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Reorganization of brain function during force production after stroke Kokotilo, Kristen J. 2008

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REORGANIZATION OF BRAIN FUNCTION DURING FORCE PRODUCTION AFTER STROKE by KRISTEN J KOKOTILO BSc (Biology), University of Alberta, 2006 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (NEUROSCIENCE) THE UNIVERSITY OF BRITISH COLUMBIA  October 2008  Kristen J Kokotilo, 2008  Abstract Damage to motor areas of the brain, caused by stroke, can produce devastating motor deficits, including aberrant control of force. After stroke, reorganization of the brain’s motor system has been identified as one of the fundamental mechanisms involved in recovery of motor control after stroke. Yet, few studies have investigated how force production and modulation are encoded in the brain after stroke and how this relates to motor outcome. Thus, the purpose of this study was to (1) understand how past neuroimaging literature has contributed to establishing common patterns of brain reorganization during both relative and absolute force production after stroke, (2) examine how brain function is reorganized during force production and modulation in individuals with stroke, and (3) relate this task-related reorganization of brain function to the amount of paretic arm use after stroke. In the second chapter, we systematically reviewed all relevant literature examining brain activation during force production after stroke. The following chapters (chapters 3 and 4) applied functional magnetic resonance imaging (fMRI) to examine the neural correlates of force production and modulation after stroke. Chapter 2 supports differences in task-related brain activation dependent on features of stroke, such as severity and chronicity, as well as influence of rehabilitation. In addition, results suggest that activation of common motor areas of the brain during force production can be identified in relation to functional outcome after stroke. Results from the subsequent two chapters (3 and 4), demonstrate that brain function reorganizes in terms of absolute, and not relative force production after stroke. Specifically, stroke participants exhibit greater activation of motor areas than healthy controls when matched for absolute force production. Moreover, there is a relationship between paretic arm usage and brain activation, where stroke participants having less paretic arm use, as measured using wrist accelerometers, exhibit higher brain activation. Results of this thesis suggest that during absolute force production, brain activation may approach near maximal levels in stroke participants at lower forces than healthy controls. Furthermore, this effect may be amplified even further in subjects with less paretic arm usage, as increased activation in motor areas occurs in participants with less arm use after stroke. Ultimately, the results from this thesis will contribute to research relevant to brain reorganization in individuals with  ii  stroke and may lead to the development of new, beneficial therapeutic interventions that optimize brain reorganization and improve functional recovery after stroke.  iii  TABLE OF CONTENTS Abstract......................................................................................................................................ii Table of Contents .....................................................................................................................iv List of Tables..........................................................................................................................viii List of Figures...........................................................................................................................ix List of Abbreviations................................................................................................................xi Acknowledgments ..................................................................................................................xii Statement of Co-Authorship .................................................................................................xiii CHAPTER 1: Introduction and Purpose ...........................................................................1 1.1 Introduction .........................................................................................................................1 1.2 Literature Review ...............................................................................................................2 Force production in Healthy Individuals ......................................................................2 Force production in Stroke Patients .............................................................................3 The Coding of Force in the Brain. ................................................................................4 Neurophysiological/Animal Studies. .........................................................4 Imaging studies..........................................................................................5 Imaging studies: Healthy Subjects. ..........................................................7 Imaging studies: Stroke Patients ..............................................................7 1.3 Purpose ...............................................................................................................................9 1.4 Research Questions and hypotheses ..................................................................................9 1.5 Rationale ...........................................................................................................................11 1.6 Significance .......................................................................................................................12 1.7 References .........................................................................................................................14 CHAPTER 2: Reorganization of brain function during force production after stroke: A Systematic Review of the Literature .............................................................................18 2.1 Abstract..............................................................................................................................18 2.2 Introduction. .....................................................................................................................19 2.3 Methods. ............................................................................................................................21 2.4 Results. ..............................................................................................................................22 Study Descriptions......................................................................................................22 Subject Characteristics of reviewed studies ...............................................................24 Imaging modalities used in reviewed studies.............................................................25 Motor tasks used in reviewed studies .........................................................................26 Influence of stroke severity on brain activation after stroke ......................................27 Differences in brain reorganization between acute and chronic stroke .....................32 Brain Activation during force modulation after stroke ..............................................33 Influence of rehabilitation on brain activation after stroke ........................................35 2.5 Discussion .........................................................................................................................36 Brain Activation after Stroke......................................................................................36 Higher levels of activation with increased severity .......................................36 Role of the Undamaged, Contralesional Hemisphere?..................................38 Differences in reorganization between acute and chronic stroke stages .......40 Influence of rehabilitation on brain activation ...........................................................41  iv  Conclusions and Limitations ......................................................................................42 Clinical implications ...................................................................................................43 2.6 Bridging Summary............................................................................................................45 2.7 References .........................................................................................................................46 CHAPTER 3: Absolute, but not relative force coding is re-organized in the brain after stroke ................................................................................................................51 3.1 Abstract..............................................................................................................................51 3.2 Introduction .......................................................................................................................52 3.3 Materials and Methods. ....................................................................................................54 Subjects. ......................................................................................................................54 Protocol. ......................................................................................................................55 fMRI Data acquisition ................................................................................................56 Behavioral data analyses.............................................................................................56 fMRI data analyses .....................................................................................................57 Absolute force analyses ..............................................................................................58 Statistical analyses ......................................................................................................59 3.4 Results ...............................................................................................................................59 Subjects .......................................................................................................................59 Clinical data ................................................................................................................60 Behavioral results........................................................................................................61 Imaging results: Relative force production ................................................................62 Areas activated during relative force production ........................................62 Effects of stroke and level of relative force on PSC ...................................63 Imaging results: Absolute force production ............................................................65 Areas activated during absolute force production.......................................65 Comparison of stroke with control group: absolute force .............................65 3.5 Discussion .........................................................................................................................67 Conclusions and Implications ...................................................................................70 Limitations ..................................................................................................................71 3.6 Bridging Summary............................................................................................................72 3.7 References ........................................................................................................................73 CHAPTER 4: Greater activation of secondary motor areas occurs with less arm use after stroke .........................................................................................................76 4.1 Abstract..............................................................................................................................76 4.2 Introduction .......................................................................................................................77 4.3 Materials and Methods. ....................................................................................................79 Subjects. ......................................................................................................................79 Accelerometer Protocol. .............................................................................................80 fMRI Protocol. ............................................................................................................80 fMRI Data acquisition ................................................................................................81 Behavioral data analysis .............................................................................................82 Force analysis..............................................................................................................82 Absolute Force analysis .................................................................................82 Relative Force analysis...................................................................................83  v  fMRI data analysis ......................................................................................................84 Statistical analysis .......................................................................................................85 4.4 Results ...............................................................................................................................86 Clinical data ................................................................................................................86 Accelerometer data .....................................................................................................87 Imaging data................................................................................................................89 Brain activation during Absolute Force Production vs. arm use...................89 Brain activation during Relative Force Production vs. arm use ....................91 Brain activation during vs. arm function (ARAT).........................................93 4.5 Discussion .........................................................................................................................93 Limitations ..................................................................................................................97 4.6 References .........................................................................................................................98 CHAPTER 5: Conclusions and General Discussion .......................................................100 5.1 Introduction .....................................................................................................................100 5.2 Summary of results..........................................................................................................100 Relative Force Production .......................................................................................100 Absolute Force Production......................................................................................101 Higher Levels of activation with increased severity? ...........................................103 5.3 Neurobiological function of recruited motor areas .....................................................104 5.4 Limitations .....................................................................................................................107 Subjects.....................................................................................................................107 Accelerometers.........................................................................................................107 Motor Task ...............................................................................................................108 5.5 Future Directions ...........................................................................................................108 Neural correlates of force production in cortical stroke........................................108 Neural correlates of force production using precision grip...................................108 Neural correlates of therapeutically drive change .................................................109 5.6 References .......................................................................................................................111 APPENDICES Appendix I: Literature table .................................................................................................114 Appendix II: Edingburgh Handedness Inventory.................................................................120 Appendix III: Fugl-Meyer Upper Extremity Motor Assessment Scale ...............................121 Appendix IV: Modified Ashworth Scale – Elbow Flexion..................................................124 Appendix V: Action Research Arm Test .............................................................................125 Appendix VI: Motor Activity Log 14...................................................................................128 Appendix VII: Consent Form ...............................................................................................131 Appendix VIII: Details of Motor Task .................................................................................136 Appendix IX: Regions of Interest defined ...........................................................................138 Appendix X: Motor Task Behavioural Data.........................................................................139 Appendix XI: Mean (SD) values for peak PSC of ROIs during relative force ....................141 Appendix XII: Graphs of mean peak PSC during relative force for both groups ................143 Appendix XIII: Mean (SD) values for peak PSC of ROIs during absolute force................147 Appendix XIV: Graphs of peak PSC for each subject during absolute force .................148 Appendix XV: R & P values for correlations peak PSC vs accelerometer activity.............152  vi  Appendix XVI: R & P values for correlations peak PSC vs ARAT ....................................156 Appendix XVII: Graphs of all correlations for all ROIs and both groups ...........................158  vii  List of Tables Table 2.1: Comparisons for subjects, modality, motor task and findings across studies demonstrating preferred recruitment of the unaffected over affected hemisphere in persons with stroke ……........................................................................................................29 Table 2.2: Comparisons for subjects, modality, motor task and findings across studies demonstrating preferred recruitment of the affected over unaffected hemisphere in persons with stroke ……........................................................................................................30 Table 2.3: Subject, modality, motor task characteristics and findings for studies demonstrating reduction in unaffected hemisphere activation over time associated with improved function in persons with stroke ……...........................................................31 Table 3.1: Patient Characteristics ..........................................................................................60 Table 4.1: Patient Characteristics: ........................................................................................86 Table 4.2: The mean (SD) activity kilocounts per day of the right and left hands for control participants and paretic and non-paretic hands for stroke participants .................88  viii  List of Figures Figure 2.1: Medial and lateral views of the non-affected hemisphere (A) and affected hemisphere (B) of the brain depicting areas having increased activation with increased severity OR decreased outcome in at least two or more studies. ......................37 Figure 3.1. Graphs showing models used to fit the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values). A. The linear model fit to the data has an R2 value of 0.9995 and thus was determined as an appropriate fit. B. The linear model fit to the data has an R2 value of 0.8403 and thus was determined not to be an appropriate fit and so a second order polynomial was used to fit the data ..............59 Figure 3.2. Lesion locations for all stroke participants taken from axial structural T1weighted MRI scans at the level of maximum infarct ........................................................61 . Figure 3.3. Average Peak PSC at each level of relative force for each group in (A) contralateral M1 and (B) ipsilateral SMA ...........................................................................64 Figure 3.4. Average Peak PSC at an absolute force level of 3.2 pressure units for each group in all (A) contralateral ROIs and (B) ipsilateral ROIs...............................................66 Figure 4.1. Graphs showing models used to fit the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values). A. The linear model fit to the data has an R2 value of 0.9995 and thus was determined as an appropriate fit. B. The linear model fit to the data has an R2 value of 0.8403 and thus was determined not to be an appropriate fit and so a second order polynomial was used to fit the data ...............83 Figure 4.2: Graph of mean activity kilocounts per day of the dominant and nondominant hands for control participants and paretic and non-paretic hands for stroke participants .............................................................................................................................88 Figure 4.3: Graph of peak PSC at an absolute force of 3.2 pressure units vs. total accelerometer activity counts for the left hand of control subjects (n=10) for contralateral caudate ..............................................................................................................89 Figure 4.4: Graphs of peak PSC at an absolute force of 3.2 pressure units vs total accelerometer activity counts for the paretic hand of stroke subjects (n=9) for: (A) contralateral (ipsilesional) caudate; (B) contralateral (ipsilesional) PM; (C) ipsilateral (contralesional) putamen; (D) ipsilateral (contralesional) M1 ...........................................90 Figure 4.5: Graphs of peak PSC at relative force of 40% MVC vs total accelerometer activity counts of the paretic hand in stroke subjects (n=10) for (A) ipsilateral (contralesional) putamen; (B) ipsilateral (contralesional) M1; (C) ipsilateral (contralesional) PM ...............................................................................................................92  ix  Figure 4.6: Graphs of peak PSC at relative force of 40% MVC vs total accelerometer activity counts of the non-paretic hand in stroke subjects (n=10) for (A) ipsilateral (ipsilesional) thalamus; (B) ipsilateral (ipsilesional) PM ...................................................92 Figure 5.1: Areas of the brain having higher activation during absolute force production in stroke participants vs. healthy controls........................................................102 Figure 5.2: Areas of the brain involved in absolute or relative force production in stroke participants from Chapters 3 and 4 of this thesis. Note: ipsilateral and contralateral are relative to the paretic hand .......................................................................104  x  List of Abbreviations ARAT – Action Research Arm Test BOLD – Blood Oxygen Level Dependent CIMT – Constraint Induced Movement Therapy EEG - Electroencephalography FM – Fugl-Meyer Motor Impairment Scale fMRI - Functional Magnetic Resonance Imaging HRF – Hemodynamic Response Function MAS – Modified Ashworth Scale MEG - Magnetoencephalography M1 – Primary Motor cortex MVC – Maximum voluntary contraction NRIS- Near Infrared Spectroscopy Imaging PET - Positron Emission Topography PFC - Prefrontal cortex PM- Premotor cortex PSC – Percent signal change ROI – Region of interest SMA - Supplementary Motor Area SMC – Sensorimotor Cortex TMS - Transcranial Magnetic Stiumlation TR – Repetition interval WD – Wallerian Degeneration  xi  Acknowledgements I would first like to thank my supervisor, Dr. Janice Eng, for her constant and generous support and encouragement, as well as her tremendous insight. Thanks also go to my committee members, Dr. Lara Boyd, Dr. Martin McKeown and Dr. Armin Curt for their time, excellent suggestions and guidance. And to Dr. Lara Boyd for her patience and help in teaching me the use (and mysteries) of AFNI. I wish to thank all of my colleagues at GF Strong Rehab Research Lab for their support and help, particularly Chihya Hung, Debbie Rand, Jocelyn Harris, Amira Tawashy, and Elmira Chan as well as my colleagues at the Brain Behavior lab at UBC: Jill Zwicker, Meghan Linsdell, Bubblepreet Rhandwa, Tara Klassen and Brenda Wessel. I am particularly grateful to all of them for their assistance in subject recruitment. I would also like to thank Samantha Johnston for her guidance in analysis techniques. The staff at UBC 3T MRI center was also an important part of this project and so I would like to thank them for their help. I am also indebted to Laurent Mingo, who built the hand grip task and Graeme McCaig for modifying the software to run the task, and above all, Dr. Martin McKeown for his initial design of both. Thank you also to all the subjects who took the time to participate in this work. Lastly, to the people that matter the most in my life, my family and Hans, for their constant support and understanding through this entire process. I could not have done it without you.  xii  Statement of Co-Authorship This thesis contains two experiments that have been conducted by the candidate Kristen J. Kokotilo, under the supervision of Dr’s. Janice Eng and Lara Boyd and with guidance from Dr. Martin McKeown, and a systematic review conducted under the supervision of Dr.’s Janice J. Eng and Lara Boyd. The collection, analysis, and documentation of all experiments and systematic review were primarily the work of the candidate..  xiii  CHAPTER 1 Introduction and Purpose 1.1 INTRODUCTION The ability to produce and modulate force output is an essential aspect of motor control, as tasks performed require the ability to control our force output to accurately suit the tasks at hand. As force production and modulation play such an important role, it is not surprising that there is an extensive literature focused on how force is encoded in the healthy brain. Studies have also examined at length, how force is controlled in situations where movement is highly disrupted. In particular, stroke often produces motor disorders highlighted by aberrant control of force and, as a result, has garnered much interest from research examining the variety of force control deficits that stroke patients can exhibit. After stroke, reorganization of the brain’s motor system occurs to try to maintain efficient communication of motor signals to spinal cord motor neurons (Ward et al., 2006) to continue to produce accurate movement. Thus, the extent of motor deficits likely depends not only on the amount of anatomic damage to the brain but also on the reorganization of brain function that occurs in the areas of the motor system after stroke. In fact, research examining reorganization of brain activation in relation to recovery after stroke has found that patients are more likely to activate motor areas, such as M1, PM, cerebellum, SMA and parietal cortex, with decreasing motor function (Ward et al., 2003). Determination of brain activation patterns associated with functional force production and modulation has great implications for current research looking at development of treatments that manipulate brain reorganization, as the motor areas targeted by such treatments need to be intact and able to produce functional movement in order for the  1  treatment be beneficial. Thus, further insight is needed on, and hence the purpose of this project is on, how the brain reorganizes during force production and modulation after stroke in humans.  1.2 LITERATURE REVIEW Force production When producing a movement, the force output of a skeletal muscle can be changed over a huge range (Clamann, 1993). The ability to accurately produce the various ranges in force required to control different movements is largely accomplished through modifying motor unit traits (Winges and Santello, 2004). Rate coding, or adjustment of motor unit firing rate frequency, and recruitment of motor units in order of increasing strength are two methods used to vary force production (Clamann, 1993). Normally, both recruitment and firing rate modulation are used in combination to produce variations in muscle force, and the relative contribution of each is determined by the type of muscle being used, as well as the level of force required (Floeter et al., 2003). In addition to these motor unit traits, in order to generate force voluntarily the motor areas of our brain must be able to communicate effectively with the motor neurons in the spinal cord that are responsible for force generation of our muscles. In the case of humans, the primary output of motor information descends from the primary motor cortex (M1) and terminates in the spinal cord, where it may connect directly with motoneurons (Dum and Strick, 1996). At the systems level, sensory information, provided by the visual and somatosensory systems, is highly significant in regulating movement. When somatosensory feedback information from the digits is eliminated, subjects are often still able to control force output  2  to match the task requirements if visual cues are still provided (Jenmalm and Johansson, 1997). Thus, visual information about objects in the environment can assist in controlling forces during movement to meet the requirements of the object (Gordon et al., 1991). Somatosensory information is also vital in controlling force output, as subjects will use somatosensory feedback to adjust force output when visual feedback is eliminated (Jenmalm and Johansson, 1997), and subjects who lack somatosensory feedback may use improper force control to manipulate objects (Nowak et al., 2004).  Force production in stroke patients As the ability to modulate force to suit the requirements of a task is extremely important in activities of daily living, neurological disorders involving aberrant force production can be extremely detrimental and debilitating. Stroke is one example of such a disorder and is the leading cause of serious, long-term, adult disability (American Heart Association, 2008). Plenty of research has focused on disturbed force production and control after stroke. In terms of deficits, many patients experience timing deficits such as prolonged time to develop force (Muller and Dichgans, 1994), to reach maximum grip force (Nowak and Hermsdorfer, 2005) or to achieve stable grip (Wenzelburger et al., 2005). Even once this timing difficulty is overcome and grip around the object is established, reduced grip force (Boissy et al., 1999) and difficulty maintaining a stable grip (Blennerhassett et al., 2006) often occur. Although there are many deficits in force production and control that are caused by stroke, most often cases that exhibit impaired performance of one aspect of force control may  3  still preserve other aspects of force control (Hermsdorfer and Mai, 1996). Thus, it may be that reorganization of the brain results in the recovery of some aspects of force control. However, prior to discussing how force may be organized in the stroke affected brain, it is vital to discuss how past research has determined the coding of force in the healthy brain.  The Coding of force in the brain Neurophysiology/animal studies The method of extracellular single cell recording in awake behaving animals was first introduced in the late 1950s (Ashe, 1997) and it was not long before it was used to study functions of the motor cortex. Since then, the encoding of force in the motor cortex has been studied widely through neurophysiological methods in the non-human primate. Although initial investigations included the relation between numerous movement parameters and the activity of cells in the motor cortices, force is the parameter that has received the most attention (Ashe, 1997). Evarts and colleagues (1968) were among the first to examine the coding of force in the motor cortex and found that variation in force rather than displacement of the wrist during movement was represented in pyramidal tract neuronal outputs. Further studies examined motor cortical responses to increasing force production and found that neurons showed increase in firing frequency associated with movement under increasing load conditions (Conrad et al., 1977). Moreover, the firing rate of corticomotor neurons appeared to increase linearly with force (Cheney and Fetz, 1980).  4  Imaging Studies While single cell recordings in non-human primates have provided invaluable insights into how movement is coded in the motor cortex, the recent advances of neuroimaging have helped to further our understanding of the coding of movement in the human brain. Over the years, a number of different imaging modalities have surfaced having the ability to measure reorganization of brain function. The development of one such modality: functional magnetic resonance imaging (fMRI) has increased immensely due to its noninvasive nature and fast speed of acquiring images (Huettel et al., 2004). The focus of this thesis is using fMRI to characterize brain activation, and so below we will provide a brief overview of the methods of fMRI use. During acquisition of the fMRI signal, two basic types of experimental designs can be used. The first, a blocked design, involves presentation of stimulus conditions in an alternating pattern, where each block is usually 10-30 s in length. In contrast, an eventrelated design presents brief conditions as individual events, and each event is separated by an interstimulus interval ranging from 2-20 s. In this latter type of design, the hemodynamic response is able to decay to baseline after each stimulus, which allows the response to each trial to be separated (Huettel et al., 2004). Regardless of design, all fMRI data consists of voxels (3D volume elements) that are repeatedly sampled over time (Huettel et al., 2004). Preprocessing techniques must be applied to the voxel data following acquisition and before statistical analysis in order to reduce variability in the data that is not associated with the experimental task (Huettel et al., 2004). These preprocessing techniques can include head motion correction, coregistration (mapping functional and structural images to each other), normalizaton (warping the brain to  5  a reference template), as well as spatial smoothing (reducing high-frequencey spatial components and spreading the intensity at each voxel over nearby voxels) (Huettel et al., 2004). With regard to the statistical analysis of fMRI experiments, one of the most common analysis methods for fMRI is a voxelwise approach, where each voxel within the brain is statistically tested to evaluate its significance relative to the experimental hypothesis (Huettel et al., 2004). To compare across groups, the brains of each individual subject are then warped to a reference template. This approach, however, may introduce inaccuracies when normalizing a brain affected by neurological disease or injury, as normalization depends on the morphology of the brain, which is often abnormal in these cases. An alternative approach is to draw regions of interest (ROIs) on the brain of each subject, and these regions are then individually analyzed for significance (Huettel et al., 2004). One of the methods of statistical fMRI ROI analysis, voxel-by-voxel analysis, is based on examination of the statistical value for each individual voxel across trials (Kimberley et al., 2008b). Alternatively, voxel counting can be used, where voxels considered active based on a threshold level are counted within a region (Kimberley et al., 2008b). Lastly, calculation of percent signal change, quantified by the signal intensity change from baseline, over all voxels in an ROI can be used as a measure of activation. In terms of reliability of analysis methods, signal change of intensity has been found to be consistently more reliable than voxel counting in healthy subjects (Kimberley et al., 2008a) and more reliable than both voxel counting and voxel by voxel analysis in subjects with stroke (Kimberley et al., 2008b).  6  Imaging studies: Healthy Subjects Although acquiring reliable data requires motion to be highly limited in imaging studies, numerous studies have been able to employ motor tasks during scanning in order to determine areas involved in force production. For example, in healthy subjects during force production via pressing a key with the index finger, fMRI and PET activation studies show that increased activation occurs in M1 and supplementary motor area (SMA) with increasing force (Dettmers et al., 1995; Dettmers et al., 1996). M1 (Cramer et al., 2002), SMA, and the premotor cortex (PM) and cerebellum (Dai et al., 2001) are also increasingly activated when more force is exerted in a squeezing task. More specifically, Ward and colleagues (2003) found that a linear increase in brain activation occurs with increasing force in contralateral sensorimotor cortex (SMC) and bilateral cerebellum whereas a non linear activation increase may be observed in other cortical areas such as the cingulate sulcus, intraparietal sulcus, and prefrontal cortex (PFC). Overall, these past studies on healthy subjects have helped to establish a group of regions in the healthy brain involved in force production and modulation that include: M1, SMA, PM, cerebellum, cingulate sulcus, intraparietal sulcus, and PFC.  Imaging Studies: Stroke Patients In terms of reorganization of brain function after stroke, many past studies have reported task-related brain activation over and above that found in healthy controls in areas such as: SMC, PM, SMA, inferior parietal cortex, PFC, anterior cingulate cortex, cerebellum and basal ganglia (Chollet et al., 1991; Cramer et al., 1997; Weiller et al., 1992). In addition, neuroimaging studies have established that reorganization of brain activation relates to recovery after stroke. For example, in studies where patients are scanned early after stroke  7  and then months later, an overall reduction of task-related activation occurs over time that is related to functional improvement of the patients in between scans (Calautti et al., 2001; Marshall et al., 2000). Although these past studies demonstrate that reorganization of brain activation occurs during movement after stroke, many employed tasks that were not performed against resistance, such as finger tapping (Cramer et al., 1997) or finger-thumb opposition (Calautti et al., 2001; Marshall et al., 2000). Unfortunately, these tasks may not be as useful as tasks involving active movement against resistance, as these latter tasks can allow different forces to be produced and thus are more highly relevant to activities of daily living (e.g., lifting a book, holding a cup). Moreover, it is difficult to ascertain the effects of stroke on task-related brain activation with individual papers due to the use of a variety of experimental protocols (e.g., varied tasks) and the heterogeneity of the population of people with stroke (e.g., types and severity of stroke, lesion location, time since injury). Reviewing all the relevant literature within one paper allows past findings to be summarized to determine commonalities and conflicts in the literature. Thus, the next chapter (chapter 2) is a systematic review that aimed to examine, more in depth, past neuroimaging literature demonstrating reorganization of brain function during force production and force modulation tasks (tasks involving active movement against resistance) after stroke. The following chapters (chapters 3 and 4) apply fMRI to examine the neural correlates of force production and modulation after stroke.  8  1.3 PURPOSE: The purpose of this study was: 1) to systematically review previous literature using neuroimaging techniques to investigate the neural correlates of force production and modulation in individuals with stroke, 2) to use functional MRI to examine how brain function is reorganized during force production and modulation in individuals with stroke, and 3) to relate reorganization of brain function during force production to the amount of paretic arm use after stroke.  1.4 RESEARCH QUESTIONS AND HYPOTHESES: Several research questions were posed in order to address the purpose of this thesis. The following are the research questions, along with our hypotheses.  Research Question 1: Have any common patterns of brain reorganization during both relative and absolute force production after stroke been established in the literature?  As a descriptive study, we proposed that common brain areas across the literature would be found that are utilized in force production and modulation. In addition, that the literature would support differences in task-related brain activation dependent on features of stroke, such as severity and chronicity.  9  Research Question 2a: How does activation intensity in cortical and subcortical motor areas change with relative force production and modulation after stroke? Hypotheses: Ho: Coding of relative force assessed by fMRI will be similar between groups during performance of a motor task with the affected hand. Hi: Coding of relative force will be different between groups during performance of a motor task with the affected hand. If probability determines that the Ho is true we will accept the Ho. Alternatively, if probability determines that the Ho is false, we will accept the Hi.  Research Question 2b: How does activation intensity in cortical and subcortical motor areas change with absolute force production after stroke? Hypotheses: Ho: Coding of absolute force assessed by fMRI will be similar between groups during performance of a motor task with the affected hand. Hi: Coding of absolute force will be different between groups during performance of a motor task with the affected hand. If probability determines that the Ho is true we will accept the Ho. Alternatively, if probability determines that the Ho is false, we will accept the Hi.  10  Research Question 3: Does reorganization of brain function (as assessed by fMRI during relative and absolute force production) in chronic stroke patients relate to the amount of paretic limb use as measured by accelerometers? Hypotheses: Ho: No relationship will occur between paretic arm use and brain reorganization during force production. Hi: A relationship will exist between paretic arm use and brain reorganization during force production. If probability determines that the Ho is true we will accept the Ho. Alternatively, if probability determines that the Ho is false, we will accept the Hi.  1.5 RATIONALE: Stroke can be devastating to many through its extensive social, psychological and physical consequences. In terms of physical consequences, stroke is the leading cause of serious, long-term, adult disability (American Heart Association, 2008). After an incidence of stroke the central nervous system (CNS) tries to compensate for its loss through plasticity, or reorganization of the brain (Teasell et al., 2005). Thus far, animal studies have provided plenty of useful evidence of brain reorganization, however studies are also needed that focus on reorganization processes in humans. For obvious reasons, techniques used in animal research cannot be applied to human studies; however the recent development of functional imaging techniques, such as fMRI, now provide an opportunity for studying brain  11  reorganization in humans. Recent studies employing fMRI techniques have focused on how the brain changes in healthy individuals while performing motor tasks at varying levels of force (eg. (Cramer et al., 2002; Ehrsson et al., 2001; Thickbroom et al., 1998)); and in stroke subjects while performing motor tasks at one level of force (eg.(Johansen-Berg et al., 2002; Staines et al., 2001)) or less commonly, increasing force levels (eg.(Ward et al., 2003; Ward et al., 2007)). Although these studies have provided extremely valuable information, many of these past studies have only looked at changes in brain activation at relative forces set at a % of maximum voluntary contraction (MVC) of each individual and not during control of absolute force production. If and how brain activation reorganizes after stroke during absolute force production is significant as the majority of daily tasks require an absolute level of force to be produced (eg. opening a jar). In addition, although past literature has examined the relationship between outcome and reorganization of brain function after stroke, there is a lack of information on the amount of paretic arm use in relation to brain reorganization during force production. Consequently, this study will examine reorganization of the brain’s motor areas during both absolute force production and increasing relative force production after stroke, and will relate it to use of the paretic arm. . 1.6 SIGNIFICANCE Reorganization of brain function after injury to the CNS has been identified as one of the fundamental mechanisms involved in the recovery of motor function. As the process of reorganization can be manipulated through treatment, such as rehabilitation, (Ward et al., 2003b) further understanding of how the brain reorganizes after injury has implications for development of treatments that manipulate brain reorganization in order to improve recovery.  12  In addition, neuroimaging studies have the potential to examine numerous questions regarding the mechanisms of current treatment options for stroke.  13  1.7 REFERENCES American Heart Association. (2008). Heart Disease and Stroke Statistics — 2008 Update. Ashe, J. (1997). Force and the motor cortex. Behavioral Brain Research. 8, 255-269. Blennerhassett, J. M., Carey, L. M., and Matyas, T. A. (2006). Grip force regulation during pinch grip lifts under somatosensory guidance: comparison between people with stroke and healthy controls. Arch Phys Med Rehabil. 87, 418-429. Boissy, P., Bourbonnais, D., Carlotti, M. M., Gravel, D., and Arsenault, B. A. (1999). 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Differential fronto-parietal activation depending on force used in a precision grip task: an fMRI study. J Neurophysiol. 85, 2613-2623. Evarts, E. V. (1968). Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol. 31, 14-27. Floeter, M. K. (2003). The Spinal Cord, Muscle, and Locomotion, in: L. R. Squire, F. E. Bloom, S. K. McConnell, J. L. Roberts, N. C. Spitzer and M. J. Zigmond (eds), Fundamental Neuroscience, 2nd edn. Academic Press, Elsevier Science: pps. 767. Gordon, A. M., Forssberg, H., Johansson, R. S., and Westling, G. (1991). Visual size cues in the programming of manipulative forces during precision grip. Exp Brain Res. 83, 477482. Hermsdorfer, J., and Mai, N. (1996). Disturbed grip-force control following cerebral lesions. J Hand Ther. 9, 33-40. Huettel, S., Song, A. W., and McCarthy, G. (2004). Functional Magnetic Resonance Imaging. Sinauer Associates, Inc.: Sunderland, Massachusetts, Jenmalm, P., and Johansson, R. S. (1997). Visual and somatosensory information about object shape control manipulative fingertip forces. J Neurosci. 17, 4486-4499. Johansen-Berg, H., Rushworth, M. F., Bogdanovic, M. D., Kischka, U., Wimalaratna, S., and Matthews, P. M. (2002). The role of ipsilateral premotor cortex in hand movement after stroke. Proc Natl Acad Sci U S A. 99, 14518-14523. Kimberley, T. J., Birkholz, D. D., Hancock, R. A., VonBank, S. M., and Werth, T. N. (2008a). Reliability of fMRI during a continuous motor task: assessment of analysis techniques. J Neuroimaging. 18, 18-27.  15  Kimberley, T. J., Khandekar, G., and Borich, M. (2008b). fMRI reliability in subjects with stroke. Exp Brain Res. 186, 183-190. Lindberg, P. G., Schmitz, C., Engardt, M., Forssberg, H., and Borg, J. (2007). Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 21, 315-326. Luft, A. R., Waller, S., Forrester, L., Smith, G. V., Whitall, J., Macko, R. F., Schulz, J. B., and Hanley, D. F. (2004). Lesion location alters brain activation in chronically impaired stroke survivors. Neuroimage. 21, 924-935. Marshall, R. S., Perera, G. M., Lazar, R. M., Krakauer, J. W., Constantine, R. C., and DeLaPaz, R. L. (2000). Evolution of cortical activation during recovery from corticospinal tract infarction. Stroke. 31, 656-661. Muller, F., and Dichgans, J. (1994). Impairments of precision grip in two patients with acute unilateral cerebellar lesions: a simple parametric test for clinical use. Neuropsychologia. 32, 265-269. Nowak, D. A., Glsauer, S., and Hermsdörfer, J. (2004). How predictive is grip force control in the complete absence of somatosensory feedback? Brain. 127, 182-192. Nowak, D. A., and Hermsdorfer, J. (2005). Grip force behavior during object manipulation in neurological disorders: toward an objective evaluation of manual performance deficits. Mov Disord. 20, 11-25. Ogawa, S., Menon, R. S., Kim, S. G., and Ugurbil, K. (1998). On the characteristics of functional magnetic resonance imaging of the brain. Annu Rev Biophys Biomol Struct. 27, 447-474. Pope, P. A., Praamstra, P., and Wing, A. M. (2006). Force and time control in the production of rhythmic movement sequences in Parkinson's disease. Eur J Neurosci. 23, 1643-1650. Schaechter, J. D., Kraft, E., Hilliard, T. S., Dijkhuizen, R. M., Benner, T., Finklestein, S. P., Rosen, B. R., and Cramer, S. C. (2002). Motor recovery and cortical reorganization after constraint-induced movement therapy in stroke patients: a preliminary study. Neurorehabil Neural Repair. 16, 326-338. Staines, W. R., McIlroy, W. E., Graham, S. J., and Black, S. E. (2001). Bilateral movement enhances ipsilesional cortical activity in acute stroke: a pilot functional MRI study. Neurology. 56, 401-404. Szaflarski, J. P., Page, S. J., Kissela, B. M., Lee, J. H., Levine, P., and Strakowski, S. M. (2006). Cortical reorganization following modified constraint-induced movement therapy: a study of 4 patients with chronic stroke. Arch Phys Med Rehabil. 87, 10521058.  16  Teasell, R., Bayona, N. A., and Bitensky, J. (2005). Plasticity and reorganization of the brain post stroke. Top Stroke Rehabil. 12, 11-26. Thickbroom, G. W., Phillips, B. A., Morris, I., Byrnes, M. L., and Mastaglia, F. L. (1998). Isometric force-related activity in sensorimotor cortex measured with functional MRI. Exp Brain Res. 121, 59-64. Ward, N. S., Brown, M. M., Thompson, A. J., and Frackowiak, R. S. (2003b). Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain. 126, 1430-1448. Ward, N. S., Newton, J. M., Swayne, O. B., Lee, L., Thompson, A. J., Greenwood, R. J., Rothwell, J. C., and Frackowiak, R. S. (2006). Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 129, 809-819. Ward, N. S., Newton, J. M., Swayne, O. B., Lee, L., Frackowiak, R. S., Thompson, A. J., Greenwood, R. J., and Rothwell, J. C. (2007). The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke. Eur J Neurosci. 25, 1865-1873. Weiller, C., Chollet, F., Friston, K. J., Wise, R. J., and Frackowiak, R. S. (1992). Functional reorganization of the brain in recovery from striatocapsular infarction in man. Ann Neurol. 31, 463-472. Wenzelburger, R., Kopper, F., Frenzel, A., Stolze, H., Klebe, S., Brossman, A., KuhtzBuschbeck, J., Gölge, M., Illert, M., and Deuschl, G. (2005). Hand coordination following capsular stroke. Brain. 128, 64-74. Winges, S. A., and Santello, M. (2004). Common input to motor units of digit flexors during multi-digit grasping. J Neurophysiol. 92, 3210-3220.  17  CHAPTER 2 Reorganization of brain function during force production after stroke: A Systematic Review of the Literature1 2.1 ABSTRACT Damage to motor areas of the brain, caused by stroke, can produce devastating motor deficits, including aberrant control of force. Reorganization of brain function has been identified as one of the fundamental mechanisms involved in recovery of motor control after stroke, and recent advances in neuroimaging have enabled study of this brain reorganization. This review focuses on neuroimaging studies that have examined reorganization of brain function during force production and force modulation after stroke. Reorganization after stroke was characterized via three factors: severity of injury, time after stroke and the impact of therapeutic interventions on brain activation during force production. Twenty-six studies meeting the inclusion criteria could be identified in MEDLINE (1980 to 2007). Relevant characteristics of studies (lesion location, chronicity of stroke, motor task) and mapping techniques varied widely. During force production, increased activation in secondary motor areas occurred in persons with more severe strokes. Also, reduced recruitment of secondary motor areas during force production was found as a function of increased time since stroke. During force modulation, increased activation in motor areas occurred with greater force generation. In addition, persons with more severe stroke showed relatively greater activation with rising force. Lastly, alteration of brain activation during and after rehabilitative interventions in persons with stroke occurred in some studies. This systematic review 1  A version of this chapter has been accepted for publication. Kokotilo, KJ., Eng, JJ., Boyd, LA. (2009) Reorganization of brain function during force production after stroke: A Systematic Review of the literature. Journal of Neurologic Physical Therapy.  18  establishes that reorganization of brain function during force production and force modulation can occur after stroke. These findings have implications for therapeutic strategies that may be able to target brain reorganization to improve functional recovery after stroke.  2.2 INTRODUCTION As humans, generation and control of force is a central part of our lives. Control of force output is required to walk, manipulate objects, and play musical instruments and sports. Even the simple act of manipulating a Styrofoam cup requires control of force, as too little may result in the cup slipping from grip and too much can result in crushing the cup and contents inside. When producing a movement, the force output of a skeletal muscle can be changed over a large range (Clamann, 1993). This ability to accurately produce the various ranges in force is largely accomplished through modifying motor unit traits (Winges and Santello, 2004). Rate coding, or adjustment of motor unit firing rate frequency, is one method used to vary force production, and force level can also be altered in steps by recruiting motor units in order of increasing strength (Clamann, 1993). Normally, recruitment and firing rate modulation are the two most common strategies used in combination to produce variations in muscle force, and the relative contribution of each is determined by the type of muscle being used, as well as the level of force required (Floeter, 2003). In order to generate force voluntarily the motor areas of our brain must be able to communicate effectively with the motor neurons in the spinal cord that are responsible for force generation of our muscles. In the case of humans, the primary output of motor information descends from the primary  19  motor cortex (M1) and terminates in the spinal cord, where it may connect directly with motor neurons (Dum and Strick, 1996). As grip force control depends on the integrity of the sensorimotor system, when injury to sensorimotor areas of the brain occurs there may be impairment in controlling force (Hermsdorfer et al., 2003). Stroke is one example of such a neurological disorder and is the leading cause of serious, long-term, adult disability (American Heart Association, 2008). Patients with stroke can experience a range of motor control deficits including exaggeration of grip force (Blennerhassett et al., 2006; Hermsdorfer and Mai, 1996; Wenzelburger et al., 2005), abnormal time to achieve stable grip (Wenzelburger et al., 2005), and difficulty in maintaining constant force during a grip task (Blennerhassett et al., 2006; Hermsdorfer and Mai, 1996). In the past, an abundance of literature has focused on irregularities in muscle fiber and motor unit properties contributing to abnormal force production and modulation after stroke. More recently, technology has enabled the study of supraspinal contributions underlying motor activity (Boyd et al., 2007; Butler and Wolf, 2007; Kimberley and Lewis, 2007). As stroke involves direct injury to the brain, it provides an appropriate model to investigate the supraspinal contributions of force control. Past literature has suggested that there are certain processes associated with stroke recovery that occur in the brain in order to generate force. Examination of spinal termination patterns of efferents from secondary cortical motor areas (supplementary motor area, cingulate motor area and premotor cortex) has shown that some corticospinal projections also originate in these areas, similar to those from M1 (Dum and Strick, 1996; Johansen-Berg et al., 2002). These findings suggest that secondary motor areas have the potential to control movement, and thus may represent a substrate for motor recovery after stroke that impacts  20  M1 (Dum and Strick, 1996). Neuroimaging techniques provide the ability to examine the brain reorganization associated with recovery after CNS damage and recent studies using these techniques have published patterns of brain area recruitment involved in force generation and modulation after stroke (eg. (Ward et al., 2007)) However, it is difficult to ascertain the effects of stroke on force generation with individual papers due to the use of a variety of experimental protocols (e.g., different muscles, varied tasks) and the heterogeneity of the population of people with stroke (e.g., types and severity of stroke, lesion location, time since injury); mixing of these factors has resulted in varying and even conflicting results. Reviewing all the relevant literature within one paper allows past findings to be summarized and contextualized to determine commonalities and conflicts in the literature. Thus, the present article systematically reviews the literature to determine how patterns of brain activation vary during force production and modulation after stroke. More specifically, the literature was synthesized to determine if brain activation patterns change during force production from the early to late stages post stroke. In addition, this review aimed to determine if the severity of stroke influences brain activation during force production. Lastly, the literature was examined to verify whether rehabilitation interventions after stroke alter brain activation patterns during force production.  2.3 METHODS Medline (1980-2007) database was used to search the literature. This database was accessed online through the local university’s library system in September 2007. The search was limited to articles written in English. Searches were performed using combinations of the key words: stroke, neuroimaging, functional magnetic resonance imaging (fMRI), 21  transcranial magnetic stiumlation (TMS), electroencephalography (EEG), magnetoencephalography (MEG), positron emission topography (PET), near infrared spectroscopy imaging (NRIS), motor. The inclusion criteria consisted of the following: (1) study participants had a diagnosis of a stroke, (2) brain plasticity in motor areas was examined, (3) study participants performed movement that was active and against resistance. The search was limited to active movement against resistance because these paradigms controlled the force produced during the motor task. In addition, active movement against resistance is highly relevant to activities of daily living (e.g., lifting a cup, opening a door). Neither theses, conference proceedings, nor case studies were included. A total of 1098 articles were identified using the key words. The titles of these references were examined and a total of 197 titles were identified as relevant and their abstracts were subsequently examined. Of the 197 abstracts identified, articles not related to the proposed question (e.g. motor imagery, passive movement, active movement against no resistance) were removed. Following this screening process, 64 articles remained for further review for appropriateness and out of these, 26 articles fell within our inclusion criteria.  2.4 RESULTS Study Descriptions Twenty-six articles were found describing brain plasticity post stroke within our search criteria. Twenty-two of these articles involved force production against resistance either at only one level or at multiple levels, but the differences in brain activation between levels was not described or the focus (Braun et al., 2007; Dong et al., 2006; Fridman et al.,  22  2004; Kopp et al., 1999; Kotani et al., 2004; Mihara et al., 2007; Mima et al., 2001; Miyai et al., 2001; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006; Newton et al., 2002; Newton et al., 2006; Serrien et al., 2004; Staines et al., 2001; Stinear et al., 2007; Strens et al., 2004; Verleger et al., 2003; Ward et al., 2003a; Ward et al., 2004; Ward et al., 2006; Werhahn et al., 2003). A subset of these studies specified a target force (ranging from 10% to 100% of maximum voluntary contraction (MVC), and at 1N). The four remaining articles involved a motor task requiring force to be produced at more than one level (Renner et al., 2005; Ward et al., 2003b; Ward et al., 2007; Woldag et al., 2004), and helped to determine brain activation in response to force modulation (varying levels of force production) of a motor task. In all of these force modulation articles, two or more target forces (ranging from 5% to 100% of MVC) were specified and the differences in brain activation between levels were described. When comparing persons with stroke and healthy controls, 14/26 studies compared the groups at equivalent relative forces (i.e., percent MVC) (Dong et al., 2006; Mima et al., 2001; Newton et al., 2002; Newton et al., 2006; Renner et al., 2005; Serrien et al., 2004; Strens et al., 2004; Verleger et al., 2003; Ward et al., 2003a; Ward et al., 2003b; Ward et al., 2004; Ward et al., 2006; Ward et al., 2007; Woldag et al., 2004). Note, in these cases, the absolute force values would be lower for the persons with stroke during force generation of the paretic limb compared to healthy controls. One study out of 26 compared the groups at an absolute force value of 1N (Braun et al., 2007). The remaining studies did not specify the target forces used. None of the 26 studies compared different rates (i.e., speed) of force generation within the same study and some studies did not specify the rate of force. Where rate and  23  force of movement were not specified, it was assumed that the subjects self-selected the movement rate and force. For studies that did specify the rate of movement, it included selfpaced (Foltys et al., 2003; Miyai et al., 2001; Stinear et al., 2007) 40% of maximum rate (Ward et al., 2003a; Ward et al., 2003b; Ward et al., 2004), 75% of maximum rate (Dong et al., 2006), 0.5 Hz (Staines et al., 2001), 1 Hz (Kopp et al., 1999), 0.4–3.0 km/h (Mihara et al., 2007), 0.2km/hr (Miyai et al., 2002), and 49.5-55.3 steps/min (Miyai et al., 2003).  Subject Characteristics of reviewed studies The number of persons with stroke in each study ranged from 2 to 25. Time after injury ranged from 10 days to 15 years; subjects were tested in the early phase after stroke (>10 days, <3 months) in nine studies (Mihara et al., 2007; Miyai et al., 2001; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006; Renner et al., 2005; Staines et al., 2001; Ward et al., 2003a; Ward et al., 2004; Woldag et al., 2004), and all but two (Renner et al., 2005; Woldag et al., 2004) studies tested subjects in the late phase after stroke (> 3 months). Time since stroke was not specified in one study (Kotani et al., 2004). The location and extent of stroke lesions was variable among studies, including exclusively subcortical lesions (12 articles (Braun et al., 2007; Fridman et al., 2004; Mihara et al., 2007; Mima et al., 2001; Miyai et al., 2001; Miyai et al., 2006; Newton et al., 2006; Renner et al., 2005; Verleger et al., 2003; Ward et al., 2006; Ward et al., 2007; Woldag et al., 2004)), exclusively cortical lesions (1 article (Staines et al., 2001)), cortical and subcortical lesions (12 articles (Dong et al., 2006; Kopp et al., 1999; Miyai et al., 2002; Miyai et al., 2003; Newton et al., 2002; Serrien et al., 2004; Stinear et al., 2007; Strens et al., 2004; Ward et al., 2003a; Ward et al.,  24  2003b; Ward et al., 2004; Werhahn et al., 2003)) and 1 article did not specify lesion location (Kotani et al., 2004).  Imaging modalities used in reviewed studies A number of imaging modalities were used to determine brain mapping in the articles, with the primary modality being fMRI (11 articles) and the others including TMS (5 articles), EEG (5 articles), MEG (2 articles), and functional NIRS (fNIRS) (4 articles). In one instance, more than one imaging modality was used to assess brain reorganization (Braun et al., 2007). These imaging modalities measure reorganization of brain function differently. In brief, fMRI measures neural activation indirectly via changes in blood oxygenation (Kimberley and Lewis, 2007). PET indexes metabolic activity within the brain while EEG records electrical impulses from the cortex directly through electrodes placed on the scalp (Boyd et al., 2007). fNIRS, or optical imaging, uses near infrared spectroscopy to measure cortical activation via changes in blood oxygenation in the cortex and can be used during human gait (Miyai et al., 2006). MEG characterizes the electromagnetic properties of neurons in the cortex (Boyd et al., 2007). These techniques (fMRI, PET, EEG, NIRS and MEG) allow measurement of changes in brain activation during overt movement. TMS measures the electrical excitability of the cortex, allowing detection of remapping in the primary motor cortex (Butler and Wolf, 2007). Importantly, only fMRI and PET allow imaging of deep brain structures such as the basal ganglia. The other technologies employed in the characterization of force control after stroke only permit characterization of the cortex of the brain, and TMS can only be used to map regions where motor responses may be evoked (i.e., primary motor cortex). (For reviews of these neuroimaging techniques and their  25  application to the sensorimotor system and rehabilitation see (Boyd et al., 2007; Butler and Wolf, 2007; Kimberley and Lewis, 2007; Rossini and Dal Forno, 2004))  Motor tasks used in reviewed studies Although all studies encompassed active movement tasks against resistance, there was some variation to the motor tasks utilized in the studies. Tasks that were performed against resistance included hand grip (Mima et al., 2001; Miyai et al., 2001; Newton et al., 2006; Serrien et al., 2004; Staines et al., 2001; Stinear et al., 2007; Strens et al., 2004; Ward et al., 2003a; Ward et al., 2003b; Ward et al., 2004; Ward et al., 2006; Ward et al., 2007; Woldag et al., 2004), pinch grip (Braun et al., 2007; Dong et al., 2006; Renner et al., 2005; Woldag et al., 2004), wrist extension (Newton et al., 2002), key/button press (Fridman et al., 2004; Kopp et al., 1999; Kotani et al., 2004; Verleger et al., 2003; Werhahn et al., 2003) and gait (Mihara et al., 2007; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006). Most studies considered movement performed by both the paretic and non-paretic limb of persons with stroke (Braun et al., 2007; Fridman et al., 2004; Kopp et al., 1999; Mihara et al., 2007; Mima et al., 2001; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006; Newton et al., 2002; Renner et al., 2005; Serrien et al., 2004; Staines et al., 2001; Strens et al., 2004; Verleger et al., 2003) , however some studies required the subjects to perform movement with only the paretic limb(Dong et al., 2006; Miyai et al., 2001; Newton et al., 2006; Stinear et al., 2007; Ward et al., 2003a; Ward et al., 2003b; Ward et al., 2004; Ward et al., 2006; Ward et al., 2007; Werhahn et al., 2003), with only the non paretic limb (Woldag et al., 2004) or with only the dominant limb (Kotani et al., 2004). All but four studies included movement  26  of the upper extremity; four studies looked at brain plasticity during gait (Mihara et al., 2007; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006).  Influence of stroke severity on brain activation after stroke Among twenty-two studies looking at production at one force level, nine showed changes in brain activation in motor areas associated with increasing severity of stroke (Miyai et al., 2001; Newton et al., 2006; Serrien et al., 2004; Stinear et al., 2007; Strens et al., 2004; Ward et al., 2003b; Ward et al., 2006; Ward et al., 2007; Werhahn et al., 2003). Specifically, during affected hand grip within a people with chronic subcortical stroke (n=11), those having greater corticospinal tract damage showed increased activation in motor areas including bilateral M1, bilateral premotor cortex (PM), supplementary area (SMA), and prefrontal cortex (PFC) (Ward et al., 2006)and affected hemisphere motor areas in general (n=3) (Newton et al., 2006). Similarly, one study examined Wallerian Degeneration (WD) of the pyramidal tract among persons with subacute, internal capsular stroke (n=18) (Miyai et al., 2001). Results showed that more persons with WD showed activation of the affected PM, and the unaffected PM was more frequently activated in persons with WD than in persons without WD (Miyai et al., 2001). In another study, persons with chronic stroke (n=20) that spared M1 were more likely to activate motor areas, such as M1, PM, cerebellum, SMA and parietal cortex, with decreasing function across outcome measures, including grip strength and timed 10 m walk (Ward et al., 2003b). Likewise, there appears to be a relationship between decreased function after stroke and changes in patterns of brain activation as demonstrated by cortico-cortico coherence during force production. Standard EEG coherence is the normalized cross-power spectrum of two simultaneously recorder signals at different  27  sites and can reveal the spatio-temporal correlation between signals (Serrien et al., 2003). In this way, it can be used to study the patterns of interaction and connectivity between different and even remote cortical areas. In comparison, directed coherence is a measure of functional coupling in the frequency domain and allows determination of the predominant direction of information flow between two coupled areas (Serrien et al., 2003). Regardless of stroke location, when function is impaired by stroke (n=25) higher levels of task-related EEG-EEG coherence between medial cortical areas of right and left hemispheres was noted in one study (Strens et al., 2004). The increases in coupling between medial cortical areas suggest that these areas may aid in compensating to produce movement during recovery (Strens et al., 2004). Using directed coherence measures, cortico-cortical coupling between the contralesional and ipsilesional sensorimotor cortices (SMC) was more likely to originate from the contralesionial, unaffected hemisphere in persons with chronic stroke in varying locations (n=25), having less functional hand movement (measured by the 9HPT and hand muscle strength) (Serrien et al., 2004). This finding implies that the unaffected hemisphere aids in generating movement in patients who do not make a full recovery. Eight of 26 studies reported whether there was a preferred recruitment of either the affected or unaffected hemisphere during a force production task in persons with stroke when compared to controls (Tables 2.1-2.2). Specifically, 3 of the 8 articles demonstrated that the unaffected hemisphere plays a large role during movement of the affected arm (Table 2.1) (total participants n=32) (Kopp et al., 1999; Newton et al., 2002; Serrien et al., 2004). Five of the 8 articles showed that motor areas of the affected hemisphere were preferentially recruited rather than areas of the unaffected hemisphere (Table 2.2) (total participants n=60) (Braun et al., 2007; Fridman et al., 2004; Mima et al., 2001; Stinear et al., 2007; Werhahn et  28  al., 2003). Two studies demonstrated a reduction in unaffected hemisphere activation over time associated with improved function in persons with stroke (Table 2.3) (Dong et al., 2006; Miyai et al., 2003). Across studies, lesion location was not a determinant of which hemisphere (contra- or ipsilesional) was recruited; individuals in this work had a mix of cortical and subcortical lesions.  29  3 months after stroke, affected hand movement-related dipole sources shifted from the affected to the unaffected hemisphere.  Behavioral measures (AAUT, MAL, WMFT, AMAT) showed improved affected arm use post treatment. The study-wide effect size was 2.38 pre- to post-treatment and 1.92 pre-treatment to follow-up EEG; key press  cortical and subcortical sparing 4-15 years M1  During affected wrist movement, increased unaffected/ipsilateral M1 activation in stroke compared to controls.  Stroke subjects less recovered had coupling between SMC's that originated from the unaffected cortex.  Findings  30  Note: MVC = maximum voluntary contraction; SMC = sensorimotor cortex; M1 = primary motor cortex; SRT = simple reaction time. Behavioral measures: 9HPT = nine hole peg test; MRC = medical research council; AAUT = actual amount of use test; MAL = motor activity log; WMFT = Wolf motor function test; AMAT = arm motor ability test.  Kopp et al., 1999 4  Newton et al. 2002 3  Serrien et al. 2004 25  Modality; Motor task  Subjects were considered recovered (n = 11) if they could perform the 9HPT EEG; isometric cortical and > 12 and had MRC power of 4/5 in each of grip task 25% subcortical months 4 muscles MVC Average Rivermead Arm Assessment fMRI; isolated, Score at time of stroke: 1.67/15; near isometric cortical Average paretic wrist extension force wrist extension and >6 at time of scan: 74% of nonparetic (10%, 20% subcortical months wrist MVC)  Author N Lesion Time Assessment of motor recovery stroke Location Post Injury  Table 2.1: Comparisons for subjects, modality, motor task and findings across studies demonstrating preferred recruitment of the unaffected hemisphere over the affected hemisphere in persons with stroke.  cortical Mean FM score of 16, range 4– fMRI, TMS; and >6 25 (out of max 32); mean squeezing of saline subcortical months NIHSS score of 4 (range 0–7). bag  Mima et al., 2001 6  Stinear et al., 2006  31  MRC of 3.6 ± 4.0 in hand and forearm muscles (range, 1–4+); cortical and >2 FM score (upper extremity) = TMS; finger flexion, Only stimulation of affected hemisphere impaired affected hand performance. subcortical months 66.3 ± 23.1% (of max score) key press  During movement of affected hand, activation was weakly lateralized towards affected hemisphere. Lateralization of M1 was more likely if motor cortex was intact.  Note: MVC = maximum voluntary contraction; SMC = sensorimotor cortex; M1 = primary motor cortex; PMd = premotor dorsal; SRT = simple reaction time. Behavioral measures: MRC = medical research council; FM = Fugl Meyer; NIHSS = National institue of health stroke scale.  Werhahn et al., 2003 20  21  Mean max power grip force: 13.5 ± 8.6 (affected hand) and 17.3 ± 5.9 (unaffected hand) subcortical > 1 year kgs  EEG, EMG; elbow flexion, wrist extension, power grip EEG-EMG coherence occurred with the at 10-20% MVC affected but not unaffected SMC.  >2 subcortical years  Crossed cortico-spinal connectivity in recovered stroke subjects showed that the affected hemisphere was recruited.  Findings  TMS; simple reaction Only TMS applied to affected hemisphere All subjects had 3+ or more on time task (SRT) via (PMd) of stroke led to delays in SRT in the the MRC scale key press affected hand.  Fridman et al., 2004 4  Braun et al., 2007 9  Modality; Motor task  MRC score: range 3–5, mean 4.22 ± 0.28; Subjects grouped >9 into fair (MRC = 4) or MEG, TMS; subcortical months excellent (MRC > 4) recovery precision grip at 1N  Author N Lesion Time Assessment of motor stroke Location Post recovery Injury  Table 2.2: Comparisons for subjects, modality, motor task and findings across studies demonstrating preferred recruitment of the affected hemisphere over the unaffected hemisphere in persons with stroke.  sparing M1 hand region  FM score: 33-62. Mean WMFT (6 items) time decreased >3 from 33.40± 37.69 to 16.59± 22.462 s for months the paretic hand after therapy (P = 0.03)  Cadence (steps per minute) and swingphase LI were used as measures for gait. Mean FM score for lower extremity: 8.5 first session, 21.9 second session. fMRI; pinch grip at 50% MVC  32  Affected hand movement showed reduction in unaffected M1 activation over time, and this reduction predicted most improvement.  Improvement in gait via changes in swingphase LI significantly correlated with changes of LI in SMC over time, where asymmetrical activation in the SMC NIRS; gait improved.  Modality; Findings Motor task  Note: MVC = maximum voluntary contraction; LI = laterality index; SMC = sensorimotor cortex; M1 = primary motor cortex. Behavioral measures: FM = Fugl Meyer; WMFT = Wolf motor function test.  Dong et al., 2006 8  Miyai et al., 2003 8  At initial cortical session: and 32-112 subcortical days  Author N Lesion Time Assessment of motor recovery stroke Location Post Injury  Table 2.3: Subject, modality, motor task characteristics and findings for studies demonstrating reduction in unaffected hemisphere activation over time associated with improved function in persons with stroke.  Differences in cortical reorganization between acute and chronic stroke Although most research assessed force control during the late, or chronic, phase after stroke, 3 studies tested at multiple time points starting in the acute phase (0-14 days poststroke; (Staines et al., 2001; Ward et al., 2003a; Ward et al., 2004). Among these studies, recruitment of motor areas changed during force production as recovery improved. In one longitudinal study, decreases in activation occurred over time, from 10-14 days post stroke to 6 months post stroke, in bilateral M1, PFC, SMA, cingulate motor area, temporal lobe, striate cortex, cerebellum, thalamus and BG during affected hand grip (Ward et al., 2003a). In addition, in a separate study the same authors determined that the recruitment of other areas such as the affected PMC and non-affected middle intraparietal sulcus, that occurred 10-14 days after stroke, disappeared by a 3 month follow-up assessment (Ward et al., 2004). Similarly, preliminary data from 2 persons with stroke demonstrated that bilateral hand movement increased activation of the affected hemisphere during early phases of recovery (2 weeks after stroke) and as recovery progressed to 6 months, the pattern no longer persisted due to increased activation associated with movement of the hemiparetic hand (Staines et al., 2001). . Brain Activation during force modulation after stroke Among force modulation studies, increased activation in motor areas occurred with increasing relative force generation in persons with stroke as well as controls (Ward et al., 2003b; Ward et al., 2007, Renner et al., 2005). In using TMS to compare activation between 16 persons with MCA stroke and 11 healthy controls, Renner et al., (2005) found that although increased excitability of the affected motor system occurred with higher force in  33  both groups, ipsilesional motor cortical excitability was decreased per relative force increment in persons with stroke when compared to controls. In contrast, when comparing fMRI activation of 20 persons with stroke that spared hand representation of M1 and 17 healthy controls during increasing relative force, Ward and colleagues (2003b) found no significant differences between the two groups. The lack of group differences may be in part due to the varied response within the stroke group that appeared to be related to recovery. In particular, persons with stroke with poorer functional outcome showed greater activation in response to increased relative grip force with the affected hand in many areas, including contralateral SMC, dorsal PMC, middle temporal gyrus, ipsilateral cerebellum, SMA, and putamen, among others (Ward et al., 2003b). Going a step further, these same authors in a separate study looked at brain activation with increasing force in relation to cortical spinal tract integrity and demonstrated that the degree to which activity in brain regions co-varies with the amount of force produced is related to the extent of corticospinal tract damage (Ward et al., 2007). More specifically, they found that persons with stroke having less corticospinal tract damage had increased activation with increasing force output in affected M1, SMA and unaffected cerebellum (Ward et al., 2007). In comparison, persons with stroke having greater corticospinal tract damage had higher activation with increasing force in affected dorsolateral PM, bilateral ventrolateral PM and unaffected cerebellum (Ward et al., 2007).  34  Influence of rehabilitation on brain activation after stroke Five studies included in this review examined motor reorganization in persons with stroke during a force production task before, after, or during an intervention. For example, one study evaluated cortical activation patterns using fNIRS during gait on a treadmill with partial body weight support (BWS; 10%) (Miyai et al., 2006). This study found that during BWS training, activation in SMC was lowered and changes in SMC activation correlated with shifts in gait performance in 6 persons with subcortical stroke (Miyai et al., 2006). Another study compared brain activation using fNIRS during gait using two different interventions under partial body weight support (Miyai et al., 2002). Results demonstrated that increased activation of cortical motor areas (including PM and preSMA) and improved gait performance occurred in walking with therapists who facilitated hip, pelvis and knee positioning rather than when therapists assisted the foot and thigh in a more mechanical pattern (Miyai et al., 2002). The same authors also examined brain activation longitudinally using the same fNIRS technique during gait before and after two months of inpatient rehabilitation (Miyai et al., 2003). Before rehabilitation, gait was associated with increased SMC activation that was greater in the unaffected vs affected hemisphere, as well as increased activation in the PM and SMA (Miyai et al., 2003). After rehabilitation, activation in the affected PM increased and asymmetry in SMC activation improved (i.e., became more equal between the hemispheres) which significantly correlated with improvement of gait parameters (Miyai et al., 2003). Dong et al., (2006) considered the impact of a 2 week bout of constraint induced movement therapy (CIMT) and found that in persons with chronic (3 months post) stroke, sparing the hand motor representation, activation of the non affected M1  35  decreased after training. This decrease in unaffected M1 activation was assessed via an increase in laterality index (LI). The increases in LI across subjects were due to either decreased unaffected M1 activation from pre to mid intervention (n=1) or an increase in affected (n=1), decease in unaffected (n=1), or a continuous reduction in bilateral (but more so in unaffected) (n=1) M1 activation from pre to post intervention. In contrast, Kopp and colleagues (1999) found that 4 persons with chronic stroke sparing the motor cortex, using CIMT, showed a shift in activation from the away from the affected hemisphere to the nonaffected hemisphere during affected hand movement.  2.5 DISCUSSION Brain Activation after Stroke Higher Levels of activation with increased severity A number of studies examined in this review investigated brain reorganization in relation to severity of stroke. All of these studies demonstrated increased activation in secondary motor areas with increasing severity of stroke, independent of imaging modality or lesion location. Figure 2.1 depicts those areas of the brain that showed higher levels of activation with increased severity or decreased outcome in persons with stroke during force production or modulation.  36  Figure 2.1: Medial and lateral views of the non-affected hemisphere (A) and affected hemisphere (B) of the brain depicting areas having increased activation with increased severity OR decreased outcome in at least two or more studies. A. Non-affected hemisphere shows increased activation in M1, SMA, PM, cingulate sulcus, intraparietal sulcus, cerebellum. B. Affected hemisphere shows increased activation in M1, SMA, PM, cingulate sulcus and intraparietal sulcus. Note: M1 = primary motor cortex, SMA = supplementary motor area, PM = premotor cortex.  This pattern of activation could be due to several reasons. Based on the similarity of corticospinal projections from numerous cortical motor areas, Ward et al., (2003b) suggested that a number of motor regions acting in parallel may generate an output to the spinal cord in order to produce movement. Thus, if damage occurs in one region, greater recruitment of secondary motor areas occurs as a compensatory mechanism. However, projections from secondary motor areas are less numerous and have an overall lower excitatory effect than those from the primary motor area (Maier et al., 2002). Thus, secondary motor recruitment also may be associated with poorer functional outcome (Ward et al., 2003b; Ward et al., 2006). The importance of intact M1 projections for the generation of voluntary movement has also been demonstrated by Wenzelburger et al., (2005) where it was noted that in persons with stroke that disrupted projections descending from M1, more severe chronic motor deficits were exhibited.  37  An alternative explanation for the association of secondary motor area recruitment with poorer functional outcome in persons with stroke is that these individuals may find certain motor tasks more effortful than recovered subjects, and thus they recruit additional motor regions (Serrien et al., 2004; Strens et al., 2004). However, the majority of studies (5/7) used force generation at a relative percentage of MVC, which eliminates the discrepancies in effort between subject groups. Strens et al., (2004) also offer the explanation that increases in activation in secondary areas may occur as a result of increased attention used by some subjects as additional compensation to generate movement. Similarly, based on data from their study, Ward et al., (2006) speculate that when performing a visuomotor task, subjects with increased stroke severity pay more attention to the motor task. This increased attention is associated with greater fronto-parietal activity, which ultimately may facilitate in recruitment of motor areas to aid in generating movement (Ward et al., 2006). In addition the effortful and attentionally demanding nature of generating movement after stroke may stimulate motor fatigue which can also affect brain activation, specifically in the SMA and frontal areas of the brain (van Duinen et al., 2007). Thus, subjects with poor functional outcome may show increased activation in secondary motor areas due to higher levels of motor fatigue.  Role of the Undamaged, Contralesional Hemisphere? The role of the undamaged, contralesional hemisphere during movement of the affected hand after stroke was addressed in 8 studies and an obvious discrepancy was noted between these studies. Some (3/8; (Kopp et al., 1999; Newton et al., 2002; Serrien et al., 2004)) report increased levels of recruitment of the contralesional hemisphere, whereas  38  others (5/8; (Braun et al., 2007; Fridman et al., 2004; Mima et al., 2001; Stinear et al., 2007; Werhahn et al., 2003)) do not. One possibility for this seeming contradiction in results may relate to the time after injury; shifts in activation from the unaffected hemisphere during the acute phase to the affected hemisphere in the chronic phase have been demonstrated after stroke (Marshall et al., 2000). However, the sole factor of time after injury cannot explain all of these findings as 7/8 studies included chronic persons with stroke. Other studies have shown that recruitment of areas in the undamaged, contralesional cortex during motor tasks is associated with poor motor performance (Nelles et al., 1999) or decreased function (Serrien et al., 2004). Moreover, some stroke patients with poor motor outcome show no motor output from the affected, ipsilesional hemisphere, while large amounts of motor activation are noted in the affected hemisphere in patients with good outcome (Bastings et al., 2002) Accordingly, two studies included in this review (Dong et al., 2006; Miyai et al., 2003) have demonstrated a reduction in unaffected hemisphere activation over time that correlated with functional improvement in persons with stroke. In this way, the balance of activation between hemispheres seems to play a role in motor function after stroke. As has been mentioned previously, projections from secondary motor areas are less numerous and have a decreased excitatory effect on the spinal cord than those from the primary motor area. Thus, recruitment of secondary motor areas has been associated with poorer functional outcome (Ward et al., 2003b; Ward et al., 2006). The findings from this review suggest that this is also true for projections descending from the undamaged, contralesional hemisphere. In addition, as persons with stroke with greater disruption of primary motor projections exhibit more severe chronic motor deficits (Wenzelburger et al., 2005), it is not surprising that during movement of the affected hand, activation is more  39  likely to be lateralized towards the affected hemisphere if the motor cortex is intact (Stinear et al., 2007). Thus, it is likely that more severe strokes are those that impart larger amounts of damage to M1 and its projections and result in increased recruitment of secondary motor areas, including the unaffected, contralesional cortex. And as secondary areas are not as adept in generating functional movement as M1, motor outcome and likely overall function are decreased in these individuals. Unfortunately, not all of the studies reporting a preferred recruitment of either hemisphere directly indexed severity of injury making it difficult to ascertain whether more severe strokes do indeed stimulate recruitment of the unaffected hemisphere during force production.  Differences in cortical reorganization between acute and chronic stroke stages The initial increase in secondary motor area activation early after stroke, demonstrated in 3 studies, likely reflects a compensatory strategy to produce functional movement of the affected hand. At a cellular level, increases in synaptogenesis (Jones et al., 1996) and dendritic branching occur in the cortex early after a lesion in rats, while over time branching is reduced (Jones and Schallert, 1992). Ward and colleagues suggest (Ward et al., 2003a) that this branching is followed by subsequent pruning back, and may explain the activation reduction seen in the chronic phase as compared to the acute phase of stroke It is also possible that changes in brain activation between the acute and chronic phase may be due to the fact that early after stroke, when motor deficits are greatest, persons with stroke pay more attention to task performance (Ward et al., 2003a) and increase error monitoring. Increases in task-related brain activation as a result of increased attention due to error awareness have been observed in a number of motor regions, including SMA and  40  cingulate cortex (Klein et al., 2007). In addition, in the acute phase, persons with stroke activate the middle parietal sulcus (Ward et al., 2003a), an area used for tasks requiring increased visuomotor attention (Nobre et al., 1997).  Influence of rehabilitation on brain activation This review provides evidence that rehabilitative interventions can alter brain activation and motor performance of persons with stroke. All 5 studies employing rehabilitation interventions using repetitive tasks demonstrated changes in brain activation post intervention (Dong et al., 2006; Kopp et al., 1999; Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006). Moreover, four of these studies identified changes in brain activation that were associated with improved upper limb (Dong et al., 2006) or lower limb (Miyai et al., 2002; Miyai et al., 2003; Miyai et al., 2006) motor performance after stroke. Although all 5 studies demonstrated altered brain activation with rehabilitation, some discrepancies were apparent in the patterns of brain activation between studies employing the same intervention. Specifically, Dong et al., (2006) showed reduction in activation of the undamaged, contralesional hemisphere after a CIMT intervention, while Kopp et al., (1999) found that the contralesional hemisphere was recruited more after a CIMT intervention. As sample sizes are low in both studies (n=4 and n=6), it is difficult to determine whether these differences are due to subject severity, stroke chronicity, lesion location or some other combination of factors. Importantly, different imaging modalities were used in these two studies (EEG vs fMRI) making a direct comparison of results difficult if not impossible. The ability to determine how patterns of brain activation shift with improved motor performance has great implications for current research designed to inform the development  41  of treatments to manipulate brain reorganization. For example, repetitive TMS applied to the cortex is being examined as a tool to promote cortical plasticity in stroke patients (Di Lazzaro et al., 2008) and could be used with other rehabilitation therapies to further promote functional motor programs (Bernad and Doyon, 2008). In addition, the use of a fully implanted cortical stimulator paired with rehabilitation therapy is being evaluated as a tool to promote plasticity and produce lasting improvements in motor control (Huang et al., 2008). Finally, as was shown by the studies of CIMT (Dong et al., 2006; Kopp et al., 1999) consideration of whether and how interventions shift activation in brain regions associated with the control of force is critical to determine the effectiveness of new treatment approaches. However, it appears that a prerequisite for these types of interventions is some degree of residual sparing of the primary motor areas and associated secondary motor regions in order to produce functional movement and allow for treatment success. Thus, the use of fMRI and other neuroimaging techniques to identify residual anatomical areas and their relative contribution to functional movement may aid in determining which patients will benefit the most from these treatments.  Conclusions and Limitations This review concludes that motor reorganization occurs with respect to force generation and modulation after stroke. Key findings across studies included that during force production increased activation in motor areas, including the undamaged, contralesional hemisphere, occurred in persons with more severe stroke, and recruitment of these motor areas often diminishes as recovery improves. With respect to force modulation, increased activation in motor areas occurred with greater force generation in persons with  42  stroke and individuals with more severe stroke showed greater activation with rising force production levels. This review provides evidence for reduced recruitment of secondary motor areas during force production as a function of time since stroke. Lastly, and very importantly, brain activation can be shifted by certain rehabilitative interventions in persons with stroke. This review has several limitations that stem from the highly varied subject characteristics and tasks that were employed across individual studies. One caveat of the conclusions formed from this review comes from our decision to only include studies that investigated the performance of active movement against resistance; thus we excluded studies employing tasks performed passively or active tapping tasks that were not against resistance. These experimental paradigms can provide valuable information on reorganization after stroke and are often used in more severe stroke populations. However, changes during these types of movements do not necessarily reflect the adaptations that take place during activities of daily living that require force generation and modulation (eg. opening a door, holding a cup) and thus were excluded.  Clinical Implications In summary, through our review of the literature we discovered that several key parameters appear to critically determine how the brain is recruited during force control and modulation after stroke. First, time since stroke is an important factor, with a return to more normal patterns of brain recruitment occurring as individuals move from the acute to chronic stage. Second, the extent of brain damage and the residual integrity of M1 and its outflow tract crucially determines whether force control requires the additional recruitment of 43  secondary and or contralesional motor areas. Thirdly, and likely in strong relationship to the extent of brain damage, the severity of stroke appears to influence whether and how force control in the hemiparetic side returns. Taken together these three factors may be used in the clinical setting to infer how the control of force may be recovered in people with stroke. Finally, it was clear from the available literature that rehabilitation interventions shift both patterns of brain activation and functional ability with respect to force control and modulation after stroke.  44  2.6 BRIDGING SUMMARY Chapter two found that during force production, increased activation in motor areas occurred in persons with more severe stroke, and recruitment of these motor areas diminished as recovery improved. With respect to force modulation, results established that increased activation in motor areas occurred with greater force generation in stroke participants that was similar to healthy controls. Yet, within the stroke group, persons with more severe stroke showed greater activation with rising force. These results from chapter 2, however, are only relevant to relative force production, as most studies used a % of MVC for their target forces. 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B., Lee, L., Frackowiak, R. S., Thompson, A. J., Greenwood, R. J., and Rothwell, J. C. (2007). The relationship between brain activity and peak grip force is modulated by corticospinal system integrity after subcortical stroke. Eur J Neurosci. 25, 1865-1873. Ward, N. S., Newton, J. M., Swayne, O. B., Lee, L., Thompson, A. J., Greenwood, R. J., Rothwell, J. C., and Frackowiak, R. S. (2006). Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 129, 809-819. Wenzelburger, R., Kopper, F., Frenzel, A., Stolze, H., Klebe, S., Brossman, A., KuhtzBuschbeck, J., Gölge, M., Illert, M., and Deuschl, G. (2005a). Hand coordination following capsular stroke. Brain. 128, 64-74.  49  Wenzelburger, R., Kopper, F., Frenzel, A., Stolze, H., Klebe, S., Brossmann, A., KuhtzBuschbeck, J., Golge, M., Illert, M., and Deuschl, G. (2005b). Hand coordination following capsular stroke. Brain. 128, 64-74. Werhahn, K. J., Conforto, A. B., Kadom, N., Hallett, M., and Cohen, L. G. (2003). Contribution of the ipsilateral motor cortex to recovery after chronic stroke. Ann Neurol. 54, 464-472. Winges, S. A., and Santello, M. (2004). Common input to motor units of digit flexors during multi-digit grasping. J Neurophysiol. 92, 3210-3220. Woldag, H., Lukhaup, S., Renner, C., and Hummelsheim, H. (2004). Enhanced motor cortex excitability during ipsilateral voluntary hand activation in healthy subjects and stroke patients. Stroke. 35, 2556-2559.  50  CHAPTER 3 Absolute, but not relative force coding is re-organized in the brain after stroke2 3.1 ABSTRACT Damage to motor areas of the brain caused by stroke can produce devastating motor impairments, including abnormal control of force. Although reorganization of brain function has been identified as an important mechanism for recovery of motor deficits, few studies have investigated how force production and modulation are encoded in the brain after stroke. We hypothesized that the coding of relative force assessed by fMRI would not be significantly altered in the brain after chronic, subcortical stroke, but the coding of absolute force would be reorganized during a task involving the paretic hand. Nine communitydwelling persons with chronic, subcortical stroke and nine healthy controls of similar age participated voluntarily. Subjects performed a squeeze motor task with the paretic hand (stroke participants) or left hand (control participants) at three target forces (10%, 40%, 70% of their measured MVC) during fMRI. A region of interest (ROI) analysis determined peak % signal change (PSC) in specific brain regions (primary motor cortex (M1), supplementary motor cortex (SMA), premotor cortex (PM), thalamus, putamen, caudate, cerebellum). When groups were matched at similar relative forces (%MVC), none of the ROIs showed a difference in brain activation between groups. However, when groups were matched for absolute force production, peak PSC was significantly (P ≤ 0.05) greater in the stroke group compared to the control group in contralateral PM, bilateral M1, ipsilateral SMA and ipsilateral thalamus and showed trends (0.05 > P < 0.1) in bilateral caudate, ipsilateral  2  A version of this chapter will be submitted for publication. Kokotilo, KJ., Eng, JJ., McKeown, MJ., Boyd, LA. Absolute, but not relative, force coding is reorganized in the brain after stroke.  51  putamen and contralateral thalamus. The results of this study indicate that reorganization occurs with respect to absolute force production after chronic, subcortical stroke. Although motor areas activate normally during increasing relative force, activation is higher in some areas when compared to controls at similar absolute forces. These results suggest that nearmaximal levels of brain activation in motor areas are reached at lower absolute forces after stroke and this may limit absolute force production. Key Words: force; stroke; reorganization; motor; fMRI  3.2 INTRODUCTION Damage to motor areas of the brain caused by stroke can produce devastating functional impairments, particularly in motor control of the affected hand. Deficits in producing and controlling force output are common, where persons with stroke moving the paretic hand can experience reduced grip force (Boissy et al., 1999), abnormal time to achieve stable grip (Wenzelburger et al., 2005), and difficulty in maintaining constant force during a grip task (Hermsdorfer and Mai, 1996, Blennerhasset et al., 2006). After brain injury, such as stroke, one mechanism that the central nervous system uses to compensate for its loss is through neural plasticity. For example, after cortical injury the greater the damage that occurs to the M1, the greater the reorganization of intact areas such as the premotor cortex (Frost et al., 2003). Past studies have now established that recovery of motor function after stroke can in fact be attributed to this plasticity, or adaptation of the typical organization of the brain (eg. Schallert et al., 2000). Although reorganization of brain function has been identified as one of the fundamental mechanisms involved in recovery of motor control after stroke, few studies have  52  made use of neuroimaging techniques to investigate how control of force is encoded in the human brain after stroke. Studies with healthy subjects have shown that increased relative force production is accompanied by increased activation in M1 as well as secondary motor areas such as the supplementary motor area (SMA) (Cramer et al., 2002; Dettmers et al., 1995; Dettmers et al., 1996), premotor cortex (PM) and cerebellum (Dai et al., 2001). Relative force (set at a percentage of maximum voluntary contraction (MVC)) allows individuals to match their effort rather than absolute force levels where factors such as age, gender and muscle strength affect force production (Jansen et al., 2008; Shinohara et al., 2003). Although few studies have examined brain activation in persons with stroke during force modulation, a similar pattern of increased activation in primary and secondary motor areas has been demonstrated with increasing relative force production after stroke, with little difference from healthy controls (Ward et al., 2003). These past studies have looked at changes in brain activation over a range of relative forces set at a percentage of MVC of each individual. Although this aids in controlling for effort and difficulty of the motor task across subjects, these studies do not provide information about brain activation during control of absolute force production. If and how brain activation differs between healthy individuals and individuals with stroke at similar absolute levels of force is important because the majority of everyday tasks require an absolute level of force to be produced (eg. pulling a door open, lifting a jug of milk). Thus, the purpose of this study was to employ functional MRI (fMRI) to examine the neural correlates of force modulation in individuals with stroke when both relative (% MVC) and absolute forces were matched with a control group. We hypothesized that, the coding of relative force assessed by fMRI would not be significantly altered in the brain after chronic,  53  subcortical stroke, but the coding of absolute force would be reorganized during a task involving the paretic hand.  3.3 MATERIALS AND METHODS Subjects Twelve individuals with hemiparesis due to subcortical stroke were recruited from the community. The inclusion criteria consisted of (1) at least 6 months post-stroke; (2) some degree of impairment (<66 on Fugl-Meyer); (3) able to use a squeeze grip; (4) able to provide informed consent; (5) able to follow instructions in English. Persons with musculoskeletal or neurological disorders in addition to stroke were excluded from the study. Fourteen healthy older adult controls of comparable age also participated. Ultimately, only 9 stroke participants and 9 healthy controls were included in analysis and reasons for inclusion/exclusion are described in the Results. All participants were right hand dominant (prior to stroke for stroke participants) according to the Edinburgh Handedness Inventory. In order to provide a measure of arm motor recovery, individuals with stroke were tested on the upper extremity portion of the Fugl-Meyer Motor Impairment Scale (FM) (maximum function = 66), the modified Ashworth Scale (MAS) for spasticity of the elbow (0, no increase in muscle tone; 4, affected part rigid in flexion or extension), the Action Research Arm Test (ARAT) (maximum function = 57) for function of the upper extremity, and grip strength using a hand held dynamometer. The local university and hospital review boards provided ethics approval and informed consent was received from all participants prior to their participation in the study.  54  Protocol The motor task consisted of a custom-built MR-compatible rubber squeeze-bulb connected to a pressure transducer. Subjects lay supine and were positioned with their elbow flexed at 90° and forearm in a resting position across their stomach with their hand pronated, gripping the squeeze-bulb. They were instructed to squeeze the bulb using an isometric handgrip. The maximum voluntary contraction (MVC) of each subject was measured prior to starting the task and all subsequent movements were scaled to this. Before the first scanning session, all subjects were trained on the motor task until familiar with the task requirements. In addition, EMG measurements were taken during this practice session to ensure mirror movements did not occur during task performance. During fMRI scanning, participants viewed a computer screen via a projection-mirror system. Each trial began with the appearance of a vertical bar on the display to cue the movement and the required force to be exerted. Using the squeeze bulb, subjects were required to squeeze until the pressure level matched the target level and then release when the target disappeared, allowing the pressure level to return to baseline. Applying greater pressure to the bulb increased the vertical height of the bar and releasing pressure from the bulb decreased the vertical height. Thus, visual feedback was given when subjects overshot or undershot the target bar. Three target force levels (10%, 40% and 70% of the measured MVC) were alternated in each trial in a pseudo-random order. An event related design was used for this study. Four runs were completed during the scan session, 24 trials occurred per run. Each squeeze trial lasted 4s; to allow the hemodynamic response to return to baseline between trials blank (resting) scan intervals were included as a baseline control and randomly inserted at the end of each squeeze trial. Blank runs jittered to last between 10s to 16s. This procedure was employed to differentiate activation trials from  55  baseline activation (Burock et al., 1998). In total each run lasted 6.8 minutes. The experiment was conducted in a total of 4 runs, and the hand required to squeeze the bulb (paretic or nonparetic) was alternated between each run. In healthy controls, the dominant hand was tested first and in stroke subjects, the non-paretic hand was tested first. Only data concerning the non-dominant hand of controls and the the paretic hand of stroke participants was analyzed.  fMRI Data acquisition A Philips Gyroscan Intera 3.0 T scanner (Philips, Best, the Netherlands), equipped with a head-coil, was used to acquire both T1-weighted anatomical images (170 axial slices) and T2*-weighted echo-planar (EPI) images (matrix size = 128 x 128, pixel size = 1.9 x 1.9 mm, TR=2000 ms, TE = 3.7) with blood oxygenation level-dependent (BOLD) contrast. Each functional run lasted 6.8 minutes. Thirty-six axial slices of 3 mm thickness were collected in each volume, with a gap thickness of 1mm. A total of 206 interleaved volumes were acquired continuously during each run.  Behavioral data analysis Custom Matlab software (Mathworks) and the Psychtoolbox (Brainard, 1997) were used to design and present stimuli, and to collect behavioral data from the response devices. The actual relative and absolute pressures exerted during the fMRI scanning session were calculated from the collected behavioral squeeze data.  56  fMRI data analysis Functional MRI data were pre-processed for each subject using Analysis of Functional Neuroimages (AFNI) software package (Cox, 1996). fMRI data were 2D and 3D motion corrected and the skull was stripped from the structural image. Functional data from all runs was concatenated and the skull stripped structural image was aligned to the concatenated functional data. A Deconvolution Analysis of functional data was then performed in AFNI. First, the impulse-response functions were estimated for each of six conditions based on the input stimulus functions and the observed fMRI time series data. The impulse-response functions were then convolved with the stimulus functions to yield the estimated response. Baseline coefficients for each run and regression coefficients for the first 6 TRs of each condition were calculated. Percent signal change (PSC) was then calculated over 6 TR intervals by averaging the baseline constants from each run to create a single baseline constant and then dividing this constant by the regression coefficients for each of the first 6 TRs for each condition. All images were then smoothed with a Gaussian blur with a root mean square deviation of 4 mm. Fourteen specific regions of interest (ROIs) were manually drawn on each aligned structural scan using Amira software. The following ROIs were drawn separately in each hemisphere, guided by a neurological atlas (Talairach and Tournoux, 1988): primary motor cortex (M1), supplementary motor cortex (SMA), premotor cortex (PM), caudate (CAU), putamen (PUT), thalamus (THA), and cerebellum (CER). Within each of these ROIs, the highest value of PSC across the 6TRs was taken as the peak PSC for each condition and used in subsequent analysis.  57  Absolute Force Analysis: Selecting the targeted absolute force is challenging because too high of a level would eliminate the majority of stroke participants who cannot achieve these levels and too low of a level would make the task trivial to the healthy controls. We aimed to select a force of at least 25% MVC which would be relevant to many functional daily tasks (Marshall and Armstrong, 2004). A single force of 3.2 pressure units was chosen based on behavioral data and this value was used to determine PSC at absolute force. This value corresponded to approximately 46% MVC for the paretic hand and 33% MVC for the non-paretic hand of stroke participants and 28% MVC for both hands of healthy controls. Three stroke participants were eliminated from further analysis as their maximum values of absolute force production were over two standard deviations away from the mean. For each ROI for each individual subject, a linear model was fit to the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values) and a coefficient of determination (R2 value) was used to determine the appropriateness of the linear model fit to the data. R2 values of greater than 0.99 were used as an appropriate fit, and the PSC value at the selected absolute force value of 3.2 pressure units was then extracted using linear interpolation. For ROIs with an R2 value of less than 0.99, a second order polynomial was fit to the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values) and the PSC value at the selected absolute force value of 3.2 pressure units was extracted (Figure 3.1).  58  A.  Peak PSC  B.  Peak PSC  Force (pressure units) Force (pressure units) Figure 3.1. Graphs showing models used to fit the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values). A. The linear model fit to the data has an R2 value of 0.9995 and thus was determined as an appropriate fit. B. The linear model fit to the data has an R2 value of 0.9318 and thus was determined not to be an appropriate fit and so a second order polynomial was used to fit the data.  Statistical Analysis: Relative force analysis: A two-way mixed MANOVA was used to assess significance of the group main factor and relative force main factor. A two-way mixed design ANOVA followed by the Student Newman-Keuls post hoc test was used to assess peak PSC between groups (stroke versus control) and within the three relative force levels (10, 40, 70%). Absolute force analysis: Differences in peak PSC between groups (stroke versus control) at 3.2 pressure units were assessed by T-tests for independent samples.  3.4 RESULTS Subjects In total, nine stroke subjects were included in our experiment (Age: mean = 63, SD = 9.8, range = 48-80; 6 men, 3 women). Data from one stroke subject was excluded from fMRI analysis due to excessive head motion and three other stroke participants were eliminated  59  from further analysis because their maximum values of absolute force production were over two standard deviations away from the mean. For control subjects, a total of nine subjects were included in our experiment (Age: mean = 64, SD = 9, range = 50-78; 6 men, 3 women). Data from two control subjects was excluded from fMRI analysis due to excessive head movements and two other control subjects were excluded after analysis because their peak PSC was over four times the standard deviation away from the mean.  Clinical Data The characteristics of all remaining stroke participants are listed in Table 3.1. Table 3.1. Patient Characteristics  Subject  Age  Sex  Time Since Stroke  Lesion Location  Grip Grip Strength Strength Non Paretic Paretic  Fugl ARA Modified Meyer (57) Ashworth (66)  15  16  63  57  0  1  72  F  6 yrs  L post limb IC, putamen, thalamus, pons  2  80  M  8 months  R caudate  34  24.7  57  57  0  3  60  F  4 yrs  R corona radiata  18  14  62  57  0  4  65  M  2.5 yrs  R putamen  41.7  14  51  53  1  5  63  M  2 yrs  R IC, putamen  38.7  30.3  61  57  1  6  55  F  2 yrs  R corona radiata  14  4.3  57  49  0  L IC, CR, Thalamus, Putamen  46  22.3  43  29  2  27  22.7  57  57  1  31.3  29  64  57  1+  7  48  M  28 yrs  8  62  M  1.5 yrs  9  61  M  2 yrs  R External capsule R External capsule  60  Seven participants had left hemiparesis and two right hemiparesis. The site of stroke was determined from the T1-weighted structural MRI. All patients had subcortical stroke encompassing basal ganglia structures or subcortical white matter (Figure 3.2). No cortical or cerebellar strokes were included.  Figure 3.2. Lesion locations for all stroke participants taken from axial structural T1-weighted MRI scans at the level of maximum infarct.  Behavioral results All subjects were able to perform the task correctly during scanning. The relative forces applied (% MVC) for low, medium and high handgrips were similar between groups. However, the absolute forces applied were significantly lower (P ≤ 0.05) at each level of relative force for the paretic hand compared to the non-paretic hand of stroke participants and both hands of healthy controls. The absolute forces applied at each level of relative force  61  were not different between the non-paretic hand of stroke participants and either hand of healthy controls.  Imaging results: Relative force production Areas activated during relative force production Among control subjects, all ROIs were activated during the motor task, with cortical regions having higher PSC than subcortical regions (eg. 0.9% vs 0.4% at high force), excluding the cerebellum (0.75% at high force). Both the ipsilateral and contralateral (relative to hand performing motor task) hemispheres had similar activation within an ROI, where only the SMA showing differences between hemispheres (1.1% for contralateral vs 0.88% for ipsilateral SMA at high force). Among participants with stroke, all ROIs were activated during the motor task, with cortical regions having higher PSC than subcortical regions (eg. 0.9% vs 0.4% at high force), excluding the cerebellum (0.6% at high force). There were some differences in activation between ipsilateral and contralateral (relative to paretic hand performing motor task) hemispheres within an ROI. The putamen and M1 showed higher activation in the ipsilateral versus contralateral hemisphere (0.41% ipsilateral vs 0.32% contralateral putamen at high force; 0.87% ipsilateral versus 0.73% contralateral M1 at high force), while the thalamus showed higher activation in the contralateral versus ipsilateral hemisphere (0.54% for contralateral versus 0.44% for ipsilateral putamen at high force). All other ROIs showed similar activation between hemispheres.  62  Effects of stroke and level of relative force on PSC In contralateral regions the two-way MANOVA produced no interaction effects (Wilks lambda = 0.802, p=0.769), no group main effect (Wilks lambda=0.780 p=0.138), but a significant relative force level main effect (Wilks lambda = 0.332, p=0.000). Similarly, in ipsilateral regions the two-way MANOVA produced no interaction effects (Wilks lambda = 0.856, p=0.936), no group main effect (Wilks lambda=0.786, p=0.153), but a significant relative force level main effect (Wilks lambda =0.373, p=0.000). The lack of a significant main effect between the stroke and control group was evident for the overall MANOVA model, as well as for two-way ANOVAs for all of the individual ROIs (bilateral M1, SMA, PM, cerebellum, caudate, putamen, and thalamus). This indicates that when the stroke and control groups were matched at similar relative forces (%MVC), the brain activation was similar at 10, 40, and 70% of MVC in all the motor areas examined (Figure 3.3).  Contralateral M1 1.2 Peak PSC  1 0.8 0.6 0.4 0.2 0 10  40  70  Relative Force (% MVC)  63  Ipsilateral SMA 1.4  Peak PSC  1.2 1 0.8 0.6 0.4 0.2 0 10  40  70  Re lativ e Force (% M VC)  Figure 3.3. Average Peak PSC at each level of relative force for each group in contralateral M1 and ipsilateral SMA. Blue squares indicate healthy participants; red circles indicate stroke participants.  The main effect of relative force was significant for the overall MANOVA model, as well as for individual two-way ANOVAs in all regions (bilateral M1, SMA, PM, cerebellum, caudate, putamen, and thalamus). The magnitude of activation of primary and secondary motor areas co-varied positively with relative force output in both groups. Thus, all ROIs had peak PSC that were amplified by increasing relative grip force output. Post-hoc Student Newman-Keuls test between the three force levels showed significance (P ≤ 0.05) between all comparisons in all ROIs except the following: bilateral putamen 10%-40%, bilateral caudate 10%-40%, bilateral SMA 10%-40%, bilateral PM 10%-40%, and bilateral cerebellum 10%-40%. No ROIs showed a decrease in peak PSC with increasing relative grip force.  64  Imaging results: Absolute force production Areas activated during absolute force production  Among controls subjects, all ROIs were activated during production of force at an absolute value of 3.2 pressure units, with cortical areas having a slightly higher PSC than subcortical regions (eg. 0.42% vs 0.3%), excluding the cerebellum (0.47%). Both the ipsilateral and contralateral hemispheres had similar activation within an ROI. Among participants with stroke, all ROIs were activated during the motor task, however cortical regions had higher PSC than subcortical regions (0.7% vs 0.4 - 0.5%). Both ipsilateral and contralateral hemispheres had similar PSC within an ROI, with exception to the thalamus, which had higher PSC in the contralateral (0.56%) versus ipsilateral (0.41%) hemisphere.  Comparison of stroke group with control group: Absolute Force Comparisons between the two groups at the same absolute force showed significantly (P < 0.05) greater peak PSC in the both ipsi- and contralateral M1, contralateral PM, , ipsilateral SMA and ipsilateral thalamus of the stroke as compared to control group, and trends towards a difference in both the ipsi- and contralateral caudate, the ipsilateral putamen, and contralateral thalamus (0.05 > P < 0.1 (Fig 3.4)). Overall, when groups were matched for an absolute force production of 3.2, peak PSC was greater in the stroke group as compared to the control group in these ROIs.  65  A  B  Figure 3.4. Average Peak PSC at an absolute force level of 3.2 pressure units for each group in all (A) contralateral ROIs and (B) ipsilateral ROIs. Red bars indicate stroke participants; blue bars indicate healthy participants. ** indicates significance at P < 0.05; * indicates trends at 0.05 > P < 0.1.  66  3.5 DISCUSSION Past literature examining task related brain activation across increasing levels of relative force production has found no differences between healthy and stroke subjects in recruitment of brain motor areas (Ward et al., 2003). Ward et al (2003) examined brain activation of chronic stroke participants with both subcortical and cortical lesions (excluding M1) during a range of grip forces set from 10-60% MVC. In comparison, our subset of stroke participants exhibited only subcortical lesions and our motor task used a slightly greater range of % of MVC (10-70%) to determine if group differences could be seen at near maximal levels of relative force production. In addition, our analysis of brain activation used an ROI approach and peak PSC as an index of activation amplitude, while the previous study (Ward et al 2003) used a voxel-wise approach, where brains were warped to a standard reference brain template. Yet even with the use of dissimilar methods of analysis, inclusion of subjects with different lesion locations, and use of a different range of grip forces, our results replicate Ward et al.’s (2003), which found no differences between stroke and controls in brain activation during increasing relative force production. As our subset of stroke subjects had only subcortical lesions, one might expect force modulatory activation in the subcortical areas to be diminished. However, most subcortical areas and all motor cortical areas examined were able to mediate modulation of relative grip force in stroke participants with no differences from healthy individuals. As previous work did not use an ROI approach, our results were able to show that all ROIs had significantly higher activation from easy to high force production; there was no recruitment of novel ROIs or switching to different ROIs as relative force production increased.  67  Importantly, when groups were matched for absolute force production, peak PSC was greater in the stroke group as compared to control group in cortical areas (bilateral M1, contralateral PM, ipsilateral SMA) as well as in some subcortical areas (ipsilateral putamen, bilateral caudate, bilateral thalamus). These results suggest that individuals with subcortical stroke use a higher fraction of their maximum brain activity when generating high forces with their paretic arm compared to healthy individuals generating the same amount of force. As absolute force of the paretic upper limb is often reduced following stroke, these nearmaximal levels of brain activation in the motor areas may actually limit absolute force production. A related finding has been reported where maximum levels of muscle activation (measured with surface EMG) appeared to limit absolute force output in the paretic upper limb of individuals with stroke (McCrea et al., 2005). Although we use the idea of “maximum” levels of brain activation, caution must be exerted with this idea. In reality, it is not known what “maximum” brain activation is. As fMRI relies on the BOLD response, which is a measure of the magnetization difference between oxy- and deoxyhemoglobin in the blood, it measures neuronal activity indirectly via this haemodynamic correlate (Arthurs and Boniface, 2002). Although some studies suggest a linear relationship exists between the hemodynamic and neuronal responses (eg. Arthurs and Boniface, 2002), others suggest that more of a nonlinear relationship exists (eg. Huttunen et al., 2008). In addition, brain damage may affect this neuro-hemodynamic coupling, as evoked changes in cerebral blood oxygenation in the stroke affected brain have been shown to differ from those in the normal brain (Sakatani et al., 2003; Murata et al., 2006). Furthermore, even with ideal neuro-hemodynamic coupling, it is not known what maximum neuronal activity is.  68  During absolute force production, our stroke participants showed increased activation of contralateral M1 in addition to other cortical and subcortical motor regions having connections with contralateral M1. The ROIs that demonstrated this increased task-specific activation are consistent with previously-reported neurobiological knowledge. For example, several neuroimaging studies have suggested that the thalamus is involved in attentional processing (Buchel et al., 1998; Tomasi et al., 2007) and thus greater activation observed in the thalamus of stroke subjects may be due to increased attention during the visuomotor task. Similarly, the PM cortex is thought to process and integrate external visual, attentional, and other information in order to produce motor output (Wise et al., 1997), indicating that greater PM activation may reflect greater use of this visuomotor integration system. In terms of activation in other areas, increased ipsilateral M1 activity has been demonstrated in healthy adults performing more challenging motor tasks (Carey et al., 2006; Verstynen et al., 2005). This suggests that the enhanced activation in ipsilateral M1 of stroke participants may be because they found the motor task more challenging. The SMA is an area important for the preparation and execution of visually cued movements (Thickbroom et al., 2000), and as a more challenging motor task would involve greater planning and execution, this would also explain the increased activation in SMA. The idea of reorganization of brain function during increased task difficulty after stroke has been examined by past studies. Such studies have demonstrated that when stroke participants and controls have been matched in terms of task difficulty, performance of a more difficult motor task increases activation in ipsilateral M1 (Cramer et al., 2001), PMv and SMA (Schaechter and Perdue, 2008) of stroke participants relative to healthy participants.  69  It is also possible that motor fatigue may have contributed to higher brain activation during matched absolute force production levels in the stroke compared to control group. A recent fMRI study has demonstrated that in healthy individuals, activation in cortical motor areas (sensorimotor cortex, SMA, PM) increases during a sustained high-force contraction, while maximal force production and EMG of the target muscles decreases with time (Post et al., 2008). Increased cortical activity during motor fatigue has also been demonstrated in the pre-SMA and frontal areas (van Duinen et al., 2007) as well as the visual cortex (Benwell et al., 2007) in healthy individuals. However, all of these studies employed highly fatiguing motor tasks, (eg. squeeze for 50 s and rest 5 s (van Duinen et al., 2007); sustained maximal contraction for 126 s (Post et al., 2008); squeeze 3 s and rest 2 s (Benwell et al., 2007)). In contrast, we chose an event-related design in order to reduce motor fatigue during task performance, where squeeze occurred for approximately 2 s, followed by 10-16s of rest. In addition, behavioral results showed that force production did not decrease over time, as it might with motor fatigue. Thus, the increased activation observed in the stroke participants during absolute force production in the current study is unlikely to be a result of increased motor fatigue.  Conclusions and Implications Taken together, our data combined with results from earlier studies suggest that during matched absolute force production individuals with stroke exhibit higher levels of activity in primary and secondary motor areas in order to maintain task performance, perhaps a result of conditions of increased task difficulty. As a result, near-maximal levels of brain activation in motor areas are reached at lower absolute forces, probably limiting absolute  70  force production in these individuals. These findings have implications for rehabilitation, as therapeutic interventions may be developed that target brain reorganization in order to improve functional recovery after stroke. In addition, it may be possible that rehabilitation targeting the strengthening of paretic muscle fibres in the upper limb after stroke may alter motor unit capability and motor fatigue and consequently reduce saturation of brain activation in cortical motor areas. Whether this can occur and subsequently translate into increased absolute force production after stroke remains to be determined.  Limitations Our analyses of absolute force production required a minimum amount of absolute force to be produced with the paretic hand, consequently limiting our sample to patients with mild and moderate impairment. Thus, our findings cannot be generalized to all stroke severities and must only be considered in the context of the mild and moderately motor impaired patients included. Also, a limitation apparent in our fMRI data analysis is that we only examined the intensity of activation and failed to examine any spatial changes in activation.  71  3.6 BRIDGING SUMMARY The third chapter found that in terms of reorganization of brain function during force production, greater brain activation occurs only during absolute force production in stroke participants compared to healthy controls. As past literature suggests that increased activation occurs as functional outcome decreases, this raises an interesting point of whether after long standing stroke the amount of paretic arm activity, which is a good indicator of upper extremity outcome (Uswatte et al., 2005), similarly relates to task-related brain activation. 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C., and Ernst, T. (2007). Different activation patterns for working memory load and visual attention load. Brain Res. 1132, 158-165. van Duinen, H., Renken, R., Maurits, N., and Zijdewind, I. (2007). Effects of motor fatigue on human brain activity, an fMRI study. Neuroimage. 35, 1438-1449. Verstynen, T., Diedrichsen, J., Albert, N., Aparicio, P., and Ivry, R. B. (2005). Ipsilateral motor cortex activity during unimanual hand movements relates to task complexity. J Neurophysiol. 93, 1209-1222. Ward, N. S., Brown, M. M., Thompson, A. J., and Frackowiak, R. S. (2003). Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain. 126, 1430-1448. Wenzelburger, R., Kopper, F., Frenzel, A., Stolze, H., Klebe, S., Brossman, A., KuhtzBuschbeck, J., Gölge, M., Illert, M., and Deuschl, G. (2005). Hand coordination following capsular stroke. Brain. 128, 64-74. Wise, S. P., Boussaoud, D., Johnson, P. B., and Caminiti, R. (1997). Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu Rev Neurosci. 20, 25-42.  75  CHAPTER 4 Greater activation of secondary motor areas occurs with less arm use after stroke3 4.1 ABSTRACT Past studies have identified reorganization of brain function as an important mechanism for recovery of motor function after stroke. The majority of past studies have assessed motor outcome through standardized laboratory measures, which are a surrogate for the actual arm activity used in real life. In comparison, the novel use of accelerometers can quantify upper limb activity outside the laboratory and can provide a real life estimate of arm and hand usage. We aimed to examine reorganization of brain function after stroke in relation to use of the paretic limb as measured by accelerometers. We hypothesized that subjects with decreased paretic arm use would exhibit greater activation of the brain’s motor areas. Ten community-dwelling persons with chronic, subcortical stroke and ten healthy controls of similar age participated voluntarily. Subjects performed a squeeze motor task with the paretic and non-paretic hand (stroke participants) or left hand (control participants) during fMRI. The activity level of the upper extremities of all subjects was quantified over 3 consecutive days using wrist accelerometers. Correlation analysis was performed to determine the relationship between arm use and peak PSC during both absolute and relative force production in the paretic and non-paretic hands of stroke participants and left hand of controls. Results from this study demonstrate that in healthy controls, only the contralateral caudate shows a relationship between decreased arm use and increased brain activation during force production. In contrast, after stroke, multiple motor areas show increased 3  A version of this chapter will be submitted for publication. Kokotilo, KJ., Eng, JJ., McKeown, MJ., Boyd, LA. Greater activation of secondary motor areas occurs with less arm use after stroke  76  activation with less paretic and non-paretic arm use during both absolute and relative force production. Significant correlations (P<0.05) were found with decreasing amounts of arm use related to greater peak PSC in areas including the contralateral SMA and caudate and ipsilateral M1, PM, thalamus and putamen. The results of this study indicate that healthy subjects with decreasing arm use do not exhibit greater activation of multiple motor areas, while stroke subjects do show a relationship between decreasing arm use and greater activation of several motor areas. These subjects may have less arm usage due to the reduced ability of secondary motor areas to generate functional movement. Alternatively, subjects who use their arm less over time may produce less primary motor activation and rely more on secondary motor areas.  4.2 INTRODUCTION Functional reorganization of the central nervous system is thought to be one of the fundamental mechanisms involved in recovery after neurological injury, such as stroke. Past animal studies have established that structural plasticity can occur in the damaged cortex and connected brain areas, and that functional recovery is associated with this plasticity (Jones and Schallert, 1992; Kolb and Gibb, 1993). Similarly, neuroimaging studies in humans have demonstrated that reorganization of brain activation relates to functional outcome after stroke (Ward et al., 2003). More specifically, persons with chronic stroke are more likely to activate motor areas, such as M1, PM, cerebellum, SMA and parietal cortex, with decreasing function across outcome measures, including the Barthel activities of daily living (ADL) index, Action Research Arm Test (ARAT) and grip strength (Ward et al., 2003). Neuroimaging studies have also examined brain reorganization in relation to rehabilitative interventions and  77  outcome measures of paretic arm use. After implementation of constraint-induced movement therapy (CIMT), persons with chronic stroke can exhibit cortical reorganization which is accompanied by increased use of the affected arm as measured by the motor activity log (MAL) (Schaechter et al., 2002; Szaflarski et al., 2006) (semi-structured interview which assesses the individual’s perception of how much and how well they use their paretic arm during ADL). Although these past studies have provided valuable information on the relationship between outcome and reorganization of brain function after stroke, the functional outcome measures evaluated in a laboratory setting are a surrogate for the actual arm activity used in real life. Yet, measuring the level of arm use in real life is significant, as one might expect that if the paretic arm is utilized less over time, activation of the brain would reflect this. The use of accelerometers is a relatively novel method of monitoring arm and hand usage in daily activities and can be used to assess actual upper limb activity outside the laboratory. Contrary to specific outcome measures of hand function, accelerometers can provide a real life estimate of the quantity and intensity of arm and hand usage. Past literature has established the reliability and validity of accelerometers for measurement of upper limb activity in stroke patients, (Uswatte et al., 2000; Uswatte et al., 2005; Uswatte et al., 2006) and a period of 3 consecutive days has been demonstrated to be a valid and objective, real-world index of arm activity (Uswatte et al., 2005; Uswatte et al., 2006). Yet, to date, there is no information on the amount of paretic arm activity use, as indexed by accelerometers, in relation to functional brain reorganization after stroke. Thus the objective of this study was to examine reorganization of brain function in chronic stroke patients using functional MRI (fMRI) and relate it to use of the paretic limb as measured by accelerometers. We hypothesized that a relationship would exist between these two  78  parameters where subjects with decreased arm use would exhibit greater activation of secondary motor areas.  4.3 METHODS Subjects Ten subjects with hemiparesis following a subcortical stroke participated (Age: mean 62.3 years; std dev 8.87; range 48-80). Subjects were at least 6 months post-stroke, had some degree of impairment (<66 on Fugl-Meyer), were free from musculoskeletal and other neurological conditions, were able to use a squeeze grip and able to follow instructions in English. Ten healthy individuals of similar age also participated (Age: mean 63.8 years; std dev 8.5; range 50-78). Approval was obtained from the local university and hospital ethics committees and all subjects provided an informed consent. All participants were right hand dominant (prior to stroke for stroke participants) according to the Edinburgh Handedness Inventory. For stroke participants motor recovery of the paretic upper extremity was assessed with the upper extremity portion of the Fugl-Meyer Motor Impairment Scale (FMA) (maximum function = 66) and grip strength using a hand held dynamometer. Function of the upper extremity was measured using the Action Research Arm test (ARAT) (maximum function = 57) and muscle tone of the elbow was assessed using the Modified Ashworth Scale (MAS) (0, no increase in muscle tone; 4, affected part rigid in flexion or extension).  Accelerometer Protocol The activity level of the upper extremities of all subjects was quantified using ActicalTM (Mini Mitter Co) accelerometers. These accelerometers were small (28X27X10  79  mm), light (17g), waterproof and had a frequency range of 0.3-3 Hz, sensitive to 0.05-2.0 Gforce and samples at 32 Hz. They stored data as activity counts every 15 seconds and detected acceleration in all 3 planes, although they were more sensitive in the vertical direction. Two wireless watch-like accelerometers were worn on each wrist of participants. Participants were asked to wear accelerometers outside the laboratory during all waking hours for 3 consecutive days. The mean total activity counts per day over the 3 consecutive days for both the paretic and non-paretic arms of stroke participants and both arms of control participants were used as the measure of upper extremity activity.  fMRI Protocol The motor task performed during fMRI scanning was a custom-built MR-compatible rubber squeeze-bulb connected to a pressure transducer. Subjects lay supine and were positioned with their elbow flexed at 90° and forearm in a resting position on their stomach with their hand pronated, gripping the squeeze-bulb. They were instructed to squeeze the bulb using an isometric handgrip. The maximum voluntary contraction (MVC) of each subject was measured prior to starting the task and all subsequent movements were scaled to this. Before the first scanning session, all subjects were trained on the motor task until familiar with the task requirements. In addition, EMG measurements were taken during this practice session to ensure mirror movements did not occur during task performance. During fMRI scanning, participants viewed a computer screen via a projection-mirror system. Each trial began with the appearance of a vertical bar on the display to cue the movement and the required force to be exerted. Using the squeeze bulb, subjects were required to squeeze until the pressure level  80  matched the target level and then release when the target disappeared, allowing the pressure level to return to baseline. Applying greater pressure to the bulb increased the vertical height of the bar and releasing pressure from the bulb decreased the vertical height. Thus, visual feedback was given when subjects overshot or undershot the target bar. Three target force levels (10%, 40% and 70% of the measured MVC) were alternated in each trial in a pseudorandom order. An event related design was used for this study, where a total of 24 trials occurred per run. Each trial lasted 4s and jittered intertrial intervals lasted between 10s – 16s, to a total of approximately 6.8 minutes per run. The experiment was conducted in a total of 4 runs; the hand required to squeeze the bulb (paretic or non-paretic for stroke participants, dominant or non-dominant for healthy controls) was alternated between each session and the non-paretic hand (for stroke participants) or dominant (for healthy controls) was tested first.  fMRI Data acquisition A Philips Gyroscan Intera 3.0 T scanner (Philips, Best, the Netherlands), equipped with a head-coil, was used to acquire both T1-weighted anatomical images (170 axial slices) and T2*-weighted echo-planar (EPI) images (matrix size = 128 x 128, pixel size = 1.9 x 1.9 mm, TR=2000 ms, TE = 3.7) with blood oxygenation level-dependent (BOLD) contrast. Each functional run lasted 6.8 minutes. Thirty-six axial slices of 3 mm thickness were collected in each volume, with a gap thickness of 1mm. A total of 206 volumes were acquired continuously during each session.  81  Behavioral data analysis Custom Matlab software (Mathworks) and the Psychtoolbox (Brainard, 1997) were used to design and present stimuli, and to collect behavioral data from the response devices. The actual relative and absolute pressures exerted during the fMRI scanning session were calculated from the collected behavioral data.  Force Analysis Force can be quantified in terms of an absolute amount of force (e.g., Newtons) or relative force (percent maximum voluntary contraction %MVC). Given that the relationship of arm activity might be different with relative versus absolute force measures, we quantified the brain activation 1) at the same absolute force across all subjects (stroke and controls) and 2) at the same relative force for each subject.  Absolute Force Analysis: We selected a force of at least 25% MVC which would be relevant to many functional daily tasks (Marshall and Armstrong, 2004) and would be low enough that the majority of stroke participants could achieve it, yet still high enough that the task was not trivial for healthy participants. A single force of 3.2 pressure units was chosen based on behavioral data, as this value corresponded to approximately 46% MVC for the paretic hand and 33% MVC for the non-paretic hand of stroke participants (n=9) and 28% MVC for both hands of healthy controls (n=10). One stroke participant was eliminated from the absolute force analysis (but not relative force analysis) as their maximum value of absolute force production failed to reach this value of 3.2 pressure units. For each ROI for each individual  82  subject, a linear model was fit to the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values) and a coefficient of determination (R2 value) was used to determine the appropriateness of the linear model fit to the data. R2 values of greater than 0.99 were used as an appropriate fit, and the PSC value at the selected absolute force value of 3.2 pressure units was then extracted using linear interpolation. For ROIs with an R2 value of less than 0.99, a second order polynomial was fit to the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values) and the PSC value at the selected absolute force value of 3.2 pressure units was extracted (Figure 4.1). A.  B.  Peak PSC  Peak PSC  Force (pressure units)  Force (pressure units)  Figure 4.1. Graphs showing models used to fit the data (over the 10, 40 and 70% MVC force values and their corresponding PSC values). A. The linear model fit to the data has an R2 value of 0.9995 and thus was determined as an appropriate fit. B. The linear model fit to the data has an R2 value of 0.9318 and thus was determined not to be an appropriate fit and so a second order polynomial was used to fit the data.  Relative force analysis: We selected a force of 40% MVC for the paretic and non-paretic hands of stroke participants (n=10) and left hand of control participants (n=10) to use in the correlation analysis for relative force. The 40% condition represented a force indicative of functional  83  daily tasks (Marshall and Armstrong, 2004) and moreover, was closest to the % MVC at the selected value of the absolute force (46% MVC) for the paretic hand of stroke participants.  fMRI data analysis The functional MRI data were pre-processed for each subject using AFNI software package (Cox, 1996). fMRI data were 2D and 3D motion corrected and the skull was stripped from the structural image. Functional data from all runs was concatenated and the skull stripped structural image was aligned to the concatenated functional data. A Deconvolution Analysis of functional data was then performed in AFNI where the impulseresponse functions were estimated for each of six conditions based on the input stimulus functions and the observed fMRI time series data. The impulse-response functions were then convolved with the stimulus functions to yield the estimated response. Baseline coefficients for each run and regression coefficients for the first 6 TRs of each condition were calculated. Percent signal change (PSC) was calculated over 6 TR intervals by averaging the baseline constants from each run to create a single baseline constant and then dividing this constant by the regression coefficients for each of the first 6 TRs for each condition. All images were smoothed with a Gaussian blur with a root mean square deviation of 4 mm. Seven specific regions of interest (ROIs) (14 bilaterally) were manually drawn on each aligned structural scan using Amira software. The following ROIs were drawn separately in each hemisphere, guided by a neurological atlas (Talairach and Tournoux, 1988): primary motor cortex (M1), supplementary motor cortex (SMA), premotor cortex (PM), caudate (CAU), putamen (PUT), thalamus (THA), and cerebellum (CER). Within each of these ROIs, the maximum value of PSC across the 6 TRs was used as peak PSC for each condition.  84  Statistical Analysis: Distribution of the data was assessed and deemed to be parametric (Shapiro-Wilks test for normality). Independent t-tests were used to determine differences between the amount of arm use in: the paretic vs. non-paretic arms of stroke participants, the paretic arm of stroke participants vs. non-dominant arm of healthy controls and the non-paretic arm of stroke participants vs. the non-dominant arm of healthy controls. We performed a correlational analysis to determine whether a relationship existed between task-related changes in activation of motor areas of the brain and activity of the upper extremity as assessed using wrist accelerometers. In stroke participants, and for each ROI, we calculated Pearson correlations between activity of the paretic arm and peak PSC at a relative force of 40% MVC and an absolute force value of 3.2 as well as between activity of the non-paretic arm and peak PSC at a relative force of 40% MVC and an absolute force value of 3.2. For healthy controls, and for each ROI, correlations were calculated between activity of the left (non-dominant arm) and peak PSC at a relative force of 40% MVC and an absolute force value of 3.2. Distribution of the ARAT scores was assessed and deemed to be non-parametric (Shapiro-Wilks test for normality). Thus, Spearman correlations were used to determine if a relationship existed between peak PSC of motor areas of the brain and functional arm activity as assessed by the ARAT.  85  4.4 RESULTS Clinical Data Characteristics of all stroke participants are listed in Table 4.1. Table 4.1. Patient Characteristics Time  Grip  Grip  Fugl ARA Modified  Lesion Subject  Age  Sex  Since  Strength  Strength  Meyer  Non Paretic  Paretic  (66)  15  16  63  57  0  34  24.7  57  57  0  18  14  62  57  0  (57) Ashworth  Location Stroke L post limb IC, 1  72  F  6 yrs  putamen, thalamus, pons  2  80  M  8 months  R caudate R corona  3  60  F  4 yrs radiata  4  65  M  2.5 yrs  R putamen  41.7  14  51  53  1  5  63  M  2 yrs  R IC, putamen  38.7  30.3  61  57  1  14  4.3  57  49  0  46  22.3  43  29  2  27  22.7  57  57  1  R corona 6  55  F  2 yrs radiata L IC, CR,  7  48  M  28 yrs  Thalamus, Putamen R External  8  62  M  1.5 yrs capsule  86  R External 9  61  M  2 yrs  31.3  29  64  57  1+  48.7  9.7  15  0  2  capsule R Basal 10  57  M  2.5 yrs Ganglia  Seven participants had experienced left hemiparesis and three right hemiparesis. The site of stroke was determined from the T1-weighted structural MRI. All patients had subcortical stroke encompassing basal ganglia structures or subcortical white matter. No cortical or cerebellar strokes were included. The majority of subjects with stroke (n=9) were generally mild to moderate stroke (FM range 43-64, mean 57), with the exception of one severely impaired subject (FM 15) who was only included in the relative, but not absolute force analysis.  Accelerometer Data The mean (SD) activity kilocounts per day for both the right and the left hands for hands for control participants and paretic and non-paretic hands for stroke participants appear in Table 4.2 and Figure 4.2. The activity counts for the paretic hand were significantly (P<0.05) lower compared to the non-paretic hand of stroke participants and both hands of healthy controls. No significant differences were found between activity counts for the nonparetic hand of stroke participants and either hand of healthy controls.  87  Table 4.2: The mean (SD) activity kilocounts per day of the dominant and non-dominant hands for control participants and paretic and non-paretic hands for stroke participants. Control Participants  Stroke Participants  mean (SD) activity kilocounts / day  mean (SD) activity kilocounts /day  Dominant Hand  Paretic Hand  Non Paretic Hand  138.5 (86.4)  259.0 (129.4)  Non-Dominant Hand  295.2 (109.4)  306.4 (134.6)  Figure 4.2: Graph of the mean activity kilocounts per day of the dominant and non-dominant hands for control participants and paretic and non-paretic hands for stroke participants.  88  Imaging Data Brain activation during Absolute Force Production versus arm use In healthy controls, there was no correlation between task-related PSC in the contralateral M1 and total activity counts. A significant correlation was only found between increasing task-related PSC in the contralateral caudate (R=-.660, P<0.05) at an absolute force of 3.2 pressure units and decreasing total activity counts of the left upper limb (n=10) (Figure 4.3). .3  R= -.660, p=0.038 .2  .1  CAU_3.2  0.0  -.1 100000  200000  300000  400000  500000  600000  TOTALACT  Figure 4.3: Graph of peak PSC at an absolute force of 3.2 pressure units vs. total accelerometer activity counts for the left hand of control subjects (n=10) for contralateral caudate  In stroke patients (n=9), there was no correlation between task-related PSC in the contralateral M1 at an absolute force of 3.2 pressure units and total activity counts for the paretic upper limb. However, correlation analysis identified several secondary motor regions in which there was a correlation between paretic arm activity and brain activation (Figure 4.4). In the contralateral (to movement of paretic hand) hemisphere, significant negative correlations (P<0.05) were found in the contralateral SMA (R = -.701) and contralateral caudate (R = -.711). In the ipsilateral (to moving paretic hand) hemisphere, significant  89  negative correlations (P<0.05) were found in the ipsilateral M1 (R = -.717) and ipsilateral putamen (R = -.787). This indicates that when matched at an absolute force, increased activation occurred in these ROIs in subjects having less activity of their paretic arm. A.  B. .8  3.0  R= -.711, p=0.032  .7  R= -.701, p=0.035  2.5  .6 2.0  .5 .4  1.5  .3 1.0  .5  .1  PM  CAU.3.2  .2  0.0 0  100000  200000  0.0  300000  0  TOTALACT  100000  200000  300000  TOTALACT  C.  D. 1.0  2.0  R= -.787, p=0.012  R= -.717, p=0.03  .8 1.5  .6 1.0  .4  .5  IPSIM1  IPSIPUT  .2  0.0 0  TOTALACT  100000  200000  300000  0.0 0  100000  200000  300000  TOTALACT  Figure 4.4: Graphs of peak PSC at an absolute force of 3.2 pressure units vs total accelerometer activity counts for the paretic hand of stroke subjects (n=9) for: (A) contralateral (ipsilesional) caudate; (B) contralateral (ipsilesional) SMA; (C) ipsilateral (contralesional) putamen; (D) ipsilateral (contralesional) M1.  90  No significant correlations were found between task-related PSC at an absolute force of 3.2 pressure units and total activity counts for the non-paretic upper limb of stroke participants (n=9).  Brain activation during Relative Force Production versus arm use In healthy controls, no significant correlations were found between task-related PSC at 40%MVC and total activity counts of the left upper limb (n=10). No correlation was found between task-related PSC in the contralateral M1 at a relative force of 40% MVC and total activity counts for the paretic upper limb of stroke participants (n=10). However, the correlation analysis did identify several ipsilateral (to movement of paretic hand) motor regions in which there was a correlation between brain activation and paretic arm activity. Significant negative correlations (P≤ 0.05) were found in ipsilateral M1 (R = -.657), ipsilateral PM (R = -.628) and ipsilateral putamen (R = -.762) (Figure 4.5). Thus, at a relative force of 40%, increased activation occurred in these ROIs in subjects with less paretic arm use. A  B .7  1.2  R= -.762, p=0.01  .5  .8  .4  .6  .3  .4  .2  .1 0  TOTALACC  100000  200000  R= -.657, p=0.039  1.0  IPSIM1ME  IPSIPUTM  .6  300000  .2  0.0 0  100000  200000  300000  TOTALACC  91  C 1.0  R= -.628, p=0.052 .8  .6  IPSIPMM  .4  .2  0.0 0  100000  200000  300000  TOTALACC  Figure 4.5: Graphs of peak PSC at relative force of 40% MVC vs total accelerometer activity counts of the paretic hand in stroke subjects (n=10) for (A) ipsilateral (contralesional) putamen; (B) ipsilateral (contralesional) M1; (C) ipsilateral (contralesional) PM.  A significant (P<0.05) correlation was found between task-related PSC at 40%MVC and total activity counts for the non-paretic upper limb of stroke participants (n=10) in the ipsilateral (to moving non-paretic hand) thalamus (R = -.655) and ipsilateral PM (R = -.657) (Figure 4.6). A  B .9  1.0  R= -.655, p=0.040  R= -.657, p=0.039  .8  .8  .7 .6  .6  .5 .4  .4  IPSIPMM  IPSTHALM  .3 .2  0.0 100000  200000  TOTALNP  300000  400000  500000  600000  .2 .1 100000  200000  300000  400000  500000  600000  TOTALNP  Figure 4.6: Graphs of peak PSC at relative force of 40% MVC vs total accelerometer activity counts of the nonparetic hand in stroke subjects (n=10) for (A) ipsilateral (ipsilesional) thalamus; (B) ipsilateral (ipsilesional) PM.  92  Brain activation versus arm function (ARAT) Spearman correlational analysis showed no significant correlations between scores on the ARAT and peak PSC during either absolute force production of 3.2 pressure units or relative force production at 40% MVC.  4.5 DISCUSSION Despite varying arm activity among individuals in the control group, a relationship between increased activation and decreasing arm activity was only found in a single area. It may be that in terms of the general population, our healthy controls were similar in their overall level of physical activity. Perhaps including a subset of healthy subjects exhibiting a wider range of activity levels, such as persons with multiple athletic pursuits and individuals who are very sedentary, will uncover a relationship in more regions. Future studies will be needed to determine this. In terms of the stroke group, our analyses of brain activation in relation to paretic arm use as measured by accelerometers demonstrated a lack of correlation in contralateral M1 during relative and absolute force production. The basis for this finding could be a result of the lesion locations for our subset of subjects. All subjects had subcortical lesions, with the majority (n=6) affecting white matter tracts that may descend from M1. If the corticospinal tract descending from M1 is damaged, then this region will be less useful in producing force output, and therefore less likely to be activated. However contrary to the unchanged activation that was observed in contralateral M1, results from this study demonstrate that increased activation in other contralateral motor  93  areas occurred in patients with less arm use after stroke during absolute force production. Specifically, the contralateral areas that showed increased activation were the SMA and caudate. Ipsilateral motor areas also appeared to compensate, as some of the ipsilateral areas showed increased activation in stroke subjects with less arm activity. These ipsilateral areas showing greater activation were the M1 and putamen during both absolute and relative force production, and PM during relative force production. Past literature has demonstrated increased activation occurs in several motor areas with increasing severity of stroke or decreasing function after stroke during performance of a motor task with the paretic hand. Specifically, chronic stroke subjects having greater corticospinal tract damage showed increased activation in motor areas including bilateral M1, bilateral PM, SMA, and prefrontal cortex (PFC) (Ward et al., 2006)and affected hemisphere motor areas in general (Newton et al., 2006). In terms of function, subjects with decreasing functional outcome are more likely to activate similar motor areas, such as M1, PM, cerebellum, SMA and parietal cortex (Ward et al., 2003). The reason for increased recruitment of these secondary motor areas may be based on the similarity of corticospinal projections from these cortical motor areas. Ward et al., (2003) suggested that a number of motor regions acting in parallel may generate an output to the spinal cord in order to produce movement, and so, if damage occurs in one region, greater recruitment of secondary motor areas can occur to compensate. However, projections from secondary motor areas are less numerous and have an overall lower excitatory effect than those from the primary motor area (Maier et al., 2002), therefore secondary motor recruitment is often associated with poorer functional outcome (Ward et al., 2003; Ward et al., 2006). As our novel results show that subjects with decreasing arm use are more likely to activate secondary motor areas, it is  94  possible that these subjects have less arm usage due to the reduced ability of secondary motor areas to generate functional movement. With respect to measures of functional movement, our correlational methods did not show any significant relationships between scores on the ARAT (a measure of functional upper limb movement in the laboratory) and brain activation during absolute force production or relative force production. In contrast, a relationship was demonstrated between decreased arm use measured by accelerometers and increased activation in five motor areas during absolute force production and two motor areas during relative force production. This may suggest that the use of accelerometers as an outcome measure of arm activity is better able to identify areas of brain plasticity in relation to arm use. The fact that 6 out of 10 of our stroke participants had identical maximum scores on the ARAT likely affected the correlational analysis for this measure. This ceiling effect of the ARAT is an example of one of the disadvantages of functional scores compared to accelerometer measures of actual arm activity. Subjects who had mild arm impairment, as shown by the Fugl-Meyer, but had no detectable deficits in arm function as measured by the ARAT, still exhibited a correlation between the brain activation and arm activity. Thus, accelerometers provide a superior way to discriminate the amount of arm use amongst mildly impaired stroke patients with a high degree of motor function that obtain maximal, or near maximal scores on measures of functional ability. As with any correlational analysis, one cannot determine any causation effect. Thus, it is possible that rather than spontaneous reorganization after stroke affecting the amount of functional arm usage, instead increased or decreased arm use may induce brain reorganization. In this way, subjects who use their arm less over time may produce less  95  primary motor activation and rely more on secondary motor areas. If this is indeed the case, then increased arm usage through rehabilitation techniques may be able to induce more beneficial reorganization of brain activation after stroke. In fact, some very small pilot studies have shown that stroke patients with increased arm use over a fixed time period due to rehabilitation training exhibit reorganization of brain activation (Lindberg et al., 2007; Schaechter et al., 2002; Szaflarski et al., 2006). For example, Lindberg et al., (2007) found that that sensorimotor activation was decreased in stroke participants compared to controls, thought to be a result of inactivity, and that repetitive passive and active arm movements increased cerebral activation in some of these areas (n=5). Szaflarski et al., (2006) used a CIMT intervention to increase paretic arm use and found that after intervention, subjects (n= 3) increased paretic arm use, as assessed by the MAL, and this was accompanied by cortical reorganization. However, there was no consistent pattern of reorganization among subjects. Another study employing CIMT (Schaechter et al., 2002) discovered that stroke subjects (n=4) increased the use of the paretic arm (detected by the MAL) and this was associated with a shift in activation toward the undamaged hemisphere. Although all of these past studies demonstrate reorganization of brain function associated with increased arm use due to rehabilitation interventions, there are no consistent patterns of activation across, or in some cases even within, studies. Furthermore, one study (n=5) found results of increased brain activation with increased arm use (Lindberg et al., 2007), that are opposite to results of decreased brain activation with increased arm use from our current study. The differing results among these studies and our current one could be due to the low n of the previous studies, or, most probably, the varying methods of arm use assessment. As mentioned, the use of accelerometers provides a reliable way to determine arm use in real life and, unlike  96  outcome measures of arm use, is not subject to differing laboratory conditions or experimenter bias. Accelerometers may be useful to quantify upper limb usage and reveal differences as a result of rehabilitation techniques aimed to increase functional arm use. In addition, longitudinal studies can be carried out to determine changes that may occur in arm usage due to recovery from injury.  Limitations One of the limitations of the study is that the use of accelerometers does not provide information on the specific tasks that the upper limb is performing, and is not able to differentiate between functional movements (i.e. eating) and non-functional movements (i.e. swing movements while walking) (Lang et al., 2007). On the other hand, non-functional movements may also be contributing to brain plasticity, as any movement would still require brain activation. Another limitation is that our analyses of absolute force production required a minimum amount of absolute force to be produced with the paretic hand, consequently limiting our sample to mild and moderately impaired patients. Thus, our results must only be considered in the context of the mild and moderately motor impaired patients included. Lastly, a limitation in our fMRI data analysis is that we only examined the intensity of activation and failed to examine any spatial changes in activation.  97  4.6 REFERENCES Brainard, D. H. (1997). The Psychophysics Toolbox. Spat Vis. 10, 433-436. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 29, 162-173. Jones, T. A., and Schallert, T. (1992). Overgrowth and pruning of dendrites in adult rats recovering from neocortical damage. Brain Res. 581, 156-160. Kolb, B., and Gibb, R. (1993). Possible anatomical basis of recovery of function after neonatal frontal lesions in rats. Behav Neurosci. 107, 799-811. Lang, C. E., Wagner, J. M., Edwards, D. F., and Dromerick, A. W. (2007). Upper extremity use in people with hemiparesis in the first few weeks after stroke. J Neurol Phys Ther. 31, 56-63. Lindberg, P. G., Schmitz, C., Engardt, M., Forssberg, H., and Borg, J. (2007). Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 21, 315-326. Maier, M. A., Armand, J., Kirkwood, P. A., Yang, H. W., Davis, J. N., and Lemon, R. N. (2002). Differences in the corticospinal projection from primary motor cortex and supplementary motor area to macaque upper limb motoneurons: an anatomical and electrophysiological study. Cereb Cortex. 12, 281-296. Marshall, M. M., and Armstrong, T. J. (2004). Observational assessment of forceful exertion and the perceived force demands of daily activities. J Occup Rehabil. 14, 281-294. Newton, J. M., Ward, N. S., Parker, G. J., Deichmann, R., Alexander, D. C., Friston, K. J., and Frackowiak, R. S. (2006). Non-invasive mapping of corticofugal fibres from multiple motor areas--relevance to stroke recovery. Brain. 129, 1844-1858. Schaechter, J. D., Kraft, E., Hilliard, T. S., Dijkhuizen, R. M., Benner, T., Finklestein, S. P., Rosen, B. R., and Cramer, S. C. (2002). Motor recovery and cortical reorganization after constraint-induced movement therapy in stroke patients: a preliminary study. Neurorehabil Neural Repair. 16, 326-338. Szaflarski, J. P., Page, S. J., Kissela, B. M., Lee, J. H., Levine, P., and Strakowski, S. M. (2006). Cortical reorganization following modified constraint-induced movement therapy: a study of 4 patients with chronic stroke. Arch Phys Med Rehabil. 87, 10521058. Talairach, J., and Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. Thieme:  98  Uswatte, G., Foo, W. L., Olmstead, H., Lopez, K., Holand, A., and Simms, L. B. (2005). Ambulatory monitoring of arm movement using accelerometry: an objective measure of upper-extremity rehabilitation in persons with chronic stroke. Arch Phys Med Rehabil. 86, 1498-1501. Uswatte, G., Giuliani, C., Winstein, C., Zeringue, A., Hobbs, L., and Wolf, S. L. (2006). Validity of accelerometry for monitoring real-world arm activity in patients with subacute stroke: evidence from the extremity constraint-induced therapy evaluation trial. Arch Phys Med Rehabil. 87, 1340-1345. Uswatte, G., Miltner, W. H., Foo, B., Varma, M., Moran, S., and Taub, E. (2000). Objective measurement of functional upper-extremity movement using accelerometer recordings transformed with a threshold filter. Stroke. 31, 662-667. Ward, N. S., Brown, M. M., Thompson, A. J., and Frackowiak, R. S. (2003). Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain. 126, 1430-1448. Ward, N. S., Newton, J. M., Swayne, O. B., Lee, L., Thompson, A. J., Greenwood, R. J., Rothwell, J. C., and Frackowiak, R. S. (2006). Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 129, 809-819.  99  CHAPTER 5: Conclusions and General Discussion  5.1 INTRODUCTION The purpose of this thesis was to understand how the brain reorganizes after chronic stroke during force production and modulation. We systematically examined past literature to determine common reorganization patterns and then used neuroimaging techniques to examine the neural correlates of both relative and absolute force production in individuals with stroke. Lastly, we related the amount of paretic arm use to brain activation during relative and absolute force production. This chapter will summarize the main results arising from the previous chapters, and will provide an overview of the neurobiological purpose of motor areas reorganized during production of force following stroke. This will be followed by limitations of the experiments included in this thesis as well as future experimental questions that might be explored.  5.2 SUMMARY OF RESULTS Relative Force Production Past neuroimaging studies in healthy subjects have helped to establish regions in the healthy brain that exhibit greater activation during increasing relative force production (%MVC). These areas include: M1, SMA (Dettmers et al., 1995; Dettmers et al., 1996) PM, cerebellum (Dai et al., 2001), cingulate sulcus, intraparietal sulcus, and PFC (Ward et al., 2003). Likewise, our ROI analysis demonstrated that all ROIs examined (bilateral M1, SMA, PM, cerebellum, caudate, putamen, and thalamus) had activation that was amplified by  100  increasing relative grip force output in both groups. Yet, when the stroke and control groups were matched at similar relative forces, the brain activation was similar in all of these motor areas. These results are comparable to Ward et al.’s (2003), which found no differences between stroke and controls in brain activation during increasing force production from 1060% MVC. Thus, individuals with stroke employ similar regions as healthy controls during relative force production and modulation, with no apparent reorganization of function in these regions after stroke. As our subset of subjects in chapters 3 and 4 (n=9) were generally mild stroke (FM range 43-64, mean 57), with the exception of one severely impaired subject (FM 15) included in chapter 4, it is possible that differences in brain activation during relative force modulation may be more apparent in comparing lower functioning individuals with stroke to healthy controls. In fact, a negative relationship was discovered between paretic arm use and brain activation in ipsilateral putamen, M1 and PM during 40% MVC, suggesting that stroke patients with less paretic arm usage, and thus likely less motor ability, exhibit higher brain activation.  Absolute Force Production Fewer studies have examined brain activation during absolute force production and modulation in healthy controls. In those that have, there is a discrepancy in results. For example, although increases in SMC activation occur during absolute force generation (Ludman et al., 1996) several studies have found no evidence of a relationship between cerebral activation and absolute force magnitude (range of 2-13.7 N tested) (Ludman et al., 1996; Muley et al., 2001). In contrast, Ehrsson and colleagues ( 2001) have observed stronger activity in the PM, rostral cingulate motor area, and the ipsilateral intraparietal cortex in  101  healthy subjects applying a small force (3.8N) in comparison to when they generated a larger force (16.6N). To our knowledge, only one study has examined activation differences between patients with stroke and healthy controls during a measured absolute force production. Using MEG to measure corticomuscular coherence that characterizes the functional connectivity between motor cortex and effector muscle, results from this study demonstrated that during precision grip at 1N, coherence does not differ between stroke patients and healthy controls (Braun et al., 2007). In contrast to this previous study, our results are based on different methodology of using fMRI to measure brain activation as indexed by peak PSC. Furthermore, our results demonstrate differences between stroke patients and healthy controls during absolute force production. More specifically, our results demonstrate that during matched absolute forces, individuals with stroke exhibit higher levels of activity in bilateral M1, contralateral PM, ipsilateral SMA and ipsilateral thalamus (Figure 5.1).  Figure 5.1: Areas of the brain having higher activation during absolute force production in stroke participants vs. healthy controls. 1 = ipsilateral SMA; 2 = ipsilateral M1; 3 = ipsilateral thalamus; 4 = contralateral PM; 5 = contralateral M1.  102  This higher activation could be a result of increased task difficulty experienced by stroke participants. Furthermore, higher activation in several of these ROIs is related to arm use, as negative relationships were found between amount of paretic arm use and activation in the contralateral SMA, ipsilateral M1, ipsilateral putamen and contralateral caudate.  Higher Levels of activation with increased severity? The systematic review in Chapter 2 investigated brain reorganization in relation to severity of stroke and results demonstrated that activation in secondary motor areas, including bilateral M1, SMA and PM, increased with greater severity or reduced outcome after stroke. The measures used to assess severity of stroke in these studies included: extent of corticospinal tract damage (Ward et al., 2006; Newton et al., 2006), presence of pyramidal tract Wallerian Degeneration (Miyai et al., 2001) and also functional outcome laboratory measures such as the ARAT, timed 10 m walk (Ward et al., 2003), 9HPT and hand muscle strength (Serrien et al., 2004). Although we too employed the ARAT as a functional outcome measure in Chapter 4 of this thesis, we found no significant relationship between decreased outcome, as measured by the ARAT, and increased activation of secondary motor areas. Although these results appear to contradict the results in Chapter 2, using an alternative outcome measure, such as paretic arm use via accelerometers, we determined that a relationship does exist between decreased arm use and increased activation of secondary motor areas, including M1, SMA and PM. Thus, these latter results appear to support the results established in Chapter 2. As mentioned previously, the lack of a relationship found using the ARAT as an outcome measure suggests that the use of accelerometers as an  103  outcome measure of arm activity is better able to identify areas of brain plasticity in relation to arm use. Subjects in Chapter 4 who had mild arm impairment, as shown by the FuglMeyer, but had no detectable deficits in arm function as measured by the ARAT, still exhibited a correlation between the brain activation and arm activity as measured by accelerometers. The ceiling effect of the ARAT is an example of one of the disadvantages that can occur with use of functional laboratory scores compared to accelerometer measures of actual arm activity.  5.3 NEUROBIOLOGICAL FUNCTION OF RECRUITED AREAS All ROIs demonstrating a role in absolute and/or relative force production with the paretic hand of stroke participants in Chapters 3 and 4 include: bilateral M1, bilateral SMA, bilateral PM, contralateral caudate, ipsilateral putamen, and ipsilateral thalamus (Figure 5.2).  Figure 5.2: Areas of the brain involved in absolute or relative force production in stroke participants from Chapters 3 and 4 of this thesis. 1 = ipsilateral M1; 2 = ipsilateral PM; 3 = ipsilateral SMA; 4 = ipsilateral thalamus; 5 = ipsilateral putamen; 6 = contralateral caudate; 7 = contralateral M1; 8 = contralateral PM.; 9 = contralateral SMA Note: ipsilateral and contralateral are relative to the paretic hand.  104  The neurobiological function for increased recruitment of these areas is discussed next. Increased ipsilateral M1 activity has been demonstrated in healthy adults performing more challenging motor tasks (Carey et al., 2006; Verstynen et al., 2005). As our motor task may have been more challenging for stroke subjects, it would require increased recruitment of the ipsilateral M1. The SMA is an area involved in preparation and execution of visually cued movements (Thickbroom et al., 2000), and as a more challenging motor task would involve greater planning and execution, this would also explain the increased activation in SMA. A more difficult task might also require increased attention for accurate performance, resulting in increased recruitment of the PM, as this area is thought to process and integrate external visual, attentional, and other information in order to produce motor output (Wise et al., 1997). Past literature examining effects of increased motor task difficulty on stroke patients compared to healthy controls has demonstrated that performance of a more difficult motor task increases activation in ipsilateral M1 (Cramer et al., 2001), PM and SMA (Schaechter and Perdue, 2008). In part, recruitment of these remote cortical areas likely also relate to the amount of damage incurred. Past literature in animals has also shown that reorganization of remote cortical areas, such as PM, occurs after cortical injury where the greater the damage to reciprocal intracortical pathways, the greater the plasticity in the intact, remote, secondary motor areas (Frost et al., 2003). In terms of subcortical areas, only the contralateral caudate and ipsilateral thalamus, and not bilateral structures, showed increased recruitment. However trends were found for recruitment of the ipsilateral caudate and contralateral thalamus. Only the ipsilateral, and not bilateral, putamen showed increased recruitment, likely due to the contralateral putamen  105  being affected by stroke in a subset of subjects (n=4). As for the increased recruitment of the ipsilateral putamen, some authors have found basal ganglia activation during motor skill learning and movement selection (Jueptner et al., 1997). In our experiments however, subjects were trained on the motor task before scanning, thus eliminating occurrence of motor skill learning during scanning. Studies have also found that the putamen is activated during prelearned movements and, moreover, when subjects pay attention to the performance of a prelearned motor task, activation occurs in the prefrontal-striatum loop (Jueptner and Weiller, 1998). As the striatum encompasses both the caudate nucleus and putamen (Jueptner and Weiller, 1998), this implies that the caudate is also activated when paying attention to the performance of a prelearned task. Similarly, several studies have suggested that the thalamus is activated during attentional processing (Buchel et al., 1998; Tomasi et al., 2007). Thus, greater activation observed in the putamen, caudate and thalamus of stroke subjects may be due to increased attention as a form of compensation in order to perform the visuomotor task. In addition, past literature has established that the caudate interacts with visual cortex to process visual information (Seger, 2008), suggesting increased caudate activation may also represent increased use of visual information in order to monitor and optimize motor output. Interestingly, the only ROI that was not recruited more in stroke participants was the cerebellum. This region has been shown to be involved in motor learning and sensory processing, specifically where it provides a prediction of the sensory consequences of movement (Nixon et al., 2003). Thus, rather than increasing their predictive ability before the visuomotor target appears, it may be that stroke patients are more likely to compensate after  106  the target appears, requiring increased attention and thus greater activation of regions such as SMA, PM, thalamus and striatum.  5.4 LIMITATIONS Subjects One of the limitations of this thesis is in the restricted subset of subjects included. Due to the effects that a lesion can have on brain morphology, we resolved only to include subjects with subcortical strokes so that the motor cortical areas we aimed to examine would be intact. In addition, our analyses of absolute force production required a minimum amount of absolute force (3.2 pressure units) to be produced with the paretic hand, consequently excluding more severely impaired patients who could not generate this force and limiting our sample to patients with mild and moderate impairment. However, we selected this targeted absolute force value because although too high of a level may eliminate stroke participants who cannot achieve these levels, too low of a level makes the task trivial to the healthy controls. Moreover, the chosen force value could still be performed by the majority of stroke participants and reflected a relative force level that was between 25-40% for all subjects, which is relevant to many functional daily tasks (Marshall and Armstrong, 2004).  Accelerometers As mentioned in chapter 4, the use of accelerometers does not provide information on the specific tasks that the upper limb is performing, and is not able to differentiate between functional movements (i.e. eating) and non-functional movements (i.e. swing movements  107  while walking) (Lang et al., 2007). However, it is likely that non-functional movements may also be contributing to brain plasticity, as any kind of movement stimulates brain activation.  Motor Task Our motor task consisted of an MRI-compatible rubber squeeze-bulb, filled with water, and connected to a pressure transducer. Using this approach as a measure of force production has potential limitations, as squeezing with a bulb measures pressure (force/area) rather than force. Thus, results may be influenced by variations in the area of hand contact applied by the subject performing the task.  5.5 FUTURE DIRECTIONS Neural correlates of force production in cortical stroke As we selected only subcortical stroke participants, to provide a more complete picture of reorganization of brain function during force production after stroke, future studies should determine whether the findings from this thesis also translate to stroke patients with cortical lesions.  Neural correlates of force production using precision grip A power grip was used as a force production task in our studies, however another form of grip task, the precision grip, can also be used to produce force. Napier (1956) argued that all prehensile movements of the human hand could be resolved into these two grips, which differ both in a functional and anatomical sense. For instance, a precision grip, where the object is pinched between the flexor aspects of the fingers and the opposing thumb  108  (Napier, 1956) is less stable than multidigit grips and so the control of finger forces needs to be more constrained at the level of individual digits (Flanagan et al. 1999). Because of this, the precision grip might be considered more challenging than the power grip and should therefore produce patterns of brain activation that reflect this. To our knowledge, only past studies in healthy humans have compared differential brain activation during performance of power and precision grip tasks (Ehrsson et al., 2000; Kuhtz-Buschbeck et al., 2008). However, results from these studies are conflicting and furthermore both grips were only performed at matching relative forces, not at matching absolute forces. To further determine the neural correlates of force production, future research should aim to compare force-related modulations of brain activity during precision and power grip tasks, both in healthy subjects and individuals with stroke.  Neural correlates of therapeutically driven change Over the past few years, a number of studies have emerged that have attempted to evaluate the effects of rehabilitation on brain reorganization after injury (Dong et al., 2006; Miyai et al., 2003; Schaechter et al., 2002). Although the methodologies of these studies have differed, they have generally demonstrated that after therapy, increased task-related activation in affected hemispheres and reduced activation in unaffected hemispheres occurs and is accompanied by an improved motor function. Thus, these past studies have established that functional imaging can be a useful marker of rehabilitative change in the injured brain. The results of this thesis demonstrate that near-maximal levels of brain activation in motor areas are reached at lower absolute forces after stroke, which may limit absolute force production. Future research should aim to determine whether rehabilitative training at higher  109  absolute forces can reduce brain activation during force production and subsequently translate into greater capability of generating increased absolute forces after stroke. In addition, if fatigue is playing a role in saturation of brain activation, then rehabilitation targeting the strengthening of paretic muscle fibres in the upper limb after stroke should alter motor unit capability and consequently also reduce saturation of brain activation in cortical motor areas. Results from this thesis have also established that differences in brain activation can be discriminated in patients with stroke based on the amount of paretic limb usage as measured by accelerometers. Thus, future research should look to use accelerometers to quantify upper limb usage and reveal differences in brain activation as a result of rehabilitation techniques aimed to increase functional arm use.  110  5.6 REFERENCES Braun, C., Staudt, M., Schmitt, C., Preissl, H., Birbaumer, N., and Gerloff, C. (2007). Crossed cortico-spinal motor control after capsular stroke. Eur J Neurosci. 25, 29352945. Buchel, C., Josephs, O., Rees, G., Turner, R., Frith, C. D., and Friston, K. J. (1998). The functional anatomy of attention to visual motion. A functional MRI study. Brain. 121 ( Pt 7), 1281-1294. Carey, J. R., Greer, K. R., Grunewald, T. K., Steele, J. L., Wiemiller, J. W., Bhatt, E., Nagpal, A., Lungu, O., and Auerbach, E. J. (2006). Primary motor area activation during precision-demanding versus simple finger movement. Neurorehabil Neural Repair. 20, 361-370. Cramer, S. C., Nelles, G., Schaechter, J. D., Kaplan, J. D., Finklestein, S. P., and Rosen, B. R. (2001). 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Miyai, I., Yagura, H., Hatakenaka, M., Oda, I., Konishi, I., and Kubota, K. (2003). Longitudinal optical imaging study for locomotor recovery after stroke. Stroke. 34, 2866-2870. Muley, S. A., Strother, S. C., Ashe, J., Frutiger, S. A., Anderson, J. R., Sidtis, J. J., and Rottenberg, D. A. (2001). Effects of changes in experimental design on PET studies of isometric force. Neuroimage. 13, 185-195. Napier, J. R. (1956). The prehensile movements of the human hand. J Bone Joint Surg Br. 38-B, 902-913. Newton, J. M., Ward, N. S., Parker, G. J., Deichmann, R., Alexander, D. C., Friston, K. J., and Frackowiak, R. S. (2006). Non-invasive mapping of corticofugal fibres from multiple motor areas--relevance to stroke recovery. Brain. 129, 1844-1858. Nixon, P.D. (2003). The role of cerebellum in preparing responses to predictable sensory events. The Cerebellum. 2, 114-122.  112  Schaechter, J. D., Kraft, E., Hilliard, T. S., Dijkhuizen, R. M., Benner, T., Finklestein, S. P., Rosen, B. 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Brain Res. 1132, 158-165. Verstynen, T., Diedrichsen, J., Albert, N., Aparicio, P., and Ivry, R. B. (2005). Ipsilateral motor cortex activity during unimanual hand movements relates to task complexity. J Neurophysiol. 93, 1209-1222. Ward, N. S., Brown, M. M., Thompson, A. J., and Frackowiak, R. S. (2003). Neural correlates of outcome after stroke: a cross-sectional fMRI study. Brain. 126, 1430-1448. Ward, N. S., Newton, J. M., Swayne, O. B., Lee, L., Thompson, A. J., Greenwood, R. J., Rothwell, J. C., and Frackowiak, R. S. (2006). Motor system activation after subcortical stroke depends on corticospinal system integrity. Brain. 129, 809-819. Wise, S. P., Boussaoud, D., Johnson, P. B., and Caminiti, R. (1997). Premotor and parietal cortex: corticocortical connectivity and combinatorial computations. Annu Rev Neurosci. 20, 25-42.  113  9 patients with fMRI, TMS to subcortical assess stroke, > 3 corticospinal months; 9 age system Ward et al., matched (CST) 2007 controls integrity 11 patients with subcortical stroke in MCA TMS applied territory, 4-11 to unaffected weeks post hemisphere Woldag et stroke; 10 during al., 2004 controls movement  20 patients with stroke not including hand region of M1, >3 months; 26 Ward et al. age matched controls 2003b fMRI  Author  N; lesion location; time post stroke Modality  No significant differences occurred between stroke and controls as relative force production increased, however there were differences within the stroke group. Stroke with poorer scores on 9 outcome measures showed increased activation in response to increase force in: (linear relationship) contralateral central sulcus, S1, PMd, middle temporal gyrus, ipsilateral cerebellum, bilateral intraparietal sulcus, as well as: (non linear relationship) SMA, cingulate sulcus, contralateral S1, cerebellum, putamen, ipsilateral superior temporal sulcus.  Findings  3 tasks: index finger abduction, pinch grip, power grip at different force levels (5, 10, 50, 100% MVC) with non affected hand  114  In stroke participants, movement of the non-affected hand caused increased excitability of the affected corticospinal system at all forces. Excitability was decreased as a whole and also was decreased per force level produced in stroke compared to controls.  In healthy controls, activation increased with increasing force output in: contralateral M1, SMA, ipsilateral cerebellum and primary visual cortex. In stroke, as CST integrity decreased, brain activation increased with isometric hand grip at increasing force output in: non-affected PMd and cerebellum, bilateral 15, 30, 45% MVC with middle frontal gyrus. As CST integrity increased (less severe) the only region affected hand that showed increased activation with increasing force was affected M1.  isometric hand grip at 10, 20, 40, 60% MVC with affected hand  Motor task  Reorganization of brain function during force production after stroke: A Systematic Review of the Literature  Appendix I: Literature table for:  Relative increases in activation in patients vs controls. Increased activation in affected PMd and Pmv in one patient and increased activation in affected dynamic, isometric M1, PMd and non affected PMv in another patient. Overall: increased hand-grip at 20% MVC damage to corticofugal pathways results in increased movement related with affected hand activity in affected motor system.  3 patients with subcortical (white matter) stroke, >20 months; 12 fMRI, DTI controls  Renner et al., 2005  Newton et al., 2006  Strokes with less functional hand movement control (via 9-hole peg test and MRC) showed that coupling between sensorimotor cortices originated from isometric grip task 25% the ipsilateral/non affected cortex during movement of affected hand of max force with whereas control and strokes with increased hand function showed that affected and noncoupling between sensorimotor cortices originated from the affected hand contralateral/affected cortex  25 cortical and subcortical stroke > 12 months; 16 healthy EEG, controls  Stinear et al., 2006  Serrien et al. 2004  115  self-paced hand squeezing of saline bag During movement of affected hand, bilateral cortical activation occurred and connected to pressure was lateralized towards affected hemisphere. Lateralization of M1 was more transducer (fMRI) with likely if motor cortex was intact. Lateralization of cortical activity did not affected hand predict functional improvement following motor practice.  21 cortical and subcortical stroke, >6 months fMRI, TMS  8 subcortical dynamic, isometric stroke patients, fMRI, TMS to hand-grip at 15, 30, 45 With greater CST damage increased recruitment of secondary motor areas Ward et al., >3 months; 10 assess CST % MVC with affected in both hemispheres: bilateral M1, bilateral PM, SMA, intraparietal sulcus, 2006 controls integrity hand dorsolateral PFC and affected superior cingulate sulcus.  In control and stroke groups, movement of affected/contralateral hand caused increased excitability of contralateral motor system but stroke had pinch grip at 10, 50% less excitability per unit force. In control and stroke, movement of ipsilateral MVsEMG for affected hand had facilitatory effect on contralateral motor system. During bilateral hand and at task, in controls, ipsilateral hand movement did not change contralateral 10,50,100% MVsEMG cortical excitability. In stroke, simultaneous activation of both hands caused for non affected hand additional facilitation of affected motor system.  16 patients with subcortical stroke in MCA TMS applied territory, 3-13 to affected hemipshere weeks post during stroke; 11 controls movement  isometric grip with light force (w/ left hand, right hand, both hands simultaneously)around a cylinder enclosed by a force-sensitive resistor  dynamic, isometric hand grip at 10 60%MVC w/ affected hand  8 (early group)/ 20 (late group) patients with cortical and subcortical stroke tested at Ward et al., 10-14 days and fMRI 2004 >3 months  Compared to controls, stroke had Increased coupling in several cortical areas: the lateral frontal region of the unaffected hemisphere, the mesiolateral region of the affected hemisphere, and over the mesial motor area during a grip task performed with the affected hand As functional outcome (via 8 outcome measures) decreased, increased coupling in mesial areas decreased.  116  Activation increased with decreasing functional outcome (via 9 outcome measures) in several motor regions (bilateral M1, PM and cerebellum, non affected S1, and cingulate area, affected SMA and PFC) independent of time after stroke. However, strokes with poorer function recruited certain areas (non affected middle intraparietal sulcus and cerebellum, and affected PM) only in early poststroke phase.  Bilateral hand movement increased activation of affected hemisphere compared to unilateral paretic hand movement during early stages of recovery. As recovery progressed, the pattern no longer persisted because of increased activation due to movement of affected hand.  Only stimulation of affected hemisphere impaired affected hand performance. Also, this impairment of affected hand performance correlated finger flexion, key press with outcome (via MRC and Fugyl Meyer) where increased impairment with the affected hand occurred in subjects with poorer outcome.  unimanual grip task EEG to around a grip force demonstrate transducer at 25% corticocortica MVC with affected and l coherence non-affected hand  20 cortical and subcortical stroke, >2 Werhahn et months; 10 al., 2003 controls TMS 2 cortical stroke patients, measured at 2 weeks, 3/4 weeks, 6/3 months post Staines et stroke; 6 al., 2001 controls fMRI  Strens et al., 2004  25 cortical and subcortical stroke, > 12 months; 16 controls  walking, treadmill training with 10% body BWS lowered activation in SMC and these changes correlated with those of weight support (BWS) improved cadence. Increased symmetry in SMC activation also correlated with both legs with increased symmetry of gait (improved gait).  6 patients with subcortical, 47108 days post stroke; 5 age Mivai et al., matched 2006 controls optical imaging system (NIRS)  Affected hand movement showed reduction in non-affected M1 activation over time. The midpoint and postintervention laterality indexes for M1 pinch grip at 50% MVC, predicted affected hand functional improvement (decreased activation in CIMT for 2 weeks with affected M1 predicted least improvement and decreased activation in non affected hand affected M1 predicted most improvement).  8 patients with stroke sparing M1 hand region, >3 months, tested before, mid, and after Dong et al., intervention; 7 controls fMRI 2006  117  Dipole sources associated w/ affected hand movement shifted from M1 and PM/SMA after stroke and before treatment further toward SMA immediately after treatment. 3 months after, affected-hand movement-related sources shifted to the opposite hemisphere. Thus affected hand movement resulted in non-affected hemisphere activation 3 months after treatment.  press one of two keys by simultaneous flexion of digits 2–4 at a repetition rate of 1 Hz, CIMT for 2 weeks with affected and non affected hand  Activation decreased over time as a function of recovery in stroke. Decreases occurred in bilateral M1, PFC, SMA, cingulate motor regions, temporal lobe, striate cortex, cerebellum, thalamus and BG. Also, in the post-acute phase, patients with more severe strokes were more likely to isometric hand grip activate: bilateral M1, postcentral sulcus, PM, superior temporal sulcus and (20%, 40% MVC) with cerebellum, contra lesional postcentral gyrus, SMA, pre-SMA, rostral cingulate sulcus, parietal cortex and cerebellar vermis the affected hand  8 cerebral stroke sparing M1, tested at 10-14 days, 4 wks, 3 & 6 mos (& 12 mos for some) post stroke; 4 age Ward et al. matched controls 2003a fMRI 4 patients with cortical and subcortical stroke, 4-15 yrs post stroke, tested at 3 time points: pre trtmt, post Kopp et al., trtmt, 3 months after trtmt 1999 EEG  TMS applied to PMd of affected hemisphere of stroke led to delays in SRT in the affected hand whereas TMS applied to PMd of non affected hemisphere of stroke or control did not.. PMd of affected hemisphere may reorganize to control M1 motor function  Activated areas more widespread for stroke vs. controls. Three areas were mainly activated in stroke during motor task: anterior M1 of unaffected hemisphere, posterior M1 of affected hemisphere and SMA of affected hemisphere.  simple reaction time task (SRT) via pressing a key with hand contralateral to stimulated hemisphere (both affected and non affected hands tested) simple pressing and force controlled pressing tasks w/ right hand only (for some this was affected hand, for others was non affected hand)  EEG and EMG to 6 patients with measure Mima et al., subcortical EEG-EMG 2001 stroke, >1 year coherence 3 patients with cortical and subcortical stroke, >6 Newton et months, 8 fMRI, event al. 2002 controls related; isolated, near isometric wrist extension (10% and 20% MVC) with affected and non affected hand  During force generation of the paretic wrist there was increased unaffected/ipsilateral M1 activation in stroke compared to controls. During force generation of the non-affected wrist M1 showed similar activation in stroke compared to controls.  118  3 tasks: elbow flexion, wrist extension, power grip at 10-20% MVC EEG-EMG coherence occurred with the affected/contralateral SMC but not with affected and non the non affected/ipsilateral side (thus functional connections to muscle after affected hand stroke came from affected motor cortex)  Both techniques showed crossed cortico-spinal connectivity in stroke with recovered motor function. Higher levels of crossed cortico-spinal connectivity from the affected M1 to affected hand correlated with increased motor function. This indicates that the affected hemisphere was recruited/reorganized rather than the non-affected hemisphere.  MEG for recruitment of CST pathway and precision grip (1N) with TMS CST affected and non integrity affected hand  4 patients with TMS, applied subcortical to M1, PMd, stroke; >2 PMv of years, 5 age affected and Fridman et matched intact al., 2004 controls hemispheres 11 patients with stroke (location and time since stroke not specified), only 3 patients Kotani et analyzed; 7 al., 2004 controls MEG  Braun et al., 2007  9 patients with subcortical stroke, >9 months, age matched controls  Miyai et al., 18 stroke 2001 patients 13 subcortical stroke, 20 months- 9 yrs post stroke; 8 Verleger et age matched al., 2003 controls  6 subcortical Miyai et al., and cortical 2003 stroke  EEG  fMRI  fNIRS  6 subcortical and cortical stroke; 32 – Miyai et al., 116 days post 2002 stroke fNIRS  Mihara et al., 2007  12 infratentorial stroke, 21-60 days post, 11 controls fNIRS  Longitudinal study before and after two months of inpatient rehabilitation Squeeze sponge; first session was 47-100 days post stroke, second session was 91-165 days post stroke key press via I) spontaneous force and II) graded force at 30, 60 or 100% MVC with affected and non affected hand  119  Activation of affected/contralateral hemisphere occurred before motor response in both stroke and control. Additional activation of the nonaffected/ipsilateral hemisphere occurred shortly after movement in strokes. This latter pattern did not occur with responses made by the unaffected hand nor in controls.  Examined Wallerian Degeneration (WD) of the pyramidal tract. Results showed that more persons with WD showed activation of the affected PM, and the unaffected PM was more frequently activated in persons with WD than in persons without WD.  Before rehabilitation: increased activation in SMC that was greater in unaffected vs affected hemisphere and increased activation in PM and SMA. After rehabilitation: improvement of asymmetrical SMC activation with significantly correlated with improved gait. Also, enhanced PM activation occurred in the affected hemisphere.  BWS with either mechanical assistance in swinging leg (CON) During gait, increased activation occurred in medial SMC of unaffected or facilitation technique hemisphere vs affected hemisphere. Also increased PM and pre SMA that enhanced swinging activation. Overall, cortical activations and gait performance were greater in FT than CON. of leg (FT)  Gait on treadmill  Cortical activations in lateral and medial PFC during acceleration phase tended to be attenuated during the steady phase of the gait period, while these activations were sustained throughout the gait period in ataxic patients. Thus, PFC may compensate for ataxic gait.  Appendix II: Edinburgh Handedness Inventory Please indicate your preferences in the use of hands in the following activities by putting a + in the appropriate column. Where the preference is so strong that you would never try to use the other hand unless forced to, put + + . If in any case you are really indifferent put a + in both columns. Some of the activities require both hands. In these cases the part of the task, or object, for which hand preference is wanted is indicated in brackets. Please try to answer all the questions, and only leave a blank if you have no experience at all of the object or task.  Left 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.  Writing Drawing Throwing Scissors Toothbrush Knife (without fork) Spoon Broom (upper hand) Striking match (match) Opening box (lid)  i ii  Which foot do you prefer to kick with? Which eye do you use when using only one?  Right  120  Appendix III: Fugl-Meyer Upper Extremity Motor Assessment Scale Test Reflexes Biceps Triceps Flexor Synergy Elevation Retraction Abduction (at least 90) External Rotation Elbow Flexion Forearm Supination Extensor Synergy Adduction/Intern. Rotation Elbow Extension Forearm Pronation  Scoring Criteria Position: Biceps – extension Triceps - flexion 0 - no reflex 2 - reflex elicited Note: If the person has active elbow flexion/extension you can skip this test. Score 2 Movement: Bring arm fully supinated to the ear of the affected side, the elbow is fully flexed, the shoulder abducted to at least 90, externally rotated, retracted and elevated. 0 - cannot be performed 1 – performed partly 2 – performed faultlessly Movement: Adduct/internally rotate the shoulder, fully extend the elbow towards the unaffected knee, forearm should be pronated. 0 - cannot be performed 1 - performed partly 2 - performed faultlessly  Max 2 2  2 2 2 2 2 2  2 2 2  Mixing Synergies Hand to Lumbar spine  0 - No specific action performed 1 - Hand passes anterior superior illiac spine 2 - Action is performed faultlessly  2  Shoulder Flexion to 90, elbow at 0  Movement: The elbow must be fully extended throughout the ROM, the forearm in mid-position between pronation and supination.  2  0 - Arm immediately abducted or elbow flexes 1 - Abduction or elbow flexion occurs late in motion 2 - Faultless motion Pronation/Supination of forearm with elbow at 90 and shoulder at 0  0 - Incorrect position and/or no pronation/supination 1 - Correct position with minimal pronation/supination 2 - Correct position and complete pronation and supination  2  Shoulder abduction to 90, elbow full extended to 0, forearm pronated  0 - Initial elbow flexion or deviation from pronated forearm 1 - Motion performed partly or if during motion elbow is flexed or forearm not kept in pronation 2 - Faultless motion  2  Shoulder flexion from 90 180, elbow at 0, and forearm in pronated  0 - Initial flexion of elbow or shoulder abduction 1 - Elbow flexion or shoulder abduction 2 - Faultless motion  2  IV Out of Synergy  121  Pronation/Supination of forearm, elbow at 0, and shoulder between 30 - 90 of flexion  Movement: The shoulder must be kept in a flexed position between 30-90 (not more than 90), elbow fully extended at 0  V Normal Reflex Activity  ***Only evaluated if stage IV has a score of 6***  Biceps and/or finger flexors and triceps  0 - at least 2 of the 3 reflexes are hyperactive 1 - one reflex is hyperactive or 2 reflexes are lively 2 - no more than one reflex is lively and none are hyperactive  2  Movement 1: Shoulder at 0, elbow at 90, forearm fully pronated, wrist at 15 of extension. If the person cannot actively attain elbow flexion to 90, you can place them in the required position. Once the position has been attained you exert some force to see if they can resist against it.  2  2  0 - Supination/Pronation not possible or elbow and shoulder position cannot be attained 1 - Elbow and shoulder properly positioned, pron/supin limited 2 - Faultless motion  VI Wrist M1: Shoulder 0, elbow 90, wrist 15 extension, forearm pronated M2: Full wrist extension/flexion M3: Shoulder 30, elbow 0, wrist 15 extension, forearm pronated M4: Full wrist extension/flexion  0 - Cannot extend wrist to required 15 1 – Extension is accomplished, but no resistance is taken 2 - Position can be maintained with some resistance Movement 2: Same position as above, but have the person move between full flexion/extension of the wrist. No resistance tested.  2 2 2  0 – volitional movement do not occur 1- cannot actively move through the range total range but can through partial 2 – full active range Movement 3: Shoulder at 30 of flexion, elbow at 0 extension, the forearm pronated. (If needed, you can support the person in this position). Wrist at 15 of extension. Resistance tested. 0 - Cannot extend wrist to required 15 1 – Extension is accomplished, but no resistance is taken 2 - Position can be maintained with some resistance Movement 4: Same position as above but have the person move between full flexion/extension of the wrist. No resistance tested. 0 – volitional movement do not occur 1- cannot actively move through the range total range but can through partial 2 – full active range  122  Circumduction  0 - Cannot be performed 1 - Jerky or incomplete circumduction 2 - Complete motion with smoothness  2  Finger mass flexion – make a fist  0 - No flexion occurs 1 - Some flexion, but not full motion 2 - Complete active flexion (compared with unaffected hand)  2  Finger Mass Extension – extend the fist  0 - No extension occurs 1 - Patient can release an active mass flexion grasp 2 - Full active extension  2  MP joints fully extended, PIPs & DIPs fully flexed. Test resistance by pulling against DIP and PIP joints  0 - Required position cannot be performed 1 - Grasp is weak 2 - Grasp maintained against reasonable resistance  2  M1: Adduct thumb with IP & MP at 0 M2: Thumb oppose to index finger Grasp can Grasp tennis ball  Movement 1: Fully adduct thumb with IP and MP at 0. Place a piece of paper between thumb and 2nd digit MCP for resistance testing. Movement 2: Thumb opposed to index finger – place paper between thumb and index finger for resistance testing.  2  VII Hand  2 2 2  0 - Function cannot be performed 1 - Paper (can, ball) can be held in place but not against a tug 2 - Paper (can, ball) is held against tug Co-ordination/Speed  Movement for the next 3 items: Finger to Nose Test You will do this with the unaffected hand first to time them, then with the affected hand. Please demonstrate. Instruction: Take the index finger of your stronger hand and place it about 12’ from your nose, then touch your nose with the index finder of your weaker hand. I want you to move your finger from your nose to your index finger, back and forth 5 times. Do this as fast as you can. You will do this with your eyes closed.  Tremor - Finger to nose  0 - Marked tremor 1 - Slight tremor 2 - No tremor 0 - Pronounced or unsystematic dysmetria 1 - Slight or pronounced dysmetria 2 - No dysmetria 0 - More than 6 seconds longer than unaffected hand 1 - 2 - 5 seconds longer 2 - less than 2 seconds  Dysmetria - Finger to nose Speed - Finger to nose  Total  2 2 2  66  123  Appendix IV: Modified Ashworth Scale – Elbow Flexion Instructions: Take the affected arm and support the elbow by placing your hand just proximal to the joint. Place your other hand just proximal to the wrist and rapidly move the forearm in a flexion, extension pattern for 5 repetitions. For the wrist, support the wrist proximal to the joint, place your hand over the palmar surface of the hand and rapidly move the wrist in a flexion, extension pattern for 5 repetitions. Please explain the process to the participant. Circle the number that corresponds with your rating for elbow and wrist tone.  Description  Elbow Flexion  No increase in muscle tone  0  Slight increase in muscle tone, manifested by a catch and release or by minimal resistance at the end of the ROM when the affected part is moved in flexion or extension  1  Slight increase in muscle tone, manifested by a catch, followed by minimal resistance throughout the remainder (less than half) of ROM  1+  More marked increase in muscle tone through most of ROM, but affected part easily moved  2  Considerable increase in muscle tone, passive movement difficult  3  Affected part rigid in flexion or extension  4  124  Appendix V: Action Research Arm Test: Only assess the affected arm Instructions: Subtests are ordered in such a way that if the person scores 3 on item one (the most difficult) the person is credited with having scored 3 on all items of the subtest. You don’t have to test the remaining subtest items. If the person scores less than 3 on item one, then item two is administered. Item two is the easies item in each of the subtests and if the person scores 0, then he/she is given a 0 for the remaining subtests. Move to the next subtest. If the person scores less than 3 on item one and more than 0 on item two, all items in the subtest must be administered. Scoring Scale: 3: 2: 1: 0:  Performs test normally Completes test, but takes abnormally long time or has great difficulty Performs test partially Can perform no part of test  Test 1: Subtest Grasp: Lift objects listed onto shelf (37.5 cm high and placed a distance of 43cm from the subject) Instructions: I want you to pick-up the object in front of you and lift it from the table onto the shelf in front of you. 1. Block 10cm (if score = 3, total = 18 go to Grip test)  Left  Right  _____  _____  2. Block 2.5cm (if score = 0, rest = 0 go to Grip test)  _____  _____  3. Block 5cm  _____  _____  4. Block 7.5cm  _____  _____  5. Ball 7.5cm  _____  _____  125  6. Stone  Test 2: Subtest Grip:  _____  _____  Left  Right  1. Pour water glass to glass (if score = 3, total = 12 go to Pinch test)  _____  _____  2. Move the 2.25cm tube a distance of 43cm (if score = 0, total is 0 go to Pinch test)  _____  _____  3. Move the 1cm tube as above  _____  _____  4. Put washer over a bolt  _____  _____  Subtest 3: Pinch Instructions: I want you to pick up the ball bearing or marble in the manner I tell you and lift it from the table onto the shelf in front of you. 1. Ball bearing of 6mm picked up between third finger and thumb (if score = 3, total = 18 go to Gross movement test)  Left  Right  _____  _____  2. Marble picked up between first finger and thumb (if score = 0, rest = 0, go to Gross movement test)  _____  _____  3. Ball bearing 6mm picked up between second finger and thumb  _____  _____  4. Ball bearing of 6mm picked up between first finger and thumb  _____  _____  5. Marble picked up between  126  third finger and thumb  _____  _____  6. Marble picked up between second finger and thumb  _____  _____  Subtest 4: Gross Movement:  Left  Right  1. Place hand behind head (if score = 3, total = 9 if score = 0, total = 0)  _____  _____  2. Place hand on top of head  _____  _____  3. Touch mouth with hand  _____  _____  Total score for ARAT_______/57  127  Appendix VI: Motor Activity Log 14 Instructions: Explain to the participant that this test looks at how much they use their affected arm in 14 daily activities. First ask the participant if they have used the affected arm to help accomplish the activity in question over the last week. If they did not use the affected arm for that activity because it was impossible (i.e. comb hair – but the person is bald), then the item is considered not applicable and dropped from the test. If they state they just did not use it but for no reason then they would score a ‘0’. After they have stated a score on the Amount of Use Scale, explain that they need to rate the quality of the movement of the affected arm during the activity in question. If they did not do the activity in the past week, ask why and indicate on the form in the space provided. 1. Hold a book ____ Amount ____ Quality ____ Did not do in the past week. Explanation: ____________________________________. 2. Use a towel to dry shelf ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 3. Pick up a glass ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________.  4. Brush teeth ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 5. Shave/Put on Make-up ____ Amount  128  ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 6. Open door with a key ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 7. Write/type ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 8. Steady self ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________.  9. Put arm through clothing ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 10. Carry object ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 11. Grasp fork/spoon ____ Amount ____ Quality  129  ____ Did not do in the past week. Explanation ____________________________________. 12. Comb hair ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________. 13. Pick up cup ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________.  14. Button clothes ____ Amount ____ Quality ____ Did not do in the past week. Explanation ____________________________________.  Amount Scale 0 – Did not use my weaker arm (not used). 1 – Occasionally tried to use my weaker arm (very rarely). 2 – Sometimes used my affected arm, but did most of the activity with my stronger arm (rarely). 3 – Used my weaker arm about half as much as before the stroke (half prestroke). 4 – Used my weaker arm almost as much as before the stroke (3/4 prestroke). 5 – Used my weaker arm as much as before the stroke (same as prestroke). How Well Scale 0 – The weaker arm was not used at all for that activity (never). 1 – The weaker arm was moved during that activity, but was not helpful (very poor). 2 – The weaker arm was of some use during that activity, but needed some help from the stronger arm, moved very slowly, or with difficulty (poor). 3 – The weaker arm was used for the purpose indicated, but movements were slow or were made only with some effort (fair). 4 – The movements made by the weaker arm were almost normal, but not quite as fast or accurate as normal (almost normal) 5 – The ability to use the weaker arm for that activity was as well as before the stroke (normal)  130  Appendix VII: Consent Form Force Production in Individuals with Stroke or Spinal Cord Injury using Functional Magnetic Resonance Imaging (fMRI) Principal Investigator:  Dr. Janice Eng, PhD PT/OT School of Rehabilitation Sciences, UBC Rehab Research Lab, GF Strong Rehab Centre  Contact number for study information and questions: 604-714-4108 Introduction: You are being invited to participate in this study because you have had a stroke or have an incomplete spinal cord injury. The purpose of this study is to investigate brain activation and movements in people who have either had a stroke or spinal cord injury. Background: Weak muslces are common consequences of stroke and spinal cord injury. How the brain reorganizes to generate movement following a stroke or spinal cord injury is not known. Noninvasive imaging methods such as Functional Magnetic Resonance Imaging (fMRI) can be used to provide detailed functional and structural information about the human brain. This study will serve as a foundation for future work to investigate ways to improve movement in individuals with stroke and spinal cord injury. Who can participate in this study? You are eligible to participate in this study if you: • had an ischemic stroke or incomplete spinal cord injury more than 6 months ago. • are age 19 or older. • are able to use a squeeze grip. • are able to follow three commands in a row in English. Who should not participate in this study? Due to the fact the fMRI involves exposure to a strong magnetic field, it is important that you do not have any of the following objects in your body: • have implanted pacing devices (i.e., pacemaker), wires, or a defibrilator. • have an artificial heart valve. • have a brain aneurysm clip. • have an electrical stimulator for nerves and bones. • have a deep brain stimulator. • have an eye or ear implant. • have an implanted drug infusion pump, coil, catheter, or filter in any blood vessel. • have a Harrington rod for scoliosis. • have metallic prostheses, shrapnel, bullets, or other metal fragments. • have dentures, braces, or retainer  131  In addition to the exclusion criteria outlined above, in order to participate in this study it is also important that you: • have not had surgery in the last 6 weeks. • have not had joint injections in the last 4 weeks. • do not have recent tattoos (including tattooed eyeliner). • do not have pierced body parts (other than ears). • are not pregnant. • have not been a metal worker or machinist at any point in time. • have not had an injury in which metal became lodged in the eye. • do not have significant concurrent disease. • do not have a known history of claustrophobia (a fear of being in narrow or enclosed spaces). What does the study involve? Time Commitment for the Study: You can expect that there will be two evaluation sessions, with the evaluation sessions taking place on separate days. The first session will take place at the Rehab Research Laboratory at G.F. Strong, and your arm movements will be assessed. During this same session, you will also be introduced to the hand movements that will be used in this study. In total, you will spend about 1hour at G.F. Strong. The second evaluation session will take place in the fMRI suite in the Purdy Pavilion at the UBC Hospital. During this time you will be asked to perform the hand movements that you previously learned while being scanned in the fMRI machine. You can expect to be at the UBC Hospital for approximately 1 hour, with your fMRI scan lasting approximately 45 minutes. Study Procedures: If you have had a previous brain scan with any hospitals in the Greater Vancouver area, we will access this scan to record the location of your stroke. On the first day of your participation in this study, you will be asked to come to the Rehab Research Laboratory at G.F. Strong. During this session, your arm and hand movements will be examined. You will move your arms and hands into different positions. Once these assessments have been completed, you will be given a chance to become familiar with the hand movements that will be used in this study. You will be instructed to squeeze a rubber ball and will be allowed to practice until you feel comfortable and confident. You will be allowed to rest at any time during the assessment and training session should you feel tired. Before you leave, you will be asked to schedule a time for the second evaluation session of this study, which will take place at the UBC Hospital. When you arrive at the fMRI suite in the Purdy Pavilion at the UBC Hospital, you will be asked to change into a cloth gown and to remove any metal objects from your body, such as watches, bracelets, rings, earrings, and metal eyeglasses. A storage locker will be provided for your belongings. You will then enter the fMRI scanning room, where you will lie on your back on a special table. You will be provided with assistance to ensure that you are able to transfer to the table safely. As the fMRI scanner emits loud noises that can be bothersome, you will be given ear plugs to ensure your comfort. If  132  required, you will also be given corrective lenses that are safe for the fMRI environment. You will then be fitted with a special head coil to prevent excessive amounts of head movement. Finally, you will be given the equipment required for the hand squeeze task, and then the table that you are lying on will be moved into the fMRI machine. Once inside the scanner, you will be asked to follow the instructions displayed on a screen. The task you will be asked to perform will be the same squeeze ball task you practiced during the training session at the Rehab Research Lab at G.F. Strong. The scan will take approximately 45 minutes. Once the scanning session is complete, you will be removed from the scanner. You will be able to communicate directly with the scanner operator at all times during the fMRI session. During the course of the scan, some people may begin to feel uncomfortably confined or agitated. If this occurs, simply tell the scanner operator and you will be removed from the scanner immediately. Once your scanning session is finished, you will be able to change and gather your belongings. Before you leave you will be given a structural image of your brain on a CD. Risks: In general, fMRI scans are considered to be very safe and are not associated with adverse health consequences. The most important safety issue associated with this fMRI study is ensuring that participants do not have any metallic objects in their bodies, as outlined in the exclusion criteria above. If at any time participant safety becomes a concern, the study will be stopped immediately. fMRI causes no pain, but there may be discomfort from being closed in or from the need to keep your head very still. You may notice a warm feeling in the area under examination; this is normal, but if it bothers you the technologist should be told. The loud tapping or knocking noises heard at certain phases of imaging can be bothersome to some people. However, in fMRI exams, the actual imaging is done in a series of very short bursts, so the noise will not last long. Some people may have the feeling of anxiousness and distress caused by spending a prolonged period of time in the enclosed space of the fMRI. If you feel overwhelmed by the anxious or distress feeling, you can tell the technologist and you will be removed from the scanner immediately. Benefits: Although you will not receive any direct medical benefit from participating in this study, you will be helping advance our knowledge of why movement problems exist following stroke and spinal cord injury. This may ultimately result in the development of new therapies to improve movement in people with stroke and spinal cord injury. New Information Available that May Affect Your Decision to Participate: If new information arises during the research study that may affect your willingness to remain in the study, you will be advised of this information. If You Withdraw Your Consent to Participate: • Your participation in this research is entirely voluntary. You may withdraw from this study at any time without providing any reasons.  133  • • •  If you decide to enter the study and to withdraw at any time in the future, there will be no penalty or loss of benefits to which you are otherwise entitled, and your future medical care will not be affected. The study investigators may decide to discontinue the study at any time, or withdraw you from the study at any time, if they feel that it is in your best interests. If you choose to enter the study and then decide to withdraw at a later time, all data collected about you during your enrollment in the study will be retained for analysis. By law, this data cannot be destroyed.  If Something Goes Wrong? Signing this consent form in no way limits your legal rights against the sponsor, investigators, or anyone else. If you have any concerns, please contact Chihya Hung (project coordinator) for further information at 604-714-4108. After the Study Is Completed: Once the study is completed and all the data are analyzed, you will be sent a report of our general findings. As the data we collect from your participation will be combined with other subjects, individual results will not be available. You may be asked if you wish to be contacted for participation in future research projects. If you check “NO”, your name will be removed from future correspondence. If you check “YES”, we may contact you in the future for participating in another study, at that time, you will be asked to sign another consent form specific to that study.  YES □  NO □  Your Cost to Participate: You may incur personal travel expenses by participating in this study. In order to defray the costs of transportation and your time, and also to show our appreciation for your participation, a $100.00 honorarium will be provided at your visit to the Brain Research Centre at UBC. After visiting the fMRI suite in the Purdy Pavilion at the UBC Hospital, if you decide to leave the study early, you will still receive the honorarium. Confidentiality: Your confidentiality will be respected. No information that discloses your identity will be released or published without your specific consent to the disclosure. However, research records and medical records identifying you may be inspected in the presence of the Investigator or his or her designate by representatives of Health Canada and the UBC Research Ethics Board for the purpose of monitoring the research. However, no records which identify you by name or initials will be allowed to leave the Investigators’ offices. Contact: If you have any questions or desire further information with respect to this study or during participation, you can contact Dr. Janice Eng or one of her associates at (604) 714-4108. If you have any concerns about your rights as a research subject and/or your experiences while participating in this study, contact the Research Subject Information Line in the University of British Columbia Office of Research Services at 604-822-8598.  134  Consent to Participate: This is not a contract and I understand that I do not give up any legal rights by signing it. By signing the form I am indicating that: • I have read and understood the subject information and consent form. • I have had the opportunity to ask questions and have had satisfactory responses to my questions. • I understand that all the information collected will be kept confidential and that the results will only be used for scientific objectives. • I understand that my participation in this study is voluntary and I am completely free to refuse to participate or to withdraw from this study at any time without changing in any way the quality of care that I receive. • I understand that I am not waiving any legal rights as a result of signing this consent form. • I have read this form and I freely consent to participate in this study. • I have been told that I will receive a dated and signed copy of this form. Printed Name of Subject  Subject Signature  Date  Printed Name of Witness  Witness Signature  Date  Printed Name of Principal Investigator/Designated Representative  Signature of Principal Investigator/Designated Representative  Date  135  Appendix VIII: Details of Motor Task I. Depiction of task visual feedback. During the rest condition, a black fixation cross appeared in the middle of the screen. Subjects were instructed to focus on this cross during the rest period. During the response condition a yellow bar indicated the target force to be reached and a green bar provided feedback of the subject’s actual exerted force.  II. Event–related design specifics Each scanning session had a total of four runs. Each event lasted 4 s and consisted of a yellow bar indicating the target %MVC to be squeezed. This was followed by a rest period that was jittered between 10-16 s with an average of 13 s. In each run there was 24 squeezes (events) and 25 rest periods, giving a total time of approximately 6.8 minutes for one run. The figure below depicts the exact timing of one scanning session. An individual run began with 4s rest. The first event was a target pressure of 70% MVC (as indicated in the figure). This was followed by a rest period of 16s, then a target pressure of 10% MVC, 16s rest, etc.  136  70% 40% 10%  16  16 14  16 10 14  16 12 10 16  12 12 14  14 12 12  16 10 14 12 10  14 10 10  137  Appendix IX: Regions of Interest defined  138  1.02 (0.4) 11.93  Mean (SD) Actual Pressure  Mean Coeff of Variation for force production 12.24  1.06 (0.4)  9.94 (0.81) 9.95 (0.76) 38.11 (0.96) 38.4 (1.29) 63.72 (2.48) 63.9 (2.4)  7.63  7.24  6.73  8.20  3.96 (1.45) 4.14 (1.51) 6.57 (2.28) 6.85 (2.35)  n=10  Mean (SD) Actual % MVC  n=10  5.40  n=10  70% MVC n=9  4.49  Stroke Non-Paretic Hand  40% MVC n=9  9.38  10% MVC n=9  9.47  24.50  16.11  Mean Coeff of Variation for force production  0.72 (0.27) 0.66 (0.33) 2.72 (0.79) 2.53 (0.97) 4.73 (1.55) 4.37 (1.85)  n=10  Mean (SD) Actual Pressure  n=10  70% MVC n=9  9.61 (1.94) 8.95 (2.78) 36.56 (2.84) 36.4 (2.72) 62.94 (4.84) 62.15 (5.21)  n=10  40% MVC n=9  Mean (SD) Actual % MVC  Stroke Paretic Hand  10% MVC n=9  Appendix X: Motor Task Behavioural Data  139  17.20  16.78  1.04 (0.23) 1.10 (0.29) 4.62 (1.17) 4.69 (1.13) 7.66 (1.81) 7.83 (1.80)  Mean (SD) Actual Pressure  Mean Coeff of Variation for force production  8.67 (1.30) 8.95 (1.52) 37.89 (1.11) 37.9 (1.05) 63.11 (3.69) 63.45 (3.64)  4.69  4.49  7.18  6.69  n=10  Mean (SD) Actual % MVC  n=10  5.50  n=10  70% MVC n=9  5.94  Controls Dominant Hand  40% MVC n=9  4.55  10% MVC n=9  4.75  15.26  15.20  Mean Coeff of Variation for force production  1.10 (0.26) 1.13 (0.26) 4.64 (1.17) 4.70 (1.12) 7.78 (1.83) 7.94 (1.81)  Mean (SD) Actual Pressure  n=10  9.1 (1.35) 37.61 (1.27) 37.55 (1.21) 63.33 (2.89) 63.65 (2.91)  n=10  70% MVC n=9  9.06 (1.42)  n=10  40% MVC n=9  Mean (SD) Actual % MVC  10% MVC n=9 Controls Non-Dominant Hand  140  Appendix XI: Mean and standard deviation values for peak PSC of all ROIs during relative force production (10%, 40%, 70% MVC). Table 2: Mean (SD) peak PSC of all ROIs during relative force production with PARETIC hand for stroke participants (n=9) (Chapter 3).  Contralateral ROIs  Mean (SD) Peak Mean (SD) Peak Mean (SD) Peak PSC at 10% MVC PSC at 40% MVC PSC at 70% MVC  Putamen  0.21 (0.09)  0.20 (0.10)  0.41 (0.16)  Caudate  0.25 (0.11)  0.38 (0.20)  0.62 (0.30)  Thalamus  0.36 (0.11)  0.47 (0.12)  0.92 (0.27)  M1  0.51 (0.24)  0.57 (0.32)  1.16 (0.32)  SMA  0.42 (0.13)  0.44 (0.21)  0.98 (0.40)  PM  0.35 (0.15)  0.45 (0.17)  0.77 (0.26)  Cerebellum  0.23 (0.11)  0.31 (0.09)  0.55 (0.15)  Ipsilateral ROIs  Mean (SD) Peak Mean (SD) Peak Mean (SD) Peak PSC at 10% MVC PSC at 40% MVC PSC at 70% MVC  Putamen  0.21 (0.08)  0.30 (0.16)  0.59 (0.34)  Caudate  0.18 (0.10)  0.29 (0.16)  0.59 (0.77)  Thalamus  0.26 (0.17)  0.36 (0.14)  0.52 (0.26)  M1  0.33 (0.11)  0.55 (0.33)  1.07 (0.48)  SMA  0.34 (0.14)  0.54 (0.33)  1.12 (0.52)  PM  0.37 (0.17)  0.49 (0.26)  0.99 (0.52)  Cerebellum  0.32 (0.09)  0.41 (0.28)  0.83 (0.50)  141  Table 2: Mean (SD) peak PSC of all ROIs during relative force production with NONDOMINANT hand for healthy controls (n=9) (Chapter 3).  Contralateral ROIs  Mean (SD) Peak Mean (SD) Peak Mean (SD) Peak PSC at 10% MVC PSC at 40% MVC PSC at 70% MVC  Putamen  0.22 (0.11)  0.28 (0.15)  0.47 (0.32)  Caudate  0.23 (0.20)  0.27 (0.12)  0.72 (1.0)  Thalamus  0.26 (0.10)  0.40 (0.16)  0.73 (0.47)  M1  0.37 (0.19)  0.58 (0.34)  0.93 (0.41)  SMA  0.38 (0.19)  0.57 (0.41)  1.01(0.69)  PM  0.33 (0.15)  0.49 (0.19)  0.94 (0.39)  Cerebellum  0.32 (0.10)  0.39 (0.21)  0.90 (0.51)  Ipsilateral ROIs  Mean (SD) Peak Mean (SD) Peak Mean (SD) Peak PSC at 10% MVC PSC at 40% MVC PSC at 70% MVC  Putamen  0.21 (0.09)  0.27 (0.12)  0.51 (0.19)  Caudate  0.22 (0.08)  0.22 (0.13)  0.38 (0.10)  Thalamus  0.21 (0.08)  0.31 (0.14)  0.55 (0.23)  M1  0.30 (0.21)  0.44 (0.16)  0.88 (0.38)  SMA  0.36 (0.23)  0.38 (0.30)  0.91 (0.22)  PM  0.36 (0.23)  0.54 (0.27)  1.0 (0.34)  Cerebellum  0.37 (0.15)  0.45 (0.18)  0.82 (0.33)  142  0.50 0.45 0.40 0.35 0.30 0.25 0.20 0.15 0.10 0.05 0.00  1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00  Peak PSC  Peak PSC  10% MVC  10% MVC  70% MVC  Force  40% MVC  70% MVC  Contralateral Thalamus  Force  40% MVC  Contralateral Putamen  Strokes  Controls  Strokes  Controls  0.00  0.20  0.40  0.60  0.80  1.00  1.20  1.40  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.80  10% MVC  10% MVC  70% MVC  Force  40% MVC  70% MVC  Contralateral M1  Force  40% MVC  Contralateral Caudate  143  Strokes  Controls  Strokes  Controls  Appendix XII: Graphs to compare mean Peak PSC at each level of relative force for each group in each ROI (chapter 3).  Peak PSC Peak PSC  Peak PSC  Peak PSC  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.80  0.90  1.00  0.00  0.20  0.40  0.60  0.80  1.00  1.20  10% MVC  10% MVC  70% MVC  Force  40% MVC  70% MVC  Contralateral Cerebellum  Force  40% MVC  Contralateral SMA  Strokes  Controls  Strokes  Controls  Peak PSC Peak PSC  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.80  0.90  1.00  10% MVC  10% MVC  70% MVC  Force  40% MVC  70% MVC  Ipsilateral Putamen  Force  40% MVC  Contralateral PM  144  Strokes  Controls  Strokes  Controls  Peak PSC  Peak PSC  10% MVC  40% MVC  70% MVC  70% MVC  Force  0.00 40% MVC  0.40  0.60  0.00  Strokes  Controls  0.80  1.00  1.20  0.20  10% MVC  Ipsilateral M1  0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.20  0.40  0.60  0.80  1.00  1.20  0.00  0.10  Force  Strokes  0.30  0.20  Controls  0.40  0.50  0.60  0.70  Ipsilateral Caudate  Peak PSC  Peak PSC  10% MVC  10% MVC  70% MVC  Force  40% MVC  70% MVC  Ipsilateral SMA  Force  40% MVC  Ipsilateral Thalam us  145  Strokes  Controls  Strokes  Controls  Peak PSC  0.00  0.20  0.40  0.60  0.80  1.00  1.20  10% MVC Force  40% MVC  70% MVC  Ipsilateral PM  Strokes  Controls  Peak PSC 0.00  0.10  0.20  0.30  0.40  0.50  0.60  0.70  0.80  0.90  10% MVC  Force  40% MVC  70% MVC  Ipsilateral Cerebellum  146  Strokes  Controls  Appendix XIII: Mean and standard deviation values for peak PSC of all ROIs during absolute force production (3.2 pressure units). Table 3: Mean (SD) peak PSC of all ROIs for healthy controls (n=9) during absolute force production with NON-DOMINANT hand and stroke participants (n=9) during absolute force production with PARETIC hand (Chapter 3). Mean (SD) peak PSC  Mean (SD) peak PSC  Putamen  0.26 (0.07)  0.32 (0.18)  Caudate  0.18 (0.09)  0.34 (0.21)  Thalamus  0.31 (0.11)  0.55 (0.37)  M1  0.41 (0.13)  0.71 (0.38)  SMA  0.48 (0.26)  0.75 (0.69)  PM  0.38 (0.16)  0.67 (0.30)  Cerebellum  0.38 (0.16)  0.51 (0.29)  Putamen  0.22 (0.09)  0.39 (0.24)  Caudate  0.20 (0.09)  0.33 (0.19)  Thalamus  0.25 (0.11)  0.41 (0.19)  M1  0.36 (0.17)  0.76 (0.53)  SMA  0.33 (0.28)  0.75 (0.46)  PM  0.46 (0.24)  0.63 (0.44)  Cerebellum  0.39 (0.17)  0.53 (0.30)  Contralateral ROIs  Ipsilateral ROIs  147  0  0.5  1  1.5  2  2.5  3  3.5  0  0.5  1  1.5  2  2.5  3  3.5  0  0  0.2  0.1  0.4  0.2 Peak PSC  0.4  0.6 Peak PSC  0.8  Contralateral Thalamus  0.3  Contralateral Putamen  1  0.5  1.2  1.4  0.6  1.6  0.7  0  0.5  1  1.5  2  2.5  3  3.5  0  -0.1  0  0.5  1  1.5  2  2.5  3  3.5  0.2  0  0.4  0.1  0.6  0.2  0.4  1 Pe ak PSC  0.8  Contralateral M1  Peak PSC  0.3  Contralateral Caudate  1.2  0.5  1.4  0.6  1.6  0.7  1.8  0.8  Appendix XIV: Graphs for each ROI of peak PSC for each subject during absolute force production (3.2 pressure units). Note: Red circles indicate stroke participants, blue squares indicate control participants.  Ab sou te F orce (Pressure Un its)  Absoute Force (Pressure Units)  Absoute Force (Pressure Units)  Absoute Force (Pressure Units)  148  Absoute Force (Pressure Units)  Absoute Force (Pressure Units)  0.1  0.2  1  1.5  2  0.7  0.8  2.5  0.9  3  0  0.5  1  1.5  2  2.5  3  3.5  0  0.3  0.4 Peak PSC  0.5  0.6  Contralate ral Ce re be llum  Peak PSC  1  0  0.5  0  0  0.5  1  1.5  2  2.5  3  3.5  0.5  1  1.5  2  2.5  3  3.5  Contralateral SM A  Absoute Force (Pressure Units)  0  0.2  0.4  Peak PSC  0.6  Contralateral PM  0.8  1  1.2  149  Absoute Force (Pressure Units)  Absoute Force (Pressure Units)  0  0.5  1  1.5  2  2.5  3  3.5  0  0.5  1  1.5  2  2.5  3  3.5  0  0  0.1  0.1  0.2  0.2  0.3 Peak PSC  0.5  0.3 Peak PSC  0.4  0.5  0.6  Ipsilateral Thalamus  0.4  Ipsilateral Putamen  0.7  0.6  0.8  0.7  0.9  0.8  1  Absoute Force (Pressure Units) Absoute Force (Pressure Units) 0  0.5  1  1.5  2  2.5  3  3.5  0  0.5  1  1.5  2  2.5  3  3.5  0  0  0.1  0.5  0.2  1  1.5  0.4  Peak PSC  Ipsilateral M1  Peak PSC  0.3  Ipsilateral Caudate  0.5  2  0.6  150  2.5  0.7  Absoute Force (Pressure Units)  Absoute Force (Pressure Units)  0  0.5  1  1.5  2  2.5  3  3.5  0  0.5  1  1.5  2  2.5  3  3.5  0  0  0.1  0.2  0.2  0.4 Peak PSC  0.8  1  0.3  0.4 Peak PSC  0.5  0.6  Ipsilateral Cerebellum  0.6  Ipsilateral SMA  0.7  1.2  0.8  1.4  0.9  1.6  1  Absoute Force (Pressure Units) 0  0.5  1  1.5  2  2.5  3  3.5  0  0.2  0.4  0.6  Peak PSC  0.8  Ipsilateral PM  1  1.2  1.4  151  1.6  Appendix XV: R and p values for correlations between peak PSC of all ROIs and arm use. Note: * significant at P ≤ 0.05 A. R and P values for correlations for peak PSC at 40% MVC vs. accelerometer total activity for PARETIC hand in stroke participants (n=10). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.593 -0.563 -0.374 0.082 -0.6 -0.321 -0.414  P-value 0.071 0.09 0.287 0.821 0.067 0.365 0.234  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.762 -0.355 -0.534 -0.657 -0.603 -0.628 -0.401  P-value 0.01 * 0.314 0.112 0.039 * 0.065 0.052 * 0.251  B. R and P values for correlations for peak PSC at 40% MVC vs. accelerometer total activity for NON PARETIC hand in stroke participants (n=10). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.453 -0.256 -0.478 -0.451 -0.613 -0.575 -0.463  P-value 0.189 0.476 0.162 0.191 0.059 0.082 0.178 152  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.497 -0.291 -0.655 -0.565 -0.594 -0.657 -0.479  P-value 0.144 0.415 0.04 * 0.089 0.07 0.039 * 0.162  C. R and P values for correlations for peak PSC at 40% MVC vs. accelerometer total activity for non-dominant hand in control participants (n=10). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.363 -0.097 -0.303 -0.08 0.267 -0.164 -0.208  P-value 0.303 0.789 0.395 0.826 0.456 0.65 0.564  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.329 -0.159 -0.195 0.504 0.45 0.408 -0.161  P-value 0.353 0.661 0.589 0.137 0.192 0.242 0.656  153  D. R and P values for correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for PARETIC hand in stroke participants (n=9). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.304 -0.711 -0.574 -0.039 -0.701 -0.539 -0.277  P-value 0.427 0.032 * 0.106 0.92 0.035 * 0.134 0.471  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.787 -0.502 -0.385 -0.717 -0.422 -0.616 -0.463  P-value 0.012 * 0.168 0.307 0.03 * 0.257 0.077 0.209  E. R and P values for correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for NON-PARETIC hand in stroke participants (n=9). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.126 -0.198 -0.496 -0.607 -0.622 -0.459 -0.449  P-value 0.746 0.61 0.174 0.083 0.074 0.214 0.226  154  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.386 -0.193 -0.655 -0.296 -0.65 -0.59 -0.504  P-value 0.305 0.618 0.055 0.437 0.058 0.094 0.167  F. R and P values for correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for non-dominant hand in control participants (n=10). Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value 0.056 -0.66 -0.07 -0.202 0.173 -0.375 -0.109  P-value 0.878 0.038 0.848 0.577 0.632 0.285 0.765  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.022 -0.268 -0.324 -0.071 0.363 0.072 0.056  P-value 0.953 0.453 0.362 0.845 0.302 0.844 0.878  155  Appendix XVI: R and p values for correlations between peak PSC of all ROIs and ARAT. A. R and P values for correlations for peak PSC at 40% MVC vs. ARAT score for PARETIC hand in stroke participants (n=10).  Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.505 -0.308 -0.137 -0.273 0.157 0.314 -0.239  P-value 0.136 0.386 0.706 0.445 0.665 0.377 0.506  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R-value -0.143 -0.328 -0.034 -0.13 -0.061 -0.417 -0.116  P-value 0.693 0.355 0.925 0.721 0.0866 0.231 0.749  156  B. R and P values for correlations for peak PSC at 3.2 pressure units vs. ARAT score for PARETIC hand in stroke participants (n=9).  Contralateral ROIs putamen caudate thalamus M1 SMA PM cerebellum  R -0.218 -0.139 -0.604 -0.376 -0.03 -0.228 -0.515  P 0.573 0.722 0.085 0.318 0.94 0.556 0.156  Ipsilateral ROIs putamen caudate thalamus M1 SMA PM  -0.317 0.129 -0.376 -0.495 -0.337 -0.337  0.406 0.741 0.318 0.175 0.376 0.376  cerebellum  -0.317  0.406  157  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  .2  .4  .6  .8  1.0  1.2  1.4  TOTALACC  0  TOTALACC  0  4. M1  PUTMED  M1MED  100000  100000  200000  200000  300000  300000  TOTALACC  0  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  TOTALACC  0  5. SMA  0.0  .1  .2  .3  .4  .5  .6  2. Caudate  CAUMED  SMAMED  100000  100000  200000  200000  300000  300000  TOTALACC  0  .1  .2  .3  .4  .5  .6  .7  TOTALACC  0  6. PM  .1  .2  .3  .4  .5  .6  .7  100000  100000  3. Thalamus  THALMED  Contralateral ROIs 1. Putamen  200000  200000  158  300000  300000  A. Correlations for peak PSC at 40% MVC vs. accelerometer total activity for PARETIC hand in stroke participants (n=10).  Appendix XVII: Graphs of correlations between peak PSC of all ROIs during force production and arm use as measured by accelerometers.  PMMED  .1  .2  .3  .4  .5  .6  .7  .8  TOTALACC  0  100000  200000  300000  IPSIPUTM  .1  .2  .3  .4  .5  .6  .7  TOTALACC  0  100000  200000  300000  0.0  .1  .2  .3  .4  .5  .6  .7  TOTALACC  0  2. Caudate  IPSICAUM  1. Putamen *  100000  Ipsilateral ROIs (*correlation was significant P≤0.05 in this ROI)  CERMED  7. Cerebellum  200000  300000  .1  .2  .3  .4  .5  .6  TOTALACC  0  3. Thalamus  IPSTHALM  100000  200000  159  300000  0.0  .2  .4  .6  .8  1.0  1.2  TOTALACC  0  0.0  .2  .4  .6  .8  1.0  TOTALACC  0  7. Cerebellum  IPSIM1ME  IPSICERM  100000  100000  200000  200000  300000  300000  0.0  .2  .4  .6  .8  1.0  TOTALACC  0  5. SMA  IPSISMAM  4. M1 *  100000  200000  300000  0.0  .2  .4  .6  .8  1.0  TOTALACC  0  6. PM*  IPSIPMM  100000  200000  160  300000  TOTALNP  200000  4. M1  100000  .2  .4  .6  .8  1.0  1.2  1.4  1.6  200000  TOTALNP  100000  .1  .2  .3  .4  .5  1. Putamen  PUTMED  M1MED  300000  300000  400000  400000  500000  500000  600000  600000  5. SMA  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  1.2  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  2. Caudate  CAUMED  Contralateral ROIs  300000  300000  400000  400000  500000  500000  600000  600000  6. PM  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  200000  TOTALNP  100000  0.0  .1  .2  .3  .4  .5  .6  3. Thalamus  300000  300000  400000  400000  500000  500000  161  600000  600000  B. Correlations for peak PSC at 40% MVC vs. accelerometer total activity for NON- PARETIC hand in stroke participants (n=10).  SMAMED  THALMED PMMED  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  1.2  300000  400000  500000  600000  IPSIPUTM  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  1.2  300000  400000  500000  600000  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  2. Caudate  IPSICAUM  1. Putamen  300000  400000  Ipsilateral ROIs (*correlation was significant P<0.05 in this ROI)  CERMED  7. Cerebellum  500000  600000  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  3. Thalamus *  IPSTHALM  300000  400000  500000  162  600000  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  7. Cerebellum  IPSIM1ME  IPSICERM  300000  300000  400000  400000  500000  500000  600000  600000 200000  TOTALNP  100000  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  1.8  5. SMA  IPSISMAM  4. M1  300000  400000  500000  600000  200000  TOTALNP  100000  .1  .2  .3  .4  .5  .6  .7  .8  .9  6. PM *  IPSIPMM  300000  400000  500000  600000  163  200000  200000  TOTALACC  100000  .2  .4  .6  .8  1.0  1.2  4. M1  TOTALACC  100000  .1  .2  .3  .4  .5  1. Putamen  300000  300000  Contralateral ROIs  400000  400000  500000  500000  600000  600000  200000  200000  TOTALACC  100000  0.0  .2  .4  .6  .8  1.0  1.2  1.4  5. SMA  300000  300000  2. Caudate  TOTALACC  100000  0.0  .1  .2  .3  .4  400000  400000  500000  500000  600000  600000  200000  6. PM  TOTALACC  100000  .1  .2  .3  .4  .5  .6  .7  .8  200000  TOTALACC  100000  .2  .3  .4  .5  .6  .7  .8  .9  300000  300000  3. Thalamus  400000  400000  500000  500000  600000  164  600000  C. Correlations for peak PSC at 40% MVC vs. accelerometer activity for non-dominant hand in healthy controls (n=10).  CAUMED SMAMED  PUTMED  M1MED  THALMED PMMED  200000  TOTALACC  100000  0.0  .2  .4  .6  .8  1.0  500000  500000  600000  600000  200000  TOTALACC  100000  0.0  .1  .2  .3  .4  .5  200000  TOTALACC  100000  0.0  .1  .2  .3  .4  .5  .6  400000  400000  2. Caudate  300000  300000  .6  IPSIPUTM  1. Putamen  Ipsilateral ROIs  CERMED  7. Cerebellum  IPSICAUM  300000  400000  500000  600000  200000  TOTALACC  100000  .1  .2  .3  .4  .5  .6  3. Thalamus  IPSTHALM  300000  400000  500000  165  600000  IPSICERM  200000  TOTALACC  100000  .1  .2  .3  .4  .5  .6  200000  TOTALACC  100000  .2  .4  .6  .8  1.0  1.2  7. Cerebellum  IPSIM1ME  .7  300000  300000  400000  400000  500000  500000  600000  600000 200000  TOTALACC  100000  0.0  .2  .4  .6  .8  1.0  1.2  5. SMA  IPSISMAM  4. M1  300000  400000  500000  600000  200000  TOTALACC  100000  0.0  .2  .4  .6  .8  1.0  1.2  6. PM  IPSIPMM  300000  400000  500000  600000  166  .2  .4  .6  .8  1.0  1.2  1.4  1.6  4. M1  0.0  .1  .2  .3  .4  .5  .6  TOTALACT  0  TOTALACT  0  100000  100000  200000  200000  300000  300000  TOTALACT  0  0.0  .5  1.0  1.5  2.0  2.5  3.0  TOTALACT  0  5. SMA*  0.0  .1  .2  .3  .4  .5  .6  .7  .8  100000  100000  200000  200000  Contralateral ROIs (*correlation was significant P<0.05 in this ROI) 1. Putamen 2. Caudate *  300000  300000  TOTALACT  0  .2  .4  .6  .8  1.0  1.2  TOTALACT  0  6. PM  0.0  .2  .4  .6  .8  1.0  1.2  1.4  3. Thalamus  100000  100000  200000  200000  167  300000  300000  D. Correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for PARETIC hand in stroke participants (n=9).  PUT.3.2  M1  CAU.3.2 SMA  THAL.3.2  PM  0.0  .2  .4  .6  .8  1.0  TOTALACT  0  100000  200000  300000  IPSIPUT  0.0  .2  .4  .6  .8  1.0  TOTALACT  0  100000  200000  300000  0.0  .1  .2  .3  .4  .5  .6  .7  TOTALACT  0  2. Caudate  IPSICAU  1. Putamen*  100000  Ipsilateral ROIs (*correlation was significant P<0.05 in this ROI)  CER  7. Cerebellum  200000  300000  .2  .3  .4  .5  .6  .7  .8  TOTALACT  0  3. Thalamus  IPSITHAL  100000  200000  300000  168  IPSICER  0.0  .5  1.0  1.5  2.0  TOTALACT  0  .2  .4  .6  .8  1.0  TOTALACT  0  7. Cerebellum  IPSIM1  4. M1*  100000  100000  200000  200000  300000  300000  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  TOTALACT  0  5. SMA  100000  200000  300000  IPSIPM  IPSISMA  0.0  .2  .4  .6  .8  1.0  1.2  1.4  TOTALACT  0  100000  6. PM  200000  300000  169  200000  TOTAL.NP  100000  .2  .4  .6  .8  1.0  1.2  1.4  200000  TOTAL.NP  100000  .1  .2  .3  .4  .5  .6  .7  .8  .9  4. M1  PUT_3.2  M1_3.2  300000  300000  400000  400000  500000  500000  600000  600000  5. SMA  200000  TOTAL.NP  100000  0.0  .2  .4  .6  .8  1.0  200000  TOTAL.NP  100000  0.0  .2  .4  .6  .8  1.0  1.2  2. Caudate  CAU_3.2  SMA_3.2  1. Putamen  300000  300000  400000  400000  Contralateral ROIs (*correlation was significant P<0.05 in this ROI)  500000  500000  600000  600000  1.0  TOTAL.NP  200000  6. PM  100000  0.0  .2  .4  .6  .8  200000  TOTAL.NP  100000  -.1  0.0  .1  .2  .3  .4  .5  .6  300000  300000  3. Thalamus  400000  400000  500000  500000  E. Correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for NON-PARETIC hand in stroke participants (n=9).  THAL_3.2 PM_3.2  170  600000  600000  200000  TOTAL.NP  100000  0.0  .1  .2  .3  .4  .5  .6  300000  400000  500000  600000  IPSIPUT_  200000  TOTAL.NP  100000  0.0  .1  .2  .3  .4  .5  .6  1. Putamen  300000  400000  500000  600000  IPSICAU_  200000  TOTAL.NP  100000  0.0  .2  .4  .6  .8  1.0  1.2  300000  2. Caudate  400000  Ipsilateral ROIs (*correlation was significant P<0.05 in this ROI)  CER_3.2  7. Cerebellum  500000  600000  200000  TOTAL.NP  100000  0.0  .1  .2  .3  .4  .5  .6  .7  3. Thalamus  IPSITHAL  300000  400000  500000  171  600000  IPSICER_  200000  TOTAL.NP  100000  -.2  0.0  .2  .4  .6  .8  200000  TOTAL.NP  100000  .1  .2  .3  .4  .5  .6  7. Cerebellum  IPSIM1_3  4. M1  300000  300000  400000  400000  500000  500000  600000  600000 200000  TOTAL.NP  100000  0.0  .2  .4  .6  .8  1.0  1.2  1.4  5. SMA  300000  400000  500000  600000  IPSIPM_3  IPSISMA_  200000  TOTAL.NP  100000  .1  .2  .3  .4  .5  .6  .7  .8  .9  6. PM  300000  400000  500000  600000  172  200000  TOTALACT  100000  .2  .3  .4  .5  .6  .7  .8  .9  4. M1  200000  TOTALACT  100000  0.0  .1  .2  .3  .4  .5  1. Putamen  PUT_3.2  M1_3.2  300000  300000  400000  400000  500000  500000  600000  600000  200000  TOTALACT  100000  .2  .4  .6  .8  1.0  200000  TOTALACT  100000  -.1  0.0  .1  .2  .3  300000  300000  2. Caudate *  5. SMA  CAU_3.2  400000  400000  Contralateral ROIs (*correlation was significant P<0.05 in this ROI)  500000  500000  600000  600000  6. PM  200000  TOTALACT  100000  0.0  .1  .2  .3  .4  .5  .6  200000  TOTALACT  100000  .2  .3  .4  .5  .6  .7  3. Thalamus  300000  300000  400000  400000  500000  500000  F. Correlations for peak PSC at 3.2 pressure units vs. accelerometer total activity for non-dominant hand in control participants (n=10).  SMA_3.2  THAL_3.2 PM_3.2  600000  600000  173  200000  TOTALACT  100000  .1  .2  .3  .4  .5  .6  .7  .8  300000  400000  500000  600000  IPSIPUT_  200000  TOTALACT  100000  .1  .2  .3  .4  1. Putamen  300000  400000  500000  600000  IPSICAU_  200000  TOTALACT  100000  0.0  .1  .2  .3  .4  .5  300000  2. Caudate  Ipsilateral ROIs (*correlation was significant P<0.05 in this ROI)  CER_3.2  7. Cerebellum  400000  500000  600000  IPSITHAL  200000  TOTALACT  100000  .1  .2  .3  .4  .5  300000  3. Thalamus  400000  500000  174  600000  200000  TOTALACT  100000  .1  .2  .3  .4  .5  .6  .7  200000  TOTALACT  100000  0.0  .2  .4  .6  .8  1.0  7. Cerebellum  IPSIM1_3  IPSICER_  300000  300000  400000  400000  500000  500000  600000  600000 200000  TOTALACT  100000  0.0  .2  .4  .6  .8  1.0  1.2  5. SMA  IPSISMA_  4. M1  300000  400000  500000  600000  IPSIPM_3  200000  TOTALACT  100000  0.0  .2  .4  .6  .8  1.0  6. PM  300000  400000  500000  600000  175  ARA  -10  .2  .4  .6  .8  1.0  1.2  1.4  4. M1  ARA  -10  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  0  0  1. Putamen  10  10  20  20  Contralateral ROIs  30  30  40  40  50  50  60  60  ARA  -10  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  5. SMA  0  0  10  10  2. Caudate  ARA  -10  0.0  .1  .2  .3  .4  .5  .6  20  20  30  30  40  40  50  50  60  60  ARA  -10  .1  .2  .3  .4  .5  .6  .7  ARA  -10  .1  .2  .3  .4  .5  .6  .7  10  0  10  6. PM  0  3. Thalamus  20  20  G. Correlations for peak PSC at 40% MVC vs. ARAT for PARETIC hand in stroke participants (n=10).  CAUMED  SMAMED  PUTMED  M1MED  THALMED PMMED  30  30  40  40  50  50  60  60  176  ARA  -10  .1  .2  .3  .4  .5  .6  .7  .8  0  10  PUTM  ARA  -10  .1  .2  .3  .4  .5  .6  .7  0  1. Putamen  10  Ipsilateral ROIs  CERMED  7. Cerebellum  20  20  30  30  40  40  50  50  60  60  ARA  -10  0.0  .1  .2  .3  .4  .5  .6  .7  0  10  2. Caudate  20  30  40  50  60  IPSTHALM  IPSICAUM  0  10  3. Thalamus  ARA  -10  .1  .2  .3  .4  .5  .6  20  30  40  50  177  60  IPSICERM  ARA  -10  0.0  .2  .4  .6  .8  1.0  0  ARA  -10  0.0  .2  .4  .6  .8  1.0  0  7. Cerebellum  IPSIM1ME  1.2  10  10  20  20  30  30  40  40  50  50  60  60  ARA  -10  0.0  .2  .4  .6  .8  1.0  5. SMA  SMAM  4. M1  0  10  20  30  40  50  60  PMM  0  6. PM  ARA  -10  0.0  .2  .4  .6  .8  1.0  10  20  30  40  50  178  60  ARA  20  .2  .4  .6  .8  1.0  1.2  1.4  1.6  ARA  20  4. M1  0.0  .1  .2  .3  .4  .5  .6  1. Putamen  PUT.3.2  M1  30  30  40  40  50  50  60  60  0.0  .5  1.0  1.5  2.0  2.5  3.0  ARA  20  5. SMA  ARA  20  0.0  .1  .2  .3  .4  .5  .6  .7  .8  2. Caudate  CAU.3.2  Contralateral ROIs  30  30  40  40  50  50  60  60  ARA  20  .2  .4  .6  .8  1.0  1.2  6. PM  ARA  20  0.0  .2  .4  .6  .8  1.0  1.2  1.4  3. Thalamus  30  30  40  40  H. Correlations for peak PSC at 3.2 pressure units vs. ARAT for PARETIC hand in stroke participants (n=9).  SMA  THAL.3.2 PM  50  50  179  60  60  0.0  .2  .4  .6  .8  ARA  20  30  IPSIPUT  0.0  .2  .4  .6  .8  1.0  ARA  20  1. Putamen  30  Ipsilateral ROIs  CER  1.0  40  40  50  50  60  60  ARA  20  0.0  .1  .2  .3  .4  .5  .6  .7  2. Caudate  IPSICAU  7. Cerebellum  30  40  50  60  ARA  20  .2  .3  .4  .5  .6  .7  .8  3. Thalamus  IPSITHAL  30  40  50  180  60  IPSICER  ARA  20  0.0  .5  1.0  1.5  .2  .4  .6  .8  ARA  20  1.0  7. Cerebellum  IPSIM1  2.0  30  30  40  40  50  50  60  60  ARA  20  0.0  .2  .4  .6  .8  1.0  1.2  1.4  1.6  5. SMA  IPSISMA  4. M1  30  40  50  60  IPSIPM ARA  20  0.0  .2  .4  .6  .8  1.0  1.2  1.4  30  6. PM  40  50  60  181  

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