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Exploring the Relationship between Motor and Frontoparietal Brain Networks in Upper Limb Motor
Outcome Post-Stroke
by
Timothy Ka-Hung Lam
A thesis submitted in conformity with the requirements for the degree of Master of Science
Rehabilitation Sciences Institute University of Toronto
© Copyright by Timothy Lam 2016
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Exploring the Relationship between Motor and Frontoparietal Brain
Networks in Upper Limb Motor Outcome Post-Stroke
Timothy Lam
Master of Science
Rehabilitation Sciences Institute
University of Toronto
2016
Abstract
Background: A network-approach may help understand how extensive neural changes arise
from localized damage. Given that motor and frontoparietal networks are implicated in motor
learning, these networks may also be implicated in motor re-learning post-stroke. This thesis
studies the relationship between motor and frontoparietal networks and movement post-stroke.
Methods: Twenty-seven chronic stroke participants underwent behavioural assessments and
magnetic resonance imaging. The resting state connectivity (rs-connectivity) within (intra-
network) and between (inter-network) motor and frontoparietal networks were correlated with
behavioural scores.
Results: Intra-network: i) Participants with higher rs-connectivity between the primary motor
cortex and supplementary motor area have less hand impairment and greater motor function; ii)
Participants with higher rs-connectivity between the dorsolateral prefrontal cortex and mid-
ventrolateral prefrontal cortex have less hand impairment. Inter-network: participants with higher
rs-connectivity between the motor and frontoparietal networks have greater motor function.
Conclusion: Connectivity of motor and frontoparietal networks may be a biomarker of
movement post-stroke.
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Acknowledgments
The seven-day work week, caffeine dependence, and stipend money paid in three equal
installments are all aspects that come with the territory of graduate school. Although these may
not be the most glamorous features of being a student, one cannot overlook the intangibles of
graduate training. Being given the opportunity to carry out a thesis project is a test of one’s
responsibility, critical thinking skills, and dedication. In addition to personal development, an
equally important aspect of graduate training is the recognition that research involves the
contributions from many people. Over the course of my graduate training, I have been fortunate
to meet and work with many individuals, and hope to maintain the relationships I have
established with these individuals for years to come. Although only one name is listed on the
first page of this thesis, this project has truly been a collective effort.
First and foremost, I would like to thank my supervisor, Dr. Joyce Chen, for giving me
the opportunity to complete my Master’s thesis under her mentorship. Joyce has been a source of
encouragement, guidance, and wisdom from the very beginning. One cannot possibly calculate
the amount of time and effort she has invested into training me. I simply hope that some of the
knowledge she has passed on to me will stay with me for many years to come.
I would also like to thank my program advisory committee, led by my co-supervisor, Dr.
Deirdre Dawson. Deirdre has been influential in ensuring that I always think about the relevance
of my project in a larger context. Dr. Jean Chen has been helpful in addressing any questions I
had about neuroimaging methods. And, Dr. Brian Levine has been instrumental in offering
valuable advice to improve this project.
In addition, I would like to thank Dr. Takako Fujioka and Deirdre for letting me use a
portion of their dataset for my thesis project. I am grateful for the opportunity to use this dataset
which also led me to meet and work with many great individuals. Most notably, I would like to
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thank Dr. Kie Honjo for her work in tracing the lesions for all the participants in the study. I also
appreciate Dr. Malcolm Binns for taking his time to discuss the appropriate statistics for my
project. In addition, I had the privilege to meet Dr. Donald Stuss, who kindly shared with me
some of his expertise on executive function. I am also fortunate to have met Rebecca Wright and
Adora Chiu. Both Rebecca and Adora always went the extra step to find or direct me to the
answers I requested about the study protocol. Finally, I would also like to acknowledge all of the
students who helped with data collection for the study.
In addition, I would like to thank my lab members and colleagues – Dr. Shinya Fujii,
Stephanie Cheung, Ashley Schipani, and Anuj Rastogi – for their support and advice over the
course of this thesis.
Lastly, I would like to thank my parents and two sisters (Karen and Doris) for their
endless support. Although my parents were not directly involved in this project, I am grateful for
their help in taking care of everything outside the lab, so I could devote as much time and effort
towards my research.
Although the journey beyond this thesis is still unknown to me, I am thankful for the
individuals I have met, the knowledge I have gained, and the paths I have taken over the course
of my graduate training.
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Table of Contents
Table of Contents
Acknowledgments.......................................................................................................................... iii
Table of Contents .............................................................................................................................v
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
List of Appendices ......................................................................................................................... ix
List of Abbreviations .......................................................................................................................x
Chapter 1 Introduction .....................................................................................................................1
Chapter 2 Background .....................................................................................................................4
1 Stroke: An overview of the epidemiology, pathophysiology, and recovery ...............................4
1.1 Brief history and epidemiology ...........................................................................................4
1.2 Pathophysiology ...................................................................................................................5
1.3 Symptoms and treatment ......................................................................................................6
1.4 Neurological recovery ..........................................................................................................6
2 Rehabilitation ..............................................................................................................................8
2.1 Definitions............................................................................................................................8
2.2 Upper limb motor deficits ..................................................................................................10
2.3 Movement in healthy individuals .......................................................................................10
2.3.1 Movement in individuals with stroke ....................................................................13
2.4 Frontoparietal network .......................................................................................................17
2.4.1 Frontoparietal network implicated in cognition .....................................................17
3 Neuroimaging ............................................................................................................................25
3.1 Relevance for stroke severity and rehabilitation ................................................................25
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3.2 Fundamental physics of magnetic resonance imaging .......................................................27
3.3 Functional magnetic resonance imaging ............................................................................29
3.4 Resting state functional magnetic resonance imaging .......................................................30
4 Summary ...................................................................................................................................34
Chapter 3 Objectives and Hypotheses ...........................................................................................34
Objectives ..................................................................................................................................34 1
2 Hypotheses ................................................................................................................................34
Chapter 4 Manuscript .....................................................................................................................35
Chapter 5 Discussion .....................................................................................................................54
1 Review of study findings ..........................................................................................................54
2 Inter-network connectivity ........................................................................................................55
2.1 Inter-network connectivity between motor and frontoparietal networks ...........................55
2.2 Potential confounds ............................................................................................................61
2.3 Anomalous cases ................................................................................................................62
3 Intra-network connectivity ........................................................................................................64
3.1 Frontoparietal network .......................................................................................................65
3.2 Motor network ...................................................................................................................67
4 Study strengths and limitations .................................................................................................68
4.1 Strengths ............................................................................................................................68
4.2 Limitations .........................................................................................................................70
5 Study implications .....................................................................................................................76
6 Future directions .......................................................................................................................77
Chapter 6 Conclusion .....................................................................................................................77
References ......................................................................................................................................78
Appendices ...................................................................................................................................107
vii
List of Tables
Table 4-1: Participant demographics and performance on clinical assessments……………......46
viii
List of Figures
Figure 2-1: Trajectory of recovery……………………………………………………………......7
Figure 2-2: International Classification of Functioning, Disability, and Health…………………9
Figure 2-3: Model of motor control…………………………………………………………......12
Figure 2-4: Trail Making Test performance……………………………………………….........20
Figure 2-5: Motor hierarchy model…………………………………………………………......21
Figure 2-6: Motor learning model…………………………………………………………........22
Figure 2-7: Physics of Magnetic Resonance Imaging…………………………………………..28
Figure 2-8: Resting state network templates……………………………………………………32
Figure 4-1: Seed masks for the resting state networks……………………………………….....42
Figure 4-2: Lesion masks of stroke participants in study……………………………………….47
Figure 4-3: Intra-network connectivity results………………………………………………….47
Figure 4-4: Inter-network connectivity results………………………………….……………....48
Figure 5-1: Inter-network connectivity results (non-significant) …………….………………...60
Figure 5-2: Motor-FP connectivity with ARAT (accounting for individual covariates)………..62
Figure 5-3: Motor-FP connectivity with ARAT (accounting for inter-network connectivity)….62
Figure 5-4: Anomalous cases for inter-network connectivity correlation…………….………...63
Figure 5-5: Trail Making Test proportion score distribution and lesion location summary…….71
Figure 5-6: Task-switching paradigm measuring the switch cost between successive trials…...73
Figure 5-7: Stem and leaf plots of motor assessment scores…………….……………………...75
Figure 5-8: Intra-network connectivity results (non-significant) …………….………………...75
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List of Appendices
Appendix 7-1: Peak Coordinates Averaged for Left Primary Motor Cortex (M1) Seed Based on
Studies with an Arm/Elbow or Hand/Finger Paradigm….……………………….……………107
Appendix 7-2: Peak Coordinates Averaged for Left DLPFC Seed Based on Studies with a Task-
switching Paradigm…………………………………………………………………………….109
Appendix 7-3: Peak Coordinates Averaged for Left Primary Visual Cortex (V1) Seed Based on
Studies with a Flashing Checkerboard Paradigm………………………………………………110
Appendix 7-4: Peak Coordinates Averaged for Left Dorsal Anterior Cingulate Cortex (dorsal
ACC) Seed Based on Studies with a Stroop Task.……………………….…………………….111
Appendix 7-5: Statistical equations…………………………………………………………....112
Appendix 7-6: Data table………………………………………………………………………113
x
List of Abbreviations
ACC Anterior cingulate cortex
AFNI Analysis of Functional NeuroImages
ARAT Action Research Arm Test
B0 External magnetic field
BA Brodmann Area
BET Brain Extraction Tool
BOLD Blood oxygen level-dependent 12C Carbon
CBF Cerebral blood flow
CMSA Chedoke-McMaster Stroke Assessment
CMSA-Arm Chedoke-McMaster Stroke Assessment: Stage of Arm Impairment
CMSA-Hand Chedoke-McMaster Stroke Assessment: Stage of Hand Impairment
CO-OP Cognitive Orientation to Daily Occupational Performance
CSF Cerebrospinal fluid
CST Corticospinal tract
dACC Dorsal anterior cingulate cortex
deoxyHb Deoxygenated hemoglobin
D-KEFS Delis-Kaplan Executive Function System
DLPFC Dorsolateral prefrontal cortex
FAST FMRIB Automated Segmentation Tool
FEAT FMRI Expert Analysis Tool
FLIRT FMRIB’s Linear Image Registration Tool
FMRI Functional magnetic resonance imaging
FMRIB Oxford Centre for Functional MRI of the Brain
FNIRT FMRIB’s Non-Linear Image Registration Tool
FP network Frontoparietal network
FSL FMRIB Software Library
Exec network Executive control network
GLM General Linear Model 1H Hydrogen
ICA Independent component analysis
ICF International Classification of Functioning, Disability, and Health
Intra-network connectivity Resting state connectivity within a network
Inter-network connectivity Resting state connectivity between two networks
M1 Primary motor cortex
MCFLIRT Motion Correction FMRIB’s Linear Image Registration Tool
mid-VLPFC mid-ventrolateral prefrontal cortex
MNI Montreal Neurological Institute
Motor-Exec connectivity Inter-network connectivity between the motor and executive control networks
Motor-FP connectivity Inter-network connectivity between the motor and frontoparietal networks
Motor-Visual connectivity Inter-network connectivity between the motor and visual networks
MRI Magnetic resonance imaging
oxyHb Oxygenated hemoglobin
PFC Prefrontal cortex
RETROICOR Retrospective image correction of physiologic motion effects in fMRI
RF Radiofrequency
Rs-fMRI Resting state functional magnetic resonance imaging
Rs-connectivity Resting state connectivity
RSN Resting state network
rTMS Repetitive transcranial magnetic stimulation
SAS Supervisory Attentional System
SMA Supplementary motor area
tDCS Transcranial direct-current stimulation
TMT Trail Making Test
TMT-2 Trail Making Test (condition #2: number sequencing)
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TMT-4 Trail Making Test (condition #4: number-letter switching)
TMT-ps Trail Making Test proportion score
tPA Tissue plasminogen activator
V1 Primary visual cortex
VLPFC Ventrolateral prefrontal cortex
WHO World Health Organization
WM White matter
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Chapter 1 Introduction
The human brain is highly inter-connected. With approximately eighty-six billion neurons
comprising the human brain, these neurons develop multiple connections (i.e., synapses) that
enable the communication with other neurons both nearby and remote from its origin
(Herculano-Houzel, 2009). As a result, it has been estimated that 0.15 quadrillion synaptic
connections in the cortex alone may be present in the human brain (Pakkenberg et al., 2003).
These numbers are not only impressive, but it suggests that the brain is also immensely complex.
The complexity and inter-connectedness of the brain is not only limited to the cellular level, but
is also found at the neural level (Sporns, 2011). A simple movement, such as reaching or
grasping, involves the communication between many brain regions (both motor and non-motor)
to successfully perform a task (Johansen-Berg & Matthews 2002; Braver, Reynolds, &
Donaldson, 2003). Taken together, the inter-connectedness of the human brain contributes to the
ability for individuals to successfully perform various actions in their environment.
The inter-connectedness of the brain is also demonstrated in individuals with stroke. Diaschisis is
a phenomenon in which focal damage to the brain can lead to widespread changes in regions
nearby and remote from the lesion, due to loss of connections with the injured area (Feeney &
Baron, 1986). Damage to the motor system can lead to changes in motor regions (e.g., primary
motor cortex (M1)) and non-motor regions (e.g., dorsolateral prefrontal cortex (DLPFC)) (Ward,
Brown, Thompson, & Frackowiak, 2003). To better understand the role of diaschisis in stroke
outcome, we can view the brain comprising distinct, yet inter-connected, networks. The use of
networks may help to explain some of the complexity in neural organization and may potentially
identify regions remote from the lesion that may influence motor outcome (Sporns, 2011).
A network-perspective of the brain has been applied to aspects of movement, such as learning a
novel motor sequence. In addition to the motor network (which includes brain regions such as
the M1, supplementary motor area, and premotor cortex (Biswal, Yetkin, Haughton, & Hyde,
1995)), a frontoparietal (FP) network (which includes regions such as the DLPFC, intraparietal
sulcus, and inferior parietal cortex (Dosenbach et al., 2007)), is thought to be involved in the
motor learning process (Hikosaka, Nakamura, Sakai, & Nakahara, 2002; Albert, Robertson, &
Miall, 2009; Kim, Ogawa, Lv, Schweighofer, & Imamizu, 2015). Although the FP network is
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more commonly associated with cognitive processes, such as task-switching performance
(Seeley et al., 2007; Li et al., 2015), the FP network is hypothesized to be involved in motor
learning by integrating visuospatial information about objects in the environment to help prepare
the proper movements (Hikosaka et al., 2002; Albert et al., 2009; Kim et al., 2015). Furthermore,
previous research has found that individuals with motor deficits post-stroke also show deficits in
task-switching performance (McDowd, Filion, Pohl, Richards, & Stiers, 2003; Pohl et al., 2007;
Serrien, Ivry, & Swinnen, 2007). This suggests that functions associated with the motor and FP
networks may not be as dissociable as how we traditionally view and study these regions and
networks. Given that motor recovery essentially requires stroke survivors to re-learn movements
(Krakauer, 2006), the integrity of the motor and FP networks may be similarly implicated in
motor outcome (and potentially task-switching) after stroke.
Motor and FP regions (such as the M1, premotor cortex, DLPFC, and parietal cortex) have been
found to be recruited during upper limb movement after stroke, however, it is unclear whether
the involvement of these motor and FP brain regions is associated with better (Weiller, Chollet,
Friston, Wise, & Frackowiak, 1992; Puh, Vovk, Sevsek, & Suput, 2007; Park et al., 2011;
Stewart, Dewanjee, Shariff, & Cramer, 2016) or worse (Ward et al., 2003; Dennis et al., 2011;
Yin et al., 2012) motor outcome. Only two studies (Park et al., 2011; Yin et al., 2012) to date
have examined the connectivity between motor and FP brain regions (specifically the M1 and
DLPFC) and its relationship with motor outcome. However, the findings from both studies are
also discrepant with each other. Specifically, Park and colleagues (2011) suggest higher
connectivity between the M1 and DLPFC is associated with better motor outcome. However,
Yin and colleagues (2012) report an opposite relationship – that is, higher connectivity between
the M1 and DLPFC is associated with worse motor outcome. Furthermore, both studies only
focus on the connectivity between specific brain regions, as opposed to the connectivity between
specific brain networks (i.e., connectivity between the entire motor and FP networks). This is an
important distinction as entire neural networks have been found to be altered after stroke (Wang
et al., 2014). A study of whether the entire motor and FP networks is related to motor outcome
may help in our understanding of the extensive neural changes to the motor network and the
relationship of the motor network with other neural networks that may be important for
movement, such as the FP network.
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Thus, the aim of my thesis is to use a holistic approach by examining the motor and FP networks
in relation to upper limb motor outcome and task-switching performance in stroke survivors. In
particular, I use resting state functional magnetic resonance imaging (rs-fMRI) to explore this
relationship. Rs-fMRI is a technique that allows us to examine the degree to which neural
activity – specifically, the blood oxygen level-dependent (BOLD) signal – is temporally
correlated or “connected” at rest (Biswal et al., 1995). Therefore, brain regions that are
connected with each other form a network. Importantly, the connectivity within an individual
network (intra-network connectivity) and between two different networks (inter-network
connectivity) is often influenced by pathology, such as stroke (Wang et al., 2014).
The objectives for my thesis are: 1) to determine the relationship between intra-network
connectivity of the motor network with motor and task-switching performance; 2) to determine
the relationship between intra-network connectivity of the FP network with motor and task-
switching performance; and 3) to explore the inter-network connectivity between the motor and
FP networks with motor and task switching performance.
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Chapter 2 Background
The aim of this chapter is to review the pertinent literature for my thesis project. As outlined in
the table of contents, the relevant literature has been broken down into three major sections. I
will first review our current understanding of stroke. Next, I will review the literature on
movement along with the importance of the frontoparietal (FP) network, in both healthy
individuals and those with stroke. Lastly, I will review some usages of brain imaging in a clinical
context and discuss the elementary physics that form the basis of magnetic resonance imaging
(MRI). Collectively, these three sections will help to build the rationale in studying the motor
and FP networks in relation to motor outcome following stroke.
1 Stroke: An overview of the epidemiology, pathophysiology, and recovery
In this section, I will review the epidemiology, pathophysiology, symptoms, and recovery of
stroke. This review will assist the reader in understanding the course of events prior to, during,
and after the stroke event.
1.1 Brief history and epidemiology
Hippocrates, the father of modern medicine (Grammaticos & Diamantis, 2008), was the first to
characterize stroke in patients who abruptly became ill (Pound, Bury, & Ebrahim, 1997).
Specifically, Hippocrates used the term ‘apoplexy’ (Sacco et al., 2013) which refers to the
sudden onset of disease just as if one was “struck by lightning” (Pound et al., 1997). Apoplexy
was perceived as a phenomenon until the mid-1600s when Jacob Wepfer discovered that
apoplexy was caused by massive bleeding or disruption to the blood supply in the brain (Pound
et al., 1997). It was not until 1962 when the Chest and Heart Association in London published a
document popularizing the term “stroke” which helped raise awareness across medical
professions from physicians to physiotherapists in taking a cooperative approach in stroke
treatment (Pound et al., 1997).
The World Health Organization (WHO) defines stroke as “rapidly developed clinical signs of
focal (or global) disturbance of cerebral function lasting more than 24 hours or leading to death,
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with no apparent cause other than of vascular origin” (Aho, Harmsen, Hatano, Marquardsen,
Smirnov, & Strasser, 1980). A recent publication by the American Heart Association expands
this definition of stroke to broadly include central nervous system infarction, ischemic stroke,
and intracerebral hemorrhage, among other subtypes (Sacco et al., 2013). Nonetheless, stroke is
highly prevalent worldwide with an estimate of 16 million first time strokes in 2005 (Kuklina et
al., 2012). In Canada, it is estimated that 50,000 individuals have a stroke (i.e., incidence) per
year (Hakim, Silver, & Hodgson, 1998). Furthermore, approximately 405,000 Canadians were
living with the effects of a stroke in 2013, with the prevalence expected to rise at least 62 percent
by the year 2038 (Krueger, Koot, Hall, O’Callaghan, Bayley, & Corbett, 2015). Although stroke
mortality has decreased over the past twenty years likely due to advances in health care (Feigin
et al., 2014), stroke remains a leading cause of lost productivity (as measured by disability
adjusted life years) (Kuklina et al., 2012).
1.2 Pathophysiology
The two major types of stroke are hemorrhagic and ischemic, with the latter making up 85
percent of the stroke cases (Hinkle & Guanci, 2007). Hemorrhagic stroke is caused by the
rupturing of blood vessels in the brain. As a result, the pooling blood causes damage by
compressing the surrounding brain tissue (Aronowski & Zhao, 2011).
Ischemic strokes can be caused by a thrombus (i.e., blood clot) that blocks blood flow in a
cerebral artery (Hinkle & Guanci, 2007). The thrombus is created from plaque formation on the
inner lining of the arterial wall (i.e., endothelium). Over time the plaque enlarges from
accumulation of cholesterol and platelets, among other components, circulating in the blood.
This is a condition known as atherosclerosis which is the hardening and narrowing of blood
vessels, and a precursor to ischemic stroke. Eventually, the thrombus is large enough to block
blood flow in the artery. Ischemic strokes can, alternatively, be caused by an embolus which
breaks off from a plaque and is carried through the bloodstream. Emboli are often from plaques
either from the heart or in the carotid artery of the neck. As the embolus circulates, it can be
wedged in cerebral vessels which then block blood supply. In both cases, the blockage of blood
flow leads to the deprivation of glucose and oxygen required for the brain tissue to function,
thereby causing stroke.
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1.3 Symptoms and treatment
Symptoms associated with stroke often depend on where the hemorrhage or ischemia is located
in the brain. However, the theory of diaschisis first suggested by von Monakow in the early
1900s states that areas remote from the damaged site can still be functionally affected due to loss
of connections between injured regions (Feeney & Baron, 1986). Therefore, a focal lesion can
lead to widespread changes in brain function and behaviour. Common symptoms of stroke
include: numbness or weakness on one side of the body, difficulty in speaking, blurred vision,
trouble coordinating movements, and balance problems (van der Worp & van Gijn, 2007).
Treatment for hemorrhagic stroke often requires surgical intervention to stop bleeding. For
ischemic strokes, the most common treatment involves the administration of tissue plasminogen
activator (tPA) which dissolves the thrombus or embolus to restore blood flow to brain regions
affected from the clot. In general, tPA is most effective if administered within three hours of
stroke symptom onset to improve neurologic outcome at three months (The National Institute of
Neurological Disorders and Stroke rt-PA Stroke Study Group, 1995). In the event that tPA is not
suitable for individuals who have a clot, endovascular treatment can be considered (Nogueira,
Schwamm, & Hirsch, 2009). Endovascular treatment involves the use of surgical devices to
remove the blood clot (Goyal et al., 2015) to restore blood flow in the vessel. Importantly,
endovascular treatment has been shown to be effective in treating individuals within six hours of
stroke onset, and functional outcome at three months has been found to be better than individuals
who were administered tPA (Badhiwala et al., 2015).
1.4 Neurological recovery
For the purposes of my thesis, “recovery” is defined as the improvement in ability – at the neural
and/or behavioural level – from the point of stroke onset (Murphy & Corbett, 2009). It is
important to acknowledge that others may define “recovery” as the full restoration of the same
abilities prior to stroke onset (Levin, Liebermann, Parmet, & Berman, 2009; Murphy & Corbett,
2009). Although this alternative definition is equally valid, “true” or “full” recovery – in the
strictest sense – is difficult to achieve, given that the stroke would have caused damage to brain
regions involved in highly specific behavioural abilities (Murphy & Corbett, 2009). Nonetheless,
both definitions are neither fully correct nor incorrect as it depends on the context/perspective
from which the definition is used. In general, the definition used for this thesis would likely
7
apply to a clinical context whereas the definition others may use would likely apply to a
theoretical perspective (Murphy & Corbett, 2009).
Recovery from stroke can be classified into three stages which depend on time from stroke onset.
The acute stage is defined as within one week of stroke onset, the subacute stage is defined as the
time period between one week and six months of stroke onset, and the chronic stage is often
defined as more than six months since stroke onset (Miller et al., 2010; Billinger, Mattlage,
Ashenden, Lentz, Harter, & Rippee, 2012). In general, the trajectory of recovery often follows a
logarithmic pattern whereby rapid recovery occurs most prominently during the acute and
subacute stages (Skilbeck, Wade, Hewer, & Wood, 1983; Kotila Waltimo, Niemi, Laaksonen, &
Lempinen, 1984; Langhorne, Bernhardt, & Kwakkel, 2011) before reaching a plateau during the
chronic stage (Rasquin, Lodder, & Verhey, 2005; Langhorne et al., 2011; Buma, Kwakkel, &
Ramsey, 2013) (Figure 2-1). Nonetheless, there is evidence to suggest that recovery can still
occur during the chronic stage of stroke (Kotila et al., 1984; Murphy & Corbett, 2009; Teasell et
al., 2012).
Figure 2-1: Trajectory of recovery
A general (logarithmic) trajectory of recovery for an individual with stroke over the course of twelve months. Rapid
improvements for an individual are typically seen at the acute (< 1 week from stroke onset) and subacute (≥ 1 week
to ≤ 6 months) stages of stroke recovery, before plateauing at the chronic stage (> 6 months). Image ideas adapted
from Langhorne et al. (2011).
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Neurological recovery of stroke can be examined at various levels ranging from the molecular
and cellular levels (small-scale) to the neural (large-scale) level (Murphy & Corbett, 2009). Gene
and protein expression is typically increased to promote cell growth nearby the lesion (Murphy
& Corbett, 2009). Furthermore, the increased expression of growth-related genes and proteins
leads to the growth of new neurons, synapses, and dendritic spines (Murphy & Corbett, 2009).
On a larger scale, recovery can be achieved through the reduction in edema (Inoue et al., 1980),
reperfusion of brain regions with decreased blood flow (i.e., penumbra) (Muir, Buchan, von
Kummer, Rother, & Baron, 2006), resolution of diaschisis (Feeney & Baron, 1986), and
recruitment of perilesional areas (Chollet & Weiller, 1994; Rossini & Dal Forno 2004; Cramer &
Chopp, 2000). Collectively, these mechanisms occur mostly during the early stages of stroke
recovery, which can lead to spontaneous improvements observed in behavioural abilities from
the point of stroke onset. Over time neurological recovery can involve reorganization of neural
connections to recover behaviour (Nudo, 2013), which can be enhanced with rehabilitation.
2 Rehabilitation
In this section, I will review the literature on motor abilities in healthy individuals and stroke
survivors from studies that examine behaviour and/or neural patterns. This review will assist the
reader in understanding the aim of my thesis to study the motor and frontoparietal networks in
relation to motor outcome and task-switching ability after stroke.
2.1 Definitions
The terminology used in this thesis will be based on the definitions provided by the World
Health Organization (WHO). The WHO developed the International Classification of
Functioning, Disability, and Health (ICF) to provide a framework for health care professionals to
view the individual through a holistic lens (World Health Organization, 2002; Lexell &
Brogårdh, 2015) (Figure 2-2). Specifically, the ICF considers both the health condition and the
contextual factors (i.e., environmental and personal) that can influence the body part (i.e., body
function/structure), the individual (i.e., activity), and the individual within a social context (i.e.,
participation) (World Health Organization, 2002). In particular, the WHO defines “body
function” as “the physiological aspects of body systems” and “body structure” as “the anatomical
support” for the function, such as a limb or organ (World Health Organization, 2013). Pathology,
such as stroke, can lead to “impairment” which the WHO defines as “problems in body function
9
or structure” (World Health Organization, 2002). Furthermore, “activity” is defined as “the
execution of a task or action” and individuals with stroke may have “activity limitations” which
the WHO defines as “difficulties an individual may have in executing activities” (World Health
Organization, 2002). Lastly, “participation” is defined as “the involvement in a life situation”
and an individual with stroke may experience “participation restrictions” which is defined as
“problems an individual may experience in involvement of life situations” (World Health
Organization, 2002). Taken together, the ICF views the deficit, the ability to perform tasks, and
the perceived involvement in life events equally important in the rehabilitation of stroke
survivors (World Health Organization, 2002; Lexell & Brogårdh, 2015).
Figure 2-2: International Classification of Functioning, Disability, and Health
A holistic perspective is taken to understand the individual. The health condition and the contextual factors (i.e.,
environmental and personal) can influence a specific body part (Body Function/Structure), the ability to perform
tasks (Activity), and the engagement in life situations (Participation) for an individual. Image adapted from the
World Health Organization (2002).
In this thesis, “rehabilitation” is defined as the intervention(s) that is/are used to optimize
recovery after stroke (Levin et al., 2009). In essence, “recovery” is the end-goal for an individual
and “rehabilitation” is the process that assists an individual towards their end-goal.
Rehabilitation can help to reduce impairment, activity limitations, and/or participation
restrictions. Specifically, rehabilitation that aims to reduce impairment attempts to restore the
structure and/or ability of the body part to the condition that was found before stroke onset.
Furthermore, rehabilitation that aims to reduce activity limitations is suggested to help the
individual with successful task completion. It is important to note that the primary concern in
10
“activity” is the ability to complete the task, rather than the way in which an individual
completes the task. This may often lead to individuals who would use compensatory techniques.
Specifically, “compensation” is defined as the adaptation and/or substitution of the strategies an
individual normally used before stroke onset to complete a given task (Levin et al., 2009).
Lastly, rehabilitation that aims to reduce participation restrictions attempts to enhance the
involvement of the individual in life situations.
Taken together, one can argue that good spontaneous recovery and effective rehabilitation may
allow for an individual to express (at varying degrees) aspects of impairment reduction,
functional improvement, and participation enhancement (Kwakkel, Kollen, & Lindeman, 2004).
Together, these aspects of recovery can aim to improve the quality of life in individuals with
stroke by increasing their level of independence to perform daily tasks (Young & Forster, 2007;
Lexell & Brogårdh, 2015). Thus, effective rehabilitation after stroke likely requires the
healthcare team to consider addressing not only the physical deficit itself, but additional factors
that can potentially influence the recovery of the deficit.
2.2 Upper limb motor deficits
In this thesis, “motor deficits” are defined as the reduced ability for an individual to perform
voluntary (upper limb) movements (Sathian et al., 2011). Based on this definition, motor deficits
can also be referred to as “hemiparesis”. Specifically, hemiparesis is commonly experienced by
individuals with stroke (Krakauer, 2005) and is a syndrome that includes weakness, spasticity,
loss of fractionated movements, and deficits in motor planning (Sathian et al., 2011). It is
estimated that 85 percent of stroke survivors acquire hemiparesis as a result of stroke which
affects their quality of life (Levin et al., 2009). At six months after stroke onset, 30 to 66 percent
of stroke survivors still have no function in the paretic limb and only 5 to 20 percent make a full
functional recovery (Kwakkel, Kollen, van der Grond, & Prevo, 2003). Thus, more research is
warranted to better understand motor deficits after stroke and subsequent ways to enhance motor
recovery.
2.3 Movement in healthy individuals
The “motor system” is defined in this thesis as the behavioural and neural components of motor
ability that are implicated in movement. Numerous models have been developed to depict the
11
organization of the motor system, based on the control (Wolpert & Kawato, 1998; Botvinick
2004; Haggard, 2008; Shadmehr & Krakauer, 2008; Cisek 2010; Mirabella 2014; Wong, Haith,
Krakauer, 2015) and/or learning (Hikosaka et al., 2002; Penhume & Steele, 2012) of movements.
Although the control and learning of movements can, at times, be thought of as complementary
processes, I will discuss each process separately in relation to the motor system. In this
subsection, I will review the literature on movement control in healthy individuals. The process
of learning movements will be discussed in subsection 2.4.1.2 (Motor learning).
In this thesis, “motor control” is defined as the ability for an individual to perform voluntary
movements (Shadmehr & Krakauer, 2008; Wong et al., 2015). Although various models for
motor control highlight different aspects of the motor system, it is evident that the control of
movements can be broken down into a few components or sub-processes (Wolpert & Kawato,
1998; Botvinick, 2004; Haggard, 2008; Shadmehr & Krakauer, 2008; Cisek & Kalaska, 2010;
Mirabella, 2014; Wong et al., 2015). In my thesis, I will focus on the model proposed by
Shadmehr and Krakauer (2008) since their model uses a more holistic approach to understand the
behavioural and neural components of the motor system. In addition to understanding how
people move (i.e., speed and coordination of movements) – which is often a common aspect of
many models for motor control – Shadmehr and Krakauer (2008) also address the motivation
that underlies why people even move in the first place.
Shadmehr and Krakauer (2008) propose a ‘cost-reward’ model of motor control whereby an
individual undergoes three sub-processes to perform movements [Figure 2-3]. Specifically,
individuals perform movements on the basis of maximizing the reward and minimizing the cost
of the outcome, as a consequence of moving. First, individuals predict the motor and sensory
costs and rewards in relation to the task in question. In particular, the basal ganglia (Jueptner &
Weiller, 1998; Groenewegen, 2003) are implicated in determining the costs and rewards.
Furthermore, the cerebellum helps to integrate this cost-reward information by developing a
model of the potential consequences or expectations as a result of moving (Ito, 2008; Stein,
2009).
12
Figure 2-3: Model of motor control
A simplified model of motor control adapted from Shadmehr and Krakauer (2008). In essence, the brain regions and
their associated functions are the primary focus of the model depicted in this figure. First, an individual performs a
“Cost-Reward Calculation” when observing a stimulus, with the intention to minimize the costs and maximize the
rewards for moving. The basal ganglia and cerebellum are involved in this initial process. Next, an individual
undergoes a “Selection” process whereby the expected costs and rewards are integrated with sensory information to
determine the proper action that is required to interact with the stimulus. The parietal cortex is implicated in this
selection process. Lastly, an individual performs the “Optimization” process whereby movements are fine-tuned to
determine the best performance. The primary motor cortex, premotor cortex, and supplementary motor area are all
involved in this optimization process.
In the second sub-process, Shadmehr and Krakauer (2008) suggest individuals would
then integrate the expectations from their cost-reward model with the actual sensory information
they receive from the environment. This sub-process is intended to help the individual determine
how their body should interact with the environment to achieve the task. The parietal cortex
(Buneo & Andersen, 2006) helps to combine the predicted cost-reward model with the actual
sensory information. This enables the individual to develop an understanding of what
movements are required.
In the third sub-process, Shadmehr and Krakauer (2008) suggest that individuals fine-
tune their movements for optimal performance. The premotor cortex, supplementary motor area
(SMA) (Tanji & Shima, 1994; Gerloff, Corwell, Chen, Hallett, & Cohen, 1997; Shima & Tanji,
1998; Hoshi & Tanji, 2000; 2004; Davare, Andres, Cosnard, Thonnard, & Olivier, 2006; Pastor-
Bernier, Tremblay, & Cisek, 2012), and primary motor cortex (M1) (Penfield & Boldrey, 1937;
Graziano, 2006; Chouinard & Paus, 2006) are thought to plan and execute the appropriate
actions to perform the task. Furthermore, the corticospinal tract (CST) – the major motor output
pathway (Jang, 2014; Scott, 2004) – helps transmit the motor processing that occurs at the M1 to
the limbs for movement. As movements are performed, sensory information, as a consequence of
moving, is received by the individual to trigger the next round of cost-reward considerations and
movements. Collectively, Shadmehr and Krakauer (2008) suggest that motor control involves the
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coordination of many motor processes – and a “network” of brain regions – to execute
movements successfully.
2.3.1 Movement in individuals with stroke
Damage to the motor system (specifically, the corticospinal tract and/or cortical motor regions)
can lead to hemiparesis in stroke survivors, whereby these individuals are generally aware of the
actions they want to perform, but have difficulty executing the proper movements for a task
(Sathian et al., 2011). According to the motor control model proposed by Shadmehr and
Krakauer (2008), stroke survivors with hemiparesis are not efficient with their movements.
Specifically, individuals may have trouble initiating movements, show less accuracy in their
movements, and are generally slower while moving, when compared to healthy individuals
(Sathian et al., 2011; Levin, Liebermann, Parmet, & Berman, 2015). As a result, many stroke
survivors with severe motor deficits often use more compensatory movements to assist in task
performance (Subramanian, Yamanaka, Chilingaryan, & Levin, 2010; Levin et al., 2015). Trunk
displacement and shoulder flexion often occur in conjunction when a stroke survivor performs a
pointing task (Subramanian 2010; Sathian 2011; Levin et al., 2015). Although compensatory
movements can sometimes be effective, these techniques can sometimes be inefficient or
harmful to the individual (Sathian et al., 2011).
2.3.1.1 Motor assessments
To assess the severity of motor deficits in individuals with stroke, motor assessments can be
administered. Scores on motor assessments can help to monitor recovery over time, select
appropriate interventions, and set realistic goals for rehabilitation (Lang, Bland, Bailey, Schaefer,
& Birkenmeier, 2013). Based on the ICF, motor assessments can be divided into three major
categories corresponding to body function/structure, activity, and participation (Langhorne et al.,
2011). Motor assessments relating to body function/structure measure the level of impairment,
such as the Chedoke-McMaster Stroke Assessment (CMSA) Impairment Inventory (Langhorne
et al., 2011). These assessments focus on the “movement quality”, which is defined in this thesis
as the ability for an individual to perform movements in the same manner prior to stroke onset
(Levin et al., 2009). Motor assessments relating to activity measure the level of activity
limitations when performing everyday tasks, such as the Action Research Arm Test (ARAT)
(Langhorne et al., 2011). Although the ARAT measures the level of motor activity in individuals,
14
it is important to note that studies (Lyle, 1981; van der Lee, De Groot, Beckerman, Wagenaar,
Lankhorst, & Bouter, 2001; Yozbatiran, Der-Yerghiaian, & Cramer, 2008) often refer to the
ARAT as measuring the level of “motor function”. To be consistent with the literature (and to
avoid potential misinterpretations whereby “motor activity” may refer to “motor (neural)
activity”), the ARAT will be referred in this thesis as an assessment for “motor function”.
“Motor function” is defined in this thesis as the ability for an individual to complete a motor task
(Levin et al., 2009). This should not be confused with the ICF terminology that relates “body
function” as a measure of “impairment”. The ICF appears to use “function” in the context of
‘normal’ physiological functioning (e.g., proper blood flow) of the body part (World Health
Organization, 2002), whereas the ARAT appears to use “function” in the context of task
completion involving the body part (Lyle, 1981; van der Lee et al., 2001; Yozbatiran et al.,
2008). Lastly, assessments relating to participation are often self-report measures that probe the
level of involvement in life situations, such as the Stroke Impact Scale (SIS) (Lang et al., 2013;
Langhorne et al., 2011).
For my thesis, I used motor assessments for impairment and function since both provide
fundamentally distinct, but complimentary information. In particular, a participant may exhibit
moderate motor impairment but have good motor function by using compensatory techniques
during task performance. I did not include a measure of participation for my thesis since I am
primarily interested in the neural patterns associated with the upper limb and its related abilities.
As a result, the study of participation in our stroke population is beyond the scope of this thesis,
given that participation involves many components beyond motor impairment and function (e.g.,
social factors, such as stigma and stereotypes, or environmental factors, such as the presence or
absence of accessibility devices) (World Health Organization, 2002).
I used the CMSA Impairment Inventory (to classify impairment) and ARAT (to measure
function) since both measures have good construct validity with other motor assessments, such
as the Fugl-Meyer assessment and Motricity Index, respectively (Gowland et al., 1993; Lyle,
1981; Hsieh, Hsueh, Chiang, & Lin, 1998; van der Lee et al., 2001; Yozbatiran et al., 2008). The
CMSA Impairment Inventory assesses the level of impairment in the arm, hand, shoulder,
posture, leg, and foot using a series of stages, which can help to classify which stage a stroke
survivor is at in their recovery. For the purposes of this thesis, however, I will only be using the
assessments from the CMSA Impairment Inventory for the arm and hand (which will be referred
15
to in this thesis as CMSA-Arm and CMSA-Hand, respectively) since I am primarily interested in
the impairment of the upper limb post-stroke. The CMSA-Arm and CMSA-Hand are assessed on
seven stages, from flaccid paralysis (Stage 1) to normal movement (Stage 7) (Gowland et al.,
1993). Successful performance on the CMSA is dependent on the participant meeting certain
requirements characterized by “normal” movement. Importantly, the CMSA-Arm and CMSA-
Hand have high intra-rater and inter-rater reliability (Gowland et al., 1993). The ARAT assesses
four aspects of movement: 1) grasp (e.g., lifting a wooden block); 2) grip (e.g., displacing a tube
from one area to another); 3) pinch (e.g., picking up a ball bearing); and 4) gross movement (e.g.,
touch top of head with hand) (Lyle, 1981). Successful performance on the ARAT tasks is
dependent on meeting the cutoff times for each task (van der Lee et al., 2001; Yozbatiran et al.,
2008). Participants are permitted to use compensatory techniques to assist in task performance
since the primary objective is to complete the given task (Levin et al., 2009). The ARAT has
high intra-rater and inter-rater reliability on the entire assessment (van der Lee et al., 2001) and
on the individual subscales (Yozbatiran et al., 2008). Collectively, the CMSA and ARAT
provide valuable information of the motor behaviour in individuals. However, to better
understand the motor deficit, we can also study the structural damage in the brain and the
resulting functional effects.
2.3.1.2 Structural and functional damage to brain regions implicated in movement
Damage to one or multiple brain regions, including – but not limited to – the basal ganglia,
SMA, M1, and/or CST can lead to hemiparesis in individuals with stroke (Shadmehr &
Krakauer, 2008; Sathian et al., 2011). As suggested in the motor control model proposed by
Shadmehr and Krakauer (2008), damage to one or more of these regions implicated in motor
processing can lead to problems in the execution of movements. Together, these findings suggest
a relationship between structural brain damage and motor deficits. Despite this relationship, the
structural damage can also lead to changes in brain function (i.e., neural activity), which is
important to examine given that the motor deficit after stroke (according to the ICF) involves an
understanding of the impairments in both the body (i.e., brain) structure and function.
Analysis of neural activity while individuals perform movements can help to assess whether
motor recovery is typical or atypical. Generally, stroke survivors show greater neural activity in
the secondary motor areas (i.e., premotor cortex, SMA) when performing simple movements
16
with one hand (Chollet, DiPiero, Wise, Brooks, Dolan, & Frackowiak, 1991; Cao 1998; Seitz,
Höflich, Binkofski, Tellmann, Herzog, & Freund, 1998; Calautti & Baron, 2003; Rehme,
Eickhoff, Rottschy, Fink, & Grefkes, 2012; Favre, Zeffiro, Detante, Krainik, Hommel, &
Jaillard, 2014). In the acute stage, individuals who elicit a pattern of neural activity different
from healthy individuals – that is, overactivity in the contralesional hemisphere (i.e., unaffected
hemisphere) – have better motor recovery when performing movements with the affected limb
(Marshall, Perera, Lazar, Krakauer, Constantine, & DeLaPaz, 2000; Feydy et al., 2002; Ward et
al., 2003; Rehme et al., 2012; Favre et al., 2014). In the chronic stage, however, individuals who
show a pattern of neural activity different from healthy individuals (i.e., greater neural activity in
contralesional hemisphere during movement) have worse motor recovery (Marshall et al, 2000;
Feydy et al., 2002; Ward et al., 2003; Rehme et al., 2012; Favre et al., 2014). Taken together, the
neural patterns elicited by stroke survivors allow us to not only understand how the brain
functions after injury, but it helps us recognize that similarities with healthy individuals may not
always be indicative of good recovery, since it depends on the stage of recovery.
2.3.1.3 Connectivity changes between brain regions implicated in movement
The temporal coupling of neural activity (i.e., connectivity) between brain regions implicated in
movement (e.g., M1, SMA, and premotor cortex) when an individual is not engaged in a task
(i.e., at rest) can also be used to assess motor recovery. Specifically, individuals with greater
connectivity between the left and right M1 have less motor impairment (Chen & Schlaug, 2013;
Park et al., 2011) and better motor function (Carter et al., 2010). Furthermore, increases in the
connectivity of these motor regions are associated with better recovery over time (Park et al.,
2011; Golestani, Tymchuk, Demchuk, Goodyear, & VISION-2 Study Group, 2013).
Collectively, these studies suggest that the temporal coupling of neural activity at rest between
motor regions – particularly the M1 – is also a sensitive measure to assess motor outcome.
In essence, both the behavioural and neural patterns that are observed in individuals with
hemiparesis provide complementary information that serve to better understand the motor deficit.
17
2.4 Frontoparietal network
In this thesis, a “network” is defined as a group of brain regions that are highly inter-connected
with each other (Sporns, 2011). Olaf Sporns (2011), a pioneer in network neuroscience, states
that the formation and dependence of networks is found in a variety of fields, including social
science (e.g., social networks), ecology (e.g., food webs), and computers (e.g.,
telecommunications). Importantly, the use of network metrics to study the relationship between
two or more regions can help to explain complex phenomena in these areas, and suggests that
networks can potentially be applied to understand other complex fields, such as neuroscience. In
the context of the brain, Sporns argues that a single component (whether it is a cell or specific
brain region) is unable to perform complex processes, such as movement or cognition, since each
component may be limited by the number of functions it can perform. However, it is through the
communication between many components and the formation of networks that enable the
execution of complex processes.
The frontoparietal (FP) network comprise many brain regions in the frontal and parietal regions,
such as the dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC),
intraparietal sulcus, and inferior parietal cortex (Beckmann, DeLuca, Devlin, & Smith, 2005).
The FP network is thought to be implicated in various functions (Cole, Reynolds, Power,
Repovs, Anticevic, & Braver, 2013), including certain cognition processes (e.g., attention and
working memory) (Dosenbach et al., 2007; Seeley et al., 2007) and motor learning (Hikosaka et
al., 2002). The role of the FP network in these functions will be discussed in the following
sections.
2.4.1 Frontoparietal network implicated in cognition
The FP network is thought to be involved in various cognitive processes, such as attention
(Seeley et al., 2007; Ptak 2012), perception (Knapen, Brascamp, Pearson, van Ee, & Blake,
2011), language production (Geranmayeh, Wise, Mehta, & Leech, 2014; Zhu et al., 2014),
working memory (Linden, 2007; Seeley et al., 2007; Khader, Pachur, Weber, & Jost, 2016),
decision making (Braunlich, Gomez-Lavin, & Seger, 2015; Waskom, Frank, & Wagner, 2016),
and response inhibition (Rubia et al., 2001). Performance on these cognitive processes is
modulated by the neural activity in regions that are part of the FP network (He, Snyder, Vincent,
Epstein, Shulman, & Corbetta, 2007; Carter et al., 2010; Dacosta-Aguayo et al., 2014; Li et al.,
18
2015). In particular, the DLPFC appears to be one region within the FP network that is
frequently reported as an area that mediates cognitive processes in both healthy individuals and
those with stroke (Dove et al., 2000; Rubia et al., 2001; Stuss, Bisschop, Alexander, Levine,
Katz, & Izukawa, 2001; Stuss & Levine, 2002; Moll et al., 2002; Zakzanis et al., 2005; Ravizza
& Carter, 2008; Knapen et al., 2011; Ptak, 2012; Hagen et al., 2014; Geranmayeh et al., 2014;
Kopp et al., 2015; Khader et al., 2016). The DLPFC is thought to be implicated in monitoring
performance by implementing the stimulus-response relationship (or ‘task rule’) and anticipating
future events (MacDonald, Cohen, Stenger, & Carter, 2000; Jamadar, Hughes, Fulham, Michie,
& Karayanidis, 2010). As a result, the DLPFC may be a critical region involved in the cognitive
processes associated with the FP network.
The FP network is both anatomically and functionally distinct from other brain networks
(Dosenbach et al., 2007; Seeley et al., 2007; Sridharan, Levitin, & Menon, 2008; Nomura et al.,
2010; Uddin, 2015). Although the FP network has been found to be involved in the same tasks as
the executive control (exec) network (which includes regions such as the anterior cingulate
cortex (ACC) and paracingulate cortex (Beckmann et al., 2005; Smith et al., 2009)), the FP and
exec networks are involved in different aspects of task performance (Dosenbach et al., 2007;
Seeley et al., 2007; Sridharan et al., 2008). Specifically, the FP network shows the greatest
involvement at the beginning and end of a given task (Dosenbach et al., 2007). As a result, the
FP network is hypothesized to be involved in task initiation and integrating feedback about
current performance to alter future task performance (Dosenbach et al., 2007). In contrast, the
exec network shows stable involvement over the course of the entire task which is hypothesized
to be involved in maintaining the goals and instructions for the current task (Dosenbach et al.,
2007). Taken together, the FP network is only one of many networks that may be involved in
task performance. Nonetheless, the FP network may play a critical role in integrating information
over time to improve subsequent task performance.
2.4.1.1 Task-switching
As discussed in the previous subsection, the FP network is implicated in various cognitive
functions. In addition to the aforementioned cognitive processes, many studies have found that
the FP network is involved in the process of task-switching (Dove et al., 2000; Sohn, Ursu,
Anderson, Stenger, & Carter, 2000; Dreher, Koechlin, Ali, & Grafman, 2002; Braver et al.,
19
2003; Elliot, 2003; Wylie, Javitt, & Foxe, 2004; Ruge, Brass, Koch, Rubin, Meiran, & von
Cramon, 2005; Wager, Jonides, Smith, & Nichols, 2005; Yeung, Nystrom, Aronson, & Cohen,
2006; Seeley et al., 2007; West, Langley, & Bailey, 2011; Li et al., 2015). In this thesis, I will
discuss in greater detail the process of task-switching, given that this is an important cognitive
process, especially when interacting in environments that are constantly changing (Ravizza &
Carter, 2008). Task-switching may also be related to movement after stroke, as previous research
found an association between individuals with motor deficits and task-switching performance
(McDowd et al., 2003; Pohl et al., 2007; Serrien et al., 2007).
“Task-switching” is defined as the ability to juggle between two or more tasks in succession
(Rubinstein, Meyer, & Evans, 2001). In task-switching, each task is given equal priority since a
response is made for the current task before switching to a successive task (Rubenstein et al.,
2001). Thus, each task does not temporally overlap. Task-switching involves many cognitive
processes – including attention, working memory, and inhibition (Rubinstein et al., 2001;
Monsell, 2003). Specifically, attention is required to focus on the current task at hand, working
memory is required to maintain task rules and/or progress when a task is not being performed,
and inhibition is required to prevent the previous task from interfering with current task
performance (Rubinstein et al., 2001; Monsell, 2003).
To assess task-switching in clinical settings, an individual may complete an assessment that
measures task-switching, such as the Wisconsin Card Sorting Test (Anderson, Damasio, Jones,
& Tranel, 1991) or the Trail Making Test (TMT) (Delis, Kaplan, & Kramer, 2001). In my thesis,
I use the TMT to assess task-switching due to its high sensitivity to detect deficits in brain
function (Stuss et al., 2001; Sánchez-Cubillo et al., 2009).
The TMT is included in the Delis-Kaplan Executive Function System (D-KEFS) assessment
battery (Delis et al., 2001). The D-KEFS TMT comprises five conditions. For my thesis,
however, I only used the TMT condition #2 (TMT-2) and TMT condition #4 (TMT-4) since
these two conditions are most analogous to the TMT version commonly used in other studies
(Stuss et al., 2001; Tombaugh, 2004). Both TMT-2 and TMT-4 contain an array of letters and
numbers randomly placed on a page. In TMT-2, participants connect the numbers in increasing
order while ignoring the letters (e.g., 1-2-3) [Figure 2-4]. In TMT-4, participants connect the
number and letters in alternating fashion (e.g., 1-A-2-B-3-C). Completion time and number of
20
errors are recorded. Age and education level of the individual can also influence TMT
performance (Tombaugh, 2004).
Figure 2-4: Trail Making Test Performance
(A) The Trail Making Test (TMT) condition #2 (Number Sequencing) requires the participant to connect the
numbers in increasing order while ignoring the letters. (B) The TMT condition #4 (Number-Letter Switching)
requires the participant to connect the numbers and letters in alternating fashion. Images ideas adapted from Delis et
al., (2001).
Although both conditions appear to be simplistic, many cognitive processes are required to
successfully perform the test (Gaudino, Geisler, & Squires, 1995; Crowe, 1998; Stuss et al.,
2001; Oosterman et al., 2010; Cepeda, Blackwell, & Munakata, 2013). Specifically, an
individual must search the visual array, read the stimuli on the page to comprehend as numbers
and letters, and move the pencil in their hand to connect the stimuli. Furthermore, an individual
requires working memory to hold their progress in memory for the previous number/letter as
they attend to the current number/letter. To account for some of these processes, the TMT
Proportion Score (TMT-ps) is often used to calculate task-switching performance on the TMT
since it takes into account the speed of an individual to scan the visual array and their movement
speed to connect the stimuli (Corrigan & Hinkeldey, 1987; Stuss et al., 2001) [Appendix 7-5].
Individuals with a lower TMT-ps value are suggested to have better task-switching performance.
Taken together, the ability for the TMT-ps to account for some of these processes enables us to
obtain a measure that is slightly more representative of task-switching performance.
2.4.1.2 Motor learning
The motor system in humans has long been viewed as a hierarchy. In the late 1800s, William
James (1890) suggested that movements can be characterized into two forms: low-level (i.e.,
reflexive actions) and high-level (i.e., voluntary movement). The ideas from James (1890) are
complementary to those of Hughlings Jackson (1889), who suggested that there are three levels
of motor centres that exist in the central nervous system of humans. Specifically, the spinal cord
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and cerebellum represent the lowest motor centre, the motor cortex is the intermediate motor
centre, and the prefrontal cortex (PFC) represents the highest motor centre. In essence, the
performance of more complex movements (Georgopoulos, 1991), such as learning a novel motor
sequence, would involve brain regions higher up on the motor hierarchy (e.g., PFC) relative to
less complex movements, such as reflexes, which may only involve regions that are lower on the
hierarchy (e.g., CST) [Figure 2-5]. Importantly, these hierarchies suggest that movement may
involve the communication between many brain regions (Wood & Grafman, 2003), including the
M1, premotor cortex, and at times the PFC, that play a collective role in the performance of
motor actions.
Figure 2-5: Motor hierarchy model
A motor hierarchy model adapted from Fuster (2001). Stimulus information from the environment will be
transmitted to the primary sensory cortex. This information will then be transferred to the unimodal association areas
(e.g., visual information to the primary visual cortex) and then to multimodal association areas (e.g., superior
parietal lobule) to develop a deeper meaning for the relevance of the stimulus. This information will then be
transmitted to the prefrontal cortex (PFC) to determine the appropriate actions to interact with the stimulus. Next,
the information will be transferred to the premotor cortex to plan the movements, and then to the primary motor
cortex to execute the action. When the action has been executed, the cycle will be repeated. Depending on the
complexity of the sensory or motor information, the information is not required to be transmitted to the multimodal
association areas and/or PFC. Complex actions, such as learning a finger tapping sequence, require the PFC to attach
relevance to these new movements and to store the movement patterns in memory.
The communication between motor and non-motor brain regions (e.g., PFC) is observed when
individuals learn movements, such as a complex finger tapping sequence. In this scenario, motor
and non-motor regions are required to learn, store, and develop associations (or meaning)
between movements that are otherwise random and disjointed. Initially, an individual learning a
movement sequence often has slow and inaccurate performance (Sakai, Ramnani, & Passingham,
2002; Penhume & Steele, 2012). However, individuals would quickly learn the proper
22
movements and show large improvements in task performance (Hardwick, Rottschy, Miall, &
Eickhoff, 2013). According to Hikosaka and colleagues (2002) [Figure 2-6], the FP regions,
caudate, and cerebellar association areas are involved in this initial learning stage which requires
more conscious effort to control the spatial position or accuracy of movements. In particular, the
FP network is thought to facilitate in the integration of visuospatial information of objects in the
environment that can possibly be used to learn and plan the proper movements (Hikosaka et al.,
2002; Albert et al., 2009; Kim et al., 2015).
Figure 2-6: Motor learning model
A motor learning model adapted from Hikosaka and colleagues (2002). Sensory information enters the ‘associative’
loop (blue square). In particular, the sensory input will first be received by the frontoparietal cortex. Afterwards, this
information is subsequently relayed to the caudate (basal ganglia) and cerebellum for reward prediction and error
monitoring in motor performance, respectively. The associative loop is often implicated during the initial stages of
motor learning to quickly encode the spatial sequence of movements. When the sensory input has been processed by
the associative loop, this information is then given to the supplementary motor area and premotor cortex which then
relay this information to the ‘motor’ loop (red square). This information will first be given to the motor cortex
before sending this information to the putamen (basal ganglia) and cerebellum to determine the predicted events and
timing of movements, respectively. The motor loop is often implicated during the later stages of motor learning to
slowly encode the proper sequence of movements for long term retention. Nonetheless, the motor cortex will help to
produce the motor output for task performance.
Over time, an individual who has learned the motor sequence has efficient and accurate
performance, to the point that these movements can be performed under less conscious control
(Roberston 2004; Hotermans, Peigneux, Maertens de Noordhout, Moonen, & Maquet, 2006;
Penhume & Steele, 2012). According to Hikosaka and colleagues (2002), learning continues to
occur in these later stages, but primarily involves the motor cortex, putamen, and cerebellum
(Hardwick et al., 2013). These regions are thought to fine-tune performance, such as improving
the speed or force of movements. Thus, FP regions show less recruitment when a movement
sequence is learned relative to when an individual is initially learning a movement sequence
(Toni, Krams, Turner, & Passingham, 1998; Debaere, Wenderoth, Sunaert, Van Hecke, &
23
Swinnen, 2004; Puttemans, Wenderoth, & Swinnen, 2005; Meister et al., 2005). Nonetheless, the
FP network appears to have an important role, especially in the early stages of motor learning, to
integrate visuospatial information which can be used to inform subsequent performance.
In the context of stroke, motor re-learning is important for motor recovery (Krakauer, 2006).
Simple movements, such as reaching or grasping, may be difficult for stroke survivors to
perform (Cirstea & Levin, 2000). Thus, stroke survivors often require movements to be re-
learned such that performance can eventually become more accurate, efficient and/or successful.
Rehabilitation interventions, such as constraint-induced movement therapy, robotic therapy, and
virtual reality, involve principles of motor learning that aim to enhance movement over time
through practice (Krakauer, 2006). Given the neural correlates implicated in motor learning
(Hikosaka et al, 2002; Penhume & Steele, 2012), the FP network, in addition to the motor
network (which includes regions such as the M1, SMA, and premotor cortex) (Beckmann et al.,
2005; Smith et al., 2009), may both be important in motor recovery after stroke.
2.4.1.3 Frontoparietal regions implicated in motor recovery
Movement, as reviewed in section 2.2 (Upper limb motor deficits) of this thesis, is often studied
as a process that works in isolation of other components (Serrien et al., 2007; Rosenbaum,
Chapman, Weigelt, Weiss, & van der Wel, 2012). However, studies on the motor hierarchy and
motor learning (as discussed in the previous subsection), suggest that movement (and motor
recovery) may involve motor and non-motor networks, such as the FP network.
There are certain motor and cognitive processes that appear to involve both the motor and FP
networks. For example, motor learning involves the motor network to plan and execute
movements (Bonzano, et al., 2015). However, frontoparietal regions – as discussed in the
previous subsection (Motor learning) – are also thought to be involved in motor learning,
perhaps with integrating visuospatial information to assist in proper movement selection
(Hikosaka et al., 2002; Albert et al., 2009; Kim et al., 2015). As discussed in section 2.4.1
(Frontoparietal network implicated in cognition) and subsection 2.4.1.1 (Task-switching),
cognitive processes, such as task-switching, involve the communication between frontoparietal
regions. However, the motor network may also be implicated in cognitive processes, such as
task-switching, given that motor regions are important in the execution of goal-directed actions
that arise from (internal) cognitive processing (Scott, 2003; Leisman, Moustafa, & Shafir, 2016).
24
Taken together, both the motor and FP networks may be involved in additional functions that
these networks are not traditionally associated with. Thus, a study of the motor and FP networks
in relation to movement and task-switching in stroke survivors is warranted, given prior evidence
that suggests both networks may be implicated in certain processes together.
Despite studies demonstrating that motor and FP regions are associated with motor outcome after
stroke, it is not clear whether the involvement of motor and FP regions is related to better
(Weiller et al., 1992; Puh et al., 2007; Park et al., 2011; Stewart et al., 2016) or worse (Ward et
al., 2003; Dennis et al., 2011; Yin et al., 2012) motor outcome. In particular, only two studies to
date have examined the connectivity between motor and frontoparietal regions. Park and
colleagues (2011) found that individuals with higher connectivity between the M1 and DLPFC
have better motor outcome. Conversely, Yin and colleagues (2012) report the opposite; that is,
individuals with higher connectivity between M1 with DLPFC have worse motor outcome. The
ability to clarify whether higher M1-DLPFC connectivity is associated with better or worse
motor outcome may eventually be useful in devising interventions that either up-regulate or
down-regulate the neural activity of these regions. I anticipate that higher M1-DLPFC
connectivity is associated with better motor outcome, given that many studies have shown that
higher connectivity between motor (Carter et al., 2010; Chen & Schlaug, 2013; Golestani et al.
2013) or FP (Seeley et al., 2007; Dosenbach et al., 2007; Li et al., 2015) regions is related to
better performance in both healthy individuals and those with stroke. Nonetheless, further
research is necessary to elucidate the relationship between motor and FP brain regions in motor
outcome after stroke.
Lastly, a recurrent idea in this chapter has been the importance of examining brain networks that
are involved in movement. There is increasing evidence to study the brain using a network-
approach (Beckmann et al., 2005; Bullmore & Sporns, 2009; Sporns, 2011) rather than to focus
on a specific brain area since many regions are often implicated in a single motor process
(Fuster, 2001; Hikosaka et al., 2002; Shadmehr & Krakauer, 2008). Based on the theory of
diaschisis (Feeney & Baron, 1986), damage to the motor system can lead to widespread effects
both nearby and remote from the lesion. In the context of brain networks, damage to a motor
region can lead to changes in connectivity both within the motor network and potentially
between other networks (Wang et al., 2014). However, the two previous studies (Park 2011; Yin
2012) that examined the connectivity between M1 and DLPFC focus on specific brain regions,
25
as opposed to entire motor and FP networks, associated with motor outcome after stroke. As a
result, it is important to explore whether changes involving the entire motor and FP networks are
also related to movement following stroke. This information can provide insight on the potential
effects a focal lesion may have on entire networks.
3 Neuroimaging
In this section, I will review the relevance of neuroimaging in stroke recovery and the physics
principles that contribute towards the formation of structural and functional brain images. I will
focus on magnetic resonance imaging (MRI) since MRI is used in my thesis to study the neural
basis of motor recovery after stroke. This review will assist the reader in understanding the
concepts that underlie the imaging and analysis techniques used in my thesis that involve resting
state functional MRI.
3.1 Relevance for stroke severity and rehabilitation
Clinical assessments are important for rehabilitation to assess the severity of deficits and to
monitor recovery over time in individuals with stroke. Although clinical assessments inform us
about behaviour, it is difficult to gain a good understanding of the neural events that underlie
behaviour. The structure and/or function of the brain as a result of stroke can, in part, help us to
understand why an individual produces certain behaviours that a clinician measures and/or
observes in the clinic. Therefore, we can use this information in determining whether certain
behaviours are typical during recovery or a cause for concern. Collectively, the use of
neuroimaging can be a valuable adjunct to clinical assessments in the understanding of specific
deficits observed in the clinic.
The information one gains from neuroimaging can, at times, be more sensitive than clinical
measures in assessing stroke severity (Burke & Cramer, 2013). For example, damage to the CST
– as discussed in section 2.3 (Movement in healthy individuals) – may lead to severe motor
deficits (Zhu, Lindenberg, Alexander, & Schlaug, 2010). The ability to determine the degree of
CST damage can be important when two individuals have different degrees of CST damage, yet
both present with the same scores on clinical assessments (see Figure 3 in Zhu et al., 2010 for
example). A measure of CST damage can help assess the degree of motor impairment and
predict the potential for an individual to regain their motor abilities. This is especially important
26
during the acute and subacute stages when behaviour may not be as predictive of future ability
since the recovery trajectory is less stable (Pineiro et al., 2000; Burke et al., 2014; Feng et al.,
2015). In particular, the presence of edema (Inoue et al., 1980), reduced blood flow to the
penumbra (Muir et al., 2006), and diaschisis (Feeney & Baron, 1986), among other events that
occur during the acute and subacute stages of stroke recovery, can influence neural function such
that behaviour observed at these time points may not be fully representative of the actual (long-
term) impairment from the stroke (Lo, 1986; Nudo, Plautz, & Frost, 2001). Therefore, the use of
neuroimaging measures, such as CST damage, can help to provide more information on the state
of the deficit and recovery potential particularly for individuals with severe damage to the CST
(and perhaps those who exceed a certain threshold for CST damage that may indicate a ‘point of
no return’ for neural function) (Burke Quinlan et al., 2015; Feng et al., 2015). Taken together,
neuroimaging measures provide distinct, but complementary, information that can be used with
behavioural assessments to better understand aspects of the deficit and recovery.
In this thesis, a “biomarker” is a measure that provides information on the state of recovery in an
individual (Ward, 2015). Biomarkers are becoming increasingly important to assess recovery
over single or multiple time points (i.e., monitoring) (Ward, 2015). Furthermore, biomarkers are
used to determine the likelihood for an individual to recover (i.e., prediction) or show a
significant response to certain interventions for rehabilitation (i.e., treatment efficacy) (Stinear,
2010; Burke & Cramer, 2013; Zheng et al., 2016). In essence, biomarkers are useful since they
provide ‘clues’ on the current or future state of recovery, which clinicians can consider when
assessing the severity of the deficit and deciding on the course of rehabilitation for an individual.
In the context of neuroimaging, it is important that biomarkers for the structural and/or
functional integrity of the brain correlate with changes in behaviour, otherwise we cannot
appreciate its significance in the context of stroke recovery. The temporal coupling of neural
activity at rest is thought to be a useful biomarker for stroke recovery since it correlates with
behaviour after stroke, such as motor deficits (Chen & Schlaug, 2013; Carter et al., 2010; Park et
al., 2011) and cognitive performance (Carter et al., 2010; Dacosta-Aguayo et al., 2014).
Furthermore, changes in the temporal coupling of neural activity at rest, throughout the different
stages of recovery (Park et al., 2011) – and even as early as the first few hours of stroke onset
(Golestani et al., 2013) – can be used to predict recovery at later time points. This is especially
important for clinicians given that clinical decisions, such as treatment plans, are often required
27
in the early stages of recovery. Previous research (Arsava 2012; Burke & Cramer, 2013; Burke
Quinlan et al., 2015; Rehme, Volz, Feis, Eickhoff, Fink, & Grefkes, 2015; Wu et al., 2015) has
shown that the use of neuroimaging biomarkers – including CST damage and temporal coupling
of neural activity – in conjunction with clinical assessments can better predict motor recovery
than the use of clinical assessments alone.
Lastly, neuroimaging can be used to study the neural correlates and/or mechanisms of recovery.
This information can be applied to the development of interventions that target specific brain
structures or processes to enhance recovery (Amadi, Ilie, Johansen-Berg, & Stagg, 2013; Ward,
2015). In particular, neural correlates thought to be important for recovery can be targets of brain
stimulation techniques. Brain stimulation applied to regions implicated in motor or cognitive
recovery (e.g., premotor cortex, DLPFC) can alter (or enhance) the neural activity of these
regions to possibly improve motor and cognitive performance, respectively, after stroke (Luber
& Lisanby 2014; Chen & Schlaug, 2016). In essence, neuroimaging offers important information
for stroke recovery, yet more research is necessary to further appreciate its relevance in clinical
settings.
3.2 Fundamental physics of magnetic resonance imaging
Magnetic resonance imaging (MRI) applies principles of nuclear magnetic resonance to exploit
the magnetic properties of subatomic particles found in the body. Subatomic particles, like
hydrogen (1H) or carbon (
12C), contain varying numbers of protons, neutrons, and electrons. In
particular, subatomic particles with an odd number of protons or neutrons, such as 1H, resemble
tiny bar magnets which exhibit the property of spin (Logothetis, 2002). A large majority of MRI
is done by exploiting the magnetic properties of hydrogen (i.e., a single proton) since it is highly
abundant in the body in the form of water (Plewes & Kucharczyk, 2012).
In the absence of an external magnetic field (B0), protons are randomly oriented. When an
external magnetic field (B0) is applied, a proportion of the protons will align in the direction of
the magnetic field (i.e., z-axis). In particular, only a small percentage of the total number of
protons in the body subjected to B0 align directly with the direction of the magnetic field. It is
estimated that only 1 in 100,000 protons align with B0 (at 1.5 Tesla) and thus contribute to the
NMR signal (Plewes & Kuckarczyk, 2012). Afterwards, a radiofrequency (RF) pulse is applied
to one slice of the brain to tip the protons in the transverse (x-y) plane (Logothetis, 2002). This
28
causes the protons in the slice to be excited and no longer in alignment with each other.
Following excitation, the protons return from the transverse plane to the direction of the external
magnetic field. Protons will vary in their rate of return to the external magnetic field which
causes protons to release energy at different frequencies (or “signals”) detected by RF receiver
coils (Plewes & Kucharczyk, 2012). Importantly, the RF receiver coils detect the collective (or
overall) signal emitted by protons, which is then recorded as a single data point in a matrix,
referred to as “k-space” (Logothetis, 2002). This process is repeated multiple times to obtain data
points for one row of the k-space. Once a row is complete, the process is repeated by applying a
RF pulse to a subsequent slice of the brain such that data points can be recorded for the next row
of the k-space. This is repeated until the entire k-space is populated with data points. Figure 2-7
summarizes this process.
The information in k-space encodes the spatial information of these protons in the brain.
However, to view this information, the raw data in k-space is transformed to create a map of the
proton signals that resembles an image of the brain (Logothetis, 2002). Thus, different tissues
(e.g., grey matter and white matter) can be depicted in an MR image due to differing amounts of
water content – and hence, differing proton densities – between tissues (Plewes & Kucharczyk,
2012).
Figure 2-7: Physics of Magnetic Resonance Imaging
A simplified schematic of some fundamental steps to generate a magnetic resonance image are depicted. Black
arrows represent hydrogen atoms (i.e., protons) for a single brain slice. (A) Protons are randomly oriented in the
29
absence of an external magnetic field. (B) Presence of an external magnetic field (B0) causes the protons to align in
the direction of B0. A radiofrequency (RF) pulse is then applied. (C) As a result of the RF pulse being applied, the
protons (originally in the z-axis) are tipped to the transverse (x-y) axis. This causes the protons to be excited. (D)
Protons are no longer in alignment with each other. Thus, protons will release energy at different frequencies as they
return to the z-axis. The different frequencies are picked up by a RF receiver coil which records the spatial
information of the protons in k-space. This process is repeated to obtain the rest of the data points in that row (or
“slice”) in k-space. Once a row has been complete, the process is repeated for the next brain slice. The data in k-
space is necessary to be transformed such that the proton signals resemble an image of the brain.
3.3 Functional magnetic resonance imaging
Functional magnetic resonance imaging (fMRI) is an imaging technique that indirectly measures
neural activity. Using the principles of MRI, protons will align in the direction of an external
magnetic field (B0). In fMRI, neural activity is indirectly measured based on changes in the
magnetic properties of blood. Specifically, hemoglobin in blood exhibits different magnetic
properties depending on whether it is oxygenated or deoxygenated (Ogawa, Lee, Kay, & Tank,
1990). When hemoglobin is oxygenated (oxyHb), it is weakly magnetic. Conversely, when
hemoglobin is deoxygenated (deoxyHb), it is highly magnetic. Importantly, the change in
concentrations between oxyHb and deoxyHb can distort the surrounding magnetic field. As a
result, the nearby proton spins become out of phase with each other (Brown, Perthen, Liu, &
Buxton, 2007).
When an individual is performing a task, the cerebral blood flow (CBF) increases to supply more
glucose (i.e., energy) to regions involved in task performance. The increase in CBF also changes
the relative concentrations of oxyHb to deoxyHb. In particular, regions involved in the task also
consume more oxygen. To accommodate the needs of these areas, the oxygen from oxyHb is
removed to supply these regions. Therefore, it is expected that an increase in deoxyHb
concentrations will follow and distort the proton signals in the blood and in tissues surrounding
the blood vessels, due to the strong magnetic properties exhibited by deoxyHb (Ogawa et al.,
1990). However, this hypothesis is not observed as individuals show an increase in oxyHb
concentration much greater than the deoxyHb concentration in regions involved in the task. It is
thought that this phenomenon results from the oxyHb level supplied by the CBF being much
greater than what is required by the brain regions involved in the task (Logothetis, 2002). Thus,
regions with higher concentration of oxyHb relative to deoxyHb will appear bright since there is
less deoxyHb (compared to baseline, prior to task performance) to distort the surrounding
magnetic field. This change in magnetic field (or “signal intensity”) is recorded as the blood
30
oxygen level-dependent (BOLD) signal (Ogawa et al., 1990; Logothetis, 2002). In essence, the
BOLD signal is an indirect measure for neural activity since it involves changes in blood flow
that is thought to result from changes in neural activity when engaged in a task (Brown et al.,
2007).
3.4 Resting state functional magnetic resonance imaging
The brain consumes one-fifth of the body’s total energy while only at rest (i.e., when not
engaged in a task), yet energy consumption of the brain increases by less than five percent during
task performance (Fox & Raichle, 2007). Thus, it may also be of scientific relevance to study the
neural patterns when the brain is at rest since considerable energy demands are required even
when the brain is not engaged in a task. Resting state functional magnetic resonance imaging (rs-
fMRI) is a novel imaging modality that measures low frequency (0.1-0.01 Hz) BOLD signal
fluctuations at rest – that is, when individuals lie in the MRI scanner but do not engage in tasks
(Fox & Raichle, 2007). These spontaneous fluctuations are not noise but represent synchronized
BOLD signals.
In rs-fMRI, brain regions in anatomically distinct locations can exhibit synchronized BOLD
signals. Biswal and colleagues (1995) demonstrated the BOLD signal in the left motor cortex is
temporally correlated with the BOLD signal in the right motor cortex when participants were not
performing any task. Brain regions that are temporally correlated (or “connected”) – that is, they
exhibit similar BOLD signal time courses – form what are known as resting state networks
(RSNs). RSNs comprise brain regions that are involved in the processing of specific
tasks/abilities (Fox & Raichle, 2007). For the purposes of this thesis, the RSNs that I refer to are
based on the networks defined by the Oxford Centre for Functional MRI of the Brain (FMRIB)
group (Beckmann et al., 2005; Damoiseaux et al., 2006; Smith et al., 2009). For example, the
motor network is a RSN that includes the M1, premotor cortex, and SMA (Beckmann et al.,
2005) [Figure 2-8]. These brain regions comprising the motor network are the same brain
regions that process movement, such as finger tapping. Similarly, the frontoparietal (FP) network
is a RSN that includes the DLPFC, VLPFC, and inferior parietal cortex (Beckmann et al., 2005).
These brain regions comprising the FP network are the same brain regions involved in cognitive
processing, such as task-switching (Damoiseaux et al., 2006).
31
The ability to study brain networks using current imaging modalities, such as rs-fMRI, has, in
part, enabled us to further understand the phenomenon of diaschisis (discussed in subsection 1.3
(Symptoms and treatment) (Carrera & Tononi, 2014). Diaschisis and neural networks are
distinct, yet related, concepts. A distinction between diaschisis and neural networks is the type of
neural changes (i.e., plasticity) that is represented by each concept. Specifically, diaschisis
primarily represents the plasticity as a result of brain damage (Carrera & Tononi, 2014). In
contrast, the connectivity of neural networks not only represents the plasticity as the result of
brain damage (Golestani et al., 2013), but may also represent plasticity as a result of transient
recovery (Park et al., 2011; Golestani et al., 2013), and/or the effect of rehabilitation (Fan et al.,
2015; Chen & Schlaug, 2016). Despite this distinction, diaschisis and brain networks are also
related since both concepts suggest the importance of neural connections and the consequences
that result from damage to these connections. Although the effect of diaschisis is most prominent
during the acute and subacute stages of stroke recovery, permanent changes in the neural activity
of remote areas in the brain can still be observed if diaschisis is not fully resolved by the chronic
stage (Feeney & Baron, 1986; Carrera & Tononi, 2014). Similarly, neural networks in chronic
stroke survivors still show differences in connectivity (often lower connectivity) relative to
healthy individuals (Park et al., 2011; Urbin, Hong, Lang, & Carter, 2014; Wang et al., 2014).
Taken together, the similarities and differences between the two concepts enable us to appreciate
that the study of neural networks has, in part, evolved from the initial concept of diaschisis.
Importantly, the same RSNs (e.g., motor, FP) can be delineated across different populations
(Beckmann et al., 2005; Damoiseaux et al., 2006; Fox & Raichle, 2007; Smith et al., 2009)
which suggests that this phenomenon may represent an intrinsic property of human
neurophysiology (Fox & Raichle, 2007). Furthermore, many studies have shown that RSNs are
highly reproducible and the metrics to study the connectivity of these RSNs show high reliability
over the course of months (Shehzad et al., 2009; Wisner, Atluri, Lim, & MacDonald, 2013) to
years (Guo et al., 2012; Choe et al., 2015). Although RSNs comprise brain regions that are found
in anatomically distinct locations, these regions show high resting state connectivity (rs-
connectivity) between each other. Given that RSNs resemble the neural patterns observed when
an individual performs a task, RSNs may be useful to predict the neural response and/or
behaviour in relation to a given task (Fox & Raichle, 2007; van den Heuvel & Hulshoff Pol,
2010). Importantly, the rs-connectivity of motor and FP networks correlate with motor and
32
cognitive performance, respectively, in individuals with stroke (Fox & Raichle, 2007; Carter et
al., 2010; Park et al., 2011; Chen & Schlaug, 2013). This suggests that RSNs may provide
information on the state of recovery following stroke. Taken together, an analysis of the motor
and FP networks may help to better understand the relationship between these networks and
motor outcome after stroke.
Figure 2-8: Resting state network templates
Templates (in yellow) of the (A) motor network, (B) frontoparietal network, (C) visual network, and (D) executive
control network overlaid on a standard Montreal Neurological Institute (MNI) brain template.
Various methods are available to delineate RSNs from rs-fMRI data (van den Heuvel & Hulshoff
Pol, 2010). One approach involves “seed-based connectivity”. A seed (i.e., region-of-interest,
ROI) comprises a group of voxels (i.e., three-dimensional pixels that form the MR image) whose
location in the brain is chosen based on a priori assumptions for its relevance in the research
question of interest (van den Heuvel & Hulshoff Pol, 2010). Once the seed is defined, we
determine other brain regions whose BOLD signals are temporally correlated with it (Fox &
Raichle, 2007). This is a hypothesis-driven approach since the seed – which is deduced from
prior literature and/or experiments – provide a ‘starting point’ to guide our interpretations of the
regions that are connected with the seed. Another approach involves “independent component
analysis” (ICA). A component comprises regions that have temporally correlated BOLD signal.
Furthermore, components are said to be “independent” of each other since the regions that form
one component are anatomically different from the regions that form another component
(Beckmann et al., 2005). This is a data-driven approach since the rs-fMRI data set is analyzed as
a whole to determine distinct components that may be related to the question of interest
(Beckmann et al., 2005; Smith et al., 2009). However, it can sometimes be difficult to interpret
ICA results since we do not have a well-defined ‘starting point’ (or hypothesis) to guide our
understanding of these results. For my thesis, I use a seed-based connectivity approach given that
I have taken a hypothesis-based approach to explore the relationship between the motor and FP
33
networks and motor outcome. Nonetheless, a data-driven approach, such as ICA, would likely
yield similar findings as the seed-based connectivity approach.
When RSNs are delineated from the rs-fMRI data, there are various methods that we can use to
further analyze the RSNs. In my thesis, I examine the connectivity between brain regions within
a RSN, such as the connectivity between the left and right M1 (both of which are part of the
motor network) (Chen & Schlaug, 2013; Carter et al., 2010; Park et al., 2011). I refer to this
analysis as “intra-network connectivity”. A second analysis technique I use is to explore the
connectivity between two separate RSNs, such as the connectivity between the motor and FP
networks (Chen et al., 2014; Onu, Badea, Roceanu, Tivarus, & Bajenaru, 2015; Wang, Liu,
Shen, Li, & Hu, 2015). I refer to this analysis as “inter-network connectivity”.
Both intra-network and inter-network connectivity analyses may be important to perform since
more recognition is starting to be placed on the fact that neural processes involve brain networks.
Traditionally, the brain has been studied using lesion patients which lead to the view that the
brain comprises regions with specialized centres (Sporns, 2011). However, this view cannot
account for the ability of the brain to produce complex patterns of behaviour, such as voluntary
movements and executive function, which involve the coordination of several brain regions
(Sporns, 2011). With the advancement in technology, such as computers and brain imaging, it is
much more feasible for neuroscientists nowadays to study how certain regions (or specialized
centres identified from lesion analyses) interact with each other (Sporns, 2011) to guide complex
behaviour, such as movement or cognitive processes (Sohn et al., 2000; Johansen-Berg &
Matthews 2002; Braver et al., 2005). Despite the emerging view to study networks and the
connections within and between these networks, previous rs-fMRI studies (Park et al., 2011; Yin
et al., 2012) that found a relationship between the M1 and DLPFC and motor outcome focused
on the connectivity between specific brain regions, as opposed to entire networks. Therefore, to
develop a deeper understanding of the brain and the changes in brain function after stroke, it is
important that we also explore the brain through a network perspective (Sporns, 2011). Taken
together, both of these analysis techniques can provide distinct, yet complementary, information
about RSNs.
34
4 Summary
The human brain is highly inter-connected, both structurally and functionally (Sporns, 2011;
Mišić & Sporns, 2016). In the case of stroke, the theory of diaschisis (Feeney & Baron, 1986)
states that focal damage in the brain can lead to changes both nearby and remote from the lesion.
Consistent with this theory, the use of network analysis may, in part, help to identify regions (or
even entire networks) that may function together, despite being in anatomically distinct locations
(Mišić & Sporns, 2016). Thus, the application of networks can potentially aid in our
understanding of the brain and its response to injury.
Motor deficits are common and persistent after stroke. Individuals with hemiparesis have
difficulty executing movements, as actions are often slow and inaccurate. Lesions to the primary
motor cortex, premotor cortex, and/or corticospinal tract (among other areas) may subsequently
lead to deficits in the planning and execution of our movements (Shadmehr & Krakauer, 2008;
Sathian et al., 2011). Importantly, individuals with motor deficits may not only show problems in
motor behaviour, but may also show deficits in other processes as well, such as task-switching
(McDowd et al., 2003; Pohl et al., 2007; Serrien et al., 2007). This suggests that the brain is
highly inter-connected, and damage to movement may lead to other processes (e.g., task-
switching) traditionally studied independently from movement (Serrien et al., 2007), also being
compromised.
Movement in healthy individuals and those with stroke involves both motor and non-motor brain
regions (Fuster 2001; Wang et al., 2014). A motor learning model by Hikosaka and colleagues
(2002) suggests that motor and frontoparietal (FP) regions are involved in the early stages of
learning a novel/complex sequence of movements. In particular, the FP regions may help provide
feedback on movement accuracy and successful task completion which can be used to modify
future motor performance (Albert et al., 2009; Kim et al., 2015). Given that motor learning
principles are often applied in interventions for motor recovery (e.g., constraint-induced
movement therapy or robotic therapy) (Krakauer, 2006), motor and FP regions may be
implicated in motor outcome as well. To date, only two studies (Park et al., 2011; Yin et al.,
2012) have shown that the connectivity between motor and FP regions (e.g., primary motor
cortex (M1) and dorsolateral prefrontal cortex (DLPFC), respectively) is related to motor
outcome after stroke. However, it is unclear whether the M1-DLPFC connectivity is associated
35
with better (Park et al., 2011) or worse (Yin et al., 2012) motor outcome. Furthermore, both
studies have only examined the connectivity between specific brain regions, as opposed to the
entire motor and FP networks, in relation to motor outcome. It is important to explore the motor-
FP relationship at the network level as well, given that many processes, such as movement,
involve the communication between multiple brain regions (Beckmann et al., 2005; Bullmore &
Sporns, 2009; Sporns, 2011).
Thus, the aim of my thesis is to better understand this brain-behaviour relationship by examining
brain networks (specifically, the motor and FP networks) in relation to motor outcome and task-
switching ability after stroke. To address this aim, I use rs-fMRI to examine the spontaneous
BOLD signal fluctuations at rest in regions that are anatomically distinct from each other.
Importantly, resting state fMRI analyses enable us to study resting state networks (RSNs), which
comprise brain regions whose BOLD signal are temporally correlated with each other. These
RSNs are thought to be important since they involve the same brain regions that are required
during the performance of certain tasks or behaviours. To study the motor and FP networks in
relation to motor outcome after stroke, I will analyze the resting state connectivity within the
motor and FP networks individually (i.e., intra-network connectivity) and between the motor and
FP networks (i.e., inter-network connectivity).
34
Chapter 3 Objectives and Hypotheses
Objectives 1
1) To determine the relationship between intra-network connectivity of the motor network with
motor impairment, motor function, and task-switching performance.
2) To determine the relationship between intra-network connectivity of the frontoparietal
network with motor impairment, motor function, and task-switching performance.
3) To determine the inter-network connectivity between the motor and frontoparietal networks
with motor impairment, motor function, and task-switching performance.
2 Hypotheses
1) Participants with higher intra-network connectivity of the motor network will have: a) less
motor impairment; b) greater motor function; and c) better task-switching performance than
those with lower intra-network connectivity of the motor network.
2) Participants with higher intra-network connectivity of the frontoparietal network will have: a)
less motor impairment; b) greater motor function; and c) better task-switching performance than
those with lower intra-network connectivity of the frontoparietal network.
3) Participants with higher inter-network connectivity between the motor and frontoparietal
networks will have: a) less motor impairment; b) greater motor function; and c) better task-
switching performance than those with lower inter-network connectivity between the motor and
frontoparietal networks.
35
Chapter 4 Manuscript
The manuscript is written in accordance with the submission guidelines for the journal Stroke.
Neural Coupling between Motor and Frontoparietal Networks Positively Correlates
with Motor Ability in Chronic Stroke Individuals Timothy K. Lam, HBSc; Deirdre R. Dawson, PhD; Kie Honjo, MD, PhD; Bernhard Ross, PhD; Malcolm
A. Binns, PhD; Donald T. Stuss, PhD; Sandra E. Black, MD; J. Jean Chen, PhD; Brian T. Levine, PhD;
Takako Fujioka, PhD; Joyce L. Chen, PhD
From the Heart and Stroke Foundation Canadian Partnership for Stroke Recovery (T.K.L., D.R.D., K.H., S.E.B.,
B.T.L., J.L.C.), Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute (T.K.L., K.H., S.E.B.,
J.L.C.), Rehabilitation Sciences Institute (T.K.L., D.R.D., J.L.C.), Rotman Research Institute, Baycrest Centre
(T.K.L., D.R.D., B.R., M.A.B., D.T.S., S.E.B., J.J.C., B.T.L., T.F.), Department of Occupational Science and
Occupational Therapy (D.R.D.), Department of Medical Biophysics (B.R., J.J.C.), Dalla Lana School of Public
Health (M.A.B.), Department of Psychology (D.T.S., B.T.L.), Department of Neurology (D.T.S., S.E.B., B.T.L),
and Department of Physical Therapy (J.L.C.), University of Toronto, Toronto, ON, Canada; Center for Computer
Research in Music and Acoustics, Department of Music, Stanford Neuroscience Institute (T.F.), Stanford University,
Stanford, CA, USA.
Correspondence to Joyce L. Chen, PhD, Sunnybrook Research Institute, 2075 Bayview Avenue,
Room M6-176, Toronto, ON, M4N 3M5, Canada, (T) 416-480-6100 x85410, (F) 416-480-4223,
E-mail: [email protected]
36
Abstract
Background and Purpose– Motor recovery is a process that involves the re-learning of
movements. Motor learning involves brain regions such as motor and frontoparietal cortex. The
integrity of these regions may thus be important for the recovery of movements after stroke.
Importantly, focal damage to one area of the brain can affect neural functioning within brain
networks implicated in the damage, and between distinct brain networks. Thus, the aim of this
study is to investigate how resting state connectivity within and between motor and
frontoparietal networks are affected post-stroke.
Methods– Twenty-seven chronic stroke participants underwent behavioral assessments and
magnetic resonance imaging. We implemented a seed-based resting state connectivity (rs-
connectivity) approach to define the motor (seed=primary motor cortex (M1)) and frontoparietal
(seed=dorsolateral prefrontal cortex (DLFPC)) networks. We analyzed the rs-connectivity within
each network (intra-network connectivity) and between both networks (inter-network
connectivity). We then performed correlations between: a) intra-network connectivity and scores
on motor and task-switching assessments; b) inter-network connectivity and scores on motor and
task-switching assessments.
Results– A) Intra-network: i) Participants with high rs-connectivity between M1 and
supplementary motor area (SMA) have less hand impairment and greater motor function than
those with low M1-SMA rs-connectivity; ii) Participants with high rs-connectivity between
DLPFC and mid-ventrolateral prefrontal cortex (mid-VLPFC) have less hand impairment than
those with low DLPFC-mid-VLPFC rs-connectivity. B) Inter-network: Participants with high rs-
connectivity between motor and frontoparietal networks have greater motor function than those
with lower motor-frontoparietal network rs-connectivity.
Conclusions– Connectivity within and between the motor and frontoparietal networks relate to
motor outcome post-stroke. We suggest that coupling between the motor and frontoparietal
networks may represent how well movements are re-learned in stroke survivors.
Key Words: resting state fMRI, motor impairment, motor function, motor network,
frontoparietal network, task-switching, chronic stroke
37
Introduction
The human brain is immensely complex, and is highly inter-connected on both anatomical and
functional levels (Mišić & Sporns, 2016). Yet, researchers have traditionally studied the
dysfunction of the motor system and thus motor behavior post-stroke in isolation (Serrien, Ivry,
& Swinnen, 2007). Growing evidence suggests that the execution of actions may also involve
other brain regions, not directly involved in movement (Ward, Brown, Thompson, &
Frackowiak, 2003; Wang et al., 2014). Thus, further research is warranted to better understand
how motor and non-motor brain regions may work together to influence motor behavior and thus
outcome from stroke. This approach may be especially important because focal damage from
stroke can lead to diaschisis, whereby areas remote from the damaged site are also affected due
to a loss of neural input or connection (Feeney & Baron, 1986). To better understand the
phenomenon of diaschisis, there is growing interest in studying how brain connectivity is
affected post-stroke (Carter, Shulman, & Corbetta, 2012). A focal lesion may have widespread
effect over the entire brain, affecting brain functioning in more than one domain (Serrien et al.,
2007; Carter et al., 2010; Burke et al., 2014).
Motor recovery for stroke survivors involves the re-learning of how to plan and execute
movements (Krakauer, 2006). It is well-established that the process of motor learning in healthy
individuals (Hikosaka, Nakamura, Sakai, & Nakahara, 2002; Dayan & Cohen, 2011), and motor
learning and recovery in stroke survivors (Carter et al., 2010; Chen & Schlaug, 2013; Lefebvre et
al., 2015) involves a network of motor regions such as the primary motor cortex (M1),
supplementary motor area (SMA), and premotor cortex (Smith et al., 2009). These regions form
a neural network, defined as regions of the brain whose neural activity are temporally coupled.
However, mounting evidence also suggests that motor learning in healthy individuals (Hikosaka
et al., 2002; Dayan & Cohen, 2011; Albert, Robertson, & Miall, 2009; Kim, Ogawa, Lv,
Schweighofer, & Imamizu, 2015) and motor learning and recovery in stroke survivors (Ward et
al., 2003; Wang et al., 2014; Lefebvre et al., 2015) involves the dorsolateral prefrontal cortex and
parietal cortex, which are part of the frontoparietal (FP) network (Smith et al., 2009). Thus, the
FP network may facilitate comprehension of the feedback about movement (Dosenbach et al.,
2007; Albert et al., 2009; Kim et al, 2015), which can be used to modify and enhance motor
performance. The FP network is often involved in complex tasks, such as the Trail Making Test
(TMT) (Seeley et al., 2007; Li et al., 2015) which assesses task-switching performance (Delis,
38
Kaplan, & Kramer, 2001; Stuss et al., 2001). Although motor learning and TMT are different
tasks, both require individuals to receive rapid feedback about current performance to determine
whether future actions require adaptation. Taken together, both the motor and FP networks may
be implicated in the re-learning of movements and thus motor recovery after stroke.
Only two studies (Park et al., 2011; Yin et al., 2012) to date have demonstrated that connectivity
between the M1 and DLPFC is associated with motor outcome. Park and colleagues (2011)
found that higher M1-DLPFC connectivity was related to better motor outcome. However, Yin
and colleagues (2012) report the opposite; specifically, higher M1-DLPFC connectivity was
related to worse motor outcome. Furthermore, both studies (Park et al., 2011; Yin et al., 2012)
examined the connectivity between specific brain regions (M1-DLPFC connectivity), as opposed
to the connectivity between entire brain networks (Motor-FP inter-network connectivity). Given
that neural networks, rather than isolated brain regions, are often implicated in movement (Mišić
& Sporns, 2016), it is important to study the brain comprising distinct, yet inter-connected,
networks that may be altered in response to stroke.
The aim of our study is to explore the connectivity within and between the motor and FP
networks in relation to motor outcome and task-switching ability after stroke. We use resting
state functional magnetic resonance imaging (rs-fMRI) to measure spontaneous blood oxygen
level-dependent (BOLD) fluctuations between spatially distinct brain regions that are temporally
correlated (Biswal, Yetkin, Haughton, & Hyde, 1995). Our objectives are: 1) To examine the
relationship for intra-network connectivity within the motor and FP networks separately with a)
motor ability; b) task-switching performance; and 2) To examine the relationship for inter-
network connectivity between the motor and FP networks with a) motor ability; b) task-
switching performance. We hypothesize the connectivity within and between the motor and FP
networks will be positively correlated with motor ability and task-switching performance.
Methods
I) Participants
Twenty-seven participants with chronic stroke gave informed written consent for a study
approved by the Baycrest Research Ethics Board. This cohort was part of a clinical trial
(NCT01721668) recruited to study the efficacy of music supported rehabilitation (MSR) in
39
chronic stroke survivors. The present study involves the analysis of a subset of the baseline (pre-
intervention) data. Our study objectives are unrelated to those of the clinical trial. Details of the
clinical trial will be reported in future publications.
Study inclusion criteria were: first-time, unilateral ischemic stroke at least six-months post onset,
English-speaking, and without moderate to severe apraxia and/or aphasia. Participants were
included if residual motor impairment was stage 2 on the Chedoke-McMaster Stroke Assessment
(CMSA) Stage of Arm and Hand Impairment, with successful completion of at least one task in
stage 3 of the CMSA. Participants require near-normal hearing verified by clinical audiometry (<
40dB 250-2000Hz), and must not be significantly depressed (as reflected by a score of less than
27 on the Center for Epidemiological Studies-Depression scale) (Radloff & Teri, 1986). If the
participant was on antidepressants, he or she must have been on a stable dosage for at least 3
months with no change during the study period. Exclusion criteria were: clinically significant
spatial neglect, dementia, psychiatric disorders, severe pain and/or fatigue, formal music training
for more than 2 years within the past 10 years or for more than 10 years in total, and concurrently
participating in another clinical intervention trial during the study period.
II) Assessments
All participants underwent a battery of behavioral assessments comprising: CMSA Stage of Arm
(CMSA-Arm), CMSA Stage of Hand (CMSA-Hand), Action Research Arm Test (ARAT), and
Delis-Kaplan Executive Function System (D-KEFS) TMT condition #2 (TMT-2) and condition
#4 (TMT-4). The CMSA is an assessment of motor impairment scored from 1 (flaccid paralysis)
to 7 (normal movement) and has good validity with the Fugl-Meyer Assessment (Gowland et al.,
1993). The ARAT is an assessment of motor function and is scored from 0 (no motor function)
to 57 (normal motor function) (Yozabatiran, Der-Yerghiaian, & Cramer, 2008). Motor
impairment involves movement quality relative to normal human physiology and motor function
involves completion of motor tasks. The TMT is an assessment of task-switching involving
number sequencing (TMT-2) and number-letter switching (TMT-4) measured according to
completion time (in seconds) (Delis, Kaplan, & Kramer, 2001).
III) MRI Protocol
40
Participants underwent MRI scanning on a Siemens MAGNETOM TrioTim 3.0-Tesla System
(Erlangen, Germany). We acquired three-dimensional T1-weighted high resolution anatomical,
T2-weighted high-resolution anatomical, fluid-attenuated inversion recovery (FLAIR), and rs-
fMRI scans. Details of scan protocols follow. Three-dimensional T1-weighted: TR, 2000 ms;
TE, 2.63 ms; flip angle, 9°; voxel size, 1x1x1 mm3; field of view (FOV), 192x256 mm
2; matrix
size, 192x256 voxels; 160 axial slices; slice thickness, 1 mm. T2-weighted: TR, 2900 ms; TE 1,
19 ms; TE 2, 102 ms; flip angle, 180°; voxel size, 0.9x0.9x3 mm3; FOV, 185.68x220 mm
2;
matrix size, 216x256 voxels; 48 axial slices; slice thickness, 3 mm. FLAIR: TR, 9000 ms; TE, 96
ms; flip angle, 165°; voxel size, 0.9x0.9x5 mm3; FOV, 185.5x224 mm
2; matrix size, 212x256
voxels; 32 axial slices; slice thickness, 5 mm. Rs-fMRI: TR, 2000 ms; TE, 27 ms; flip angle, 70°;
voxel size, 3x3x3 mm3; 180 volumes; FOV, 192x192 mm
2; matrix size, 64x64 voxels; 40 axial
slices; slice thickness, 3 mm; gap, 0.5 mm. Participants were instructed to keep their eyes open
and fixated on a cross. We collected physiologic information (heart beat and respiration) using a
pulse oximeter and respiration belt.
IV) Data Analysis
IVa) Behavioral Analyses
Performance on the TMT involves several cognitive processes (Crowe, 1998; Stuss et al., 2001).
Performance on the TMT-2 and TMT-4 requires the participant to visually search an array of
numbers and letters on a page and to connect the numbers and/or letters in numerical or
alphabetical order. To perform the TMT-4, the participant needs to alternate between numbers
and letters, a task that implicates task-switching (Stuss et al., 2001). Task-switching can be
measured by calculating the proportion score (TMT-ps), which removes the influence of the
participant’s speed to visually search and connect the array of numbers on the page for TMT-2,
isolating the time for task-switching in TMT-4 (Stuss et al., 2001). The TMT-ps is calculated by
subtracting the completion time for TMT-2 from the completion time for TMT-4, and then
dividing this difference by the completion time for TMT-2, or simply: TMT-ps= (TMT-4 –
TMT-2)/TMT-2.
IVb) Image Processing
i) Lesion
41
Stroke lesions were manually traced by a neurologist (KH) who was blinded to the current study
objectives and to the participant demographics. Lesions were traced on T1-weighted images
using the imaging software ITK-SNAP (Yushkevich et al., 2006). T2-weighted and FLAIR
images were simultaneously reviewed to confirm lesion tracings. In addition, all other visible
infarctions (including lacunar infarction), Virchow-Robin vascular spaces, and subdural
hematoma were traced. A mask was then created from all tracings and used to exclude these
areas from the registration procedure and statistical analyses. Lesion masks were non-linearly
registered to standard Montreal Neurological Institute (MNI) space with an affine transformation
using FMRIB’s Non-Linear Image Registration Tool (FNIRT). Lesion volume was determined
with the lesion masks in standard space. Images of the lesion mask were flipped along the x-
plane such that the lesions were displayed on the right side of the structural and functional
images for all analyses.
ii) Preprocessing and Nuisance Regressors
Physiological regressors (heart beat and respiration artifacts) were first removed from the raw
data using the RETROICOR algorithm (Glover, Li, & Ress, 2000) in AFNI. All subsequent
analyses of the data, were performed using FMRIB Software Library (FSL) (Jenkinson,
Beckmann, Behrens, Woolrich, & Smith, 2012) version 5.0. The rs-fMRI images were
preprocessed using the FMRI Expert Analysis Tool (FEAT). Preprocessing steps included head
motion correction, slice time correction to the middle slice, spatial smoothing with a Gaussian
kernel of 6 mm full width at half maximum, and high pass temporal filtering at 0.01 Hz. FSL’s
Brain Extraction Tool (BET) was used to remove non-brain tissue.
The time series for cerebrospinal fluid (CSF), white matter (WM), and head motion were
extracted as nuisance regressors from the rs-fMRI data. To obtain the time series of CSF and
WM, we created masks of the CSF and WM by segmenting the T1-weighted image using
FMRIB’s Automated Segmentation Tool (FAST). The CSF and WM masks were eroded twice to
ensure the masks only contained voxels in CSF and WM. Next, the masks were transformed to
functional space using FMRIB’s Linear Image Registration Tool (FLIRT). The mean time series
of CSF and WM was extracted from voxels in each CSF and WM mask, respectively, from the
preprocessed rs-fMRI data. The time series for the six motion parameters were obtained with
MCFLIRT. The average time series for CSF, WM, and six motion parameters were included as
42
regressors of no interest in the GLM. The residual rs-fMRI data, free of these nuisance
regressors, was then used in further analyses.
IVc) Seed Masks
The RSNs of interest include: motor, FP, visual, and exec (Smith et al., 2009). We selected the
following seeds for each RSN: M1 for motor, DLPFC for FP, primary visual cortex (DeYoe et
al., 1996) (V1) for visual, and ACC for exec [Figure 4-1]. The visual network was chosen as a
control since we do not expect regions implicated in the processing of basic visual stimuli, such
as flashing checkerboards (Laird et al., 2011), to influence performance on the motor
assessments used in this study. The exec network was chosen as a second control since it is also
implicated in cognition (response conflict), but is thought to be functionally distinct from the FP
network (MacDonald et al., 2000; Dosenbach et al. 2007; Seeley et al., 2007).
Figure 4-1: Seed masks for the resting state networks
Seed masks for the following resting state networks: (A) motor network, (B) frontoparietal network, (C) visual
network, and (D) exec network are overlaid on a Montreal Neurological Institute (MNI) template.
All seeds were placed in the left hemisphere so that the seed did not overlap with the lesion
mask, which was located in the right side of the image for all participants. Each seed was defined
by taking the average peak coordinate (in Montreal Neurological Institute (MNI) 2 mm standard
space) from a set of relevant fMRI studies (described below) and creating an 8 mm radius around
it. Seed masks were non-linearly transformed into functional space using FNIRT. For each seed,
the time course for all voxels in the mask was extracted from the residual rs-fMRI data.
Given our interest in the motor outcome of the arm and hand, the M1 seed was derived from
studies reported in a meta-analysis (Mayka, Corcos, Leurgans, & Vaillancourt, 2006) with
arm/elbow and hand/finger representations [Appendix 7-1, coordinates in MNI space]. The peak
coordinate averaged across arm/elbow paradigms was (–28, –24, 62) and the peak coordinate
averaged across hand/finger paradigms was (–36, –20, 56). To have both peak coordinates
43
occupy equal volumes in a single M1 seed, we used a 6 mm radius (instead of an 8 mm radius) to
ensure the total voxel count (242 voxels) was as close as possible to the total voxel count in an 8
mm seed (257 voxels). To ensure the total voxel counts in the M1 seed and 8 mm seeds were
equal, we added 15 voxels to the most superior voxel (–28, –24, 68) of the M1 seed. Specifically,
we created a 4-voxel-by-4-voxel-by-1-voxel (8 mm x 8 mm x 2 mm) rectangular prism
(inclusive from x= –24 to x= –30, y= –20 to y= –26, and z= 68 to z= 66) to surround the most
superior voxel of the M1 seed. Importantly, the added voxels were part of the motor cortex,
according to the Jülich Histological Atlas (Eickhoff et al., 2007).
The DLPFC seed was derived from studies reported in a meta-analysis (Niendam, Laird, Ray,
Dean, Glahn, & Carter, 2012) summarizing task-switching paradigms [Appendix 7-2,
coordinates in MNI space]. The peak average coordinate was (–43, 25, 24).
The V1 seed was derived from studies that utilized a flashing checkerboard paradigm that is
known to elicit V1 activity [Appendix 7-3, coordinates in MNI space]. The peak average
coordinate was (–6, –88, 8).
The ACC seed was derived from studies reported in a meta-analysis (Niendam et al., 2012)
summarizing Stroop Task paradigms, which implicate the ACC in response inhibition
[Appendix 7-4, coordinates in MNI space]. The peak average coordinate was (–3, 25, 29).
IVd) Intra-Network Connectivity
We first determined whether intra-network motor connectivity correlated with performance on
the behavioral assessments. To extract the motor network, we performed a first-level FEAT
analysis. Here we input the mean time series of the M1 seed as an explanatory variable (EV) in
the GLM to find voxels that shared a similar time course with the left M1, for each participant.
Next, we performed a group-level FEAT analysis whereby outputs from the first-level FEAT
were input, as well as the demeaned score for a behavioral assessment of interest (CMSA-Arm,
CMSA-Hand, ARAT, or TMT-ps). In total, four group-level FEAT analyses were performed,
one for each assessment. The contrast for the first EV gives voxels correlated with the M1 seed.
The contrast for the second EV determines voxels whose correlation with the seed is modulated
by performance on the behavioral assessment. Five additional covariates (age, time since stroke,
lesion volume, sex and years of education) were also included in the GLM. The same procedure
44
was repeated to determine the relationship between intra-network connectivity within the FP
network and performance on the motor and task-switching assessments.
IVe) Inter-Network Connectivity
We next determined whether inter-network connectivity between the motor and FP networks
(Motor-FP) was correlated with performance on the motor and task-switching assessments. We
delineated the motor and FP networks following the methods described above. However, we did
not include the above-mentioned covariates in the GLM since we controlled for these variables at
a later point in the analyses. To standardize the regions included in the motor and FP networks
for each participant, we created a mask for each of the motor and FP networks by applying a
cluster threshold at z >2.5, p< 0.05 to the resulting group image maps, which were then
binarized. The masks were non-linearly transformed to the functional space of each participant.
Although the motor and FP networks are spatially distinct, any overlapping voxels were removed
from the motor network. We then extracted the BOLD time course from voxels of the motor and
FP network masks, from each participant’s residual resting state fMRI data. Next, Pearson’s
correlation was computed on these time series as a measure of inter-network connectivity. This
correlation coefficient was then correlated with scores from the behavioral assessment of
interest. The same procedure was repeated for inter-network connectivity between the motor and
visual networks (Motor-Visual) and between the motor and exec networks (Motor-Exec).
V) Statistical Analysis
Va) Intra-network Connectivity
To ensure identification of the motor network, a mask of the motor network defined in a healthy
set of participants by Smith et al. (2009) was voxel thresholded at z >3.1. This mask was
inputted as a pre-threshold mask to restrict the statistical analysis to regions typically found in
the motor network of healthy individuals. The same procedure was repeated to define a mask of
the FP network and inputted as a pre-threshold mask for the FP network analysis.
Cluster thresholding was applied to correct for multiple comparisons. Clusters were considered
significant at z >2.5, p< 0.05. We report the peak coordinate and number of voxels (#voxels) in
45
significant clusters. Non-significant analyses are reported with voxel thresholding at z >2.5, p<
0.01, uncorrected (see Figure 5-8 for images from non-significant analyses).
Vb) Inter-network Connectivity
A Pearson’s correlation was computed for each participant between time series of the motor and
FP networks. The Pearson’s r-value represents the rs-connectivity between the motor and FP
networks. A Fisher’s r-to-z transformation was applied, which yielded rs-connectivity values that
are normally distributed. To determine the correlation between inter-network connectivity and
performance on clinical assessments, a partial Spearman’s correlation was calculated between
the Fisher z-score with the behavioral assessment scores (CMSA-Arm, CMSA-Hand, ARAT,
and TMT-ps). Covariates (age, lesion volume, and years of education) were controlled for in the
Spearman’s correlation. The same procedure was repeated to determine the correlation between
inter-network connectivity and performance on clinical assessments for the Motor-Visual and
Motor-Exec conditions. A total of 12 Spearman’s correlations were performed (to determine how
the three inter-network connectivity conditions (Motor-FP, Motor-Visual, Motor-Exec) correlate
with each of the four behavioral assessments). We applied a Bonferroni correction to correct for
multiple comparisons with correlations considered significant at p< 0.004 (i.e., 0.05/12 tests).
We then used the Williams T2 test statistic (Steiger, 1980) to assess whether two dependent
correlation coefficients are significantly different from each other [Appendix 7-5]. A T2-test
value greater than 1.71 (degrees of freedom=24) was considered significant at p< 0.05. The T2-
test value was assessed using a one-sided test of significance since we expected the correlation
coefficient between Motor-FP connectivity with behavioral assessment scores to be higher than
that associated with the control conditions. All statistics were performed using SPSS version
22.0.
Results
Participant demographics and performance on behavioral assessments are summarized in
Table 4-1 (group average data) and Appendix 7-6 (individual participant data). Lesion tracings
of all participants are displayed in Figure 4-2.
46
Table 4-1: Participant Demographics and Performance on Behavioral Assessments
Characteristic (N=27)
Age, years 61.3±10.7 (40-79)
Sex
Male
Female
18 (66%)
9 (33%)
Time since stroke, years 5.5±6.5 (0.6-25)
Lesion location
Left hemisphere
Right hemisphere
11 (41%)
16 (59%)
Lesion volume, cm3 65.6±101.8 (0.5-401.6)
Years of education 15.6±2.8 (10-20)
Dominant hand affected*
Yes
No
10 (38%)
16 (62%)
Chedoke-McMaster Stroke Assessment
Stage of Arm Impairment 3 (2-5)
Stage of Hand Impairment 3 (2-5)
Action Research Arm Test 25.3±21.6 (0-57)
Delis-Kaplan Executive Function System
Trail Making Test (Condition 2) (seconds) 49.4±24.3 (28-127)
Trail Making Test (Condition 4) (seconds) 139.1±82.3 (52-380)
Trail Making Test Proportion Score 0.9±1.16 (0.7-5.8) Data are presented as mean±SD (range) for continuous variables and n (%) for categorical variables. Chedoke-
McMaster Stroke Assessment is presented as median (range). *Handedness data was not collected for the first
participant.
I) Intra-network Connectivity
Participants with higher rs-connectivity between left M1 and midline SMA have significantly
higher CMSA-Hand stage (i.e., less hand impairment) [z=4.06; p<0.001; peak coordinate: 10, –2,
48; #voxels in cluster=662] and higher ARAT score (i.e., greater motor function) [z=3.10;
p=0.03; peak coordinate: 10, –32, 62; #voxels in cluster=188] than those with lower M1-SMA
rs-connectivity [Figure 4-3, panels A & B]. Intra-network connectivity of the motor network
was not significantly correlated with CMSA-Arm [z=3.32; p<0.001; coordinate: –8, –4, 52].
Intra-network connectivity of the motor network was not significantly correlated with TMT-ps
[z=2.91; p<0.002; coordinate: –8, –36, 46].
Participants with higher rs-connectivity between left DLPFC and left BA 45a (mid-ventrolateral
prefrontal cortex (mid-VLPFC)) have higher CMSA-Hand stage [z=3.54; p=0.02; peak
coordinate: –48, 34, 16; #voxels in cluster=228] than those with lower DLPFC-mid-VLPFC rs-
connectivity [Figure 4-3, panel C]. Intra-network connectivity of the FP network was not
47
significantly correlated with either CMSA-Arm [z=3.48; p<0.001; coordinate: –50, 38, 16] or
ARAT scores [z=3.12; p<0.001; coordinate: –48, 38, 12]. Intra-network connectivity of the FP
network was not significantly correlated with TMT-ps [z=3.30; p<0.001; coordinate: –48, –64, –
10].
Figure 4-2: Lesion masks of stroke participants in study
Individual lesion maps (in red) are displayed on the right hemisphere and superimposed on the standard Montreal
Neurological Institute template. The transverse slice of the lesion with the largest cross-sectional area is shown. (“s”
indicates subject).
Figure 4-3: Intra-network connectivity results
The rs-connectivity between the left primary motor cortex (M1) (seed) and midline supplementary motor area
(SMA) is correlated with performance on the Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-Hand)
(A) and Action Research Arm Test (B). The rs-connectivity between the left dorsolateral prefrontal cortex (DLPFC)
(seed) and left Brodmann Area (BA) 45a (mid-ventrolateral prefrontal cortex (mid-VLPFC)) is correlated with
CMSA-Hand stage (C).
48
II) Inter-Network Connectivity
Motor-FP connectivity is positively correlated with the ARAT score (rs(22)=0.578, p=0.003)
[Figure 4-4]. Motor-Visual connectivity and Motor-Exec connectivity were not significantly
correlated with the ARAT (rs(22)=0.245, p=0.248; rs(22)=0.359, p=0.085, respectively). The
correlation between Motor-FP connectivity and the ARAT was significantly higher than the
correlation between Motor-Exec connectivity with the ARAT (p=0.037, one-sided). The
correlation between Motor-FP connectivity with the ARAT showed a trend towards significance
as being higher than the correlation between Motor-Visual connectivity and the ARAT (p=0.078,
one-sided). Correlations between the Motor-FP connectivity and the CMSA-Arm and CMSA-
Hand scores were not significant (rs(22)=0.394, p=0.057; rs(22)=0.522, p=0.009, respectively).
The correlation between the Motor-FP connectivity and the TMT-ps was not significant
(rs(22)=0.233, p=0.296).
Figure 4-4: Inter-network connectivity results
Partial correlation graphs of the residuals for the inter-network connectivity between the motor and frontoparietal
networks (Motor-FP) with Action Research Arm Test (ARAT). Asterisks represent significant partial Spearman’s
correlations. A significant correlation was found between Motor-FP and ARAT (A). No significant correlation was
found for the inter-network connectivity between the motor and visual networks (Motor-Visual) and ARAT (B) or
between the inter-network connectivity between the motor and exec networks (Motor-Exec) and ARAT (C). The
correlation coefficient (rs-value) for the Motor-FP connectivity and ARAT is significantly higher than the rs-value
for the Motor-Exec connectivity and ARAT.
Discussion
In this study, we examined the relationship between rs-connectivity of the motor and FP
networks and motor outcome after stroke. We report three main findings: 1) rs-connectivity
between the motor and FP networks correlates with motor function; 2) within the FP network, rs-
connectivity between left DLPFC and left mid-VLPFC correlates with motor impairment; 3)
within the motor network, rs-connectivity between left M1 and midline SMA correlates with
49
motor impairment and function. Collectively, our results suggest that motor and FP networks
separately – and importantly their association – may be related to motor outcome after stroke.
I) Inter-Network Connectivity
To our knowledge, a novel finding from our study is that Motor-FP network connectivity is
positively correlated with motor function. That is, individuals with higher Motor-FP connectivity
have better motor function, and those with lower Motor-FP connectivity have worse motor
function. This relationship was selective to the motor and FP networks as neither the Motor-
Visual nor Motor-Exec connectivity correlated with motor function.
Our results support findings from Park and colleagues (2011) who found that individuals with
higher rs-connectivity between M1 and DLPFC have better motor outcome than those with lower
M1-DLPFC rs-connectivity. However, we show for the first time that the relationship between
the motor and FP networks may not only be limited to specific regions, but may also involve the
entire network. The discrepant results from Yin and colleagues (2012) may, in part, be due to the
different methods used in their study. To determine the relationship between rs-connectivity and
motor outcome, both our study and Park et al. (2011) use a correlational analysis whereas Yin et
al. (2012) use a subtraction analysis.
One interpretation of the inter-network results is that motor and FP networks may be important
in the execution of motor sequences that are fluid and purposeful after stroke. In healthy
individuals, the FP network is thought to facilitate in the learning of movements that are
challenging (Hikosaka et al., 2002; Albert et al., 2009; Kim et al., 2015). In stroke survivors,
even simple movements can be challenging to perform, and thus the FP network may similarly
assist in organizing a set of movements. The FP network may be involved in the integration of
feedback (Dosenbach et al., 2007), such as movement accuracy (Albert et al., 2009; Kim et al.,
2015). The integration of this information may enable individuals to quickly adapt and re-learn
the proper movements during rehabilitation. Furthermore, the FP network may be involved in the
consolidation of motor skills (Robertson, 2009; Kantak, Sullivan, Fisher, Knowlton, & Winstein,
2010), which can help to maintain the practice effects gained from rehabilitation. Taken together,
coupling between motor and FP networks may involve the integration of motor sequence
information to enhance motor performance after stroke.
50
Alternatively, the coupling between the motor and FP networks may help provide cognitive
resources to plan and execute movements. The FP network is implicated in aspects of cognition
(Seeley et al., 2007; Dosenbach et al., 2007; Li et al., 2015), which may help provide attentional
and working memory resources (Seeley et al., 2007; Dosenbach et al., 2007) that potentially aid
stroke survivors to successfully perform a sequence of movements. In particular, the involvement
of the FP network may assist individuals to focus on the most relevant information from the
environment and to efficiently manipulate this information. Thus, stroke survivors can select the
appropriate movements to interact with stimuli and/or complete a given task. This interpretation
is consistent with previous research that has shown cognitive function is positively correlated
with motor outcome (Mullick, Subramanian, & Levin, 2015). Furthermore, the use of cognitive
strategies is suggested to enhance motor outcome in stroke survivors (Sharma, Pomeroy, &
Baron, 2006; McEwen, Polatajko, Huijbregts, & Ryan, 2015). Together, motor and FP coupling
may involve the application of cognitive resources to supplement the motor process. It is
important to note that this interpretation is not mutually exclusive from the first interpretation
since cognitive processes, such as attention or memory, are also involved in motor learning
(Taylor & Ivry, 2012). Therefore, both interpretations may be simultaneously valid.
II) Intra-Network Connectivity
Within the FP network, we also report novel findings that rs-connectivity between DLPFC and
mid-VLPFC correlates with hand impairment. One interpretation is that mid-VLPFC and DLPFC
may be involved in the retrieval and reconsolidation of motor skills. Based on a working memory
model by Petrides (2005), the mid-VLPFC and DLPFC are thought to function in a hierarchical
manner. Specifically, the mid-VLPFC is involved in ‘lower order’ functions with active retrieval
of past experiences (Petrides, 2005; Badre & Wagner, 2006). Thus, the mid-VLPFC may select
the most relevant movement pattern for the current task, from a set of alternative movement
patterns. The DLPFC is involved in ‘higher-order’ functions with the manipulation of
information from various brain regions (Petrides, 2005). Thus, the DLPFC may use the selected
movement pattern from mid-VLPFC to organize and coordinate movements for task
performance. The ability to retrieve the appropriate movement pattern and apply the movements
for successful task performance may lead to reconsolidation of the movement pattern. Taken
together, the mid-VLPFC and DLPFC may help strengthen motor skills previously re-learned to
enhance motor performance.
51
Alternatively, the DLPFC and mid-VLPFC may be involved in determining and monitoring the
proper actions required for a motor task. Based on an executive function model by Stuss and
Alexander (2007), the mid-VLPFC is involved in setting the stimulus-response relationship,
which may help to determine the appropriate movements to interact with a stimulus in the
environment. Stuss and Alexander (2007) also suggest the DLPFC is involved in monitoring task
execution, which may help to adjust performance and anticipate events. Taken together, the
coupling between DLPFC and mid-VLPFC may help stroke survivors select and optimize their
movements.
Interestingly, the DLPFC and mid-VLPFC are also thought to mediate task-switching (Badre &
Wagner, 2006). In our study, however, we did not find a relationship between our intra-network
or inter-network connectivity measures and TMT performance. One reason for this could be
related to the TMT assessment itself. The TMT-ps measure may not be sensitive enough to
assess task-switching performance, given that the TMT implicates many cognitive processes,
such as attention and working memory (Gaudino, Geisler, & Squires, 1995; Crowe, 1998; Stuss
et al., 2001; Oosterman et al., 2010; Cepeda, Blackwell, & Munakata, 2013). This makes it
difficult to dissociate the different cognitive processes that may be related to motor and/or FP
network connectivity after stroke. Furthermore, the influence of motor processes (e.g., motor
speed) (Crowe, 1998) involved in the TMT is accounted for in the TMT-ps measure (Stuss et al.,
2001). This makes the TMT-ps measure more related to cognitive processes only, rather than a
measure involving both motor and cognitive processes. Thus, a paradigm more representative of
task-switching motor performance may better relate to our intra- network and inter-network
connectivity measures. An alternative paradigm that can be used to measure task-switching is
discussed in the section Limitations and Future Directions.
Our finding that higher rs-connectivity between M1 and SMA correlates with less hand
impairment and greater motor function is consistent with prior literature. Previous findings have
shown greater activity (Favre et al., 2014) and higher coupling (Grefkes et al., 2008; Rehme,
Eickhoff, Wang, Fink, & Grefkes, 2011) between M1 and SMA during movement is associated
with better motor recovery. We suggest that stroke survivors who exhibit greater temporal
coupling between M1 and SMA might have better motor outcome than those with lower M1-
SMA rs-connectivity because they engage in motor planning processes (Tanji & Shima, 1994) to
prepare and sequence movements. Specifically, the SMA is implicated in organizing movements
52
prior to and during movement execution. Therefore, the coupling between M1 and SMA is
important for the development of motor plans that contribute towards the execution of
purposeful, appropriate, and coordinated movements.
III) Limitations and Future Directions
As previously discussed, one limitation was that we did not find a significant relationship for
intra-network or inter-network connectivity with TMT-ps. The TMT is a cognitive-based task-
switching assessment since it involves shifting between abstract concepts (numbers and letters).
Future studies may consider assessing Motor-FP coupling using a motor-based task-switching
paradigm in which participants alternate hand use during a finger tapping task (Serrien et al.,
2007). This may be a more appropriate task since both motor and cognitive processes are
implicated, whereby individuals apply cognitive processes (task-switching) in the context of
movement (Serrien et al., 2007).
Furthermore, we found no significant relationship between intra-network or inter-network
connectivity and CMSA-Arm assessment. One reason may involve the fact that a majority of our
participants (fourteen of twenty-seven) scored stage 3 on the CMSA-Arm, which may lead to
difficulty in interpolating results from a correlational analysis between rs-connectivity and
CMSA-Arm. Future studies may also consider using additional measures to assess motor
outcome to determine whether certain aspects of movement, such as speed or force, are
associated with motor and FP network connectivity. It is also important to determine whether the
results from our study can be replicated in other stroke populations. Lastly, future work may
consider exploring in greater detail the network characteristics (e.g., hubs) (Mišić & Sporns,
2016) of the motor and FP networks to better understand the critical regions and/or mechanisms
that may underlie the relationship between inter-network connectivity and motor outcome after
stroke.
Conclusion
Collectively, our findings suggest that rs-connectivity within and between the motor and FP
networks are associated with motor outcome. We suggest that temporal coupling between the
motor and FP networks may, in part, represent how well stroke survivors can re-learn
movements after stroke.
53
Sources of Funding
The clinical trial was funded by the Canadian Institutes for Health Research (CIHR
MOP119547) and Heart and Stroke Foundation (HSF 000421). T.K.L. was funded by the
Canadian Partnership for Stroke Recovery Start-up Funds, Rehabilitation Sciences Institute, and
Ontario Graduate Scholarship.
Disclosures
None.
54
Chapter 5 Discussion
1 Review of study findings
The aim of my thesis is to explore the motor and frontoparietal (FP) networks and their
association with motor outcome and task-switching ability after stroke. Three major findings are
reported from this study:
1) Individuals with higher inter-network connectivity between the motor and FP networks
(Motor-FP connectivity) have higher Action Research Arm Test (ARAT) scores (i.e., better
motor function) than those with lower Motor-FP connectivity. Thus, these results support
hypothesis 3b.
2) Individuals with higher resting state connectivity (rs-connectivity) between the left
dorsolateral prefrontal cortex (DLFPC) and left mid-ventrolateral prefrontal cortex (mid-
VLPFC) have higher CMSA-Hand stage (i.e., lower hand impairment) than those with lower
DLPFC-mid-VLPFC rs-connectivity. Thus, these results support hypothesis 2a.
3) Individuals with higher rs-connectivity between the left primary motor cortex (M1) and
midline supplementary motor area (SMA) have better CMSA-Hand stage and higher ARAT
scores than those with lower M1-SMA rs-connectivity. Thus, these results support hypotheses 1a
and 1b.
These results add to the literature suggesting that both the motor and FP networks may be
important for motor outcome after stroke. Specifically, the inter-network and intra-network
results demonstrate that individuals with higher coupling in neural activity between and within
the motor and FP networks have better motor outcome. We interpret these findings in the context
of motor learning, given prior models (Hikosaka et al., 2002) and/or studies (Albert et al., 2009;
Kim et al., 2015) that have suggested the FP network is implicated in motor learning in healthy
individuals. Nonetheless, other interpretations of our results are certainly possible and will be
discussed in this chapter.
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2 Inter-network connectivity
2.1 Inter-network connectivity between motor and frontoparietal networks
The results from this study demonstrate that individuals with higher rs-connectivity between the
motor and FP networks (Motor-FP connectivity) have better motor function than those with
lower Motor-FP connectivity. Importantly, this relationship appears to be specific to the motor
and FP networks, as neither the inter-network connectivity between the motor and visual
networks (Motor-Visual connectivity) nor the inter-network connectivity between the motor and
executive control networks (Motor-Exec connectivity) correlated with the ARAT score [Figure
4-4]. Similarly, a comparison of the correlation coefficients using the Williams’ T2 test statistic
for dependent correlations suggests that the correlation for Motor-FP connectivity with the
ARAT is significantly greater than the correlation for Motor-Exec connectivity with the ARAT.
To my knowledge, only two rs-fMRI studies (Park et al., 2011; Yin et al. 2012) have examined
the relationship between rs-connectivity of motor and FP brain regions with motor outcome.
However, these two studies present discrepant results. In particular, Park and colleagues (2011)
found that individuals with higher rs-connectivity between M1 and DLPFC at the subacute stage
post-stroke have better motor outcome at the chronic stage. These findings support our results.
Conversely, Yin and colleagues (2012) found that individuals with higher rs-connectivity
between M1 and DLPFC have worse motor outcome at the chronic stage.
The results from our study are consistent with the findings from Park et al. (2011) in which
higher rs-connectivity between the M1 and DLPFC is associated with better motor outcome.
However, we demonstrate for the first time that this relationship is also found between the motor
and FP networks. Previous studies (Park et al., 2011; Yin et al., 2012) use a “brain-specific
approach” whereby the focus is on the rs-connectivity between specific motor and FP regions
(i.e., M1 and DLPFC) that are correlated with motor outcome. However, there is increasing
evidence to suggest that a brain-specific approach may not be sufficient to explain complex
processes, such as movement, given that these processes do not simply involve single (or
isolated) regions (Fuster, 2001; Hikosaka et al., 2002; Shadmehr & Krakauer, 2008). Studies that
examine neural activity in a paradigm for movement often find many regions that are associated
with performance (Sohn et al., 2000; Cunnington, Windischberger, Deecke, & Moser, 2002;
56
Johansen-Berg & Matthews, 2002; Braver et al., 2005). This suggests that movement is a process
that involves multiple brain regions, or in other words, a “network”.
As discussed in section 2.4 (Frontoparietal network), a “network” comprise regions that have its
neural activity temporally correlated with each other (Sporns, 2011). In this study, we used a
seed-based connectivity approach to specifically examine the motor and FP networks
holistically. Given the approach we used, two alternative methods may be suggested: 1) use a
“brain-specific approach” to examine the connectivity of the M1 and/or DLPFC seed with the
entire brain; and 2) use network metrics (i.e., graph theory) (Bullmore & Sporns, 2011) to study
the motor and FP networks. The first method can help determine whether a single region in one
network (e.g., M1 seed) is connected with regions in other networks (e.g., FP network).
However, it does not provide us a holistic view of whether these two networks couple together
(which is part of the research question). The second method can provide details on the network
characteristics underlying our findings and is a logical progression of the current study.
Nonetheless, I believe the results from our study are still merited, particularly from a ‘proof of
concept’ perspective. Specifically, it is important to first determine whether these two networks
couple together such that we have a rationale to study the motor and FP networks in greater
detail, with graph theory analysis approaches.
Networks allow us to view the brain holistically and to hypothesize and/or determine remote
regions in the brain that may communicate during certain tasks. The communication within and
between networks not only produces complex neural patterns, but these patterns are necessary
precursors for the performance of complex behaviours. Although the brain-specific approach is
often used in neuroanatomy to link brain structure with a specific function, a network approach
may help to better understand the brain by exploring its organization, its functions, and its ability
to recover after injury, such as stroke. Consistent with this rationale to explore the brain from a
network perspective, our results suggest that motor outcome not only involves specific brain
regions, but may also involve entire motor and cognitive networks.
One potential explanation for the discrepant results by Yin et al. (2012) could be related to their
method in exploring the relationship of rs-connectivity between motor and FP regions and motor
outcome. Specifically, stroke survivors were categorized according to whether they had a
‘mild/moderate’ or a ‘severe’ motor impairment. As a result, Yin and colleagues (2012)
57
performed subtraction analyses (e.g., ‘severe’ subtract ‘mild/moderate’) to determine rs-
connectivity differences between groups. However, the categorization of ‘mild/moderate’ versus
‘severe’ is a course measure of impairment which limits their ability to determine whether
participants placed in the same group have rs-connectivity differences between each other. In
contrast, correlational analyses (used in both our study and Park et al. (2011)) determine whether
a relationship exists between rs-connectivity and motor performance. Furthermore, correlational
analyses allow us to detect anomalous cases or outliers that could influence the results, which
may warrant further investigation of these cases (Poldrack, 2007). Thus, the different methods
used in analyzing the rs-fMRI data may have contributed to the different results found in the
study by Yin and colleagues (2012).
Our inter-network results are interpreted in the context of integrating motor sequence
information. Specifically, individuals with higher Motor-FP connectivity may recruit the motor
and FP networks more effectively to perform a sequence of movements efficiently and fluidly
after stroke. In healthy individuals, motor learning is thought to involve the motor and FP
networks (Hikosaka et al., 2002; Ma, Narayana, Robin, Fox, & Xiong, 2011; Vahdat, Darainy,
Milner, & Ostry, 2011; Bonzano et al., 2015) when initially learning complex movements (e.g.,
finger tapping based on a random sequence, or reaching for a target in which arm trajectory is
interfered with). In stroke survivors, simple or routine movements, such as reaching or grasping,
can be regarded as complex given that individuals with motor deficits may have difficulty
initiating and coordinating movements (Cirstea & Levin, 2000; Handley, Medcalf, Hellier, &
Dutta, 2009; Subramanian et al., 2010; Levin et al., 2015). As discussed in section 2.4.1.2 (Motor
learning), the FP network is suggested to integrate feedback (Dosenbach et al., 2007) about
movement accuracy that can be used to update and/or enhance subsequent performance on the
motor task (Albert et al., 2009; Kim et al., 2015). Furthermore, the FP network may use feedback
from task performance to learn compensatory techniques to aid in task completion. Taken
together, the coupling between motor and FP networks may be important to perform a sequence
of movements after stroke.
Alternatively, Motor-FP connectivity may represent a relationship between movement and
cognition. Specifically, individuals with higher Motor-FP connectivity may recruit the FP
network more effectively to provide cognitive resources (e.g., attention, working memory) that
assist in task performance (Dosenbach et al., 2007; Seeley et al., 2007). In healthy individuals,
58
motor and cognitive brain regions (e.g., M1, premotor cortex, DLPFC) are required to quickly
update motor plans and movements in response to changing motor task goals (e.g., different
target locations) (Rounis, Yarrow, & Rothwell, 2007; Jamadar et al., 2010). Furthermore, greater
cognitive load (as defined by dual- or multi-tasking paradigms in which participants perform a
motor task and simultaneously perform other irrelevant tasks) has been shown to reduce motor
performance in both healthy individuals (Au & Keir, 2007; Guillery Mouraux, & Thonnard,
2013) and those with stroke (Dennis et al., 2011). Behaviourally, individuals with better
cognitive status following their stroke have less arm impairment (Burke et al., 2014; Mullick et
al., 2015) and show better performance on activities of daily living (Heruti et al., 2002; Leśniak,
Bak, Czepiel, Seniów, & Członkowska, 2008). Given that the FP network is implicated in
various cognitive processes (as discussed in subsection 2.4.1 (Frontoparietal network implicated
in cognition)), the FP network may help provide cognitive resources to improve motor
performance, perhaps through better concentration and anticipation of movements. It is
important to note that this interpretation involving the motor-cognitive relationship is not
mutually exclusive from the first (i.e., motor learning) interpretation, as cognitive processes (e.g.,
attention, memory) are also required for learning (in general). Thus, both interpretations can be
equally valid and may depend on whether the FP network has a more prominent motor-role (for
motor learning) or a more prominent cognitive-role (to provide cognitive resources, such as
attention, decision making, or response inhibition).
In addition to our primary interpretations, an alternative explanation for our results is that
individuals with high Motor-FP connectivity may be imagining their movements in preparation
to perform a motor task. A few recent studies (Sharma, Baron, & Rowe, 2009; Hétu et al., 2013;
Pilgramm et al., 2016; Zhang et al., 2016) have found that both the motor and FP networks are
involved in motor imagery. Furthermore, damage to the FP network from stroke has been shown
to cause deficits in the ability for individuals to imagine their movements (Oostra, Van Bladel,
Vanhoonacker, & Vingerhoets, 2016). Motor imagery has been suggested to be an effective
intervention to enhance movement after stroke (Stevens & Stoykov, 2003; Sharma et al., 2006;
Dodakian, Campbell Stewart, & Cramer 2014). It is hypothesized that motor imagery enables
individuals to place greater focus on their movements (e.g., understanding why certain actions
are important) and to improve their ability to plan movements prior to actual performance
(Hanakawa, Dimyan, & Hallett, 2008). Thus, individuals with greater Motor-FP connectivity
59
may employ greater use of motor imagery, which subsequently improves the process of motor
execution.
Another interpretation of our Motor-FP connectivity results may involve a distinction between
“domain-general” versus “domain-specific” networks. Specifically, the FP network can be
classified as a “domain-general network” since it is implicated in various (cognitive) processes,
such as attention (Seeley et al., 2007; Ptak 2012), language production (Geranmayeh et al., 2014;
Zhu et al., 2014), working memory (Linden, 2007; Seeley et al., 2007; Khader et al., 2016), and
task-switching (Dove et al., 2000; Sohn et al., 2000; Rubia et al., 2001; Li et al., 2015). Cole and
colleagues (2013) suggest that domain-general networks, such as the FP network, are “flexible”
because its pattern of neural activity can be altered in real-time (when tasks are novel or difficult)
to closely match the pattern of neural activity for “domain-specific networks”, defined as
networks that are implicated in specific tasks, such as the motor network implicated in
movement. It is thought that domain-general networks may act as a ‘supervisor’ to monitor and
assist the domain-specific networks such that tasks involving the domain specific network can be
performed more effectively (Cole et al., 2013). Thus, the ability for the FP network to closely
match its neural activity with the motor network may help individuals to better learn, plan, and
execute their movements (Cole et al., 2013). In stroke, an individual whose FP network is more
flexible not only would show similar patterns of neural activity between the motor and FP
networks, but the FP network may assist the motor network (whose functions may be
compromised due to damage to the motor system) to enhance motor performance. It is important
to note that this interpretation is more mechanistic in nature. Specifically, this interpretation can
explain why the motor and FP networks would show neural coupling, but the benefit of this
coupling still rests on some aspect of motor learning, planning or cognition. As a result, this
interpretation may not be entirely exclusive from the aforementioned interpretations.
Lastly, the additional analyses involving the correlation between Motor-FP connectivity with the
CMSA-Arm and Hand stage were not significant, following correction for multiple comparisons
[Figure 5-1]. Nonetheless, a similar trend involving a positive association between Motor-FP
connectivity and each of the motor assessments is found. This suggests that the relationship
between the motor and FP networks is not necessarily specific to performance on the ARAT, but
that these results similarly apply to measures of arm and hand impairment. Thus, the motor and
FP networks may be important to enhance the collective aspects of motor outcome after stroke.
60
Figure 5-1: Inter-network connectivity results (non-significant)
Inter-network results that did not meet the threshold set for statistical significance in this study. Scatterplots of the
residuals for the inter-network connectivity and behavioural assessment score, after controlling for age, lesion
volume, and years of education, are shown. The inter-network connectivity between the motor and FP networks
(Motor-FP connectivity) with Chedoke-McMaster Stroke Assessment Stage of Arm (CMSA-Arm) (A), Chedoke-
McMaster Stroke Assessment Stage of Hand (CMSA-Hand) (D), and Trail Making Test (TMT) Proportion Score
(G). The inter-network connectivity between the motor and visual networks (Motor-Visual connectivity) with
CMSA-Arm (B), CMSA-Hand (E), and TMT Proportion Score (H). The inter-network connectivity between the
motor and executive control (Motor-Exec connectivity) with CMSA-Arm (C), CMSA-Hand (F), and TMT
Proportion Score (I).
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2.2 Potential confounds
In our partial correlation between Motor-FP connectivity and the ARAT score, we accounted for
three covariates: age, lesion volume, and years of education. In particular, age (Roski et al.,
2013) and years of education (Marques, Soares, Magalhães R, Santos, & Sousa, 2015) have been
shown to greatly affect the connectivity of resting state networks. The raw correlation between
Motor-FP connectivity and ARAT was not significant (rs(25) = 0.14; p = 0.49). We found a
significant correlation, however, when accounting for the three aforementioned covariates in our
partial correlation between Motor-FP connectivity and ARAT (rs(25) = 0.58; p < 0.01).
However, we cannot be certain whether a single covariate is primarily suppressing the
relationship between Motor-FP connectivity and ARAT. Thus, an additional analysis was
performed comparing the partial correlation between Motor-FP connectivity and ARAT, with
only a single covariate accounted for in the correlation. The correlations between Motor-FP
connectivity and ARAT, after accounting for only a single covariate of age (rs(24) = 0.19; p =
0.35), lesion volume (rs(24) = 0.32; p = 0.12), or years of education (rs(24) = 0.28; p = 0.17) were
not significant [Figure 5-2]. These results suggest that there does not appear to be one covariate
in particular (i.e., age, lesion volume, or years of education) that may be significantly
suppressing the relationship between Motor-FP connectivity and ARAT. Instead, it appears that a
combined effect of the covariates may be involved in suppressing the relationship between
Motor-FP connectivity and ARAT.
In our study, we used the Williams’ T2 test statistic to show that the correlation between Motor-
FP connectivity and the ARAT score is significantly higher than the two control conditions: 1)
Motor-Visual connectivity with ARAT; and 2) Motor-Exec connectivity with ARAT. However,
we cannot rule out the possibility that the Motor-Visual or Motor-Exec connectivity is
influencing the significant correlation between Motor-FP connectivity and ARAT. Thus, an
additional analysis was performed by accounting for the Motor-Visual or Motor-Exec
connectivity as an additional covariate in our partial correlation between Motor-FP connectivity
and ARAT. The correlation between Motor-FP connectivity and ARAT remains significant, even
after accounting for Motor-Visual connectivity (rs(22) = 0.56; p < 0.01) or Motor-Exec
connectivity (rs(22) = 0.50; p = 0.02) [Figure 5-3]. These results further suggest that the
relationship between Motor-FP connectivity and the ARAT score appears to be specific to these
two networks.
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Figure 5-2: Motor-FP connectivity with ARAT (accounting for individual covariates)
(A) Raw Spearman’s correlation between Motor-FP connectivity and ARAT. (B) Scatterplot of the residuals for the
Spearman’s correlation between Motor-FP connectivity and ARAT, while accounting for age, lesion volume, and
years of education. Scatterplot of the residuals for the Spearman’s correlation between Motor-FP connectivity and
ARAT, while accounting for age (C), lesion volume (D), or years of education (E). Asterisks represent significant
correlation.
Figure 5-3: Motor-FP connectivity with ARAT (accounting for inter-network connectivity)
Scatterplot of the residuals for the Spearman’s correlation between Motor-FP connectivity and ARAT, while
accounting for Motor-Visual connectivity (A) or Motor-Exec connectivity (B).
2.3 Anomalous cases
In the context of our correlation between Motor-FP connectivity with ARAT, an outlier generally
refers to either a case with low Motor-FP connectivity but high ARAT score (or vice versa). The
partial regression plot between Motor-FP connectivity with ARAT contained two anomalous
data points, but importantly, these cases were not outliers [Figure 5-4, panel A]. Specifically,
these two data points have the largest residuals (i.e., lowest Motor-FP connectivity) relative to
the rest of the data. However, a statistical analysis of these data points using Weisberg’s t-
statistic and Cook’s distance (Stevens, 1984) [Appendix 7-5] suggests these two cases do not
appear to be outliers and do not appear to be the most influential points in our correlation
between Motor-FP connectivity and the ARAT score.
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Figure 5-4: Anomalous cases for inter-network connectivity correlation
(A) Scatterplot of the residuals for the Spearman’s correlation for the inter-network connectivity between the motor
and FP networks (Motor-FP connectivity) with the Action Research Arm Test (ARAT), after accounting for age,
lesion volume, and years of education. Two anomalous cases have been marked on the scatterplot (referred to as
‘green-circled case’ and ‘red-circled case’ in this figure). The lesion mask (in blue) for the green-circled case (B)
and red-circled case (C) is overlaid on a lesion map for all twenty-seven participants in the study. All lesions are
transformed to standard (Montreal Neurological Institute (MNI)) space.
There does not appear to be one prominent factor that can explain these two anomalous data
points. Specifically, the covariates (i.e., age, lesion volume, and years of education) we included
in our partial Spearman’s correlation for these two cases are not extreme values relative to the
rest of the data. Furthermore, the lesion location alone does not seem sufficient to fully explain
the low Motor-FP connectivity seen for these two cases. This is because twelve other participants
have similar lesion locations as what is found for these two anomalous cases [Figure 5-4, panels
B & C]. The Motor-FP connectivity values for participants who also had similar lesion locations
as these two cases ranged from 0.5 to 0.9, which is greater than the median Motor-FP
connectivity value in this data set. Taken together, a combination of factors may have influenced
the low Motor-FP connectivity value observed for these two anomalous cases.
There is a possibility that the Motor-FP connectivity at baseline may be lower in some
individuals not because of any underlying pathology, but could be a result of their genetic make-
up (Glahn et al., 2009) and/or life experiences, such as stress levels (Soares et al., 2013) or social
support (Pillemer, Holtzer, & Blumen, 2016). Although a significant association is found
between Motor-FP connectivity with the ARAT score, correlation does not equal causation.
Given that our study is cross-sectional and not a prospective one, we are not aware of the Motor-
FP connectivity prior to stroke onset for each participant. As a result, we cannot determine
whether the Motor-FP connectivity we found for these anomalous cases are below, at, or above
the Motor-FP connectivity at baseline (i.e., before stroke onset). Furthermore, our life
experiences can indirectly affect the connectivity within and between networks (Sporns, 2011).
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For example, less social support has been found to be related with lower connectivity in the
motor and FP networks (Pillemer et al., 2016). In particular, it has been suggested that
individuals with less social support have greater age-related decline (Pillemer et al., 2016), which
may lead to dysfunctional or disconnected networks (Sala-Llonch, Bartrés-Faz, & Junqué, 2015).
Thus, our study only focuses on one aspect that may influence motor outcome, but in reality
there are many factors that need to be considered when interpreting these results.
Collectively, these two cases provide a reminder of the complexities that surround clinical
research. As discussed in section 3.1 (Relevance for stroke severity and rehabilitation),
neuroimaging measures alone cannot provide a complete interpretation of the stroke survivor and
their motor deficit (Stinear, 2010; Arsava 2012; Burke & Cramer, 2013; Burke Quinlan et al.,
2015; Rehme, Volz, Feis, Eickhoff, Fink, & Grefkes, 2015; Ward, 2015; Wu et al., 2015). Thus,
it is important that we integrate information from various sources (including neuroimaging,
clinical behaviour, and life history, among other factors) to not only better understand the motor
deficit itself, but also the individual.
3 Intra-network connectivity
For the intra-network analyses, we are interested in the neural changes to areas not damaged by
the lesion. Thus, the location of the lesion is not the primary concern, but rather the main focus is
how the neural activity of these non-damaged brain regions within the motor and FP networks
are subsequently affected. Although the clusters from the intra-network analyses are reported in
the left hemisphere, a contributing factor to these results may be related to the fact we decided to
display the lesions in all participants on the right hemisphere to better generalize our findings.
Thus, our findings may not necessarily be specific to the left hemisphere per se, but the same
results may also be found if we displayed all lesions on the left hemisphere and performed our
analyses on the right hemisphere instead. As a result, the interpretations of our findings (in the
section below) are not specific to a single hemisphere, but can apply to either hemisphere.
Nonetheless, we do not expect the fact we displayed all lesions to the same hemisphere to
significantly impact our results, given that most of the RSNs are bilateral (Beckmann et al.,
2005).
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3.1 Frontoparietal network
We found that individuals with higher rs-connectivity between DLPFC and mid-VLPFC have
less hand impairment relative to those with lower DLPFC-mid-VLPFC rs-connectivity. We
suggest that rs-connectivity between DLPFC and mid-VLPFC may be involved in motor
learning, with the retrieval and reconsolidation of motor skills. Based on the working memory
model by Petrides (1996; 2002), the mid-VLPFC is implicated in the active retrieval of
information (Petrides, 1996; 2002). Active retrieval is a process whereby information about a
specific stimulus or context is recalled among competing alternatives (i.e., the recall does not
easily trigger a memory trace) (Petrides, 2002). I speculate that individuals use active retrieval to
recall their previous (movement) experiences that are relevant for the current situation, such that
the appropriate response can be determined. A motor task, such as to reach and grasp a spatula
on the table, can be performed in numerous ways (Bernstein, 1967). For example, an individual
can extend their arm, and then extend their fingers to grasp the spatula without moving their
trunk (Fujii, Lulic, & Chen, 2016). Alternatively, an individual can flex their trunk forward and
extend their fingers to grasp the spatula without extending their arm (Fujii et al., 2016). The fact
that many movement patterns exist to perform a single motor task suggests that the process of
active retrieval may also be important for individuals to select the appropriate movement pattern
from competing alternatives (Hummel, Andres, Altenmüller, Dichgans, & Gerloff, 2002). In the
case of stroke, when an individual receives an instruction to reach and grasp a spatula, the mid-
VLPFC may help to retrieve the movement pattern from their last successful attempt and inhibit
the movement patterns that were unsuccessful in achieving the motor task. Thus, the movement
pattern retrieved from a successful attempt can help inform the individual of the appropriate
movements they previously used that can be applied to the current task.
The DLPFC is thought to be important in monitoring performance to help correct and anticipate
movements (MacDonald et al., 2000; Petrides, 2000; Jamadar et al., 2010). I speculate that when
the appropriate movement pattern is selected (via the mid-VLPFC), the DLPFC will hold this
movement pattern in working memory such that the same movements can be applied to the
current motor task (Petrides, 1995). Interestingly, the DLPFC also has anatomical connections to
the SMA and premotor cortex (Koski & Paus, 2000; Ridderinkhoff, van den Wildenberg,
Segalowitz, & Carter, 2004) which suggests that information of the movement pattern held in
working memory by the DLPFC may be transferred to the SMA and premotor cortex to develop
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motor plans in preparation for action. In the case of our spatula example, the DLPFC may hold
the movement pattern that was selected from the mid-VLPFC and transmit this information to
the SMA and premotor cortex to develop a motor plan that resembles the successful movement
pattern used previously. Furthermore, the DLPFC may help to monitor performance by
comparing the movement pattern that was previously learned with current movement execution,
and if necessary, update the movement pattern based on the current motor experience. Thus, the
ability to successfully perform the movement using a movement pattern that was learned
previously enables the reconsolidation of the motor skill. Taken together, the mid-VLPFC and
DLPFC may be important to retrieve, apply, and reconsolidate the motor skills for a given task.
Alternatively, individuals with high rs-connectivity between DLPFC and mid-VLPFC may be
more capable in adjusting or adapting their movements. An attentional model proposed by
Mitchell (2011) suggests the DLPFC and mid-VLPFC may be important to adapt to novel or
dynamic situations. In particular, Mitchell (2011) suggests the mid-VLPFC is primarily involved
in modulating the relationship between the task goal and action (i.e., stimulus-response
relationship). A change in the environment, such as a different end target location, while an
action is being executed, may require the mid-VLPFC to quickly inhibit movement, re-assess the
stimulus-response relationship, and then determine the proper course of action. When the mid-
VLPFC has modulated the stimulus-response relationship, Mitchell (2011) hypothesizes the
DLPFC is implicated in (re)-focusing attention to place greater emphasis on relevant
information, such as stimulus features or prior experiences, that would assist in task completion.
This model appears to be highly applicable in real-life situations, given that our surrounding
environment is constantly changing to some degree (Ravizza & Carter, 2008). In stroke,
individuals who can better adapt to different conditions and focus on the relevant information
will likely select and execute the proper actions for task performance. This interpretation may
not be mutually exclusive from the motor learning interpretation since prior motor experiences
may be retrieved to help adapt current performance.
Interestingly, the mid-VLPFC and DLPFC are thought to be important for task-switching (Badre
& Wagner, 2007; Jamadar et al., 2010) in setting the task goal and monitoring performance
(Rubinstein et al., 2001; Badre & Wagner, 2007). In our study, however, the rs-connectivity
between the mid-VLPFC and DLPFC did not correlate with task-switching performance. One
reason for why we did not find a relationship with task-switching performance could relate to the
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type of paradigm that was used. Although the Trail Making Test (TMT) may be a sensitive
assessment to detect problems in cognitive function, it is not an ideal assessment to assess task-
switching given that many other cognitive processes, such as attention and working memory, are
also implicated in the performance of the TMT (Gaudino, Geisler, & Squires, 1995; Crowe,
1998; Stuss et al., 2001; Oosterman et al., 2010; Cepeda, Blackwell, & Munakata, 2013). Given
that TMT performance is often measured as the total time to complete the assessment, this value
can broadly determine whether cognitive function (in general) is ‘abnormal’. However, it does
not clearly determine which cognitive function(s) specifically contribute to ‘abnormal’
performance. The TMT will be further discussed in subsection 4.2 (Limitations).
3.2 Motor network
From this study, we found that individuals with greater rs-connectivity between M1 and SMA
have less hand impairment and better motor function than those with lower M1-SMA rs-
connectivity. This result is in line with previous literature that has found the coupling between
M1 and SMA is associated with better motor outcome following stroke (Grefkes et al., 2008;
Park et al., 2011; Rehme et al., 2011; Favre et al., 2014). We suggest that stroke survivors with
greater rs-connectivity between M1 and SMA are possibly using more motor planning to assist in
movement selection and execution. The SMA is involved in planning and temporal sequencing
of movements (Tanji & Shima, 1994; Gerloff et al., 1997; Shima & Tanji, 1998). Given that the
SMA is often recruited prior to movement execution, it is thought that the SMA is important to
set the movement trajectory and develop the proper/optimal order of movements that can be used
to inform action execution (Nachev, Kennard, & Husain, 2008). The motor plans developed by
the SMA may also involve information on the kinematics (e.g., speed) and kinetics (e.g., force)
of movement (Picard & Strick, 2003; Padoa-Schioppa, Li, & Bizzi, 2004). These results are also
consistent with our discussion in section 2.3 (Movement in healthy individuals), involving the
motor control model by Shadmehr and Krakauer (2008). Specifically, their model recognizes that
the SMA is one region important for motor planning that enables individuals to fine-tune their
movements such that performance can be accurate and precise. Taken together, stroke survivors
who have greater coupling between the M1 and SMA during movement are possibly more
organized with their actions since they can better prepare and anticipate the required movements
for task performance.
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4 Study strengths and limitations
4.1 Strengths
One of the strengths in this thesis involves the use of clinical assessment scores, as opposed to
performance on behavioural paradigms, to correlate with neuroimaging measures. Many
previous studies that examined the neural relationship between motor and FP regions and motor
outcome after stroke used an experimental paradigm, such as a finger tapping task (Ward et al.,
2003; Puh et al., 2007; Stewart et al., 2016). Importantly, performance on these paradigms may
not always be indicative of performance on clinical assessments. Thus, the findings from my
thesis can potentially be better understood within a clinical perspective given that the degree of
rs-connectivity was correlated with performance on specific motor assessments. Furthermore, an
interesting component of my thesis was the inclusion of assessments for motor impairment and
activity. Many previous studies only use a single motor assessment that either measures motor
impairment (Park et al., 2011; Yin et al., 2012; Chen & Schlaug, 2013) or function (Carter et al.,
2010). In rehabilitation, however, important distinctions are made between “impairment” and
“function” (World Health Organization, 2002; Levin et al., 2009). Therefore, it is important to
determine whether differences in rs-connectivity would be associated with impairment, function,
or both since it can provide insight on the motor recovery pattern (i.e., restore or compensate
motor ability). In this study, the intra-network and inter-network findings were similar between
the motor impairment and function measures, prior to corrections for multiple comparisons. This
suggests that motor and FP networks are not necessarily limited to an individual aspect of motor
ability (e.g., impairment or function), but instead appear to be involved in the collective aspects
of motor ability (e.g., impairment and function).
The use of two different approaches to analyze the motor and FP networks in relation to motor
outcome is also one of the strengths in this thesis. Overall, I use a hypothesis-driven approach for
my thesis to determine the specific networks (i.e., motor and FP) that warrant investigation. For
the intra-network analysis, however, a data-driven approach is used for the group-level FEAT
analyses to determine which regions within the motor or FP networks that had its rs-connectivity
with the seed modulated by performance on motor or task-switching assessments. This approach
is useful to determine all the regions within the network – as opposed to limiting our analyses to
specific brain regions within a single network – that have its rs-connectivity with the seed
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modulated by behavioural performance. Although a whole-brain approach is certainly feasible,
we decided to restrict our intra-network analyses within the motor and FP networks since these
were the two networks of primary interest in our study. Thus, I believe a whole-brain approach is
beyond the scope of the study objectives and any results outside the motor and FP networks may
be difficult to interpret within the context of the a priori approach we have taken with regards to
the motor and FP networks involved in motor re-learning of stroke survivors. For the inter-
network analysis, our results add to the current literature (Park et al., 2011; Yin et al., 2012)
since we show that the relationship between motor and FP regions and motor outcome is not only
limited to M1-DLPFC rs-connectivity, but is also found between entire motor and FP networks.
As discussed in subsection 2.1 (Inter-network Connectivity between Motor and Frontoparietal
Networks) of this chapter, it is important to explore this relationship through a network
perspective given that movement is a complex process that involves the communication between
many motor and non-motor regions.
Lastly, one of the strengths in this study is the use of various methods to study motor deficits
post-stroke. In particular, three main methods were used in this study: 1) lesion analysis; 2) brain
imaging; and 3) behavioural assessment. The combination of all three methods enables us to
obtain a more ‘holistic’ understanding of our stroke population and their motor deficits.
Specifically, each method by itself has its limitations. For example, lesion analysis can provide
information on the critical brain regions to perform certain processes (Stuss & Alexander, 2007),
such as movement, however, it provides little information about how the lesioned areas function
together with the regions spared from damage (Rorden & Karnath, 2004). Brain imaging is
useful to understand how certain regions function (or couple together) in response to task
performance (or at rest) (Rorden & Karnath, 2004). However, a major weakness of brain
imaging is the difficulty to determine whether certain regions are critical in task performance and
their specific role(s) for the task (Rorden & Karnath, 2004). Lastly, behavioural assessments
provide important information about the behavioural deficits as a result of pathology, but little
information can be gleaned from the neural processes involved in behaviour. In essence, each
method individually has its weaknesses which limit our ability to make interpretations of our
results. Importantly, however, the advantage of our study is that we use multiple methods to
address the weaknesses of each method separately to obtain a better understanding of the neural
processes associated with post-stroke behaviour in our participants.
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4.2 Limitations
A limitation of this study was that we only used data at one time point. As a result, we are
limited in the interpretations that we can make about the motor and FP networks with motor
recovery. In the strictest sense, interpretations involving motor “recovery” cannot be made in
cross-sectional studies since recovery implies studying a change over time. Furthermore, a
limitation of this study was the fact that the sex distribution in our stroke population may not be
representative of the stroke population found worldwide. In particular, stroke incidence is 33%
higher in males as opposed to females (Appelros, Stegmayr, & Terént, 2009), whereas stroke
prevalence is approximately 15% higher in females as opposed to males (possibly due to the fact
that females typically have a longer life expectancy than males) (Reeves et al., 2008). In our
study, however, we included eighteen (66%) participants who were male and nine (33%)
participants who were female. Given that we are examining neural measures and motor
performance during the chronic stage, it may be more ideal if we had more females in our study.
However, we did control for sex distribution in our study when relevant. Specifically, we
included sex as a covariate in the GLM of our intra-network analyses. Furthermore, sex did not
correlate with our inter-network measures or behavioural performance scores, thus we did not
include sex as a covariate in our partial Spearman’s correlations for the inter-network analyses.
Another limitation of my thesis was that we did not find a relationship between any of our neural
measures and TMT performance. The FP network was related to motor outcome, which is
consistent with prior studies that suggest the FP network is implicated in motor learning
(Hikosaka et al., 2002; Albert et al., 2009; Kim et al., 2015). However, given the FP network did
not correlate with TMT-ps, we are not certain of the relationship between the FP network and
cognitive performance, as previously reported in other studies (Dove et al., 2000; Sohn et al.,
2000; Seeley et al., 2007; Hagen et al., 2014; Li et al., 2015).
One reason there may have not been a significant relationship between the FP network and
TMT-ps could be due to our analysis technique. As shown in Figure 5-5 (panel A), twenty-two
(81%) of our participants, had a TMT-ps value less than two. With very little spread in our data
set, it is difficult to interpolate relationships between TMT-ps performance and our connectivity
measures when performing a correlational analysis. An alternative approach may be to separate
our participants into two groups: 1) participants with ‘normal’ TMT-ps values who likely do not
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have cognitive problems as a result of stroke; and 2) participants with ‘abnormal’ TMT-ps values
who may potentially have cognitive problems as a result of stroke. In this thesis, for example, I
consider the TMT-ps measure of 1.4 as the critical point, such that a TMT-ps value less than 1.4
is an indication of ‘normal’ cognitive function whereas a TMT-ps value greater than 1.4 is a
potential indication of cognitive problems [Appendix 7-5]. Given these TMT-ps guidelines,
eleven of twenty-seven (i.e., 40%) participants have a TMT-ps value less than 1.4 and thus,
sixteen of twenty-seven (i.e., 60%) participants have a TMT-ps value greater than 1.4 [Figure 5-
5, panel A]. With relatively equal numbers in each subgroup, an analysis comparing the intra-
network and inter-network connectivity between the two subgroups may possibly be more
appropriate given the distribution of TMT-ps values in our stroke population.
Figure 5-5: Trail Making Test proportion score distribution and lesion location summary
(A) Stem and leaf plot depicting the distribution of participant performance for the Trail Making Test Proportion
score (TMT-ps). The number in brackets represents the total number of participants for a given stem. (B) A table
summarizing the lesion location for all participants in the study. Frontal lesions were further classified according to
the anatomical divisions of the frontal lobes (Petrides, 2005). The number of participants with a lesion that spans a
stated location is listed in the table. It is important to note that some participants would be counted in more than one
lesion location if their lesion spans more than a single region. The number in brackets represents the number of
participants with a lesion only in the stated region.
Furthermore, Stuss and colleagues (2001) showed that lesion location may influence TMT
performance. Specifically, individuals who were most impaired had lesions in the dorsolateral
frontal area while those who were less impaired had lesions in ventromedial or orbitofrontal
areas. Although lesion location would be important to examine, the number of participants in our
study may not be feasible to study the relationship between FP network and TMT performance in
relation to lesion location. Specifically, if we were to separate our participants based on lesion
location [Figure 5-5, panel B], we have few participants in the different lesion location (e.g.,
dorsolateral, inferior medial) and even fewer individuals with lesions localized to a single frontal
72
region. Thus, it may be challenging to perform analyses with a small number of participants in
each of the lesion location subgroups. Lastly, our understanding of the relationship between the
FP network and task-switching (specifically TMT performance) is based on studies with healthy
participants (Dove et al., 2000; Sohn et al., 2000; Seeley et al., 2007; Hagen et al., 2014) (or
those with hypertension (Li et al., 2015)). Behaviourally, studies have reported that stroke
survivors show impaired performance on the TMT (Stuss et al., 2001; Kopp et al., 2015). To my
knowledge, however, there has been no prior study that examined FP network connectivity with
TMT-ps performance in stroke survivors. Thus, it is difficult to conclude whether the non-
significant relationship seen in our study between the FP network and TMT-ps is expected or
unexpected since the neural basis of TMT performance after stroke is not as well-studied as in
healthy individuals.
As mentioned in subsection 5.3.1 (Frontoparietal network), the TMT is a noisy measure to assess
task-switching performance. To my knowledge, the TMT Proportion Score (TMT-ps) – which
was used for this study – can be considered as the gold standard to assess task-switching
performance on the TMT (Corrigan & Hinkeldey, 1987; Stuss et al., 2001). However, the TMT-
ps may not be a truly sensitive measure for task-switching, given that performance could also be
influenced by other factors, such as reading ability (Crowe, 1998) which is required to
comprehend the array of numbers and letters on the test page. Alternative assessments for task-
switching, such as the Wisconsin Card Sorting Test (Anderson et al., 1991), may be useful if we
want to correlate our neural measures with performance on clinical assessments.
In addition to the TMT being a noisy measure, the demand placed on working memory processes
may influence TMT performance. Specifically, the TMT requires individuals to not only
remember the task-switching rule (i.e., alternate between number and letter sequencing), but to
also remember the number and letter they previously sequenced. This demonstrates that working
memory is required to know the subsequent number or letter that needs to be found on the page
(Gaudino, Geisler, & Squires, 1995; Crowe, 1998). As a result, task-switching performance may
be hindered over the course of the test since the working memory requirements accumulate from
the beginning (Crowe 1998).
To reduce the working memory demands encountered with the TMT, we can use a task-
switching paradigm (perhaps implemented on a computer) that measures reaction time to switch
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between two successive trials [Figure 5-6] (Dove et al., 2000; Sohn et al., 2000; Braver et al.,
2003; Badre & Wagner 2006; Shallice et al., 2008b). This approach only requires individuals to
remember the task-switching rule since each trial is independent of each other. Therefore,
working memory is relatively constant across the experiment since individuals are required to
only focus on the current task. As a result, performance on these paradigms is likely more
representative of task-switching. Taken together, the inability to calculate a relatively ‘pure’
measure of task-switching using the TMT may have led to a null finding between the FP network
and task-switching performance.
Figure 5-6: Task-switching paradigm measuring the switch cost between successive trials
An example of a task-switching paradigm in which a cue is provided (i.e., “NUMBER” or “LETTER”) and the
participant is then required to press the left or right button, based on the cue that was given. It is expected that a
participant who is given different cues on two successive trials (e.g., “NUMBER” and “LETTER”) would produce a
switch cost. This paradigm is an example of a ‘focused approach’ since the switch cost is determined based on the
difference in reaction time between two successive trials (e.g., Trial 1 and 2). Thus, each trial is independent of the
other trials since the participant is not required to remember their previous response. Image ideas adapted from
Badre and Wagner (2006).
It can also be argued that task-switching paradigms can be classified as ‘cognitive-based’
(Allport, Styles, & Hsieh, 1994; Rogers & Monsell, 1995; Aron, Monsell, Sahakian, & Robbins,
2004; Shallice et al., 2008b) or ‘motor-based’ (Serrien et al., 2007; Serrien, 2009; Tallet et al.,
2009). The TMT can be considered as a cognitive-based paradigm since participants switch
between sequencing abstract concepts, such as letters and numbers. In contrast, a motor-based
task-switching paradigm may involve switching hands during a finger tapping task (Serrien et
al., 2007; Serrien, 2009; Tallet et al., 2009). Although both types of paradigms are considered
“task-switching”, it is possible that different processes are required for each type (Badre &
Wagner, 2007; Serrien et al., 2007). Previous studies have shown that the motor network showed
increased coupling when participants switched hands while finger tapping. Thus, future studies
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may consider exploring the intra-network connectivity of the FP network and the Motor-FP
connectivity with performance on a motor-based task-switching paradigm.
Another limitation involves the lack of variability across participant scores on the CMSA-Arm.
As seen on the stem and leaf plot in Figure 5-7 (panel A), fourteen of the twenty-seven
participants scored a stage 3 on the CMSA-Arm. Given that the CMSA-Arm stage is the same
for more than half of the participants, the use of a correlational analysis may not be sensitive to
show whether differences in rs-connectivity are associated with the level of arm impairment.
This is likely one reason there were no significant relationships between our intra-network or
inter-network connectivity measures and CMSA-Arm stage. In particular, the intra-network
analyses for the motor and FP networks with CMSA-Arm stage show a similar pattern of
findings (to our results reported in Figure 4-3) when viewed at lower thresholds [Figure 5-8,
panels A & B]. This suggests that a similar relationship involving the motor and FP intra-
network connectivity with CMSA-Arm stage is likely present, and may also be significant if we
had a more equal distribution of our participants in the other CMSA-Arm stages. Taken together,
interpolation from a correlational analysis can become more difficult when a majority of the data
points cluster in one area (i.e., at a single CMSA-Arm stage) since the line-of-best-fit will be
largely influenced by the minority of data points elsewhere in the scatterplot.
In contrast, the CMSA-Hand and ARAT have a more variable distribution of scores [Figure 5-7,
panels B & C], which may allow for better interpolation when using a correlational analysis.
This is because we can determine whether differences in hand impairment or motor function are
associated with differences in rs-connectivity. It is important to note, however, that the
significant relationship between FP intra-network connectivity and CMSA-Hand stage (reported
in Figure 4-3) was not significant for the ARAT. However, the relationship between the FP
intra-network connectivity and the ARAT showed a similar pattern of findings when viewed at
lower thresholds [Figure 5-8, panel C]. This suggests that a similar relationship involving the FP
intra-network connectivity and ARAT is likely present, even if the relationship may not be as
strong as the significant results with CMSA-Hand stage.
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Figure 5-7: Stem and leaf plots of motor assessment scores
Stem and leaf plots depicting the distribution of behavioural scores in participants for the (A) Chedoke-McMaster
Stroke Assessment Stage of Arm (CMSA-Arm), (B) Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-
Hand), and (C) Action Research Arm test (ARAT). The number in brackets represents the total number of
participants for a given stem.
Figure 5-8: Intra-network connectivity results (non-significant)
Intra-network results that did not meet the threshold set for statistical significance in this study. For illustration
purposes, voxels with z >2.5 are shown. (A) The rs-connectivity between the left primary motor cortex (seed) with
midline supplementary motor area (SMA) was not significantly modulated by performance on Chedoke-McMaster
Stroke Assessment Stage of Arm (CMSA-Arm). The rs-connectivity between the left dorsolateral prefrontal cortex
(seed) with left Brodmann Area (BA) 45a (mid-ventrolateral prefrontal cortex (mid-VLPFC)) was not significantly
modulated by performance on the CMSA-Arm (B) or the Action Research Arm Test (C).
Lastly, a general limitation not necessarily specific to my thesis, but applies more generally to
the field of neuroimaging analyses involves the method I used to create the seed masks for this
study. Specifically, the seed masks created in standard (Montreal Neurological Institute (MNI))
space were required to be transformed to functional space for use in each participant. As a result,
the location of the seed mask in functional space may correspond to a similar – but not the same
– region as what was determined in standard space. This can be interpreted more of a limitation
with the imaging analysis software rather than a limitation of the study design itself.
Nonetheless, one suggestion to possibly improve seed localization in functional space is to have
participants perform a simple fMRI paradigm (e.g., finger tapping) which is expected to elicit a
BOLD response in the region(s) of interest (e.g., M1). Thus, we can derive a seed mask directly
in functional space given that the brain regions activated during the simple fMRI paradigm are
specific to each participant. Furthermore, the proposed method is a way to verify the accuracy of
the transformation matrix used to transform the seed mask from standard to functional space
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since an overlap between the seed mask (in functional space) and the activated regions would
suggest a relatively accurate transformation matrix.
5 Study implications
The results from my thesis suggest that the coupling between the motor and FP networks may be
associated with better motor outcome after stroke. As a result, our study offers three main
implications: 1) to inform clinicians on the importance of using a holistic approach in motor
rehabilitation; 2) to provide potential targets for therapeutic interventions, such as brain
stimulation; and 3) to better understand the mechanism(s) that underlie behavioural interventions
for motor recovery that apply a learning component.
From a short-term perspective, this study suggests the importance of using a holistic approach in
motor rehabilitation. Currently, interventions for motor rehabilitation primarily focus on the use
of simple motor tasks performed in repetition to re-learn movements (Page, 2003; Takeuchi &
Izumi, 2013). This suggests that motor deficits are primarily viewed as a motor issue that can
improve with the practice of movements. However, the results from this study demonstrate that
movement post-stroke also involves non-motor regions (and specifically the FP network). Thus,
interventions that focus on non-motor aspects, such as cognitive-motor training (e.g., motor
imagery) which implicates the FP network (Sharma et al., 2006), may also be used in
conjunction with traditional motor rehabilitation to help improve motor performance in stroke
survivors.
In the long-term, the motor and FP networks may represent potential targets for therapeutic
interventions. In particular, interventions involving brain stimulation techniques, such as
repetitive transcranial magnetic stimulation (rTMS) (Pascual-Leone, Walsh, & Rothwell, 2000)
or transcranial direct-current stimulation (tDCS) (Filmer, Dux, & Mattingley, 2014), can be
applied to both motor and FP regions to possibly enhance motor recovery. Importantly,
stimulation of one region may influence the entire network associated with that region. Thus,
rTMS and tDCS applied to both the M1 and DLPFC may not only enhance the coupling within
the motor and FP networks, respectively, but the coupling between these two networks may be
increased as well. Given that our results demonstrate that individuals with greater coupling
between the motor and FP networks have better motor outcome, brain stimulation applied to both
networks may help improve movement after stroke. Nonetheless, future research can test the
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validity of this implication and determine the target locations that yield the best results after
stimulation.
Furthermore, a better understanding of the relationship between motor and FP networks for
motor outcome may help to better understand the effectiveness of current behavioural
interventions that apply learning strategies for motor recovery, such as Cognitive Orientation to
Daily Occupational Performance (CO-OP) (McEwen et al., 2015) or motor imagery (Sharma et
al., 2006). In CO-OP, for example, individuals are required to make guided discoveries in their
approach to perform a (motor) task (McEwen et al., 2015). Therefore, I speculate the following
events may occur. Prior to CO-OP, the Motor-FP connectivity may be low in individuals with
stroke and thus, task performance is not successful. During CO-OP, guidance from the clinician
may help to strengthen the Motor-FP connectivity such that greater FP network recruitment and
hence, more feedback about task accuracy (Albert et al., 2009; Kim et al., 2015), can improve
performance. After CO-OP, the Motor-FP connectivity is considerably stronger than before the
intervention which enables individuals to apply their learning and CO-OP training to novel tasks.
In essence, a better understanding of the neural mechanisms by which these behavioural
interventions may operate can help to stimulate research in the development of strategies that
stimulate the FP network to possibly enhance motor recovery after stroke.
6 Future directions
Despite finding a relationship between the motor and FP networks, future studies are required to
replicate findings and test the interpretations suggested in this thesis. For example, the
application of effective connectivity (Friston, 2011) or graph theory (Bullmore & Sporns, 2009)
may help to better understand the relationship between motor and FP networks and motor
outcome. Briefly, effective connectivity can provide information on how two brain regions may
influence each other by determining the direction of the rs-connectivity between two regions
(Friston, 2011). Alternatively, graph theory can provide information on the network
characteristics, such as the hub or connector (Bullmore & Sporns, 2009). Hubs are major regions
within a network and connectors are regions that link two networks together. Network
characteristics can help to determine the critical regions that underlie the relationship between
the motor and FP networks.
78
Furthermore, future studies may consider exploring the change in coupling between the motor
and FP networks before and after an intervention for motor rehabilitation. This may help us
better understand the significance of Motor-FP network coupling in motor recovery after stroke.
Given that the data used in this thesis is part of a larger clinical trial, it is quite feasible to
perform a future study that measures the change in Motor-FP network coupling before and after
an intervention (i.e., music-supported rehabilitation). In this scenario, I hypothesize that stroke
survivors who show greater behavioural improvements in motor ability post-intervention would
also show greater Motor-FP network coupling than stroke survivors who show less (or no)
improvements in motor ability post-intervention.
Although the primary interpretation of our results in this thesis was in the context of motor re-
learning in stroke survivors, it is important for future studies to test the validity of these
interpretations. The ability to examine changes, if any, between the motor and FP networks after
rehabilitation may be due to motor re-learning (Albert et al., 2009; Kim et al, 2015), but can also
be, in part, explained by a ‘ripple’ or experience-dependent effect (Stevens, Buckner, Schacter,
2010). The ‘ripple effect’ suggests that brain regions recently activated during a simple task,
such as button pressing (a simple task that does not require much learning) (Tung et al., 2013),
can subsequently modulate the neural activity at rest in networks that involve the same brain
regions previously activated for the task (Stevens & Spreng, 2014). This suggests that the effect
of learning a complex movement or simply repeating a task that implicates both motor and FP
networks (e.g., motor imagery (Sharma et al., 2009; Oostra et al., 2016)) may both lead to
changes in network connectivity (and perhaps motor performance). The ability to tease apart and
test the influence of these two interpretations may eventually have important implications on the
interventions and/or strategies for motor rehabilitation. Taken together, the findings from my
thesis can hopefully stimulate further research to explore the motor and FP network relationship
in stroke survivors with motor deficits.
77
Chapter 6 Conclusion
Motor outcome after stroke not only involves the integrity of the motor system, but may also
involve the integrity of the non-motor (i.e., frontoparietal) regions. In my thesis, three major
results are found: 1) individuals with higher rs-connectivity between the motor and FP networks
have better motor function than those with lower Motor-FP connectivity; 2) individuals with
higher rs-connectivity between DLPFC and mid-VLPFC have less hand impairment than those
with lower DLPFC-mid-VLPFC rs-connectivity; and 3) individuals with higher rs-connectivity
between M1 and SMA have less hand impairment and better motor function than those with
lower M1-SMA rs-connectivity. These results suggest that the rs-connectivity within and
between the motor and FP networks may, in part, be related to better motor outcome. In essence,
the results from my thesis demonstrate that motor deficits may involve other factors (i.e.,
networks) beyond the motor system and that the coupling between motor and FP networks may
represent one important component to enhance movement after stroke.
78
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Appendices
Appendix 7-1: Peak Coordinates Averaged for Left Primary Motor Cortex (M1) Seed
Based on Studies with an Arm/Elbow or Hand/Finger Paradigm
Supplementary Table IA: Arm/Elbow Paradigm
First Author (Year) Journal Paradigm x y z
Alkadhi (2002) Am J Neuroradiol Elbow Movement -29 -25 61
Lotze (2000) Neuroreport Elbow Movement -28 -24 64
Average Coordinates -28 -24 62 All peak coordinates are from healthy participants.
Supplementary Table IB: Hand/Finger Paradigm
First Author (Year) Journal Paradigm x y z
Adam (2003) Cogn Brain Res Finger Key Press -34 -14 62
Alkadhi (2002) Am J Neuroradiol Hand Movement &
Finger Movement
-36.5 -21 58
Baraldi (1999) Neurosci Lett Finger Movement -31 -15 51
Bischoff-Grethe (2002) J Neurosci Button Press -30 -28 42
Calhoun (2002) Hum Brain Mapp Hand Driving -33 -13 60
Choi (2001) Exp Brain Res Hand Gestures
Culham (2003) Exp Brain Res Grasp vs. Reach -29 -18 56
Cunnington (2002) Neuroimage Finger Press -41 -17 60
De Weerd (2003) Neuroimage Hand Tapping -42 -17 49
Debaere (2001) Neuroimage Wrist
Flexion/Extension
-40.5 -24 64.5
Dhamala (2003) Neuroimage Rhythmic Finger
Tapping
-36 -28 64
Dreher (2002) Eur J Neurosci Button Press -28 -16 60
Ehrsson (2000) J Neurophysiol Precision &
Power Grip
-34 -20 56
Ehrsson (2001) J Neurophysiol Grip -36 -20 48
Ehrsson (2003) J Neurophysiol Finger Grip -40 -36 60
Foltys (2003) Neuroimage Hand Clenching -33 -29 56
Hamzei (2002) Exp Brain Res Writing -24 -16 54
Hamzei (2002) Neuroimage Pinch Grip -42 -12 58
Haslinger (2002) Cogn Brain Res Finger Movement -42 -19 56
Indovina (2001) Exp Brain Res Button Press -38 -20 68
Indovina (2001) Neuroimage Finger Movement -34.4 -26.4 57.5
Ino (2003) Neurosci Res Clock Drawing -34 -12 56
Jancke (2000) Cogn Brain Res Finger Tapping -49 -20 49
Jantzen (2002) Neurosci Lett Finger Flexion -33.5 -17 57
Johansen-Berg (2002) Exp Brain Res Button Pressing -28 -8 54
Kawashima (2000) J Neurophsyiol Visually Cued Finger
Movement
-34 -18 60
108
Kobayashi (2003) Neuroimage Finger Movement -40.8 -17.2 57.4
Koski (2002) Cereb Cortex Finger Movement -40 -24 58
Kuhtz-Buschbeck (2001) Eur J Neurosci Natural Grip -40 -24 52
Liddle (2001) Hum Brain Mapp Finger Press -38 -26 65
Lotze (2000) Neuroreport Finger Tap -40 -16 56
Lotze (2003) Brain Voluntary Wrist
Movement
-38 -20 58
Matsuo (2003) Cogn Brain Res Finger Movement -38 -18 58
Mattay (1998) Psychiatry Res:
Neuroimaging Sect
Thumb Opposition -36 -22 68
Mayville (2002) Hum Brain Mapp Pinch -39 -28 56
Muller (2002) Cogn Brain Res Finger Tapping -33 -21 60
Rao (1997) J Neurosci Finger Tapping -35 -24 55
Riecker (2003) Neuroimage Finger Tapping -45 -24 60
Rotte (2002) Stereotact Funct Neurosurg Finger Movement -35 -18 59
Rowe (2002) Brain Finger Tapping -50 -28 48
Sakai (2000) J Neurosci Button Press -36 -16 46
Schubotz (2001) Cereb Cortex Finger Tapping -35 -19 61
Stippich (2002) Neurosci Lett Hand Movement -37.14 -24.5 55.21
Stoeckel (2003) Neuroimage Hand Movement -44 -17 43
Sugio (2003) Neuroreport Grasp -32 -20.5 53.5
Toma (2003) Neurosci Lett Button Press -44 -15 50
Toni (1998) Neuroimage Finger Movements -40 -18 64
Toni (2002) J Cogn Neurosci Finger Press -26 -22 66
Vaillancourt (2003) J Neurophysiol Grip -15 -16 63
Watanabe (2002) Neuroimage Finger Press -32 -8 62
Winterer (2002) Neuroimage Button Press -35 -26 50
Average Coordinates -36 -20 56 All peak coordinates are from healthy participants.
109
Appendix 7-2: Peak Coordinates Averaged for Left DLPFC Seed Based on Studies with a
Task-switching Paradigm
First Author (Year) Journal Paradigm x y z
Braver (2003) Neuron Word Switching -46 15 21
Cools (2004) J Neurosci Color Switching -48 24 21
Dreher (2003) Cereb Cortex Letter Switching -52 12 36
Luks (2002) Neuroimage Number Switching -38 31 23.5
Ruff (2001) Neuroimage Color-Word
Switching
-44 22 24
Smith (2004) Hum Brain Mapp Shape-Position
Switching
-28 47 17
Average Coordinates -43 25 24 All peak coordinates are from healthy participants.
110
Appendix 7-3: Peak Coordinates Averaged for Left Primary Visual Cortex (V1) Seed
Based on Studies with a Flashing Checkerboard Paradigm
First Author (Year) Journal Paradigm x y z
Di Russo (2002) Hum Brain Mapp Circular
Checkerboard
-4 -81 5
Kim (2011) Cereb Cortex Checkerboard
Wedge
-7.2 -92.8 4.3
Miki (2001) Ophthalmic Res Varying Check Size -4 -90 25
Moradi (2003) Neuroimage Varying Presentation
Regions
-8.45 -85.9 1.35
Wan (2006) Neuroimage Varying Frequency
and Contrast
-7.6 -92.8 3.2
Average Coordinates -6 -88 8 *PubMed Search used the following keywords: “visual cortex”, “flashing checkerboard”, “checkerboard”, “fmri”,
and “human”. All peak coordinates are from healthy participants.
111
Appendix 7-4: Peak Coordinates Averaged for Left Dorsal Anterior Cingulate Cortex
(dorsal ACC) Seed Based on Studies with a Stroop Task
First Author (Year) Journal Paradigm x y z
Coderre (2008) Brain Lang Word Stroop -6.5 41 3.5
de Zubicaray (2001) Hum Brain Mapp Word Stroop -4 42 -4
Heckers (2004) Am J Psychiatry Number Stroop 0 18 46
Leung (2000) Cereb Cortex Word Stroop -6 23 38
Milham (2001) Cogn Brain Res Word Stroop 0 10 44
Milham (2005) Hum Brain Mapp Word Stroop -4 14 48
Average Coordinates -3 25 29 All peak coordinates are from healthy participants.
112
Appendix 7-5: Statistical equations
Statistic Equation Critical value
(A) Trail Making Test
proportion score 𝑇𝑀𝑇𝑝𝑠 =
(𝑇𝑀𝑇4 − 𝑇𝑀𝑇2)
𝑇𝑀𝑇2 > 1.4
(B) Williams’ T2 test
𝑇2 = (𝑟𝑗𝑘 − 𝑟𝑗ℎ)√(𝑁 − 1)(1 + 𝑟𝑘ℎ)
2 (𝑁 − 1𝑁 − 3
) |𝑅| + 𝑟2(1 − 𝑟𝑘ℎ)3 > 1.71
(C) Weisberg’s t statistic
𝑡𝑖 = 𝑟𝑖√𝑛 − 𝑝′ − 1
𝑛 − 𝑝′ − 𝑟𝑖2 < 3.56
(D) Cook’s Distance (CDi) 𝐶𝐷𝑖 =
(�̂� − �̂�(−𝑖))′𝑋′𝑋(�̂� − �̂�(−𝑖))
(𝑝 + 1)𝑀𝑆𝑟𝑒𝑠 < 1
Statistical equations used for this thesis.
(A) The Trail Making Test (TMT) proportion score was used to assess task-switching performance (based on
completion times for number sequencing (TMT condition #2 (TMT-2)) and number-letter switching (TMT condition
#4 (TMT-4)). The critical value is based on TMT-2 and TMT-4 completion times (obtained from the Delis-Kaplan
Executive Function System Manual) for an average 60-year old. The critical value was chosen for this age group
since the average age of the participants in this study is approximately 60 years. An individual who has a TMT-ps
value greater than 1.4 may have some degree of cognitive impairment.
(B) The Williams’ T2 test was used to determine whether two dependent correlation coefficients are significantly
different from each other. For this study, a T2 value greater than 1.71 suggests the correlation coefficients between
the two dependent conditions are significantly different.
(C) The Weisberg’s t statistic was used to determine whether the residual for an individual case is an outlier. For this
study, a ti value less than 3.56 suggests an individual case is not a significant outlier.
(D) Cook’s Distance (CDi) was used to determine influential cases in the data set. A CDi value less than (1) suggests
an individual case is not an influential data point.
11
3
Ap
pen
dix
7-6
: D
ata
Tab
le
Ab
bre
via
tion
s: C
MS
A-A
rm (
Ched
oke-
McM
aste
r S
tro
ke
Ass
essm
ent:
Sta
ge
of
Arm
Im
pai
rmen
t, 1
-7);
CM
SA
-Han
d (
Ch
edok
e-M
cMas
ter
Str
ok
e A
sses
smen
t: S
tag
e
of
Han
d I
mp
airm
ent,
1-7
); A
RA
T (
Act
ion R
esea
rch A
rm T
est,
0-5
7);
TM
T-p
s (T
rail
Mak
ing
Tes
t P
ropo
rtio
n S
core
); M
oto
r-F
P c
on
nec
tiv
ity
(In
ter-
net
wo
rk
con
nec
tiv
ity b
etw
een m
oto
r an
d f
ronto
par
ieta
l n
etw
ork
s) (
Fis
her
’s z
-tra
nsf
orm
); M
oto
r-V
isu
al c
on
nec
tivit
y (
Inte
r-n
etw
ork
co
nn
ecti
vit
y b
etw
een
mo
tor
and
vis
ual
net
wo
rks)
(F
ish
er’s
z-t
ransf
orm
); M
oto
r-E
xec
conn
ecti
vit
y (
Inte
r-n
etw
ork
co
nn
ecti
vit
y b
etw
een
mo
tor
and
ex
ecu
tiv
e co
ntr
ol
net
wo
rks)
(F
ish
er’s
z-t
ran
sfo
rm);
Sex
(Mal
e/F
emal
e).
Part
icip
an
t C
MS
A-
Arm
(sta
ge)
CM
SA
-
Ha
nd
(sta
ge)
AR
AT
T
MT
-ps
Moto
r-F
P
con
nec
tivit
y
(Fis
her
’s z
)
Moto
r-V
isu
al
con
nec
tivit
y
(Fis
her
’s z
)
Moto
r-E
xec
con
nec
tivit
y
(Fis
her
’s z
)
Age
(yea
rs)
Sex
(M/F
)
Tim
e
sin
ce
stro
ke
(yea
rs)
Ed
uca
tion
(yea
rs)
Les
ion
volu
me
(cm
3)
1
2
2
9
0.8
90
0.4
90
0.0
99
-0
.033
76.4
17
F
7.1
67
14
35.5
16
2
4
4
49
1.5
00
0.1
97
0.4
02
-0
.173
64.2
50
F
1.1
67
16
1.9
20
3
4
4
50
1.5
40
0.2
93
0.0
51
0.3
18
73.9
17
M
1.1
67
12
2.3
20
4
5
5
48
3.3
18
0.4
32
0.1
53
0.0
81
79.1
67
M
1.1
67
10
31.9
20
5
3
2
4
1.6
07
0.3
96
0.5
19
0.4
33
40.6
67
M
1.1
67
15
321.0
96
6
3
3
37
1.4
25
1.1
29
0.6
34
0.8
76
44.8
33
M
2.8
33
17
31.0
64
7
3
4
26
0.8
64
0.6
65
0.2
09
0.3
54
72.4
17
M
5.2
50
19
0.5
04
8
2
2
6
1.7
59
0.4
50
0.4
59
0.3
54
61.8
33
M
18.1
67
17
26.0
56
9
3
3
13
0.9
42
0.2
98
0.5
94
0.0
98
55.6
67
F
5.9
17
14
134.0
56
10
3
3
6
1.8
15
1.1
01
0.7
64
0.9
04
54.0
83
M
1.0
83
22
76.3
28
11
4
4
5
7
2.8
24
0.5
23
0.1
28
0.2
30
55.9
17
M
2.8
33
12
6.8
40
12
4
2
3
1
1.4
72
0.5
39
0.4
81
0.5
85
47.1
67
M
0.7
50
16
3.5
12
13
3
4
3
9
1.7
17
1.0
85
0.1
16
0.8
48
67.8
33
M
0.5
83
18
144.6
32
14
2
2
0
1.5
56
0.3
98
0.5
42
0.3
15
73.1
67
M
1.8
33
16
9.1
04
15
2
2
0
1.6
67
0.7
07
0.2
23
0.6
35
53.5
83
M
2.7
50
15
161.5
44
16
3
5
5
3
1.5
11
0.8
43
0.7
93
0.8
43
56.9
17
M
3.8
33
16
5.8
40
17
3
2
4
1.8
22
0.9
72
-0
.034
0.3
93
41.5
83
F
25.4
17
18
107.7
92
18
4
4
4
8
1.1
78
0.4
05
0.0
52
0.1
83
66.5
00
M
4.9
17
15
3.7
12
19
3
4
4
1
5.7
94
0.6
71
0.1
48
0.1
23
71.1
67
F
8.2
50
11
182.4
56
20
3
2
4
0.9
75
0.3
26
0.4
62
0.2
79
63.6
67
M
0.9
17
13
9.4
80
21
3
4
4
3
0.7
35
1.0
96
0.5
08
0.6
40
54.9
17
F
4.1
67
20
7.9
44
22
3
2
0
1.4
49
0.5
23
0.0
18
0.2
80
67.4
17
M
11.5
00
16
17.1
68
23
2
2
2
3.3
29
0.0
94
0.2
91
0.0
72
53.8
33
F
1.0
00
17
4.8
08
24
3
2
0
1.3
26
0.6
67
0.0
04
0.4
13
66.5
83
F
21.9
17
16
401.6
24
25
4
4
5
5
4.4
42
0.9
18
0.6
72
0.5
20
75.3
33
F
5.3
33
12
9.5
84
26
4
5
4
9
1.7
84
0.9
00
0.5
41
0.5
58
58.4
17
M
3.9
17
19
28.0
00
27
3
2
1
1
0.8
57
0.3
38
0.3
58
0.0
78
59.5
83
M
1.8
33
16
7.8
96