Characterizing Cortical and Spinal Markers of Lower Limb ...€¦ · master’s student has been critical to the completion of this work, and for that I am truly grateful. I would

Characterizing Cortical and Spinal Markers of Lower Limb Movement Preparation

by

Tyler Saumur

A thesis submitted in conformity with the requirements for the degree of Master of Science

Rehabilitation Science Institute University of Toronto

© Copyright by Tyler Saumur 2017

ii

Characterizing Cortical and Spinal Markers of Lower Limb

Movement Preparation

Tyler Saumur

Master of Science

Rehabilitation Sciences Institute

University of Toronto

2017

Abstract

Preparation for an action involves a variety of inhibitory and excitatory processes that influence

the efficiency and scaling of the movement. The purpose of this thesis was to identify the cortical

and spinal contributions regulating excitability while preparing for differentially cued lower limb

tasks and how individual strategy influences these measures. Twenty-six participants were

presented with two reaction time tasks (simple and complex) using a GO/NO-GO paradigm.

During the foreperiod, transcranial magnetic stimulation and/or percutaneous electrical

stimulation were performed to evoke a muscle response in tibialis anterior as measures of

corticospinal and spinal excitability, respectively. Analyses showed no significant effect of task

predictability or strategy on cortical and spinal measures; corticospinal and spinal excitability

were modulated to a similar extent irrespective of the task. Future work should investigate other

potential modifiers of preparatory excitability such as arousal and environment.

iii

Acknowledgments

There are many people to thank who have helped me throughout my master’s degree and

made this all possible. Firstly, I would like to thank the Rehabilitation Sciences Institute and

specifically Dr. George Mochizuki for their unwavering support and taking a chance on me (cue

the imposter syndrome). The mentorship and guidance offered throughout my tenure as a

master’s student has been critical to the completion of this work, and for that I am truly grateful.

I would like to take the time to acknowledge my committee members, Dr. Chetan Phadke and

Dr. Robert Chen for their insightful perspectives, thought-provoking questions, and continued

support over the past two years.

To all of my fellow students and lab mates in RSI and other departments who I have

gotten to know throughout my graduate experience, thank you for making it a truly great time.

Whether I was in need of a participant for my research, needed assistance with award

applications, or just some socialization in the ever-isolating academic world which we immerse

ourselves in, I never needed to look far.

Thank you to all of my participants who eagerly got involved in my work, which without

them, would not be possible. I am ever grateful for your flexibility in scheduling, insightfulness,

and trust.

Lastly, I would like to thank my friends and family for their support over the past two

years. Your continuous confidence in me always gave me a boost when I needed it most.

Laura – thank you for your love and patience.

iv

Table of Contents

Acknowledgments .......................................................................................................................... iii

Table of Contents ........................................................................................................................... iv

List of Tables ............................................................................................................................... viii

List of Figures ................................................................................................................................ ix

List of Appendices ....................................................................................................................... xiii

Chapter 1: Literature Review .......................................................................................................... 1

Introduction ................................................................................................................................ 1

1.1 Background ......................................................................................................................... 1

Motor Preparation ...................................................................................................................... 2

2.1 Preparation and Motor Programs ........................................................................................ 2

2.2 Motor Preparation and Central Set ..................................................................................... 3

2.3 Modifiers of Central Set in the Context of Balance Control .............................................. 4

Reaction Time – An Index of Motor Preparation ...................................................................... 5

3.1 Extrinsic Characteristics Influencing Reaction Time ......................................................... 6

3.2 Go and No-Go Responses ................................................................................................... 7

Assessing Excitability of the Central Nervous System .............................................................. 8

4.1 Hoffmann’s Reflex .............................................................................................................. 8

4.2 Motor Evoked Potentials ................................................................................................... 10

Modulation of Preparatory Excitability ................................................................................... 12

5.1 Inhibitory Control of Movement ....................................................................................... 12

5.2 Excitatory Control of Movement ...................................................................................... 13

5.3 Combining Stimulation Methods ...................................................................................... 14

Clinical Implications ................................................................................................................ 14

6.1 Influences of Aging on Motor Tasks ................................................................................ 14

v

6.2 Negative Biasing of the CNS ............................................................................................ 15

Rationale and Objectives .......................................................................................................... 16

Chapter 2: Co-Modulation of Corticospinal and Spinal Excitability During Preparation for

Lower Limb Movement ........................................................................................................... 20

Introduction .............................................................................................................................. 20

Methods .................................................................................................................................... 23

2.1 Participants ........................................................................................................................ 23

2.2 Experimental Protocol ...................................................................................................... 23

2.2.1 Equipment and Procedures ................................................................................... 23

2.2.2 Preparatory Strategy .............................................................................................. 24

2.2.3 Reaction Time Tasks ............................................................................................. 24

2.2.4 Single-Pulse Transcranial Magnetic Stimulation .................................................. 25

2.2.5 Percutaneous Electrical Stimulation ..................................................................... 25

2.2.6 Electromyography ................................................................................................. 26

2.3 Data Analysis .................................................................................................................... 26

2.3.1 EMG Analysis ....................................................................................................... 26

2.4 Statistical Analysis ............................................................................................................ 27

2.5 Secondary Analyses .......................................................................................................... 28

Results ...................................................................................................................................... 29

3.1 Primary Results ................................................................................................................. 29

3.1.1 Strategies and Errors ............................................................................................. 29

3.1.2 Reaction Time ....................................................................................................... 30

3.1.3 Corticospinal Excitability ..................................................................................... 31

3.1.4 Spinal Excitability ................................................................................................. 32

3.1.5 Relationship Between Corticospinal and Spinal Excitability ............................... 33

3.1.6 Muscle Activity of Motor Response ..................................................................... 34

vi

Table 2. 2x4 repeated measures ANOVA summary table for primary variables of interest. .. 35

3.2 Secondary Results ............................................................................................................. 35

3.2.1 Task Optimization – Reaction Time ..................................................................... 35

3.2.2 Effect of Time and Task Order on Corticospinal Excitability .............................. 36

3.2.3 Effect of Time on Behavioural Measures ............................................................. 36

3.2.4 Recruitment Curves .............................................................................................. 37

3.2.5 Adaptive Tuning ................................................................................................... 37

3.2.6 Excitatory and Inhibitory Control ......................................................................... 38

3.2.7 TMS Timing .......................................................................................................... 39

3.3 Results Normalized to Baseline ........................................................................................ 40

3.3.1 Relative Corticospinal and Spinal Excitability ..................................................... 40

3.3.2 Relative Relationship Between Corticospinal and Spinal Excitability (% M-

Max) ...................................................................................................................... 41

3.3.3 Alternative Classifications of Preparatory Control ............................................... 42

Discussion ................................................................................................................................ 44

4.1 Excitatory and Inhibitory Control ..................................................................................... 44

4.2 Parallel Modulation of Cortical and Spinal Connections ................................................. 46

4.3 Gradual Increase in Corticospinal Excitability Associated with Adjustment in

Preparatory Processing ...................................................................................................... 48

4.4 Context and Strategy ......................................................................................................... 49

4.5 Conclusions ....................................................................................................................... 50

Chapter 3: General Discussion and Conclusions .......................................................................... 52

Summary of Findings ............................................................................................................... 52

Revisiting the Conceptual Model ............................................................................................. 52

2.1 Predictability ..................................................................................................................... 53

2.2 Strategy ............................................................................................................................. 54

2.3 Potential Modifiers ............................................................................................................ 54

vii

Implications for Rehabilitation Science ................................................................................... 57

3.1 Cues as Rehabilitation Tools ............................................................................................ 57

3.2 Deficiencies in Preparatory Excitability in Stroke ............................................................ 57

3.3 Aging and Preparing for Temporally-Urgent Movements ................................................ 58

Limitations and Future Directions ........................................................................................... 59

4.1 Limitations ........................................................................................................................ 59

4.2 Future Directions .............................................................................................................. 62

Final Conclusions ..................................................................................................................... 62

References ..................................................................................................................................... 64

Appendices .................................................................................................................................... 82

viii

List of Tables

Table 1. Summary of preparatory strategies ................................................................................. 30

Table 2. 2x4 repeated measures ANOVA summary table for primary variables of interest. ....... 35

Table 3. Summary of control types based on MEP measures normalized to baseline ................. 42

ix

List of Figures

Figure 1. Motor program adapted from Schmidt, 1982. ................................................................. 3

Figure 2. Graph demonstrating the relationship between arousal and performance adapted from

Hebb, 1955 ...................................................................................................................................... 4

Figure 3. Graphical representation of the Hick-Hyman law. .......................................................... 6

Figure 4. Reflex loop activated when stimulating a mixed nerve using percutaneous electrical

stimulation of the reflex circuitry. Initial response is caused by direct activation of an alpha

motor neuron (blue), whereas the second response is a result of the volley traveling to the spinal

cord along the Ia sensory nerve where it synapses to an alpha motor neuron resulting in a second

action potential in the muscle (red). .............................................................................................. 10

Figure 5. Magnetically stimulating the motor cortex results in the depolarization of interneurons

and a measurable downstream action potential known as a motor-evoked potential (MEP). ...... 10

Figure 6. Conceptual model outlining the potential influences of predictability and strategy on

regulating sensorimotor gain. ........................................................................................................ 17

Figure 7. Diagram of experimental TMS set up. .......................................................................... 23

Figure 8. Contingent Negative Variation paradigm for the GO and GO/NO-GO reaction time

tasks. Transcranial or nerve stimulation/percutaneous electrical stimulation was applied at two

seconds following the warning tone. ............................................................................................ 25

Figure 9. Schematic demonstrating the temporal features of the collected electromyography

(EMG) measures. .......................................................................................................................... 27

Figure 10. Mean errors recorded for each reaction time task and separated based on preparatory

strategy. Data suggests those who kept the same strategy for both conditions had an increase in

errors for the GO/NO-GO task, whereas those who changed strategy types improve or see no

difference in errors. Solid lines indicate individuals who used the same strategy for both tasks

and dotted line represents those who switched strategies depending on the task. Error bars denote

standard error of the mean. ........................................................................................................... 29

x

Figure 11. A) Mean reaction time recorded for each reaction time task and separated based on

preparatory strategy. GO/NO-GO task elicits significantly slower reactions. B) Mean reaction

time coefficient of variation recorded for each reaction time task and separated based on

preparatory strategy. Solid lines indicate individuals who used the same strategy for both tasks



Figure 12. A) Mean motor-evoked potential (MEP) amplitude of BASELINE, GO, and GO/NO-

GO conditions. No apparent modulation of MEP was seen between conditions. B) Mean MEP

amplitude for each reaction time task and separated based on preparatory strategy. Anticipatory

strategy appeared to elicit higher preparatory corticospinal excitability although not significant.

Solid lines indicate individuals who used the same strategy for both tasks and dotted line

represents those who switched strategies depending on the task. Error bars denote standard error

of the mean. ................................................................................................................................... 32

Figure 13. A) No difference in H-reflex amplitude was observed between tasks and baseline. B)

Mean H-reflex amplitude for each reaction time task and separated based on preparatory

strategy. Spinal excitability appeared stable and unchanged between tasks regardless of the

strategy implemented. Solid lines indicate individuals who used the same strategy for both tasks



Figure 14. Plot of 8 participants who completed both H-reflex and motor-evoked potential

measures. Strong correlation was found between corticospinal and spinal measures for the

GO/NO-GO task (open circle, dotted line) and a trend towards a positive correlation was

observed for the GO task (closed square, solid line). ................................................................... 33

Figure 15. A) Mean integrated electromyographic activity (iEMG) recorded for each reaction

time task and separated based on preparatory strategy. B) Mean iEMG coefficient of variation

recorded for each reaction time task and separated based on preparatory strategy. A significant

increase in muscle activity variability was found in the GO/NO-GO task. Solid lines indicate

individuals who used the same strategy for both tasks and dotted line represents those who

switched strategies depending on the task. Error bars denote standard error of the mean. .......... 34

xi

Figure 16. Mean MEP amplitudes for all 60 reaction time trials irrespective of task condition.

Trials are presented in order they were performed. A significant effect of time on MEP amplitude

was observed. Error bars denote standard error of the mean. ....................................................... 35

Figure 17. A) Mean reaction time for the GO/NO-GO task separated by 10 trial bins. Visually,

reaction time appears to speed up as the familiarity with the trial progresses. B) Mean iEMG

variability recorded during the GO condition and separated by 10 trial bins. A significant effect

of time was seen on muscle response variability, with the last 10 trials having the largest

variability. This may point to a lack of attention throughout a simple task. Error bars denote


Figure 18. Individual recruitment curve of tibialis anterior H-reflex and M-wave. Experimental

stimulator intensity was set to evoke an H-reflex amplitude of 50% Hmax. For this participant

that would correspond with an intensity of ~53 V. ....................................................................... 38

Figure 19. A) Mean reaction time coefficient of variation recorded for each reaction time task

and separated based on cortical control. A significant interaction was found between

corticospinal type and task, likely driven by the group which switched from excitatory to

inhibitory control between the GO and GO/NO-GO task. B) Mean iEMG recorded for each

reaction time task and separated based on spinal control. A significant interaction between

control type and task was observed, indicating a change in control from GO to GO/NO-GO

increases the size of muscle response. Solid lines indicate individuals who used the same control

for both tasks and dotted line represents those who switched control depending on the task. Error

bars denote standard error of the mean ......................................................................................... 39

Figure 20. Contingent Negative Variation paradigm for the GO and GO/NO-GO reaction time

tasks. Transcranial or nervous stimulation/percutaneous electrical stimulation was applied at

three timepoints throughout the preparatory foreperiod (indicated by an arrow). ........................ 40

Figure 21. A) Mean MEP amplitude expressed as a percentage of baseline for each reaction time

task, separated based on preparatory strategy. B) Mean H-reflex amplitude as percentage

baseline for each reaction time task and separated based on preparatory strategy. Solid lines

indicate individuals who used the same strategy for both tasks and dotted line represents those

who switched strategies depending on the task. Error bars denote standard error of the mean. .. 41

xii

Figure 22. Plot of H-reflex and MEP amplitudes made relative to M-Max (n=8 for each task).

Strong correlation was found between cortical and spinal measures for the GO/NO-GO task

(open circle, dotted line) and the GO task (closed square, solid line). ......................................... 41

Figure 23. A) Mean reaction time recorded for each reaction time task and separated based on

corticospinal control. A significant interaction was found between control type and task, as well

as effect of task. B) Mean iEMG coefficient of variation (CoV) recorded for each reaction time

task and separated based on corticospinal control. A significant interaction between control type

and task was observed. Solid lines indicate individuals who used the same control for both tasks

and dotted line represents those who switched control depending on the task. Error bars denote


Figure 24. Mean iEMG CoV recorded for each reaction time task and separated based on spinal

control. A significant interaction between control type and task was observed. Solid lines

indicate individuals who used the same control for both tasks and dotted line represents those

who switched control depending on the task. Error bars denote standard error of the mean. ...... 44

Figure 25. Proposed conceptual model outlining potential modifiers of set which influence CNS

excitability to a greater extent than predictability. ........................................................................ 55

xiii

List of Appendices

Appendix 1. Data collection sheet ................................................................................................ 82

Appendix 2. Chi square table of preparatory strategy proportions ............................................... 86

Appendix 3. ANOVA tables comparing the effect of condition and strategy on errors and

reaction time .................................................................................................................................. 87

Appendix 4. ANOVA tables comparing the effect of condition and strategy on reaction time

variability (CoV) ........................................................................................................................... 88

Appendix 5. Paired t-test comparing reaction times for conditions performed with PES and TMS

....................................................................................................................................................... 88

Appendix 6. ANOVA table comparing baseline, GO, and GO/NO-GO corticospinal excitability

....................................................................................................................................................... 88

Appendix 7. ANOVA tables comparing the effect of condition and strategy on corticospinal

excitability ..................................................................................................................................... 89

Appendix 8. ANOVA table comparing baseline, GO, and GO/NO-GO spinal excitability ........ 89

Appendix 9. ANOVA tables comparing the effect of condition and strategy on spinal excitability

....................................................................................................................................................... 89

Appendix 10. Correlations between MEP and H-Reflex Amplitudes .......................................... 90

Appendix 11. ANOVA tables comparing the effect of condition and strategy on iEMG ............ 90

Appendix 12. ANOVA tables comparing the effect of condition and strategy on iEMG variability

(CoV) ............................................................................................................................................ 91

Appendix 13. Correlation tables of excitability measures with behavioural measures ................ 91

Appendix 14. ANOVA table of the effect of time and task order on corticospinal excitability .. 92

Appendix 15. ANOVA tables of the effect of time on behavioural measures.............................. 93

xiv

Appendix 16. ANOVA tables outlining the effect of a NO-GO tone on the subsequent

corticospinal excitability ............................................................................................................... 95

Appendix 17. ANOVA tables and t-tests on corticospinal excitatory and inhibitory control ...... 96

Appendix 18. ANOVA tables and t-tests on spinal excitatory and inhibitory control ................. 98

Appendix 19. ANOVA tables of stimulation timing analyses .................................................... 100

Appendix 20. Supplementary Relative Value Secondary Analyses ........................................... 103

1

Chapter 1: Literature Review

Introduction

1.1 Background

In the most basic terms, movement is defined as the process or act of moving

(“movement,” 2015). Despite the simplistic nature of this description, the notion of a process

remains pervasive in its definition, and this process changes as the environment (and the

contextual cues it offers), change as well. Once the environmental surroundings have been

subjectively interpreted, an individual must then prepare for the ensuing action. Certain scenarios

such as a driver approaching a traffic light that has just turned amber, and determining whether to

stop or continue driving require a degree of temporal urgency when selecting the most

contextually appropriate behaviour. This compulsory need to perform the correct action, and do

so in a timely and efficient manner, requires the central nervous system (CNS) to optimize

incoming information and task performance.

Conceptually, task optimization is a process in which the CNS utilizes the available

contextual information to prime relevant pathways, and enhances the efficiency of the system as

it prepares for the impending movement to perform it quickly and accurately. These adjustments

occur not just at the cortical level, but at the spinal level as well. Determining if these

modifications occur independently or in tandem can help further knowledge of how different

physiological components involved in preparation for movement operate. Principally, adjusting

the gain of the system to augment its sensitivity will further tune the processes necessary to

optimize the imminent action. Cues in the environment and the value associated with them will

also influence the attention and anticipation an individual experiences during motor preparation.

In addition, the strategy one implements when performing a motor task may also play a role in

underlying preparatory processes and potentially behavioural features as well. Generally, as a

response becomes increasingly predictable, the CNS can stereotype its processing to the

predicted action to improve motor performance. In contrast, if a situation is less certain, a general

state of readiness is optimal to augment processing. Understanding the type of strategy one

employs in these types of situations can help determine if one’s perceived strategy can have a

marked influence on processing greater than the predictability of the task itself or vice-versa.

2

The concepts of generalized motor preparation and task-specific preparation are inherent

based upon the relevant context of the present situation. As more information is presented, the

level of biasing of the CNS increases in parallel to execute the task more efficiently. While task

performance and execution have been studied in detail, the processing and priming that occurs

prior to movement execution is less well understood, particularly for the lower limb. In addition,

studying how both the cortical and spinal components of the CNS influence contextually-

appropriate movement can advance knowledge of motor preparation’s influence on motor

control. This thesis explores the influence of external cues and strategy on preparatory processes

and the impact of these factors on task optimization.

Motor Preparation

2.1 Preparation and Motor Programs

Generally, the more information available preceding a motor response, the more efficient

an individual will be at performing the movement. This task optimization involves various

preparatory processes that make adjustments to the system to align with the motor plan and

context for the impending movement. A simplistic model of the information processing that

occurs during motor preparation can be seen in Figure 1, with the internal processes represented

by the larger box. Once a stimulus has been presented, it must first be identified by the system

and coded to select an appropriate response. A generalized motor program that meets the criteria

set out by the external stimulus would then be recruited to produce an optimized movement. This

model is effective when external variability is minimized and the CNS can select a response

quickly and accurately due to the biased nature of the action; however, for scenarios in which

predictability of a response is lower and the need to rely on supplemental external information

becomes critical, a closed loop model of control may better explain the preparatory mechanisms

at work. A closed loop system of control implements feedback throughout the action, and

compares this feedback to the perceived ideal of how the movement should be performed

(Adams, 1971). This closed loop incorporates various sensory systems to correct the movement

as needed, or allows the action to continue on course as initiated. These two popular models of

motor control create a foundation on which other theories are formed, based on the type of

movement being studied.

3

2.2 Motor Preparation and Central Set

Central set is the anticipatory regulation of sensorimotor gain to optimize processing and

task performance (Evarts, 1975; Prochazka, 1989). Many factors can influence it such as: prior

experience, current state, prior warning, affect, and arousal. These factors likely shape an action

at the level of the control centre and are not necessarily involved in the online feedback that

would be utilized in a closed loop system. When this occurs, the control system makes its best

interpretation of the information available when recruiting a motor program; this strategy of the

CNS may be disadvantageous in more general scenarios that may be unfamiliar to an individual

(Greene, 1972). Set can however be adjusted to help fine tune processing to optimize responses

in more familiar circumstances.

In the case of the everyday environment, balance and perturbation responses are

commonly studied in the context of set. The cortex can modify central set for postural responses

specifically, through two pathways – one involving the cerebellum and the other involving the

basal ganglia. Postural and compensatory responses are examples of a movement in which an

open loop system is required based on the temporal urgency and discrete nature of the response.

If one is perturbed, the basal ganglia are likely involved in automating response selection through

a generalized motor program and executing the context-specific movement (Jacobs & Horak,

2007). Conversely, the cerebellum’s role in preparatory control involves adapting and tuning the

system based on the anticipated response requirements (Jacobs & Horak, 2007; Timmann &

Horak, 1997). Together, the basal ganglia allow for modifications of set when preparing and

adjusting postural responses in a variety of scenarios. As these pathways are also involved in

motor control more broadly, these connections likely modulate set similarly for adapting and

adjusting other movements as well.

Figure 1. Motor program adapted from Schmidt, 1982.

4

2.3 Modifiers of Central Set in the Context of Balance Control

As mentioned briefly in the

previous section, various factors can

influence the ability to optimize

information processing when preparing for

temporally-urgent responses. Related to

lower limb movement and responses,

temporal urgency is often displayed in

scenarios of reactive balance and postural

adjustments, and relies on features of

central set to maintain control and

stability. This set-driven scaling tightly regulates responses to offset impending threats to

stability and can alter compensatory strategies related to postural perturbations. One important

factor influencing this scaling is context. When the environmental context of a task becomes

increasingly threatening, postural control is regulated to a greater extent (Adkin, Frank,

Carpenter, & Peysar, 2000; Brown & Frank, 1997). This change in strategy is a consequence of

the increased risk of imbalance and falling which may result in increased arousal.

Unpredictability of a condition can also increase the postural anxiety of an individual, which

results in overt upscaling of preparatory cortical activity (Mochizuki, Boe, Marlin, & McIlroy,

2010), and larger postural responses in muscles with smaller displacements of the lower limbs

(Carpenter, Frank, Adkin, Paton, & Allum, 2004). The latter example may demonstrate a

negative feature influencing set-related scaling and likely involves individuals being outside of

the optimal threshold of arousal (Hebb, 1955; Figure 2), whereas the former may be a

compensatory mechanism whereby individuals prepare for a worst-case scenario. Arousal is not

only tightly linked to the context of a condition, but ones experience with the condition as well

(Maki & Whitelaw, 1993). As one’s familiarity with a task increases, their behaviour can become

habituated and this can be independent of the potential threat of the situation (Brown & Frank,

1997). Conversely, if one is exposed to a high postural threat condition initially, this can affect

the scaling of postural responses of subsequent trials of lower consequence and vice-versa

(Adkin et al., 2000; Horak, Diener, & Nashner, 1989). When preparing for motor tasks, these

factors influencing central set are not mutually exclusive, as demonstrated by these various

Figure 2. Graph demonstrating the relationship between

arousal and performance adapted from Hebb, 1955.

5

examples in which contexts and familiarity can affect one’s perception of a task and

consequently their behaviour as well.

Reaction Time – An Index of Motor Preparation

Reaction time is often referred to as an index of motor preparation. A faster reaction time

is perhaps the most obvious behavioural outcome of the impact that relevant information has on

preparatory processing. This notion was first explored around 150 years ago by Franciscus

Donders. Donders investigated the impact of various stimuli identification tasks on reaction time,

using methods now referred to as simple, choice, and discrimination reaction time tasks. In a

simple reaction time task, there is one stimulus with one response. For example, depressing a

pedal with the right leg every time a light turns green. For the choice reaction time task, there are

multiple stimuli which elicit different responses; building on the first example, when the light

turns green the participant depresses the right pedal and every time the light is orange the left

pedal is selected. The last reaction time task, the discrimination task, involves a response only

when a certain stimulus is presented. For example, a response is required only when the green

light appears and not the orange. These reaction time tasks outlined by Donders help create a

foundation for understanding the processes involved in motor preparation.

Donders postulated that the difference between the discrimination and simple reaction

time tasks was indicative of the time it took for stimulus discrimination; conversely, the

difference between the discrimination and choice reaction time tasks was thought to be indicative

of the response selection stage of processing (Donders, 1969; translated by W.G. Koster). Since

these experiments, multiple researchers have disputed this concept largely due to the inability to

truly isolate different processing stages when removing or adding another (Sternberg, 1969);

however, the fundamental underpinnings remain pervasive to date. As reaction time is an

inherent characteristic of motor preparation, the next section will concern itself with the some of

the most commonly explored extrinsic characteristics of the reaction time paradigm.

6

3.1 Extrinsic Characteristics Influencing Reaction Time

About a century after Donders’ experiments,

Hick and Hyman furthered reaction time research by

investigating the relationship between the number of

stimulus-response alternatives and reaction time (Figure

3). A law was developed which allowed for the

prediction of an individual’s reaction time if their

simple reaction time and the number of response options

were known. Essentially, as an additional choice is

added as a potential response, an individual’s reaction

time will increase logarithmically (Hick, 1952; Hyman,

1953). Adding multiple response options is perhaps the most influential factor on preparatory

processing, as the CNS becomes less able to bias its tuning to a particular response.

Another level of complexity to preparatory processing is the probability of selecting the

correct response. For example, if there are three different response choices but one of them is

selected 90% of the time, an individual will intrinsically bias their responses to the choice most

frequently selected. This probability can be manipulated by using a precuing technique which

can display varying amounts of information regarding the impending movement. If a subject can

maintain visual spatial attention on the information given by the precue for a single effector

throughout the foreperiod, reaction time becomes increasingly faster as more information is

presented (Adam & Pratt, 2004; Eversheim & Bock, 2002; Rosenbaum, 1980). To further

complicate response biasing during reaction time tasks, the probability of the precue providing

correct information can also influence one’s reaction time. Biasing one’s responses based on

available information creates a cost-benefit trade-off phenomenon whereby reaction time will be

slower when the selected response is not the one prepared for, but faster when the stereotyped

preparation is correct.

One last example of factors that must be considered for performing tasks that require

quick volitional movement are the complexity, urgency, and accuracy of the movement. A

simple discrete task that does not require high spatial accuracy, such as lifting one’s foot

upwards as quickly as possible following a tone can be performed much faster than someone

Figure 3. Graphical representation of the

Hick-Hyman law.

7

having to slam on the brakes of their car as a ball rolls in front of it (Green, 2000; Mulder et al.,

2004; Saumur & Mochizuki [unpublished data]). This is because depressing a pedal with

sufficient force requires multiple degrees of freedom as well as heightened motor control to

transfer the foot from the accelerator to the brake. However, if one were to consider taking a

reactive step to recover from a perturbation to ensure a fall does not occur, this can actually be

performed faster than the simple dorsiflexion of the foot (Maki & McIlroy, 2007; Patel & Bhatt,

2015; Zettel, McIlroy, & Maki, 2008) . So, if a reactive step requires a higher level of control and

specificity, how is it that an individual can initiate one faster than a simple movement of the

foot? Generally, if a task requires additional complexity and accuracy like the foot pedal

example, it would be expected to take longer to execute; in the case of reactive stepping

however, there is a certain level of automaticity and central programming involved that emerges

with the urgency and threat presented by the scenario (see Dietz, 1992 for review). When

manipulating variables involved with reaction time, it is an important distinction to ensure that

the tasks being compared are of similar physiological wiring. Manipulating complexity and

accuracy generally allows for the manipulation of preparatory processing within a similar

network of connections, however, urgency should be manipulated with some caution to ensure

reflex pathways aren’t recruited if drawing comparisons to different tasks.

In addition to the influence of response alternatives, cuing, and movement

complexity/accuracy there are many other characteristics of a reaction time paradigm which

influence motor preparation. These features include, but are not limited to: foreperiod length

consistency, stimulus-response compatibility, and repetition of a movement (see Magill, 2010 for

a brief review). Ultimately, the CNS can respond to stimuli more efficiently and rapidly when the

most accurate information regarding a condition is presented and the least amount of variability

is introduced. In regards to temporally-urgent movements, being able to perform rapidly and

correctly are perhaps the most important features of task optimization.

3.2 Go and No-Go Responses

Another means of altering motor preparation is by manipulating the certainty of a task

being executed. This can be accomplished by implementing a go/no-go reaction time paradigm

in which participants perform the instructed movement when a “go” signal is presented and

inhibit the action when a “no-go” stimulus is presented. The go/no-go task is similar to a choice

8

reaction time task in that there is more than one potential response; however, it differs in that

response inhibition can be studied to a greater extent. Response inhibition is largely mediated

through the fronto-basal-ganglia circuit and specifically involves a competition between

pathways leading to the basal ganglia’s output structures – the substantia nigra pars reticulata and

globus pallidus pars interna (Chambers, Garavan, & Bellgrove, 2009; Stinear, Coxon, & Byblow,

2009). When a stop signal is presented, a hyper-direct pathway from the interior frontal gyrus

overrides the set-related response selection and cancels the response execution signal which

travels through the basal ganglia’s direct pathway (Chambers et al., 2009). This relationship to

central set is important in understanding the influence of task certainty on motor preparation and

the neural correlates involved; implementing a go/no-go paradigm provides a means to explore

this relationship by manipulating task certainty.

Assessing Excitability of the Central Nervous System

Motor preparation and movement are reliant upon corticospinal circuitry to transmit

signals to other sites in the CNS. These signals can be altered through various excitatory and

inhibitory connections. Measurement of how these systems modify response preparation

develops one’s understanding of healthy motor control. Furthermore, in elite athletes and in those

with various neurological disorders, it can help assess physical function and pathology of the

enhanced or diminished connections. The following section will explore various methodologies

involved in measuring and assessing CNS function – specifically the modulation of cortical and

spinal excitability. Some of the more popularized methodologies will be discussed here.

4.1 Hoffmann’s Reflex

The H-reflex is the electrical analog of the T-reflex, directly stimulating the Ia sensory

nerves to produce a muscular response, while avoiding stimulation of the muscle spindle, to

more directly measure spinal excitability. The H-reflex has been widely studied for the past 100

years because of its simple elicitation and repeatability in the lower limb muscles (see Palmieri et

al., 2004 for a review). When utilizing percutaneous electrical stimulation (PES) to produce an

H-reflex, there are various methodologies which can be utilized to understand different aspects

of the spinal pathway in the neural control of movement (Misiaszek, 2003). One common use of

the H-reflex is to understand the connectivity and wiring of the corticospinal tract. This can be

explored to understand various inhibitory and excitatory connections which optimize everyday

9

movement. As an example, when flexing the calf muscles to point one’s toe, the H-reflex of the

associated antagonist (foot dorsiflexors) becomes suppressed due to the excitatory connections

from the foot’s plantar flexors synapsing on the Ia inhibitory interneuron of the dorsiflexors. The

result is reciprocal inhibition which allows for minimal co-contraction when the plantar flexors

are activated. Similarly, presynaptic inhibition of the effector muscle can be studied by passively

or actively moving the muscle and using PES to understand the spinal circuitry. Furthermore, the

H-reflex can be used to examine state-dependent modulation in spinal excitability through

various experimental methods involving reaction time tasks. This process can help advance the

understanding of the roles of attention, arousal, or task complexity on spinal neural structures,

and ultimately inform how these modifiers fine-tune and optimize the motor response. Apart

from looking at task-dependent changes of the H-reflex, PES can be used to probe various other

aspects of spinal circuitry such as: motoneuron pool excitability, changes in pre-synaptic

inhibition, functional damage and adaptations of spinal structures following injury, adaptive

plasticity through training and aging, and estimating the maximum capacity of motor neurons

activated in a given state (Misiaszek, 2003; Palmieri et al., 2004; Zehr, 2002).

One of the main limitations to interpreting H-reflex changes is that they are highly

modulated by presynaptic inhibition. Another type of stimulation – cervicomedullary stimulation

– is a technique that can be used to assess the excitability of the corticospinal tract (see Taylor &

Gandevia, 2004 for a review) and is immune to changes in presynaptic inhibition (Jackson,

Baker, & Fetz, 2006; Nielsen & Petersen, 1994). By magnetically stimulating at the

cervicomedullary junction, one can activate axons within a predominantly monosynaptic

component of the corticospinal tract to produce a cervicomedullary evoked potential (CMEP) in

an innervated muscle. Comparing CMEPs to motor evoked potentials (MEPs) elicited by

transcranial magnetic stimulation (TMS) allows for a direct comparison of the influence of spinal

motor neurons and the motor cortex on excitability (Petersen, Taylor, & Gandevia, 2002). One of

the drawbacks of cervicomedullary stimulation however, is the pain and discomfort associated

with it (Avela & Gruber, 2010). This makes PES a more favourable methodology to administer

to gain insight to the peripheral contribution to spinal excitability modulation.

10

Many factors can influence H-reflex amplitude such as: level of background muscle

activity, stimulus intensity, the size of the reflex itself, and post-activation depression (Zehr,

2002). By controlling for these factors, state changes in spinal excitability can be determined. For

the purposes of this study, spinal excitability will be defined as the net change in excitability of

the alpha motor neuron internally and the influence of connections pre- and post-synaptically, as

well as at the synapse itself.

4.2 Motor Evoked Potentials

Developed by Merton and Morton in 1980, transcranial

electrical stimulation (TES) elicits action potentials in muscles by

stimulating the brain electrically through the scalp. Through

corticospinal neurone activation of the motor cortex, a descending

volley is propagated to the innervated muscle producing an MEP

(Rothwell et al., 1994). However, due to the higher current

needed to overcome the resistance of the scalp and skull, pain and

discomfort were frequently reported amongst recipients (Hallett,

2000). Despite TES providing the first insight into understanding

Figure 5. Magnetically stimulating

the motor cortex results in the

depolarization of interneurons and a

measurable downstream action

potential known as a motor-evoked

potential (MEP).

Figure 4. Reflex loop activated when stimulating a mixed nerve using percutaneous electrical stimulation of

the reflex circuitry. Initial response is caused by direct activation of an alpha motor neuron (blue), whereas the

second response is a result of the volley traveling to the spinal cord along the Ia sensory nerve where it

synapses to an alpha motor neuron resulting in a second action potential in the muscle (red).

11

corticospinal excitability changes, a solution to the discomfort caused by the device was being

explored.

In 1985, an alternative to TES was proposed by Barker and colleagues through the

invention of TMS. TMS functions as a consequence of Faraday’s law, by which a magnetic field

can induce an electric current in a conducting material – such as the brain, which can then

propagate to an innervated muscle. The results of magnetic stimulation were simple operation

and less pain experienced by subjects, leading to its uptake in neurophysiological studies and

clinical interventions. TMS primarily acts through depolarizing interneurons, whereas TES

directly stimulates corticospinal neurons (Day et al., 1989; Di Lazzaro et al., 1998; Hess, Mills,

& Murray, 1987; Rothwell, Thompson, Day, Boyd, & Marsden, 1991). It should be noted that

TES and TMS cannot actually differentiate between cortical, subcortical, and spinal excitability

changes (Chen, 2000; Cracco, Cracco, Maccabee, & Amassian, 1999) and therefore measures

corticospinal excitability and not purely cortical excitability.

There are numerous stimulation paradigms that employ TMS, all of which allow for the

investigation of various pathophysiologies (see Badawy et al., 2012 for a review). The most basic

of methods, is single-pulse TMS which can measure motor threshold and changes in the

conductive properties of ion channels and neurotransmitters (Ziemann, Lonnecker, Steinhoff, &

Paulus, 1996). Like the H-reflex elicited by PES, the MEP elicited by single-pulse TMS can

provide insight into state-dependent changes and function, but of corticospinal circuitry as

opposed to spinal circuitry. The main limitation of single-pulse TMS is its inability to

differentiate cortical and spinal influences. Conversely, paired-pulse methods allow for the study

of inhibitory and facilitatory cortico-cortical circuits by having two stimuli in close succession,

with the first conditioning pulse influencing the modulation of the later test pulse (Ferbert et al.,

1992; Kujirai et al., 1993; Nakamura, Kitagawa, Kawaguchi, & Tsuji, 1997). Paired-pulse

paradigms can be applied intracortically or interhemispherically to investigate changes in the

excitability of intracortical circuits or the interaction between the two hemispheres of the brain

respectively. One last method of TMS is repetitive TMS, which modifies cortical excitability and

can be used for neurological interventions to facilitate or dampen cortical excitability as required

based on the underlying physiology of the disease (Chen et al., 1997; Pascual-Leone, Valls-Solé,

Wassermann, & Hallett, 1994). These TMS techniques can advance human neurophysiology

research through non-invasive and safe methods.

12

Modulation of Preparatory Excitability

Using the various central and peripheral stimulatory methods described, the processing

that occurs during motor preparation can be studied. Preparatory activity is thought to be

dynamic in nature, changing as more information is revealed throughout the foreperiod.

Understanding the cognitive and spinal control of excitatory and inhibitory pathways involved in

task optimization can help develop basic motor control research and various neurological

disorders in which motor control and preparation are impaired. This section will discuss some of

the proposed mechanisms of spinal and supraspinal control during movement preparation.

5.1 Inhibitory Control of Movement

Motor preparation involves competing excitatory and inhibitory processes which

summate to produce a net motor response. Both spinal and cortical inputs control motor output

through a vast network of connections. Multiple studies using reaction time tasks to investigate

preparatory excitability have demonstrated decreased corticospinal excitability during the latter

part of the preparatory foreperiod as evidenced by diminished single-pulse MEPs in the agonist

muscle (Davranche et al., 2007; Duque & Ivry, 2009; Duque, Lew, Mazzocchio, Olivier, & Ivry,

2010; Hasbroucq, Kaneko, Akamatsu, & Possamaï, 1997, 1999; Touge, Taylor, & Rothwell,

1998; van Elswijk, Schot, Stegeman, & Overeem, 2008). H-reflex findings during the

preparatory foreperiod have also mirrored this inhibition (Duque et al., 2010; Hasbroucq et al.,

1999; Komiyama & Tanaka, 1990; Requin, Bonnet, & Semjen, 1977; Touge et al., 1998). This

model of inhibitory activity is thought to be a means of impulse control for the CNS to prevent

premature actions, particularly in the event that the action has already been selected and is not to

be performed until the presentation of the imperative tone (Duque & Ivry, 2009; Duque et al.,

2010). The impulse control theory involves the priming of the selected action being suppressed at

the level of the spinal cord (Duque et al., 2010) until the action is to be initiated, at which point

the inhibition is lifted similar to a dam raising its floodgates to allow water – or in this case top-

down signals – to pass through. A second theory of inhibitory control proposed by Duque and

colleagues (2010) is the competition resolution hypothesis which occurs upstream. The

mechanism involves corticospinal suppression when a potential relevant muscle is not selected

for the task such as in a bimanual choice reaction time task. The non-selected muscle is inhibited

to assist in choosing the correct response based on the accrual of additional information. Both

13

proposed theories of inhibitory control ultimately function to ensure the correct action is selected

and at the appropriate time. The theories however do not necessarily account for a control

mechanism to enhance reaction time and optimize task performance temporally.

5.2 Excitatory Control of Movement

Another proposed mechanism of motor preparation is through excitatory connections of

the corticospinal tract. There is currently less evidence for this control process; however, an

increase in MEP (Davranche et al., 2007; Mars, Bestmann, Rothwell, & Haggard, 2007; van den

Hurk et al., 2007; van Elswijk, Kleine, Overeem, & Stegeman, 2007) and H-reflex amplitude

(Brunia & Vuister, 1979) has also been observed during the latter half of the preparatory period.

van Elswijk and colleagues (2007) utilized a paradigm which demonstrated that the expectancy

of a relevant cue transiently increases corticospinal excitability. It was hypothesized that this

increase in excitability was likely not mediated by intracortical networks, but through indirect

projections to the spinal cord as the expectancy of an imperative stimulus did not show any

differential effects between paired-pulse and single-pulse techniques. This expectancy-driven

facilitation of cortical excitability likely primes the appropriate pathways for the impending

response. In contrast to the inhibitory control theory, increases in MEP and H-reflex amplitude of

the agonist muscle have been observed when the prior information regarding the impending

response was provided and individuals could fully prepare. It was hypothesized that if response

selection and programming is held online, cortical excitability is modulated due to influences of

the precentral cortex on preparatory processing (van den Hurk et al., 2007). A longer foreperiod

(ie. 4 s) may also allow for a general rise in excitability associated with processes involved in

motor preparation (Brunia & Vuister, 1979).

The discrepancy between the inhibitory and excitatory control findings may be explained

by various factors such as: length of foreperiod, timing of stimulation pulses, the interpretation

and enforcement of instructions, the lab groups conducting the experiments, tonic activation

levels of the agonist muscle, the reaction time paradigm implemented, and whether “practice”

trials were first completed. Overall, the general consensus regarding spinal and cortical tuning

during preparation based on majority of research indicates an initial facilitation following the

warning stimulus, followed by a general inhibition exhibited in the prime mover carrying into the

14

initial response time (Frank, 1986). This may however be limited to shorter foreperiods (1 s or

less).

5.3 Combining Stimulation Methods

The majority of the aforementioned research examines CNS gain modulation utilizing

TMS and PES techniques independently; however, measuring MEPs through TMS does not

isolate cortical influence on corticospinal excitability, as an MEP is the net result of cortical

interneurons, fast corticospinal pathways, and spinal motoneuron connections (Badawy et al.,

2012; Cracco et al., 1999). Measuring H-reflex modulation in addition to changes in MEP

amplitude can provide supplemental information regarding corticospinal control of a motoneuron

pool, assuming that a conditioning volley does not alter motor cortex excitability, presynaptic

inhibition of Ia terminals, or its transmission through an interneuronal relay (Pierrot-Deseilligny

& Burke, 2005). In those studies which measured both corticospinal and spinal excitability

changes, MEP and H-reflexes were both inhibited or unchanged during preparation in the agonist

muscle (Duque et al., 2010; Hasbroucq et al., 1999; Touge et al., 1998). By dissociating spinal

and supraspinal influence on motoneuron excitability during motor preparation, further

conclusions can be drawn regarding optimization of preparatory processing.

Clinical Implications

6.1 Influences of Aging on Motor Tasks

It is no mystery that reaction time slows with age. This is observed not only in the crude

simple reaction time task, but also in more complicated paradigms such as go/no-go (Fozard,

Vercruyssen, Reynolds, Hancock, & Quilter, 1994) and choice reaction time tasks (Simon &

Pouraghabagher, 1978). These age-dependent deficiencies in information processing may be

attributed to stimulus encoding issues (Simon & Pouraghabagher, 1978) and could have obvious

implications related to community-based daily activities. For example, French and colleagues

(1993) demonstrated that thoroughness and hesitancy were the two most important factors of

predicting accident liability in seniors. Thoroughness was related to planning well and using

logical decision-making, whereas hesitancy consists of items associated with changing ones’

mind. Indecisiveness in motor preparation associated with age is perhaps a key feature that could

be translated to accidents and falls. Interestingly, Lajoie and Gallagher (2004) identified balance

15

confidence and reaction time as two highly significant predictors of falls in elderly, with the

other predictor being Berg Balance score. Balance confidence has also been identified as a

contributor to poor balance control and gait characteristics and falls in stroke (Belgen, Beninato,

Sullivan, & Narielwalla, 2006; Schinkel-Ivy, Inness, & Mansfield, 2016; Schinkel-Ivy, Wong, &

Mansfield, 2016), Parkinson’s disease (Mak & Pang, 2009), and individuals with hip fracture

history (Kulmala et al., 2007).

Understanding the physiological origins responsible for reaction times slowing with age

is important. One potential mechanism is dampened cortical and spinal excitability which may be

explained by an age-dependent decrease in the number of cortical and spinal neurons (Eisen,

Siejka, Schulzer, & Calne, 1991; Henderson, Tomlinson, & Gibson, 1980; Kallio et al., 2010;

Koceja, Markus, & Trimble, 1995; Rossini, Desiato, & Caramia, 1992; Scaglioni, Narici,

Maffiuletti, Pensini, & Martin, 2003; Tomlinson & Irving, 1977). Recently, Duque et al. (2016)

compared corticospinal excitability of younger and older adults when performing a motor

inhibition task. MEP suppression was not present to the same extent in the older participants as

the younger; due to the slower reaction times and fewer errors made by the older group, the

authors postulated that this difference in preparatory cortical activity may be a product of

weighing accuracy of higher importance than speed, leading to potentially different processes

being recruited. It may be that these decreases in cortical and spinal neuron populations do not

necessarily result in lower global excitability, but instead result in less regulation and control in

preparatory processing.

6.2 Negative Biasing of the CNS

Thus far, stereotyping of preparatory processes has been presented as a positive feature of

the CNS. This biasing however, can prove detrimental in situations where the environment or

context suddenly change (Greene, 1972). An extreme example which demonstrates this

phenomena is the withdrawal reflex (Hagbarth & Finer, 1963). This spinal reflex is meant to

protect the body from damaging stimuli such as stepping on a tack, by stereotyping connections

and bypassing higher order structures to allow for a rapid withdrawal from the harmful stimulus.

In the lower limb however, unexpectedly jabbing a pin into the back of an individual’s calf

would result in a biased withdrawal response of the leg flexors, resulting in the individual flexing

their knee and consequently pushing the pin further into the calf. Following this realization, the

16

person would then correct their response and extend the knee away from the harmful stimulus.

Although the response was performed rapidly with automaticity, it in fact produced a negative

outcome.

In addition to this hardwired reflexive response, negative overcompensatory

physiological arousal during preparation has also been revealed in those with motor control

impairments. For example, Smith et al. (2012) found that participants with Parkinson’s disease

have increased beta event-related desynchronization when attempting to scale their postural

responses to the predicted perturbation magnitude compared to health controls. This increased

cortical activity is likely maladaptive, as those with Parkinson’s were not able to scale their

posture to the predicted perturbation magnitude. Similarly, individuals post-stroke demonstrate

higher levels of physiological arousal when preparing for a perturbation, regardless of the source

(investigator-initiated vs. participant-initiated; Pollock, 2014). This increase in physiological

activity was detrimental as heightened postural muscle activity limits the range of limb

displacement in response to a perturbation. Although arousal, increased cortical activity, and

biased pathways in anticipation of a motor response can be advantageous as previously

discussed, these examples outline scenarios in which they can be disadvantageous.

Understanding preparatory processing to a greater extent in healthy populations might help

further the grasp of what occurs in older and motor control impairment populations.

Rationale and Objectives The previous literature explores various processes involved in motor preparation,

methods to probe these processes, and different clinical and societal applications of motor

control research. Despite this growing body of literature, some questions remain unanswered.

How does cortical and spinal contributions to modulating motor output compare to one another?

Does altering the certainty of a response occurring modify these influences on lower limb motor

output? Can an individual’s perceived preparatory strategy alter their corticospinal and spinal

excitability? Developing a greater central understanding of the preparatory processing involved

in set-related adjustments of responses can provide greater insight to the biomechanical postural

adjustments measured at a more simplistic and neurophysiological level. This requires applying a

relatively simple and discrete task that manipulates the predictability of action and requires

action of only a single effector. Using central and peripheral stimulation methods, the modulation

17

of corticospinal and spinal excitability that occurs when preparing for both predictable and

unpredictable scenarios can be measured; specifically, by utilizing a simple (GO) and complex

(GO/NO-GO) reaction time paradigm, predictability can be manipulated while keeping other

aspects of the environmental context such as threat and urgency relatively low. Utilizing both

bottom up and top down methods in parallel can allow for further conclusions to be drawn

regarding the influence of cortical and spinal inputs on motor output; since top down approaches

require transmission of information through the spinal cord, they do not allow for isolation of

cortical influences on movement. Because unpredictable scenarios are present in everyday life,

understanding how individuals adjust their sensitivity to incoming stimuli and adjust their

strategies accordingly can provide insight into how performance can be optimized for complex

tasks (Figure 6). To date, there has been relatively little research investigating individuals’

perceptions of their preparatory strategies. Instead, inferences are made on based upon the

neurophysiological or biomechanical correlates measured and how they are modulated

accordingly.

Figure 6. Conceptual model outlining the potential influences of predictability and strategy on regulating

sensorimotor gain.

18

Accordingly, the objectives of this thesis were to:

1. Investigate the task-specific corticospinal excitability modulation during motor

preparation for lower limb movement

2. Investigate the task-specific spinal excitability modulation during motor preparation for

lower limb movement

3. Determine whether cortical and spinal contributions modulating motor output vary for

lower limb tasks

4. Understand the role of preparatory strategy on cortical and spinal changes in excitability

It was hypothesized that low predictability tasks will result in the facilitation of both

corticospinal and spinal excitability. Although increased excitability for known tasks in which

individuals can facilitate preparation may be intuitive, evidence has shown that unpredictable,

novel, and arousing tasks result in increased gain and activity of the CNS (Brunia & Vuister,

1979; Mars et al., 2007; Mochizuki et al., 2010; Prochazka, 1989; van Elswijk et al., 2007). This

is due to the incoming information being crucial to the performance and success of the motor

task. In addition, more repetitive tasks can lead to biasing of the CNS and dampening of its

sensitivity as it can allocate resources to more urgent processes. Secondly, it was hypothesized

that these task-dependent changes in sensorimotor gain will be modulated to a similar extent for

cortical and spinal connections (Badawy et al., 2012; Cracco et al., 1999; Pierrot-Deseilligny,

1997). Lastly, it was hypothesized that individuals that engage in an anticipatory preparation

strategy compared to a sit-and-wait preparation strategy will have a higher level of cortical and

spinal gain facilitation. Previous work has postulated that an anticipatory strategy is associated

with an increase in preparatory cortical activity (Cheung, 2015; Mochizuki et al., 2010) More

specifically, it is hypothesized the anticipatory preparation will result in increased arousal and

sensitivity to incoming stimuli to optimize motor output.

Developing an understanding of how these inputs are regulated in healthy individuals can

help advance motor preparation research at both ends of the motor control spectrum. At one end

is the elite athlete such as a soccer goalkeeper who needs to acquire a large quantity of

information and respond at tremendous speeds to kick away incoming shots. This level of

19

preparation requires high levels of rapid processing, biased from previous experience and the

current context of the situation. At the other end of the spectrum are individuals with motor

control impairments such as anterior lobe cerebellar disorders. These individuals may require a

high level of conscience attention to their postural movements due to difficulty in scaling

response magnitudes. These individuals would have impairments utilizing their previous

experience and current context to anticipate future responses accordingly. By understanding the

central and peripheral relationship to modifying pathways involved in motor preparation at a

fundamental level, research investigating motor preparation in various populations can be

advanced.

20

Chapter 2: Co-Modulation of Corticospinal and Spinal Excitability During Preparation for Lower Limb Movement

Introduction

Motor preparation is an anticipatory behaviour humans utilize in everyday contexts.

Tasks which require temporally-urgent responses such as suddenly depressing the brakes to

avoid an accident while driving further accentuate the importance of readiness and motor

preparation. Preparatory processes pre-activate certain brain structures to engage relevant

pathways and disengage those that are not uninvolved. This priming improves the signal-to-noise

ratio to optimize the impending information, allowing responses to be performed rapidly and

precisely (Brunia, 1999).

To understand the physiological components of motor preparation, many studies utilize a

chronometric paradigm in which a warning stimulus is presented, priming the subject for a

subsequent imperative stimulus in which a response may be selected. The warning stimulus can

provide varying degrees of information related to the impending response. In the length of time

between the stimuli (foreperiod), various cognitive processes occur – evidenced by an increase in

cortical activity – to optimize incoming information. This contingent negative variation (CNV)

wave can be manipulated through response expectancy and is thought to occur once a participant

begins to anticipate the imperative tone due to the temporal delay following the warning signal

(Macar & Bonnet, 1997; Walter, Cooper, Aldridge, McCallum, & Winter, 1964). Recently,

Cheung (2015) demonstrated CNV onset occurring in a window of 1600 to 800 ms (3 s

foreperiod) prior to the commencement of the imperative tone and was thought to represent

processes associated with central set.

Set or central set refers to a state of readiness for a stimulus, in which the gain of

sensorimotor pathways is adjusted to suit a particular context (Evarts, 1975; Prochazka, 1989). In

a similar paradigm to Cheung (2015), Mochizuki and colleagues (2010) found that pre-

perturbation cortical activity in the preparatory window is scaled to perturbation size if the

impending amplitude is known; when perturbation amplitude was unpredictable, N1 amplitude

was larger – evident of the CNS adjusting its set for the “worst-case scenario” instead of a “sit-

and-wait” approach. These proposed strategies were based upon the preparatory cortical activity

21

of the individuals; however, subjectively asking participants their strategy and then comparing

the neurophysiological underpinnings may provide additional understanding of the effect

strategy has on actual processes. Currently, manipulating response expectancy can modify the

influence of set on a particular task, and the preparatory processes involved.

Motor preparation involves competing excitatory and inhibitory processes which

summate to produce a net motor response. Both spinal and supraspinal inputs control motor

output through a vast network of connections. Multiple studies using reaction time tasks to

investigate preparatory excitability have demonstrated decreased corticospinal excitability during

the latter part of the preparatory foreperiod as evidenced by diminished MEPs in the agonist

muscle (Davranche et al., 2007; Duque et al., 2010; Hasbroucq et al., 1997, 1999; Touge et al.,

1998; van Elswijk et al., 2008); however, an increase in MEP amplitude has also been observed

(Davranche et al., 2007; Mars et al., 2007; van den Hurk et al., 2007; van Elswijk et al., 2007). In

regards to H-reflex findings during the preparatory foreperiod, mixed results have also been

observed, with inhibition (Duque et al., 2010; Hasbroucq et al., 1999; Komiyama & Tanaka,

1990; Requin et al., 1977; Touge et al., 1998) and facilitation (Brunia & Vuister, 1979) of spinal

excitability being found during the latter half of preparation. The discrepancy between these

findings may be explained by various factors such as: length of foreperiod, timing of stimulation,

tonic activation levels of the agonist muscle, the reaction time paradigm implemented, and

whether “practice” trials were first completed. Overall, the general consensus regarding spinal

and cortical tuning during preparation suggests an initial facilitation following the warning

stimulus, followed by a general inhibition exhibited in the prime mover carrying into the initial

response time (Frank, 1986).

The majority of the aforementioned research examines CNS gain modulation utilizing

transcranial magnetic stimulation (TMS) or percutaneous electrical stimulation (PES) techniques

independently; however, measuring MEPs through TMS does not isolate cortical influence on

corticospinal excitability, as an MEP is the net result of cortical interneurons, fast corticospinal

pathways, and spinal motoneuron connections (Badawy et al., 2012; Cracco et al., 1999).

Measuring H-reflex modulation in addition to changes in MEP amplitude can provide

supplemental information regarding corticospinal control of a motoneuron pool, assuming that a

conditioning volley does not alter motor cortex excitability, presynaptic inhibition of Ia

terminals, or its transmission through an interneuronal relay (Pierrot-Deseilligny & Burke, 2005).

22

In those studies which measured both corticospinal and spinal excitability changes, MEP and H-

reflexes were both inhibited or unchanged during preparation in the agonist muscle (Duque et al.,

2010; Hasbroucq et al., 1999; Touge et al., 1998). By dissociating spinal and supraspinal

influence on motoneuron excitability during motor preparation, further conclusions can be drawn

regarding optimization of preparatory processing.

The majority of work investigating preparatory control of both corticospinal and spinal

excitability employs an upper limb paradigm. Due to anatomical and functional differences

between upper and lower limbs, transferability of neurophysiological findings should be made

cautiously. The upper limb and hands are non-weight-bearing and involved in fine motor skills.

Conversely, lower limb musculature such as tibialis anterior are functionally relevant for tasks

such as anticipatory postural adjustments (Burleigh, Horak, & Malouin, 1994), toe clearance

during normal gait (Winter & Yack, 1987), and stair ascent/descent (McFadyen & Winter, 1988).

Understanding the cortical and spinal mechanisms involved in lower limb motor control –

specifically in unpredictable scenarios – can have direct applications related to how the CNS

modulates its excitability in the constantly changing environment one is exposed to daily.

Accordingly, the purpose of this study was to determine whether the cortical and spinal

contributions to modifying motor output differ from one another during preparation for

temporally-urgent lower limb movement. In addition, this study also aimed to determine how this

modulation varies with response expectancy and preparatory strategy. It was hypothesized that

during preparation for a highly predictable reaction time task, corticospinal and spinal

excitability would be lower compared to the responses measured for the low predictability

reaction time task. This is consistent with previous studies which have found a diminution of

MEP and H-reflex responses as an adapted mechanism to optimize performance (Hasbroucq et

al., 1997, 1999). In addition, it was hypothesized that individuals that recruit an anticipatory

preparation strategy compared to a sit-and-wait preparation strategy will have a higher level of

cortical and spinal gain facilitation. More specifically, the anticipatory preparation will result in

increased arousal and sensitivity to incoming stimuli to optimize motor output.

23

Methods

2.1 Participants

Twenty-six participants (27.1 ± 5.3 years, 11 F:15 M) participated in this study. Subjects

were recruited from the General Toronto Area, and provided written informed consent prior to

study participation. Participants were free of any neuromuscular disorders or impairments and

were excluded if they met any of the potential contraindications for TMS as outlined by Rossi et

al. (2009). This study was approved by the Research Ethics Board at Sunnybrook Research

Institute.

2.2 Experimental Protocol

2.2.1 Equipment and Procedures

Participants were seated in a height-adjustable chair, positioned to allow for a 90° angle

at the hip joint, 120° angle at the knee joint, and 120° ankle angle. The participant’s right foot

was placed in a custom designed foot dynamometer and strapped into the foot plate (Marsh, Sale,

McComas, & Quinlan, 1981). For the purposes of this study, the dynamometer was used to

support the foot. No force data was collected. The left foot rested comfortably on a wooden stand

built to the same height as the foot plate (see Figure 7). Subjects were randomized to determine

whether corticospinal or spinal excitability

measures were recorded first. All measurements

were taken in the same position and lasted

approximately one to two hours depending on the

measures obtained. Following baseline excitability

measurements, participants performed a simple

(GO) and complex (GO/NO-GO) task. Each block

consisted of single-pulse TMS being performed

during the preparatory foreperiod. A small subset

(n=8) also completed both reaction time tasks with

H-reflex measures recorded.

Figure 7. Diagram of experimental TMS set up.

24

2.2.2 Preparatory Strategy

To probe whether preparatory strategy influences corticospinal and spinal excitability,

participants were given a questionnaire to subjectively report their preparatory strategy

(Appendix 1). Participants were asked for each reaction time task whether they “actively

prepared for a Go tone” or “waited until the tone sounded before preparing to move”. These

strategy types were initially classified by Cheung (2015), where electroencephalographic (EEG)

activity changes could result in a “sit-and-wait” or “worst-case scenario” preparatory strategy.

By asking participants what strategy they implemented, it allowed for pre-determined

categorization of the data. Participants were also provided additional space to expand on their

strategy if they felt that these classifications did not accurately portray how they prepared.

Participants were not made aware that they would be asked about the strategy ahead of time, to

not bias their cognition and performance on the reaction time tasks.

2.2.3 Reaction Time Tasks

Condition order was randomized between participants. For the GO condition, a warning

tone was played, followed by an imperative “go” tone 3 s later (Figure 8). A constant foreperiod

length of 3 s was utilized to keep temporal features of the task consistent. Once the “go” tone

was presented, the subjects were instructed to dorsiflex their right foot as quickly as possible.

Emphasis was placed on the speed of the response rather than the quality and accuracy of the

response to decrease errors associated with poor attention (Shalgi, O’Connell, Deouell, &

Robertson, 2007; Sinclair & Hammond, 2009). Blocks consisted of 30 trials, with 12 s between

each trial. For the GO/NO-GO condition, participants were to perform similarly to the “go” tone

upon presentation; however, in addition to the “go” tone being presented, a “no-go” tone also

appeared in six of the 30 trials in which participants were instructed to not perform the motor

task. These six “no-go” tones were presented randomly throughout the 30 trials to alter task

predictability. A ratio of 4:1 was utilized to promote active movement preparation and

manipulate response expectancy (Walter et al., 1964). Following the presentation of the warning

tone, stimulation of the leg motor cortex or stimulation of the peroneal nerve occurred, evoking a

small muscular response in tibialis anterior. Participants were made aware of which condition

was going to be performed prior to the commencement of the block. Participants were not

provided with practice trials in order to ensure reaction time was not influenced by prior

experience with the reaction time paradigm.

25

2.2.4 Single-Pulse Transcranial Magnetic Stimulation

Single-pulse TMS (monophasic, 1 ms duration) was performed using a Magstim 2002

magnetic stimulator with a 110mm double cone coil to stimulate the left leg region of the motor

cortex (MagStim Company Ltd, Whitland UK). Previous work has demonstrated no significant

difference between leg moto excitability of either cortex, thus only the left leg region was

stimulated for consistency and repeatability (Smith, Stinear, Alan Barber, & Stinear, 2017). The

coil was rotated to produce a posterior to anterior current in the cortex. The centre of the coil was

initially placed 2-3 cm lateral and posterior to Cz of the scalp. The coil was then moved to

determine the lowest threshold area for producing an MEP in tibialis anterior. The resting motor

threshold was ascertained as the lowest stimulator output needed to produce an MEP with a 50

µV peak-to-peak amplitude in 5 of 10 consecutive trials. The mean stimulator intensity for

resting motor threshold trials was 48.6% of total output. The experimental stimulator output was

adjusted to 110% of the resting motor threshold intensity, to study facilitation or diminution of

the MEP during experimental trials. During the experiment, TMS was automatically triggered 2 s

after the warning tone was presented using LabView software (LabView 2012, National

Instruments, Austin TX).

2.2.5 Percutaneous Electrical Stimulation

H-reflexes of the tibialis anterior were evoked using a bipolar stimulating electrode with

felt tips (Alpine Biomed, Skovlunde, Denmark). The H-reflex was elicited by stimulating the

peroneal nerve, located laterally in the popliteal fossa and directly posterior to the fibular head.

One millisecond square wave pulses were produced using a stimulator and stimulus isolation unit

(Models S48 & SIU5, Grass Technologies, West Warwick RI). Once the optimal location was

determined for electrode placement, the electrode was strapped into place using a fixation strap.

Figure 8. Contingent Negative Variation paradigm for the GO and GO/NO-GO reaction time tasks. Transcranial or

nerve stimulation/percutaneous electrical stimulation was applied at two seconds following the warning tone.

26

A tensor bandage was then wrapped around the knee to further secure the stimulation electrode.

A recruitment curve protocol was then performed to determine the experimental stimulus

intensity. Experimental intensity was set to a level which corresponded to 50% of the maximum

H-reflex amplitude produced during the ascending portion of the recruitment curve; this intensity

was implemented to eliminate the influence of antidromic collision and improve reliability of the

measure (Grosprêtre & Martin, 2012). The mean stimulator output was 52 V for experimental

trials. For the reaction time tasks, PES was triggered using the same program as the TMS trials.

2.2.6 Electromyography

Surface electromyography (EMG) was collected from the right tibialis anterior using self-

adhesive Ag-AgCl recording electrodes (30 mm Medi-Trace 130, Mansfield, MA, USA). Two

recording electrodes were placed on the belly of tibialis anterior, with a Velcro strap ground

electrode placed securely around the ankle. Skin was prepared using an abrasive preparation gel

(NuPrep, Weaver and Company, Aurora, CO, USA) and cleansed using an alcohol swab (70%

isopropyl HealthCare Plus swabs, Canadian Custom Packaging, Toronto, ON, Canada) prior to

electrode placement. Surface EMG signals were sampled at 1000 Hz (Power 1401 mkII,

Cambridge Electronic Design, Cambridge, UK), amplified by 5000, and band-pass filtered at 10-

1000 Hz online using an amplifier (Model QP511, Grass Technologies, West Warwick, RI,

USA). Data was then saved and stored offline for analysis.

2.3 Data Analysis

2.3.1 EMG Analysis

Muscle activity was collected using data acquisition software (Spike2 Version 7.17,

Cambridge Electronic Design, Cambridge, UK). EMG signals were rectified and low-pass

filtered offline using a second-order Butterworth filter at 10Hz for the reaction time behavioural

measures (reaction time and iEMG). Files were then converted to another data acquisition

software to run a custom analysis program (Signal 6.02, Cambridge Electronic Design,

Cambridge, UK). MEP and H-reflex amplitudes were calculated as the peak-to-peak amplitudes

of the raw EMG 0.02 to 0.06 s after stimulation. Responses were excluded from analysis if the

peak-to-peak amplitude was below 6 µV as it was undifferentiable from background noise.

Reaction time was determined as the difference between the time of the imperative tone and

27

onset of muscle activity (defined as two standard deviations above resting activity). Reaction

times were discarded if faster than 100 ms. Muscle activity of the imperative tone response was

calculated as integrated EMG (iEMG) –defined as the area under the curve of the rectified signal

from the onset of activity to the peak EMG response (Figure 9).

2.4 Statistical Analysis

All statistical tests were run using IBM SPSS Statistics 24 (IBM, Armonk USA) and all

values are represented as means and standard deviations unless otherwise stated. Data was tested

for normality using the Shapiro-Wilk Test, and if violated, a log transformation was performed.

Data was excluded if they met any of the following criteria: 1) exhibited > 2 standard deviations

of EMG activity during stimulation; 2) had a brief muscle contraction within 100 ms of

stimulation which resulted in reaction time being modified by +/- 1 standard deviation; 3) had a

reaction time faster than 100 ms; 4) responded incorrectly to an imperative tone. To determine if

corticospinal and spinal excitability during the conditions were different than baseline, a one-way

repeated measures analysis of various was performed (ANOVA). To test the hypothesis that task

condition and strategy affects errors, reaction time, corticospinal/spinal excitability, and iEMG,

2x4 repeated measures ANOVAs were performed for each variable. These analyses were also

conducted on coefficient of variation, as it allows for the assessment of performance variability

and the stability of responses within an individual. Coefficient of variation was calculated as the

standard deviation divided by the mean, and multiplied by 100%. Strategies could be categorized

Figure 9. Schematic demonstrating the temporal features of the collected electromyography (EMG) measures.

28

into four possible options based on either a sit-and-wait or anticipatory strategy choice: sit-and-

wait for both tasks (SAW), anticipatory for both tasks (AP), sit-and-wait for GO and anticipatory

for GO/NO-GO (SAWxAP), or anticipatory for GO and sit-and-wait for GO/NO-GO

(APxSAW). To determine whether there was a relationship between corticospinal and spinal

modulation, a Pearson’s correlation was run for each task. Statistical significance was set at p ≤

0.05.

2.5 Secondary Analyses

Secondary analyses were conducted to better understand the corticospinal and spinal

modulation occurring in the study. To determine the relationship between corticospinal and

spinal excitability and behaviour, a Pearson’s correlation was conducted, comparing MEP and H-

reflex amplitude to reaction time. In addition, to determine the influence of task order, a 2x2

repeated measures ANOVA was performed; trials for each task were subsequently split into

thirds to create 6 time points which represent the first 10 trials, middle 10 trials, and last 10 trials

of the experiment and a repeated measures ANOVA was performed. To determine if time had an

effect on behaviour, a repeated measures ANOVA was conducted for each condition, with

reaction time, reaction time coefficient of variation, iEMG, or iEMG coefficient of variation as

the within-subject factors. A repeated measures ANOVA was performed to determine if there

was corticospinal tuning following a “no-go” trial in the GO/NO-GO condition. The

corticospinal excitability, reaction time, and iEMG measures during the subsequent “go” trial

were determined. To understand the influence of excitatory vs. inhibitory modes of preparatory

control, participants were divided into groups depending on whether they had an increase or

decrease in excitability compared to baseline. A paired t-test was used to determine if

participants had a significant modulation in corticospinal excitability compared to baseline. A

2x4 repeated measures ANOVA was then performed.

To test the effects of stimulation timing on corticospinal excitability and to analyze overt

changes in excitability, 4 participants were recruited. Stimulation was performed at either -2.0, -

1.0, or -0.5s and was randomized across the 30 trials for both tasks with 10 trials for each time

point. Participants also had baseline excitability measures taken before and immediately after the

reaction time tasks. Due to the pilot nature of these experiments, no statistical analyses were

performed.

29

Normalization procedures were performed and MEP and H-reflex amplitudes were

expressed as a percentage of baseline to create relative values for each participant for the

following analyses. Two-by-four repeated measures ANOVAs were performed for

corticospinal/spinal excitability, reaction time, reaction time coefficient of variation, iEMG, and

iEMG coefficient of variation. To better understand the relationship between the modulation of

H-reflex and MEP amplitudes, the measures were calculated as a percentage of M-max for the 8

participants who performed both the TMS and PES tests. A Pearson’s correlation was then

performed to study the relationship between these measures. Lastly, to account for ‘neutral’

excitability (i.e. measures that did not change substantially from baseline) and to account for

natural variability which can occur with MEP and H-reflex measures, excitatory and inhibitory

control was defined as 10% above and below baseline excitability respectively; neutral control

was defined as 90-110% of baseline MEP and H-reflex amplitude. A 2x6 repeated measures

ANOVA was then performed.

Results

3.1 Primary Results

3.1.1 Strategies and Errors

There was no significant difference in

proportions of strategies [χ2(1) = 0.078, p =

0.780], despite it appearing that individuals

tended to select an anticipatory strategy for

the GO condition (73% of participants) and a

sit-and-wait preparatory strategy for the

GO/NO-GO condition (62% of participants;

Table 1). Errors were described as a

participant prematurely reacting to an

imperative tone (ie. a reaction time less than

100 ms or incorrectly responding to the

imperative “go” or “no-go” tone). After

reviewing the trials, 2.70% of trials resulted

in an error for both the GO and GO/NO-GO condition and were discarded. One participant

Figure 10. Mean errors recorded for each reaction time

task and separated based on preparatory strategy. Data

suggests those who kept the same strategy for both

conditions had an increase in errors for the GO/NO-GO

task, whereas those who changed strategy types

improve or see no difference in errors. Solid lines

indicate individuals who used the same strategy for

both tasks and dotted line represents those who

switched strategies depending on the task. Error bars

denote standard error of the mean.

30

prematurely anticipated an imperative tone for the GO condition in 18 of 30 trials and was

excluded from the error analysis. No effect of condition [F(1,21) = 0.710, p = 0.409] or strategy

[F(3,21) = 0.645, p = 0.594] was found. A trend towards a significant interaction between

condition and strategy [F(3,21) = 2.698, p = 0.072; Figure 10] was present however. This

interaction may suggest increased errors switching from the GO to GO/NO-GO condition in

participants who kept the same preparatory strategy (either SAWxSAW or APxAP).

3.1.2 Reaction Time

Analysis revealed a significant main effect of task [F(1,22) = 64.360, p < 0.001], with

GO/NO-GO (379 ms ± 13 ms) resulting in significantly slower reaction time than the GO

condition (270 ms ± 12 ms; Figure 11). No significant interaction between task and strategy

[F(3,22) = 2.025, p = 0.140] or effect of strategy alone [F(1,22) = 0.988, p = 0.140] was found

(Appendix 8 and Appendix 9). The data from the sample which included all participants (n = 26)

was used for this analyses as opposed to the subgroup with spinal excitability measures.

Reaction time coefficient of variation was then assessed. No effect of task condition

[F(1,22) = 0.539, p = 0.539] or strategy [F(1,22) = 1.439, p = 0.258], and no significant

interaction effects were seen [F(3,22) = 0.129, p = 0.942] (Appendix 10 and Appendix 11).

When comparing reaction time of participants who performed both H-reflex and MEP

procedures, there were no significant differences in the reaction times between the two

procedures [t(15) = 1.1557, p = 0.140] (Appendix 12).

Table 1. Summary of preparatory strategies

GO

SAW AP

GO

/NO

-GO

SAW 4 12

AP 3 7

SAW = sit-and-wait strategy; AP = anticipatory strategy

31

3.1.3 Corticospinal Excitability

Due to outliers in the data, the MEP amplitude data was log transformed. Analysis

revealed no significant difference between BASELINE, GO, and GO/NO-GO conditions

[F(2,50) = 0.359, p = 0.700] following a repeated measures ANOVA (Appendix 2). Following

this analysis, the influence of preparatory strategy and task condition on corticospinal excitability

was investigated. The results showed that task [F(1,22) = 2.794, p = 0.109], strategy [F(1,22) =

1.487, p = 0.246], and task-strategy interaction [F(3,22) = 0.758, p = 0.530] were not significant

following a two-way repeated measures ANOVA with task (GO and GO/NO-GO) as the within

subject factor and strategy as the between subject factors (Appendix 3 and 4). Upon review of the

data, there may be an effect of strategy or task independently on MEP amplitude (GO/NO-GO

and anticipatory preparation leading to increase MEP) based on the trends seen in Figure 12,

however this study was underpowered to account for strategy as initial sample size calculations

were based on the effect of condition on corticospinal excitability.

A

Figure 11. A) Mean reaction time recorded for each reaction time task and separated based on preparatory

strategy. GO/NO-GO task elicits significantly slower reactions. B) Mean reaction time coefficient of variation

recorded for each reaction time task and separated based on preparatory strategy. Solid lines indicate individuals

who used the same strategy for both tasks and dotted line represents those who switched strategies depending on

the task. Error bars denote standard error of the mean.

B

32

3.1.4 Spinal Excitability

Analysis revealed no significant difference amongst the BASELINE, GO, and GO/NO-

GO H-reflex amplitudes [F (2,14) = 0.255, p = 0.779] following a repeated measures ANOVA

(Appendix 5). Following this analysis, the influence of preparatory strategy and task condition on

spinal excitability was investigated similarly to the corticospinal excitability measures. Analysis

revealed no effect of task condition [F(1,5) = 0.252, p = 0.637] or strategy [F(1,5) = 1.361, p =

0.337 on spinal excitability], and no significant interaction [F(3,5) = 0.045, p = 0.956] (Appendix

6 and Appendix 7). Based on Figure 13, strategy seems to influence spinal excitability greater

than condition based on the horizontal nature of the lines between task.

Figure 12. A) Mean motor-evoked potential (MEP) amplitude of BASELINE, GO, and GO/NO-GO conditions.

No apparent modulation of MEP was seen between conditions. B) Mean MEP amplitude for each reaction time

task and separated based on preparatory strategy. Anticipatory strategy appeared to elicit higher preparatory

corticospinal excitability although not significant. Solid lines indicate individuals who used the same strategy for

both tasks and dotted line represents those who switched strategies depending on the task. Error bars denote

standard error of the mean.

A B

33

3.1.5 Relationship Between Corticospinal and Spinal Excitability

A Pearson’s correlation was performed to investigate if there was any relationship

between corticospinal and spinal excitability measures for the two tasks (Figure 14). Analyses

showed a significant correlation between MEP and H-reflex measures of the participants for the

GO/NO-GO condition [r = 0.724, p = 0.038] and showed a trend towards significance for the GO

condition as well [r = 0.649, p = 0.082] (Appendix 13).

Figure 14. Plot of 8 participants who completed both H-reflex and motor-evoked potential measures. Strong

correlation was found between corticospinal and spinal measures for the GO/NO-GO task (open circle, dotted line)

and a trend towards a positive correlation was observed for the GO task (closed square, solid line).

Figure 13. A) No difference in H-reflex amplitude was observed between tasks and baseline. B) Mean H-reflex

amplitude for each reaction time task and separated based on preparatory strategy. Spinal excitability appeared

stable and unchanged between tasks regardless of the strategy implemented. Solid lines indicate individuals who

used the same strategy for both tasks and dotted line represents those who switched strategies depending on the

task. Error bars denote standard error of the mean.

A B

34

3.1.6 Muscle Activity of Motor Response

Results indicated no effect of task [F(1,22) = 0.029, p = 0.867] or strategy [F(1,22) =

0.723, p = 0.549] on iEMG, and no significant interaction between condition and strategy

[F(3,22) = 1.114, p = 0.365] following a two-way repeated measures ANOVA (Appendix 14 and

Appendix 15). Secondary analyses also investigated the influence of condition and strategy on

the variability of the iEMG as measured by coefficient of variation. iEMG variability increased

for the GO/NO-GO condition (42.151 ± 2.444 mV·s) compared to the GO condition (37.831 ±

2.231 mV·s) and appears to largely affect the groups which did no alter their strategy between

tasks [F(1,22) = 4.826, p = 0.039] (Figure 15). No effect of strategy [F(1,22) = 0.257, p = 0.856]

and no significant interaction between task and strategy on iEMG variability [F(3,22) = 1.582, p

= 0.220] (Appendix 16 and Appendix 17). This variability in motor performance may be due to

the adjustment of preparatory processes and adaptation to the more complex task.

Figure 15. A) Mean integrated electromyographic activity (iEMG) recorded for each reaction time task and

separated based on preparatory strategy. B) Mean iEMG coefficient of variation recorded for each reaction time

task and separated based on preparatory strategy. A significant increase in muscle activity variability was found in

the GO/NO-GO task. Solid lines indicate individuals who used the same strategy for both tasks and dotted line

represents those who switched strategies depending on the task. Error bars denote standard error of the mean.

A B

35

Table 2. 2x4 repeated measures ANOVA summary table for primary variables of interest

Variables Interaction Effect of Task Effect of Strategy

F value p value F value p value F value p value

Error 2.698 0.072 0.710 0.409 0.645 0.594

Reaction Time 2.025 0.140 64.360 <0.001* 0.988 0.140

Cortical Excitability 0.758 0.530 2.794 0.109 1.487 0.246

Spinal Excitability 0.045 0.956 0.252 0.637 1.361 0.337

iEMG 1.114 0.365 0.029 0.867 0.723 0.549

* denotes p value less than 0.05

3.2 Secondary Results

3.2.1 Task Optimization – Reaction Time

Analysis revealed no significant relationship between corticospinal excitability and

reaction time for the GO [r = -0.146, p = 0.477] or GO/NO-GO condition [r = 0.255, p = 0.209].

No significant relationships were observed between spinal excitability and reaction time as well

[GO: r = -0.445, p = 0.270; GO/NO-GO: r = -0.333, p = 0.420].

Figure 16. Mean MEP amplitudes for all 60 reaction time trials irrespective of task condition. Trials are

presented in order they were performed. A significant effect of time on MEP amplitude was observed. Error bars


36

3.2.2 Effect of Time and Task Order on Corticospinal Excitability

The log-transformed MEP data was again utilized for the time analysis. No individual

effect of task [F(1,24) = 0.788, p = 0.383] or order [F(1,24) = 0.004, p = 0.948] was found on

corticospinal excitability. However, a significant interaction between task and the order of the

tasks [F(1,24) = 5.005, p = 0.035] was observed. This analysis shows that for the GO and

GO/NO-GO conditions elicited different responses depending on the order in which the task was

performed (ie. the second task demonstrated a higher modulation of corticospinal excitability).

Subsequently, it was determined whether significant differences in corticospinal excitability were

seen across various time points throughout the trials. A repeated measures ANOVA revealed a

significant effect of time on corticospinal excitability [F(5, 125) = 2.455, p = 0.037] (Figure 16).

Multiple pairwise comparisons were performed using a Bonferroni correction and revealed no

significant differences existed between each interval (p > 0.05).

3.2.3 Effect of Time on Behavioural Measures

There were no significant effects of time on reaction time [GO: F(2,50) = 1.075, p =

0.349; GO/NO-GO: F(2,50) = 1.069, p = 0.351] or iEMG [GO: F(2,50) = 1.445, p = 0.245;

GO/NO-GO: F(2,50) = 1.039, p = 0.361]. The small visible trend in reaction time over the

course of the trials may have to do with motor learning in the GO/NO-GO condition (Figure

17A). The effect of time on variability was then assessed to determine if participants were more

consistent over in their behavioural measures or more variable. There was no significant

difference over time for reaction time [GO: F(2,50) = 0.291, p = 0.740; GO/NO-GO: F(2,50) =

0.714, p = 0.494]. Time had a significant effect on iEMG variability for the GO condition

[F(2,50) = 3.830 p = 0.028], but not the GO/NO-GO condition [F(2,50) = 1.642, p = 0.204], in

which variability was higher for the latter third of the trials (Figure 17B). This may point to a

dampening in arousal and attention as predictable, simple tasks progress.

37

3.2.4 Recruitment Curves

At the beginning of the experiment, recruitment curves were generated for each

participant to determine their Hmax so that an appropriate experimental stimulation intensity

could be determined accordingly. Participants had a mean Hmax of 0.29 mV ± 0.15 mV and a

mean Mmax of 0.88 mV ± 0.39 mV. The mean Hmax:Mmax ratio was 0.38. Figure 18 illustrates a

recruitment curve for a single participant.

3.2.5 Adaptive Tuning

Analysis revealed no effect of the “no-go” trial on the corticospinal excitability of the

subsequent “go” trial [F(1,24) = 0.736, p = 0.399]. There was also no effect of the “no-go” trial

on the proceeding reaction time [F(1,24) = 0.187, p = 0.669] or muscle response [F(1,24) =

0.178, p = 0.677]. Only strategy had an effect on corticospinal excitability [F(1,24) = 5.196, p =

0.032] and reaction time [F(1,24) = 5.268, p = 0.031], with the sit-and-wait preparatory strategy

resulting in a lower corticospinal modulation and faster reaction time.

Figure 17. A) Mean reaction time for the GO/NO-GO task separated by 10 trial bins. Visually, reaction time

appears to speed up as the familiarity with the trial progresses. B) Mean iEMG variability recorded during the GO

condition and separated by 10 trial bins. A significant effect of time was seen on muscle response variability, with

the last 10 trials having the largest variability. This may point to a lack of attention throughout a simple task. Error

bars denote standard error of the mean.

A B

38

3.2.6 Excitatory and Inhibitory Control

Majority of participants either demonstrated a global facilitation (n = 11) or inhibition (n

= 8) of corticospinal excitability for both tasks, with a small proportion demonstrating inhibitory

control for the GO and excitatory for the GO/NO-GO (n = 5), and a smaller proportion

exhibiting the opposite (n =2). For the GO condition, 13 participants exhibited facilitatory

modulation of corticospinal excitability and 13 demonstrated a dampening of excitability and

their changes were significantly different compared to baseline [t(12) = 3.862, p = 0.002; t(12) =

-5.172, p < 0.001]. Similarly for the GO/NO-GO task, participants also exhibited significantly

different modulation in their corticospinal excitability [Inhibitory Group: t(9) = -3.780, p =

0.004; Excitatory Group: t(15) = 5.559, p < 0.001]. A repeated measures ANOVA revealed a

significant interaction between condition and excitability control type on reaction time variability

[F(3,22) = 4.362, p = 0.015]. This may indicate a slight increase in variability with task

complexity when individuals utilize the same control type (Figure 19A).

Figure 18. Individual recruitment curve of tibialis anterior H-reflex and M-wave. Experimental stimulator

intensity was set to evoke an H-reflex amplitude of 50% Hmax. For this participant that would correspond with

an intensity of ~53 V.

39

In the spinal excitability subgroup, 5 of the 8 participants demonstrated the same control

at the cortical and spinal level. For the GO condition, only the excitatory control group

demonstrated a significant modulation of spinal excitability [t(4) = 2.771, p = 0.005] which is

likely due to only 3 participants demonstrating inhibitory spinal control. Conversely for the

GO/NO-GO task, only the inhibitory control group exhibited a significant change in spinal

excitability [t(3) = -3.899, p = 0.030]. No significant effect of control type was found on reaction

time [F(2,5) = 2.554, p = 0.172] or its variability [F(2,5) = 1.973, p = 0.234], only task alone

influenced reaction time [F(1,5) = 22.256, p = 0.005]. For muscle response, there was a

significant interaction between task and control type [F(2,5) = 33.680, p = 0.001]. Although

post-hoc tests could not be performed, it appears that spinal inhibitory control may result in less

response variability and better motor control (Figure 19B).

3.2.7 TMS Timing

Timing of stimulation during the preparatory foreperiod was varied (Figure 20).

Stimulation time appeared to affect the speed of reaction time for the GO and GO/NO-GO task,

with the -0.5s window resulting in the slowest reaction time (GO: 38 ms ± 51 ms slower;

GO/NO-GO: 72 ms ± 58 ms slower). No apparent effect of stimulation time on the other

Figure 19. A) Mean reaction time coefficient of variation recorded for each reaction time task and separated based on

cortical control. A significant interaction was found between corticospinal type and task, likely driven by the group

which switched from excitatory to inhibitory control between the GO and GO/NO-GO task. B) Mean iEMG recorded

for each reaction time task and separated based on spinal control. A significant interaction between control type and

task was observed, indicating a change in control from GO to GO/NO-GO increases the size of muscle response. Solid

lines indicate individuals who used the same control for both tasks and dotted line represents those who switched

control depending on the task. Error bars denote standard error of the mean.

A B

40

measures was observed. When looking at overt changes in excitability, there appeared to be no

global difference between initial and final baseline; three of the four participants did show a

decrease in MEP amplitude however, which is contrary to the hypothesis that MEP amplitude

would increase globally as there was an effect of time on corticospinal excitability in the larger

group.

3.3 Results Normalized to Baseline

3.3.1 Relative Corticospinal and Spinal Excitability

No significant main effects of task [F(1,22) = 2.498, p = 0.128] or strategy [F(3,22) =

0.945, p = 0.436] were observed (Figure 21A). Also, no significant interaction between task and

strategy was observed for corticospinal excitability [F(3,22) = 0.289, p = 0.833]. Similarly, no

significant main effects of task [F(1,5) = 0.022, p = 0.889] or strategy observed [F(2,5) = 1.252,

p = 0.362] were observed, and no significant interaction between task and strategy was found for

spinal excitability [F(2,5) = 0.044, p = 0.957].

Figure 20. Contingent Negative Variation paradigm for the GO and GO/NO-GO reaction time tasks. Transcranial

or nervous stimulation/percutaneous electrical stimulation was applied at three timepoints throughout the

preparatory foreperiod (indicated by an arrow).

41

B A

Figure 21. A) Mean MEP amplitude expressed as a percentage of baseline for each reaction time task, separated

based on preparatory strategy. B) Mean H-reflex amplitude as percentage baseline for each reaction time task and

separated based on preparatory strategy. Solid lines indicate individuals who used the same strategy for both tasks

and dotted line represents those who switched strategies depending on the task. Error bars denote standard error of

the mean.

3.3.2 Relative Relationship Between Corticospinal and Spinal Excitability (% M-Max)

For the GO task,

corticospinal and spinal

excitability were

significantly positively

correlated (r = 0.881, p

= 0.004). A significant

relationship was also

observed for the

GO/NO-GO task (r =

0.945, p < 0.001). The

relationship is plotted in

Figure 22. Majority of

the clustering in data

points are observed at lower amplitudes of corticospinal and spinal excitability, whereas the data

becomes more linear with higher changes in MEP and H-reflex amplitude.

Figure 22. Plot of H-reflex and MEP amplitudes made relative to M-Max (n=8

for each task). Strong correlation was found between cortical and spinal measures

for the GO/NO-GO task (open circle, dotted line) and the GO task (closed square,

solid line).

42

3.3.3 Alternative Classifications of Preparatory Control

The distribution of participants for corticospinal control is presented in Table 3. There

was a significant interaction between corticospinal control type and task following a repeated

measures ANOVA [F(5,20) = 2.942, p = 0.038]. Simple main effects were unable to be

determined due to groups having fewer than two values. A significant effect of corticospinal

control type on corticospinal excitability was also found [F(5,20) = 14.308, p < 0.001]. A

significant interaction was also observed between task and control type on reaction time [F(5,20)

= 5.164, p = 0.003]. Although simple main effects could not be performed, it appears that the

groups which shifted from excitatory to neutral control from GO to GO/NO-GO, and those

which switched from inhibitory to excitatory demonstrated the largest slowing in reaction time

between the tasks (Figure 23A). Lastly, a significant interaction was observed between task and

corticospinal control type for iEMG variability [F(5,20) = 3.617, p = 0.017]. This is likely due to

all corticospinal control types demonstrating an increase in variability for the GO/NO-GO task

with the exception of the inhibitory-excitatory group. No other significant interactions or effects

were found for reaction time, reaction time variability, iEMG, or iEMG variability, p > 0.05.

Table 3. Summary of control types based on MEP measures normalized to baseline

GO

Excitatory Inhibitory Neutral

GO

/NO

-GO

Excitatory 10 2 0

Inhibitory 0 8 0

Neutral 1 3 2

43

For spinal excitability, the majority of participants kept the same control type for both

tasks (excitatory – 3, neutral – 2, inhibitory – 1), with 1 participant switching from inhibitory-

neutral control and 1 participant demonstrating excitatory-neutral control. Only 2 participants

demonstrated the same corticospinal and spinal control type for both tasks. Similar to

corticospinal excitability, there was a significant effect of spinal control type [F(4,3) = 12.668, p

= 0.032] and a significant interaction between task and control type [F(4,3) = 20.944, p = 0.016]

was also found. No significant interaction was found on reaction time, with only task having a

significant main effect [F(1,4) = 28.819, p = 0.013]. A significant effect of spinal strategy type

was identified[F(4,3) = 14.436, p = 0.027], which appears to show those with excitatory-

excitatory control and excitatory-neutral control having increase variability, specifically for the

GO/NO-GO task. A significant interaction was also observed between task and spinal control

type on iEMG variability [F(4,3) = 10.481, p = 0.041]. No other significant interactions or

effects were found for reaction time, reaction time variability, iEMG, or iEMG variability, p >

0.05.

B A

Figure 23. A) Mean reaction time recorded for each reaction time task and separated based on corticospinal

control. A significant interaction was found between control type and task, as well as effect of task. B) Mean iEMG

coefficient of variation (CoV) recorded for each reaction time task and separated based on corticospinal control. A

significant interaction between control type and task was observed. Solid lines indicate individuals who used the

same control for both tasks and dotted line represents those who switched control depending on the task. Error bars


44

Discussion

The present study aimed to

understand the effect of task

predictability and preparatory strategy

on sensorimotor gain. Specifically, this

study set out to characterize the

modulation of corticospinal and spinal

excitability evoked by a GO and

GO/NO-GO condition, and how these

modifications in preparatory set related

to each other. In addition, the influence

of a sit-and-wait and anticipatory

preparation strategy on excitability

tuning was also explored. The main findings of this study were: (1) corticospinal and spinal

excitability are regulated to a similar degree during low predictability tasks and this relationship

appears to be maintained in predictable tasks, (2) altering task predictability does not manipulate

corticospinal or spinal tuning, (3) preparatory strategy may modify corticospinal and spinal

preparatory pathways. Secondary analyses revealed a significant interaction of task and the order

tasks were presented on corticospinal excitability. Specifically, corticospinal excitability was

facilitated and increasingly tuned in the second condition, regardless of whether the task was the

GO or GO/NO-GO condition. This change in excitability may represent adjustments in set-

related to motor learning of the reaction time tasks and changes in preparatory processes. The

findings of this study will be discussed further in the subsequent sections.

4.1 Excitatory and Inhibitory Control

This study demonstrated an excitatory mechanism of preparatory control for the lower

limb in addition to an inhibitory mode of control, regulated at the level of the cortex and the

spinal cord. For the GO condition, in which participants had prior knowledge of the impending

action, half of the participants exhibited an excitatory mode of control, whereas half of the

participants showed a dampening of corticospinal excitability. The schema of control utilized by

Figure 24. Mean iEMG CoV recorded for each reaction time

task and separated based on spinal control. A significant

interaction between control type and task was observed. Solid

lines indicate individuals who used the same control for both

tasks and dotted line represents those who switched control

depending on the task. Error bars denote standard error of the

mean.

45

the CNS may be influenced by strategy. Interestingly, for the GO and GO/NO-GO condition, the

groups which had the highest level of corticospinal tuning were those which implemented an

anticipatory preparation strategy. Although this study was not initially powered for testing the

effects of strategy and predictability on corticospinal and spinal tuning, it appears that if

observing the tasks independent of each other that strategy may modulate the descending

preparatory drive generated by the CNS.

Contrary to the majority of preparation studies in the literature, the excitatory control

exhibited by some of the participants in this study may be a consequence of a longer foreperiod,

the stimulation timing, and the novel GO/NO-GO paradigm implemented. The inhibitory control

findings have generally implemented a preparatory period of less than one second, which may

accentuate the need for strategies to suppress descending drive and require impulse control and

closer to the imperative tone. Indeed, when studying three different stimulation time points, the

period closest to the imperative tone did demonstrate the greatest dampening. In contrast to short

foreperiods, longer foreperiods may allow for the emergence of later preparatory processes that

are not present (Brunia & Vuister, 1979). With a longer period during which to prepare,

impending drive may build over time, priming the relevant pathways, whereas shorter

foreperiods may not allow for the accumulation of these processes (van den Hurk et al., 2007).

The heterogeneity of preparatory control demonstrated by the participants seems to point to

differing preparation strategies at an individual level; this point is further supported by no

apparent effect of excitatory or inhibitory control on reaction time. In contrast, analysis of the

normalized data revealed an interaction between corticospinal control type and task on reaction

time. It appears that those who switched control types between the two tasks (specifically from

excitatory to neutral and inhibitory to excitatory), saw an exaggerated slowing of their reaction

time compared to groups which kept the same corticospinal control. This supports the point that

it may not matter what preparatory control one uses, as long as it is consistent across different

contexts.

These findings appear to support the parallel pathway model proposed by Cohen and

colleagues (2010) which outlines two separate pathways. A “priming” and “breaking” pathway

from the cortex to the spinal cord allow for the retention of online task-related information, while

in tandem applying a general inhibition to prevent premature movement. Individuals which

employ an anticipatory preparation strategy may exert greater excitatory influence on

46

downstream pathways by conserving task-related processes. Conversely, those who used a sit-

and-wait preparatory strategy may have been activating the “breaking” pathway to a higher

extent than the “priming” pathway, resulting in dampened corticospinal excitability. Despite

differences in control type, no apparent differences were found in behaviour except for reaction

time variability. This may point to changes in excitability control being advantageous in

stabilizing motor performance in varying contexts. Indeed, although MEPs provide insight to

state changes of pre and post-synaptic elements, they may not necessarily have causal relevance

to motor behaviour (Bestmann & Krakauer, 2015).

Due to the complexity of the CNS, fully understanding all the connections and pathways

that influence the resultant response from artificially stimulating the brain is difficult. Another

view on the representation of MEP amplitude changes during motor preparation is that they

signify the influence of direct or indirect connections to the primary motor cortex (Bestmann &

Krakauer, 2015). This interpretation involves regions of the brain associated with decision-

making and preparation influencing the output from the primary motor cortex and the tuning

involved during preparation (Bestmann et al., 2008; Klein-Flügge, Nobbs, Pitcher, & Bestmann,

2013; Klein-Flügge & Bestmann, 2012). Context may be a modifier of this motor output, leading

to biasing of the system. Predictability has been shown previously to result in higher

corticospinal excitability (Bestmann et al., 2008) and biasing of the system. However, depending

on the type of context the task presents itself with, predictable tasks can result in habituation and

decreased need to be aware of incoming stimuli, resulting in a decrease in sensorimotor gain

(Prochazka, 1989). The findings of this study do not necessarily support one theory over the

other, with 9 participants having higher corticospinal excitability for the GO or predictable

condition and 17 participants having a higher corticospinal excitability for the GO/NO-GO task,

where probability of a response was lower. Overall, it appears that the state changes of the CNS

recorded do not necessarily inform the optimization of the impending movement and may be

tailored individually to each person based upon their resting state and strategy implemented.

4.2 Parallel Modulation of Cortical and Spinal Connections

The strong positive correlation between corticospinal and spinal excitability observed in

both tasks may demonstrate undifferentiated tuning of preparatory set at both the cortical and

spinal level. A lack of difference between MEP and H-reflex amplitude likely suggests that

47

presynaptic inhibition does not modulate preparatory processing, as a depression in H-reflex

amplitude independent of MEP amplitude changes would indicate presynaptic influences

(Nielsen & Petersen, 1994). When analyzing the raw mode of control utilized by the CNS for

both tasks, 5 of the 8 participants demonstrated the same type of control at the corticospinal and

spinal level (either inhibitory or excitatory). These concurrent changes in spinal motor neuron

and primary motor cortex sensitivity possibly reflect a similar mechanism of volitional

movement control. In a study by Touge and colleagues (1993), corticospinal and spinal

excitability were both inhibited with corticospinal being modified to a greater extent. This trend

was also found in the present results, although not exclusively (GO: 3 of 8 participants; GO/NO-

GO: 4 of 8 participants). The temporal features associated with changes in corticospinal and

spinal excitability during the preparatory period may account for these differences (Hasbroucq et

al., 1999).

H-reflexes and MEPs of similar size do not necessarily recruit the same population of

motoneurons (Nielsen, Morita, Baumgarten, Petersen, & Christensen, 1999). It has been shown

that for tibialis anterior specifically, Ia afferent input may only activate a small proportion of

motor neurons even during muscle contraction (Morita et al., 2000). This can be demonstrated by

the difficulty in eliciting an H-reflex in tibialis anterior at rest and the absence of modulation

with increased levels of contraction. In this study however, for H-reflex testing, only participants

in which an H-reflex could be recorded in quiescent tibialis anterior were recruited; this allowed

for the investigation of preparatory modulation of excitability in a resting muscle prior to

movement. By evoking responses in quiet muscles during a reaction time task, typical behaviour

for GO/GO-NOGO reaction time paradigms can be observed. If participants were tonically

contracting tibialis anterior, response inhibition would be increasingly difficult to study as

participants would already have descending drive to the effector muscle prior to the imperative

tone.

In contrast to H-reflexes, MEPs in tibialis anterior can be evoked relatively easily and

consistently, and are almost exclusively produced compared to other lower limb musculature

(Brouwer & Qiao, 1995). It has been postulated that the biasing towards cortically evoked ankle

flexor responses may be due to the need to overcome the tonic contraction of the postural ankle

extensors (Preston & Whitlock, 1963). This would align with the importance of tibialis anterior

for day-to-day movement such as achieving foot clearance during gait and stair ascent/descent

48

(McFadyen & Winter, 1988; Winter & Yack, 1987). The similarities in corticospinal and spinal

excitability changes found are in agreement with studies that have demonstrated both Ia and

corticospinal inputs are activated in a similar fashion when recruiting motor neurons during

muscle activation. Since the present study did not apply conditioning stimuli that could

differentially alter corticospinal or spinal excitability, it would be expected that MEP and H-

reflex amplitudes would be modified similarly (Pierrot-Deseilligny & Burke, 2005).

4.3 Gradual Increase in Corticospinal Excitability Associated with Adjustment in Preparatory Processing

A significant effect of time on corticospinal excitability was observed throughout the

experiment, irrespective of which task was performed first. Because no practice trials were given

to participants, it may be that this gradual increase in corticospinal excitability displays

reorganizational processes within the primary motor cortex during preparation. Preparatory

pathways may have been optimized as participants adjusted to the reaction time paradigm; this

possibly includes learning the temporal features of the tones and stimulation, and the most

efficient way to perform the task. The initial increase in corticospinal excitability observed may

be associated with the novelty of the task, explaining why the second interval resulted in an

initial dampening of corticospinal excitability, follow by a gradual rise. Previous studies have

observed an overt plastic change in resting corticospinal excitability following active training

trials demonstrating increased input strength and intracortical processing (Lotze, Braun,

Birbaumer, Anders, & Cohen, 2003; Muellbacher, Ziemann, Boroojerdi, Cohen, & Hallett, 2001;

Perez, Lungholt, Nyborg, & Nielsen, 2004). In pilot data collected which manipulated different

stimulation time points, no global change was seen in resting corticospinal excitability, perhaps

illustrating that these changes were specific to preparatory processing and did not effect

connections at rest but specifically primed pathways active during motor preparation. The

learning associated with this task is possibly related to understanding the paradigm and timing

and not the motor task itself. Since dorsiflexion is not a novel task and no instructions were given

related to size of the response, it is unlikely that these would not have accounted for MEP

changes. The repetition of the movement however, may have resulted in a brief change in the

cortical representation of the lower limb (Classen, Liepert, Wise, Hallett, & Cohen, 1998). This

may explain why the change in corticospinal excitability appears to occur in the latter half of the

task compared to the first 30 trials (see Figure 16). Despite this change in excitability during

49

learning, there is no strong support that a change in MEP amplitude effects motor output and

performance as witnessed in the present study as well (Bestmann & Krakauer, 2015).

Interestingly, a gradual increase in excitability during motor preparation and not the task itself

was observed, perhaps demonstrating a change in the processes involved in motor planning.

4.4 Context and Strategy

The results of the corticospinal excitability analysis were in contrast to the expected

influence of context and strategy. Work by Miller and Low (2001) has shown that modulation of

preparatory processes does not necessarily prepare for potential alternative responses, but may

bias towards adjusting gain for the expected response. In the present study, it may be that no

differences in corticospinal excitability were seen between the two conditions because both

responses required the same movement (ie. dorsiflexion), so participants could have activated

processes associated with the “go” tone regardless of the potential choices. For the GO/NO-GO

condition, since the “go” tone was present 80% of the time, participants may have mirrored their

preparation modulation of the GO condition as they began to realize the high ratio of “go” to

“no-go” tones. This may provide some explanation for the lack of influence predictability had on

corticospinal and spinal excitability as a whole on participants. In contrast, Mochizuki et al.

(2010) found participants would modulate their cortical activity to the highest level of postural

threat if there was equal chance of a large or small perturbation occurring. It may be that in

situations where probability of response is similar, that the condition that offers a higher threat to

the system will be favoured during preparation for unpredictable scenarios. In the context of the

present experiment, since the threat of a simple seated motor task is quite low, participants may

have recruited similar preparatory processes for the both GO and GO/NO-GO condition as the

“favoured” task for both would involve dorsiflexion of the foot. An alternative explanation for no

apparent effect of condition on corticospinal and spinal excitability may be a result of changes in

the time-course of the preparatory modulation that could not be captured with the current

paradigm (Frank, 1986; Hasbroucq et al., 1999).

Despite the absence of significant effects of task and strategy on corticospinal

excitability, the influence of strategy and task may be present independently. The classification

of the preparatory strategies originated from work by Cheung (2015), where it was postulated

that EEG activity variability may be attributed to individuals either implementing a “sit-and-

50

wait” or “worst-case scenario” preparatory strategy. A strategy is similar to the concept of motor

preparation, however a strategy provides information related to the relationship between

responses and the possible stimuli present (Dixon & Just, 1986). For the purposes of this

experiment, these strategies were referred to as sit-and-wait or anticipatory preparation. These

strategies were subjectively probed by asking participants if they actively prepared for a “go”

tone and then inhibited their response as needed or if they waited until the tone sounded before

preparing to move. The subjective strategies of the individuals were collected to further

dichotomize participants a priori and to determine whether there was a relationship between

corticospinal and spinal excitability, and the strategy implemented. Although preliminary, it does

appear that in line with the hypotheses that an anticipatory strategy of control was associated

with a higher corticospinal excitability. Individuals even showed a modulation of corticospinal

excitability when they switched strategies between tasks (Figure 12). Interestingly, work

investigating internal versus external strategies found no difference in corticospinal excitability

between the two, suggesting that the primary motor cortex is not exclusively involved in internal

strategy models (Bode, Koeneke, & Jäncke, 2007). MEP amplitude however, was facilitated

during mental rotation compared to baseline, suggesting a spill-over from adjacent brain areas or

the direct involvement of the primary motor cortex in imagining. In the present study, it may be

that anticipatory preparation recruited processes of the primary motor cortex to greater extent as

mental visualization techniques may be employed in anticipation of the imperative tone. It would

be interesting to directly manipulate preparatory strategy by instructing participants to use

anticipatory or sit-and-wait preparation for a block of trials and observe the effects of the

strategies directly on corticospinal and spinal excitability.

4.5 Conclusions

The present study demonstrated that there was no effect of task predictability and strategy

on corticospinal and spinal excitability. This finding may be attributed to lack of threat to the

system within the two tasks not requiring a change in preparation related to the condition. It is

speculated that low-threat tasks that adjust predictability may instead gradually alter preparatory

processes over time and the predictability of a response does not overtly influence the sensitivity

of the CNS and the importance of attending to an imperative tone. This study also found a strong

relationship between corticospinal and spinal modulation between tasks, suggesting

undifferentiated tuning of inputs at both levels of the CNS during preparation. Further work

51

should investigate the effects of strategy on corticospinal and spinal excitability by directly

manipulating the strategy an individual implements.

52

Chapter 3: General Discussion and Conclusions

Summary of Findings

This thesis sought to understand set-related changes in corticospinal and spinal

excitability during temporally-urgent lower limb motor preparation using transcranial and

peripheral stimulation techniques. To probe these changes, a novel CNV paradigm was

implemented during a simple and complex reaction time task. Participants were asked to

subjectively describe their preparatory strategy to better investigate the impact of strategy on

corticospinal and spinal excitability. Participants demonstrated both inhibitory and facilitatory

modes of cortical and spinal control and these were not locked to the predictability of the task or

strategy utilized. In addition, corticospinal and spinal excitability were modulated to a similar

degree in both tasks, indicating similar modes of control. Furthermore, there appeared to be a

task-independent effect of order and time, in which a gradual increase in corticospinal

excitability was observed. The change in excitability may represent altered preparatory processes

forming throughout the progression of the tasks. The following section will explore these

findings in greater detail and the potential clinical implications and future directions of the work.

Revisiting the Conceptual Model

The initial proposed model for motor preparation (refer to Figure 6) sought to address set-

related adjustments of CNS gain and the influence of predictability and strategy on the

modifications. It was originally proposed that lower predictability scenarios would result in a

heightened corticospinal and spinal excitability to increase sensitivity for incoming information,

allowing for accurate and rapid responses. Given that a simple, highly predictable scenario would

result in repetition, it was hypothesized that the excitability during this task would be dampened

in comparison to the low predictability task; with repetition, the CNS can bias its responses

allowing less attention needed to focus on the identity of the imperative tone, allowing for the

allocation of resources to other processes. Secondly, the model predicted that corticospinal and

spinal excitability would be modified similarly for each task, indicative of both spinal and

supraspinal influences being responsible for modifying preparatory excitability. Lastly, it was

hypothesized that subjective strategy can modify corticospinal and spinal excitability during

motor preparation. Specifically, anticipatory strategies would result in a higher modulation of

53

corticospinal and spinal excitability, due to actively preparing for the task instead of passively

sitting and waiting for the identity of the imperative tone.

2.1 Predictability

One of the major objectives of this thesis was to probe the influence of task predictability

on preparatory CNS gain. Task predictability was explored by implementing two reaction time

tasks where there was a 100% probability of a response or an 80% chance of a response. Results

of the study showed no significant effect of predictability on corticospinal or spinal excitability.

Previous studies have shown significant modulation of corticospinal and spinal excitability

during choice reaction time tasks (Davranche et al., 2007; Duque et al., 2010; Hasbroucq et al.,

1999). These studies however, implemented a short foreperiod and the type of modulation

observed was increased dampening. In contrast to the majority of other studies, Davranche and

colleagues (2007) found a stable increase in corticospinal excitability during their long

foreperiod duration (2.5 s), whereas the shorter foreperiod (500 ms) demonstrated the usual

inhibitory control. It was suggested that a longer foreperiod affords less temporal predictability

of the imperative tone and subsequently poorer levels of preparation.

Touge et al. (1998) compared the preparatory excitability changes during a choice

reaction time task to a baseline state during the warning signal and did not identify changes in

corticospinal excitability when implementing a choice reaction time task. The authors

hypothesized that the reason for not observing an alteration may have been related to participants

already having a high corticospinal activation in anticipation of the warning tone compared to

complete rest. In the present study, however, baseline was measured separately from the reaction

time tasks themselves so this would not explain the null findings. Alternatively, the intensity of

the stimulator during baseline may provide an explanation. Given that baseline was recorded at

110% for resting motor threshold and that it was the first exposure participants had to a

stimulation intensity of that level, the novelty of the stimulation intensity may have caused an

increase in corticospinal excitability. Implementing a go/no-go paradigm versus a choice reaction

time task may elicit a greater sit-and-wait approach due to the alterative response being “no

response” instead of altering direction of the response or the limb to respond. Another potential

confounder between the current study and others related to the reaction time paradigm is the

ratio, and number, of response choices. If one response is favoured over the other, preparatory

54

processes may bias themselves to the expected response (Miller & Low, 2001). To conclude,

influences of predictability may be limited due to a large foreperiod and lack of alternative

responses implemented in this paradigm.

2.2 Strategy

Strategy can be defined as an internal representation specifying the perceptual

requirements associated with an impending movement, and the decision rules and responses to

be potentially generated (Dixon & Just, 1986; Logan, Zbrodoff, & Fostey, 1983; Logan &

Zbrodoff, 1982). Factors that may influence an individual’s strategy include the information that

can be gained from cues and the task environment, one’s cognitive abilities, and the structure and

weighting of the task outcomes (Logan et al., 1983). For the present study, it was determined

whether one’s subjective evaluation of strategy aligned with the hypothesized modulation of

CNS excitability. Results indicated no significant effect of strategy on corticospinal or spinal

excitability; however, visually it appeared that there may be potential for an association between

strategy and CNS gain. This study may be the first that has attempted to probe the effects of

subjective preparatory strategy on neurophysiological adjustments. Previously, individual

strategies have been characterized based on the modulation of preparatory cortical activity

(Cheung, 2015; Mochizuki et al., 2010). An advantage to probing participants’ strategies is that it

allows for pre-determined stratification of participants and may potentially expose physiological

and behavioural differences. If preparatory strategy were to influence these factors, manipulating

strategy may be a mechanism that can be used to alter preparatory processes and behaviour.

Investigating prefrontal pathways and up- or downregulating specific areas associated with

higher-order function and strategies may allow for further study of strategy and intracortical

networks involved in preparation.

2.3 Potential Modifiers

Although predictability and strategy were two factors explored in this study, there are

many other potential modifiers that may play a larger role in adjusting preparatory set and may

contribute to the conceptual model outlined. One factor that may account for changes in CNS

sensitivity is context. Context can involve the presentation of stimuli or the saliency of the cues.

Of particular importance is the subjective value individuals place on the cue and the amount of

information available from the cue. In the present study, the two task conditions varied the

55

saliency of the warning tone, with the GO condition providing full information about the identity

of the imperative tone and its temporal features (ie. 3 s from imperative tone), whereas the

GO/NO-GO condition only gave information related to the temporal nature of the response.

Altering the amount of information related to the identity of the imperative tone may allow for

further investigation of the effect of context on CNS excitability. For example, multiple warning

tones could be presented that may bias a certain response (ie. 80% chance imperative tone will be

go or no-go). In relation to task order, previous research has demonstrated that presenting a more

threatening or complex condition first can affect the scaling of subsequent trials and this is also

evident in the reverse as well (Adkin et al., 2000; Horak et al., 1989). In the present study, it may

be that for the GO/NO-GO condition, the timing of the first no-go is critical to subsequent

preparation. Adjusting the paradigm to further explore this may further knowledge related to the

effect of context on corticospinal and spinal excitability.

Figure 25. Proposed conceptual model outlining potential modifiers of set which influence CNS

excitability to a greater extent than predictability.

56

Another potential factor that may be responsible for set-related changes to corticospinal

and spinal excitability is environment. As mentioned earlier in Chapter 1, environmental

stressors present in a task can modulate cortical activity and response sizes. Perhaps one of the

most studied environmental stressors is risk of falling. Previous research has shown that

increasing and varying the balance threat to subjects results in upregulated postural control and

overt preparatory scaling of cortical activity (Adkin et al., 2000; Brown & Frank, 1997;

Carpenter et al., 2004; Mochizuki et al., 2010). In the present study, the lack of system threat or

repercussions for an error may have resulted in a lack of corticospinal/spinal excitability

modulation. To exhibit large changes in preparatory processes, it may be that threat needs to be

present to challenge the system, and alter processes and strategy on a larger scale where

consequences for an error may be present.

Although modifiers such as context and the state of the environment can likely alter

preparatory processes, one’s experience may also modify these pathways. In the present study,

an effect of time on preparatory corticospinal excitability was found. This may represent an

optimization of preparatory processes or potential habituation. This habituation was not observed

behaviourally however, as time had no influence on the reaction times of the participants. The

familiarity of the task has been previously shown to result in habituation independent of

difficulty or potential threat (Brown & Frank, 1997). This would align with the present finding in

which no significant effect of task was found on corticospinal excitability, but time and task

order did alter corticospinal excitability. It may be that input strength and intracortical processing

associated with optimizing preparation were adjusted to meet the task demands throughout the

duration of the trial. Previous studies have demonstrated a global increase in resting corticospinal

excitability training trials, potentially reflecting a change in cognitive processing and motor

function (Lotze et al., 2003; Muellbacher et al., 2001; Perez et al., 2004). Experience with the

task and repetition of the movement may result in a temporary modification in the lower limb

cortical representation (Classen et al., 1998). Verifying the role of experience with a novel

paradigm and task on preparatory processing should be further explored by implementing

practice trials.

Based on the findings of this study, the conceptual model framing this thesis has been

adjusted to represent the current hypothesized modifiers of set-related preparatory processing

(Figure 25). In this model, context, environment, experience, and threat act to adjust

57

corticospinal and spinal excitability to appropriately adjust sensorimotor gain and optimize task

performance. The results of this study did not confirm the initial proposed model in which

predictability and strategy influenced both corticospinal and spinal control. It is suggested that

future work should aim to individually target these aspects of a task that may alter preparatory

processing to confirm the proposed model.

Implications for Rehabilitation Science

3.1 Cues as Rehabilitation Tools

Developing an understanding of the neurophysiological state changes in an intact CNS

may allow for translation to various populations where processing and slowing of reactions may

be impaired. In this study, two auditory cues were implemented – a warning and an imperative

tone – to prompt individuals for the task. By having individuals attend to cues, one can

manipulate attention and improve motor systems. The ability to utilize auditory cues alone as a

method of gait rehabilitation has been explored in many populations such as Parkinson’s disease

(Benoit et al., 2014; Lopez et al., 2014; McIntosh, Brown, Rice, & Thaut, 1997; Thaut et al.,

1996) and stroke (Prassas, Thaut, McIntosh, & Rice, 1997; Schauer, 2003; Thaut et al., 2007;

Thaut, McIntosh, Prassas, & Rice, 1993; Thaut, McIntosh, & Rice, 1997). The use of cues and

rhythmic auditory stimulation can improve various gait parameters such as: gait velocity,

cadence, stride length (Benoit et al., 2014; Lopez et al., 2014; McIntosh et al., 1997; Schauer,

2003; Thaut et al., 1996; Thaut et al., 1997; Thaut et al., 2007), muscle activation patterns (Thaut

et al., 1993; Thaut et al., 1996; Thaut et al., 1997), stride symmetry (Prassas et al., 1997;

Schauer, 2003; Thaut et al., 1993; Thaut et al., 2007), centre of mass vertical displacement

(Prassas et al., 1997) and specific aspects of motor control such as temporal perception and

synchronization (Benoit et al., 2014). Developing knowledge around preparatory processing of

cues and the contextual nuances associated with them can advance and optimize rehabilitation

approaches that use cues to modify motor behaviour. The responsiveness of the CNS to cues

could perhaps provide markers of rehabilitation efficacy.

3.2 Deficiencies in Preparatory Excitability in Stroke

Cerebrovascular accidents have a well-established impact on motor function due to

lesions interrupting communication within the corticospinal tract. And while motor control

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impairments as a result of stroke have been studied in detail, there is also evidence of

deficiencies in motor preparation as well (Battaglia et al., 2006; Hummel et al., 2009; Murase,

Duque, Mazzocchio, & Cohen, 2004; Platz et al., 2000; Pollock, 2014). For example, preparatory

brain activity has shown topographical differences for self-initiated finger movements and may

be explained by a compensatory reliance on motor and premotor areas to promote excitatory

drive to downstream elements (Platz et al., 2000); this overcompensatory activity is similar to the

study discussed in Chapter 1 by Pollock (2014) in which stroke patients exhibited increased

anticipatory muscle activity in both self-initiated and externally-sourced perturbations. The

presence of compensatory mechanisms to overcome intracortical and inter-hemispheric

inhibition of the lesioned hemisphere may be one potential explanation (Battaglia et al., 2006;

Hummel et al., 2009; Murase et al., 2004). Implementing the simple and complex reaction time

tasks employed in this current thesis to the stroke population using single or paired-pulse

techniques may be useful in understanding underlying pathology related to preparatory drive to

the lower limb and how unpredictability modifies the gain of the CNS in this population.

Overcoming physiological deficits induced by progressive or abrupt changes in the CNS may be

important to improving gait initiation and reaction time in this context.

3.3 Aging and Preparing for Temporally-Urgent Movements

Humans encounter uncertainty in the face of situations that require rapid responses in

everyday scenarios. Whether it is engaging in sports such as soccer or hockey, or driving to the

supermarket, there is still the notion of temporal urgency in an ever-changing, unpredictable

environment. As one ages, the ability to encode the incoming information which informs the

nature of motor response can become impaired (Simon & Pouraghabagher, 1978). It is suggested

that the movement speed and cognitive pathways may be disrupted at various locations within

the network as one ages (Salthouse & Madden, 2013). Furthermore, emphasis should be placed

on higher order processes related to stimulus decoding and interpretation, instead of the sensory

and motor processes themselves when the task is increasingly complex and unpredictable

(Cantin, Lavallière, Simoneau, & Teasdale, 2009; Cerella, 1985). When a task becomes more

complex, the risk of taking greater than two seconds to respond can occur in as many as one

quarter of trials in those over the age of 70 (Salvia et al., 2016). In this amount of time when

driving, the window to decide to brake quickly may have already passed and can lead to an

59

accident; therefore, creating a need to understand the physiological and cognitive changes that

are causing this slowness in responses and heightened uncertainty.

Recently, Duque and colleagues (2016) identified a less enhanced preparatory inhibition

of corticospinal excitability and lower resting excitability levels as well with age. The authors

hypothesized that due to slower reaction time and fewer errors, this lack of modulation may be

due to the strategy employed by the older participants favouring accuracy over speed. It would

be intriguing to see if these findings would occur when stimulating the leg motor region using a

similar paradigm as the one used in this thesis as majority of studies focus on upper limb and

utilize shorter preparatory foreperiods. In addition, one would speculate that if older individuals

were asked the same questions related to preparatory strategy, that they may be more inclined to

select a passive strategy (ie. sit-and-wait). Within the present study, participants that

demonstrated a sit-and-wait preparatory strategy appeared to potentially show lower levels of

preparatory corticospinal excitability compared to those utilizing an anticipatory control strategy.

Since the current paradigm favoured an increase in corticospinal excitability versus the typical

inhibition demonstrated in the literature, the prediction that a sit-and-wait strategy results in a

less enhanced “typical” response by the CNS would align with preliminary findings. It would be

intriguing to implement an intervention in which older individuals are told to employ the two

strategy types to determine whether modulation in preparatory excitability can occur in this

population and potentially improve reaction time (although there is still no strong established

relationship between MEP amplitude and behaviour as discussed earlier).

Limitations and Future Directions

4.1 Limitations

In the present reaction time task, a fixed foreperiod length as well as fixed stimulation

window was utilized, allowing for increased anticipation and predictability of the imperative tone

timing. To facilitate engagement in active preparation, a 4:1 tone ratio was implemented for the

GO/NO-GO condition. The limitation of using TMS over a non-disruptive neurophysiological

technique such as EEG, is that individuals may actually use the TMS stimulus as a

supplementary warning tone which re-“sets” the system. Indeed, multiple participants did

comment on using the TMS or PES stimulation window as a cue to initiate preparation for the

imperative tone. Previous work has found that TMS during the foreperiod can enhance reaction

60

time (Sinclair & Hammond, 2008, 2009). Based on the present paradigm however, it is unlikely

that an “intersensory facilitation effect” or “StartReact effect” associated with a startling acoustic

stimulus was present due to the large delay between the stimulus and the imperative tone, and

both the tone and the stimulation producing an auditory cue (Romaiguere, Possamai, &

Hasbroucq, 1997; Valldeoriola et al., 1998; Valls-Solé, Rothwell, Goulart, Cossu, & Muñoz,

1999). The TMS and PES may have simply provided a supplemental cue for participants to

enhance preparation. This may limit the effects of investigating preparatory changes in

individuals if they did not engage in preparation until stimulation occurred. In contrast, EEG is a

passive system and does not physically interrupt the preparatory foreperiod, providing less

predictability for individuals. The use of EEG may have also allowed for an objective measure to

relate preparatory strategy types to, as participants’ subjective perceptions of the preparation type

were the only measures utilized.

The disruption to the motor output system that TMS causes may also influence individual

responses negatively. In the present study, when piloting different windows of stimulation, the -

500 ms timepoint resulted in significantly slower reaction time. If the stimulation did not truly

influence the motor response, one would expect no difference in reaction time regardless of the

stimulation timing. Compared to the hand area of motor cortex, stimulating the deeper leg motor

region tends to result in evoked potentials being produced in other muscles throughout the body.

This disruption and distraction may account for these differences in reaction time. Implementing

one stimulation window may limit the scope of the preparatory foreperiod within this study. The

justification for using this time point was based on previous EEG work using this paradigm

which identified the -1 s window as an area of increasing preparatory activity (Cheung, 2015;

Chin, Shirzadi, Cheung, & Mochizuki, submitted; Mochizuki et al., 2010). Nonetheless, probing

other timepoints within the preparatory foreperiod may have resulted in differences in both the

cortical and spinal modulation of excitability as the time-course of these two measures may vary

(Hasbroucq et al., 1999).

Another potential limitation is the validity of probing the primary motor cortex relatively

early in the preparatory period. In motor preparation, frontal and pre-frontal areas play a large

role as components associated with cue selection and pathway priming. For example, the left-

dorsolateral prefrontal cortex, lateral prefrontal cortex, and (pre-) supplementary motor areas

have been reported to contribute to cue-based motor preparation, motor planning, and action

61

selection and inhibition (Deecke, 1987; Duque, Labruna, Verset, Olivier, & Ivry, 2012;

MacDonald III, Cohen, Stenger, & Carter, 2000; Nachev, Kennard, & Husain, 2008; Nakayama,

Yamagata, Tanji, & Hoshi, 2008; Sohn, Ursu, Anderson, Stenger, & Carter, 2000). This complex

network of brain regions includes portions of the corticospinal tract, connecting with the basal

ganglia and projecting to the primary motor cortex. This interconnectivity creates issues related

to the sensitivity of the MEP and its ability to accurately reflect all of these changes in activity of

various structures. Although measuring corticospinal excitability at the level of the primary

motor cortex may account for some of the excitability changes in these projections, the temporal

features associated with the activation of these areas may not necessarily align with the optimal

activity of the primary motor cortex. For example, EEG studies have found that supplementary

motor area activity precedes that of the primary motor cortex during motor preparation (Deecke,

1987). Developing research that targets these brain areas using non-invasive brain stimulation

may further the understanding of the different brain areas involved in motor preparation during a

paradigm of varying predictability.

When interpreting changes in single-pulse MEPs and H-reflexes, there are several

limitations related to being able to identify where the modulation of excitability occurred. When

measuring an MEP, changes in excitability could occur at the level of the brain, spinal cord, and

peripheral motor properties as they are all components of the cortico-spinal system (Ziemann &

Rothwell, 2000). One alternative explanation as to why MEP and H-reflexes were significantly

related could be that corticospinal and spinal excitability was not modulated by the brain, but by

other inputs to the spinal cord that would modulate both MEP and H-reflex amplitude. In

addition, by only collecting an initial recruitment curve from participants, we operated under the

assumption that baseline excitability did not change over time or with each condition. However,

it is possible that baseline excitability varied over time. As a result, factors such as fatigue or

task-dependent modulation of baseline activity could not be accounted for in the later blocks of

trials. Using other techniques such as paired-pulse TMS or cervicomedullary stimulation could

provide additional information regarding the site of preparatory modulation.

The final limitation of this study is that it was conducted on young, healthy individuals.

This was done to specifically understand the effects of task predictability on corticospinal and

spinal excitability in a presumably intact CNS. Although no differences between conditions and

strategy were found between the tasks, it would be interesting to compare these physiological

62

changes to older populations or those with motor control deficits where information encoding is

impaired to see if differences are present and perhaps accentuated.

4.2 Future Directions

The current thesis demonstrated the absence of influence of task predictability and

strategy on corticospinal and spinal excitability in preparing for movement. One of the biggest

factors determining the outcomes of studies looking at changes in neurophysiological measures

is the heterogeneity of the responses. Participants ranged in the strategy types utilized and these

were not necessarily task-dependent. To help control for heterogeneous responses, it would be

beneficial to directly explore the effects of strategy on corticospinal and spinal excitability by

instructing participants to employ a certain strategy type (either anticipatory or sit-and-wait) for

both the simple and complex tasks to observe the concurrent effect on CNS gain modulation.

It was hypothesized that the poor influence of task predictability on corticospinal and

spinal excitability may be due to the reaction time tasks not inducing a heightened state of

arousal or threat to the system. To account for these inconsistencies, future work could directly

manipulate threat by implementing a simple perturbation task that would allow for excitability

changes to be measured using a coil and stimulation electrode. In addition, arousal could be

directly measured through skin conductance to further one’s understanding of factors that

influence set-related adjustments of CNS sensitivity.

Final Conclusions

To conclude, this thesis explored corticospinal and spinal markers associated lower limb

movement preparation. No effect of task predictability and strategy was observed on

corticospinal and spinal excitability and this may be attributed to lack of threat or arousal

generated by the reaction time tasks. An effect of time on corticospinal excitability was

speculated to be caused by a gradual modification of preparatory processes over time. This study

also found a strong relationship between corticospinal and spinal modulation between tasks,

suggesting undifferentiated tuning of inputs at both levels of the CNS during preparation. Further

work should investigate the effects of other modifiers of motor preparation and central set on

corticospinal and spinal excitability such as context and environment. Furthermore, studies

63

should investigate different brain areas associated with motor preparation and how these are

affected by motor control deficits.

64

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Appendices

Appendix 1. Data collection sheet

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84

85

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Appendix 2. Chi square table of preparatory strategy proportions

Chi-Square Tests

Value df

Asymptotic Significance (2-

sided) Exact Sig. (2-

sided) Exact Sig. (1-

sided)

Pearson Chi-Square .078a 1 .780 Continuity Correctionb .000 1 1.000 Likelihood Ratio .078 1 .781 Fisher's Exact Test 1.000 .562 Linear-by-Linear Association .075 1 .784 N of Valid Cases 26

a. 2 cells (50.0%) have expected count less than 5. The minimum expected count is 2.69. b. Computed only for a 2x2 table

87

Appendix 3. ANOVA tables comparing the effect of condition and strategy on errors and reaction time

Tests of Within-Subjects Effects – Errors

Measure: MEASURE_1

Source Type III Sum of

Squares df Mean Square F Sig.

factor1 Sphericity Assumed .817 1 .817 .710 .409

Greenhouse-Geisser .817 1.000 .817 .710 .409

Huynh-Feldt .817 1.000 .817 .710 .409

Lower-bound .817 1.000 .817 .710 .409

factor1 * Strategy Sphericity Assumed 9.313 3 3.104 2.698 .072

Greenhouse-Geisser 9.313 3.000 3.104 2.698 .072

Huynh-Feldt 9.313 3.000 3.104 2.698 .072

Lower-bound 9.313 3.000 3.104 2.698 .072

Error(factor1) Sphericity Assumed 24.167 21 1.151

Greenhouse-Geisser 24.167 21.000 1.151

Huynh-Feldt 24.167 21.000 1.151

Lower-bound 24.167 21.000 1.151

Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average



Intercept 29.400 1 29.400 12.998 .002 Strategy 4.380 3 1.460 .645 .594 Error 47.500 21 2.262

Tests of Within-Subjects Effects – Reaction Time Measure: MEASURE_1



Condition Sphericity Assumed .126 1 .1

26

64.360 .000

Greenhouse-Geisser .126 1.000 .126 64.360 .000

Huynh-Feldt .126 1.000 .126 64.360 .000

Lower-bound .126 1.000 .126 64.360 .000

Condition * Strategy Sphericity Assumed .012 3 .004 2.025 .140


Huynh-Feldt .012 3.000 .004 2.025 .140

Lower-bound .012 3.000 .004 2.025 .140

Error(Condition) Sphericity Assumed .043 22 .002

Greenhouse-Geisser .043 22.000 .002

Huynh-Feldt .043 22.000 .002

Lower-bound .043 22.000 .002

Tests of Between-Subjects Effects

Measure: MEASURE_1

Transformed Variable: Average

Source Type III Sum of Squares df Mean Square F Sig.

Intercept 4.343 1 4.343 683.846 .000

Strategy .019 3 .006 .988 .417

Error .140 22 .006

88

Appendix 4. ANOVA tables comparing the effect of condition and strategy on reaction time variability (CoV)

Tests of Within-Subjects Effects Measure: MEASURE_1


Condition Sphericity Assumed 17.697 1 17.697 .390 .539

Greenhouse-Geisser 17.697 1.000 17.697 .390 .539

Huynh-Feldt 17.697 1.000 17.697 .390 .539

Lower-bound 17.697 1.000 17.697 .390 .539

Condition * Strategy Sphericity Assumed 17.499 3 5.833 .129 .942


Huynh-Feldt 17.499 3.000 5.833 .129 .942

Lower-bound 17.499 3.000 5.833 .129 .942

Error(Condition) Sphericity Assumed 997.673 22 45.349


Huynh-Feldt 997.673 22.000 45.349

Lower-bound 997.673 22.000 45.349




Intercept 23347.359 1 23347.359 318.625 .000 Strategy 316.351 3 105.450 1.439 .258 Error 1612.058 22 73.275

Appendix 5. Paired t-test comparing reaction times for conditions performed with PES and TMS

Paired Samples Test

Paired Differences

t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean

95% Confidence Interval of the Difference

Lower Upper

Pair 1 MEP_RT - H_RT

.02229 .05723 .01431 -.00821 .05278 1.557 15 .140

Appendix 6. ANOVA table comparing baseline, GO, and GO/NO-GO corticospinal excitability




Condition Sphericity Assumed .010 2 .005 .359 .700


Huynh-Feldt .010 1.577 .006 .359 .650

Lower-bound .010 1.000 .010 .359 .554



Huynh-Feldt .669 39.421 .017

Lower-bound .669 25.000 .027

89

Appendix 7. ANOVA tables comparing the effect of condition and strategy on corticospinal excitability




Condition Sphericity Assumed .017 1 .017 2.794 .109


Huynh-Feldt .017 1.000 .017 2.794 .109

Lower-bound .017 1.000 .017 2.794 .109

Condition * Strategy Sphericity Assumed .014 3 .005 .758 .530


Huynh-Feldt .014 3.000 .005 .758 .530

Lower-bound .014 3.000 .005 .758 .530



Huynh-Feldt .131 22.000 .006

Lower-bound .131 22.000 .006


Measure: MEASURE_1 Transformed Variable: Average



Intercept 42.781 1 42.781 693.334 .000 Strategy .275 3 .092 1.487 .246 Error 1.357 22 .062

Appendix 8. ANOVA table comparing baseline, GO, and GO/NO-GO spinal excitability






Huynh-Feldt .000 1.867 .000 .255 .764

Lower-bound .000 1.000 .000 .255 .629



Huynh-Feldt .010 13.070 .001

Lower-bound .010 7.000 .001

Appendix 9. ANOVA tables comparing the effect of condition and strategy on spinal excitability






Huynh-Feldt .000 1.000 .000 .252 .637

Lower-bound .000 1.000 .000 .252 .637

Condition * Strategy Sphericity Assumed 4.193E-5 2 2.096E-5 .045 .956

90

Greenhouse-Geisser 4.193E-5 2.000 2.096E-5 .045 .956

Huynh-Feldt 4.193E-5 2.000 2.096E-5 .045 .956

Lower-bound 4.193E-5 2.000 2.096E-5 .045 .956



Huynh-Feldt .002 5.000 .000

Lower-bound .002 5.000 .000

Appendix 10. Correlations between MEP and H-Reflex Amplitudes Correlations

Condition MEP_raw H_raw

Go MEP_raw Pearson Correlation 1 .649

Sig. (2-tailed) .082

N 8 8

H_raw Pearson Correlation .649 1


N 8 8

NoGo MEP_raw Pearson Correlation 1 .734*


N 8 8

H_raw Pearson Correlation .734* 1


N 8 8

*. Correlation is significant at the 0.05 level (2-tailed). b. Cannot be computed because at least one of the variables is constant.

Appendix 11. ANOVA tables comparing the effect of condition and strategy on iEMG

Tests of Within-Subjects Effects

Measure: MEASURE_1



Condition Sphericity Assumed 1.245E-8 1 1.245E-8 .029 .867


Huynh-Feldt 1.245E-8 1.000 1.245E-8 .029 .867

Lower-bound 1.245E-8 1.000 1.245E-8 .029 .867

Condition * Strategy Sphericity Assumed 1.447E-6 3 4.822E-7 1.114 .365

Greenhouse-Geisser 1.447E-6 3.000 4.822E-7 1.114 .365

Huynh-Feldt 1.447E-6 3.000 4.822E-7 1.114 .365

Lower-bound 1.447E-6 3.000 4.822E-7 1.114 .365

Error(Condition) Sphericity Assumed 9.524E-6 22 4.329E-7

Greenhouse-Geisser 9.524E-6 22.000 4.329E-7

Huynh-Feldt 9.524E-6 22.000 4.329E-7

Lower-bound 9.524E-6 22.000 4.329E-7






Intercept .568 1 .568 28.267 .003 Strategy .055 2 .027 1.361 .337 Error .100 5 .020

91



Intercept .000 1 .000 21.171 .000 Strategy 3.235E-5 3 1.078E-5 .723 .549 Error .000 22 1.491E-5

Appendix 12. ANOVA tables comparing the effect of condition and strategy on iEMG variability (CoV)




Condition Sphericity Assumed 276.600 1 276.600 4.826 .039


Huynh-Feldt 276.600 1.000 276.600 4.826 .039

Lower-bound 276.600 1.000 276.600 4.826 .039

Condition * Strategy Sphericity Assumed 273.698 3 91.233 1.592 .220


Huynh-Feldt 273.698 3.000 91.233 1.592 .220

Lower-bound 273.698 3.000 91.233 1.592 .220

Error(Condition) Sphericity Assumed 1261.006 22 57.318


Huynh-Feldt 1261.006 22.000 57.318

Lower-bound 1261.006 22.000 57.318




Intercept 64558.356 1 64558.356 263.244 .000 Strategy 188.768 3 62.923 .257 .856 Error 5395.313 22 245.242

Appendix 13. Correlation tables of excitability measures with behavioural measures Correlations

log_Go_MEP Go_RT

log_Go_MEP Pearson Correlation 1 -.146


N 26 26

Go_RT Pearson Correlation -.146 1


N 26 26

Correlations

log_NoGo_MEP NoGo_RT

log_NoGo_MEP Pearson Correlation 1 .255


N 26 26

NoGo_RT Pearson Correlation .255 1


N 26 26

92

Correlations

Go_H_Raw Go_RT_H

Go_H_Raw Pearson Correlation 1 -.445


N 8 8

Go_RT_H Pearson Correlation -.445 1


N 8 8

Correlations

NoGo_H_Raw NoGo_RT_H

NoGo_H_Raw Pearson Correlation 1 -.333


N 8 8

NoGo_RT_H Pearson Correlation -.333 1


N 8 8

Appendix 14. ANOVA table of the effect of time and task order on corticospinal excitability



Task Sphericity Assumed .004 1 .004 .788 .383


Huynh-Feldt .004 1.000 .004 .788 .383

Lower-bound .004 1.000 .004 .788 .383

Task * First_Task Sphericity Assumed .025 1 .025 5.005 .035


Huynh-Feldt .025 1.000 .025 5.005 .035

Lower-bound .025 1.000 .025 5.005 .035

Error(Task) Sphericity Assumed .119 24 .005


Huynh-Feldt .119 24.000 .005

Lower-bound .119 24.000 .005



Intercept 55.275 1 55.275 812.641 .000 First_Task .000 1 .000 .004 .948 Error 1.632 24 .068



Time Sphericity Assumed .132 5 .026 2.455 .037


Huynh-Feldt .132 4.238 .031 2.455 .047

Lower-bound .132 1.000 .132 2.455 .130

Error(Time) Sphericity Assumed 1.348 125 .011

Greenhouse-Geisser 1.348 89.254 .015

Huynh-Feldt 1.348 105.955 .013

Lower-bound 1.348 25.000 .054

93

Pairwise Comparisons Measure: MEASURE_1

(I) Time (J) Time Mean Difference (I-J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

1 2 .024 .026 1.000 -.061 .109

3 -.007 .029 1.000 -.100 .086

4 -.019 .029 1.000 -.112 .074

5 -.035 .028 1.000 -.126 .056

6 -.069 .035 .888 -.182 .044

2 1 -.024 .026 1.000 -.109 .061

3 -.031 .022 1.000 -.104 .042

4 -.043 .031 1.000 -.143 .057

5 -.059 .035 1.000 -.173 .055

6 -.093 .035 .207 -.207 .021

3 1 .007 .029 1.000 -.086 .100

2 .031 .022 1.000 -.042 .104

4 -.012 .027 1.000 -.100 .076

5 -.028 .027 1.000 -.116 .060

6 -.062 .023 .180 -.136 .012

4 1 .019 .029 1.000 -.074 .112

2 .043 .031 1.000 -.057 .143

3 .012 .027 1.000 -.076 .100

5 -.016 .023 1.000 -.091 .059

6 -.050 .032 1.000 -.154 .055

5 1 .035 .028 1.000 -.056 .126

2 .059 .035 1.000 -.055 .173

3 .028 .027 1.000 -.060 .116

4 .016 .023 1.000 -.059 .091

6 -.034 .025 1.000 -.115 .048

6 1 .069 .035 .888 -.044 .182

2 .093 .035 .207 -.021 .207

3 .062 .023 .180 -.012 .136

4 .050 .032 1.000 -.055 .154

5 .034 .025 1.000 -.048 .115

Based on estimated marginal means a. Adjustment for multiple comparisons: Bonferroni.

Appendix 15. ANOVA tables of the effect of time on behavioural measures



Time Sphericity Assumed .001 2 .000 .465 .631


Huynh-Feldt .001 1.628 .000 .465 .592

Lower-bound .001 1.000 .001 .465 .502

Error(Time) Sphericity Assumed .042 50 .001


Huynh-Feldt .042 40.710 .001

Lower-bound .042 25.000 .002



Time Sphericity Assumed .006 2 .003 1.069 .351


Huynh-Feldt .006 1.822 .003 1.069 .347

Lower-bound .006 1.000 .006 1.069 .311

Error(Time) Sphericity Assumed .135 50 .003

94


Huynh-Feldt .135 45.541 .003

Lower-bound .135 25.000 .005



Time Sphericity Assumed 1.635E-6 2 8.177E-7 1.445 .245


Huynh-Feldt 1.635E-6 1.593 1.027E-6 1.445 .247

Lower-bound 1.635E-6 1.000 1.635E-6 1.445 .241

Error(Time) Sphericity Assumed 2.829E-5 50 5.658E-7


Huynh-Feldt 2.829E-5 39.821 7.104E-7

Lower-bound 2.829E-5 25.000 1.132E-6



Time Sphericity Assumed 8.301E-7 2 4.151E-7 1.039 .361


Huynh-Feldt 8.301E-7 2.000 4.151E-7 1.039 .361

Lower-bound 8.301E-7 1.000 8.301E-7 1.039 .318

Error(Time) Sphericity Assumed 1.997E-5 50 3.993E-7


Huynh-Feldt 1.997E-5 50.000 3.993E-7

Lower-bound 1.997E-5 25.000 7.987E-7



Time Sphericity Assumed 35.869 2 17.934 .291 .749


Huynh-Feldt 35.869 2.000 17.934 .291 .749

Lower-bound 35.869 1.000 35.869 .291 .594

Error(Time) Sphericity Assumed 3080.337 50 61.607


Huynh-Feldt 3080.337 50.000 61.607

Lower-bound 3080.337 25.000 123.213



Time Sphericity Assumed 81.736 2 40.868 .714 .494


Huynh-Feldt 81.736 2.000 40.868 .714 .494

Lower-bound 81.736 1.000 81.736 .714 .406



Huynh-Feldt 2860.051 50.000 57.201

Lower-bound 2860.051 25.000 114.402

95



Time Sphericity Assumed 541.740 2 270.870 3.830 .028


Huynh-Feldt 541.740 2.000 270.870 3.830 .028

Lower-bound 541.740 1.000 541.740 3.830 .062



Huynh-Feldt 3535.946 50.000 70.719

Lower-bound 3535.946 25.000 141.438



Time Sphericity Assumed 455.618 2 227.809 1.642 .204


Huynh-Feldt 455.618 2.000 227.809 1.642 .204

Lower-bound 455.618 1.000 455.618 1.642 .212



Huynh-Feldt 6935.711 50.000 138.714

Lower-bound 6935.711 25.000 277.428

Appendix 16. ANOVA tables outlining the effect of a NO-GO tone on the subsequent corticospinal excitability

Multivariate Testsa

Effect Value F Hypothesis df Error df Sig.

factor1 Pillai's Trace .030 .736b 1.000 24.000 .399

Wilks' Lambda .970 .736b 1.000 24.000 .399

Hotelling's Trace .031 .736b 1.000 24.000 .399

Roy's Largest Root .031 .736b 1.000 24.000 .399

factor1 * PrepStrat_NoGo Pillai's Trace .002 .056b 1.000 24.000 .815

Wilks' Lambda .998 .056b 1.000 24.000 .815



Design: Intercept + PrepStrat_NoGo Within Subjects Design: factor1 b. Exact statistic Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average


Intercept 51.343 1 51.343 892.454 .000 PrepStrat_NoGo .299 1 .299 5.196 .032 Error 1.381 24 .058



Intercept 7.178 1 7.178 1027.233 .000 PrepStrat_NoGo .037 1 .037 5.268 .031 Error .168 24 .007

96

Appendix 17. ANOVA tables and t-tests on corticospinal excitatory and inhibitory control

Paired Samples Test – GO MEP Amplitude to Baseline

Control_Go

Paired Differences

t df Sig. (2-tailed) Mean

Std. Deviation

Std. Error Mean


Lower Upper

IC Pair 1

log_Go_MEP - log_Baseline_MEP

-.14006 .09763 .02708 -.19906 -.08106 -5.172 12 .000

EC Pair 1

log_Go_MEP - log_Baseline_MEP

.13647 .12742 .03534 .05947 .21347 3.862 12 .002

Paired Samples Test – GO/NO-GO MEP Amplitude to Baseline

Control_NoGo

Paired Differences


Std. Deviation

Std. Error Mean


Lower Upper

IC Pair 1

log_NoGo_MEP - log_Baseline_MEP

-.16668 .13946 .04410 -.26645 -.06692 -3.780 9 .004

EC Pair 1

log_NoGo_MEP - log_Baseline_MEP

.14089 .10139 .02535 .08687 .19492 5.559 15 .000

Tests of Within-Subjects Effects – MEP Amplitude Measure: MEASURE_1


task Sphericity Assumed .001 1 .001 .264 .612


Huynh-Feldt .001 1.000 .001 .264 .612

Lower-bound .001 1.000 .001 .264 .612

task * VAR00002 Sphericity Assumed .047 3 .016 3.596 .030


Huynh-Feldt .047 3.000 .016 3.596 .030

Lower-bound .047 3.000 .016 3.596 .030

Error(task) Sphericity Assumed .097 22 .004


Huynh-Feldt .097 22.000 .004

Lower-bound .097 22.000 .004

Tests of Between-Subjects Effects – MEP Amplitude




Intercept 36.634 1 36.634 623.069 .000 VAR00002 .339 3 .113 1.923 .155 Error 1.294 22 .059

Tests of Within-Subjects Effects – Reaction Time Measure: MEASURE_1



task Sphericity Assumed .124 1 .124 55.374 .000


Huynh-Feldt .124 1.000 .124 55.374 .000

Lower-bound .124 1.000 .124 55.374 .000

task * VAR00002 Sphericity Assumed .006 3 .002 .848 .482


Huynh-Feldt .006 3.000 .002 .848 .482

Lower-bound .006 3.000 .002 .848 .482

Error(task) Sphericity Assumed .049 22 .002

97


Huynh-Feldt .049 22.000 .002

Lower-bound .049 22.000 .002

Tests of Between-Subjects Effects – Reaction Time




Intercept 3.906 1 3.906 597.003 .000 VAR00002 .015 3 .005 .743 .538 Error .144 22 .007

Tests of Within-Subjects Effects – Reaction Time Variability Measure: MEASURE_1



task Sphericity Assumed 43.312 1 43.312 1.497 .234


Huynh-Feldt 43.312 1.000 43.312 1.497 .234

Lower-bound 43.312 1.000 43.312 1.497 .234

task * VAR00002 Sphericity Assumed 378.621 3 126.207 4.362 .015


Huynh-Feldt 378.621 3.000 126.207 4.362 .015

Lower-bound 378.621 3.000 126.207 4.362 .015

Error(task) Sphericity Assumed 636.551 22 28.934


Huynh-Feldt 636.551 22.000 28.934

Lower-bound 636.551 22.000 28.934

Tests of Between-Subjects Effects – Reaction Time Variability




Intercept 21366.371 1 21366.371 327.381 .000 VAR00002 492.589 3 164.196 2.516 .085 Error 1435.820 22 65.265

Tests of Within-Subjects Effects – iEMG Measure: MEASURE_1



task Sphericity Assumed 1.156E-7 1 1.156E-7 .258 .617


Huynh-Feldt 1.156E-7 1.000 1.156E-7 .258 .617

Lower-bound 1.156E-7 1.000 1.156E-7 .258 .617

task * VAR00002 Sphericity Assumed 1.112E-6 3 3.707E-7 .827 .493


Huynh-Feldt 1.112E-6 3.000 3.707E-7 .827 .493

Lower-bound 1.112E-6 3.000 3.707E-7 .827 .493

Error(task) Sphericity Assumed 9.859E-6 22 4.481E-7


Huynh-Feldt 9.859E-6 22.000 4.481E-7

Lower-bound 9.859E-6 22.000 4.481E-7

98

Tests of Between-Subjects Effects – iEMG Measure: MEASURE_1 Transformed Variable: Average



Intercept .000 1 .000 21.161 .000 VAR00002 3.450E-5 3 1.150E-5 .776 .520 Error .000 22 1.481E-5

Tests of Within-Subjects Effects – iEMG Variability Measure: MEASURE_1



task Sphericity Assumed 245.086 1 245.086 3.951 .059


Huynh-Feldt 245.086 1.000 245.086 3.951 .059

Lower-bound 245.086 1.000 245.086 3.951 .059

task * VAR00002 Sphericity Assumed 170.064 3 56.688 .914 .450


Huynh-Feldt 170.064 3.000 56.688 .914 .450

Lower-bound 170.064 3.000 56.688 .914 .450

Error(task) Sphericity Assumed 1364.640 22 62.029


Huynh-Feldt 1364.640 22.000 62.029

Lower-bound 1364.640 22.000 62.029

Tests of Between-Subjects Effects – iEMG Variability Measure: MEASURE_1 Transformed Variable: Average



Intercept 62005.868 1 62005.868 300.634 .000 VAR00002 1046.569 3 348.856 1.691 .198 Error 4537.511 22 206.251

Appendix 18. ANOVA tables and t-tests on spinal excitatory and inhibitory control

Paired Samples Test – GO H-Reflex Amplitude to Baseline

Control_Go_H

Paired Differences


Std. Deviation

Std. Error Mean


Lower Upper

IC Pair 1

Go_H_Raw - Baseline_Raw

-.0302075 .0210876 .0121749 -.0825919 .0221770 -2.481 2 .131

EC Pair 1

Go_H_Raw - Baseline_Raw

.0334949 .0270308 .0120886 -.0000683 .0670581 2.771 4 .050

Paired Samples Test - GO/NO-GO H-Reflex Amplitude to Baseline

Control_NoGo_H

Paired Differences


Std. Deviation

Std. Error Mean


Lower Upper

IC Pair 1

NoGo_H_Raw - Baseline_Raw

-.0289135

.0148326 .0074163 -.0525155 -.0053116 -3.899 3 .030

EC Pair 1

NoGo_H_Raw - Baseline_Raw

.0387904 .0384911 .0192456 -.0224576 .1000384 2.016 3 .137

99

Tests of Between-Subjects Effects – Reaction Time

Effect Value F Hypothesis df Error df Sig.

Task Pillai's Trace .817 22.356b 1.000 5.000 .005

Wilks' Lambda .183 22.356b 1.000 5.000 .005

Hotelling's Trace 4.471 22.356b 1.000 5.000 .005

Roy's Largest Root 4.471 22.356b 1.000 5.000 .005

Task * Control_H Pillai's Trace .006 .014b 2.000 5.000 .986

Wilks' Lambda .994 .014b 2.000 5.000 .986



a. Design: Intercept + Control_H Within Subjects Design: Task b. Exact statistic




Intercept .858 1 .858 1028.464 .000 Control_H .004 2 .002 2.554 .172 Error .004 5 .001

Tests of Within-Subjects Effects – Reaction Time Variability

Measure: MEASURE_1


Task Sphericity Assumed 233.263 1 233.263 4.381 .091


Huynh-Feldt 233.263 1.000 233.263 4.381 .091

Lower-bound 233.263 1.000 233.263 4.381 .091

Task * Control_H Sphericity Assumed 89.467 2 44.733 .840 .485


Huynh-Feldt 89.467 2.000 44.733 .840 .485

Lower-bound 89.467 2.000 44.733 .840 .485

Error(Task) Sphericity Assumed 266.202 5 53.240


Huynh-Feldt 266.202 5.000 53.240

Lower-bound 266.202 5.000 53.240




Intercept 7345.908 1 7345.908 141.346 .000 Control_H 205.058 2 102.529 1.973 .234 Error 259.855 5 51.971

Tests of Within-Subjects Effects – iEMG Measure: MEASURE_1



Task Sphericity Assumed 9.002E-8 1 9.002E-8 1.806 .237


Huynh-Feldt 9.002E-8 1.000 9.002E-8 1.806 .237

Lower-bound 9.002E-8 1.000 9.002E-8 1.806 .237

Task * Control_H Sphericity Assumed 3.106E-7 2 1.553E-7 3.117 .132


Huynh-Feldt 3.106E-7 2.000 1.553E-7 3.117 .132

Lower-bound 3.106E-7 2.000 1.553E-7 3.117 .132

Error(Task) Sphericity Assumed 2.492E-7 5 4.983E-8

100


Huynh-Feldt 2.492E-7 5.000 4.983E-8

Lower-bound 2.492E-7 5.000 4.983E-8





Intercept 5.772E-5 1 5.772E-5 193.240 .000 Control_H 1.120E-6 2 5.599E-7 1.875 .247 Error 1.493E-6 5 2.987E-7

Tests of Within-Subjects Effects – iEMG Variability Measure: MEASURE_1



Task Sphericity Assumed .668 1 .668 .066 .808


Huynh-Feldt .668 1.000 .668 .066 .808

Lower-bound .668 1.000 .668 .066 .808

Task * Control_H Sphericity Assumed 685.099 2 342.550 .5540 .001


Huynh-Feldt 685.099 2.000 342.550 33.680 .001

Lower-bound 685.099 2.000 342.550 33.680 .001



Huynh-Feldt 50.853 5.000 10.171

Lower-bound 50.853 5.000 10.171





Intercept 12309.019 1 12309.019 438.002 .000 Control_H 749.728 2 374.864 13.339 .010 Error 140.513 5 28.103

Appendix 19. ANOVA tables of stimulation timing analyses Tests of Within-Subjects Effects – GO Reaction Time Measure: MEASURE_1


StimTime Sphericity Assumed .004 2 .002 5.589 .043


Huynh-Feldt .004 1.049 .004 5.589 .095

Lower-bound .004 1.000 .004 5.589 .099

Error(StimTime) Sphericity Assumed .002 6 .000


Huynh-Feldt .002 3.148 .001

Lower-bound .002 3.000 .001

Tests of Within-Subjects Effects – GO/NO-GO Reaction Time Measure: MEASURE_1



101


Huynh-Feldt .017 2.000 .009 8.127 .020

Lower-bound .017 1.000 .017 8.127 .065



Huynh-Feldt .006 6.000 .001

Lower-bound .006 3.000 .002

Paired Samples Test – GO and GO/NO-GO Reaction Times Post-Hoc

Paired Differences


Std. Deviation

Std. Error Mean


Lower Upper

Pair 1 Go_RT_1 - Go_RT_2 .00112 .00563 .00281 -.00783 .01008 .399 3 .717 Pair 2 Go_RT_2 - Go_RT_25 -.03884 .03419 .01710 -.09325 .01557 -2.272 3 .108 Pair 3 Go_RT_1 - Go_RT_25 -.03772 .02997 .01498 -.08541 .00997 -2.517 3 .086 Pair 4 NoGo_RT_1 - NoGo_RT_2 -.01638 .03988 .01994 -.07984 .04708 -.821 3 .472 Pair 5 NoGo_RT_2 - NoGo_RT_25 -.07075 .03746 .01873 -.13035 -.01114 -3.777 3 .033 Pair 6 NoGo_RT_1 - NoGo_RT_25 -.08713 .05777 .02889 -.17905 .00480 -3.016 3 .057

Tests of Within-Subjects Effects – GO MEP Amplitude Measure: MEASURE_1


StimTime Sphericity Assumed 7.982E-5 2 3.991E-5 .099 .907


Huynh-Feldt 7.982E-5 2.000 3.991E-5 .099 .907

Lower-bound 7.982E-5 1.000 7.982E-5 .099 .773



Huynh-Feldt .002 6.000 .000

Lower-bound .002 3.000 .001

Tests of Within-Subjects Effects – GO/NO-GO MEP Amplitude Measure: MEASURE_1




Huynh-Feldt .001 1.165 .001 1.922 .254

Lower-bound .001 1.000 .001 1.922 .260



Huynh-Feldt .001 3.494 .000

Lower-bound .001 3.000 .000

Tests of Within-Subjects Effects – GO iEMG Measure: MEASURE_1


StimTime Sphericity Assumed 1.655E-6 2 8.275E-7 2.412 .170


Huynh-Feldt 1.655E-6 2.000 8.275E-7 2.412 .170

Lower-bound 1.655E-6 1.000 1.655E-6 2.412 .218

Error(StimTime) Sphericity Assumed 2.058E-6 6 3.431E-7


Huynh-Feldt 2.058E-6 6.000 3.431E-7

102

Lower-bound 2.058E-6 3.000 6.861E-7

Tests of Within-Subjects Effects – GO/NO-GO iEMG Measure: MEASURE_1


StimTime Sphericity Assumed 4.067E-7 2 2.033E-7 .614 .572


Huynh-Feldt 4.067E-7 2.000 2.033E-7 .614 .572

Lower-bound 4.067E-7 1.000 4.067E-7 .614 .490

Error(StimTime) Sphericity Assumed 1.987E-6 6 3.311E-7


Huynh-Feldt 1.987E-6 6.000 3.311E-7

Lower-bound 1.987E-6 3.000 6.622E-7

Tests of Within-Subjects Effects – GO Reaction Time Variability Measure: MEASURE_1


StimTime Sphericity Assumed 135.693 2 67.846 3.877 .083


Huynh-Feldt 135.693 2.000 67.846 3.877 .083

Lower-bound 135.693 1.000 135.693 3.877 .144

Error(StimTime) Sphericity Assumed 104.999 6 17.500


Huynh-Feldt 104.999 6.000 17.500

Lower-bound 104.999 3.000 35.000

Tests of Within-Subjects Effects – GO/NO-GO Reaction Time Variability Measure: MEASURE_1




Huynh-Feldt 177.097 1.044 169.649 1.619 .292

Lower-bound 177.097 1.000 177.097 1.619 .293



Huynh-Feldt 328.090 3.132 104.764

Lower-bound 328.090 3.000 109.363

Tests of Within-Subjects Effects – GO iEMG Variability Measure: MEASURE_1


StimTime Sphericity Assumed 13.875 2 6.937 .173 .845


Huynh-Feldt 13.875 1.627 8.527 .173 .806

Lower-bound 13.875 1.000 13.875 .173 .705



Huynh-Feldt 240.170 4.882 49.197

Lower-bound 240.170 3.000 80.057

103

Tests of Within-Subjects Effects – GO/NO-GO iEMG variability Measure: MEASURE_1




Huynh-Feldt 663.065 2.000 331.532 4.500 .064

Lower-bound 663.065 1.000 663.065 4.500 .124



Huynh-Feldt 442.058 6.000 73.676

Lower-bound 442.058 3.000 147.353

Appendix 20. Supplementary Relative Value Secondary Analyses Tests of Within-Subjects Effects Measure: MEASURE_1


tASK Sphericity Assumed 370.969 1 370.969 1.789 .196


Huynh-Feldt 370.969 1.000 370.969 1.789 .196

Lower-bound 370.969 1.000 370.969 1.789 .196

tASK * Overt_Control Sphericity Assumed 3049.582 5 609.916 2.942 .038


Huynh-Feldt 3049.582 5.000 609.916 2.942 .038

Lower-bound 3049.582 5.000 609.916 2.942 .038

Error(tASK) Sphericity Assumed 4146.269 20 207.313


Huynh-Feldt 4146.269 20.000 207.313

Lower-bound 4146.269 20.000 207.313



Intercept 314977.397 1 314977.397 292.421 .000 Overt_Control 77059.082 5 15411.816 14.308 .000 Error 21542.758 20 1077.138



tASK Sphericity Assumed .142 1 .142 117.738 .000


Huynh-Feldt .142 1.000 .142 117.738 .000

Lower-bound .142 1.000 .142 117.738 .000

tASK * Overt_Control Sphericity Assumed .031 5 .006 5.164 .003


Huynh-Feldt .031 5.000 .006 5.164 .003

Lower-bound .031 5.000 .006 5.164 .003

Error(tASK) Sphericity Assumed .024 20 .001


Huynh-Feldt .024 20.000 .001

Lower-bound .024 20.000 .001

104



Intercept 3.146 1 3.146 480.726 .000 Overt_Control .028 5 .006 .845 .534 Error .131 20 .007



tASK Sphericity Assumed 229.982 1 229.982 5.708 .027


Huynh-Feldt 229.982 1.000 229.982 5.708 .027

Lower-bound 229.982 1.000 229.982 5.708 .027

tASK * Overt_Control Sphericity Assumed 728.817 5 145.763 3.617 .017


Huynh-Feldt 728.817 5.000 145.763 3.617 .017

Lower-bound 728.817 5.000 145.763 3.617 .017

Error(tASK) Sphericity Assumed 805.888 20 40.294


Huynh-Feldt 805.888 20.000 40.294

Lower-bound 805.888 20.000 40.294



Intercept 50002.289 1 50002.289 225.044 .000 Overt_Control 1140.295 5 228.059 1.026 .429 Error 4443.786 20 222.189





Huynh-Feldt 77.396 1.000 77.396 4.243 .132

Lower-bound 77.396 1.000 77.396 4.243 .132

Task * Overt_Control_H Sphericity Assumed 1528.023 4 382.006 20.944 .016


Huynh-Feldt 1528.023 4.000 382.006 20.944 .016

Lower-bound 1528.023 4.000 382.006 20.944 .016



Huynh-Feldt 54.718 3.000 18.239

Lower-bound 54.718 3.000 18.239



Intercept 117364.304 1 117364.304 419.260 .000 Overt_Control_H 14184.763 4 3546.191 12.668 .032 Error 839.796 3 279.932

105





Huynh-Feldt 36.756 1.000 36.756 2.244 .231

Lower-bound 36.756 1.000 36.756 2.244 .231

Task * Overt_Control_H Sphericity Assumed 686.806 4 171.702 10.481 .041


Huynh-Feldt 686.806 4.000 171.702 10.481 .041

Lower-bound 686.806 4.000 171.702 10.481 .041



Huynh-Feldt 49.146 3.000 16.382

Lower-bound 49.146 3.000 16.382



Intercept 13244.046 1 13244.046 903.667 .000 Overt_Control_H 846.274 4 211.569 14.436 .027 Error 43.968 3 14.656

Correlations

MMax_MEP_GO MMax_H_Go

MMax_MEP_GO Pearson Correlation 1 .962**


N 8 8

MMax_H_Go Pearson Correlation .962** 1


N 8 8

**. Correlation is significant at the 0.01 level (2-tailed). Correlations

MMax_MEP_GO MMax_H_Go

Spearman's rho MMax_MEP_GO Correlation Coefficient 1.000 .881**

Sig. (2-tailed) . .004

N 8 8

MMax_H_Go Correlation Coefficient .881** 1.000

Sig. (2-tailed) .004 .

N 8 8

**. Correlation is significant at the 0.01 level (2-tailed).

Documents

Characterizing Cortical and Spinal Markers of Lower Limb ...€¦ · master’s student has been critical to the completion of this work, and for that I am truly grateful. I would