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A joint compensatory and default mode network closely related to motor performance in Parkinson's disease Galley, Shawna Lynn 2012

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A joint compensatory and default mode network closely related to motor performance in Parkinson’s disease  
 by  Shawna Lynn Galley  B.A., Honours, Simon Fraser University, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES  (NEUROSCIENCE)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May, 2012 © Shawna Lynn Galley, 2012  ABSTRACT The motor symptoms in Parkinson’s disease only appear after extensive dopaminergic nigral cell loss, suggesting the presence of redundancy or compensatory mechanisms that serve to delay symptom onset and maintain motor function. Previous studies have demonstrated altered activity in a premotorparietal-cerebellar circuit, frequently interpreted, but not necessarily established to be compensatory. Unfortunately, it is difficult to differentiate compensatory from direct disease-related if a clear relationship between brain activity and motor performance is not rigorously established. Accordingly, the present thesis investigated fMRI connectivity patterns that predicted motor performance in 12 Parkinson’s patients and 11 healthy controls. Subjects performed a manual tracking task employing a rubber squeeze bulb that incorporated different sinusoidal frequencies and varying amounts of visual guidance. Motor performance was then assessed by first fitting linear dynamical systems models such that the desired tracking performance was the input and the actual tracking performance was the output. A feature of the models (damping ratio) was then used as a metric of performance. The group fMRI connectivity networks were derived by a conditional dependence statistical method, which distinguished between direct and indirect connectivity. Damping ratio from the behaviour models was then predicted by the fMRI connectivity strengths using a sparse linear regression method, and a leave-one-out validation procedure. In both patients and controls, damping ratio could be accurately predicted with fMRI connectivity patterns. In controls, premotor-cerebellar and cingulate  
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  connections were associated with increased damping ratio and enhanced performance. However, in patients the strength of premotor-cerebellar  and  visuomotor connections were associated with improved motor performance, while connection strengths within the default mode network were associated with worse performance. Simultaneous modelling of fMRI and behaviour is a powerful tool to assess compensatory changes in Parkinson’s subjects. The current thesis provides strong evidence that altered activity in some parts of the premotor-parietalcerebellar network is, in fact, compensatory as previously speculated, as greater connectivity within this network contributed to maintenance of performance. Furthermore, activation of this compensatory network impairs the ability of the inferior parietal cortex to normally de-activate as part of the default mode network, possibly making patients susceptible to non-informative, extraneous stimuli.  
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  PREFACE This thesis was conducted under the supervision of Dr. Martin J. McKeown and in collaboration with Drs. Meeko Oishi and Jane Wang in the Electrical and Computer Engineering department at UBC. The behavioural analysis (damping ratio derrived from LDS models) was conducted in collaboration with Dr. Oishi’s Masters student, Pouria Talebifard.  The PCfdr and LASSO analysis were  conducted in collaboration with Dr. Wang’s PhD student, Aiping Liu. My role in this work was to help design and implement the experimental paradigm, conduct the experiment and collect data, perform the fMRI preprocessing, interpret the results and write the manuscript. Editing and revision of the final manuscript for chapter 2 was done in collaboration with Dr. McKeown and Dr. Oishi. I assisted with the development of a semi-automatic ROI segmentation pipeline, which was done in collaboration with Dr. Jingyun Chen while he was under the supervision of Dr. Faisal Beg in the Medical Image Analysis Laboratory at SFU. Dr. Edna Ty recruited the PD subjects and helped conduct the experiments. I recruited the healthy controls. All work conducted for this thesis was under UBC Research Ethics Board and Vancouver Coastal Health Authority approval. The UBC Clinical Research Ethics Board Certificate number is H04-70177, and the Vancouver Coastal Health Authority Research number is V04-0091.  
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  TABLE OF CONTENTS ABSTRACT....................................................................................................................................ii
 PREFACE ......................................................................................................................................iv
 TABLE
OF
CONTENTS ............................................................................................................... v
 LIST
OF
TABLES....................................................................................................................... vii
 LIST
OF
FIGURES ....................................................................................................................viii
 LIST
OF
ABBREVIATIONS .......................................................................................................ix
 ACKNOWLEDGMENTS .............................................................................................................. x
 DEDICATION...............................................................................................................................xi
 CHAPTER
1
­
INTRODUCTION................................................................................................1
 1.1
Parkinson’s
disease
and
the
basal
ganglia ..........................................................................1
 1.2
Compensatory
mechanisms
in
Parkinson’s
disease ........................................................3
 1.2.1
Compensation
at
the
synaptic
level:
dopamine ....................................................................... 4
 1.2.2
Non‐dopaminergic
compensation
at
the
synaptic
level ....................................................... 6
 1.3
Macroscopic,
systems
level
compensatory
mechanisms ...............................................7
 1.3.1
Systems
level
compensation
investigated
with
PET.............................................................. 8
 1.3.2
Task‐related
systems
level
compensation ................................................................................. 9
 1.3.3
Compensatory
hyperactivity
in
the
premotor‐parieto‐cerebellar
network..............10
 1.4
Compensatory
changes
in
functional
connectivity ....................................................... 12
 1.4.1
Connectivity
at
rest.............................................................................................................................13
 1.4.2
Task
related
connectivity.................................................................................................................13
 1.5
Visually
guided
movement
in
Parkinson’s
disease ....................................................... 14
 1.5.1
The
basal
ganglia
and
the
cerebellum
are
interconnected................................................16
 1.5.2
Visual
guidance
of
movement ........................................................................................................19
 1.5.3

The
role
of
the
cerebellum
in
the
visual
guidance
of
movement...................................19
 1.5.4

The
role
of
premotor
and
parietal
cortex
and
the
cerebellum
in
the
visual
 guidance
of
movement .................................................................................................................................21
 1.6
Study
aims
and
hypothesis .................................................................................................... 23
  CHAPTER
2
–
A
JOINT
COMPENSATORY
AND
DEFAULT
NETWORK
CLOSELY
 RELATED
TO
MOTOR
PERFORMANCE
IN
PARKINSON’S
DISEASE .......................... 28
 2.1
Introduction ............................................................................................................................... 28
 2.2
Material
and
methods ............................................................................................................. 31
 2.2.1
Participants ............................................................................................................................................31
 2.2.2
Experimental
paradigm ....................................................................................................................32
 2.2.3
Data
acquisition ...................................................................................................................................34
 2.2.4
fMRI
pre‐processing
and
analysis ................................................................................................35
 2.2.5
Behavioural
analysis ..........................................................................................................................35
 2.2.6
Connectivity
analysis:
PCfdr ...........................................................................................................37
 2.2.7
Relationship
between
behavioural
and
connectivity
analysis:
LASSO ........................38
 2.3
Results .......................................................................................................................................... 39
 2.3.1
Behavioural............................................................................................................................................39
 2.3.2
Connectivity ...........................................................................................................................................39
 
  v
  2.3.3
Effect
of
noise
level
on
functional
connectivity......................................................................39
 2.3.4
Relationship
between
connection
strength
and
damping
ratio:
healthy
controls..40
 2.3.5
Relationship
between
connection
strength
and
damping
ratio:
patients
off
 medication .........................................................................................................................................................40
 2.4
Discussion ................................................................................................................................... 41
  CHAPTER
3
–
CONCLUSION.................................................................................................. 57
 3.1
Summary
of
findings................................................................................................................ 57
 3.2
Study
significance
and
suggestions
for
future
research.............................................. 60
 3.3
Limitations .................................................................................................................................. 62
 BIBLIOGRAPHY ....................................................................................................................... 65
 
 
 
 
 
  
  vi
  LIST OF TABLES Table 2-1 Participant characteristics………………………………………………..53 Table 2-2 Region of interest (ROI) legend…………………………………………54  
  vii
  LIST OF FIGURES Figure 1-1 The Basal ganglia………………………………………………………….27 Figure 2-1 Experimental task.…………………………………………………………52 Figure 2-2 Connectivity patterns of patients and controls derived from PCfdr…..55 Figure 2-3 Network predicting motor performance in healthy controls……………56 Figure 2-4 Network predicting motor performance in PD participants…………….57  
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  LIST OF ABBREVIATIONS ACC
 BG
 CB
 DA
 EEG
 EG
 EMG
 fMRI
 IG
 MVC
 PD
 PET
 PMd
 PMv
 pre‐SMA
 SMA
 SPECT
 UPDRS
  
  anterior
cingulate
cortex
 
 basal
ganglia
 
 
 cerebellum
 
 
 dopamine
 
 
 electroencephalography
 
 externally
guided
 
 magnetoencephalography
 
 functional
magnetic
resonance
imaging
 internally
guided
 
 maximum
voluntary
contraction
 Parkinson's
disease
 
 positron
emission
tomography
 dorsal
premotor
cortex
 
 ventral
premotor
cortex
 
 pre‐supplementary
motor
area
 supplementary
motor
area
 
 single
photon
emission
tomography
 Unified
Parkinson's
Disease
Rating
Scale
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  ACKNOWLEDGMENTS I would like to thank my supervisor, Dr Martin McKeown, for his continued support and guidance throughout my graduate studies. I came to UBC hoping to gain experience in fMRI and to gather a breadth of knowledge in Parkinson’s disease. I am grateful for the opportunity to have received both. I would like to thank our collaborators Drs. Oishi and Wang and their students, Pouria Talebifard and Aiping Liu for their extensive help and contribution to the completion of this thesis. I would like to thank the members of my supervisory committee for their guidance, encouragement and time. I am very grateful to Dr. Edna Ty for her unwavering help in recruiting subjects and conducting the experiments with me, and for being a wonderful person with which to work. Edna, your constant encouragement and support were invaluable. A special thanks to all those who participated in this study, for without you, this research would not be possible. Thanks to the friends and family who encouraged me through my many years of study. Dr. Jessica Green, you have been a generous mentor and a true friend. Last but not least, thank you to Kevin for your love and patience and for being my personal cheerleader through this process. 
 
 
 
 
 
 
 
 
 
  
  x
  DEDICATION  
 For my mother. For being the single mother who taught me the value of independence and an education by raising me while completing a B.Sc. For your unconditional love. For reminding me that giving me life gave you the right to take it away if I didn’t behave myself  I think that kept me on track.  
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  CHAPTER 1 - INTRODUCTION 1.1 Parkinson’s disease and the basal ganglia Parkinson’s disease (PD) is the most common form of movement disorder affecting approximately 1% of the population above the age of 65 (Lang & Lozano, 1998a, 1998b). The cardinal motor features of PD include rigidity, bradykinesia (slowness of movement), postural instability, and tremor (Fahn, 2003). Further, though motor symptoms dominate the clinical picture, PD is associated with several nonmotor symptoms, such as: 1) neuropsychiatric disturbances (depression, anxiety, apathy); 2) sleep disorders (REM sleep behaviour disorders, excessive daytime somnolence); 3) autonomic symptoms (bladder  disturbances,  orthostatic  hypotension,  constipation);  4)  sensory  symptoms (pain, olfactory disturbance) and 5) dementia (Chaudhuri & Schapira, 2009). PD is associated with a twofold risk of mortality and thus a significant reduction in patients’ life expectancy when compared to healthy, age-matched controls (Bennett et al., 1996). PD is characterized pathologically by progressive degeneration in the dopaminergic (DA) neurons of the substantia nigra pars compacta (SNc), which results in a significant reduction of striatal dopamine levels (Bergman & Deuschl, 2002), and the presence of intraneuronal cytoplasmic eosinophilic inclusion bodies termed Lewy bodies in the substantia nigra (Gibb & Lees, 1988). The subtantia nigra is a brain structure belonging to the basal ganglia (BG), a group of interconnected nuclei including the caudate, putamen, globus pallidus interna 
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  (GPi) and externa (GPe), and subthalamic nucleus (STN). Together with the cerebral cortex and thalamus, the BG forms the motor loop responsible for the control of voluntary movement (Albin, Young, & Penney, 1989). The classic description of the BG (Albin, et al., 1989; Delong, 1990) affirms that there are two segregated BG pathways that begin in the striatum and converge on the output structures of the BG (GPi and substantia nigra pars reticulata (SNr)). The BG are comprised of a direct and indirect pathway. The direct pathway is a ϒ-amino butyric acid (GABA)ergic inhibitory pathway, while the indirect pathway is a polysynaptic disinhibitory pathway through the GABAergic GPe and STN. Striatal projections in the direct pathway express D1 dopamine receptors while those in the indirect pathway express D2 receptors. Dopamine facilitates movement via excitatory D1 receptors in the direct pathway and inhibits transmission via D2 receptors in the indirect pathway. The opposing effect of dopamine in these pathways results in a net effect of facilitating movement in the BG. In PD, the dopamine depleted state leads to decreased activation of D1 and D2 receptors, which results in excessive inhibitory output of the GPi and SNr and inhibition of the thalamo-cortical motor loop (Brainard, 1997; Lang & Lozano, 1998b; Obeso et al., 2000) (figure 1). The etiology of PD is largely unknown, however, several genetic (Lesage & Brice, 2009) and environmental (Priyadarshi, Khuder, Schaub, & Priyadarshi, 2001) risk factors have been identified. Further, oxidative stress, mitochondrial dysfunction, deficient neurotrophic support and immune factors have been implicated as possible mechanisms underlying nigral cell death in PD (Lang & Lozano, 1998a). 
  2
  1.2 Compensatory mechanisms in Parkinson’s disease  The motor symptoms in PD only occur after an estimated 50% of dopaminergic nigral cells and 60-80% of striatal dopamine levels have been lost (Fearnley & Lees, 1991; Lee et al., 2000). Further, imaging measures of pathological  disease  progression,  such  as  18  F-dopa  positron  emission  tomography (PET) and single-photon emission computed tomography (SPECT), do not necessarily correlate with clinical measures of disability, such as the Unified Parkinson’s Disease Rating Scale (UPDRS) (Morrish, Sawle, & Brooks, 1996; Nurmi et al., 2001). The length of this presymptomatic phase varies significantly among patients with PD, however, neuroimaging studies estimate it to be approximately 5 years (Marek et al., 2001; Morrish, et al., 1996). Though the nature of this preclinical period has not been fully elucidated, the lack of observed motor pathology despite significant cell loss indicates the existence of redundancy and/or compensatory mechanisms that serve to delay the onset of symptoms and preserve an optimal level of motor function (Zigmond, 1997; Zigmond, Abercrombie, Berger, Grace, & Stricker, 1990). It is sometimes difficult to differentiate compensatory changes from direct disease-related changes. This requires a rigorous definition of compensation. For the purpose of this discussion, compensation will be defined as “any change, morphological or functional, seen in the damaged brain, that acts to maintain performance of the impaired function.” In addition to the preclinical stage, the rate of progression of PD also varies widely among individual patients. Factors that may contribute to a more rapid motor decline include baseline motor and cognitive impairment, older age and 
  3
  lack of tremor at onset (Marras, Rochon, & Lang, 2002). The heterogeneous pattern of disease progression in PD can potentially be explained by individual differences in compensatory mechanisms. Therefore, the investigation of compensatory changes in PD is essential to the development of sensitive biomarkers allowing for a more accurate prognosis for patients, and a clearer understanding of compensatory mechanisms can better guide the design and interpretation of clinical trials (Marras, et al., 2002). 1.2.1 Compensation at the synaptic level: dopamine  Compensatory mechanisms can occur on different temporal and spatial scales at both the synaptic and systems level. The focus of this work is on macroscopic, systems level compensatory mechanisms in PD, however, a brief discussion of compensation at the molecular level is warranted. As with other monoamines, dopamine transmission involves six processes: 1) biosynthesis; 2) packaging into vesicles by type 2 vesicular monoamine transporters (VMAT2); 3) release into the synaptic cleft in response to presynaptic action potential; 4) interaction with pre- and postsynaptic receptors; 5) reuptake of extracellular DA via the plasma membrane DA transporter (DAT1 in the case of the BG); and 6) enzymatic degradation of remaining DA by catechol-Οmethyltransferase (COMT) (Squire et al., 2008). There are several presynaptic mechanisms that have been proposed to compensate for dopamine deficiency in PD, including increased synthesis and  
  4
  release of DA and down-regulation of DA reuptake into presynaptic terminals (Zigmond, et al., 1990). Basal extracellular levels are maintained at relatively normal levels through direct somatodendritic release, indirect action of DA collaterals on the soma and negative feedback from striatal GABAergic afferents (Geffen, Jessell, Cuello, & Iversen, 1976; Grace & Bunney, 1985; Llinas, Greenfield, & Jahnsen, 1984). Studies have shown that the down-regulation of the dopamine transporters (DAT) plays an important role in DA homeostasis by reducing the amount of reuptake resulting in increased DA availability (Lee et al., 2000; Sossi et al., 2009). An increase in the rate of dopamine turnover has also been documented as an early compensatory mechanism in PD (de la FuenteFernandez et al., 2001; Sossi et al., 2002; Sossi et al., 2004; Sossi, de la FuenteFernandez, Schulzer, Adams, & Stoessl, 2006). Dopaminergic supersensitivity has been proposed as a postsynaptic compensatory mechanism in PD where there is an increase in dopamine receptor sensitivity as well as an increase in receptor numbers (Lee et al., 1978;Morissette et al., 1996). Zigmond and colleagues (1990) hypothesized that presynaptic mechanisms of compensation occur first in order to maintain sufficient DA concentrations in the striatum. Once these presynaptic changes can no longer conserve adequate levels of dopamine, postsynaptic changes come into play to promote the optimal use of dopamine by remaining neurons (Zigmond, et al., 1990).  
  5
  1.2.2 Non-dopaminergic compensation at the synaptic level  The aforementioned evidence that compensation results from the adaptive properties of DA neurons is taken from rodent models and patients clinically diagnosed with PD. Nigrostrial degeneration may be significantly advanced in these cases, therefore, some reports have questioned whether the same compensatory mechanisms are involved in early-stage, pre-diagnosed PD (for review see Bezard, Gross & Brotchie, 2003). Investigations using the progressive N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine  (MPTP)  primate  model  of  parkinsonism, in which DA cell loss occurs over a month long period, have demonstrated that alterations in DA metabolism and release are present at later stages of neurodegeneration. Further, there is a biphasic relationship between D2like receptor binding and the level of striatal dopaminergic denervation such that an initial decrease is seen during the presymptomatic period followed by an upregulation in postsynaptic receptors once striatal DA homeostasis can no longer be conserved (Bezard et al., 2001). These findings prompted examination of the role of neurotransmission in other BG projections in functional compensation. The glutamatergic inputs from the STN to the SNc were examined as a potential compensatory pathway in the presymptomatic period of PD (Bezard, Bioulac, & Gross, 1998; Bezard, Boraud, Bioulac, & Gross, 1997b). Transitory blockage of SNpc glutamatergic inputs by intracranial injections of kynurenic acid  
  6
  had no effect in primates prior to MPTP treatment or during the first stage of treatment. However, once clinical signs appeared, motor abnormalities were significantly aggravated by blockage of glutamatergic input (Bezard, Boraud, Bioulac, & Gross, 1997a; Bezard, et al., 1997b). It was further demonstrated that blockage of glutamatergic afferents to SNc with kynurenic acid induced parkinsonian motor abnormalities in asymptomatic MPTP monkeys and increased the severity of parkinsonism in symptomatic monkeys. Thus, glutamatergic inputs to SNc appear to be implicated in compensatory phenomena at different stages of experimental Parkinsonism (Bezard, et al., 1998). 1.3 Macroscopic, systems level compensatory mechanisms  In addition to the above-described compensatory mechanisms at the synaptic level within the basal ganglia, systems level changes outside the BG are involved in functional compensation as well. Indeed, overactivity in output regions of the BG is observed in presymptomatic primate models of Parkinsonism, suggesting structures downstream of the basal ganglia can provide compensation that delay the onset of symptoms (Bezard, Crossman, Gross & Brotchie, 2001b). No brain region works in isolation and the basal ganglia is but one of several structures associated with the production of movement. Moreover, the basal ganglia are anatomically and functionally connected to other regions. As a consequence, disruption of transmission through the basal ganglia has the potential to perturb a number of networks within the brain.  
  7
  Macroscopic tomography  (PET),  changes  can  functional  be  assessed  magnetic  using  resonance  positron  imaging  emission  (fMRI)  and  electroencephalographic or magnetoencephalographic (EEG/MEG) methods. These techniques measure neural activity based on hemodynamic, metabolic and electromagnetic properties respectively. A main advantage of PET is the ability to label biological substrates with radiotracers in order to assess their in vivo function. Labeling glucose ([18F]-flurodeoxyglucose [FDG]) or water ([15O]-H2O) allows for the examination of local metabolism and blood flow. This technique revealed that the metabolic changes in Parkinson’s disease follow a characteristic pattern. 1.3.1 Systems level compensation investigated with PET  Investigation of resting state regional metabolism using 18F-FDG PET in patients with early, unmedicated PD reveals a ‘Parkinson’s disease-related spatial covariance pattern (PDRP)’ (Eidelberg et al., 1994). This pattern is characterized by relative hypometabolism of the lateral premotor cortex, supplementary motor area (SMA), dorsolateral prefrontal cortex (DLPFC) and parieoto-occipital association areas. Metabolic increases, seemingly compensatory, are observed in sensorimotor cortex, putamen, globus pallidus, thalamus, pons and cerebellum. These metabolic abnormalities are already present in the presymptomatic phase of the disease (Tang, Poston, Dhawan, & Eidelberg, 2010). Additionally, this pattern is consistently reported in several populations of PD patients (Moeller et al., 1999), demonstrates high test-retest reproducibility (Ma, Tang, Spetsieris, 
  8
  Dhawan, & Eidelberg, 2007), serves to monitor disease progression (Eidelberg et al., 1995) and differentiates between Parkinson’s disease and atypical Parkinsonism (Eckert et al., 2008; Eidelberg, et al., 1994). The PDRP is also sensitive to therapeutic modulation and clinical improvement associated with levodopa administration (Feigin et al., 2001) and surgical treatment (Eidelberg et al., 1996; Fukuda et al., 2004). 1.3.2 Task-related systems level compensation  In addition to examining complex spatial patterns at rest, several studies have investigated voluntary movement in PD using PET and regional cerebral blood flow (rCBF) as a correlate of neural activity; and with fMRI and the bloodoxygen level dependent signal as an indirect measure of neuronal activity. Unlike PET, however, the BOLD signals from fMRI do not represent a simple physical quantity that can be directly compared across participants. Rather, most fMRI experiments require a contrast between an experimental task (e.g. finger tapping) and a control task (e.g. rest). Therefore, experiments suggesting ‘hyperactivation’ and ‘hypoactivation’ of specific brain regions must be interpreted with this in mind. Several studies have interpreted patterns of hyperactivity during voluntary movement in PD to represent the compensatory recruitment of parallel motor circuits to overcome a functional deficit of the striatocortical motor loops. PET has revealed that PD patients off medication demonstrate underactivation of SMA and overactivation of the ipsilateral cerebellar hemisphere relative to healthy controls during a finger-to-thumb opposition motor task (Rascol et al., 1997). Other studies 
  9
  have confirmed that activity in the lateral premotor-parietal-cerebellar circuit is increased in PD participants, whilst activity in mesial-SMA-cingulate is reduced. Samuel and colleagues (1997) reported reduced rCBF in mesial frontal and prefrontal areas along with overactivity in the lateral premotor and inferolateral parietal regions in patients compared to controls during sequential finger movements. This pattern was corroborated by an fMRI investigation during which Parkinson’s patients exhibited a relative decrease in signal amplitude in SMA and right DLPFC, while concomitant BOLD increases were seen bilaterally in primary sensorimotor cortex, lateral premotor cortex, inferior parietal cortex and anterior cingulate cortex (ACC) (Sabatini et al., 2000). This same pattern of hyperactivity, with the addition of precuneus, appears to scale linearly with increasing task difficulty (Catalan, Ishii, Honda, Samii, & Hallett, 1999). Motor sequence learning in PD patients leads to a more widespread cortical activation than in controls and to cerebellar activation in PD patients alone (Mentis et al., 2003). Similarly, the spatial extent of activation within motor regions is increased in PD, and normalized after medication, so that L-dopa can produce a focusing effect (Ng, Palmer,  Abugharbieh,  &  McKeown,  2010).  This  implies  that  another  compensatory mechanism may be to recruit increased cortical regions, presumably to excite defective basal ganglia circuits. 1.3.3 Compensatory hyperactivity in the premotor-parieto-cerebellar network  Several studies lend additional support for the assumption that the cerebellum, premotor and parietal regions increase activity to compensate for 
  10
  degeneration of the basal ganglia to maintain near-normal motor function (Glickstein & Stein, 1991b). For example, during an externally guided fingertapping task, umedicated patients demonstrated greater activation in cerebellum, frontostriatal circuit, right inferior frontal gyrus, and insular cortex. In contrast, the only network exhibiting an overall signal increase in patients during internally generated movement was the cerebellothelamic pathway (Cerasa et al., 2006). Similarly, during a right handed thumb-pressing task, patients off medication exhibited hyperactivation in both cerebellar hemispheres and left primary motor cortex (M1). Further, a significant negative correlation was found between ipsilateral cerebellum and contralateral putamen, suggesting that activity in the cerebellum increases to compensate for reduced activation in the putamen in idiopathic PD (Yu, Sternad, Corcos, & Vaillancourt, 2007). Likewise, greater activation of pre-motor areas appears to be compensatory in the preclinical stage of genetic PD (van der Vegt, van Nuenen, Bloem, Klein, & Siebner, 2009). A  recent  study  suggests  that  another  important  mechanism  for  compensation in PD is achieved by means of “motor reserve” and “novel area recruitment” (NAR) (Palmer, Ng, Abugharbieh, Eigenraam, & McKeown, 2009). Active motor reserve, a concept drawn from cognitive reserve (Stern, 2002), is defined as an increased recruitment of a task-related network that monotonically increases with task difficulty in healthy participants in order to maintain performance. This is distinguished from NAR, whereby novel areas or networks are recruited as additional resources to maintain a near-normal level of performance as task difficulty increases. Evidence for both these effects in PD 
  11
  were demonstrated when subjects were asked to provide sinusoidal force production at three different speeds (0.25, 0.5, and 0.75 Hz) (Palmer, Ng, et al., 2009). Multiple linear regression analyses revealed that activity linearly increased with movement speed in regions of the basal ganglia in healthy controls, most notably in bilateral putamen and thalamus. Off medication, PD patients appear to maximally recruit this same network at lower speeds, suggesting that PD subjects tap into this motor reserve to maintain task performance. To perform the task at higher speeds, patients exhibit NAR by shifting to a compensatory network that included the cerebello-thalamo-cortical loop, consistent with prior studies. 1.4 Compensatory changes in functional connectivity  As previously mentioned, neurons and neural populations do not function in isolation and static images of neural activation during a particular task do not convey how brain regions are interconnected. Therefore, it may be insufficient to simply examine discrete regions of activation in relation to neurological disease. Studies that investigate interactions between brain regions are beginning to play an increasingly important role in the understanding of brain function (Jiang, He, Zang, & Weng, 2004). Further, the concept of neural networks associated with specific tasks is necessary for understanding organized behaviour of the brain beyond the simple mapping of brain activity (Lee, Harrison, & Mechelli, Horwitz, 2003; 2003). Researchers have thus begun to explore inter-regional connectivity during rest or task performance, which is typically described in terms of functional connectivity or effective connectivity (Friston, 1994). 
  12
  1.4.1 Connectivity at rest  Studies alluding to “connectivity” can refer to functional connectivity, the temporal correlation between spatially distinct neurophysiological events, or effective connectivity, a connectivity pattern that reveals the strength and directionality of information flow (Friston, 1994). A recent study compared the functional connectivity in the motor network between healthy controls and PD patients off and on levodopa during the resting state (Wu, Wang, et al., 2009). Patients off medication demonstrated decreased connectivity in the SMA, left DLPFC, and left putamen, and increased connectivity in the left cerebellum, left M1, and left parietal cortex. Administration of levodopa relatively normalized these connectivity patterns in patients. Similar observations were reported in a subsequent study comparing regional homogeneity (ReHo) patterns in PD subjects and healthy controls (Wu, Long et al., 2009). ReHo decreases were found in the putamen, thalamus, and supplementary motor area of PD participants, while increases were seen in cerebellum, primary sensorimotor cortex, and premotor area. These results are consistent with the regional hypoand hyperactivation patterns seen in the prior imaging studies discussed above. 1.4.2 Task related connectivity  Few studies have examined connectivity changes in PD during task performance. During automatic (well learned) movement, PD patients off medication show increased effective connectivity in SMA, cerebellum and cingulate motor areas relative to controls (Wu, Chan, & Hallett, 2010). Likewise, 
  13
  during the performance of self-initiated movement, connectivity between putamen and M1, SMA, premotor cortex and cerebellum is decreased in patients compared to healthy controls. Conversely, connectivity between M1, pre-supplementary motor area (pre-SMA), parietal cortex and cerebellum is increased in patients relative to controls. In other words, the striatum-cortical and striatum-cerebellar connections are weakened in PD while the connections between corticocerebellar motor regions are strengthened, presumably to compensate for basal ganglia dysfunction (Wu et al., 2011). A study that jointly examined amplitude and connectivity changes found distinct alterations in connectivity between PD subjects and controls (Palmer, Li, Wang, & McKeown, 2010). Most notably, patients demonstrated increased activation amplitude in the cerebello-thalamo-cortical pathway relative to controls as the task frequency (speed of movement required) increased. Further, PD subjects alone demonstrated increased interhemispheric connectivity within the cerebello-thalamo-cortical (CTC) pathway during sinusoidal force production. Only amplitude changes, however, were modulated by task difficulty. These results indicate that connectivity changes may represent more permanent plastic changes that are relatively task-independent. 1.5 Visually guided movement in Parkinson’s disease  The findings summarized above strongly suggest that a premotor-parietalcerebellar network is involved in compensation in PD. The motor deficits in PD are primarily involved in the volitional initiation of movement, however, a number  
  14
  studies have suggested that patients may at least partially overcome this deficit by relying on external visual or auditory cues (Chuma, Reza, Ikoma, & Mano, 2006; Georgiou et al., 1993; Jahanshahi et al., 1995; Lewis, Byblow, & Walt, 2000; Oliveira, Gurd, Nixon, Marshall, & Passingham, 1997). The lateral premotor-posterior parietal network is implicated in visually guided movement (Wise, Boussaoud, Johnson, & Caminiti, 1997) as is the cerebellum (Stein & Glickstein, 1992). A potential mechanism through which visual cues assist patients with PD involves an intact premoto-parieto-cerebellar pathway that may allow patients to bypass the compromised basal ganglia (Glickstein & Stein, 1991b). A switch to more visually guided motor networks as a compensatory strategy is in line with the clinical observation that PD patients become more reliant on external cues to perform movement successfully (Cunnington, Iansek, & Bradshaw, 1999; Glickstein & Stein, 1991b; Praamstra, Stegeman, Cools, & Horstink, 1998). Likely the most dramatic case of the use of visual cues by PD patientis is that of kinesia paradoxa (Glickstein & Stein, 1991a). In kinesia paradoxa, PD subjects described as "frozen" have anecdotally gained the sudden ability to move in urgent situations. Motor urgency, closely related to kinesia paradoxica, has been shown to involve cerebellar circuits in PD patients, and speed of movement demonstrates a significant negative covariation with rCBF in left parasagittal cerebellar hemisphere, with shorter movement times associated with greater activation of the CB in PD patients (Ballanger et al., 2008).  
  15
  PD patients demonstrate marked improvement in gait when stepping across lines placed transversely to their walking direction at suitable intervals (Martin et al., 1967) and with the use of an L-shaped walking aid (Dunne, Hankey, & Edis, 1987). The neural underpinnings of paradoxal gait induced by visual cues in PD were investigated by Hanakawa and colleagues (1999) using 99mTchexamethylpropyleneamine SPECT. Both patients and controls demonstrated increased activity in cerebellar hemispheres and posterior parietal cortex during gait on a treadmill guided by transverse visual cues relative to parallel visual cues. Moreover, during the transverse guided condition PD patients showed increased activity in lateral premotor cortex relative to controls. In addition, the properties of stimuli that are effective in helping patients guide their movements (e.g., transverse stripes on the floor) are similar to those of visual signals that are relayed by mossy fibers via the pontine nuclei to the CB. The receptive fields of neurons along this pathway are tuned to horizontal gratings in the lower visual field, therefore, a staircase or stripes on the floor serve as a horizontal grating which activate the neurons that relay mossy fiber visual information by way of the pontine nuclei to the CB (the role of the cerebellum in the visual guidance of movement is detailed bellow)(Glickstein & Stein, 1991b; Suzuki & Keller, 1984). 1.5.1 The basal ganglia and the cerebellum are interconnected  Superficially, the subcortical systems of the basal ganglia and cerebellum (CB) have much in common. Both systems influence cerebral cortical activity via the thalamus, are linked with the cerebral cortex via recurrent circuits (Afifi & 
  16
  Bergman, 1998) and affect multiple aspects of motor, cognitive, and affective behaviour (Alexander, Delong, & Strick, 1986; Middleton & Strick, 2000). Whereas the exact way that each system influences motor output has not been fully elucidated (Jueptner & Weiller, 1998a), most models of the BG emphasize the importance of movement selection in the context of reward (Bar-Gad & Bergman, 2001), and the BG are most active when a subject must perform an action that is internally guided (IG, e.g. recalled from memory) and selected from many potential candidates of action (Jueptner & Weiller, 1998a; van Donkelaar, Stein, Passingham, & Miall, 1999b, 2000). The cerebellum, traditionally associated with pure motor control, is now considered to be essential for the development of "forward models," such as predicting the sensory consequences of motor actions (Blakemore, Frith, & Wolpert, 2001; Miall & Jenkinson, 2005). Cerebellar activity is normally associated with externally guided (EG, e.g. visually guided) movements where sensorimotor integration is important (Jeuptner et al., 1996; M. Jueptner & Weiller, 1998b; van Donkelaar, Stein, Passingham, & Miall, 1999a; van Donkelaar, et al., 2000). In fact, during an active/passive execution of a motor task consisting of flexion and extension of the elbow, 80-90% of the neocerebellar signal can be attributed to sensory information processing (Jueptner & Weiller, 1998b). The outputs from the BG and cerebellum project to neighbouring thalamic nuclei (ventroanterior (VA) and ventrolateral (VL), respectively), which also demonstrate differential involvement in EG and IG tasks (MacMillan, Dostrovsky, Lozano, & Hutchison, 2004; Vaillancourt, Thulborn, & Corcos, 2003b). Anatomical studies using transneuronally transported viruses have demonstrated that  
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  projections from the BG and the cerebellum through the thalamus to the cortex constitute multiple 'parallel' channels forming circuits (Bostan & Strick, 2010). Although previously considered in isolation, recent evidence has demonstrated that the BG and CB are anatomically connected. Bostan and Strick (2010) describe a disynaptic projection from the CB to the BG and a reciprocal projection from the BG to the CB. Hoshi and colleagues (Hoshi, Tremblay, Feger, Carras, & Strick, 2005), using transneuronal transport of rabies in non-human primates, revealed a disynaptic pathway that links the dentate nucleus (an output stage of cerebellum) with the putamen (input nuclei of the BG) and the globus pallidus externa (GPe; output nuclei of the BG) via the thalamus. Thus, output from the CB influences the striatum, the target of which includes striatal neurons in the indirect pathway of the BG. Similarly, Bostan et al. (Bostan, Dum, & Strick, 2010) observed a disynaptic connection linking the subthalamic nucleus and cerebellar cortex via the pontine nuclei. Interestingly, these projections appear to be topographically organized, such that projections from the dentate nucleus to primary motor and premotor areas originate from its motor domain, whereas projections from the dentate to prefrontal and parietal areas originate from its nonmotor domain (Bostan & Strick, 2010). In sum, these results demonstrate an anatomical substrate for two-way communication between the cerebellum and the basal ganglia. An implication of this finding is that activity in one of these systems may directly influence the function of the other. This can lead us to assume that abnormal activity in one structure may influence the other, resulting in negative  
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  consequences. Alternatively, activity in one of these structures may compensate for abnormal activity or degeneration in the other.  1.5.2 Visual guidance of movement  Vision plays an extremely important role in the guidance of movement. This is demonstrated by the fact that even when using vision to guide movement proves to be an inferior strategy to employing other sensory systems, vision nonetheless takes precedence (Stein & Glickstein, 1992). For example, novice fencers react fastest when they are touched by the foil while blindfolded. However, if they can both see and feel their opponent, their reaction time increases. In this case, the dominance of vision hinders performance but vision is used for motor guidance regardless (Jordan, 1972). Visually guided movements are typically attributed to projections from the visual striate cortex to the posterior parietal cortex (PPC). This occipito-parietal pathway is primarily associated with visual guidance of action and recognition of an object’s position in space. This pathways is also referred to as the ‘dorsal’ or ‘where’ pathway. The occipitoparietal pathway is differentiated from the occipito-temporal pathway, which is also referred to as the ‘ventral’ or ‘what’ pathway and is associated with object recognition (Ettlinger, 1990; Mishkin & Ungerleider, 1982). 1.5.3 The role of the cerebellum in the visual guidance of movement  Stein and Glickstein (1992) have noted that it is unlikely for visuomotor 
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  transmission to take place wholly within the cortex. Cortical projections from PPC are sent via the superior longitudinal bundle to the frontal lobe, taking an ambagious route to the frontal eye fields, then to premotor cortex and motor cortex proper. This route is interrupted by several relays and is far too lengthy to guide movements that are required to respond rapidly to visual signals. This conjecture was confirmed by lesion studies that demonstrate damage to the superior longitudinal bundle while preserving occipito-parieto-pontine connections does not arrest visual guidance of movement (Myers, McCurdy, & Sperry, 1962). Thus, it has long been recognized that subcortical structures, such as the cerebellum, contribute to the visual guidance of movement. Indeed, from a neuroanatomical perspective, pathways from the PPC to the cerebellum allow the cerebellum to gain access to visual afferents. Likewise, these afferents project back to the cortex allowing the cerebellum to influence the visual guidance of movement. To guide voluntary movement, visual afferents from the PPC reach the lateral cerebellum via the pontine nuclei of the brainstem where they terminate as mossy fibers. From the cerebellum, fibers project from the dentate nucleus to the nucleus ventralis lateralis of the thalamus, and from the thalamus to the premotor cortex, thence to the primary motor cortex. In turn, the primary motor and premotor cortex send projections to the cerebellum via the pontine nuclei. These projections either return to the motor cortex via the interpositus deep nucleus of the cerebellum, or to the spinal cord via the red nucleus through the rubrospinal tract (Stein, 1986; Stein & Glickstein, 1992). 
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  The crucial role of the cerebellum in the visual guidance of movement is confirmed by lesion studies in non-human primates reporting that damage to the cerebellum severely disrupts visually guided movement (Baizer, Kralj-Hans, & Glickstein, 1999; Brooks, Kozlovsk.Ib, Atkin, Horvath, & Uno, 1973; Miall, Weir, & Stein, 1987). Holmes was the first to describe that patients with cerebellar dysfunction display symptoms of ataxia, dysmetria and dysdiadochokinesia, which can be collectively described as uncoordinated movement (Holmes, 1939). Further, patients with cerebellar dysfunction show severe impairments in visually guided movement, such as delayed response times to target motion and significant increases in error and variability during visually guided manual tracking (Vandonkelaar & Lee, 1994). Specifically, smooth pursuit movements become jerky and sporadic with patients often overshooting their target (Hallett, Shahani, & Young, 1975). An interesting observation is that these jerky uncoordinated movements become smooth if vision of the limb performing them is occluded (Beppu, Nagaoka, & Tanaka, 1987), this highlights the major reliance on visual feedback in cerebellar patients (Bastian, 2006). 1.5.4 The role of premotor and parietal cortex and the cerebellum in the visual guidance of movement  In addition to the cerebellum, the parietal and premotor cortex are heavily involved in visuomotor control processes (Caminiti, Ferraina, & Johnson, 1996; Milner & Goodale, 1993). The selective involvement of lateral premotor cortex during movements which are under sensory guidance has been suggested for decades (Passingham, 1988). Removal of the lateral premotor cortex in non
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  human primates results in animals who are slow to perform visually cued tasks (Passingham, 1985). Studies using transcranial magnetic stimulation (TMS) in humans demonstrate that premotor cortex stimulation disrupts an early stage of movement selection leading to the conclusion that the premotor cortex is important for selecting movements after a visual cue (Schluter, Rushworth, Passingham, & Mills, 1998). Neuroimaging in humans has confirmed the role of the premotor-parietalcerebellar system in visuomotor control. The medial and lateral cerebellum are activated by visually guided joystick movements confirming the role of the corticoponto-cerebellar pathway in visually guided movements (Ellermann, Siegal, Strupp, Ebner, & Ugurbil, 1998). Significant activation of the intermediate cerebellum, dentate nucleus, putamen and lateral thalamus (Vaillancourt, Thulborn, & Corcos, 2003a) as well as premotor and parietal cortex is also associated with visually guided movement (Hamzei et al., 2002; Vaillancourt, et al., 2003a). Anatomical studies in monkeys have confirmed that the superior parietal lobule and posterior parietal cortex are connected to the dorsal premotor cortex while the inferior parietal cortex (Wise, et al., 1997) is connected to the ventral premotor cortex (Cavada & Goldmanrakic, 1989). It is highly likely that analogous connections exist in the human brain. Therefore, an intact premotorparieto-cerebellar loop may provide a means of generating volitional movement by way of conscious or subconscious use of sensory cues in Parkinson’s patients (Samuel et al., 1997).  
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  1.6 Study aims and hypothesis  To summarize the themes in the preceding introductory sections, motor symptoms in Parkinson’s disease only appear after extensive dopaminergic nigral cell loss (Fearnley & Lees, 1991; C. S. Lee, et al., 2000), suggesting the presence of redundancy or compensatory mechanisms that serve to delay symptom onset and maintain motor function (Zigmond, 1997; Zigmond, et al., 1990). Compensation can occur on different temporal and spatial scales at both the synaptic and systems level and is defined as “any change, morphological or functional, seen in the damaged brain, that acts to maintain performance of the impaired function.” Previous studies have demonstrated altered activity in a premotor-parietalcerebellar network in PD patients at rest (Eidelberg et al., 1994) as well as during task performance (Rascol et al., 1997; Samuel et al., 1997; Sabatini et al., 2000; Catalan et al., 1999; Cerasa et al., 2006; Yu et al., 2007; Palmer et al., 2009; van der Vegt et al., 2009). Changes in connectivity are also reported in this same network in PD patients during the resting state (Wu et al., 2009a; 2009b) and during task performance (Wu et al., 2010; Palmer et al., 2010; Wu et al., 2011). These findings are strongly suggestive of a compensatory switch to a more visually guided motor network involving the lateral premotor, parietal and cerebellar regions in order to compensate for a dysfunctional basal ganglia motor loop (Glickstein & Stein, 1991). The lateral premotor-posterior parietal network and the cerebellum are heavily implicated in the visual guidance of movement (Stein & Glickstein, 1992; 
  23
  Wise et al., 1997). Recruitment of these regions may account for the clinical observation that PD patients become more reliant on external cues to perform movement successfully (Cunnington, et al., 1999; Glickstein & Stein, 1991b; Praamstra, et al., 1998); and why they may overcome deficits in volitional movement with the use of external visual cues (Chuma et al., 2006; Georgiou et al., 1993; Jahanshahi et al., 1995; Lewis et al., 2000; Oliveira et al., 1997). Although the consistent finding of altered activity in the premotor-parietocerebellar circuit strongly implicates this network as a compensatory network in PD patients, it is difficult to differentiate compensation from direct disease changes or epiphenomenal changes if a clear relationship between brain activity and motor performance is not established. Furthermore, with the exception of Yu et al. (2007) and Palmer et al. (2009a; 2009b; 2010), differences in signal amplitude and connectivity were simply assumed to be compensatory, but this hypothesis was not directly tested. In addition, no study to date has investigated whether connectivity differences in PD patients are associated with maintained motor performance. Accordingly, the present study aimed to investigate whether a clear relation exists between connectivity patterns and motor performance in Parkinson’s patients and healthy controls. In order to rigorously quantify motor performance in the MR scanner, we employed linear dynamical systems (LDS) models, which have been demonstrated to provide a sensitive measure of motor performance in PD (Au, Lei, Oishi, & McKeown, 2010; Oishi, TalebiFard, & McKeown, 2011; Stevenson et al., 2011). Further, as activation patterns tend to be task dependent (Taniwaki et al., 2006), we employed a task that required 
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  varying amounts of visual and internal guidance to detect robust results that were relatively independent of a particular task. Finally, rather than simply examining discrete loci of hypo/hyperactivation associated with task performance, we elected instead to investigate patterns of task-related connectivity. Static images of neural activation during a particular task do not convey how brain regions are interconnected. In addition, connectivity changes may provide more valuable information than merely examining amplitude changes given that brain regions may project to overlapping areas, which would render regional changes difficult to interpret in isolation. To examine the relationship between motor performance and connectivity patterns in PD patients and healthy controls, we did the following: First, we examined motor performance in healthy controls and PD patients using root mean square (RMS) error. We expected to find no differences between groups in this measure. The absence of significant differences in RMS error between groups would indicate that differences in connectivity could not be attributed to differences in motor performance accuracy. Second, we used the damping ratio parameter derived from LDS models as a marker of motor performance. While RMS error provides an indication of overall  tracking  accuracy,  damping ratio  derived through LDS  models  characterizes how a given motor performance is achieved. The damping ratio signifies the tendency of actual tracking behaviour to undershoot or overshoot and oscillate around the desired trajectory. A higher damping ratio is typically  
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  associated with better performance in that subjects display less oscillation and overshoot around the desired trajectory. In contrast, a lower damping ratio is associated with less damping and more overshoot in the error response (Au, et al., 2010). Third, using a sparse regression method we examined the relationship between damping ratio and a linear combination of connections between ROIs. Put simply, a positive association between the connection strength amongst two ROIs and damping ratio can be interpreted as a compensatory connection in PD subjects. This is because the strength of that connection would be associated with better performance (i.e. higher damping ratio). We anticipated that if such connections existed, they would involve the lateral premotor, parietal and cerebellar regions in patients.  
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  Figure 1-1 The basal ganglia in (A) normal’s and (B) Parkinsonism White arrows depict excitatory connections; black arrows depict inhibitory connections. supplementary motor area(SMA); pre motor cortex (PMC); motor cortex (MC). The putamen is connected to the internal segment of the globus pallidus (GPi) by direct and indirect projections (via the external segment of the globus pallidus (GPe) and the subthalamic nucleus (STN)). In Parkinsonism, there is severe damage to the SNc. Arrow width indicates the importance of the activity of individual projection systems. Inactivation of putaminal projection increases GPi activity, through an increase in excitatory drive from the STN and a decrease in direct inhibitory input from the striatum. The resulting overinhibition of thalamo-cortical circuits may account for some of the parkinsonian motor signs (from Bezard & Gross, 1998).  
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  CHAPTER 2 – A JOINT COMPENSATORY AND DEFAULT NETWORK CLOSELY RELATED TO MOTOR PERFORMANCE IN PARKINSON’S DISEASE  2.1 Introduction  The motor symptoms in Parkinson’s disease (PD) include rigidity, bradykinesia (slowness of movement), postural instability, and tremor. These clinical features of PD only occur after an estimated 50% of dopaminergic nigral cells and 60-80% of striatal dopamine levels have been lost (Fearnley & Lees, 1991; C. S. Lee, et al., 2000). Further, imaging measures of pathological disease progression, such as  18  F-dopa PET, do not necessarily correlate with clinical  measures of disability, such as the Unified Parkinson’s Rating Scale (UPDRS)(Morrish, et al., 1996). The lack of observed motor pathology despite significant cell loss indicates the existence of compensatory mechanisms, and/or redundancy, which serve to delay the onset of symptoms and preserve an optimal level of function (Bezard & Gross, 1998; Zigmond, et al., 1990). The motor deficits of PD are related primarily to volitional initiation of movement, but a number studies have suggested that patients may at least partially overcome this deficit by relying on external visual or auditory cues (Chuma, et al., 2006; Georgiou, et al., 1993; Jahanshahi, et al., 1995; Lewis, et al., 2000; Oliveira, et al., 1997). A potential mechanism through which visual cues assist patients with PD involves an intact cerebellar pathway that may allow patients to bypass the compromised basal ganglia (BG) (Glickstein & Stein, 
  28
  1991b). Support for this conjecture comes from several imaging studies that report increased activity of the lateral premotor-parietal-cerebellar circuit in PD patients. For example, Samuel et al. (1997), using H215O PET, found the presence of overactivity in the lateral premotor and inferolateral parietal regions in PD patients relative to controls while participants performed sequential finger movements. The authors suggested that overactivity in these regions represented an adaptive reorganization in the PD brain whereby brain areas typically involved in cued movement are recruited to facilitate volitional movement. Various additional studies have reported increased activity in a lateral premotor, parietal cortex and cerebellum circuit in PD patients relative to controls. PD patients demonstrate increased regional homogeneity in the cerebellum, primary sensorimotor area and premotor cortex during the resting state (Wu, Long, et al., 2009). With task related activation, neuroimaging studies report increased activity in premotor, parietal cortex and cerebellum in PD patients during bimanual anti-phase movements (Wu, Wang, Hallett, Li, & Chan, 2010); dual task performance (Palmer et al., 2009; T. Wu & Hallett, 2005); sequential finger movements (Catalan, et al., 1999; Mallol et al., 2007; Sabatini, et al., 2000); automatic movements (sequential finger movements with simultaneous counting task) (Wu & Hallett, 2005); auditory cued thumb pressing (Yu, et al., 2007); volitional limb movement (Haslinger et al., 2001); visuomotor tracking (Turner, Grafton, McIntosh, DeLong, & Hoffman, 2003); visually guided sinusoidal force production (Palmer, et al., 2010); both internally and externally guided finger  
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  tapping (Cerasa, et al., 2006); and “urgent” motor contexts (Ballanger, et al., 2008). Rather than examining discrete loci for hyper/hypo activation as purported markers of compensatory activity, a few studies have alternately examined changes in connectivity between brain regions. Connectivity changes may provide more valuable information than merely examining amplitude changes given that brain regions may receive input from different regions with varying influences, rendering overall regional changes in activation difficult to interpret in isolation. During the resting state, PD patients off medication demonstrate increased connectivity in left cerebellum, left primary motor cortex (M1) and left parietal cortex (Wu, Wang, et al., 2009). A more recent study examined effective connectivity during self-initiated movement and observed increased connectivity between M1, pre-supplementary motor area (pre-SMA), premotor cortex, parietal cortex and cerebellum in unmedicated patients compared to controls (Wu et al., 2011). Palmer and colleagues (2010) also found increased connectivity within the cerebello-thalamo-cortical loop in patients off medication relative to controls while patients performed a sinusoidal force production task at various frequencies. Although these studies are suggestive of cerebellar, lateral premotor and parietal regions acting as compensatory regions in PD patients, it is difficult to differentiate compensation from direct disease changes or even epiphenomenal changes if a clear relationship between brain activity and motor performance is not established. Accordingly, the present study investigates whether a clear relation exists between connectivity patterns and motor performance in 
  30
  Parkinson’s patients and in healthy controls. In order to rigorously quantify motor performance, we employ linear dynamical systems (LDS) models, which have been demonstrated to provide a sensitive measure of motor performance in PD. (Au, et al., 2010; Oishi, et al., 2011; Stevenson, Oishi, et al., 2011). In order to detect robust results that are relatively independent of a particular task, we employ a task that requires varying amounts of visual and internal guidance. 2.2 Material and methods  2.2.1 Participants  Twelve volunteers with clinically diagnosed PD agreed to participate in the study (9 male, 3 female, mean age 60 + 9.8 years, all right handed). Patients were recruited from the Pacific Parkinson’s Research Centre (PPRC)/Movement Disorders Clinic at the University of British Columbia. All patients had mild to moderately severe PD (Hoehn & Yahr stage 1-3)(Hoehn & Yahr, 1967). Exclusion criteria included atypical Parkinsonism, presence of other neurological and psychiatric disorders and use of antidepressants, hypnotics and dopamine blocking agents. All patients were taking L-dopa (mean daily dose 625 + 351.9). Other PD medications included dopamine agonists (seven patients). We recruited eleven healthy, aged-matched control participants without active neurological disorders (6 male, 5 female, mean age 59.5 + 6.1 years, 10 right handed, 1 left handed). All patients withdrew from L-dopa for a minimum of 12 hours before the 
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  study and withdrew all other Parkinson’s medication for at least 18 hours, consistent  with  CAPSIT-PD  (Core  Assessment  Program  for  Surgical  Interventional Therapies in Parkinson's Disease) guidelines (Langston et al., 1992). The mean Unified Parkinson’s Disease rating Scale (UPDRS) motor score during the off-levodopa sate was 29.5. Participant characteristics are shown in Table 2-1. All participants gave written informed consent. The study conformed to the Code of Ethics of the World Medical Association (Declaration of Helsinki) and was approved by the Ethics Board of the University of British Columbia.  2.2.2 Experimental paradigm  Participants used a custom-built, in-house designed, rubber squeeze bulb connected via water-filled, low-compliance tubing to a precision pressure transducer (Honeywell, Inc., Plymouth, MN, model PPT0100AWN2VA) outside the scanner room, which allowed us to dynamically monitor motor performance in the scanner. Participants lay in the MRI scanner while viewing the computer screen via a projection mirror system attached to the head coil. Each subject had his or her maximum voluntary contraction (MVC) measured during a 1 hr training session prior to the scanning session. Movements were scaled such that target output was between 5 and 15% of each individual’s MVC. The participants were asked to squeeze the bulb using an isometric grip with their right hand and to keep their hand position constant throughout the study. The participants used the squeeze bulb to control the width of a horizontal bar that was shown between a  
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  vertically scrolling, undulating pathway (see figure 2-1). Applying greater pressure to the bulb increased the width of the bar, while releasing pressure from the bulb decreased the width of the bar. The participants were asked to keep the edges of the bar within the pathway. The pathway of the symmetric tracks was divided into 30-s blocks that were either “static force” blocks, whereby participants were required to steadily squeeze at 10% of MVC to keep within the tracks, or “varying force” blocks where subjects needed to squeeze between 5-15% of MVC to stay within the tracks. “Varying force” blocks were further subdivided into noise-free blocks, where subjects were instructed to follow the track as accurately as possible, and noisy blocks where the track was corrupted by visual noise, but the participants are asked to maintain a smooth trajectory (squeeze as if it was noiseless). In order to ensure that our results were not restricted to a certain motor speed (although we note that connectivity patterns tend to be robust to changes in motor speed (Palmer, et al., 2010)), and provide a rich enough input for the LDS models, the track in “varying force” blocks consisted of a sweep smoothly varying between 0Hz-1Hz (or decreasing from 1Hz-0Hz). In noisy blocks, the visual pathway was corrupted with varying noise levels: if p(t) represents the trajectory of the track in noise-free blocks, a noise block consisted of α*noise + (1-α)*p(t) with α set to: 0%, 20% and 40% presented in random order. The motor task allowed modulation from a visually-guided task (0% noise) to a partially internally-guided task (20% and 40% noise) where the visual guidance was less informative. 
 A block design was used to provide long enough periods to accurately assess the connectivity patterns such that each functional  
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  run consisted of 30s of each noise level sweeping both up and down, which was interspersed with 30s static force blocks and 10s blocks of rest. Each participant performed 3 functional runs of 7 minutes each. The visual stimuli were coded with Matlab software (Natick, MA) and Psychtoolbox (Brainard, 1997; Pelli, 1997).  2.2.3 Data acquisition  A Philips Achieva 3.0 T scanner (Philips Achieva 3.0T; Philips Medical Systems, Netherlands) equipped with a head-coil was used to collect functional MRI data, consisting of echo-planar (EPI) T2*-weighted images with blood oxygenation level-dependent (BOLD) contrast (repetition time 1985 ms, echo time 37 ms, flip angle 90°, field of view (FOV) 240.00 mm, matrix size = 128 x 128, pixel size 1.9 x 1.9 mm). Thirty-six axial slices were acquired, with 3mm thickness (1 mm gap thickness). The FOV was selected to include both the dorsal surface of the brain and the cerebellum ventrally. To facilitate anatomical localization of activation for each participant, a high-resolution (1x1x1 mm), whole brain 3dimensional T1-weighted image was acquired, consisting of 170 axial slices. Head motion was minimized using a memory foam pillow placed around the participant’s head within the coil.  
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  2.2.4 fMRI pre-processing and analysis  Slice time correction, isotropic reslicing of voxels and initial motion correction was performed with SPM99. The data were then further motion corrected with MCICA, a computationally expensive but highly accurate method for motion correction that is more suitable to accommodate the larger head motion observed in older and PD participants (Liao, Krolik, & McKeown, 2005). We did not spatially normalize participant data to a common atlas template as this has been shown to incur excessive error (Nieto-Castanon, Ghosh, Tourville, & Guenther, 2003), particularly in small subcortical regions such as the basal ganglia, which is of particular importance in PD (Ng et al. 2009; Chen et al., 2009). Regions of Interest (ROI) were extracted using a combined Freesurfer (Harvard, MA) and Large Deformation Diffeomorphic Metric Mapping (LDDMM) method (Khan, Wang, & Beg, 2008). We chose 48 Freesurfer-derived ROIs in addition to the 8 ROIs as defined by the Human Motor Area Template (HMAT) (Mayka, Corcos, Leurgans, & Vaillancourt, 2006).  2.2.5 Behavioural analysis  Quantification of motor performance was achieved by calculating root mean square (RMS) error as well as examining the parameters of fitted LDS models. RMS error was computed by comparing the behavioural data from the squeeze bulb, i.e. the actual force output, to the target force output. This 
  35
  performance measure was compared between groups to examine differences in tracking accuracy. Though RMS error is one of the most common tracking performance metrics, it appears to be relatively insensitive at detecting performance differences between PD patients and controls (Au, et al., 2010; Oishi, et al., 2011; Stevenson, Talebifard, Ty, Oishi, & McKeown, 2011). RMS error provides an indication of overall tracking accuracy, but damping ratio derived through LDS models can characterize how a given motor performance is achieved. The damping ratio signifies the tendency of actual tracking behaviour to undershoot or overshoot and oscillate around the desired trajectory. LDS models of participants’ tracking performance were computed using standard system identification techniques (Ljung, 1992). We presume to model each subject’s tracking behaviour as a “black box” system, with the desired trajectory as the input, and an individual’s actual tracking as the output. We then fit a standard linear, second order dynamical system model to each subject’s tracking data, with the goal of reproducing the subject’s motor performance. Standard optimization approaches are used to assess the parameters of the model (Ljung, 1992). For models whose predictive error are within acceptable tolerances (e.g., measured through autocorrelation of residuals), key parameters of the model then characterize all possible system responses.  This  characterization enables prediction for each subject of their output (actual performance), given any arbitrary input (target performance) with a similar bandwidth to the input sequence. For full technical details see (Oishi, et al., 2011).  
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  2.2.6 Connectivity analysis: PCfdr  The connectivity network was computed with the PCfdr algorithm (Li, Wang, McKeown, & Ieee, 2008). This method is designed to accommodate a large number of ROIs but relatively few time points, typical for fMRI experiments. It takes the mean time course from each ROI (after linear detrending) and determines the conditional (in)dependence of each pair of ROIs conditional on all other ROIs. The algorithm is able to asymptotically curb the false discovery rate (FDR) (Benjamini & Yekutieli, 2001) of inferred connections under a userspecified level. In other words, the expected ratio of spurious connections to all the connections learnt with the algorithm is controlled under a predefined threshold (Li & Wang, 2009). The method estimates a p-value with hypothesis tests for the existence of each possible connection, and applies an FDRcontrol procedure to the p-values collectively to adjust the effect of simultaneously testing multiple connections. We set the FDR threshold at 1% in this study. Connectivity networks were calculated for each subject group independently. The connection strength between each significantly-connected ROI for each individual subject was then estimated by initializing a Bayesian Network with the PCfdr group structure (Li, Wang, Palmer, & McKeown, 2008).  
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  2.2.7 Relationship between behavioural and connectivity analysis: LASSO  With 49 ROIs, the number of potential regressors exceeded the number of participants. Therefore, we employed the LASSO method (Friedman, Hastie, & Tibshirani, 2010) to examine the relationship between damping ratio and a linear combination of connections between ROIs. This method imposes a sparsity constraint on the regression co-efficients, and thus assumes that only a relatively few number of connections between ROIs are important for predicting behavioural parameters. To prevent over-fitting, we employed a leave-one-out validation procedure: One subject was set aside, and the remaining subjects were used to create a statistical model relating the connection between ROIs and damping ratio. This model was then used to take the connectivity measures of the subject set aside, and predict damping ratio for that subject, which was then compared with the actual damping ratio. The process was then repeated for all subjects. Since in some cases the LASSO approach might select a different subset of connections to predict the damping ratio when different subjects are included, we only considered connections that were robustly selected every time a different subject was left out of the analysis. For all analyses, p values were corrected for multiple comparisons using a false discovery rate (FDR) approach.  
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  2.3 Results  2.3.1 Behavioural  RMS error was not significantly different between PD and controls (p = 0.1139). There was no difference in damping ratio between groups at any noise level (p>0.05). 2.3.2 Connectivity  Figure 2-2 summarizes all the connections that were found to be significant in healthy controls and patients: The connection from left M1 to left cerebellum was seen only in Parkinson’s subjects; regions of the basal ganglia were connected to premotor, inferior parietal, inferior temporal cortex and cuneus in patients while basal ganglia connections in controls were more wide ranging; left and right cerebellum were connected in patients but not controls; parietal regions were heavily connected to the cerebellum in patients (right and left inferior parietal cortex to right cerebellum and left superior parietal cortex to cerebellar vermis) while this was not seen in controls. 2.3.3 Effect of noise level on functional connectivity  Using a combined PCfdr / Bayesian network approach, we determined the connectivity patterns in both groups. We examined each connection determined  
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  to be significant by the PCfdr procedure to see if its strength was affected by noise level. No connection was significantly modulated by noise (p > 0.05). 2.3.4 Relationship between connection strength and damping ratio: healthy controls  Using a sparse regression method, the following connections were positively associated with damping ratio in controls: left cerebellum to left PMv (p = 0.0005), right caudal middle frontal cortex (BA6) to left ACC (p = 0.0285), and left cerebellum to right superior temporal cortex (p = 0.0015). The following connections were negatively associated with damping ratio: left supramarginal gyrus (BA 40, parietal association cortex) to left temporal pole (p = 0.0044) and the left cerebellum with right caudal middle frontal cortex (p = 5 x 10-6). 2.3.5 Relationship between connection strength and damping ratio: patients off medication  The following connections were positively associated with damping ratio in PD subjects: left cerebellum to left PMd (p = 0.052), left caudate to left PMv (p = 0.0005), left S1 to right pre-SMA (p = 0.0097), left lateral occipital cortex (BA 19) to right putamen (p = 0.0369), and left temporal pole to left superior temporal gyrus (p = 0.0367). The following connections were negatively associated with damping ratio in patients: left lateral occipital cortex to left pre-SMA (p = 0.0009), right cuneus to 
  40
  left caudal middle frontal cortex (BA 40, parietal association area) (p = 0.0473), and left inferior temporal cortex to left inferior parietal cortex (p = 0.0039). 2.4 Discussion  We used damping ratio as a marker for motor performance. Damping ratio is often used in engineering systems to characterize system response, because it indicates the amount of overshoot or undershoot that can occur in response to input stimuli.  It is one way for engineers to mathematically describe what  constitutes a desirable response for a system. For example, tradeoffs often exist in creating systems to be responsive to changes in the input stimulus, yet not so overly sensitive that noise and other disruptive processes are unduly emphasized. Damping ratio indirectly provides a measure of responsiveness (or overresponsiveness, in the case of significant overshoot). For manual tracking, a higher damping ratio is typically associated with better performance, in that subjects display less oscillation and overshoot around the desired trajectory, while a lower damping ratio indicates less damping and more overshoot in the error response (Au, et al., 2010). Figure 2-3 depicts the network predicting motor performance in healthy controls. A stronger connection between left cerebellum and left PMv was associated with better motor performance in controls. This finding likely reflects the use of cerebellar motor loops while the task remained at least partly externally guided (Taniwaki, et al., 2006). The positive association between the connection from left cerebellum and right superior temporal cortex and damping ratio may 
  41
  reflect activity in response to perceived motion of the hand (Pelphrey, Morris, Michelich, Allison, & McCarthy, 2005). Finally, the positive relationship between damping ratio and the connection from right caudal-middle-frontal gyrus and left ACC is in keeping with these regions’ involvement in motor control (for review see Paus, 2001). We found some connections that were negatively associated with damping ratio in controls. Stronger connections between the left supramarginal gyrus (BA 40, parietal association cortex) and left temporal pole as well as stronger connections between left cerebellum and right caudal-middle-frontal gyrus may imply a difficulty in switching from externally cued to internally generated movement or difficulty de-weighting ambiguous visual stimuli (see discussion of the role of these regions in visually guided movement below) in some participants. As in controls, we found a tightly bound relationship between a quantitative measure of motor performance (damping ratio) and connectivity patterns in PD patients (figure 2-4), but the actual connections were different. PD subjects recruited parts of a premotor-parieto-cerebellar network in order to maintain performance during a task in which visual guidance of required motor output varied. Additionally, connectivity between striatum, motor, sensory-motor and visual areas was associated with improved motor performance in patients off medication. Our results are consistent with several imaging studies that have conjectured that PD patients increase activity in cerebellar motor circuits to compensate for degeneration of the striato-thalamo-cortical loop to maintain near normal motor function (Glickstein & Stein, 1991b). Samuel et al. (1997) examined 
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  rCBF changes associated with performance of sequential finger movements in PD patients and controls. Similar to our findings that connections from left CB to left PMd and left caudate to left PMv were associated with improved motor performance, the authors observed relative overactivity in lateral premotor and inferior parietal cortex, which they interpreted to represent a compensatory switch to a more visually guided network. Similarly, Catalan and colleagues (1999) reported increased rCBF in premotor and parietal cortices while patients performed sequential finger movements which the authors also interpreted as compensatory activity in response to striatal dysfunction. PD patients are reported to have difficulty performing specific types of tasks such as those requiring simultaneous bimanual movements, complex sequential movements and internally generated or volitional movements. Our results demonstrate that patients recruit the premotor-parieto-cerebellar network in order to maintain motor performance during a task that becomes partially internally guided (a type of task they have difficulty with). In line with this finding, Wu and Hallett (2005) reported that PD patients demonstrated increased activity in the cerebellum, premotor and parietal regions relative to controls when performing automatic movements. These same regions exhibited increased activity in patients while they executed a simultaneous bimanual task (Wu & Hallett, 2008). Further, it has been shown that when task difficulty is gradually increased (e.g., by increasing the speed of movement required) patients maximally recruit the “normal” network at slower speeds and must switch to the compensatory cerebellar network at higher speeds to maintain near normal motor 
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  output (Palmer, Ng, et al., 2009). In sum, PD subjects recruit a compensatory premotor-parieto-cerebellar circuit while they perform tasks they find difficult. In contrast to the studies described above, the present experiment focused on the relationship between neural connectivity and motor performance in PD. In line with our findings, Wu et al. (2011) reported that PD patients demonstrate increased effective connectivity between M1, pre-SMA, premotor cortex, parietal cortex and cerebellum compared to controls during self-initiated movement. Palmer and colleagues (2010) examined both amplitude and functional connectivity changes in PD patients while they performed a visuomotor tracking task at three different frequencies. Although patients demonstrated increased activation amplitude in the cerebello-thalamo-cortical pathway relative to controls as the task frequency increased, connectivity changes remained static across all three task frequencies in both groups. PD patients were lacking several connections within the BG that were observed in controls but demonstrated increased connectivity within a compensatory cerebello-thalamo-cortical pathway, which was robustly present at all three frequencies. This is consistent with our observation that connectivity did not change across noise conditions, and suggests that underlying functional networks related to voluntary movement in PD may be fixed and may represent permanent plastic changes. Further, these observations imply that functional connectivity analysis in PD may be a more sensitive method of detecting plastic changes that are relatively invariant to the type of task.  
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  The observation that the strength of connection within regions of a premotor-cerebellar network (left CB to left PMv), striatal premotor regions (left caudate to left PMv), as well as sensory motor (left S1 to right pre-SMA) and visuomotor (left lateral occipital cortex to right putamen) are associated with improved motor performance in PD is in keeping with the conjecture that PD subjects switch to circuits involved in cued movement to overcome difficulties in generating volitional movement, as is frequently observed clinically (Chuma, et al., 2006; Georgiou, et al., 1993; Jahanshahi, et al., 1995; Lewis, et al., 2000; Oliveira, et al., 1997). The most dramatic exemplification of this phenomena is referred to as Kinesia Paradoxa (Glickstein & Stein, 1991b) or paradoxical movement, during which PD subjects described as "frozen" have anecdotally gained the sudden ability to move in urgent situations. Further, PD patients demonstrate marked improvement in gait when stepping across lines placed transversely to their walking direction at suitable intervals (Martin et al., 1967) and with the use of an L-shaped walking aid (Dunne, et al., 1987). The neural underpinnings of paradoxal gait induced by visual cues in PD were investigated by Hanakawa and colleagues (Hanakawa, et al., 1999) using  99  mTc-  hexamethylpropyleneamine SPECT. Both patients and controls demonstrated increased activity in cerebellar hemispheres and posterior parietal cortex during gait on a treadmill guided by transverse visual cues relative to parallel visual cues. Moreover, during the transverse guided condition PD patients showed increased activity in lateral premotor cortex relative to controls. Therefore, it is reasonable to assume that connectivity in the cerebellum, visuomotor and premotor regions in  
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  patients represents compensatory recruitment of visually guided motor networks to maintain performance. The premotor, parietal and cerebellar regions are heavily implicated in visuomotor control. The PMd is well documented as being a part of the visuomotor control network that uses proprioceptive, visual and attentional information to compute output that contributes to the selection, preparation and execution of movement (Wise, et al., 1997). What’s more, the premotor cortex receives input from deep cerebellar nuclei (Schell & Strick, 1984) as well as from inferior and superior parietal association areas which project back to the CB via the pontine nuclei (Schmahmann & Pandya, 1989). In addition, the properties of the stimuli that are effective in helping patients guide their movements (e.g., transverse stripes on the floor) are similar to those of visual signals that are relayed by mossy fibers via the posterior parietal cortex and the pontine nuclei to the CB (Glickstein & Stein, 1991). The receptive fields of the visual neurons along this pathway tend to be tuned to horizontal gratings in the lower visual field, therefore, transverse visual stimuli may activate the neurons along the visuomotor pathway (Glickstein & Stein, 1991b; Suzuki & Keller, 1984). Thus, our finding that increased connectivity within these areas predicts improved motor performance provides convincing evidence that patients engage a visuomotor circuit to facilitate performance of volitional movement. Seeing as several studies have interpreted overactivity in the inferior parietal region as compensatory in PD (Catalan, et al., 1999; Hanakawa, et al., 1999; Mallol, et al., 2007; Sabatini, et al., 2000; Samuel, et al., 1997) a key result 
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  is that we observed connectivity to this region (i.e. left inferior temporal cortex to left inferior parietal cortex) to be associated with worse performance in patients. A possible explanation for this finding is that activation of the compensatory network involving a premotor-parietal-cerebellar circuit impairs the ability of the inferior parietal cortex to normally de-activate as part of the default mode network (DMN). The DMN encompasses the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, lateral parietal and temporal cortices (Raichle et al., 2001). In short, these areas are typically active during the resting state and become “deactivated” during task performance. The leading hypothesis assumes that deactivation of these regions represent a reduction in inhibitory processes as well as a shift in attentional processes from self-reflection to goal directed behaviour (Raichle, et al., 2001). Relative dysfunction in the DMN has been reported in unmedicated PD patients during performance of a card-sorting task (van Eimeren, Monchi, Ballanger, & Strafella, 2009). While healthy control participants demonstrated deactivation in the mPFC, PCC and precuneus during task performance, patients showed deactivation in mPFC but significantly less deactivation of the PCC and precuneus. Further, the authors reported that greater deactivation in the PCC and precuneus was associated with better performance. Considering the parietal part of the DMN is responsible for allocating visuospatial attention according to anticipation (Small et al., 2003), increased activity in this region may make subjects with PD less able to ignore external stimuli (van Eimeren, et al., 2009). DMN dysfunction in PD may be related to nigrostriatal DA denervation as 
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  unmedicated PD patients failed to deactivate the posterior midline and lateral regions of the DMN. In contrast, patients given L-dopa showed deactivation in PCC and lateral temporal cortex (Delaveau et al., 2010). In the present study, increased connectivity between DMN regions and a motor area (right cuneus to left caudal middle frontal cortex (Brodmann are 6)) and within the DMN (left inferior temporal cortex to left inferior parietal cortex) was associated with worse performance in patients. Thus, engaging the premotor, cerebellar and parietal regions as a compensatory strategy may come at a price. When the inferior parietal cortex is recruited as part of a compensatory visuomotor network, it may fail to normally deactivate as part of the DMN during task performance. As a consequence, other areas of the DMN may remain engaged during task performance as well. Moreover, activation of the inferior parietal cortex to recruit a visuomotor system may render patients susceptible to non-informative, extraneous visual stimuli during the “noisy” conditions of the task leading to decreased damping ratio and increased overshoot of the target. This interpretation is consistent with a card-sorting task demonstrating a lack of release of the inferior parietal part of the DMN in PD subjects, making them more susceptible to interference of extraneous and irrelevant information (van Eimeren, et al., 2009). Furthermore, Stevensen and colleagues (2011a) found that PD patients were unable to adequately de-weight less informative visual input. The authors speculated this to be the result of cerebellar hyperactivity in patients, though our findings would suggest that overactivity of the parietal cortex, i.e., another part of the lateral premotor, parietal cortex and cerebellar circuit, may be 
  48
  responsible. Alternatively, attenuated deactivation in the DMN of patients may be directly related to striatal dopamine deficiency. Tomasi and colleagues (2009) reported that dopamine transporter (DAT) availability in caudate and putamen shows a negative correlation with deactivation in the ventral parietal regions of the DMN during a visuospatial attention task. They interpreted this finding to reflect the fact that higher DAT availability would lead to lower and shorter DA increases resulting in weaker DA signalling. DA is postulated to enhance task specific signalling while decreasing background neuronal activity (Volkow et al., 2008), thus, decreased DA availability may lead to task-irrelevant neuronal processing in the DMN. In summary, using simultaneous modelling of fMRI and motor behaviour, we found convincing evidence that altered activity in a premotor-parietalcerebellar network is, in fact, compensatory as previously speculated, as greater connectivity within this network was positively associated with improved motor performance. Yet, activation of this compensatory network, which included the inferior parietal cortex, came at a price; the inferior parietal cortex was unable to normally de-activate as part of the default mode network, making patients susceptible to non-informative, extraneous stimuli.  
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  Figure 2-1 Experimental task performed in MRI scanner  Subject used their right hand to squeeze a bulb which increased the width of a bar. The bar did not move horizontally or vertically – it only changed size. There was a smoothly scrolling background that contained two symmetric tracks. In the noise-free blocks, subjects are instructed to follow the track as much as possible. The track was divided into 30-s blocks (interspersed with rest). During movement blocks subjects were required to squeeze between 5-15% of MVC. During static blocks subjects squeezed at 10% of MVC. During movement blocks, the track consisted of a “sweep” smoothly varying between 0Hz-1Hz (or decreasing from 1Hz-0Hz). During “noisy” blocks, the track was corrupted by noise (20 or 40%), but the subjects were required to maintain a smooth trajectory as if it were noiseless, partially relying on internally generated commands, as opposed to simply following the track.  
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  Table 2-1 Participant characteristics  
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  Table 2-2 Region of interest (ROI) legend This table lists the ROIs examined in the study.  
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  Figure 2-2 Connectivity patterns of patients and controls derived from PCfdr Please refer to the ROI legend in Table 2-2. Most notable observations: The connection from left M1 to left cerebellum was seen only in Parkinson’s subjects; regions of the basal ganglia were connected to premotor, inferior parietal, inferior temporal cortex and cuneus in patients while basal ganglia connections in controls were more wide ranging; left and right cerebellum were connected in patients but not controls; parietal regions were heavily connected to the cerebellum in patients (right and left inferior parietal cortex to right cerebellum and left superior parietal cortex to cerebellar vermis) while this was not seen in controls.  
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  A  B Figure 2-3 Network predicting motor performance in healthy controls (A) Depicts the connections that were associated with damping ratio in healthy controls. Thick lines represent positive association between connection strength and damping ratio; thin lines represent negative relationship between connection strength and damping ratio. Significant connections: positive: L CBL PMv (p = 0.0005), R caudal middle frontal L ACC (p = 0.0285), and L CB R superior temporal (p = 0.0015); negative: L supramarginal gyrus L temporal pole -6 (p = 0.0044) and the L CB R caudal middle frontal (p = 5 x 10 ). (B) Depicts the relationship between damping predicted from connectivity strength and behaviourally derived damping ratio.  
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  A  B Figure 2-4 Network predicting motor performance in PD (A) Depicts the connections that were associated with damping ratio in PD. Thick lines represent positive association between connection strength and damping ratio; thin lines represent negative relationship between connection strength and damping ratio. Significant connections:  
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  positive: L_CBL_ PMd (p = 0.052), L_CAUL_ PMv (p = 0.0005), L_S1  R_pre-SMA (p = 0.0097), L lateral occipital cortex  R_putamen (p = 0.0369), and L temporal pole  L superior temporal gyrus (p = 0.0367); negative: L lateral occipital cortex  L pre-SMA (p = 0.0009), R cuneus  left caudal middle frontal (p = 0.0473), and L inferior temporal cortex L inferior parietal cortex (p = 0.0039). (B) Depicts the relationship between damping ratio predicted from connectivity strength and behaviourally derived damping ratio.  
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  CHAPTER 3 – CONCLUSION  3.1 Summary of findings  The aim of this thesis was to provide persuasive evidence for the existence of macroscopic, systems level compensatory mechanisms in individuals with clinically diagnosed early-stage, mild to moderate Parkinson’s disease. Whereas previous studies used simple button press or finger-tapping tasks to examine task-related activation, we employed an in-house designed response device allowing for a task that required dynamic, continuous movement. Consequently, we were able to quantify motor performance and examine the relationship between motor performance and differences in connectivity between PD subjects and healthy controls. We observed a tightly bound relationship between a quantitative measure of motor performance (damping ratio) and connectivity patterns in PD patients (figure 2-4). We demonstrated that participants with PD recruit parts of a premotor-parieto-cerebellar network to maintain performance during a task in which visual guidance of required motor output varied (left cerebellum to left PMd and left caudate to left PMv). Additionally, connectivity between striatum, motor, sensory-motor and visual areas was associated with improved motor performance in patients (left primary sensory motor area to right pre-SMA; left lateraloccipital cortex to right putamen). Our results are consistent with prior studies reporting increased task-related activity (e.g., Samuel et al., 1997; Yu et al., 2007; Palmer 
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  et al., 2009a; 2010; Wu et al., 2010) and connectivity (Palmer et al., 2010; Wu et al., 2011) in the lateral premotor, parietal and cerebellar regions. A  previous  experiment  has  shown  that  compensatory  cerebellar  hyperactivity monotonically increases with increasing task difficulty (Palmer et al., 2010). If this activity was purely disease-related, we would expect that cerebellar hyperactivation would remain static across variations in the task. By definition, compensatory mechanisms are recruited to maintain motor performance, therefore, they should show a degree of correlation with the difficulty of the task. Our study extends this prior evidence by demonstrating that activity in a visuomotor pathway is directly related to improved task performance. Our results support the hypothesis that PD patients recruit a visuomotor pathway as a compensatory strategy to maintain near-normal motor performance. The lateral premotor-posterior parietal network and the cerebellum are heavily implicated in the visual guidance of movement (Stein & Glickstein, 1992; Wise et al., 1997). Recruitment of these regions may account for the clinical observation that PD patients become more reliant on external cues to perform movement successfully (Cunnington, et al., 1999; Glickstein & Stein, 1991b; Praamstra, et al., 1998); and why they may overcome deficits in volitional movement with the use of external visual cues (Chuma et al., 2006; Georgiou et al., 1993; Jahanshahi et al., 1995; Lewis et al., 2000; Oliveira et al., 1997). Here we show that activity in visuomotor regions demonstrates a strong relationship with improved motor performance in participants with PD.  
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  An intriguing observation from the present study is that we found connectivity to the inferior parietal cortex (i.e. left inferior temporal cortex to left inferior parietal cortex) to be associated with worse performance in patients. This is a key finding as several studies have interpreted overactivity in the inferior parietal region to be compensatory in PD (Catalan, et al., 1999; Hanakawa, et al., 1999; Mallol, et al., 2007; Sabatini, et al., 2000; Samuel, et al., 1997). We’ve interpreted this finding to represent dysfunction in the DMN in participants with PD. It is likely that activation of the compensatory network involving a premotorparietal-cerebellar circuit impairs the ability of the inferior parietal cortex to normally de-activate as part of the DMN. We also found that connectivity between a DMN region and a motor area (right cuneus to left caudal middle frontal cortex (Brodmann are 6)) was associated with worse performance in patients. Thus, engaging the premotor, cerebellar and parietal regions as a compensatory strategy may come at a price. When the inferior parietal cortex is recruited as part of a compensatory visuomotor network, it may fail to normally deactivate as part of the DMN during task performance. As a consequence, other areas of the DMN may remain engaged during task performance as well. A lack of release from the DMN may render patients susceptible to noninformative, extraneous visual stimuli during the “noisy” conditions of the task leading to decreased damping ratio and increased overshoot of the target. This interpretation is supported by the observation that PD subjects demonstrate attenuated deactivation of the inferior parietal part of the DMN during a card-  
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  sorting task, which made them more susceptible to interference of extraneous and irrelevant information (van Eimeren, et al., 2009). 3.2 Study significance and suggestions for future research  A strength of this work is the use of our MR compatible squeeze-bulb device and task that required dynamic, continuous movement. Most fMRI motor tasks involve simple movements such as finger tapping or button presses, making the dynamics of movements hard to quantify. Here we were able to quantify motor performance and examine the relationship between motor performance and differences in connectivity between PD subjects and healthy controls. This allows us to draw more compelling conclusions regarding the relationship between connectivity changes and improved motor performance in patients with PD. An additional strength of this work is that we did not spatially normalize participant data to a common atlas template, which is standard practice in most fMRI research. Segmentation of ROIs was performed on an individual subject basis, which removed the possibility of mis-registration to standard atlases. Template-based warping has been shown to incur excessive error (NietoCastanon, et al., 2003), particularly in small subcortical regions such as the basal ganglia, which is of particular importance in PD (Ng et al. 2009; Chen et al., 2009). This likely increased the accuracy of our results. Identifying compensatory mechanisms has considerable implications for Parkinson’s disease. It is suggested that compensatory mechanisms increase in  
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  parallel with increasing degeneration over the course of the pre-clinical stage of PD. Eventually, these mechanism will level off or fail and will no longer be able to mask the symptoms of the disease and maintain normal motor performance. Differentiation of compensatory mechanisms from disease-related changes is important for longitudinal studies that utilize neuroimaging techniques to assess the progression of PD. These studies will detect changes in both disease-related and compensatory activity and it will be important to ascertain how changes in the premotor-parieto-cerebellar network are associated with symptom severity over time. Future studies are needed, but it would be interesting to explore whether or not activation of the premotor-parieto-cerebellar network is still present in patients with more sever PD. Unfortunately, it may not be feasible to conduct such a study as these patients may not be able to endure overnight withdrawal from medication or the demands of the MR scanning and task. From a clinical perspective, compensatory mechanism may serve as a useful biomarker for identifying those who may be in the pre-clinical phase of the disease. Application of this would of course be restricted to individuals with a genetic predisposition to PD. Therefore, future investigations of the presence of compensatory recruitment in asymptomatic carriers of known PD-related genetic mutations may be warranted. From a therapeutic standpoint, discriminating between disease-related changes and compensatory changes is essential since therapies should focus on targeting only those changes that are a consequence of the disease itself. Conversely, 
  compensatory  mechanisms  could  serve  as  a  target  for 61
  complimentary therapies that serve to enhance or prolong the effects of these mechanisms in order to delay disease progression. Future investigations may focus on the neurochemical underpinnings of compensatory premotor-parietocereballar recruitment. Additionally, the value of occupational interventions involving visually cued movement or assistive devices such as the L-shaped cane (Dunne, et al., 1987) should be explored further. 3.3 Limitations  Conclusions drawn from this experiment are from a population of patients with early stage, mild to moderate idiopathic Parkinson’s disease. Therefore, whether or not our findings can be generalized to individuals who are in the preclinical phase or have severely advanced PD remains to be determined. It is only possible to investigate the pre-clinical, asymptomatic phase of PD in individuals who are identified with a genetic predisposition to the disease. However, genetic forms of PD may take a different course to idiopathic PD, therefore, results from such studies may not relate to all forms of Parkinsonism. As suggested above, it may be useful to conduct investigations with participants with later stage PD, however, the increasing severity of tremor and bradykinesia in these patients may present problems with task performance as well as artifacts with a motion sensitive technique such as fMRI. We employed a task that required varying levels of visual and internal guidance. Whether or not our findings are applicable to all forms of volitional movement remains to be determined. However, increased recruitment of the 
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  premotor, parietal and cerebellar regions has been found during bimanual antiphase movements (Wu et al., 2010); dual task performance (Palmer, Eigenraam, et al., 2009; T Wu & Hallett, 2008); sequential finger movements (Catalan, et al., 1999; Mallol, et al., 2007; Sabatini, et al., 2000); automatic movements (sequential finger movements with simultaneous counting task) (Wu & Hallett, 2005); auditory cued thumb pressing (Yu, et al., 2007); volitional limb movement (Haslinger, et al., 2001); visuomotor tracking (Turner, et al., 2003); visually guided sinusoidal force production (Palmer, et al., 2010); both internally and externally guided finger tapping (Cerasa, et al., 2006); and “urgent” motor contexts (Ballanger, et al., 2008). Although patterns of activation are task dependent (Taniwaki, et al., 2006). The consistent finding of premotor-parieto-cerebellar recruitment across this wide range of tasks indicates that this form of compensation is a general mechanism that exists across various movement parameters. The standard for studying PD patients in the off medication state according CAPSIT-PD guidelines (Langston, et al., 1992) is 12 hour L-dopa withdrawal and 18 hour dopamine agonist withdrawal. This method was established out of concern for interfering with participants’ treatment plans for longer time periods; time periods longer than 12 hours would not be justifiable on ethical grounds. This practically defined off medication state is universally adopted in PD research. Further, its validity is supported by the symptomatic state in which patients present at the time of study commencement.  
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  Though the return of symptoms is a convincing indication of the wearing-off of medication, we can not be certain that no residual effects are present. Therefore, we may not be able to claim with complete certainty that the off medication state is not influenced by lingering effects of L-dopa and is representative of brain function of drug-naïve patients. Unfortunately, recruitment of drug-naïve patients typically isn’t feasible as the majority of patients begin prompt therapy subsequent to diagnosis due to the impact of symptoms. In light of this possibility, comment should be made regarding the metabolic and vascular effects of L-dopa, particularly since this may influence changes in the BOLD response. It has been shown that L-dopa can reduce blood flow as measured by rCBF within specific regions such as the prefrontal cortex during a working memory task (Cools, 2006). Similar findings were observed in healthy individuals given methylphenidate (Mehta et al., 2000).  
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