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Galvanic vestibular stimulation : the effects on postural instability in Parkinson's Disease Tran, Stephanie 2017

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GALVANIC VESTIBULAR STIMULATION: THE EFFECTS ON POSTURAL INSTABILTY IN PARKINSON’S DISEASE  by  Stephanie Tran  B.Sc., The University of Western Ontario, 2015  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Neuroscience)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  October 2017  © Stephanie Tran, 2017 ii  Abstract Postural instability and gait disorders (PIGD) are cardinal symptoms of Parkinson’s disease (PD) and a major source or morbidity; however, current treatments are largely ineffective. Galvanic vestibular stimulation (GVS) is a non-invasive stimulation technique previously reported to improve motor responsiveness in neurodegenerative disorders when applied at subthreshold levels. The response to GVS depends on the type of electrical signal and plane of stimulation (i.e. mediolateral (ML) or anterior/posterior (AP)). As such, the effect of subthreshold GVS in PD is not fully understood. This study is the first to investigate the comparative effects of GVS configurations by manipulating the type of electrical signal (stochastic vestibular stimulation (SVS) or multisine vestibular stimulation (MVS)) and plane of stimulation (ML or AP). Three types of stimuli are used: SVS-ML, SVS-AP and MVS-AP. Subthreshold GVS was first examined during standing, using a stable force platform, then during gait, using an electronic walkway and a dual task paradigm (serial-3 subtraction).  Without stimulation, standing and gait patterns in PD were distinguishable from healthy age-matched controls (HC). Compared to HC, PD participants showed increased amplitude of postural sway, increased gait variability and decreased bilateral coordination. These baseline differences indicate that PIGD symptoms in PD are pathological, and not age-related, changes, that exist even with anti-Parkinsonian medication.  Subthreshold GVS resulted in an effect at the level of the force plate, observed through significant coherences between ground reaction forces. SVS-AP reduced frequency of sway during standing and stride time variability during a dual task walk in PD participants to levels similar to those in HC. Multisine stimulation did not show superiority over stochastic stimulation.  iii  This study demonstrates the feasibility of subthreshold GVS as a potential method for improving PIGD symptoms and concludes that stochastic GVS, delivered in the AP configuration, is the most promising of the three tested methods for reducing sway frequency and improving stride time variability in people with PD.   iv  Lay Summary People suffering from Parkinson’s disease (PD) often show problems with balance and stability. Current treatments for PD are not effective and do not improve this symptom, thus it is crucial to research new treatment options. Galvanic vestibular stimulation (GVS) is a method of delivering electrical signals to the “balance” organ called the vestibular system. Using low levels of stimulation, rat models of PD have been shown to have improved motor responses. This study aims to use GVS at low levels to improve standing balance and walking in people with PD. After testing, the results showed that PD participants had balance issues, such as increased movements during standing called sway, and inconsistent walking patterns. There was a mild effect of GVS stimulation that resulted in less frequent swaying and more consistent walking in people with PD. This study demonstrates GVS as a potential method for treating balance issues in PD. v  Preface Chapter 2 is based on work conducted in UBC’s Neural Control of Postural and Movement Lab with Dr. Mark Carpenter. I was responsible for all the data collection, along with help from Elizabeth Pasman and Christina Jones, both whom helped with recording. I was also responsible for the data analyses, with help from Mahsa Shafiee and Saurabh Garg, both of whom helped with writing scripts for the MAR-LASSO, coherence and regression analyses. Participant recruitment and scheduling was conducted by Christina Jones.  Chapter 3 is based on work conducted in UBC’s Pacific Parkinson Research Centre with Dr. Martin McKeown. I was responsible for all the data collection with recording help from Christina Jones, who also recruited and scheduled participants. I was also responsible for all the data analyses, with help from Mahsa Shafiee, who helped with writing scripts for assessing bilateral coordination between legs using the phase coordination index.   Approval of this study was obtained by the University of British Columbia’s Clinical Research Ethics Board (Certificate #H09-02016) and the Vancouver Coastal Health Ethics Committee (Certificate #V09-02016).   vi  Table of Contents Abstract .......................................................................................................................................... ii	Lay Summary ............................................................................................................................... iv	Preface .............................................................................................................................................v	Table of Contents ......................................................................................................................... vi	List of Tables ................................................................................................................................ ix	List of Figures .................................................................................................................................x	List of Abbreviations ................................................................................................................... xi	Acknowledgements .................................................................................................................... xiii	Dedication .....................................................................................................................................xv	Chapter 1: Introduction ................................................................................................................1	1.1	 Parkinson’s disease ......................................................................................................... 1	1.1.1	 Prevalence and socio-economic impact of Parkinson’s disease ............................. 1	1.1.2	 Symptoms and pathophysiology of Parkinson’s disease ........................................ 1	1.1.3	 Pharmacological and surgical treatments ................................................................ 2	1.2	 Postural instability and gait disorders in Parkinson’s disease ........................................ 4	1.2.1	 Postural control ....................................................................................................... 4	1.2.2	 Falls in Parkinson’s disease .................................................................................... 5	1.2.3	 Parkinson’s disease and static balance .................................................................... 7	1.2.4	 Parkinson’s disease and gait ................................................................................... 8	1.3	 Galvanic vestibular stimulation ...................................................................................... 9	1.3.1	 The vestibular system ............................................................................................. 9	1.3.2	 Galvanic vestibular stimulation at suprathreshold levels ...................................... 10	vii  1.3.3	 Galvanic vestibular stimulation at subthreshold levels ......................................... 12	1.3.4	 Multisine vestibular stimulation ........................................................................... 13	1.3.5	 Galvanic vestibular stimulation in Parkinson’s disease ........................................ 13	1.4	 Purpose .......................................................................................................................... 14	Chapter 2: Galvanic vestibular stimulation on quiet stance in Parkinson’s disease .............15	2.1	 Introduction ................................................................................................................... 15	2.2	 Methods......................................................................................................................... 15	2.2.1	 Participants ............................................................................................................ 15	2.2.2	 Measures ............................................................................................................... 17	2.2.3	 Stimulation ............................................................................................................ 17	2.2.4	 Procedure .............................................................................................................. 18	2.2.5	 Data analysis ......................................................................................................... 19	2.3	 Results ........................................................................................................................... 21	2.3.1	 Descriptive statistics ............................................................................................. 21	2.3.2	 Effect of GVS at the level of the force plate (GRF outputs) ................................ 24	2.3.3	 Quiet stance without stimulation .......................................................................... 24	2.3.4	 Quiet stance with stimulation ................................................................................ 28	2.4	 Discussion ..................................................................................................................... 33	Chapter 3: Galvanic vestibular stimulation on gait in Parkinson’s disease ...........................34	3.1	 Introduction ................................................................................................................... 34	3.2	 Methods......................................................................................................................... 34	3.2.1	 Participants ............................................................................................................ 34	3.2.2	 Measures ............................................................................................................... 35	viii  3.2.3	 Stimulation ............................................................................................................ 35	3.2.4	 Procedure .............................................................................................................. 36	3.2.5	 Data analysis ......................................................................................................... 37	3.3	 Results ........................................................................................................................... 39	3.3.1	 Descriptive statistics ............................................................................................. 39	3.3.2	 Differences in gait patterns between HC and PD ................................................. 39	3.3.3	 Effect of dual task ................................................................................................. 42	3.3.4	 Effect of GVS ....................................................................................................... 44	3.4	 Discussion ..................................................................................................................... 46	Chapter 4: Discussion ..................................................................................................................47	4.1	 Static posturography and gait patterns reveal baseline differences between Parkinson’s disease and age-matched controls ............................................................................................. 47	4.2	 Vestibular stimulation produces unique effects in Parkinson’s Disease during static balance and gait ......................................................................................................................... 47	4.2.1	 Effect of GVS during static balance ..................................................................... 48	4.2.2	 Effect of GVS during gait ..................................................................................... 49	4.3	 Stochastic resonance in the vestibular system .............................................................. 50	4.4	 Vestibular contributions during static versus dynamic balance .................................... 51	4.5	 Limitations .................................................................................................................... 51	4.6	 Significance ................................................................................................................... 52	4.7	 Future directions ........................................................................................................... 52	4.8	 Conclusions ................................................................................................................... 53	References .....................................................................................................................................54	ix  List of Tables Table 2.1. Individual participant demographics…………………………………………………22 Table 2.2. Mean participants’ demographics and descriptive statistics………………………….23 Table 2.3. Mean RMS- and MPF-COP for each GVS configuration OFF and ON-stimulation...23 Table 2.4. Three-way mANOVA comparing effects of group, stimulation and plane of stimulation for each COP parameter………………………………………………….31 Table 2.5.  Three-way mANOVA comparing effects of group, stimulation and signal waveform for each COP parameter………………………………………………………………32 Table 3.1. Individual participant demographics…………………………………………………38 Table 3.2. Mean participants’ demographics and descriptive statistics…………………………38 Table 3.3. Gait patterns during OFF-single test for PD and HC………………………………...40 Table 3.4. Dual Task Costs (%)………………………………………………………………….42  x  List of Figures Figure 1.1. Model of pathophysiology of the basal ganglia in Parkinson’s disease………………3 Figure 1.2. Postural sway………………………………………………………………………….5 Figure 1.3. GVS electrode placements ……………….............………………………………....11 Figure 1.4. Sample of 60 seconds of stochastic and multisine stimulation……………………...12 Figure 2.1. Experimental set-up…………………..……………………………………………...16 Figure 2.2. Regression analysis (MAR-LASSO) methods and results…………………….…….25 Figure 2.3. Coherences of the GVS signal and the GRF between ON and OFF………………...26 Figure 2.4. Difference of coherences between the GVS signal and the GRF….....…….………..27 Figure 2.5. COP summary measures: RMS-COPAP, RMS-COPML, MPF-COPAP, MPF-COPML 30 Figure 3.1. Percent difference between PD and HC of each gait parameter during single-task walk, OFF-stimulation……………………………………………….……………...41 Figure 3.2. Dual Task Costs (OFF-stimulation) for PD and HC………………………………...43 Figure 3.3. Effect of stimulation for PD and HC using SVS-AP and SVS-ML during single  and dual tasks for gait variability and bilateral coordination parameters.……....…..45 xi  List of Abbreviations a-synuclein Alpha-synuclein ABC  Activities-specific Balance Confidence  AP  Anterior-posterior BG  Basal ganglia BoS  Base of support CI  Confidence interval COM  Center-of-mass COP  Center-of-pressure CV  Coefficient of variation DA  Dopamine DBS  Deep brain stimulation DTC  Dual task costs GA  Gait asymmetry GABA  γ-Aminobutyric acid GRF  Ground reaction force GVS  Galvanic vestibular stimulation L-DOPA Levodopa ML  Mediolateral MDS  Movement Disorder Society MoCA  Montreal Cognitive Assessment  MPF  Mean power of frequency MPF-COP Mean power frequency of COP  xii  MVS   Multisine vestibular stimulation PCI  Phase coordination index PD  Parkinson’s disease PIGD  Postural instability and gait disorders PPN  Pedunculopontine nucleus RMS   Root-mean-square RMS-COP Root-mean-square of COP  SNc  Substantia nigra pars compacta STN  Subthalamic nucleus SVS  Stochastic vestibular stimulation UPDRS Unified Parkinson’s Disease Rating Scale   xiii  Acknowledgements To start, I would like to thank the faculty, staff and my fellow students at UBC, who have inspired me to pursue research in this field. I owe particular thanks to Dr. Martin McKeown for being the best supervisor. Thank you for being a mentor and someone I can always turn to for questions and advice.   To Dr. Mark Carpenter, thank you for collaborating on this project. Thank you for offering your lab equipment, advice and support. To Elizabeth Pasman and the rest of the Neural Control of Posture and Movement Laboratory, thank you for being such a massive help in the early stages of my study and for teaching me the basics.  To Christina Jones, a big thank you for being my partner-in-crime throughout the years. Thank you for being quick at recruiting, helping with all my recording sessions, reading and editing my thesis, brainstorming/trouble-shooting with me and, of course, being a great friend!  To Mahsa Shafiee, thank you for all the hard work you put into this project. Thank you for always keeping me positive and motivated!   To Saurabh Garg, thank you for helping me with analyses and putting up with me barging into your office!  To the Pacific Parkinson’s Research Centre (PPRC), thank you for all the support. It has been a blast working with you and being a part of this lab! xiv  To the Mottershead Foundation, without you, this project would not have been possible. Thank you for your kind donations.  To my friends: Shams, Marlo, Amna, Kassie, Fati, Maria, Deena and Ben, thank you for all the late-nights and the fun times. Vancouver would not be the same without any of you. Thank you to Shirley Wong for helping me draw some figures! A special thank you to Shams, Marlo and Maria for helping me read and edit this thesis! In particular, Shams thanks for being someone I can turn to no matter what (you’re my #1).   To Francis, thank you for the never-ending love and support. I couldn’t have stayed sane throughout this process without you. Thank you for being a big-part of this milestone with me.  To my siblings, thank you for always being a constant support, even across the country. A special thank you to my big sister, Karen Tran, for helping me read and edit this thesis! I miss you all every day.  To my mom and dad, thank you for being my inspiration and for teaching me the value of hard work. This one’s for you!  xv  Dedication       To my family  1  Chapter 1: Introduction 1.1 Parkinson’s disease 1.1.1 Prevalence and socio-economic impact of Parkinson’s disease Parkinson’s disease (PD) is the second most common neurodegenerative disorder1. World-wide prevalence of the disease increases with age, affecting 1-2% of the population over 65 years2,3. In Canada alone, more than 25 people are diagnosed with PD every day4. It is estimated that the number of Canadians with PD will double from 84,700 incidences in 2011 to more than 163,700 incidences in 20314. Compared to other neurological conditions in Canada, PD has the highest rate of prescription drug use and is among the highest for annual health care costs and out-of-pocket expenses4. As the aging population grows, the socio-economic burden of PD will increase, thus it is important to manage the disease and minimize its impacts. 1.1.2 Symptoms and pathophysiology of Parkinson’s disease James Parkinson first described PD in 1817 as a “shaking palsy” with three cardinal motor symptoms: tremor, rigidity and bradykinesia5. Postural instability develops with disease progression and is a fourth cardinal symptom6. Non-motor symptoms in PD have recently been recognized and include: neuropsychiatric features, sleep disorders, sensory dysfunction, pain and fatigue2.  The cause of PD is unknown but the presence of Lewy bodies is a pathological hallmark of PD3. It is suggested that the abnormal protein aggregates of alpha-synuclein (α-synuclein) in Lewy bodies mediate neuronal death7 however, the function of α-synuclein is not understood. Motor symptoms of PD are attributed to the degeneration of dopaminergic neurons in the substantia nigra pars compacta (SNc) of the basal ganglia (BG), which is involved in voluntary motor control8. Figure 1.1 summarizes the classic model of basal ganglia pathophysiology in 2  Parkinson’s disease. Symptoms are typically observed when 50% of the dopamine (DA) neurons in the SNc are lost9. Neuropathology outside the DA nigrostriatal system has also been reported in areas such as the locus coeruleus, dorsal vagal nucleus, and the neocortex, which elucidate the non-motor symptoms10.  1.1.3 Pharmacological and surgical treatments Due to the complexity and idiopathic nature of PD, there is currently no cure. Current treatments are symptomatic and do not slow the progression of the disease. Two broad treatments are available for PD: pharmacological (i.e. Levodopa; L-DOPA) or surgical treatment (i.e. deep brain stimulation; DBS). L-DOPA is highly effective for bradykinesia and rigidity, whereas non-motor and postural instability and gait disorders (PIGD), show little to no response11. Further, long-term use of L-DOPA leads to side-effects of dyskinesia (i.e. involuntary and often jerky muscle movements). L-DOPA responsiveness is a primary selection criteria when determining suitability for DBS surgery12. DBS in the subthalamic nucleus (STN-DBS) improves appendicular symptoms (i.e. tremor, rigidity and bradykinesia) and L-DOPA-induced dyskinesias but fails to improve PIGD13,14, especially if these symptoms were unresponsive to L-DOPA before surgery11. Recently, the pedunculopontine nucleus (PPN), also known as the mesencephalic locomotor region, has been suggested as a target for DBS for PIGD14 because it has strong connections with the BG and shows cholinergic degeneration in PD15. Bilateral stimulation of the STN and PPN has been shown to improve balance and gait16,17. However, this procedure comes with complications: it is highly invasive and is reserved only for those in advanced stages of PD.  3   Figure 1.1 Model of pathophysiology of the basal ganglia in Parkinson's disease. The basal ganglia (BG) are comprised of four subcortical nuclei: the striatum (which includes the caudate nucleus and the putamen), the globus pallidus (which has an external segment (GPe) and an internal segment (GPi)), the substantia nigra (which has the pars compacta (SNc) and the pars reticulata (SNr)) and the subthalamic nucleus (STN). The BG receives input from cortical areas and outputs via the GPi and SNr, which inhibit the thalamus (Thal). There are two pathways in the BG: the direct pathway, which facilitates movement, and the indirect pathway, which suppresses movement. Dopamine from the SNc facilitates the direct pathway and inhibits the indirect pathway. In healthy states, movement is regulated by a balance between the direct and indirect pathways. In PD however, the degeneration of dopamine in the SNc leads to increased activity of the indirect pathway and decreased activity of the direct pathway. This pathological change causes hyperactivity of the GPi and SNr, which inhibit the thalamus and reduces cortical activation. Adapted from Rodriguez-Oroz et al. (2009).  Brain slice modified and obtained from https://i.pinimg.com/originals/bc/36/cd/bc36cde22a00df55645e6041cab1b5d9.jpg      CortexA.	Healthy B.	Parkinson’s	DiseasePutamen PutamenSNc SNcCortexGPeSTNGPeThal ThalGPi/SNr GPi/SNrSTN4  1.2 Postural instability and gait disorders in Parkinson’s disease 1.2.1 Postural control Individuals with PD experience declining postural stability, which refers to the ability to maintain the body’s center-of-mass (COM) within its base of support (BoS)19,20. Due to internal (e.g. breathing) and external (e.g. visual and proprioceptive stimuli) perturbations, the body continuously corrects for movements of the COM. These COM displacements occur in an oscillatory fashion, creating postural sway21. This sway is observed even during quiet stance, an upright position without self-initiated movements (see Figure 1.2). Stability is maintained through postural control22, the complex interaction of sensorimotor processes to continuously counteract gravity and prevent falls22,23. Standing and walking require different postural control mechanisms24. During quiet, unperturbed stance, anterior/posterior (AP) sway is controlled by ankle muscles (plantarflexors and dorsiflexors) and mediolateral (ML) sway is controlled by hip muscles (abductors and adductors)24. As such, human balancing is modeled as a multi-link inverted pendulum24. During walking, more dynamic postural control is required since the BoS is in constant movement24. For gait initiation, the COM must accelerate ahead of the BoS to initiate a forward fall. The opposite occurs for gait termination, where the COM is returned to the BoS24. Proper placement of the swing foot is crucial for preventing falls during gait24.  The BG is highly involved in postural control through thalamic-cortical-spinal loops, the PPN and the reticulospinal tract25. BG functions relevant to postural control include: sensorimotor integration, motor flexibility, regulation of postural tone, selection of postural strategies and execution of automatic motor plans 25,26. Pathological changes to the BG as observed in PD lead to postural instability. 5  		Figure 1.2 Postural sway. Balance requires control of the centre-of-mass (COM) within its base of support (BoS). Sway can occur in both the anterior/posterior (AP) and mediolateral (ML) directions.  1.2.2 Falls in Parkinson’s disease Postural instability is a debilitating symptom of PD as it increases the risk of falls27. A meta-analysis of six prospective studies (n = 473 PD patients) reports a fall rate of 46% (95% confidence interval (CI): 38-54%). Even in early-stage PD with no history of falls, a high incidence of falls is observed (fall rate = 21%, 95% CI: 12-35%)28. People with PD have higher frequencies of falls in a community-dwelling environment28 as well as compared to healthy age-matched controls (HC)29.    COM COMBoS BoSML AP6  Falls can lead to increased risk of loss of independence, nursing home admissions30, fall-related injuries31,32 and, in severe cases, mortality33. In a study of falls risk in PD, 59% of 118 PD patients report one or more falls within a three-month period. Of these falls, 40% result in injuries31. An eight-year retrospective study in New South Wales, Australia examined fall-related hospitalizations in people over 65 years32. Nearly 8500 fall-related admissions were for people with PD. Of these falls, 67% are due to injury and 35% are due to fractures. Further, PD patients have higher rates of fall admissions and longer median duration of stay.   The most robust method of predicting falls is determining whether an individual has a history of falls (i.e. one or two falls within the past year)28; however, this method cannot prevent falls before they manifest. The Unified Parkinson’s Disease Rating Scale (UPDRS) is considered the “gold standard” for assessing PD symptoms34. This assessment includes standardized guides and subdivided into four sections to rate: (1) mood and behaviour, (2) activities of daily living, (3) motor symptoms and (4) complications of therapy. From the UPDRS, a PIGD subscore can be calculated by summing the following items: arising from chair (3.9), gait (3.10), freezing of gait (3.11), postural stability (3.12) and posture (3.13)35. The retropulsion (i.e. pull) task of the UPDRS (item 3.12) is a “gold standard” for assessing postural instability in PD36. This pull test requires a trained examiner to briskly pull the patient’s shoulders, forcing him or her to take a step. A normal response requires one to two steps to recover balance, while three or more steps suggest postural decline. Previous studies have found that total UPDRS scores28,37,38 and PIGD subscores39 can predict falls. However, this clinical method of assessing postural instability to predict falls is subjective and qualitative, and may rely on the ability of the rater.   7  1.2.3 Parkinson’s disease and static balance  Static posturography is a safe and popular method of assessing postural decline in a laboratory setting40. Static posturography uses a force platform to measure postural control through changes in center-of-pressure (COP), the vertical point of application of the ground force vector, during quiet stance. Because COP represents COM at the level of the support surface40, COP has been used as indirect measure of postural sway41 and an index of stability in standing41,42. From this point on, sway will be used interchangeably with COP, and not COM, activity for this thesis.   Static posturography studies examining quiet stance in PD are inconclusive. Differences between PD and age-matched controls have reported: (1) no difference in sway43–45, (2) decreased sway46,47 and (3) increased sway21,48–50. The heterogeneity of results may be explained by differences in methodology between studies. COP measures are sensitive to hardware and software design, subject selection, data collection (i.e. sampling frequencies, trial durations, “first-trial effects”, data analysis (i.e. type of COP parameters) and interpretation40,51. Specific to PD, COP activity varies with medicated state (“on” vs. “off”)46,50,52 and disease severity46,53,54. Despite the popularity of posturography, the meaning of sway is not understood. As such, there is still no standard protocol, making it difficult to compare results.   There are suggestions for improving clinical utility of posturography. First, a range of COP parameters has been reviewed and root-mean-square (RMS) of the COP (RMS-COP) is recommended for assessing postural control55. RMS-COP represents the standard deviation of the COP displacement. It is found to have both intersession reliability and intrasubject consistency and can detect changes in both the AP and ML directions55. Next, trial durations of at least 60 seconds are suggested to increase reliability of COP measures56. Finally, to detect pathological changes, a homogenous sample should be included (i.e. similar disease duration, severity, 8  symptomology and medicated state)51. Following these guidelines will improve the clinical relevance and allow comparisons with previous studies related to static posturography in PD.  1.2.4 Parkinson’s disease and gait Pathological changes in PD gait are more visually obvious than those observed in static balance. Characteristic gait disorders in PD include: stooped posture, festination (or shuffling of gait), and freezing57. Slow gait velocities and smaller step sizes are also observed due to bradykinesia. Although gait biomarkers for PD have not been established, potential gait-related biomarkers have been suggested in a recent review58. These biomarkers include: reduced arm swing, trunk rotation, double support time, gait velocity, and stride length and increased gait variability (i.e. stride-time variability). Increased stride-time variability is observed in PD59 and has been suggested as a predictor of falls in older adults60, people with PD 61 and people with multiple sclerosis62.  As the BG regulates automaticity of posture and gait, walking becomes less automatic with PD and requires more attention25. People with PD often show difficulties with more attention-demanding gait tasks such as turning, dual-tasking and avoiding obstacles25. Spatiotemporal patterns of gait are more affected in PD compared to HC during a dual task63.  Both motor (e.g. carrying or manipulating an object) and cognitive (e.g. calculations, verbal/conversational or memory) tasks affect PD gait. It is unknown which type of task has a greater impact but more complex tasks tend to have a greater effect. Because postural control of walking is more dynamic than standing, the risk of falls is increased53,64. Recurrent falls are associated with freezing and disease severity65. As gait disorders have a large impact on quality of life, improving gait in PD is crucial.   9  1.3 Galvanic vestibular stimulation 1.3.1 The vestibular system The vestibular system is an important component of postural control24 and consequently, fundamental to our understanding of postural instability in PD. The vestibular system is composed of the otolith organs (saccule and utricle) and the semi-circular canals, which exist in each ear66. The otolith organs sense information about linear accelerations66. The saccule detects vertical accelerations while the utricle detects horizontal accelerations66. The semi-circular canals sense information about angular accelerations66. Both otolith organs and semi-circular canals detect changes in acceleration using hair cells, which have cell bodies in the vestibular ganglion67. The vestibular ganglion combines with the cochlear nerve and becomes the vestibulocochlear nerve (cranial nerve VIII)68.  Vestibular afferents travel through the vestibulocochlear nerve to the four ipsilateral vestibular nuclei located in the brainstem67,68. The lateral vestibular nucleus projects via the lateral vestibulospinal tract to control balance and extensor tone in the limbs67. The medial vestibular nucleus and inferior vestibular nucleus project via the medial vestibulospinal tract to control the position of the head and neck using vestibulocollic reflexes67. The superior vestibular nucleus and medial vestibular nucleus project via the medial longitudinal fasciculus to control eye muscles and reflexes such as the vestibulo-ocular reflex67.  The vestibular nuclei project via ascending fibers to the cerebellum and other higher cortical areas. Connections with the cerebellum are important for balance, eye control, movement coordination and postural adjustments67,68. Connections with the hippocampus are important for spatial orientation66. Finally, connections to the thalamus are important for conscious perception of head orientation and self-movement67. With the integration of visual and proprioceptive inputs, 10  the vestibular system allows for spatial orientation in a three-dimensional space and coordination of movement and stabilization of the head and eyes with respect to the environment. As the vestibular system functions as the “balance” organ of the body, targeting this system to enhance its functions may be a feasible approach to improve postural instability. 1.3.2 Galvanic vestibular stimulation at suprathreshold levels For over a century, galvanic vestibular stimulation (GVS) has been used as a non-invasive method to deliver electrical currents to vestibular afferents and elicit vestibular reflexes at suprathreshold levels (i.e. above sensory perception) (for reviews, see:69,70). This method involves passing small currents of < 5 mA across the mastoid processes to target the underlying vestibular nerves and modulate neural firing69. Anodal currents decrease firing rates while cathodal currents increase firing rates71. A whole-body response of the neck, trunk and legs causes a standing subject sway toward the anode72. GVS mediates its effects at the level of the hair-cell, providing a pure vestibular stimulus71. GVS induces an illusion of angular acceleration of the head, which is consistent with activation of the semi-circular canals, thus the semi-circular canals are suggested to be the basis of GVS responses73,74.     The effects of GVS vary based on electrode placement (see Figure 1.3). Binaural bipolar GVS (i.e. one electrode over each mastoid process) induces ML sway and has been widely used in the literature75. In this configuration, the semi-circular canals detect a signal of a roll toward the cathode, causing a compensatory reaction and an observed sway of the head and trunk toward the anode. If the subject’s head is turned (i.e. over one shoulder), binaural bipolar GVS induces AP sway. AP sway can also be induced with equivalent stimulation on both mastoids using binaural monopolar GVS (i.e. two sets of electrode pairs with the cathodes on the mastoids and anodes on an ipsilateral reference point on the body)76. In this configuration, signals from the lateral semi-11  circular canals cancel from the equal stimulation on each ear, causing a compensatory pitch movement as modulated firing is detected from the anterior and posterior canals77. Typically, sway responses to GVS are observed using direct constant current signals of ~ 1 mA69.  Figure 1.3 GVS electrode placements. One electrode pair on the mastoids mediates mediolateral sway. Two pairs of electrodes with equal polarity on the mastoids mediates anterior/posterior sway. Fewer studies have examined the effects of GVS on gait. During straight-line walking, binaural bipolar (ML direction) GVS biases the vestibular system toward the cathode and causes lateral displacements of the feet toward the anode78,79. The effects of GVS are dampened when visual information is available and are more pronounced when the subject has their eyes closed80. The magnitude of displacement toward the anode positively correlates with the magnitude of the stimulus intensity whereby increasing the GVS current intensity creates greater displacements in sway and/or walking pattern81. These magnitude changes are more evident in older adults compared to younger adults and are only observed in younger adults when vision is artificially impaired (i.e. blurred vision)82. Based on this finding, young adults are suggested to be more effective than older adults at re-weighting vestibular inputs when accurate visual information is available. The effects of GVS are also phase-dependent as the largest responses to GVS are observed at the start of support phase at the moment of heel contact83,84.  12  1.3.3 Galvanic vestibular stimulation at subthreshold levels While suprathreshold stimulation induces sway and changes trajectory of gait, subthreshold (i.e. below sensory perception, typically < 1 mA) stimulation is suggested for clinical use. Using stochastic vestibular stimulation (SVS) applied 60% below nociceptive thresholds (mean stimulation: 0.33 ± 0.20 mA), improvements in motor responses are found in people with PD and multi-system atrophy85. This improvement is attributed to stochastic resonance principles, a phenomenon in which signal detection is enhanced by adding low levels of noise to a non-linear system (i.e. the nervous system)86. In healthy adults, improvements in standing87 and walking88 are observed following recommended amplitudes of 0.1 to 0.5 mA of SVS89. In a hemiparkinsonian rat model, SVS improves locomotion through increased γ-Aminobutyric acid (GABA) regulation in the substantia nigra in a dopamine-independent manner90. Together, these studies suggest clinical uses for SVS. As SVS may mediate its effects through non-dopaminergic pathways, it may prove to be effective for treatment-resistant symptoms in PD such as PIGD.    Figure 1.4 Sample of 60 seconds of stochastic and multisine stimulation. 13  1.3.4 Multisine vestibular stimulation A new method of vestibular stimulation has been suggested as a clinical research tool (see Figure 1.4 for comparison between stochastic and multisine signal waveforms). Forbes et al. (2014) used multisine electrical signals to demonstrate that multisine vestibular stimulation (MVS) evokes similar motor responses to SVS but with additional benefits. Namely, in comparison to SVS, MVS increases signal-to-noise ratios of stimulus-evoked motor responses, enhances motor output and reduces subject discomfort91. These aspects make MVS a beneficial tool for clinical research use. 1.3.5 Galvanic vestibular stimulation in Parkinson’s disease  Two studies have used SVS for postural instability in PD. First, Pal, et al. (2009) used binaural monopolar (AP direction) SVS and observed a small, but statistically significant, reduction in COP displacements in five PD participants, “on”-medication. In this study, cathodes were placed on the mastoids for a “bicathodal” stimulation and each participant was stimulated between 0 and 0.5 mA. COP amplitudes were recorded for 26 second trials in two separate conditions (eyes open and eyes closed), while standing on a force plate covered with foam to decrease proprioceptive feedback. A 4.5% decrease in COP amplitude was observed in the eyes-closed condition following stimulation at an intensity of 0.1 mA. No changes were observed above 0.1 mA. Next, Samoudi and colleagues (2015) examined binaural bipolar (ML direction) SVS to assess recovery from AP perturbations in PD participants (n = 10). The stimulation was applied at 45% below perceptual threshold (mean threshold: 0.5 ± 0.25 mA). COP was assessed using a force plate during four ten-second trials of standing with eyes open and eyes closed, then repeated on foam. Stimulation improved balance reaction times to perturbation and total sway path of COP during “off”-medication quiet stance. These studies suggest the potential of SVS for decreasing 14  PD postural instability. However, both studies had small sample sizes and tested postural stability using trial durations of less than 60 seconds, which reduces the reliability of COP displacement measures.  1.4 Purpose The effects of SVS-AP and SVS-ML have not yet been compared for postural instability in PD and MVS has yet to be used in the PD population. Moreover, subthreshold SVS has not been examined during PD gait. For this thesis, GVS will be tested in PD during quiet stance using static posturography (Chapter 2) and gait using an electronic walkway (Chapter 3). Three different GVS configurations will be compared: SVS-ML, SVS-AP and MVS-AP. The objective is to further investigate the feasibility of GVS in PD and to determine an effective method of vestibular stimulation for improving postural instability.  It is hypothesized that: 1) subthreshold GVS will induce a measurable change in postural sway in PD and 2) there will be systematic differences in postural sway and gait patterns between the three different types of stimuli.  15  Chapter 2: Galvanic vestibular stimulation on quiet stance in Parkinson’s disease 2.1 Introduction  For this chapter, the effects of subthreshold GVS are examined during standing balance in PD participants, “on”-medication, and age-matched controls, while standing with eyes-closed. This study is the first to investigate the comparative effects of GVS configurations to manipulate the plane of stimulation (ML or AP) and electrical signal waveforms (stochastic or multisine). Three types of stimuli are used: SVS-ML, SVS-AP and MVS-AP.  2.2 Methods 2.2.1 Participants Thirteen PD participants (6 females, 7 males, 67 ± 4.81 years of age) were recruited by the Pacific Parkinson’s Research Centre at the University of British Columbia Hospital. Thirteen HC participants (7 females, 6 males, 66.5 ±  3.9 years of age) were recruited through the Changing Aging program, UBC campus, and the community of Vancouver. Due to dizziness, one female HC was unable to complete the study. The remaining HC participants were age- and gender-matched to the PD participants. None of the participants had a history of brain surgery, neurological disorders, or medical issues that may influence balance. Informed written consent was obtained and the study was approved by the University of British Columbia’s Clinical Research Ethics Board (Certificate #H09-02016) and the Vancouver Coastal Health Ethics Committee (Certificate #V09-02016).    16   Figure 2.1 Experimental set-up. An electrical signal input (stochastic or multisine) is converted into an analogue signal using a digital acquisition module (DAQ). The analogue signals are passed through two constant current linear isolated stimulators. Electrodes are placed in the binaural monopolar configuration. To stimulate in the mediolateral plane, two electrodes on the mastoids are stimulated. To stimulate in the anterior/posterior plane, four electrodes are placed with both cathodes placed on the mastoids. Ground reaction forces and moments are measured by the force plate and output to the recording computer. For Chapter 3, an electronic walkway is used instead of a force plate to measure spatiotemporal patterns of gait.      Force	PlateComputer+-+- DAQStimulatorsDigital	input	signalForce	and	moment	output	 signalsDigital	acquisition	module17  2.2.2 Measures PD participants were assessed using the Movement Disorders Society-sponsored revision of the UPDRS (MDS-UPDRS). All participants completed the Activities-Specific Balance Confidence (ABC) Scale and answered the following questions: “Have you fallen in the past six months (i.e. fallen to the floor or ground unintentionally)? If so, how many times and what activities were engaged prior to the fall(s)? In general, do you have a fear of falling?”   2.2.3 Stimulation Vestibular stimulation was delivered through foam electrodes (Kendall™ 130 Foam Electrodes, USA) placed in a binaural monopolar configuration for AP and ML stimulation (see Figure 2.1 for the experimental set-up). Each electrode pair was placed on the mastoid process and over the ipsilateral acromion. Nuprep® skin prep gel was used to clean skin for better electrode contact and to reduce resistance during stimulation. Stimulation waveforms were generated on a computer using MATLAB (R2015b, Mathworks, USA) and converted to an analogue signal using digital acquisition module (NI USB-6343, X Series Multifunction DAQ, National Instruments, USA). The analogue signals were then passed through two constant current linear isolated stimulators (STMISOLA, Biopac, USA), which output the stimulation to the electrodes.   Two types of electrical signal waveforms were used: stochastic and multisine. The stochastic signal was zero-mean white noise with a Gaussian distribution ranging from 0.4 to 30 Hz. This type of stochastic signal was previously used in PD and healthy controls87,88,93. A 0 to 30 Hz frequency range was chosen to effectively stimulate vestibular hair cells involved in both posture, which respond to lower frequencies, and lower limb myogenic responses, which respond to higher frequencies89,94. The multisine signal was generated between 0.4 to 24.8 Hz following a logarithmic distribution to focus power at lower frequencies. This MVS signal was previously used 18  in healthy controls and suggested to be used over SVS signals for research and clinical applications91. All electrical stimuli were tested at an imperceptible level to blind participants to the presence or absence of stimulation.  2.2.4 Procedure PD participants were tested in their best clinical “on”-medication state (typically one hour after medication intake) to observe dopamine-unresponsive postural instability symptoms. Prior to testing, all participants underwent a sensory thresholding task (for methods, see 95). Electrical stimuli were delivered at 70% below individual cutaneous sensory thresholds. Separate thresholds were determined for stochastic and multisine signals. The mean current intensity used was 0.22 ± 0.02 mA for SVS and MVS stimuli. On average, HC received stimulation of 0.18 ± 0.18 mA, while PD received 0.25 ± 0.24 mA. For binaural bipolar stimulation, only two electrodes were stimulated with the anode placed on the left mastoid and the cathode placed on the right mastoid. For binaural monopolar design, all four electrodes were stimulated with both cathodes were placed on the mastoids and anodes placed on the ipsilateral acromion. Participants were blind to the type of GVS received throughout testing.   Six standing trials were conducted: 2 MVS-AP, 2 SVS-AP and 2 SVS-ML. Participants stood quietly on a force plate (#K00407, Bertec, USA) with bare feet and hands by their sides for all trials. Feet were placed apart a width equal to each participant’s individual foot length and the position of the feet was marked on the force platform for consistency between trials. Each trial was 160 seconds. One practice trial was conducted to avoid “first trial effects”, where the first trial is typically different than subsequent trials51. Within each trial, there was 60 seconds of continuous OFF-stimulation and 60 seconds of continuous ON-stimulation with ten seconds of ramping period 19  prior to stimulation. A duration of 60 seconds per condition was chosen to increase the reliability of the COP summary measures56. The order of OFF and ON stimulation were randomized between trials to avoid fatigue effects. Ground reaction force (GRF) and moment outputs were collected at a sampling frequency of 2000 Hz (Power 1401 with Spike 5 software, Cambridge Electronic Design).  2.2.5 Data analysis All data was processed and analyzed using MATLAB© (R2017a, The Mathworks, USA). The data were filtered using a 4th order Butterworth low-pass filter at 3.5 Hz. The filtered data were then separated into 60 seconds of OFF-stimulation and 60 seconds of ON-stimulation for analyses. COP in the AP and ML directions were calculated from the GRF and moments. Mean COP was removed to create an unbiased signal. RMS and MPF of COP were calculated from the unbiased signal to measure the amplitude and frequency of sway, respectively, in the AP and ML planes.  To investigate the effect of GVS at the level of force plate, shear GRF outputs were analyzed in both time and frequency domain using two methods: a regression analysis and a coherence analysis (see Figures 2.2 and 2.3, respectively). Time domain analysis was performed using multivariate second order auto-regression (MAR) with regularized fitted least square (Figures 2.2). The LASSO (least absolute shrinkage and selection operator) algorithm with two lags and 10-fold cross validation was used to solve the MAR problem. GVS was considered as a binary interaction variable (for OFF and ON). The sparse coefficient vector was estimated and the interactions between GRFs for each group in the time domain were investigated, by comparing the estimated coefficients between the variables. Significant interaction weights were considered with p < 0.05.  20  Coherence analysis was performed to evaluate the linear relationship between the shear GRF outputs and the GVS signal in the frequency domain. Coherence peaks with values above 95% confidence level, were estimated between GVS and force in the corresponding plane of stimulation direction (Fy in GVS-AP and Fx in GVS-ML). Raw and unfiltered GRF outputs were used for this analysis. To further evaluate the statistical significance of these estimated peaks and how much they represent the relationship to GVS, pooled coherences for all subjects between stimulation and forces were also calculated and compared for both ON and OFF conditions. Pooled coherence estimates across subjects (separately pooled for HC and PD) and coherence differences were computed using Neurospec 2.0 (see: 96).  The comparison test is formed by subtraction of spectral coefficients and tests for null hypothesis of equal coherence estimates.   Statistical analyses were conducted using SPSS (Version 21, IMB Corp ©, Chicago, IL). Pearson’s r correlation coefficients are calculated between the MoCA scores and the following clinical measures: total UPDRS score, UPDRS item 3.12 (postural instability) and PIGD subscore. Clinical measures and COP summary measures were tested for normality using Shapiro-Wilk tests. To compare groups without stimulation, independent samples t-tests were used on the clinical measures and independent samples Mann-Whitney U Tests were used on the COP summary measures. Two separate three-way mixed ANOVAs (mANOVA) were conducted to examine the effect of GVS on COP as a measure of postural control. The first mANOVA was used to examine the effect of group (PD vs. HC), stimulation (OFF vs. ON) and plane of stimulation (SVS-ML vs. SVS-AP) for each COP summary measure. The second mANOVA was used to examine the effect of group (PD vs. HC), stimulation (OFF vs. ON) and signal (stochastic vs. multisine) for each COP summary measure. An a level of 0.05 was used to indicate statistical significance. All data is reported mean ± SD unless otherwise stated. 21  2.3 Results 2.3.1 Descriptive statistics Tables 2.1 and 2.2 report participants’ demographics and clinical measures. Pearson’s r correlation coefficients were calculated between ABC scores and clinical UPDRS scores. There was a significant negative relationship between ABC scores and total UPDRS scores, r(11) =  -0.729, p < 0.001. This correlation indicates that PD symptoms worsened as balance confidence decreased. There were no significant correlations between ABC scores and postural instability subscores [r(11) = -0.119, p = 0.70] or PIGD subscores [r(11) = -0.401, p = 0.174].  Independent t-tests were conducted to compare baseline characteristics between groups. There was no significant difference in age between PD (67 ± 4.8 years) and HC (66.7 ± 4.1 years); t(23) = -0.17, p = 0.853. For the ABC scale, there was a significant difference between PD scores (86.94 ± 10.9%) and HC scores (96.14 ± 3.74%); t(23) = -0.277, p = 0.011.  This result suggests less confidence in the PD participant group in balanced-related activities of daily living compared to controls. No participants reported falling in the six months prior to testing.    22    Subject ID Group Sex Age (years) ABC Scale (%) PD Duration (years) MDS-UPDRS: total score MDS-UPDRS: PIGD subscore S001 HC M 62 100 - - - S002 HC M 66 88.70 - - - S003 HC F 71 96.25 - - - S004 HC F 61 94.38 - - - S005 PD F 72 66.25 2 32 2 S006 PD F 61 76.88 9 21 1 S007 PD F 68 90.31 2 16 2 S008 PD M 60 97.19 2 12 0 S009 PD M 63 99.13 3 10 1 S010 PD F 75 66.25 5 26 2 S011 PD M 65 91.88 2 17 0 S012 HC F 68 98.69 - - - S013 PD F 64 86.56 2 27 4 S014 HC F 65 95.06 - - - S015 HC M 62 96.56 - - - S016 PD M 65 85.00 3 21 0 S017 PD F 68 89.25 7 7 0 S018 PD M 69 98.13 8 13 1 S019 PD M 75 91.25 3 27 0 S020 HC F 71 99.06 - - - S021 HC M 72 99.38 - - - S022 PD M 66 92.19 7 4 0 S023 HC F 63 97.19 - - - S024 HC M 71 96.25 - - - S025 HC M 65 98.13 - - - S026 HC F  68 89.06 - - - Table 2.1. Individual participant demographics. ABC scale: Activities Balance Confidence Scale ranging from 0% (no confidence) to 100% (completely confident) for balance ability. MDS-UPDRS: Unified Parkinson’s Disease Rating Scale (motor assessment). MDS-UPDRS PIGD subscore: sum of UPDRS items 3.9-3.12.   23     Parkinson’s Disease  Healthy Controls  p-values Age 67 ± 4.8  66.7 ± 4.1  0.853 ABC (%) 86.94 ± 10.9  96.14 ± 3.74  0.011  H&Y  1.54 ± 0.78  	-  	- MDS-UPDRS:                Total                3.12                3.9-3.12  18.69 ± 7.64 0.46 ± 0.97 1.38 ± 1.45  	-	-	-  	-	-	- Table 2.2. Mean participants’ demographics and descriptive statistics. Mean and standard deviations of demographics and clinical measures. ABC scale: Activities Balance Confidence Scale ranging from 0% (no confidence) to 100% (completely confident). H&Y: Hoehn & Yahr Scale, ranging from 0 (unilateral involvement only with minimal or no functional disability) to 5 (confinement to bed or wheelchair unless aided). MDS-UPDRS: Unified Parkinson’s Disease Rating Scale (motor assessment). MDS-UPDRS item 3.12: postural instability subscore. MDS-UPDRS items 3.9-3.12: postural instability and gait disorders subscore. Data not applicable to healthy control participants are represented with a dash (-). p-values are computed using Student’s t-test for independent samples. Bolded p-values represent statistical significance (p < 0.05).     RMS-COPAP RMS-COPML MPF-COPAP MPF-COPML PD OFF 12.56 ± 3.50 14.17 ± 5.89 13.86 ± 3.77 14.36 ± 5.63 9.57 ± 4.27 9.93 ± 4.43 9.45 ± 3.61 9.26 ± 3.59 0.29 ± 0.17 0.26 ± 0.19 0.25 ± 0.14 0.27 ± 0.18 0.22 ± 0.15 0.23 ± 0.17 0.22 ± 0.12 0.25 ± 0.18  MVS-AP  SVS-AP  SVS-ML           HC OFF 9.26 ± 3.87 8.68 ± 3.48  9.13 ± 3.55   9.54 ± 3.82     5.94 ± 3.73 5.35 ± 3.19 5.92 ± 3.17   6.16 ± 3.70    0.28 ± 0.13 0.30 ± 0.12 0.25 ± 0.12    0.28 ± 0.13        0.20 ± 0.08 0.21 ± 0.07 0.17 ± 0.05  0.22 ± 0.11  MVS-AP  SVS-AP  SVS-ML Table 2.3. Mean RMS- and MPF-COP for each GVS configuration OFF and ON stimulation. Mean and standard deviations of COP summary measures. RMS COP: root mean square center-of-pressure, representing the amplitude of sway. MPF COP: mean power frequency center-of-pressure, representing the frequency component of sway. AP: anterior-posterior plane. ML: mediolateral plane. OFF represents the overall mean of no stimulation during MVS-AP, SVS-AP, and SVS-ML trials.   24  2.3.2 Effect of GVS at the level of the force plate (GRF outputs) GRF outputs were explored to determine whether GVS at subthreshold levels was able to mediate an effect. Through time domain analysis, MAR-LASSO regression results showed significant coefficients in both groups for each GVS configuration (see Figure 2.2). For the difference of coherences between ON and OFF conditions, the most consistent significant values (surpassing 95% confidence level) were observed following SVS-ML stimulation at the frequencies under 0.6 Hz (see Figure 2.3). These results indicate that GVS with low amplitudes of stimulation is capable of influencing GRFs and their interactions.  2.3.3 Quiet stance without stimulation Non-parametric Wilcoxon rank-sum tests were conducted for each dependent variable to test the hypothesis that static posturography is different between groups. Without stimulation, significant differences were observed for RMS-COP between HC and PD (p < 0.0001 for both AP and ML planes). Increased RMS-COPAP and RMS-COPML are observed in PD (12.56 ± 3.5 mm and 9.57 ± 4.26 mm, respectively) compared to HC (9.26 ± 3.87 mm and 5.94 ± 3.73 mm, respectively). No significant group differences were observed for MPF-COP (p = 0.74 for MPF-COPAP and p = 0.90 for MPF-COPML). These results indicate that PD participants are distinguishable from healthy age-matched controls during eyes-closed static posturography by greater amplitude, but not frequency, of sway.   25   Figure 2.2. Regression analysis (MAR-LASSO) methods and results. A) regression is used to determine the effects of GVS on each corresponding GRF and their relationships to other GRFs. Corresponding GRFs are AP force for MVS-AP and SVS-AP, and ML force for SVS-ML. The response of the GRF of interest is predicted by the other GRFs, delayed versions of those GRFs and an interaction term of the GRFs and GVS ON-stimulation. B) The AP, ML and vertical GRFs are labelled. The effect of GVS on these GRFs (based on estimated significant coefficients) are represented in read in i-vi). The top row of subjects represents PD participants following: i) MVS-AP, ii) SVS-AP and iii) SVS-ML. The bottom row of subjects represents HC participants following iv) MVS-AP, v) SVS-AP and vi) SVS-ML. GVS mediates an effect in both groups following all three types of stimulation.  PD PD PDHC HC HCMLAPVerticali) ii) iii)iv) v) vi)B)A)*GVSOFFGVSONGRF	of	interestOtherGRFsDelayedversionsGVS OFFGVS ON=WeightsPredictors+  !PDMVS-AP SVS-MLSVS-APHCi. ii. iii.iv. v. vi.26   Figure 2.3. Coherences of the GVS signal and the GRF between ON and OFF. A) The experimental design using raw, unfiltered GRF data. Differences of coherences are in figures B-D. HC is represented by blue and PD is represented by red. Coherences between the GVS signal and GRFs during OFF-stimulation are represented by dashed lines. Coherences between the GVS signal and GRFs during ON-stimulation are represented by solid lines. Solid black horizontal lines represent the 95% confidence intervals for coherence estimates. B) Coherences between MVS-AP and the AP force. C) Coherences between SVS-AP and the AP force. D) Coherences between SVS-ML and the ML force. Significant peaks are observed in both OFF-stimulation and ON-stimulation for all three types of stimuli.  Force	PlateComputer+-+- DAQStimulatorsDigital	input	signalForce	and	moment	output	 signalsDigital	acquisition	moduleCoherenceanalysisA) B)C) D)27    Figure 2.4. Difference of coherences of the GVS signal and the GRF between ON and OFF conditions. HC is represented by blue and PD is represented by red.  Solid black horizontal lines represent 95% confidence intervals for coherence estimates and frequency resolution is at 0.1 Hz. A) Difference of coherences between MVS-AP and the AP force. B) Difference of coherences between SVS-AP and the AP force. C) Difference of coherences between SVS-ML and the ML force. Significant values were observed following SVS-ML in PD participants for frequencies under 0.6 Hz.  B)C)A)0.4	Hz28  2.3.4 Quiet stance with stimulation Table 2.3 reports mean RMS- and MPF-COP results for OFF-stimulation and ON-stimulation for each GVS configuration. Two mANOVAs were conducted to compare (1) stochastic stimulation of ML and AP planes and (2) stochastic and multisine stimulation signals. F-ratios and p-values are reported in Tables 2.4 and 2.5, respectively. Figure 2.3 summarizes the effect of stimulation. Outliers were assessed by inspection of a boxplot. The outliers were kept in the final analysis because they did not affect the results as assessed by comparison of the results with and without the outliers. All variables except MPF-COPML showed homogeneity of variances as assessed by Levene’s test of equal variances (p > 0.05). MPF-COPML measurements were logarithmically transformed to adjust for the positive skew in data prior to the analysis of variance.  A three-way mANOVA was conducted to understand the effect of group (PD vs. HC), stimulation (OFF vs. ON) and plane of stimulation (ML vs. AP) for each COP summary measure. There were significant effects of group for RMS-COPAP (F(1,23) = 7.467, p = 0.012) and RMS-COPML (F(1,23) = 5.385, p = 0.030), with PD showing increased RMS-COP compared to HC. These results suggest that with group differences in COP amplitude exist with and without stimulation. There was a significant effect of plane for MPF-COPAP (F(1,23) = 4.439, p = 0.046). SVS-ML showed higher MPF-COPAP (0.292 ± 0.026 Hz) compared to SVS-AP (0.261 ± 0.036 Hz). There were also significant effects of stimulation for RMS-COPAP (F(1,23) = 4.387, p = 0.047) and MPF-COPAP (F(1,23) = 5.613, p = 0.027). Stimulation increased RMS-COPAP (11.818 ± 0.948 mm) compared to no stimulation (10.826 ± 0.735 mm) while stimulation decreased MPF-COPAP (0.263 ± 0.291 Hz) compared to no stimulation (0.291 ± 0.028 Hz). Finally, there was an interaction effect for stimulation and plane for MPF-COPML (F(1,23) = 5.175, p = 0.033). MPF-COPML showed a 29  slight increase with SVS-ML stimulation and a slight decrease with SVS-AP stimulation; however, no main effects were significant following pairwise comparisons with Bonferroni-corrections.  A three-way mANOVA was conducted to understand the effect of group (PD vs. HC), stimulation (OFF vs. ON) and signal waveform (stochastic vs. multisine) for each COP summary measure. Again, there were significant main effects of group for RMS-COPAP (F(1,23) = 7.424, p = 0.012) and RMS-COPML (F(1,23) = 6.315, p = 0.019), with PD showing increased RMS-COP compared to HC. There was a significant interaction effect for group and stimulation for RMS-COPAP (F(1,23) = 8.017, p = 0.009). Pairwise comparisons were conducted, which showed a significant simple main effect of group (p = 0.012) and no significant simple main effect of stimulation (p =0.182). Significant interactions for signal and stimulation were observed for MPF-COPAP (F(1,23) = 4.559, p = 0.044) and MPF-COPML (F(1,23) = 11.281, p = 0.003). For both planes, MPF-COP showed a slight increase with MVS-AP stimulation and a slight decrease with SVS-AP stimulation; however, the simple main effects were not significant.    30    Figure 2.5. COP summary measures: RMS-COPAP, RMS-COPML, MPF-COPAP, MPF-COPML for A) MVS-AP stimulation, B) SVS-AP stimulation, and C) SVS-ML stimulation. RMS- and MPF-COP are used to represent the amplitude and frequency of sway, respectively. HC is represented by blue stars and PD is represented by red diamonds. Error bars represent standard deviation. Stochastic stimulation increases RMS-COPAP and decreases MPF-COPAP (B and C). Regardless of group, SVS-AP (B) decreases while MVS-AP (A) and SVS-ML increase MPF-COP.  	    31    RMS-COPAP  RMS-COPML  MPF-COPAP  MPF-COPML  F p-values  F p-values  F p-values  F p-values Group 7.467 0.012  5.385 0.030  0.012 0.913  0.122 0.730             Stimulation 4.387 0.047  0.013 0.909  5.613 0.027  0.057 0.813 Stimulation * Group 3.259 0.084  0.059 0.810  1.172 0.290  0.844 0.368             Plane 0.493 0.490  1.889 0.183  4.439 0.046  0.008 0.931 Plane * Group 0.327 0.573  0.182 0.674  0.014 0.906  2.110 0.160             Stimulation * Plane 0.815 0.376  1.903 0.181  0.070 0.794  5.175 0.033             Stimulation * Plane * Group 0.122 0.730  1.893 0.182  0.337 0.567  0.056 0.518 Table 2.4. Three-way mANOVA comparing the effects of group, stimulation and plane of stimulation for each COP parameter. 2 (group: PD vs HC) x 2 (stimulation: OFF vs. ON) x 2 (plane of stimulation: ML vs. AP) mANOVA for each COP parameter. Bolded p-values represent statistical significance (p < 0.05).     32    RMS-COPAP  RMS-COPML  MPF-COPAP  MPF-COPML  F p-values  F p-values  F p-values  F p-values Group 7.424 0.012  6.315 0.019  0.021 0.885  0.287 0.597             Stimulation 1.893 0.182  0.091 0.765  0.259 0.615  0.005 0.946 Stimulation * Group 8.017 0.009  1.399 0.249  1.984 0.172  0.117 0.735             Signal 0.141 0.711  0.460 0.504  1.273 0.271  0.150 0.702 Signal * Group 0.768 0.390  3.302 0.082  1.139 0.297  0.929 0.345             Stimulation * Signal 0.738 0.399  1.294 0.264  4.559 0.044  11.281 0.003             Stimulation * Signal * Group 0.001 0.971  0.001 0.978  0.580 0.454  1.087 0.308 Table 2.5. Three-way mANOVA comparing the effects of group, stimulation and signal waveform for each COP parameter. 2 (group: PD vs HC) x 2 (stimulation: OFF vs. ON) x 2 (signal: stochastic vs. multisine) mANOVA for each COP parameter. Bolded p-values represent statistical significance (p < 0.05).     33  2.4 Discussion Despite the relatively mild balance symptoms in the PD participants, larger COP displacements were observed at baseline compared to HC. This result aligns with two other studies that demonstrate greater postural sway measured by COP displacement when comparing PD standing balance to that of HC50,54. In studies with PD participants tested while on dopaminergic medications, there have been mixed results with reports of (1) increased sway52,54, (2) decreased sway46 or (3) no difference in sway97 in comparison to HC participants. Notably, the two studies which report decreased or no changes in sway versus HCs, differed in methodology using test conditions with vision, rather than without vision, which was used in the current study. Of these studies, Johnson et al. is most comparable to the present study as participants were also tested under the conditions of eyes-closed and “on”-medication. Johnson’s larger sample study (n = 48 PD, 17 HC) showed increased sway area and sway path in people with PD (H & Y stages I-III) compared to HC. The current study was able to replicate these findings in a smaller sample of participants, showing a greater amplitude of postural sway in PD participants.    34  Chapter 3: Galvanic vestibular stimulation on gait in Parkinson’s disease 3.1 Introduction  For this chapter, the effects of subthreshold GVS are examined in walking balance in PD participants, “on”-medication, and age-matched controls. A dual task paradigm (serial-3 subtraction) is used. This study is the first to investigate GVS during gait in PD and to compare GVS configurations. Two types of stimuli are used: SVS-ML and SVS-AP, to manipulate the plane of stimulation. MVS-AP is not used in this study because it did not show superiority over stochastic signals (see Chapter 2).  3.2 Methods 3.2.1 Participants Five PD participants (3 females, 2 males, 66.6 ± 5.50 years) were recruited by the Pacific Parkinson’s Research Centre at the University of British Columbia Hospital. Five HC participants (3 females, 2 males, 69.8 ±  5.40 years) were recruited through the Changing Aging program, UBC campus, and the community of Vancouver. None of the participants had a history of brain surgery, neurological disorders, or medical issues that may influence balance. Informed written consent was obtained and the study was approved by the University of British Columbia’s Clinical Research Ethics Board (#H09-02016) and the Vancouver Coastal Health Ethics Committee (#V09-02016).   35  3.2.2 Measures PD participants were assessed using the MDS-UPDRS. All participants completed the Montreal Cognitive Assessment (MoCA) Scale and answered the following fall-related questions: “Have you fallen in the past six months (i.e. fallen to the floor or ground unintentionally)? If so, how many times and what activities were engaged prior to the fall(s)? In general, do you have a fear of falling?”   3.2.3 Stimulation Vestibular stimulation was delivered through foam electrodes (Kendall™ 130 Foam Electrodes, USA) placed in a binaural monopolar configuration for AP and ML stimulation (See 2.1 for experimental set-up). Each electrode pair was placed on the mastoid process and over the ipsilateral acromion. Nuprep® skin prep gel was used to clean skin for better electrode contact and to reduce resistance during stimulation. The stimulation waveform was generated on a computer using MATLAB (R2017b, Mathworks, USA) and converted to an analogue signal using digital acquisition module (NI USB-6343, X Series Multifunction DAQ, National Instruments, USA). The stochastic signal was zero-mean white noise with a Gaussian distribution ranging from 0.4 to 30 Hz, which has been previously used in PD and healthy controls87,88,93. The signals were then passed through two constant current linear isolated stimulators (STMISOLA, Biopac, USA), which output the stimulation to the electrodes. All electrical stimuli were tested at an imperceptible level to blind participants to the presence or absence of stimulation.    36  3.2.4 Procedure PD participants were tested in their best clinical “on”-medication state to observe dopamine-unresponsive postural instability symptoms. Prior to testing, all participants underwent a sensory thresholding task. Electrical stimuli were delivered at 70% below individual cutaneous sensory thresholds. The mean current intensity used was 0.2 ± 0.11 mA. On average, HC received stimulation of 0.22 ± 0.11 mA, while PD received 0.18 ± 0.12 mA. For binaural bipolar stimulation, only two electrodes were stimulated with the anode placed on the left mastoid and the cathode placed on the right mastoid. For binaural monopolar design, all four electrodes were stimulated with both cathodes were placed on the mastoids and anodes placed on the ipsilateral acromion. Participants were blind to the type of GVS received throughout testing.   Participants were required to walk down a seven-metre long electronic walkway (GAITRite, Platinum S174, Sparta, NJ). Single (walking only) and dual task (serial-3 subtraction) paradigms were used. Practice trials for single and dual task walking were conducted to avoid “first trial effects”, where the first trial is typically different than subsequent trials51. Three stimulation profiles (OFF, SVS-ML and SVS-AP) were used. The order of stimulation profiles was randomized for each participant. Six tests were conducted: (1) OFF-stimulation with a single task (OFF-single), (2) SVS-ML stimulation with a single task (ML-single), (3) SVS-AP stimulation with a single task (AP-single), (4) OFF-stimulation with a dual task (OFF-dual), (5) SVS-ML stimulation with a dual task (ML-dual) and (6) SVS-AP stimulation with a dual task (AP-dual). Six trials were conducted for each test with a two-minute rest between tests. Spatial and temporal gait parameters were collected with a sampling frequency of 120 Hz. 37  3.2.5 Data analysis Ten parameters were used to assess gait, which have been previously used by Wuehr et al. (2015, 2016). Five parameters were used to characterize the mean spatiotemporal patterns: stride time, stride length, base of support (BoS), double support time percentage and swing time percentage. These parameters were computed by the GAITRite system. Five parameters were used to characterize bilateral coordination and gait variability: gait asymmetry (GA) and phase coordination index (PCI), following equations from Plotnik, Giladi and Hausdorff (2007), as well as the coefficient of variation (CV) of stride time, stride length and BoS. These parameters were calculated off-line using MATLAB.  Statistical analyses were conducted using SPSS (Version 21, IMB Corp ©, Chicago, IL). Pearson’s r correlation coefficients are calculated between the MoCA scores and the following clinical measures: total UPDRS score, UPDRS item 3.10 (gait) and PIGD subscore. Independent samples t-tests were used to compare age, MoCA scores and OFF-stimulation states between groups. Dual task costs (DTCs) were calculated (!"#$%&'()$*&'()$* ∗ 100%), which was used to determine the effect of the dual task. Independent samples t-tests were used to compare DTCs of each gait parameter between groups. Percent difference of PD was calculated (./%0101 ∗ 100%) for each parameter and expressed as a percent of HC. A three-way mixed ANOVA (mANOVA) was used to examine the effect of group (PD vs. HC), task (single vs. dual) and stimulation (OFF vs. SVS-ML vs. SVS-AP) for each parameter. An a level of 0.05 was used to indicate statistical significance. All data is reported mean ± SD unless otherwise stated.    38    Subject ID Group Sex Age (years) MoCA PD Duration (years) MDS-UPDRS: total score MDS-UPDRS: PIGD subscore G001 PD F 75 28 5 26 2 G002 PD F 62 29 9 21 1 G003 PD M 69 26 8 13 1 G004 PD F 62 29 4 11 0 G005 PD M 65 25 3 17 0 G006 HC M 63 28 - - - G007 HC F 77 27 - - - G008 HC F 69 30 - - - G009 HC M 67 28 - - - G010 HC F 73 27 - - - Table 3.1. Individual participant demographics. MoCA: Montreal Cognitive Assessment.  MDS-UPDRS: Unified Parkinson’s Disease Rating Scale (motor assessment). MDS-UPDRS PIGD subscore: sum of UPDRS items 3.9-3.12. Data not applicable to healthy control participants are represented with a dash (-).     Parkinson’s Disease  Healthy Controls  p-values Age 66.6 ± 5.50  69.8 ± 5.40  0.381 MoCA 27.4 ± 1.82  28 ± 1.22  0.557  H&Y  1.8 ± 0.84  	-  	- MDS-UPDRS:                Total                3.10                3.9-3.12  17.6 ± 6.07 0.6 ± 0.55 0.8 ± 0.84  	-	-	-  	-	-	- Table 3.2. Mean participants’ demographics and descriptive statistics. Mean and standard deviations of demographics and clinical measures each group. MoCA: Montreal Cognitive Assessment. H&Y: Hoehn & Yahr Scale, ranging from 0 (unilateral involvement only with minimal or no functional disability) to 5 (confinement to bed or wheelchair unless aided). MDS-UPDRS: Unified Parkinson’s Disease Rating Scale Part III (motor assessment). MDS-UPDRS item 3.10: gait subscore. MDS-UPDRS items 3.9-3.12: postural instability and gait disorders subscore. Data not applicable to healthy control participants are represented with a dash (-). p-values are computed using Student’s t-test for independent samples. Bolded p-values represent statistical significance (p < 0.05).  39  3.3 Results 3.3.1 Descriptive statistics  Tables 3.1 and 3.2 reports participants’ demographics and clinical measures. Pearson’s r correlation coefficients were calculated between MoCA scores and clinical UPDRS scores. There were no significant correlations between MoCA scores and (1) total UPDRS scores [r(3) = 0.177, p = 0.776], (2) gait subscores [r(3) = 0.201, p = 0.746], or (3) PIGD subscores [r(3) = 0.230, p = 0.709]. Independent t-tests were conducted to compare baseline characteristics between groups. There was no significant difference in age between PD (66.6 ± 5.50 years) and HC (69.8 ± 5.40 years); t(8) = 0.928, p = 0.381. There was also no difference in MoCA scores between groups (PD: 27.4 ± 1.82; HC: 28 ± 1.22); t(8) = 0.612, p = 0.557. No participants reported falling in the six months prior to testing or fear of falling. 3.3.2 Differences in gait patterns between HC and PD OFF-single test condition was used to compare group differences in gait patterns. Table 3.3 and Figure 3.1 summarize the results of each parameter during OFF-single. There were no significant differences between groups following independent t-tests for each of the ten gait parameters. A one-sample t-test was conducted to determine whether the percent difference across all parameters was significantly different from zero. A significant difference was found, t(9) = 2.377, p = 0.041. The most notable changes in PD compared to HC were in the variability and bilateral coordination variables. Most notably, GA in PD (5.00 ± 4.23 %) showed a 146.1 % difference compared to HC (2.033 ± 0.811 %) and PCI showed a 105.9 % difference in PD (6.57 ± 6.12 %) from HC (3.192 ± 0.983 %). Additionally, CV of stride time, stride length and BoS in PD changed by 69.5, 20.84 and 42.765 %, respectively.   40      Parkinson’s Disease  Healthy Controls  Percent Difference (%)   Mean SD  Mean SD  Stride time  1.168 0.190  1.122 0.130  4.10 Stride length  129.622 9.251  119.892 13.006  8.12 BoS  8.291 3.432  8.844 2.606  -6.25 Double support  24.76 3.471  24.370 3.514  1.60 Swing  37.38 2.018  37.720 1.836  -0.90          CV stride time  4.318 2.807  2.547 0.296  69.55 CV stride length  3.096 0.956  2.562 0.863  20.84 CV BoS  28.763 11.912  20.147 6.565  42.77 GA  5.004 4.229  2.033 0.811  146.13 PCI  6.570 6.120  3.192 0.983  105.85 Table 3.3. Gait patterns during OFF-single test for PD and HC. Mean and standard deviations of each gait parameter during OFF-single. BoS: base of support. Double support: double support time (percentage of gait cycle). CV: coefficient of variation. GA: gait asymmetry. PCI: phase coordination index. Percent difference of PD, calculated as a percent of HC.  41   Figure 3.1. Mean percent difference between PD and HC of each gait parameter during single-task walk, OFF-stimulation. Large changes are observed between PD and HC for gait variability (CV of stride time, stride length and BoS), gait asymmetry (GA) and bilateral coordination (phase coordination index; PCI). A one-sample t-test confirmed the changes were significantly different from zero, p = 0.041. The most notable changes in PD compared to HC were in the variability and bilateral coordination variables (see Table 3.3 for values).-20 020406080100120140160Stride time Stride lengthBoS Double supportSwing CV stride timeCV stride lengthCV BoS GA PCIPercent Difference (%)Percent Difference (single-task OFF-stimulation) 42     Parkinson’s Disease  Healthy Controls   Mean SD  Mean SD Stride time  9.934 5.742  6.926 6.157 Stride length  -8.626 5.347  -3.112 10.274 BoS  1.264 2.907  3.772 6.430 Double support  9.384 7.240  4.172 14.153 Swing  -3.014 2.163  -0.178 5.288        CV stride time  14.718 50.237  27.608 66.852 CV stride length  36.166 56.189  40.490 44.915 CV BoS  15.000 28.197  -0.540 24.064 GA  -23.546 36.345  4.724 93.034 PCI  25.910 54.894  16.950 49.718 Table 3.4. Dual Task Costs (%). Mean and standard deviations of dual task costs (%) for each gait parameter. BoS: base of support. Double support: double support time (percentage of gait cycle). CV: coefficient of variation. GA: gait asymmetry. PCI: phase coordination index. Percent difference of PD, calculated as a percent of HC.   3.3.3 Effect of dual task  Table 3.4 and Figure 3.2 summarize DTC results. Independent t-tests and non-parametric Mann-Whitney U tests were conducted to compare DTC of each parameter between groups. No significant differences were found. In both groups, larger DTC of gait variability and bilateral coordination parameters were observed compared to spatiotemporal parameters.  43   	Figure 3.2. Dual Task Costs (OFF-stimulation) for PD and HC. Large changes in gait variability and bilateral coordination parameters are observed in both groups during a dual task. -60-40-20020406080Stride Time Stride LengthBoS Double Swing CV stride timeCV stride lengthCV BoS GA PCIDual Task Costs (%)PD HC44  3.3.4 Effect of GVS  Three-way mANOVAs were conducted to examine the effect of group (PD vs. HC), task (single task walk vs. dual task walk) and stimulation (OFF vs. SVS-ML vs. SVS-AP) for each parameter. There was a significant effect of task for stride time (F(1,8) = 27.401,  p = 0.001), BoS (F(1,8) = 8.207,  p = 0.021), and CV stride length (F(1,8) = 5.759,  p = 0.043). Stride length increased with dual task from 1.152 ± 0.060 cm to 1.244 ± 0.068 cm. BoS increased with dual task (8.982 ± 0.935 cm) compared to single task (8.495 ± 0.930 cm). There was also an increase in CV stride length with 3.102 ± 0.317 % for single task and 3.852 ± 0.553 % for dual task. A significant group and stimulation was found for CV BoS, F(2,16) = 7.124, p = 0.006; however there were no significant simple main effects when pairwise comparisons with Bonferroni corrections were conducted.    There was a trend for decreased gait variability and bilateral coordination parameters observed in PD following stimulation in dual task walks. Figure 3.3 summarizes the effects of OFF- and ON-stimulation for each group during single and dual tasks. Both types of stimulation (SVS-ML and SVS-AP) decrease CV of stride time in PD during single task. During a dual task, SVS-AP stimulation in PD decreases CV stride time (3.257 ± 1.00 %) from OFF-stimulation (4.467 ± 2.67 %) to values close to HC OFF-dual (3.161 ± 1.50 %). Both types of stimulation (SVS-AP and SVS-ML) mildly decrease CV stride length, CV BoS and PCI in PD during dual task (see Table 4). Conversely, the effect of stimulation during single tasks are mixed and there are no evident trends for spatiotemporal parameters (i.e. stride time, stride length, BoS, double support and swing). 45   			Figure 3.3. Effect of stimulation for PD and HC using SVS-AP and SVS-ML during single and dual tasks for gait variability and bilateral coordination parameters. For each parameter, the change from OFF- to ON-stimulation is plotted for each group. HC is represented by blue stars and PD is represented by red diamonds. SVS-AP stimulation is represented by a solid line and SVS-ML is represented by a dashed line.  During a dual task, CV stride time decreases in PD following SVS-AP stimulation to levels similar to HC OFF-stimulation.  Similarly, both types of stimulation decrease CV stride length, CV base of support and phase coordination index during dual task walking. 46  3.4 Discussion Interestingly, gait impairments were observed in PD despite the mild disease severity of the sample of PD participants. This result is supported by an earlier study that found similar differences in people who were newly diagnosed with PD and not yet treated with medication 101. PD participants showed increased gait variability (CV of stride time, stride length and BoS) and decreased bilateral coordination (GA and PCI) compared to healthy, older adults. The present results confirm previously established instances of gait disturbances in PD (for reviews see: 58,63,102). In particular, the current study observed large changes in PD gait patterns compared to HC during dual tasks, especially for swing, CV of BoS, GA and PCI. These findings parallel earlier work that has also reported exacerbated gait impairments in PD during dual tasks63.  Decreased stride length is a hallmark of gait in PD 103 that was not observed in this study. One possible explanation for this discrepancy is the fact that this study tested only L-DOPA medicated PD participants, versus non-medicated participants. L-DOPA has been found to be effective for stride length, but not for temporal gait patterns (e.g. stride time or stride time variability)104. Because PD participants were tested during their best clinical “on” state, L-DOPA may have increased stride length to levels similar to those of HC participants. A second explanation for not observing decreased stride length may be due to overall differences in disease severity. Decreased stride length is most often a result of shuffling of gait, during which appropriate stride lengths cannot be generated105. Since the tested sample of PD participants had mild disease severity (mean H & Y stage = 1.8 ± 0.84), shuffling of gait symptoms likely have not yet manifested, thus explaining why stride length was unaffected.  47  Chapter 4:  Discussion Following investigation of GVS applied during both quiet stance and dynamic movement, two main findings were revealed: 1. Without stimulation, PD differed from HC in both static posturography and gait patterns.  2. The effects of stimulation varied based on group (HC vs. PD), plane of stimulation (ML vs. AP) and type of electrical signal (stochastic vs. multisine), with potential beneficial effects observed following SVS-AP stimulation in PD.  4.1 Static posturography and gait patterns reveal baseline differences between Parkinson’s disease and age-matched controls Compared to HC, PD participants showed increased amplitude of postural sway in both AP and ML directions, increased gait variability and decreased bilateral coordination. With dual task walking, both gait variability and bilateral coordination became exacerbated during dual task walking. These baseline differences between groups indicate that PIGD symptoms in PD are pathological, not age-related, deteriorations of postural control and exist even in mild PD “on”-medication. 4.2 Vestibular stimulation produces unique effects in Parkinson’s Disease during static balance and gait  This study is the first to examine the comparative effects of GVS configurations to manipulate the plane of stimulation (AP or ML) and electrical signals (stochastic or multisine). Three types of stimuli are used: SVS-ML, SVS-AP and MVS-AP. The influence of subthreshold GVS is investigated first in static balance then during gait.   48  4.2.1 Effect of GVS during static balance GVS with subthreshold stimulation showed an effect at the level of the force plate measured by shear GRF outputs. With coherence analysis, significant coherence differences above 95% confidence level were observed between ON and OFF conditions for SVS-ML and its corresponding GRF and at frequencies under 0.6 Hz. However, with subthreshold stimulation, the observed coherence values are generally much lower than suprathreshold GVS, which has been shown to have significant coherences between 0 to 20 Hz for GRF and lower leg EMG responses94,106. With stochastic stimulation, regardless of plane of stimulation (SVS-ML or SVS-AP), amplitude of sway increased in the AP direction while frequency of sway decreased in the AP direction. Differences in stimulation effects were most evident in the frequency domain. In both groups (PD and HC), SVS-AP decreased while SVS-ML and MVS-AP increased COP frequency. Overall, SVS-AP mildly reduces frequency of sway. Multisine stimulation was not used for the following gait study as it did not appear to be superior over stochastic stimulation. The results of this study agree with Pal et al. who studied PD participants “on”-medication using SVS-AP. Pal found a significant reduction in five PD participants when standing with eyes-closed on a non-compliant surface (foam) using a stimulation amplitude of 0.1 mA. As such, no significant reductions are expected at ~ 0.2 mA, which was used in the current study. Similar group differences are observed in the present study and in Pal’s study as reductions in COP are only observed in PD participants but not in HC participants. Samoudi et al. found a decrease in sway path when standing eyes-closed; however, PD participants (n = 10) were “off”-medication and thus, cannot be compared to this study where PD participants were tested “on”-medication. Compared to these two studies, the present study uses a larger sample size and longer trial durations of 60 seconds to increase reliability of COP displacement measures56. However, these 49  two studies used foam to decrease proprioceptive inputs, which was not utilized in the present study.  Differences in SVS stimulation in the AP and ML planes may be due to biomechanics of the body. ML sway relies on active control of hip muscles, where AP sway relies on passive control of ankle muscles107,108. Balance requires both hip and ankle strategies to maintain postural control; hip strategy contributes to ML sway while ankle strategy contributes to AP sway. A recent study reported that HC and PD predominantly use an ankle strategy during quiet stance109. For this reason, AP stimulation may be more effective than ML stimulation for postural stability during quiet standing.  4.2.2 Effect of GVS during gait  A trend of decreased stride time variability is observed in PD following both SVS-ML and SVS-AP in the single task paradigm. Interestingly, in the dual task walking condition, SVS-AP was capable of decreasing PD stride time variability to levels close to those of HC at baseline. Since increased stride time variability has been suggested to be a predictor of falls60–62, stimulation-induced reductions in variability may provide one potential avenue for preventative therapy. Stimulation also mildly decreases stride length variability, BoS variability and bilateral coordination (PCI) during dual tasks, providing further support for SVS-AP as a promising technique for lessening gait-related injuries in PD.  The results of this study correspond with Mulavera et al., who examined the effect of subthreshold SVS in healthy adults during walking. A stimulation amplitude of between 0.1 to 0.5 mA was suggested to improve walking stability in healthy adults. The current study used an average amplitude of 0.2 ± 0.11 mA, and similarly found reductions in stride time variability.  50  The trend of decreased variability and bilateral coordination is predominantly observed in the dual task condition and in PD participants. Because the PD participants had mild symptoms (H&Y stages I and II, Total MDS-UPDRS: 17.6 ± 6.07), it is possible that the effects of GVS are only observed when gait impairments are exacerbated with dual tasks. Moreover, gait disorders in PD have been attributed to attention.  People with PD use a “posture second strategy”, meaning that postural stability is compromised to effectively complete a dual task110. During dual tasks, sensory contributions are reduced to allow for cognitive processing111. Improvements following stimulation during dual tasks may be attributed to stochastic resonance principles, where signal detection is enhanced using low levels of noise in a non-linear system (i.e. the human nervous system). Stochastic stimulation may increase vestibular inputs to account for the decrease in sensory integration caused by a dual task. Similar reductions during dual tasks are not observed in HC, which may be due to different sensory-reweighting and compensatory responses.  4.3 Stochastic resonance in the vestibular system  In this study, the effect of stochastic stimulation is small, which may be due to the physiology of the vestibular system. Vestibular primary afferents have a tonic discharge and are spontaneously active112. While cathodal currents of vestibular stimulation increase firing rates of vestibular afferents69, this increased firing may not be as detectable in the vestibular system as other sensory receptors that are silent and activate once they reach a firing threshold. Thus, the effects of stochastic resonance are reduced.    51  4.4 Vestibular contributions during static versus dynamic balance This study observes a reduction in stride time variability in PD to values close to HC during a dual task walk. Similar reductions were not observed during quiet stance. As contribution of vestibular system varies with sensory re-weighting, the effect of GVS may also depend on the balance task. While standing balance predominantly relies on proprioceptive inputs when standing on a firm surface, it fully relies on vestibular inputs when the surface being stood on is unstable and vision is removed113. Conversely, vestibular contributions are more involved during dynamic balance tasks such as transitioning into gait initiation (Bent, McFadyen, & Inglis, 2005). This may explain why subthreshold GVS may have a larger effect during dynamic tasks compared to static tasks. 4.5 Limitations This study is limited by a relatively small sample size and large inter-subject variability. Furthermore, as various stages of PD may exhibit different sway patterns, variability in disease severity is another limitation115. Of the PD group, none of the participants showed balance impairments through clinical assessments (H&Y Stages I and II), except one participant who showed minimal difficulty walking (H&Y Stage III). Regardless of this, PD participants were still distinguishable from HC, showing increased amplitude of postural sway and altered gait patterns. Proprioceptive inputs are not removed in this study, which may limit the influence of GVS through sensory re-weighting. Finally, there is no standardized method for analyzing posturography. Mixed results between studies may be attributed to methodology (e.g. sampling frequency and trial duration) and COP parameters. There exists no standard set of parameters to assess sway because it is still unknown why sway occurs during stance.   52  4.6 Significance   The effects of GVS on balance have been predominantly studied in the healthy population. The value of this study and its findings is threefold. First, the effects of SVS-ML and SVS-AP have not been compared in standing balance and gait in both HC and PD groups, so this study provides the first comparison of these two configurations of stochastic stimulation. Second, a subthreshold multisine signal has not been previously used to study quiet standing and/or gait in people with PD, and this study demonstrates that it produces only minimal effects, particularly when compared to SVS. Finally, subthreshold SVS had not previously been tested during gait in people with PD as potential means to improve walking stability, which this study finds to indeed be a feasible strategy. This study has the potential to impact clinical treatments for PD by setting the groundwork for the development of a portable, non-pharmacological, non-surgical treatment for PD. Further, this study supports the use of GVS as a feasible technique in the PD population, since no PD participants complained of negative side-effects following stimulation and overall, the technique was well-tolerated. 4.7 Future directions  A larger randomized control trial should be conducted to determine the effects of GVS on gait. PD participants with more prominent PIGD symptoms should be tested to determine the effect of GVS on treatment-resistant symptoms. As vestibular inputs are highly involved in dynamic tasks, it is possible GVS may have a greater influence during dynamic tasks. Future studies should investigate different dynamic balance paradigms, and can include longer walkways, walking with obstacles, different types of dual tasks (motor and cognitive) and manipulations of gait speed. 53  4.8 Conclusions  This study demonstrates the feasibility of GVS as a potential method of improving balance and gait disturbances commonly seen in PD. Subthreshold SVS-AP may reduce frequency of sway during standing and stride-time variability during in single and dual task walking in PD participants. Thus, we conclude that stochastic GVS, delivered in the AP configuration, is the most promising of the three tested methods of reducing postural sway and improving gait in those with PD.   54  References 1. Hirtz, D. et al. How common are the ‘common’ neurologic disorders? Neurology 68, 326–337 (2007). 2. Massano, J. O. & Bhatia, K. P. Clinical Approach to Parkinson’s Disease: Features, Diagnosis, and Principles of Management. Cold Spring Harb. Perspect. Med. 2, (2012). 3. 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