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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.
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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
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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
and dotted line represents those who switched strategies depending on the task. Error bars denote
standard error of the mean. ........................................................................................................... 31
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
and dotted line represents those who switched strategies depending on the task. Error bars denote
standard error of the mean. ........................................................................................................... 33
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
standard error of the mean. ........................................................................................................... 37
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
standard error of the mean. ........................................................................................................... 43
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
denote standard error of the mean.
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
denote standard error of the mean.
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
58
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
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
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
Greenhouse-Geisser .012 3.000 .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
Source Type III Sum of Squares df Mean Square F Sig.
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
Greenhouse-Geisser 17.499 3.000 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
Greenhouse-Geisser 997.673 22.000 45.349
Huynh-Feldt 997.673 22.000 45.349
Lower-bound 997.673 22.000 45.349
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed .010 2 .005 .359 .700
Greenhouse-Geisser .010 1.502 .006 .359 .640
Huynh-Feldt .010 1.577 .006 .359 .650
Lower-bound .010 1.000 .010 .359 .554
Error(Condition) Sphericity Assumed .669 50 .013
Greenhouse-Geisser .669 37.550 .018
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed .017 1 .017 2.794 .109
Greenhouse-Geisser .017 1.000 .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
Greenhouse-Geisser .014 3.000 .005 .758 .530
Huynh-Feldt .014 3.000 .005 .758 .530
Lower-bound .014 3.000 .005 .758 .530
Error(Condition) Sphericity Assumed .131 22 .006
Greenhouse-Geisser .131 22.000 .006
Huynh-Feldt .131 22.000 .006
Lower-bound .131 22.000 .006
Tests of Between-Subjects Effects
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed .000 2 .000 .255 .779
Greenhouse-Geisser .000 1.527 .000 .255 .722
Huynh-Feldt .000 1.867 .000 .255 .764
Lower-bound .000 1.000 .000 .255 .629
Error(Condition) Sphericity Assumed .010 14 .001
Greenhouse-Geisser .010 10.691 .001
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed .000 1 .000 .252 .637
Greenhouse-Geisser .000 1.000 .000 .252 .637
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
Error(Condition) Sphericity Assumed .002 5 .000
Greenhouse-Geisser .002 5.000 .000
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
Sig. (2-tailed) .082
N 8 8
NoGo MEP_raw Pearson Correlation 1 .734*
Sig. (2-tailed) .038
N 8 8
H_raw Pearson Correlation .734* 1
Sig. (2-tailed) .038
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
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed 1.245E-8 1 1.245E-8 .029 .867
Greenhouse-Geisser 1.245E-8 1.000 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
Tests of Between-Subjects Effects
Measure: MEASURE_1 Transformed Variable: Average
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
Intercept .568 1 .568 28.267 .003 Strategy .055 2 .027 1.361 .337 Error .100 5 .020
91
Source Type III Sum of
Squares df Mean Square F Sig.
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)
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of
Squares df Mean Square F Sig.
Condition Sphericity Assumed 276.600 1 276.600 4.826 .039
Greenhouse-Geisser 276.600 1.000 276.600 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
Greenhouse-Geisser 273.698 3.000 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
Greenhouse-Geisser 1261.006 22.000 57.318
Huynh-Feldt 1261.006 22.000 57.318
Lower-bound 1261.006 22.000 57.318
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Sig. (2-tailed) .477
N 26 26
Go_RT Pearson Correlation -.146 1
Sig. (2-tailed) .477
N 26 26
Correlations
log_NoGo_MEP NoGo_RT
log_NoGo_MEP Pearson Correlation 1 .255
Sig. (2-tailed) .209
N 26 26
NoGo_RT Pearson Correlation .255 1
Sig. (2-tailed) .209
N 26 26
92
Correlations
Go_H_Raw Go_RT_H
Go_H_Raw Pearson Correlation 1 -.445
Sig. (2-tailed) .270
N 8 8
Go_RT_H Pearson Correlation -.445 1
Sig. (2-tailed) .270
N 8 8
Correlations
NoGo_H_Raw NoGo_RT_H
NoGo_H_Raw Pearson Correlation 1 -.333
Sig. (2-tailed) .420
N 8 8
NoGo_RT_H Pearson Correlation -.333 1
Sig. (2-tailed) .420
N 8 8
Appendix 14. ANOVA table of the effect of time and task order on corticospinal excitability
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Task Sphericity Assumed .004 1 .004 .788 .383
Greenhouse-Geisser .004 1.000 .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
Greenhouse-Geisser .025 1.000 .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
Greenhouse-Geisser .119 24.000 .005
Huynh-Feldt .119 24.000 .005
Lower-bound .119 24.000 .005
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
Intercept 55.275 1 55.275 812.641 .000 First_Task .000 1 .000 .004 .948 Error 1.632 24 .068
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed .132 5 .026 2.455 .037
Greenhouse-Geisser .132 3.570 .037 2.455 .058
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed .001 2 .000 .465 .631
Greenhouse-Geisser .001 1.546 .001 .465 .582
Huynh-Feldt .001 1.628 .000 .465 .592
Lower-bound .001 1.000 .001 .465 .502
Error(Time) Sphericity Assumed .042 50 .001
Greenhouse-Geisser .042 38.647 .001
Huynh-Feldt .042 40.710 .001
Lower-bound .042 25.000 .002
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed .006 2 .003 1.069 .351
Greenhouse-Geisser .006 1.709 .003 1.069 .343
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
Greenhouse-Geisser .135 42.719 .003
Huynh-Feldt .135 45.541 .003
Lower-bound .135 25.000 .005
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 1.635E-6 2 8.177E-7 1.445 .245
Greenhouse-Geisser 1.635E-6 1.516 1.079E-6 1.445 .246
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
Greenhouse-Geisser 2.829E-5 37.891 7.466E-7
Huynh-Feldt 2.829E-5 39.821 7.104E-7
Lower-bound 2.829E-5 25.000 1.132E-6
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 8.301E-7 2 4.151E-7 1.039 .361
Greenhouse-Geisser 8.301E-7 1.913 4.339E-7 1.039 .359
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
Greenhouse-Geisser 1.997E-5 47.832 4.174E-7
Huynh-Feldt 1.997E-5 50.000 3.993E-7
Lower-bound 1.997E-5 25.000 7.987E-7
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 35.869 2 17.934 .291 .749
Greenhouse-Geisser 35.869 1.931 18.578 .291 .741
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
Greenhouse-Geisser 3080.337 48.267 63.819
Huynh-Feldt 3080.337 50.000 61.607
Lower-bound 3080.337 25.000 123.213
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 81.736 2 40.868 .714 .494
Greenhouse-Geisser 81.736 1.958 41.753 .714 .492
Huynh-Feldt 81.736 2.000 40.868 .714 .494
Lower-bound 81.736 1.000 81.736 .714 .406
Error(Time) Sphericity Assumed 2860.051 50 57.201
Greenhouse-Geisser 2860.051 48.940 58.439
Huynh-Feldt 2860.051 50.000 57.201
Lower-bound 2860.051 25.000 114.402
95
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 541.740 2 270.870 3.830 .028
Greenhouse-Geisser 541.740 1.949 277.952 3.830 .029
Huynh-Feldt 541.740 2.000 270.870 3.830 .028
Lower-bound 541.740 1.000 541.740 3.830 .062
Error(Time) Sphericity Assumed 3535.946 50 70.719
Greenhouse-Geisser 3535.946 48.726 72.568
Huynh-Feldt 3535.946 50.000 70.719
Lower-bound 3535.946 25.000 141.438
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Time Sphericity Assumed 455.618 2 227.809 1.642 .204
Greenhouse-Geisser 455.618 1.982 229.874 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
Error(Time) Sphericity Assumed 6935.711 50 138.714
Greenhouse-Geisser 6935.711 49.551 139.972
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
Hotelling's Trace .002 .056b 1.000 24.000 .815
Roy's Largest Root .002 .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
Source Type III Sum of Squares df Mean Square F Sig.
Intercept 51.343 1 51.343 892.454 .000 PrepStrat_NoGo .299 1 .299 5.196 .032 Error 1.381 24 .058
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
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
95% Confidence Interval of the Difference
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
t df Sig. (2-tailed) Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
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
Source Type III Sum of Squares df Mean Square F Sig.
task Sphericity Assumed .001 1 .001 .264 .612
Greenhouse-Geisser .001 1.000 .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
Greenhouse-Geisser .047 3.000 .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
Greenhouse-Geisser .097 22.000 .004
Huynh-Feldt .097 22.000 .004
Lower-bound .097 22.000 .004
Tests of Between-Subjects Effects – MEP Amplitude
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
task Sphericity Assumed .124 1 .124 55.374 .000
Greenhouse-Geisser .124 1.000 .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
Greenhouse-Geisser .006 3.000 .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
Greenhouse-Geisser .049 22.000 .002
Huynh-Feldt .049 22.000 .002
Lower-bound .049 22.000 .002
Tests of Between-Subjects Effects – Reaction Time
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
task Sphericity Assumed 43.312 1 43.312 1.497 .234
Greenhouse-Geisser 43.312 1.000 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
Greenhouse-Geisser 378.621 3.000 126.207 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
Greenhouse-Geisser 636.551 22.000 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
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
task Sphericity Assumed 1.156E-7 1 1.156E-7 .258 .617
Greenhouse-Geisser 1.156E-7 1.000 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
Greenhouse-Geisser 1.112E-6 3.000 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
Greenhouse-Geisser 9.859E-6 22.000 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
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
task Sphericity Assumed 245.086 1 245.086 3.951 .059
Greenhouse-Geisser 245.086 1.000 245.086 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
Greenhouse-Geisser 170.064 3.000 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
Greenhouse-Geisser 1364.640 22.000 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
Source Type III Sum of
Squares df Mean Square F Sig.
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
t df Sig. (2-tailed) Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
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
t df Sig. (2-tailed) Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
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
Hotelling's Trace .006 .014b 2.000 5.000 .986
Roy's Largest Root .006 .014b 2.000 5.000 .986
a. Design: Intercept + Control_H Within Subjects Design: Task b. Exact statistic
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of Squares df Mean Square F Sig.
Task Sphericity Assumed 233.263 1 233.263 4.381 .091
Greenhouse-Geisser 233.263 1.000 233.263 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
Greenhouse-Geisser 89.467 2.000 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
Greenhouse-Geisser 266.202 5.000 53.240
Huynh-Feldt 266.202 5.000 53.240
Lower-bound 266.202 5.000 53.240
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
Task Sphericity Assumed 9.002E-8 1 9.002E-8 1.806 .237
Greenhouse-Geisser 9.002E-8 1.000 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
Greenhouse-Geisser 3.106E-7 2.000 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
Greenhouse-Geisser 2.492E-7 5.000 4.983E-8
Huynh-Feldt 2.492E-7 5.000 4.983E-8
Lower-bound 2.492E-7 5.000 4.983E-8
Tests of Between-Subjects Effects
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of
Squares df Mean Square F Sig.
Task Sphericity Assumed .668 1 .668 .066 .808
Greenhouse-Geisser .668 1.000 .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
Greenhouse-Geisser 685.099 2.000 342.550 33.680 .001
Huynh-Feldt 685.099 2.000 342.550 33.680 .001
Lower-bound 685.099 2.000 342.550 33.680 .001
Error(Task) Sphericity Assumed 50.853 5 10.171
Greenhouse-Geisser 50.853 5.000 10.171
Huynh-Feldt 50.853 5.000 10.171
Lower-bound 50.853 5.000 10.171
Tests of Between-Subjects Effects
Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of
Squares df Mean Square F Sig.
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed .004 2 .002 5.589 .043
Greenhouse-Geisser .004 1.020 .004 5.589 .097
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
Greenhouse-Geisser .002 3.059 .001
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed .017 2 .009 8.127 .020
101
Greenhouse-Geisser .017 1.492 .011 8.127 .036
Huynh-Feldt .017 2.000 .009 8.127 .020
Lower-bound .017 1.000 .017 8.127 .065
Error(StimTime) Sphericity Assumed .006 6 .001
Greenhouse-Geisser .006 4.476 .001
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
t df Sig. (2-tailed) Mean
Std. Deviation
Std. Error Mean
95% Confidence Interval of the Difference
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 7.982E-5 2 3.991E-5 .099 .907
Greenhouse-Geisser 7.982E-5 1.493 5.346E-5 .099 .856
Huynh-Feldt 7.982E-5 2.000 3.991E-5 .099 .907
Lower-bound 7.982E-5 1.000 7.982E-5 .099 .773
Error(StimTime) Sphericity Assumed .002 6 .000
Greenhouse-Geisser .002 4.479 .001
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed .001 2 .000 1.922 .226
Greenhouse-Geisser .001 1.064 .001 1.922 .258
Huynh-Feldt .001 1.165 .001 1.922 .254
Lower-bound .001 1.000 .001 1.922 .260
Error(StimTime) Sphericity Assumed .001 6 .000
Greenhouse-Geisser .001 3.191 .000
Huynh-Feldt .001 3.494 .000
Lower-bound .001 3.000 .000
Tests of Within-Subjects Effects – GO iEMG Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 1.655E-6 2 8.275E-7 2.412 .170
Greenhouse-Geisser 1.655E-6 1.800 9.196E-7 2.412 .179
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
Greenhouse-Geisser 2.058E-6 5.399 3.812E-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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 4.067E-7 2 2.033E-7 .614 .572
Greenhouse-Geisser 4.067E-7 1.938 2.098E-7 .614 .568
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
Greenhouse-Geisser 1.987E-6 5.815 3.416E-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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 135.693 2 67.846 3.877 .083
Greenhouse-Geisser 135.693 1.815 74.766 3.877 .092
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
Greenhouse-Geisser 104.999 5.445 19.285
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 177.097 2 88.548 1.619 .274
Greenhouse-Geisser 177.097 1.017 174.067 1.619 .293
Huynh-Feldt 177.097 1.044 169.649 1.619 .292
Lower-bound 177.097 1.000 177.097 1.619 .293
Error(StimTime) Sphericity Assumed 328.090 6 54.682
Greenhouse-Geisser 328.090 3.052 107.492
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 13.875 2 6.937 .173 .845
Greenhouse-Geisser 13.875 1.223 11.346 .173 .747
Huynh-Feldt 13.875 1.627 8.527 .173 .806
Lower-bound 13.875 1.000 13.875 .173 .705
Error(StimTime) Sphericity Assumed 240.170 6 40.028
Greenhouse-Geisser 240.170 3.669 65.463
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
Source Type III Sum of Squares df Mean Square F Sig.
StimTime Sphericity Assumed 663.065 2 331.532 4.500 .064
Greenhouse-Geisser 663.065 1.675 395.969 4.500 .079
Huynh-Feldt 663.065 2.000 331.532 4.500 .064
Lower-bound 663.065 1.000 663.065 4.500 .124
Error(StimTime) Sphericity Assumed 442.058 6 73.676
Greenhouse-Geisser 442.058 5.024 87.996
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
Source Type III Sum of Squares df Mean Square F Sig.
tASK Sphericity Assumed 370.969 1 370.969 1.789 .196
Greenhouse-Geisser 370.969 1.000 370.969 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
Greenhouse-Geisser 3049.582 5.000 609.916 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
Greenhouse-Geisser 4146.269 20.000 207.313
Huynh-Feldt 4146.269 20.000 207.313
Lower-bound 4146.269 20.000 207.313
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
tASK Sphericity Assumed .142 1 .142 117.738 .000
Greenhouse-Geisser .142 1.000 .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
Greenhouse-Geisser .031 5.000 .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
Greenhouse-Geisser .024 20.000 .001
Huynh-Feldt .024 20.000 .001
Lower-bound .024 20.000 .001
104
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
Intercept 3.146 1 3.146 480.726 .000 Overt_Control .028 5 .006 .845 .534 Error .131 20 .007
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
tASK Sphericity Assumed 229.982 1 229.982 5.708 .027
Greenhouse-Geisser 229.982 1.000 229.982 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
Greenhouse-Geisser 728.817 5.000 145.763 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
Greenhouse-Geisser 805.888 20.000 40.294
Huynh-Feldt 805.888 20.000 40.294
Lower-bound 805.888 20.000 40.294
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Task Sphericity Assumed 77.396 1 77.396 4.243 .132
Greenhouse-Geisser 77.396 1.000 77.396 4.243 .132
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
Greenhouse-Geisser 1528.023 4.000 382.006 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
Error(Task) Sphericity Assumed 54.718 3 18.239
Greenhouse-Geisser 54.718 3.000 18.239
Huynh-Feldt 54.718 3.000 18.239
Lower-bound 54.718 3.000 18.239
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
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
Tests of Within-Subjects Effects Measure: MEASURE_1
Source Type III Sum of Squares df Mean Square F Sig.
Task Sphericity Assumed 36.756 1 36.756 2.244 .231
Greenhouse-Geisser 36.756 1.000 36.756 2.244 .231
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
Greenhouse-Geisser 686.806 4.000 171.702 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
Error(Task) Sphericity Assumed 49.146 3 16.382
Greenhouse-Geisser 49.146 3.000 16.382
Huynh-Feldt 49.146 3.000 16.382
Lower-bound 49.146 3.000 16.382
Tests of Between-Subjects Effects Measure: MEASURE_1 Transformed Variable: Average
Source Type III Sum of Squares df Mean Square F Sig.
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**
Sig. (2-tailed) .000
N 8 8
MMax_H_Go Pearson Correlation .962** 1
Sig. (2-tailed) .000
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).