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Inter-limb Coordination in Balance Control: Implications for
understanding Balance after Traumatic Brain Injury
by
Olinda Dulce Habib Perez
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Rehabilitation Sciences Institute
University of Toronto
© Copyright by Olinda Dulce Habib Perez 2018
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Inter-limb Coordination in Balance Control: Implications for
understanding Balance after Traumatic Brain Injury
Olinda Dulce Habib Perez
Doctor of Philosophy
Rehabilitation Sciences Institute
University of Toronto
2018
Abstract
Coordination of forces generated in the lower extremities is essential for maintaining
balance and recovery from postural instability. Temporal synchronization of force amplitude
from each limb in upright stance illustrates the importance of the integrity of the central
nervous system (CNS) and its contribution to the regulation of the centre of mass within its
base of support in producing appropriate coordination between both limbs. Weight-bearing
symmetry is also important to postural control and the maintenance of balance in response to
upcoming perturbations. Therefore, inter-limb coordination in balance control is critical when
the integrity of the CNS has been compromised as a result of a traumatic brain injury (TBI), and
its compromise can lead to increased risk of falling. Balance impairments after TBI are common
and result from multiple TBI-related sequelae, which includes cognitive deficits and
sensorimotor and coordination impairments. Impaired balance performance is typically seen
after TBI; however, the motor contributions that influence postural control are not well
understood in this population. The overall purpose of this dissertation was to further
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understand the role of inter-limb coordination in postural control and to determine the extent
to which inter-limb coordination is altered in individuals following moderate-to-severe TBI. The
decoupling of inter-limb synchronization was particularly important in preparation for balance
perturbations that were less predictable and required stepping responses. Balance asymmetry
and poor static balance control were found in individuals with TBI. There was no change in
inter-limb synchronization and as a result of large within-group variability, inter-limb synchrony
post-TBI did not fall outside healthy control norms. Additionally, anticipatory and reactive
balance control mechanisms were also impaired after injury. Although there were
improvements in some balance measures, balance impairments persist one year after injury.
The findings of this dissertation are the first to quantify balance asymmetry after TBI and
provide further evidence that asymmetry and inter-limb coordination are essential components
of balance control after TBI. Furthermore, these findings suggest that future rehabilitation
intervention may benefit from balance training with a focus on asymmetry to reduce of risk of
falling and that other TBI-related sequelae may also be impeding on the recovery of balance
outcomes.
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Acknowledgments
This work could not have been completed without the support of many individuals.
Firstly, I would like to thank my supervisor, Dr. George Mochizuki, for his continuing support
and research expertise throughout this journey. I feel fortunate to have had the opportunity to
learn from and work with Dr. Mochizuki prior to beginning my doctoral research – you were my
mentor before you were my supervisor. Thank you for providing me the opportunity in
becoming an independent researcher and re-igniting my passion for research and, most of all,
for teaching me that real life matters. I would also like to thank my committee members, Dr.
Robin Green and Dr. William McIlroy, for their support, insight, and guidance. And, I would like
to thank Drs. George Mochizuki, Robin Green, and William McIlroy for continuously challenging
my scientific thinking throughout our meetings and advancing my writing abilities through their
feedback.
I am very grateful to the many wonderful professors and scientists that I learned from at
the Rehabilitation Sciences Institute, Toronto Rehabilitation Institute, and Sunnybrook Research
Institute. Throughout my graduate training, I have worked alongside exceptional graduate
students and staff. I am thankful to the many members of the Mobility Team who continually
made coming into the lab every day an enjoyable experience. To the present (and former)
students of the GMo lab, it has been a pleasure to share a desk area with you. I would also like
to thank Drs. Jodi Edwards and Jonathan Singer for their encouragement, mentorship, and
allowing me to pick their brain on many matters. I would like to thank and express my upmost
appreciation to Dr. Dina Brooks, whom I have repeatedly called my s-hero and who went above
and beyond her responsibilities as Graduate Coordinator. Thank you for your mentorship and
believing in me when I was least confident.
I would like to thank my wonderful friends, inside and outside of academia, for their
support and encouragement, and reminding me of life outside of graduate school. Thank you to
my family, and in particular my parents, who have always supported my career aspirations with
their unconditional love and encouragement. To my life partner in crime, Nathan Gara, for
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always supporting my career ambitions through his daily love and patience. Thank you for
sharing a desk with me throughout this journey.
My upmost gratitude goes to the participants in the Toronto Rehabilitation Recovery
Study, who I hope I have advocated for in my doctoral work. I would also like to thank the
following funding agencies for supporting my research throughout my doctoral degree: the
NSERC CREATE Collaborative Academic Rehabilitation Engineering Award (CARE), Ontario
Graduate Scholarship, Toronto Rehabilitation Institute Ontario Student Opportunity Trust Fund,
Ontario Neurotrauma Foundation – Réseau provincial de recherché en adaptation-readaptation
(ONF-REPAR), and Rehabilitation Sciences Institute at the University of Toronto. This thesis
could not have been completed without the many individuals who volunteered their time and
the multiple sources of funding.
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Dedication
To my mother and father, who instilled curiosity in me from a young age, and for their ongoing
support and wisdom throughout this journey; and to Nathan, who has always shared my
curious nature and has celebrated the highs and supported me through the lows.
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Table of Contents
ACKNOWLEDGMENTS ...................................................................................................................... IV
DEDICATION .................................................................................................................................... VI
TABLE OF CONTENTS ....................................................................................................................... VII
LIST OF TABLES ................................................................................................................................ XI
LIST OF FIGURES .............................................................................................................................. XII
LIST OF ABBREVIATIONS .................................................................................................................. XV
LIST OF APPENDICES ....................................................................................................................... XVI
CHAPTER 1 : INTRODUCTION ..............................................................................................................1
1.1 REFERENCES ........................................................................................................................................4
CHAPTER 2 : BACKGROUND ................................................................................................................5
2.1 BALANCE CONTROL ..............................................................................................................................6
2.1.1 Anticipatory and Reactive Balance Control ........................................................................... 10
2.1.2 Inter-limb Coordination in Balance Control ........................................................................... 13
2.2 TRAUMATIC BRAIN INJURY .................................................................................................................. 16
2.2.1 Prevalence, Incidence, and Mechanism ................................................................................. 16
2.2.2 Features of Traumatic Brain Injury Influence Control of Balance .......................................... 18
2.2.3 Characteristics of Balance Control after Traumatic Brain Injury ........................................... 21
2.3 RATIONALE ...................................................................................................................................... 24
2.3.1 Research Purpose, Questions, and Hypotheses ..................................................................... 29
2.3.2 Thesis Questions .................................................................................................................... 30
2.4 REFERENCES ..................................................................................................................................... 32
CHAPTER 3 : THE EFFECTS OF PREDICTABILITY ON INTER-LIMB POSTURAL SYNCHRONIZATION PRIOR
TO BOUTS OF POSTURAL INSTABILITY ............................................................................................... 45
3.1 ABSTRACT ........................................................................................................................................ 46
3.2 INTRODUCTION ................................................................................................................................. 47
3.3 METHODS ........................................................................................................................................ 49
3.3.1 Participants ............................................................................................................................ 49
3.3.2 Instrumentation and Protocol ............................................................................................... 50
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3.3.3 Data Analysis ......................................................................................................................... 55
3.3.4 Secondary Analyses ............................................................................................................... 55
3.3.5 Statistical Analysis ................................................................................................................. 56
3.4 RESULTS .......................................................................................................................................... 57
3.4.1 Inter-limb Synchrony in Preparation for Instability ............................................................... 57
3.4.2 Quiet Standing ....................................................................................................................... 60
3.4.3 Secondary Analysis ................................................................................................................ 60
3.5 DISCUSSION ..................................................................................................................................... 61
3.5.1 Condition-dependent Differences in Preparatory COP .......................................................... 61
3.5.2 Perturbation Type of Preparatory COP Activity ..................................................................... 63
3.6 CONCLUSIONS .................................................................................................................................. 64
3.7 REFERENCES ..................................................................................................................................... 66
CHAPTER 4 : CHARACTERIZATION OF BALANCE CONTROL AFTER MODERATE-TO-SEVERE TRAUMATIC
BRAIN INJURY: A LONGITUDINAL RECOVERY STUDY .......................................................................... 69
4.1 ABSTRACT ........................................................................................................................................ 70
4.2 INTRODUCTION ................................................................................................................................. 72
4.3 METHODS ........................................................................................................................................ 74
4.3.1 Participants ............................................................................................................................ 74
4.3.2 Study Design .......................................................................................................................... 76
4.3.3 Data Collection and Procedures ............................................................................................ 76
4.3.4 Data Processing and Analysis ................................................................................................ 77
4.3.5 Statistical Analyses ................................................................................................................ 78
4.3.6 Secondary Analyses ............................................................................................................... 79
4.4 RESULTS .......................................................................................................................................... 80
4.4.1 Demographic Characteristics ................................................................................................. 80
4.4.2 Cognitive Characteristics ....................................................................................................... 82
4.4.3 Clinical Measures of Balance ................................................................................................. 82
4.4.4 Net Measures of Balance ....................................................................................................... 83
4.4.5 Measures of Asymmetry ........................................................................................................ 85
4.4.6 Secondary Analyses ............................................................................................................... 89
4.5 DISCUSSION ..................................................................................................................................... 90
4.5.1 Balance Improvements Within the First Five Months after TBI ............................................. 91
4.5.2 Asymmetry after TBI is evident .............................................................................................. 93
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4.5.3 Study Limitations ................................................................................................................... 95
4.6 CONCLUSIONS .................................................................................................................................. 96
4.7 REFERENCES ..................................................................................................................................... 97
CHAPTER 5 : SPECTRAL ANALYSIS OF CENTRE OF PRESSURE IDENTIFIES ALTERED BALANCE CONTROL IN
INDIVIDUALS WITH MODERATE-SEVERE TRAUMATIC BRAIN INJURY ............................................... 101
5.1 ABSTRACT ...................................................................................................................................... 102
5.2 INTRODUCTION ............................................................................................................................... 103
5.3 METHODS ...................................................................................................................................... 106
5.3.1 Participants .......................................................................................................................... 106
5.3.2 Data Collection and Procedure ............................................................................................ 107
5.3.3 Data Analysis ....................................................................................................................... 107
5.3.4 Statistical Analysis ............................................................................................................... 108
5.4 RESULTS ........................................................................................................................................ 109
5.4.1 Spectral Measures of Balance ............................................................................................. 111
5.4.2 Inter-limb Coordination ....................................................................................................... 113
5.4.3 Spatial Measures of Balance ............................................................................................... 118
5.5 DISCUSSION ................................................................................................................................... 120
5.5.1 Anticipatory and Reactive Balance Control Mechanisms are Impaired after TBI................ 120
5.5.2 Inter-limb Coordination after TBI ........................................................................................ 122
5.5.3 The Role of the Postural Challenge ...................................................................................... 124
5.6 CONCLUSION .................................................................................................................................. 125
5.7 REFERENCES ................................................................................................................................... 126
CHAPTER 6 : DISCUSSION ............................................................................................................... 131
6.1 SUMMARY OF FINDINGS ................................................................................................................... 132
6.2 INTER-LIMB COORDINATION IN BALANCE CONTROL .............................................................................. 135
6.2.1 The Organization and Control of Inter-limb Coordination in Balance Control .................... 137
6.2.2 Inter-limb Synchrony in Anticipatory Balance Control......................................................... 138
6.2.3 Inter-limb Coordination in Balance Control after Traumatic Brain Injury ........................... 140
6.2.4 Inter-limb Coordination and Asymmetry: Does it contribute to balance impairment in
traumatic brain injury? .................................................................................................................... 145
6.3 BALANCE RECOVERY AFTER TRAUMATIC BRAIN INJURY .......................................................................... 148
6.4 LIMITATIONS AND FUTURE DIRECTIONS............................................................................................... 151
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6.5 IMPLICATIONS FOR REHABILITATION ................................................................................................... 155
6.6 SUMMARY AND CONCLUSIONS .......................................................................................................... 157
6.7 REFERENCES ................................................................................................................................... 159
APPENDICES .................................................................................................................................. 169
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List of Tables
Table 3-1: Summary of perturbation types and their corresponding responses for each
condition in the experimental paradigm. ..................................................................................... 51
Table 3-2: Mean and standard deviations for external perturbation characteristics. ................. 53
Table 4-1: Participant demographics of traumatic brain injury (TBI) and healthy control (HC)
participants. .................................................................................................................................. 81
Table 4-2: Relationship between inter-limb synchronization (Rxy(0)) and standing balance
measures (centre of pressure root mean square (COP RMS) and absolute stance load symmetry)
for eyes open. ............................................................................................................................... 90
Table 5-1: Mean and standard deviation (SD) participant demographics of traumatic brain injury
(TBI) and healthy control (HC) participants. .............................................................................. 110
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List of Figures
Figure 2-1: The theoretical framework of postural control. Postural control emerges from the
interaction of the individual, task, and environment. [From: A. Shumway-Cook & M. Woollacott,
Motor Control: Translating Research into Clinical Practice 4th Ed. (2012)]. ................................. 6
Figure 2-2: The theoretical framework of inter-limb coordination. Inter-limb coordination
emerges from the interaction of the individual, task, and the environment. [Adapted from: A.
Shumway-Cook & M. Woollacott, Motor Control: Translating Research into Clinical Practice 4th
Ed. (2012)]. .................................................................................................................................... 26
Figure 2-3: Theoretical framework of the impairments after traumatic brain injury (TBI).
Irrespective of TBI, cognition, psychiatric, and sensorimotor factors affects balance control. The
bidirectional arrow in the theoretical frameworks demonstrates that balance control can be
considered interrelated with inter-limb coordination. ................................................................ 28
Figure 2-4: Subcomponent of theoretical framework of the sensorimotor impairments after
traumatic brain injury (TBI) that affect balance control and inter-limb coordination that will be
the focus of this dissertation. ....................................................................................................... 29
Figure 3-1: Mean force profile across trials from one participant for external perturbations in
small (S, dash) and large (L, solid) conditions (top). Experimental set up for external
perturbations (bottom). ................................................................................................................ 54
Figure 3-2: Anteroposterior (AP) cross-correlation (Rxy(0)) group means and standard error (SE)
for quiet standing (QS), and all conditions in internal and external perturbations for (A); AP
Rxy(0) individual data for external (B) and internal (C) perturbations. Mediolateral (ML) Rxy(0)
group means and SE for QS, and all conditions in internal and external perturbations for (D); ML
Rxy(0) individual data for external (E) and internal (E) perturbations. Conditions include blocked
(B) and random (R) small (S) and large (L) perturbations (BS, BL, RS, RL). ................................... 58
Figure 3-3: Means and standard error bars from first 5 (First5) and last 5 (Last5) trials in
external (A) and internal (B) perturbations for all participants (n=12). ....................................... 59
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Figure 4-1: Anteroposterior (AP) (A) and mediolateral (ML) (B) centre of pressure root mean
square (COP RMS) group means and standard error for TBI participants across recovery in eyes
open (EO; black) and eyes closed (EC; dark grey). The solid and dashed lines represent mean
and 95% confidence intervals for COP RMS in healthy controls in each condition, respectively.
....................................................................................................................................................... 84
Figure 4-2: Anteroposterior (AP) (A) and mediolateral (ML) (B) inter-limb synchrony (Rxy(0))
group means and standard error for TBI participants across recovery in eyes open (EO; black)
and eyes closed (EC; dark grey). The solid and dashed lines represent mean and 95% confidence
intervals for Rxy(0) in healthy controls in each condition, respectively. ....................................... 86
Figure 4-3: Mean and standard error of absolute stance load symmetry ratio of the less loaded
limb for TBI participants across recovery in eyes open (EO; black) and eyes closed (EC; dark
grey). The solid and dashed lines represent mean and 95% confidence intervals for the absolute
stance load symmetry ratio in healthy controls in each condition, respectively. ........................ 87
Figure 4-4: Time in unipedal stance in the left (UL; black) and right (UR; grey) leg group mean
and standard error for TBI participants across recovery (A). Anteroposterior (AP) (B) and
mediolateral (ML) (C) centre of pressure root mean square (COP RMS) group means and
standard error for TBI participants across recovery in the left (UL; black) and right (UR; grey) leg.
The solid and dashed lines represent mean and 95% confidence intervals in healthy controls in
each condition, respectively. Note that there are no dashed lines for UR in panel A. ................ 89
Figure 5-1: – Net centre of pressure mean integral of the power spectral density (PSD) in low
(A) and high (B) frequencies in the anteroposterior (AP) direction, and the low (C) and high (D)
frequencies in the mediolateral (ML) direction. Group means and standard error for TBI
participants across recovery are displayed for eyes open (EO; black) and eyes closed (EC; grey).
The solid and dashed lines represent mean and 95% confidence intervals for the net COP mean
integral of the PSD in healthy controls in each condition, respectively. ................................... 112
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Figure 5-2: Mean and standard deviation for the net centre of pressure (COP) power spectral
density (PSD) for healthy controls in the anteroposterior (AP) and mediolateral (ML) direction
for eyes open (EO and eyes closed (EC) conditions (top row). Individual data net COP PSD from
a sample of four TBI participants across recovery [T1 (black), T2 (grey), and T3 (light grey)] in the
AP and ML direction in both EO and EC conditions (row 2 to 5). .............................................. 113
Figure 5-3: Mean and standard error (SE) anteroposterior (AP) (A) and mediolateral ML (B)
inter-limb coherence in low frequencies and AP (C) and ML (D) inter-limb coherence in for high
frequencies. Group means ±SE for TBI participants are displayed for eyes open (EO; black) and
eyes closed (EC; grey) conditions across recovery. The solid and dashed lines represent mean
and 95% confidence intervals for the mean inter-limb coherence in healthy controls in each
condition, respectively. .............................................................................................................. 115
Figure 5-4: Individual limb centre of pressure (COP) mean integral of the power spectral density
(PSD) in low (A) and high (B) frequencies in the anteroposterior (AP) direction, and the low (C)
and high (D) frequencies in the mediolateral (ML) direction. Group means and standard error
for traumatic brain injury participants across recovery are displayed across time. Lower weight-
bearing (LWB; solid) and higher weight-bearing (HWB; open) limb are represented in eyes open
(EO; black) and eyes closed (EC; grey). Group means and 95% confidence intervals (depicted
with error bars) are displayed for healthy control (HC) data. ................................................... 117
Figure 5-5: Anteroposterior (AP) (A) and mediolateral (ML) (B) centre of pressure root mean
square (COP RMS) group means and standard error for TBI participants across recovery in eyes
open (EO; black) and eyes closed (EC; grey) conditions. The solid and dashed lines represent
mean and 95% confidence intervals for COP RMS in healthy controls in each condition,
respectively. ............................................................................................................................... 119
Figure 6-1: The theoretical framework of inter-limb coordination. Inter-limb coordination
emerges from the interaction of the individual, task, and the environment. [Adapted from: A.
Shumway-Cook & M. Woollacott, Motor Control: Translating Research into Clinical Practice 4th
Ed. (2012)]. ................................................................................................................................. 136
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List of Abbreviations
ANOVA – Analysis of Variance
AP - Anteroposterior
APA – Anticipatory Postural Adjustment
BOS – Base of Support
CNS – Central Nervous System
COM – Centre of Mass
COP – Centre of Pressure
EO – Eyes Open
EC – Eyes Closed
EPA – Early Postural Adjustments
HC – Healthy Controls
HWB – High Weight-Bearing
ICF – International Classification of
Function, Disability and Health
LWB – Low Weight-Bearing
ML – Mediolateral
PSD – Power Spectral Density
RMS – Root Mean Square
TBI – Traumatic Brain Injury
SMA – Supplementary Motor Area
List of Appendices
Appendix A – Relationship between inter-limb synchrony and mediolateral postural sway
variability
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Chapter 1 : Introduction
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Effective balance control requires a multifaceted interaction of neural and
musculoskeletal systems. The interaction of these two systems are responsible for generation
and coordination of forces and torques when controlling the optimal position of the centre of
mass (COM) within the base of support (BOS) during upright balance (Woollacott & Shumway-
Cook, 1996). To maintain quiet upright bipedal stance, the central nervous system (CNS)
generates muscle contractions in the lower extremities that produce adequate forces and
torque from each limb. Relatively equal force production with temporally synchronized
moments from each limb during bipedal stance demonstrates the importance of the integrity of
the CNS and its contribution to the regulation of the COM within the BOS in producing
appropriate coordination between both limbs in healthy individuals and brain injured
populations. Additionally, the ability to coordinate the upcoming decoupling of weight shifting
prior to anticipatory postural adjustments is important for postural stability. Thus, the
coordination of the lower extremities is not only important for quiet standing, but also for the
preparation and reaction for postural instability.
Though maintenance of upright balance appears to be a simple motor task, upright
balance has demonstrated to be a challenging task for individuals post-TBI (Basford et al., 2003;
Geurts, Ribbers, Knoop, & van Limbeek, 1996; Lehmann et al., 1990; Pickett, Radfar-Baublitz,
McDonald, Walker, & Cifu, 2007; Wöber et al., 1993). Traumatic brain injury (TBI) is a leading
cause of disability, which leads to multiple impairments that directly and indirectly affect
balance control (Colantonio, Croxford, Farooq, Laporte, & Coyte, 2009). Furthermore, there is
growing evidence to suggest that individuals with TBI have balance asymmetries and unilateral
motor weakness that may place these individuals at an increased risk of falling (Choi et al.,
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2012; Drijkoningen et al., 2015; Newton, 1995). Balance control is essential for mobility and
daily tasks. Advancing understanding of the underlying changes in control, such as sensorimotor
components of balance, would enhance knowledge of how weight-bearing asymmetry and
inter-limb coordination are affected after TBI.
While there exist many factors that may contribute to balance control and TBI, including
cognitive, psychiatric, sensorimotor and behavioural components, the primary goal of this
dissertation is to better understand the role of inter-limb coordination in postural control and
to determine the extent to which TBI affects inter-limb coordination. The background literature
of this dissertation begins with an overview of balance control and the inter-limb coordination
in balance control, followed by an outline of TBI and how TBI-related sequelae may impede
balance control. Finally, a description of all studies that have characterized balance after
moderate-to-severe TBI is presented.
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1.1 References
Basford, J. R., Chou, L. S., Kaufman, K. R., Brey, R. H., Walker, A., Malec, J. F., . . . Brown, A. W.
(2003). An assessment of gait and balance deficits after traumatic brain injury. Archives
of Physical Medicine and Rehabilitation, 84(3), 343-349. doi:10.1053/apmr.2003.50034
Choi, G. S., Kim, O. L., Kim, S. H., Ahn, S. H., Cho, Y. W., Son, S. M., & Jang, S. H. (2012).
Classification of cause of motor weakness in traumatic brain injury using diffusion tensor
imaging. Archives of Neurology, 69(3), 363-367. doi:10.1001/archneurol.2011.1930
Colantonio, A., Croxford, R., Farooq, S., Laporte, A., & Coyte, P. C. (2009). Trends in
hospitalization associated with traumatic brain injury in a publicly insured population,
1992-2002. Journal of Trauma, Injury, Infection and Critical Care, 66(1), 179-183.
doi:10.1097/TA.0b013e3181715d66
Drijkoningen, D., Caeyenberghs, K., Vander Linden, C., Van Herpe, K., Duysens, J., & Swinnen, S.
P. (2015). Associations between Muscle Strength Asymmetry and Impairments in Gait
and Posture in Young Brain-Injured Patients. Journal of Neurotrauma, 32(17), 1324-
1332. doi:10.1089/neu.2014.3787
Geurts, A. C., Ribbers, G. M., Knoop, J. A., & van Limbeek, J. (1996). Identification of static and
dynamic postural instability following traumatic brain injury. Archives of Physical
Medicine and Rehabilitation, 77(7), 639-644.
Lehmann, J. F., Boswell, S., Price, R., Burleigh, A., deLateur, B. J., Jaffe, K. M., & Hertling, D.
(1990). Quantitative evaluation of sway as an indicator of functional balance in post-
traumatic brain injury. Archives of Physical Medicine and Rehabilitation, 71(12), 955-
962.
Newton, R. A. (1995). Balance abilities in individuals with moderate and severe traumatic brain
injury. Brain Injury, 9(5), 445-451.
Pickett, T. C., Radfar-Baublitz, L. S., McDonald, S. D., Walker, W. C., & Cifu, D. X. (2007).
Objectively assessing balance deficits after TBI: Role of computerized postography.
Journal of Rehabilitation Research and Development, 44(7), 983-990.
Wöber, C., Oder, W., Kollegger, H., Prayer, L., Buaumgartner, C., Wöber-Bingöl, Ç., . . . Deecke,
L. (1993). Posturographic measurment of body sway in survivors of severe closed head
injury. Archives of Physical Medicine and Rehabilitation, 74(11), 1151-1156.
Woollacott, M. H., & Shumway-Cook, A. (1996). Concepts and methods for assessing postural
instability Journal of Aging and Physical Activity, 4(3), 214-233.
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Chapter 2 : Background
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2.1 Balance Control
A well-established model of balance (postural) control describes postural control as an
interdependent interaction of the individual with the task and the environment (Shumway-
Cook & Woollacott, 2012). Using this model, the organization of postural control is defined by
factors within the individual, the task, and the environment (Shumway-Cook & Woollacott,
2012) (Figure 2-1).
Figure 2-1: The theoretical framework of postural control. Postural control emerges from the
interaction of the individual, task, and environment. [From: A. Shumway-Cook & M. Woollacott,
Motor Control: Translating Research into Clinical Practice 4th Ed. (2012)]
Postural control is a complex task resulting from the combination of the interaction of
multiple sensorimotor processes and the interrelationship of multiple body segments (Horak,
2006; Shumway-Cook & Woollacott, 2012). Additionally, the reception and integration of
sensory information and the output of the motor system are critical for postural control (Lin &
Postural Control
Environment
E
T
Task
I
Individual
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Woollacott, 2005; Peterka & Loughlin, 2004; Stelmach & Worringham, 1985). The core function
of postural control is the maintenance of equilibrium, which requires the ability to maintain the
centre of mass (COM) within an individual’s base of support (BOS) (Woollacott & Shumway-
Cook, 1996). During quiet standing, the gravitational forces acting on the body are balanced by
the central nervous system (CNS) producing active forces against gravity (Horak & Macpherson,
2011). This is achieved by matching the resultant external torque with the torque of the muscle
acting around lower extremity joints (Balasubramaniam & Wing, 2002). Additionally,
maintaining balance produces minor oscillations that reflect continuous and intermittent
muscle activity (Balasubramaniam & Wing, 2002). These small oscillations are measured with
force plates through the centre of pressure (COP), which is a single point position of the vertical
ground reaction force within the BOS, and represents the average of all the pressures over the
standing surface area (Winter, 1995). During quiet upright bipedal stance, the lower extremities
generate appropriate forces and torque from each limb, from the muscle activations, to
maintain balance by keeping the COM within its BOS.
Upright bipedal stance has been modeled as an inverted pendulum that pivots about
the ankle joint (Winter, 1995). In bipedal stance, a net COP lies between both feet; however,
each foot also possesses its own distinct COP. Moreover, the net COP in single leg standing lies
within the foot in contact with the surface. Net anteroposterior (AP) COP displacements are
produced by ankle dorsiflexors and plantarflexors, while net mediolateral (ML) COP
displacements result from a combination of ankle evertors and invertors, and hip abductors and
adductors. Ankle evertors and invertors are responsible for the individual foot ML COP
displacements, while hip adductors and abductors account for the dynamic control of loading
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and unloading and have a greater contribution to the net ML COP (Winter, 1995). The net COP
continuously moves with respect to the COM to offset or prevent falling while maintaining
balance, and does so by appropriately generating forces from each limb.
During upright standing, the net COP has been typically analyzed in two domains that
provide insight to the postural sway and balance strategies and mechanisms. The most
commonly reported COP measures include spatial domain measures, which describe the
variability in the COP and depict the amplitude of postural sway (Duarte & Freitas, 2010). The
frequencies of the COP oscillations have been shown to be important as they identify postural
strategies and mechanisms (Winter, Patla, Prince, Ishac, & Gielo-Perczak, 1998; Zatsiorsky &
Duarte, 1999). The COP signal, like most other signals, can be broken down to several simple
sinusoids of different frequencies by taking the signal in the time domain and transforming it
into the frequency domain (Winter & Patla, 1997). This type of transformation is conducted by
applying a Fourier transform, where the results display the amplitude of the signal at distinct
frequencies (Winter & Patla, 1997). COP oscillations that depict postural sway has been
generally characterized as consisting of mostly low frequencies (Zatsiorsky & Duarte, 1999). In
particular, COP oscillations with frequencies less than 0.4 Hz are characterized as low frequency
components and are considered to be exploratory (Carpenter, Murnaghan, & Inglis, 2010;
Zatsiorsky & Duarte, 1999), slow migration of the COP to a reference point from the CNS
(Latash, Ferreira, Wieczorek, & Duarte, 2003), or error corrections to equilibrium (Kiemel, Oie,
& Jeka, 2006), suitable for feedforward information (Carpenter et al., 2010; Gatev, Thomas,
Kepple, & Hallett, 1999). Feedforward control of movement inherently reflects predictive or
anticipatory models of behaviour (Massion, 1992; Wolpert & Kawato, 1998). In contrast, COP
9
oscillations with frequencies greater than 0.4 Hz are characterized as higher frequency
components and have been categorized as corrective in response to temporary instability
(Singer & Mochizuki, 2015), representing reactive control mechanisms (Schinkel-Ivy, Singer,
Inness, & Mansfield, 2016).
Frequency decomposition of the COP has been conducted in neurologically impaired
populations such as individuals post-stroke (Paillex & So, 2003; Schinkel-Ivy et al., 2016; Singer
& Mochizuki, 2015; Yanohara et al., 2014), individuals with Huntington’s disease (Myklebust et
al., 2009), Down syndrome (Rigoldi, Galli, Mainardi, Crivellini, & Albertini, 2011), multiple
sclerosis (Kanekar, Lee, & Aruin, 2014), and the elderly population (Bauer et al., 2010; Kirchner,
Schubert, Getrost, & Haas, 2013; Prieto, Myklebust, Hoffmann, Lovett, & Myklebust, 1996;
Suarez et al., 2013). Specifically, individuals post-stroke demonstrate increased amplitude in the
power spectra throughout various frequency ranges in low and high frequencies (Paillex & So,
2003; Singer & Mochizuki, 2015). In addition, individuals post-stroke who demonstrate an
inability to recover from postural instability using stepping reactions produce increased power
in the COP in high frequencies (Schinkel-Ivy et al., 2016). Older adults with bilateral vestibular
hypofunction demonstrate greater spectral energy in frequencies between 0.1 to 0.78 Hz when
compared to a control group (Suarez et al., 2013). In contrast, individuals with multiple sclerosis
demonstrate a reduced proportion of frequencies produced in low frequencies when compared
to healthy controls (Kanekar et al., 2014). Individuals with Huntington’s Disease (Myklebust et
al., 2009) and healthy older adults (Prieto et al., 1996) both demonstrate increased frequencies
compared to healthy young and older adults in the frequencies bounding the 95% of the power
in the range of 0.15 – 5 Hz. Power spectra across a range of frequencies are further increased
10
when vision is omitted and the BOS is reduced in older adults (Bauer et al., 2010). Together, this
body of literature demonstrates that, in general, individuals with neurological impairments and
older adults displayed increased power spectra and/or increased frequencies during upright
stance. Thus, frequency decomposition of COP oscillations in upright stance can provide insight
into the impairments in anticipatory and reactive control mechanisms in populations that may
be at increased risk of falling such as individuals with TBI.
2.1.1 Anticipatory and Reactive Balance Control
Anticipatory balance control prepares the sensory and motor systems for postural
demands induced by self-generated movements or predictable externally-generated
perturbations in a feedforward manner (Massion, 1994; Wolpert & Kawato, 1998). Anticipatory
balance control is dependent on the physiological readiness of the CNS, also known as ‘central
set’, and is influenced by prior experiences and current context (Prochazka, 1989). Prior
experiences in addition to upcoming stimuli, enables descending commands of the CNS to
prepare and modify contextually appropriate postural responses (Horak, Diener, & Nashner,
1989; Prochazka, 1989). In anticipating postural perturbations, muscle activation can be
initiated prior to the perturbation onset and diminish subsequent instability (Cordo & Nashner,
1982; Horak, Esselman, Anderson, & Lynch, 1984). One important aspect of anticipatory control
is the ability of the CNS to modulate cortical and muscular activity for postural control
strategies (Mochizuki, Boe, Marlin, & McIlRoy, 2010). The role of the CNS in predicting the
postural perturbations that correspond to volitionally planned movements result to two
feedforward postural adjustments, early postural adjustments (EPAs) and anticipatory postural
11
adjustments (APAs) (Krishnan, Latash, & Aruin, 2012). Although EPAs and APAs contribute to
feedforward control, their timing and roles during anticipatory control are different. Recent
literature has demonstrated that EPAs are visible 400-500 ms prior to self-initiated or expected
perturbations by minimizing the effects of upcoming expected perturbations, preceding APAs
(Klous, Mikulic, & Latash, 2012; Krishnan et al., 2012). Conversely, APAs are immediately
detected 100-150 ms prior to volitional postural perturbations in postural muscles and, in turn,
COP displacements (Aruin, 2002; Aruin, Forrest, & Latash, 1998), and are responsible for
generating the forces and moments against expected perturbations (Klous et al., 2012).
The timing and the amplitude of forces produced during anticipatory control plays an
important role in the ability to coordinate between the two limbs. For example, during gait
initiation, APAs are biomechanically essential for ML stability by initially increasing the load of
the swing leg followed by the unloading forces from the swing leg to take a step (Breniere & Do,
1986; Brunt et al., 1991; Jian, Winter, Ishac, & Gilchrist, 1993). The loading and unloading from
muscular activations generates a decoupling motion of the COP and COM in order to propel the
COM forwards and towards the stance leg (Burleigh, Horak, & Malouin, 1994; Fiolkowski, Brunt,
Bishop, & Woo, 2002; Jian et al., 1993). Although APAs occur invariably in gait initiation
(Breniere & Do, 1986; Brunt et al., 1991; Brunt, Liu, Trimble, Bauer, & Short, 1999; Delval et al.,
2012; Jian et al., 1993; MacKinnon et al., 2007), they are also detected in forward and
backwards single step movements (Degani, Donna-Dos-Santos, & Latash, 2007; Gélat &
Breniere, 2000; Ito, Azuma, & Yamashita, 2003), upward leg raises (Hall, Brauer, Horak, &
Hodges, 2010), rising on the toes (Cowan, Hodges, & Bennell, 2001), and asymmetrical
perturbations (Chen, Lee, & Aruin, 2015; Mochizuki, Ivanova, & Garland, 2004). Predictable
12
bilateral and unilateral perturbations that do not require a change in BOS have demonstrated
different postural muscle activations prior to the self-initiated perturbations. Bilateral arm
raises have activated postural bilateral muscles prior to self-initiated bilateral shoulder flexion
perturbations, while unilateral arm perturbations elicit more unilateral muscle activations
(Mochizuki et al., 2004). These examples illustrate the capacity of the CNS to appropriately
synchronize and desynchronize the amplitude of forces produced by the lower extremities in
preparation for the postural task. Therefore, the coordination between the lower limbs is
critical for anticipatory feedforward postural control.
Coordination between the lower extremities is also important for reactive balance
control. Reactive balance is essential for unpredictable perturbations and considered to be a
cortical or high-level cognitive processing that control balance reactions (Jacobs & Horak, 2007;
Maki & McIlroy, 2007). Balance control strategies that occur in response to external
perturbations include the fixed-support and change-in-support strategies. Fixed-support
strategies produces stabilizing joint moments about the ankle or hip to maintain the body’s
COM within the existing BOS with feet-in-place ankle and hip strategies, respectively (Horak &
Nashner, 1996). As feet-in-place strategies require both limbs to maintain contact with the BOS,
temporally synchronized force amplitude is required for ongoing postural stability. In contrast,
change-in-support strategies evoke a hand or foot displacement to expand the size of the BOS
to recapture the COM. Because this strategy generates larger forces, it provides increased
stability (Maki & McIlroy, 1997) and is considered one’s ‘preferred’ response (Jensen, Brown, &
Woollacott, 2001; Maki & McIlroy, 1997). Unlike the invariability of APAs in volitional stepping,
APAs do not always precede perturbation evoked stepping reactions. The attenuation or
13
omission of APAs in compensatory stepping is influenced by the unpredictability of the
perturbation (McIlroy & Maki, 1993). Change-in-support stepping is rapidly generated in
comparison to that of volitional stepping and result in truncated APAs (McIlroy & Maki, 1999).
As a result of these rapid reactions, weight-bearing symmetry may alter reactive balance
control. Weight-bearing asymmetry has demonstrated to decrease stepping thresholds during
lateral perturbations when perturbations were towards the limb with greater load (de Kam,
Kamphuis, Weerdesteyn, & Geurts, 2016). Individuals with balance asymmetry from a brain
injury, such as stroke, have demonstrated impaired reactive balance control and are at
increased risk of falls (Inness, Mansfield, Bayley, & McIlroy, 2016; Inness, Mansfield, Lakhani, &
McIlroy, 2014; Mansfield, Inness, Lakhani, & McIlroy, 2012). Additionally, individuals post-
stroke demonstrated poor coordination between limbs and this has been reflective in reactive
balance control mechanisms (Mansfield, Danells, Inness, Mochizuki, & McIlroy, 2011;
Mansfield, Mochizuki, Inness, & McIlroy, 2012; Schinkel-Ivy et al., 2016). Thus, the ability to
coordinate between the lower limbs in response to unpredictable perturbations is essential to
prevent the risk of falling.
2.1.2 Inter-limb Coordination in Balance Control
Temporally synchronized force amplitude production from each limb during bipedal
stance illustrates the importance of the integrity of the CNS and its contribution to the
regulation of the COM within its BOS in producing appropriate coordination between both
limbs. In quiet standing, individuals with an intact CNS generate relatively equal vertical ground
reaction forces from each limb by distributing approximately 50% of their body weight between
14
limbs (Winter, Prince, Stergiou, & Powell, 1993). This implies that the coordination between the
lower extremities is critical for the task of upright bipedal standing. For the purpose of this
thesis, inter-limb coordination is defined as the ability to produce relatively symmetrical force
amplitudes from each limb by temporally synchronizing ankle and hip moments while standing.
Like the net COP, individual limb COP can be examined in various domains. The
neurophysiological contributions within the COP signal during bipedal stance can be examined
in spatial, temporal, and frequency domains. Spatial domain measures have been used to
characterize postural asymmetry through various computations of individual limb COP and
forces (Duarte & Freitas, 2010). Temporal domain measures provide insight into the
relationships between two signals and have been applied to the COP of each limb and the
muscle activations of different muscles (Mochizuki, Ivanova, & Garland, 2005; Nelson-Wong,
Howarth, Winter, & Callaghan, 2009; Winter et al., 1993). Specifically, temporal relationships
describe the neurophysiological synchronicity between two time-varying signals. In balance
control, this relationship has been characterized as inter-limb synchronization, a depiction of
inter-limb coordination (Nelson-Wong et al., 2009). Inter-limb synchrony is calculated as the
cross-correlation function between two signals as a function of time by iteratively shifting one
signal forwards and backwards in time over the length of the signal (Nelson-Wong et al., 2009;
Singer, Mansfield, Danells, McIlroy, & Mochizuki, 2013; Winter et al., 1993). The magnitude of
the cross-correlation function ranges from -1.0 to 1.0 and illustrates the strength of the
correlation of the COP time series. Postural control in healthy individuals illustrates high in-
phase and anti-phase inter-limb synchrony relationships in the AP and ML direction,
respectively (Winter et al., 1993) with little to no phase lags between limbs (Mochizuki et al.,
15
2005; Winter et al., 1993). During quiet standing, a reduction in the cross-correlation function
suggests a reduced relationship or coupling between the left and right limb, which in turn
would indicate poor inter-limb coordination. Neurologically impaired populations with weight-
bearing asymmetry have demonstrated reduced inter-limb synchrony (Mansfield et al., 2011;
Mansfield, Mochizuki, et al., 2012; Singer et al., 2013) and the two measures have been linked
in post-stroke individuals (Mansfield et al., 2011). This highlights the importance of weight-
bearing symmetry for inter-limb coordination. In some cases, individuals with weight-bearing
asymmetry display an offset in the relationship between the left and right foot COP, which
results in a phase lag where one COP signal ‘leads’ the other COP signal. Though
electromechanical delay exists between muscle activation and force production (Nelson-Wong
et al., 2009), the interpretation of the phase-lag between the left and right COP signals found in
individuals with weight-bearing asymmetry post-stroke remains unclear (Mansfield et al.,
2011). Accordingly, a decrease in the relationship between the left and right COP resulting in
reduced inter-limb synchrony, despite a phase-lag between the signals, would indicate poor
inter-limb coordination.
More recently, balance asymmetry and inter-limb coordination have been quantified in
the spectral domain by analyzing the frequency relationship of the COP from each limb through
coherence analysis (Myklebust et al., 2009). Analogous to inter-limb synchrony, the coherence
of two time-varying signals range from 0 to 1.0 and can also be considered a representation of
inter-limb coordination indicating the degree of correlation between signals at each frequency.
According to Myklebust et al. (2009), individual limb COP is produced by the same postural
control system by the CNS and should be highly correlated. Reduced inter-limb coherence
16
would reflect disorders in the central postural control system and/or additionally peripherally
introduced inputs or noise. Inter-limb COP coherence in healthy young and elderly individuals
was compared to individuals with Huntington’s Disease, and the study found greater coherence
in the healthy groups in comparison to the neurologically impaired population (Myklebust et
al., 2009). Thus, a reduction in inter-limb coherence, like in inter-limb synchrony, would be
indicative of poor inter-limb coordination in relation to the postural control system that drives
the frequencies that control anticipatory and reactive control mechanisms. The combination of
spatial, temporal, and spectral analysis may provide much insight to postural asymmetry and
inter-limb coordination in postural control.
2.2 Traumatic Brain Injury
2.2.1 Prevalence, Incidence, and Mechanism
Traumatic brain injury (TBI) is defined as a disruption in the normal function of the brain
as a result of blunt or penetrating trauma or from acceleration and/or deceleration forces
(Coronado et al., 2012) and is one of the leading causes of disability in North America (Centers
for Disease Control and Prevention, 2006; Colantonio, Croxford, Farooq, Laporte, & Coyte,
2009). In the United States, approximately 16% of emergency visits each year have a primary or
secondary diagnosis of TBI (Centers for Disease Control and Prevention, 2015). In Canada, it is
estimated that 3710 men and 1240 women per 100,000 who have experienced a TBI are
currently living in long-term care facilities, and the projected incidence rate of TBI over the next
20 years is expected to increase by 28% to 70 new cases per 100,000 per year (Public Health
Agency of Canada, 2014). The prevalence of adults with TBI in Canada is bimodal, with peaks in
17
young and older adults aged 20-34 and older than 75 years, respectively (Public Health Agency
of Canada, 2014). The most commonly reported mechanisms of TBI are falls, being struck by an
object, and motor vehicle incidents (Centers for Disease Control and Prevention, 2015;
Colantonio et al., 2010).
TBI occurs when the head or brain undergoes a transfer of kinetic energy through a
rapid acceleration and/or deceleration or from direct impacts to the skull, resulting in focal
coup and contre-coup injuries as well as diffuse injuries, including traumatic axonal injury
(Gaetz, 2004; Povlishock & Katz, 2005). When the brain experiences moderate-to-severe
acceleration and deceleration forces, the combination of focal and diffuse injuries is common
(Gaetz, 2004). Focal coup injuries are generally associated with acceleration trauma, where
contusions, haemorrhages and/or haematomas appear beneath the point of impact in the
either extradural, subarachnoid, subdural and intracerebral areas (Gaetz, 2004). Contre-coup
injuries are associated with acceleration and deceleration trauma and typically occur on the
opposite side of the impact when the cerebral cortex makes contact with the cranium (Gaetz,
2004; Shaw, 2002). Focal injuries result in neuronal damage at the site of injury and to the
surrounding cerebrovasculature and are considered primary injuries of TBI. Secondary injuries
from focal trauma result from ischemia, extracellular glutamate neurotoxicity, and cytotoxic
edema (Gaetz, 2004). Furthermore, the effects of acceleration and deceleration forces result in
widespread axonal tissue damage or lesions, known as diffuse or traumatic axonal injury of the
white matter (Gaetz, 2004; McGinn & Povlishock, 2015). The spectrum of TBI from focal and
diffuse axonal injury leads to cell injury, dysfunction or neuronal death with neuropathological
changes that affect neurotransmitter regulation (McGinn & Povlishock, 2015) and white matter
18
connectivity (Sharp, Scott, & Leech, 2014; Shumskaya, van Gerven, Norris, Vos, & Kessels,
2017). Consequently, TBI leads to cognitive impairments, psychiatric deficits, sensory
impairments, motor dysfunction, and behavioural changes (Iaccarino, Bhatnagar, & Zafonte,
2015; Walker & Pickett, 2007).
2.2.2 Features of Traumatic Brain Injury Influence Control of Balance
There are number of impairments that occur after TBI, including but not limited to,
cognitive deficits, psychiatric deficits, behavioural changes, motor impairments, and balance
impairments. According to the International Classification of Function, Disability and Health
(ICF), impairments are related to the physiological or structural changes of the body (de Kleijn-
de Vrankrijker, 2003; Laxe et al., 2014). Given the scope of this thesis, the current section will
describe the impairments and dysfunctions after TBI that specifically relate to balance control.
Balance impairments after TBI are commonly reported and persist many years after injury
(Ponsford et al., 2014; Walker & Pickett, 2007). Given the pathophysiology of TBI, balance
impairments in individuals with TBI likely stem from dysfunction in multiple body systems (e.g.
sensory, motor, cognitive, soft tissue) (Campbell & Parry, 2005). Though balance control may
appear to be a simple motor ability, research has recognized that balance control uses higher-
level cortical resources (Shumway-Cook & Woollacott, 2000; Varghese, Beyer, Williams,
Miyasike-daSilva, & McIlroy, 2015), and thus, can be quite challenging after TBI. Balance control
engages cortical and subcortical structures in static balance control (Varghese et al., 2015) and
dynamic balance control (Jacobs & Horak, 2007; Maki & McIlroy, 2007; Varghese et al., 2015).
Neurological insults like TBI negatively affect balance control by altering activation in brain
19
networks, compromising the integrity of white matter connections, and altering the properties
of the CNS that are responsible for muscle activation and sensorimotor feedback (Di Russo,
Incoccia, Formisano, Sabatini, & Zoccolotti, 2005; Ghajar & Ivry, 2008).
A number of studies have reported that individuals with moderate-to-severe TBI
experience a range of cognitive dysfunctions including attention deficits (Owens et al., 2017;
Shah et al., 2017), reduced concentration (Barman, Chatterjee, & Bhide, 2016; Belmont, Agar, &
Azouvi, 2009), executive function deficits (Merkley, Larson, Bigler, Good, & Perlstein, 2013;
Stuss, 2011; Zimmermann et al., 2015), memory deficits (Azouvi, Vallat-Azouvi, & Belmont,
2009; Slovarp, Azuma, & Lapointe, 2012; Vakil, 2005), and reduced speed of processing
(Dymowski, Owens, Ponsford, & Willmott, 2015; Madigan, DeLuca, Diamond, Tramontano, &
Averill, 2000; Willmott, Ponsford, Hocking, & Schonberger, 2009). The consequences of
cognitive impairments after TBI may have an implication on balance control as attention,
executive function, and visuo-spatial memory are important for balance control (Dalton,
Sciadas, & Nantel, 2016; Little & Woollacott, 2014; Muir-Hunter et al., 2014; van Iersel, Kessels,
Bloem, Verbeek, & Olde Rikkert, 2008). Studies have shown that performing cognitive tasks
while maintaining balance increased or decreased postural sway (Shumway-Cook & Woollacott,
2000; Siu & Woollacott, 2007; Vuillerme & Vincent, 2006; Woollacott & Shumway-Cook, 2002),
increased muscle latencies to perturbations (Anan, Fujiwara, Yaguchi, & Kiyota, 2014),
decreased muscle amplitudes (Rankin, Woollacott, Shumway-Cook, & Brown, 2000), and
increased reaction time for postural strategies (Remaud, Boyas, Caron, & Bilodeau, 2012) and
stepping responses (Uemura, Oya, & Uchiyama, 2013). In addition to cognitive dysfunctions,
psychiatric factors, such as depression and anxiety are also common after TBI (Bombardier et
20
al., 2010; Failla, Juengst, Graham, Arenth, & Wagner, 2016; Guillamondegui et al., 2011; Hart et
al., 2016; Jorge & Arciniegas, 2014; Khan et al., 2016; Lewis & Horn, 2017; Osborn, Mathias,
Fairweather-Schmidt, & Anstey, 2016). Depression and increased level of anxiety in other
populations have been shown to negatively impact balance control (Adkin, Frank, Carpenter, &
Peysar, 2000; Carpenter, Frank, & Silcher, 1999; Carpenter, Frank, Silcher, & Peysar, 2001;
Casteran, Putot, Pfitzenmeyer, Thomas, & Manckoundia, 2016; Sturnieks, Delbaere, Brodie, &
Lord, 2016). These cognitive and psychiatric sequelae of TBI have implications in balance
control, and in turn, may place this population at increased fall risk (McKechnie, Pryor, & Fisher,
2015). While these factors are important in fully understanding the relationship between TBI
and balance, they are not the focus of this dissertation.
Sensorimotor impairments are common after TBI (Debert, Herter, Scott, & Dukelow,
2012; Shumskaya et al., 2017) and are among the multi-systemic body impairments that
contribute to increased risk of falling after TBI (McKechnie, Pryor, & Fisher, 2017). When
function of either system is disrupted as a result of a TBI, motor impairments, including
strength imbalances between limbs and coordination deficits (Khan, Baguley, & Cameron, 2003;
Walker & Pickett, 2007) may become apparent. Unilateral motor weakness, balance
asymmetries and poor inter-limb coordination have been documented following TBI (Arce, Katz,
& Sugarman, 2004; Choi et al., 2012; Drijkoningen et al., 2015; Newton, 1995). Muscle strength
asymmetry has been associated with poor balance performance and greater step time
variability during mobility (Drijkoningen et al., 2015). Greater attention has been placed on the
implications of gait asymmetry after TBI (Ochi, Esquenazi, Hirai, & Talaty, 1999; Vasudevan,
21
Glass, & Packel, 2014; Williams, Morris, Schache, & McCrory, 2009); however, the extent of
balance asymmetry after TBI has as not yet been quantified.
2.2.3 Characteristics of Balance Control after Traumatic Brain Injury
Several studies have characterized features of balance control after TBI using a number
of different approaches. One of the first studies quantifying postural control after TBI
demonstrated that path length and radial displacement of the COP in TBI participants was
greater than healthy controls by almost two-fold. The authors reported that individuals with
greater sway in static balance assessments exhibited a tendency to fall (Lehmann et al., 1990).
Additionally, greater COP radial displacements were found post-TBI in balances tasks where
visual information was omitted suggesting that individuals with TBI have an increased reliance
of visual information than healthy controls (Lehmann et al., 1990). Importantly, this study also
identified strong test – retest reliability (0.72 – 0.99) of COP radial displacement and path
length, implying consistency of the measures and patient characteristics. Similarly, Wöber et al.
(1993) analyzed the association between the depth of brain lesions in individuals with TBI using
magnetic resonance imaging and subsequent postural imbalance. Postural imbalance was
calculated as the Chi-Square of clinical markers of severity, including the Glasgow Coma Scale
and Post Traumatic Amnesia, and posturographic measures of sway. The study found positive
correlations between the depth of brain lesions and the severity of subsequent postural
imbalance, indicating that individuals with TBI with deeper lesions were more likely to produce
greater postural sway (Wöber et al., 1993). Also, individuals with a Glasgow Coma Scale score of
less than 7 demonstrated an increased likelihood of postural imbalance (Wöber et al., 1993). In
22
a subsequent study, static and dynamic balance control was analyzed after mild to severe TBI in
various task conditions and compared to healthy controls (Geurts, Ribbers, Knoop, & van
Limbeek, 1996). Additionally, the weight shifting was analyzed with a visual feedback balance
task. The study found that after six months of injury, TBI participants had greater COP RMS in
both the AP and ML directions than healthy controls, in particular with the omission of vision.
TBI produced less weight-bearing balance tasks than healthy controls, demonstrating difficulty
with this task. Thus, these two studies demonstrated that static and dynamic balance control
were impaired six months post-TBI (Geurts et al., 1996).
Other posturographic measures of balance have been reported throughout the
literature. Basford et al. (2003) used sensory organization tests to analyze static balance in a
small sample of individuals with mild to severe TBI. Overall sensory organization tests scores
demonstrated that TBI participants who self-reported balance instability demonstrated lower
sensory organization test scores when compared to healthy controls (Basford et al., 2003), with
lower sensory organization test scores indicative of poorer balance control. Basford et al.
(2003) also demonstrated vestibular impairment in the majority of TBI participants, using
caloric irrigation tests. However, because the overall sensory organized tests scores were
reported, vestibular function during balance control remained unknown (Basford et al., 2003).
The study suggested that subtle complaints of postural instability from individuals with TBI
need further investigation (Basford et al., 2003). Balance control was assessed in individuals
with severe TBI undergoing inpatient rehabilitation using the sensory organization test and
found that in comparison to normative values, individuals with TBI had difficulty with
somatosensory and vestibular probing conditions (Pickett, Radfar-Baublitz, McDonald, Walker,
23
& Cifu, 2007). Thus, despite having good functional independence, other sensory impairments
may result in reduced postural stability after TBI.
Though cross-sectional studies provide an overview of a population at one specific time
point, studies that look at changes over time can provide insight to the recovery of balance
control. Static balance has been analyzed over a short period of time (i.e. two to six weeks)
within an inpatient rehabilitation setting in individuals with moderate-to-severe TBI (Wade,
Canning, Fowler, Felmingham, & Baguley, 1997). This study found that postural sway decreased
between the two assessments, as depicted by sway index, as a result of inpatient rehabilitation
(Wade et al., 1997). Though Wade et al. (1997) demonstrated an improvement in balance after
TBI, it remains unknown whether improvement persists over the course of recovery. Walker
and Picket (2007) studied motor impairments after severe TBI over the course of two years. The
study found that two years following acute rehabilitation, approximately one third of
individuals exhibited neuromotor abnormalities in neurological examinations and tandem gait
(Walker & Pickett, 2007). More recently, Ponsford et al. (2014) reported functional outcomes in
mild to severe TBI at three time points over a span of 10 years. In their larger cohort of 141
individuals with TBI, over 60% of individuals with TBI reported balance problems two years after
injury; however, this dropped to 40% by five years after injury and then rose once again to 55%
10 years post injury. The study found that individuals with moderate-to-severe injuries were
more likely to report balance impairment 10 years post injury in comparison to individuals with
mild TBI (Ponsford et al., 2014). Though studies such as Ponsford et al. (2014) and Walker and
Pickett (2007) examined a large sample size longitudinally, their measure to characterize
postural instability included a neurological examination (i.e. Romberg test), rather than a
24
quantitative measure that provides a more detailed characterization of the specific impairment.
Therefore, there is a need to understand balance control using quantitative measures of
balance across the course of recovery and compare to a healthy control group to understand
where along recovery they begin to resemble normative values.
In summary, few studies have analyzed posturographic measures in individuals with TBI
(Brauer, Broome, Stone, Clewett, & Herzig, 2004; Geurts et al., 1996; Lehmann et al., 1990;
Wade et al., 1997). Greater postural sway has been linked with individuals with increased TBI
severity (Wöber et al., 1993). Evidence has identified balance impairments up to 10 years post-
injury through self-report and neurological examinations (Ponsford et al., 2014), with a couple
of studies identifying impaired dynamic balance (Arce et al., 2004; Geurts et al., 1996).
Therefore, there is further need to quantify postural control across recovery in individuals with
moderate-to-severe TBI and identify the impairments of postural control mechanisms in this
population.
2.3 Rationale
As previously stated, postural control occurs from an interaction of the individual with
the task and the environment (Shumway-Cook & Woollacott, 2012). In recent years,
measurement of individual limb contributions to balance (inter-limb coordination), has been a
useful approach for characterizing balance dyscontrol and neurological asymmetries after
neurological injury (i.e. stroke) in static balance (Mansfield et al., 2011; Mansfield, Mochizuki, et
al., 2012; Singer et al., 2013), and TBI during dynamic balance tasks (Arce et al., 2004).
25
However, inter-limb coordination within other components of postural control, such as
anticipatory and reactive balance control, and after TBI during static balance tasks and has not
been considered. This represents an important gap in the literature because weight-bearing
asymmetry has been linked to inter-limb coordination measures in individuals with neurological
injuries (Mansfield et al., 2011), but has not been examined in individuals post-TBI.
Recent studies have demonstrated unilateral motor weakness (Choi et al., 2012;
Drijkoningen et al., 2015) that persists following TBI. Additionally, qualitative observations of
asymmetrical stance prior to platform perturbations (Newton, 1995) and poor inter-limb force
amplitude coordination during bimanual load lifting have been reported in individuals with TBI
(Arce et al., 2004). Drijkoningen et al. (2015) found a negative relationship between leg muscle
strength asymmetry and decreased balance stability, indicating greater asymmetrical muscle
strength was associated with poorer balance performance. Additionally, increased leg muscle
strength asymmetry was associated with increased step time variability during gait
(Drijkoningen et al., 2015). Greater emphasis has been placed on the implications of gait
asymmetry after TBI (Ochi et al., 1999; Vasudevan et al., 2014; Williams et al., 2009); however,
balance asymmetry after TBI has as yet to be quantified and the implications on balance control
are unknown. With the current evidence that motor and balance asymmetries occur after TBI,
inter-limb coordination during static balance tasks may be an important determinant in
understanding recovery of balance impairments after TBI. Therefore, postural control and inter-
limb coordination are to be considered interrelated within the Shumway-Cook and Woollacott
(2012) conceptual model, and inter-limb coordination can be assumed to have a similar
interaction between the individual, the task and the environment (Figure 2-2).
26
Figure 2-2: The theoretical framework of inter-limb coordination. Inter-limb coordination
emerges from the interaction of the individual, task, and the environment. [Adapted from: A.
Shumway-Cook & M. Woollacott, Motor Control: Translating Research into Clinical Practice 4th
Ed. (2012)]
After TBI, balance impairments are commonly reported and persist many years after
injury (Ponsford et al., 2014; Walker & Pickett, 2007). The literature on postural control has
identified the importance of cortical resources for static and dynamic control (Jacobs & Horak,
2007; Richard et al., 2017; Siu & Woollacott, 2007; Woollacott & Shumway-Cook, 2002). Though
balance impairments after TBI may arise from various mechanisms of injury, the high incidence
of focal and diffuse injuries and edema from TBI (Gaetz, 2004) have the potential to impair the
cortical structures and connective white matter, ultimately impacting postural control after
Inter-limb
Coordination
Environment
E
T
Task
I
Individual
27
injury. Furthermore, previous studies have proposed that predictive or anticipatory control is
impaired after TBI (Ghajar & Ivry, 2008), and this may transfer to balance control.
Given that inter-limb coordination is not well understood in dynamic balance tasks and
these tasks may be impaired after TBI, it is important to establish an understanding of inter-
limb coordination and dynamic balance control in healthy individuals. Thus, for the purpose of
this thesis, healthy individuals and individuals with TBI will be studied. Additionally, because of
the large gap in the literature quantifying postural control post-TBI throughout the course of
recovery using quantitative balance measures, it is imperative to advance the understanding of
these features as they relate to TBI. As previously described in the literature, cognitive,
psychiatric, and sensorimotor components affect balance control (Figure 2-3). Irrespective of
TBI, there is evidence to suggest that cognition, psychiatric, and sensorimotor factors impact
various aspects of balance (postural) control. The focus of this thesis is on the sensorimotor
components that affect balance control after TBI (Figure 2-4). Therefore, this thesis will
establish new knowledge about the control of balance and inter-limb coordination, changes in
postural control across recovery after TBI, and the mechanisms that contribute to altered
postural control following TBI.
28
Figure 2-3: Theoretical framework of the impairments after traumatic brain injury (TBI).
Irrespective of TBI, cognition, psychiatric, and sensorimotor factors affects balance control. The
bidirectional arrow in the theoretical frameworks demonstrates that balance control can be
considered interrelated with inter-limb coordination.
29
Figure 2-4: Subcomponent of theoretical framework of the sensorimotor impairments after
traumatic brain injury (TBI) that affect balance control and inter-limb coordination that will be
the focus of this dissertation.
2.3.1 Research Purpose, Questions, and Hypotheses
The overall purpose of this thesis was to better understand the role of inter-limb
coordination in postural control and to determine the extent to which inter-limb coordination is
altered in individuals following moderate-to-severe TBI. Inter-limb coordination was
characterized with temporal and spectral balance measures from each limb.
30
2.3.2 Thesis Questions
To meet the overall purpose, this thesis was divided into three studies, looking at the role of
inter-limb coordination in postural control and how it changes after a moderate-to-severe TBI.
1) How does the predictability of postural instability affect inter-limb coordination? Inter-
limb coordination, assessed using COP temporal synchronization between limbs, was
analyzed prior to bouts of temporally predictable and internally and externally applied
postural instability. It was hypothesized that (1) inter-limb synchrony would be highest
when perturbation magnitudes were predictable and of low magnitude; (2) inter-limb
synchrony would be higher in internally generated perturbations; and, (3) inter-limb
synchrony would change from the start to the end of repeated exposure to
perturbations when perturbation magnitudes were known.
2) How does balance control and inter-limb coordination change across recovery after
moderate-to-severe TBI? Inter-limb coordination and asymmetry were examined after
moderate-to-severe TBI comparing balance tasks with varying levels of difficulty. Spatial
measures of net COP and temporal measures of individual limb COP were used to
characterize asymmetry and inter-limb coordination after moderate-to-severe TBI
across recovery. It was hypothesized that (1) net balance measures would improve over
the course of recovery; (2) individuals with TBI would demonstrate postural asymmetry
in measures of asymmetry, including inter-limb synchrony, stance load symmetry, and
postural sway and time in unipedal stance; and (3) measures of postural asymmetry
would improve across recovery.
3) How do anticipatory and reactive balance control mechanisms after moderate-to-severe
TBI change during recovery? By applying spectral analysis on the individual limb COP
31
and net COP, this study informed the conceptual model about the frequencies of sway
associated with anticipatory and reactive mechanisms at different stages of recovery. It
was hypothesized that (1) spectral measures of net COP would improve across time in
individuals with TBI; (2) improvements across time in inter-limb coherence in individuals
with TBI would be detectable; and (3) the individual limb applying the most vertical
force (i.e., highest weight-bearing) would demonstrate increased COP power spectral
density and decrease across recovery.
32
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45
Chapter 3 : The effects of predictability on inter-limb postural
synchronization prior to bouts of postural instability
Habib Perez, O., Singer, J. & Mochizuki, G. (2016). The effects of predictability on inter-limb
postural synchronization prior to bouts of postural instability. Gait and Posture, 46, 167-172.
46
3.1 Abstract
Anticipatory balance control optimizes balance reactions to postural perturbations. Predictive
control is dependent on the ability of the central nervous system to modulate gain in
accordance with specific task demands. Inter-limb synchronization is a sensitive measure of
individual limb contributions to balance control and may reflect the coordination of gain
modulation in preparation for instability. The purpose of the study was to determine whether
gain modulation in advance of predictable bouts of instability was reflected in the extent of
inter-limb synchronization. Two adjacent force plates were used to collect centre of pressure
(COP) data from 12 healthy young adults (27.5 ±3.4 years). Participants prepared for internal
and external balance perturbations using a cueing paradigm with three auditory warning tones
followed by an imperative tone. Perturbations were delivered in blocked and randomized
conditions with two perturbation magnitudes (small and large). Inter-limb synchrony was
calculated using the cross-correlation function of the COP excursions from the left and right
foot for three seconds prior to perturbation onset in the anteroposterior (AP) and mediolateral
(ML) direction. Inter-limb synchrony decreased in the AP and ML directions as perturbation
magnitude became more unpredictable. The need to take a step or not knowing whether a step
was required prior to postural instability reduced ML inter-limb synchrony. No differences were
found between internal and external perturbations. Modulation of postural set was evident in
the extent of inter-limb synchrony.
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3.2 Introduction
Postural control is not an automatic process, but instead requires attentional resources
to optimize control (Kerr, Condon, & McDonald, 1985; Rankin, Woollacott, Shumway-Cook, &
Brown, 2000; Remaud, Boyas, Caron, & Bilodeau, 2012; Shumway-Cook & Woollacott, 2000; Siu
& Woollacott, 2007). Reliance on these resources for generating contextually appropriate
postural responses implies that top-down processes likely regulate balance control and are
engaged when preparing for balance responses in the event of postural instability. Anticipatory
balance control optimizes reactions to perturbations induced by self-generated movements or
for externally-generated perturbations whose characteristics are known.
Anticipatory control is dependent on the extent of central nervous system (CNS)
readiness, also known as ‘central set’ (Prochazka, 1989) and is influenced by prior experience
and current context (Horak, Diener, & Nashner, 1989). Prior experience with predictable stimuli
enables descending commands to prepare and modify postural responses (Horak et al., 1989;
Prochazka, 1989). By anticipating perturbations, postural muscle activity can be initiated prior
to perturbation onset and mitigate subsequent instability (Cordo & Nashner, 1982; Horak,
Esselman, Anderson, & Lynch, 1984). The role of set modulation is supported by studies
characterizing the cortical potentials associated with the factors of central set (i.e. the
predictability and amplitude) of the upcoming balance perturbations (Mochizuki, Boe, Marlin, &
McIlRoy, 2010; Mochizuki, Sibley, Cheung, & McIlroy, 2009; Mochizuki, Sibley, Esposito,
Camilleri, & McIlroy, 2008).
One important feature of anticipatory control is the extent to which gain modulation
affects postural control strategies. Previous work has demonstrated centre of pressure (COP)
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shifts (i.e. early postural adjustments; EPA) prior to self-initiated forward postural sway (Klous,
Mikulic, & Latash, 2012) that were distinct from anticipatory postural adjustments. EPAs
optimize postural control in preparation for volitional or reactive instability (Klous et al., 2012).
Additionally, bilateral activation of postural muscles with comparable magnitude and onset in
advance of self-initiated bilateral shoulder-flexion perturbations demonstrated a more
unilateral activation pattern for unilateral perturbations (Mochizuki, Ivanova, & Garland, 2004).
These examples illustrate the capacity of the CNS to modulate gain in accordance with task
demands by coupling or decoupling between-limb coordination as necessary given current
contextual requirements or previous experience (i.e. depending on whether the conditions
under which postural instability is experienced are known or familiar) (Jacobs & Horak, 2007).
The extent of inter-limb synchronization of COP displacements reflects the coordination of
muscle activation patterns between limbs (Winter, Prince, Stergiou, & Powell, 1993) and is a
sensitive measure of balance control (Mansfield, Danells, Inness, Mochizuki, & McIlroy, 2011;
Mochizuki, Ivanova, & Garland, 2005; Singer, Mansfield, Danells, McIlroy, & Mochizuki, 2013).
Thus, measuring the extent of between-limb coordination in advance of postural instability may
enhance understanding of the processes by which humans prepare for and optimize balance-
correcting responses.
The purpose of this study was to determine whether changes in inter-limb postural
synchronization prior to bouts of postural instability are indicative of modulation in postural
set. Consistent with studies identifying modifiability in pre-perturbation cortical activity related
to postural set alteration (Mochizuki et al., 2010) and because high inter-limb synchrony may
contribute to optimal balance control in quiet standing (Mansfield et al., 2011; Mochizuki et al.,
49
2005; Singer et al., 2013; Winter et al., 1993), we hypothesized that inter-limb synchrony would
be highest when perturbation magnitudes were predictable and of low magnitude. Conversely,
when the parameters of instability were unpredictable and required the execution of
compensatory stepping responses, we expected a reduction in inter-limb synchronization (i.e.
decoupling of between-limb synchrony). As volitional movements have an additional level of
saliency/predictability due to internal models for the intended movement, we predicted that
individuals would demonstrate better control of movement through higher levels of inter-limb
synchrony when perturbations were internally generated. Lastly, we expected that inter-limb
synchrony would change from the start to the end of repeated exposure to perturbations when
perturbation magnitudes were known, but would not differ when magnitudes were
unpredictable. Preliminary data has been presented in abstract form (Habib Perez & Mochizuki,
2014).
3.3 Methods
3.3.1 Participants
Fifteen healthy adults volunteered for the study. Three participants were excluded –
two reported to be in the first trimester of their pregnancy, while another participant did not
meet our inclusion criteria of normal/corrected visual acuity during testing. Data were collected
from twelve participants (seven males, 27.5±3.4 years, 172.7±9.6cm, 68.8±11.6kg) who
provided written, informed consent prior to the start of study. Participants completed the
Waterloo Footedness Questionnaire (Elais, Bryden, & Bulman-Fleming, 1998) to determine
lower extremity dominance. Dominant limb was defined as the limb used most frequently for
50
manipulation of object and providing support during an activity (Lakhani, Mansfield, Inness, &
McIlroy, 2011). The study was approved by the Research Ethics Board at Sunnybrook Research
Institute.
3.3.2 Instrumentation and Protocol
Two force plates (Bertec Corp., Columbus, OH, USA) were arranged with the Y-axes in
parallel and separated by 5mm. Ground reaction forces and moments from each plate were
sampled at 500Hz using a 16-bit acquisition system (Power 1401 mk2, Spike2 v7.02 software,
Cambridge Electronic Design, Cambridge, UK). External perturbations were applied using a
5.5cm aluminum rod (tipped with a dense rubber cap) attached to a load cell (MLP-300,
Transducer Techniques, Temecula, CA, USA). Load cell signals were sampled at 1000Hz and
down-sampled to align to the collection of the force channels for analysis (LVC-U5, A-Tech
Instruments Ltd., Toronto, Canada).
Each foot was positioned on a separate force plate. Foot placement was standardized
across individuals (medial aspects of each foot 0.185m apart) at the start of each trial. For
baseline measures, one 30-second quiet standing (QS) trial was collected. Participants then
were presented with a series of three auditory warning cues (1-second inter-cue interval) while
maintaining a fixed gaze on a target approximately 3m ahead of them. On the presentation of a
fourth auditory tone (a different tone for each condition), participants experienced a postural
perturbation.
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Participants completed two sessions at least one week apart, each consisting of internal
or external perturbations. Each session consisted of three conditions: a block of small (BS), a
block of large (BL) and a block of randomly presented small (RS) or large (RL) perturbations
(Table 3-1).
Table 3-1: Summary of perturbation types and their corresponding responses for each
condition in the experimental paradigm.
Perturbation Type External Internal
Conditions Response
Blocked Small BS Feet-in-place No Step
Blocked Large BL Change-in-support Step
Randomized Small RS Feet-in-place No Step
Randomized Large RL Change-in-support Step
Thirty trials of each condition were presented as a block in the S and L conditions and a set of
60 trials was presented in random order for the random condition for a total of 120 trials. Inter-
trial duration was 15 seconds and participants were given a 5-minute break in between each
condition. Internal perturbations were performed first and the order of BS and BL conditions
was randomized among participants. The random condition was always presented last to allow
familiarization with the different auditory tones associated with either the S or L conditions for
both Internal and External perturbations.
52
Internal perturbations – For the L conditions, participants were instructed to take a volitional
step in the anterior direction with their dominant foot as quickly as possible immediately after
hearing the imperative tone. For the S conditions, participants were instructed to not take a
step immediately after the imperative tone (i.e. prepare not to step).
External perturbations – External perturbations were delivered manually using an aluminum
rod, similar to previous work (Adkin, Campbell, Chua, & Carpenter, 2008). Perturbations were
applied directly over the spinous processes (approximate location T5) to reduce any torques
about the trunk. A rectangular foam pad (15.5cm x 20.5cm x 1.5cm) was placed between the
two scapulae to reduce the impact of the rod on the spinous processes. Small perturbations
associated with the S condition resulted in feet-in-place reactions, while large perturbations
associated with the L conditions resulted in stepping responses (see Figure 3-1 and Table 3-2).
Force thresholds for each participant were determined by exposing the participant to various
levels of force magnitudes in random order (i.e. calibration trials). This allowed the
experimenter to determine the force threshold for small and large perturbations, which evoked
a feet-in-place and stepping reactions, respectively. The same experimenter performed all
perturbations.
Prior to each set of blocked trials, participants were informed about the number and
magnitude of the perturbation. For the random trials, participants were informed that they
would experience either a small or large perturbation. In all conditions, participants were
instructed to “use whatever strategy you need to regain your balance”.
53
Table 3-2: Mean and standard deviation for external perturbation characteristics. Load cell
onset (LConset), load cell peak force (LCpeak), the time at which peak occurred (LCpeak(τ)), and
the percentage of the load cell peak force applied at the onset of postural activity (%LC-
COPonset) is listed.
Conditions BS BL RS RL
LConset (ms) 121 ±155 195 ±177 162 ±173 176 ±192
LCpeak (N) 87.9 ±27.8 215.0 ±61.5 108.9 ±29.6 225.4 ±57.3
LCpeak(τ) (ms) 484 ±144 598 ±165 507 ±156 570 ±165
%LC-COPonset (%) 58.0 ±18.2 47.2 ±20.3 39.9 ±15.0 39.5 ±10.2
54
Figure 3-1: Mean force profile across trials from one participant for external perturbations in
small (S, dash) and large (L, solid) conditions (top). Experimental set up for external
perturbations (bottom).
55
3.3.3 Data Analysis
Centre of Pressure – Raw data were low-pass filtered using a zero-lag, fourth-order,
Butterworth filter with a cut-off frequency of 10 Hz. Anteroposteior (AP) and mediolateral (ML)
COP under each limb were calculated. The cross-correlation function was calculated using the
left and right mean-removed AP and ML COP waveforms. Cross-correlation coefficients at zero
phase-lag (Rxy(0)) were calculated at incremental phase-shifts defined by the sampling rate, by
iteratively shifting the right limb COP forwards and backwards in time over the entire length of
the record (Singer et al., 2013), over the 3 second duration prior to perturbation (i.e. during the
3 seconds of preparation for internal or external instability). Similarly, for baseline measures of
standing balance, Rxy(0) were also calculated for quiet standing over a 30 second duration. To
determine the extent of set modulation within a condition, the AP Rxy(0) for the first five trials
(First5) and the last five trials (Last5) were analyzed. These variables were evaluated both in
internal and external perturbation conditions.
3.3.4 Secondary Analyses
For External Perturbations (Table 3-2), load cell magnitude and timing characteristics
were analyzed to confirm consistency in perturbation within each condition (i.e. S or L). Load
cell onset (LConset) was defined as the time with respect to the imperative tone at which the
load cell increased more than 0.6 N within 0.014 s, a rate of 40 N/s. Load cell peak force
(LCpeak) was taken as the maximum value and the time point which this peak occurred was
defined as LCpeak(τ). The percentage of load cell peak applied at the onset of postural activity
(%LC-COPonset) was also measured. Onset of postural activity was defined as the time point
56
where the net AP COP position was 8mm greater than the mean AP COP during the 200ms just
prior to the imperative tone (McIlroy & Maki, 1999).
3.3.5 Statistical Analysis
Data from all trials from each participant were evaluated for normality. A 4 × 2 within-
subject analysis of variance (ANOVA) was conducted to analyze the effects of Condition (BS, BL,
RS, RL) and Perturbation Type (Internal, External) on AP and ML COP measures. Separate,
paired t-tests were used to compare Rxy(0) in the AP direction in the First5 and Last5 trials for
each condition to investigate the role of set modulation within a condition. Additionally, a
paired t-test was conducted between QS and the S condition in Internal Perturbations. We
hypothesized that no differences would be found between QS and S.
To identify whether S and L perturbations were significantly different in magnitude and
temporally consistent in external perturbations, separate one-way repeated measures ANOVA
were conducted on the following load cell outcome measures: LConset, LCpeak, LCpeak(τ), %LC-
COPonset. For all ANOVAs, Greenhouse-Geisser corrected degrees of freedoms were used if the
assumption of sphericity was not met.
57
3.4 Results
3.4.1 Inter-limb Synchrony in Preparation for Instability
A significant main effect of Condition was found for Rxy(0) in the AP direction
(F2.1,23.2=5.02, p=0.014, partial η2 =.31). Bonferroni adjusted post-hoc analysis identified a
tendency towards significant differences between BS (Rxy(0)=0.83) and BL (Rxy(0)=0.78; p=0.057)
and RS (Rxy(0)=0.77; p=0.09). For ML synchrony, a significant main effect of Condition was found
for Rxy(0) (F3,33=9.32, p<0.0005, partial η2 =.46). Bonferroni adjusted post-hoc analysis revealed
that BS (Rxy(0)=-0.26) was significantly more synchronized than all other conditions (Rxy(0) range
-0.05 to -0.07; p≤0.04; Figure 3-2). The results demonstrate that inter-limb synchrony
decreased as perturbation magnitude became more unpredictable. No significant main effects
of Perturbation Type were found.
58
Figure 3-2: Anteroposterior (AP) cross-correlation (Rxy(0)) group means and standard error (SE)
for quiet standing (QS), and all conditions in internal and external perturbations for (A); AP
Rxy(0) individual data for external (B) and internal (C) perturbations. Mediolateral (ML) Rxy(0)
group means and SE for QS, and all conditions in internal and external perturbations for (D); ML
Rxy(0) individual data for external (E) and internal (E) perturbations. Conditions include blocked
(B) and random (R) small (S) and large (L) perturbations (BS, BL, RS, RL). * denotes a significant
difference in condition (p≤0.05)
Paired t-test demonstrated that inter-limb synchrony (Rxy(0)) for conditions in External
perturbations was not significantly different between the First5 and Last5 trials in BS, BL and RS
conditions (p>0.05); however, RL did demonstrate a significant reduction from the First5 to
59
Last5 trials (p=0.01; Figure 3-3A). Internal perturbations conditions showed a similar pattern as
in External perturbations, but no significance was found between the First5 and Last5 trials in
each condition (Figure 3-3B).
Figure 3-3: Means and standard error bars from first 5 (First5) and last 5 (Last5) trials in
external (A) and internal (B) perturbations for all participants (n=12). * denotes significant
paired t-test differences at p < 0.05.
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3.4.2 Quiet Standing
Participants demonstrated high AP inter-limb synchrony at zero phase lag (Rxy(0)=0.86
±0.03) and did not differ to BS (Internal). ML inter-limb synchrony at zero phase lag in QS
(Rxy(0)=-0.52 ±0.10) was significantly more synchronized than BS (Internal) (Rxy(0)=-0.23 ±0.12;
t(11)= -5.83 p<0.05.
3.4.3 Secondary Analysis
3.4.3.1 Characteristics of External Perturbations
The characteristics of the external perturbations were quantified to determine whether
S and L perturbations differed in magnitude and temporal characteristics (Table 3-2). Peak force
was significantly lower for small perturbations (BS and RS) compared to large perturbations
(F1.3,14.6=129.1, p<0.00005, partial η2 =.92); however, there was also a significant difference
between BS and RS (p<0.05). In spite of these differences, the stepping behaviors were
appropriately suited to the specific task condition. That is, all participants executed a feet-in-
place compensatory response for small perturbations in both the Block and Random conditions.
A significant main effect of Condition for %LC-COPonset (F3,33=5.50, p=0.004, partial η2 =.33)
demonstrated that random conditions required less force, with COP onset requiring significant
less force application in random conditions (RS and RL) than BS (p≤0.02).
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3.5 Discussion
This study examined whether set modulation was revealed in the extent of inter-limb
postural synchronization prior to bouts of postural instability. As hypothesized, our results
demonstrated relatively higher levels of AP and ML inter-limb synchrony when the magnitude
of the perturbation was most predictable and of low magnitude. Contrary to our hypothesis,
there were no significant differences across perturbation types (Internal vs. External) in inter-
limb synchrony during preparation.
3.5.1 Condition-dependent Differences in Preparatory COP
Information about the magnitude, timing and direction of the forthcoming perturbation
influences preparation for instability (Horak et al., 1989; Maki & McIlroy, 2007). In our study,
timing and direction were maintained across conditions while perturbation magnitude in the
forward direction remained the varying factor. Previous studies have shown responses to
instability which scale to the magnitude of the perturbation, and in addition, scale in magnitude
in parallel with the cortical activity associated with the anticipated differences in magnitude
(Mochizuki et al., 2010). Importantly, in conditions where the amplitude of forthcoming
instability was unknown, the amplitude of cortical potentials scaled in magnitude to the larger
of two perturbation amplitudes. These findings indicate that the CNS scales readiness to the
magnitude of imminent instability or to the larger of possible perturbation amplitudes. The
current study found that the randomization of the perturbation magnitudes resulted in a
decrease in the extent of inter-limb COP synchrony in comparison to when trials were blocked.
In the ML direction preparing for a step or not knowing the magnitude of the perturbation also
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elicited different preparatory COP excursions. Evidence from the perturbation force thresholds
(%LC-COPonset) demonstrated that randomized conditions required less force for individuals to
begin moving in a forward direction. Combined, these findings suggests that randomization of
perturbations reduces the threshold needed for evoking a response.
The paradigm used in this study provided cueing prior to perturbation onset, which
redirects attention to the corresponding task. Concurrent visual tracking tasks and balance
perturbations reduce the amplitude of post-perturbation cortical evoked potentials and are
associated with increased AP COP displacement and muscle activity (Quant, Adkin, Staines,
Maki, & McIlroy, 2004). Inter-limb synchrony was highest in both AP and ML directions during
BS conditions, and in comparison to QS, AP inter-limb synchrony was not different. Accordingly,
BS conditions may have required minimal additional motor tasks in comparison to other
blocked and random conditions. Thus, the increased inter-limb synchrony in BS, representing
efficient control, may be associated with lower cognitive demands and suggests that the other
conditions required more attentional resources to prepare for instability resulting in decreased
inter-limb synchrony. Alternately, the reduction in inter-limb synchrony from QS to BS and from
BS to the other conditions in the ML direction may reflect a decoupling of the COP during
movement preparation to enable EPAs to effectively contribute to balance reactions (Klous et
al., 2012). The combinations of high inter-limb synchrony in the AP direction combined with the
lower ML inter-limb synchrony may, therefore, represent the most efficient method to prepare
for unpredictable perturbations of different magnitudes. Decoupling of inter-limb synchrony
prior to a perturbation may arise as a result of the different roles of each limb during
compensatory stepping.
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3.5.2 Perturbation Type of Preparatory COP Activity
The results demonstrated that inter-limb synchrony did not differ between Internal and
External perturbations. Self-initiated movements use different neural networks during
preparation when tasks are associated with timing and choice of movements (Hoffstaedter,
Grefkes, Zilles, & Eickhoff, 2013). This may have contributed to different strategies
implemented and greater between-subject variability in inter-limb synchrony within Internal
perturbations in comparison to External perturbations (Figure 3-2C/F). Between-subject
variability may have also resulted from differences in arousal levels (Maki & Whitelaw, 1993;
Sibley, Mochizuki, Frank, & McIlroy, 2010) associated with positioning of the experimenter
behind the participant.
Similarities in the magnitude of inter-limb synchrony during preparation of postural
instability between perturbation types suggest that regardless of the mechanism that drives the
execution of movement it may be the information present in cues themselves (i.e. timing, task
requirements) that is common and salient. If the physiological importance of set is to use past
experience and current context to optimize movement, the motor task being performed may
be irrelevant. During preparation, self-generated or externally generated perturbations elicited
similar physiological characteristics (Mochizuki et al., 2009). In a different motor task, whole
body-balance disturbances paired with auditory cues elicited faster reaction times compared to
auditory cueing alone. This was maintained when whole body-balance disturbances were
omitted (Lakhani, Miyasike-Dasilva, Vette, & McIlroy, 2013). This work demonstrates that the
current context of balance instability and auditory cuing led to an adaptation of the task
64
requirements. Similarly, inter-limb synchrony may be associated with scaling of set rather than
distinct preparation for self-initiated movements.
The only difference between perturbation types was observed in the First5 and Last5
comparison in which trial-by-trial modulation was probed. During External perturbations, inter-
limb synchrony significantly decreased in the condition where perturbation magnitude was
unknown. Differences in AP inter-limb synchrony between the First5 and the Last5 trials were
observed in RL condition. No such differences were observed for Internal perturbations.
Consistent with the idea that reduced synchrony with Large perturbations allows for efficient
decoupling of bilateral control, it is possible that in the initial RL trials of External perturbations,
individuals were unaware of the extent to which decoupling should have occurred. By the time
they got to the Last5 trials, participants learned to use decoupling of inter-limb synchrony to
optimize balance responses in situations where perturbation magnitude was unknown. In
contrast, overt motor planning for the stepping response for internally generated perturbations
may have already used decoupling of the limbs within the first few trials.
3.6 Conclusions
In examining whether set modulation is evident in changes in inter-limb synchronization
prior to bouts of postural instability, it was determined that inter-limb synchrony decreased as
perturbation magnitude became more unpredictable. Moreover, larger or unpredictable
perturbations reduced preparatory ML inter-limb synchrony. These findings identify
65
characteristic adaptations to control bilateral activity in preparation for instability and provide
important insight into preparatory postural control.
66
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Singer, J. C., Mansfield, A., Danells, C. J., McIlroy, W. E., & Mochizuki, G. (2013). The effect of
post-stroke lower-limb spasticity on the control of standing balance: Inter-limb spatial
and temporal synchronisation of centres of pressure. Clinical Biomechanics, 28(8), 921-
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Chapter 4 : Characterization of balance control after
moderate-to-severe traumatic brain injury: A longitudinal
recovery study
Habib Perez, O., Green, R. E. & Mochizuki, G. Characterization of balance control after
moderate-to-severe traumatic brain injury: A longitudinal recovery study. Under Review in
Physical Therapy
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4.1 Abstract
Background: Balance impairments after traumatic brain injury (TBI) are common and persist
after injury. Postural asymmetries in balance have been reported, but not quantified across
recovery.
Objective: To characterize balance recovery after moderate-to-severe TBI with a focus on
postural asymmetry.
Methods: A secondary analysis of longitudinal balance data from 45 participants with
moderate-to-severe TBI was conducted. Balance was assessed with force plates at
approximately two, five and twelve months post-injury in two bipedal stances and two unipedal
stances for TBI participants. Single-visit data from age-matched healthy control was collected
for visual comparison. Spatial and temporal centre of pressure (COP) measures were calculated
from force plates in the anteroposterior (AP) and mediolateral (ML) directions.
Results: Despite improvements in net ML COP postural sway from two-to-five months post
injury, there were no changes across recovery in AP postural sway. Postural sway in TBI was
greater than normative values at all time points in both directions. Inter-limb synchrony did not
change across recovery in either direction. TBI weight-bearing asymmetry was lower than
normative values at all time points and did not change across recovery. The characteristics of
unipedal stance differed between limbs.
Conclusions: The absence of recovery in ML COP postural sway, inter-limb synchrony, and
weight-bearing symmetry indicate that ML control may contribute to balance impairments after
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TBI. These impairments may extend to dynamic balance tasks and may also place individuals
with TBI at higher risk of falls.
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4.2 Introduction
Traumatic brain injury (TBI) is among the leading causes of disability in North America
(Colantonio, Croxford, Farooq, Laporte, & Coyte, 2009). Balance control, defined as the ability
to maintain one’s center of mass within the base of support, is often impaired after TBI (Walker
& Pickett, 2007). These impairments persist years after injury, particularly in individuals with
more severe TBIs (Ponsford et al., 2014). Previous studies of balance control after moderate-to-
severe TBI have characterized balance impairments at different stages of recovery or have
employed cross-sectional comparisons of balance measures to those of healthy controls or to
published normative values. These studies have found impairments in balance at the acute and
chronic stages post injury (Basford et al., 2003; Geurts, Ribbers, Knoop, & van Limbeek, 1996;
Lehmann et al., 1990; Pickett, Radfar-Baublitz, McDonald, Walker, & Cifu, 2007; Wade, Canning,
Fowler, Felmingham, & Baguley, 1997; Wöber et al., 1993).
Longitudinal examinations of balance impairment after injury have been sparse. One
study showed that improvements in postural sway are observed within the first months of
injury (Wade et al., 1997). To our knowledge, in the only longitudinal balance study beyond the
early recovery phase, postural stability was examined over a two-year recovery window using
the Romberg test (Walker & Pickett, 2007) rather than quantitative analysis of posturographic
measures. While the authors (Walker & Pickett, 2007) reported an improvement in the number
of individuals who completed the Romberg test post-injury, no longitudinal studies employing
posturographic outcomes to quantitatively measure balance impairment have been undertaken
beyond the first months of injury (Wade et al., 1997). Thus, it remains unclear whether
73
continued balance recovery might be observed in the later stages of recovery with more
sensitive outcome indices.
Typical posturographic analyses of balance report net measures of control (i.e.,
contributions of both limbs) as their primary outcome (Brauer, Broome, Stone, Clewett, &
Herzig, 2004; Geurts et al., 1996; Wade et al., 1997). Net measures of balance identify postural
sway metrics in the anteroposterior (AP) and mediolateral (ML) directions, reflecting ankle and
hip motion control, respectively (Winter, 1995). Although net postural sway quantifies overall
balance control, it lacks specificity with regard to the individual limb contributions to control
processes (Winter, Prince, Stergiou, & Powell, 1993). This is especially important considering
evidence that unilateral motor weakness and asymmetrical stance persist following TBI (Choi et
al., 2012; Drijkoningen et al., 2015; Newton, 1995). The extent of asymmetry specific to balance
control has not yet been quantified. Because TBI may contribute to asymmetrical balance,
which is linked to elevated fall risk in other neurologically-injured populations (Keenan, Perry, &
Jordan, 1984; Mansfield, Mochizuki, Inness, & McIlroy, 2012), it is important to characterize
recovery trajectories in the context of asymmetry.
Balance asymmetry has been investigated in neurologically impaired populations, such
as stroke, where unilateral weakness/impairment is common (Keenan et al., 1984). Between-
limb centre of pressure (COP) synchronization (the spatial and temporal relationships between
the COP of both feet) is a sensitive measure of balance control during static standing in healthy
adults (Mochizuki, Ivanova, & Garland, 2005; Winter et al., 1993) and in adults after stroke
(Mansfield, Danells, Inness, Mochizuki, & McIlroy, 2011; Mansfield et al., 2012; Singer,
Mansfield, Danells, McIlroy, & Mochizuki, 2013; Singer & Mochizuki, 2015). In stroke patients,
74
synchronization is reduced and delayed during standing balance tasks (Mansfield et al., 2011).
In addition, single leg standing identifies postural asymmetry that may be an indicator of
unilateral weakness or impairment. Because both bipedal and unipedal stances are used in
functional balance assessments in other neurologic populations across recovery to identify
underlying balance deficits (Inness et al., 2011; Mancini & Horak, 2010), they may also identify
postural asymmetries after TBI.
The purpose of this study was to characterize the recovery trajectory of balance control
following TBI with a specific focus on measures of postural asymmetry. It was hypothesized that
(1) net balance measures would improve over the course of recovery; (2) individuals with TBI
would demonstrate postural asymmetry in measures of asymmetry, including inter-limb
synchrony, stance load symmetry, and postural sway and time in unipedal stance; and (3)
measures of postural asymmetry would improve across recovery.
4.3 Methods
4.3.1 Participants
The experimental group (TBI participants) was drawn from the database of The Toronto
Rehab Traumatic Brain Injury Recovery Study (Christensen et al., 2008; Till, Colella, Verwegen, &
Green, 2008), a prospective longitudinal study of cognitive and motor recovery, for which
motor assessments were undertaken at approximately 2, 5 and 12 months post-injury. The
study was carried out on a large, urban acquired brain injury program. Inclusion criteria for the
Recovery Study included moderate-to-severe TBI as indicated by a Glasgow Coma Scale (GCS) of
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12 or less and/or post traumatic amnesia (PTA) of 1 day or more and/or positive neuroimaging
findings; out of PTA by 3 months post-injury; aged between 17 and 75; able to use at least one
upper extremity; and, functional command of English. Exclusion criteria for the larger study
included: past history of TBI and history of psychotic or neurological illness. An additional
inclusion criterion for the current study was the completion of a motor assessment at each time
point. Over the course of the Recovery Study, different technologies were used to assess
balance. Initially, assessments were conducted on a single force plate. Later in the Recovery
Study, assessments were conducted on dual (adjacent) force plates in order to obtain data from
each individual limb. Consequently, an exclusion criterion for the current study was the use of
mixed force plate techniques (i.e. single and dual force plates) across different time points for
an individual participant. Another exclusion criterion was the presence of lower extremity
orthopedic injury.
Ninety-seven participants with clinically-confirmed TBI completed instrumented balance
assessments, 51 of whom completed all three balance assessments using either a single or dual
force plate approach. Six of these individuals were excluded for lower extremity orthopedic
injuries. Therefore, 45 TBI participants were included in the analysis, with 14 and 31
participants completing balance assessments post-injury on single and dual force plates,
respectively. An age-matched group of 22 healthy control (HC) participants were recruited from
the university and local community and assessed on one occasion. All participants (or their
substitute decision maker) provided informed consent for the original study. The current study
was approved by the Research Ethics Board at the Toronto Rehabilitation Institute.
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4.3.2 Study Design
The study employed a secondary analysis of prospectively collected data. Within-
subjects analysis enabled characterization of recovery over time. The primary balance-related
outcome measures included root mean square (RMS) of the net (both feet combined) COP in
the AP and ML directions, cross-correlation of the COP in the AP and ML directions (all in
bipedal stance), and unipedal stance time and RMS in unipedal stance.
4.3.3 Data Collection and Procedures
Posturographic (force plate) and clinical measures of balance (The Community Balance
and Mobility Scale [CB&M]) (Howe, Inness, & Wright, 2011) were assessed at approximately 2-
months (T1), 5-months (T2), and 12-months (T3) post injury. Data were collected using either a
single force plate (AccuSway, Advanced Mechanical Technology, Inc. (AMTI)) or dual force plate
(AMTI, Watertown Massachusetts) arrangement.
For the single force plate assessments, participants placed both feet on one force plate.
For the dual force plates, the plates were positioned with the Y-axes in parallel and separated
by 1 mm, with participants placing one foot on each force plate. For both the single and dual
force plate arrangements, participants stood in two bipedal and two unipedal conditions:
standardized position (McIlroy & Maki, 1997) with eyes open (EO) and eyes closed (EC), and
unipedal stance with the right leg (UR) and the left leg (UL). For all bipedal conditions,
participants were instructed to stand quietly for 45 seconds looking straight ahead. For
77
unipedal stances, participants were instructed to maintain their balance without touching the
ground as best as possible for 30 seconds. Ground-reaction forces and moments from each
plate were sampled at 50 Hz for all TBI participant data. The sampling rate for all TBI
assessments was kept consistent regardless of whether the single or dual force plate
arrangement was used. HC data were collected at one time point for all balance tasks for visual
comparison.
Comprehensive neuropsychological assessments were conducted, including measures of
attention and executive function (Trails Making Tests [TMT] A and B, respectively) (Lezak,
1995), Symbol Digit Modalities Test (SDMT) oral (Smith, 1982), and estimated pre-morbid IQ, as
measured by the Wechsler Test of Adult Reading (WTAR) (Wechsler, 2001). In the current
study, WTAR was included for patient characterization. The TMT and SDMT, two highly
sensitive TBI measures, were employed to demonstrate the presence of cognitive impairment
and to provide evidence of cognitive recovery (Perianez et al., 2007; Reitan & Wolfson, 1985).
4.3.4 Data Processing and Analysis
Ground-reaction forces and moments were low-pass filtered using a 4th ordered dual-
pass Butterworth filter with a 10 Hz cut off frequency prior to processing. In bipedal stance, the
net COP was calculated. The root mean square (RMS) of the AP and ML COP time series was
calculated for all participants. For bipedal stance, inter-limb synchrony was measured by
calculating the cross-correlation function between the left and right mean-removed AP and ML
COP waveforms. Inter-limb synchrony was defined by the cross-correlation coefficients at zero
78
phase-lag (Rxy(0)), which were calculated at incremental phase-shits defined by the sampling
rate, by iteratively shifting the right limb COP forwards and backwards in time over the entire
length of the record (Singer et al., 2013). The magnitude of the coefficient (ranging from -1.0 to
1.0) illustrates the strength of correlation of the COP time series.
Weight-bearing symmetry was calculated using two measures. Stance load symmetry
was defined as a ratio of the vertical force applied onto one force plate, relative to the sum of
the vertical forces applied onto both force plates. This was determined for both the left and
right side. Absolute stance load symmetry was defined as the stance load symmetry ratio of
the less-loaded limb (regardless of whether it was the left or right side) (Mansfield et al., 2011).
Unipedal stance time and RMS were used to characterize asymmetry in weakness and
postural instability, respectively. Time in unipedal stance was calculated as the time from foot
lift off to the first drop down contact if the participant could not sustain the full 30 seconds.
RMS during unipedal stance was only calculated for the duration over which participants
maintained a unipedal standing position. All balance data were processed using MATLAB
R2014a (MathWorks, Natick, USA).
4.3.5 Statistical Analyses
Statistical analyses were performed using SPSS v23.0 (IBM SPSS Statistics, Armonk, USA).
Descriptive and exploratory analyses of changes in attention and executive function over time
in the TBI group were conducted using a one-way analysis of variance (ANOVA). To test the
hypothesis that net measures of balance (net AP and ML COP RMS) would improve across
79
recovery for TBI participants, a series of 3×2 two-way ANOVA were conducted to assess the
main effects of Time (T1, T2, T3) and Condition (EO, EC). A one-way ANOVA was used to probe
the effect of Time on a clinical and complementary measure of balance (the CB&M) in the TBI
group. To test the hypothesis that postural symmetry would improve across recovery, a series
of 3×2 two-way ANOVA was conducted to analyze the effects of Time and Condition on features
of the Rxy(0) in the AP and ML direction. Because the Rxy(0) values are bounded by values of -1
and 1, a Fisher transform was applied. Hypothesis testing for improvement in postural
symmetry over time also included paired t-tests to determine whether stance load symmetry
was different between the left and right foot at each time point in TBI participants and a 3×2
two-way ANOVA was conducted to analyze the effects of Time and Condition on absolute
stance load symmetry. The final probe of the hypothesis of improved symmetry over time, as
measured by unipedal standing duration and AP and ML RMS, were a series of 3×2 within-
subjects ANOVAs with Time (T1, T2, T3) and Leg (Left, Right) as factors.
Data were log-transformed if the values were not normally distributed. In cases where
the assumption of sphericity was not met, Greenhouse-Geisser values were reported. Statistical
significance was set at p<0.05.
4.3.6 Secondary Analyses
Additional exploratory analyses were conducted to test relationships between: inter-
limb synchrony and postural sway and inter-limb synchrony and absolute stance load symmetry
80
in individuals with TBI. Spearman correlations were conducted between Rxy(0) and COP RMS
and absolute stance load symmetry (Mansfield et al., 2011).
4.4 Results
4.4.1 Demographic Characteristics
All demographic information for TBI participants and HC are listed in Table 4-1. TBI
participants were assessed on average at 64.7 (±28.1), 156.2 (±36.5), and 401.5 (±60.6) days
post injury at T1, T2 and T3, respectively. There was no significant difference in age between
TBI and HC participants (t(51.0) = 1.7, p>0.05, d = 0.42) and there were no significant
differences in mass between the HC group and TBI group at T1 (t(65) = 1.3, p>0.05, d = 0.34). A
significant increase in mass within the TBI group across Time (F1.3,61.5 = 42.6, p<0.001, partialη2 =
.49) from T1 to T2 (p<0.001) and T2 to T3 (p<0.001) was observed. The HC group completed
more years of education in comparison to TBI participants (t(64) = -3.0, p=0.004, d = 0.77).
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Table 4-1 – Participant demographics of traumatic brain injury (TBI) and healthy control (HC)
participants. Demographics of TBI participants for single and dual force plate collections are
also listed. TBI severity was characterized by the lowest post-traumatic amnesia (PTA) scale and
the number of TBI participants in each category is described below. The Community Balance
and Mobility (CB&M) scale is listed for the TBI group. Cognitive function includes years of
education, Wechsler Test of Adult Reading (WTAR), Trails Making Tests (TMT) A and B, and
Symbol Digit Modalities Test (SDMT).
TBI HC
Force plate
arrangement
Single & Dual
(all participants)
Single Dual Dual
Sample size 45 (36 M/9 F) 14 (12 M/2 F) 31 (24 M/7 F) 22 (10 M/12 F)
Age 43.2 (±17.3) 36.6 (±13.8)
Mass (kg)
T1 74.1 (±13.5) 69.6 (±12.8)
T2 78.7 (±14.3) * --
T3 82.0 (±15.9) *# --
Severity (PTA)
Moderate 1 0 1 --
Severe 9 3 6 --
Very Severe 23 7 16 --
Extremely Severe 7 3 4 --
CB&M Scale
T1 60.8 (±20.3)
T2 71.3 (±18.0)*
T3 74.2 (±16.7)*
Cognitive Function
Years of education 14.5 (±3.2)☨ 17 (±3.4)
WTAR 114.6 (±9.1) --
TMT A (t-score)
T1 41.5 (±10.7) 35.3 (±8.1) 44.2 (±10.7) --
T2 47.4 (±13.3)* 37.9 (±8.5) 51.7 (±12.9) --
T3 48.8 (±14.3)* 39.2 (±9.3) 53.0 (±14.1) --
TMT B (t-score)
T1 47.3 (±14.5) 42.6 (±11.0) 49.6 (±15.5) --
T2 49.7 (±13.7) 41.9 (±8.1) 53.4 (±14.3) --
T3 50.9 (±12.5) 44.2 (±9.1) 53.9 (±12.8) --
SDMT oral
T1 47.8 (±16.2) 40.5 (±11.9) 51.0 (±16.9) --
T2 52.5 (±17.3) * 43.2 (±13.8) 56.6 (±17.3) --
T3 57.1 (±20.0) *# 46.8 (±17.2) 62.7 (±19.7) --
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*denotes significant differences within TBI participants at p<0.05 from T1 value. #denotes
significant differences within TBI participants at p<0.05 from T2 value. ☨ denotes significant
differences between HC and TBI at p<0.05. Note that PTA severity was obtained from 40 of the
45 TBI participants. TMT A, TMT B and SDMT oral was obtained from 42, 41 and 42 TBI
participants respectively. WTAR was obtained from 28 TBI participants at 12 months post
injury.
4.4.2 Cognitive Characteristics
Data for the WTAR, TMT and SDMT oral are presented in Table 4-1. There was a
significant effect of Time for the TMT A (F2,82 = 14.2, p<0.001, partialη2 = .26) and the SDMT oral
(F2,82 = 30.0, p<0.001, partialη2 = .42), with improvements across recovery for both measures.
Bonferroni adjusted post-hoc analysis identified that TMT A significantly increased from T1 and
T2 (p=0.001) and T1 and T3 (p<0.001). Bonferroni adjusted post-hoc analysis demonstrated that
SDMT oral significantly increased from T1 and T2 (p<0.001) and from T2 and T3 (p=0.001). No
significant change across Time was observed on the TMT B measure.
4.4.3 Clinical Measures of Balance
Analysis of the CB&M scale identified a significant effect of Time (F1.6,62.0 = 31.8, p<0.05,
partial η2 = .46). Bonferroni post-hoc tests identified that the improvements in clinical balance
measures were statistically significant only between T1 and T2 (p<0.001) and between T1 and
T3 (p<0.001). There was no statistically significant difference between T2 and T3 (p>0.05).
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4.4.4 Net Measures of Balance
4.4.4.1 Bipedal COP RMS
The 3×2 ANOVA demonstrated that there were no changes across recovery in AP RMS;
however, ML RMS decreased across time. There was a significant within-subjects reduction in
the measure of ML RMS across Time (F1.6,66.7=4.32, p=0.024, partial η2 = .10) for TBI participants.
Bonferroni adjusted post-hoc analysis identified a significant reduction for the ML RMS from T1
to T2 (p=0.015). There was a significant Condition effect in AP RMS, with TBI participants
producing greater RMS in EC compared to EO (F1,42=55.0, p<0.001, partial η2 = .57). Figure 4-1
demonstrates the results for TBI participants and normative ranges of HC. AP and ML RMS for
TBI participants fell outside the HC ranges at all time points.
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Figure 4-1 – Anteroposterior (AP) (A) and mediolateral (ML) (B) centre of pressure root mean
square (COP RMS) group means and standard error for TBI participants across recovery in eyes
open (EO; black) and eyes closed (EC; dark grey). The solid and dashed lines represent mean
and 95% confidence intervals for COP RMS in healthy controls in each condition, respectively.
Asterisks denotes significant difference across Time (T1 and T2) for TBI participants at p<0.05.
85
4.4.5 Measures of Asymmetry
4.4.5.1 Inter-limb COP Temporal Synchrony
Inter-limb synchrony did not change across recovery in either the AP or ML direction;
there were no significant effects of Time for AP Rxy(0) and ML Rxy(0) (Figure 4-2A and B, p>0.05).
A significant Condition effect was found in AP Rxy(0) (F1,27=14.9, p=0.001, partial η2=.36), and
approaching significance for ML Rxy(0) (p=0.058). AP Rxy(0) in EC was significantly higher than in
EO. AP inter-limb synchrony in TBI participants was within normal ranges of HC; however, mean
ML inter-limb synchrony in TBI participants was outside the HC ranges in EO.
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Figure 4-2 – Anteroposterior (AP) (A) and mediolateral (ML) (B) inter-limb synchrony (Rxy(0))
group means and standard error for TBI participants across recovery in eyes open (EO; black)
and eyes closed (EC; dark grey). The solid and dashed lines represent mean and 95% confidence
intervals for Rxy(0) in healthy controls in each condition, respectively.
87
4.4.5.2 Weight-bearing Symmetry
Paired t-tests within the TBI participants demonstrated that there were no stance load
asymmetries between the left and right limbs (p>0.05) at each time point. Absolute stance load
symmetry did not significantly change across recovery for TBI participant (p>0.05) and there
were no significant differences between EO and EC (p>0.05). However, TBI participants at all
time points fell outside HC ranges in both EO and EC across their recovery (Figure 4-3).
Figure 4-3 – Mean and standard error of absolute stance load symmetry ratio of the less loaded
limb for TBI participants across recovery in eyes open (EO; black) and eyes closed (EC; dark
grey). The solid and dashed lines represent mean and 95% confidence intervals for the absolute
stance load symmetry ratio in healthy controls in each condition, respectively.
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4.4.5.3 Unilateral Stance
Of the 31 participants whose data were collected on dual force plates, two participants
were unable to complete unipedal stance at T1 and were excluded from the within-subjects
ANOVA analyses. Unilateral stance duration demonstrated asymmetries in TBI participants
(Figure 4A). A significant main effect of Time (F2,56=10.1, p<0.001, partial η2 = .27) and Leg
(F1,28=6.3, p=0.018, partial η2 = .18) was observed. The left leg maintained a significantly longer
duration in unilateral stance in comparison to the right leg (p=0.018). Bonferroni adjusted post-
hoc analysis demonstrated that TBI participants significantly improved unilateral stance
duration between the first two time points (p<0.001); however, time in unipedal stance showed
a trend towards deteriorating at T3 (p=0.067). Unipedal stance duration in TBI participants fell
outside HC ranges throughout their recovery. Asymmetries in TBI participants were not
identified in unilateral stance RMS. The 3×2 within-subjects ANOVA revealed no significant
main effects of Time or between Leg in AP and ML RMS in TBI participants (p>0.05; Figure 4-4B
and 4-4C).
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Figure 4-4 – Time in unipedal stance in the left (UL; black) and right (UR; grey) leg group mean
and standard error for TBI participants across recovery (A). Anteroposterior (AP) (B) and
mediolateral (ML) (C) centre of pressure root mean square (COP RMS) group means and
standard error for TBI participants across recovery in the left (UL; black) and right (UR; grey) leg.
The solid and dashed lines represent mean and 95% confidence intervals in healthy controls in
each condition, respectively. Note that there are no dashed lines for UR in panel A. Asterisk in
(A) denotes significant difference between T1 and T2 in TBI participants
4.4.6 Secondary Analyses
Exploratory analyses of Rxy(0) were conducted with COP RMS and absolute stance load
symmetry to test relationships between these measures. Reduced AP Rxy(0) was significantly
associated with greater ML RMS across all three time points, with the strongest relationship at
T1 (rs = -0.55 p = 0.002; see Table 4-2). Increased ML Rxy(0) was significantly correlated to lower
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ML RMS at T1 and T2. There were no statistically significant relationships between AP COP RMS
with either AP or ML Rxy(0). Absolute stance load symmetry did not demonstrate any statically
significant relationships with AP or ML Rxy(0). Scatter plots for significant relationships are
shown in Appendix A.
Table 4-2 – Relationship between inter-limb synchronization Rxy(0) and standing balance
measures (centre of pressure root mean square (COP RMS) and absolute stance load symmetry)
for eyes open. Spearman correlation coefficients and the associated p-values are shown in
parentheses. Statistically significant correlations and p-values are highlighted in bold font.
AP Rxy(0) ML Rxy(0)
AP COP RMS
T1 0.082 (0.67) - 0.053 (0.79)
T2 0.27 (0.14) - 0.17 (0.36)
T3 0.14 (0.46) - 0.19 (0.31)
ML COP RMS
T1 - 0.55 (0.002) 0.49 (0.007)
T2 - 0.40 (0.027) 0.49 (0.005)
T3 - 0.40 (0.026) 0.28 (0.13)
Absolute stance load symmetry
T1 0.20 (0.30) - 0.22 (0.25)
T2 0.10 (0.58) - 0.22 (0.25)
T3 0.28 (0.13) - 0.009 (0.96)
4.5 Discussion
This study characterized balance control across recovery after TBI with a focus on
quantifying postural asymmetry. As hypothesized, improvements were observed from two to
five months of injury in the ML direction; however, in contrast to the study hypotheses, net
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measures of balance in the AP direction did not improve across recovery in individuals with TBI.
Individuals with TBI demonstrated postural asymmetry in comparison to normative data ranges
of absolute stance load asymmetry, reduced ML inter-limb synchrony, and lower unipedal
stance time. In contrast to the study hypothesis, postural asymmetry measures did not improve
across recovery. Overall, the findings indicated that some balance impairments do improve
over the course of recovery after moderate-to-severe TBI from 2 to 5 months post-injury,
though not beyond.
4.5.1 Balance Improvements Within the First Five Months after TBI
Overall ML balance control, as measured by ML RMS, improved from T1 to T2 post-
injury. Additionally, functional balance and mobility (CB&M) improved from T1 to T2, but
plateaued from T2 to T3. Consistent with previous studies (Wade et al., 1997; Walker & Pickett,
2007), the greatest improvements in balance control occurred within the first five months after
TBI. Net ML postural sway is controlled by hip abductors and adductors, and reflects the ability
to load and unload the vertical force from one limb to another (Winter et al., 1993). The
reduction in ML sway from two months to five months after injury suggests that the CNS may
prioritize the reduction ML RMS to increase stability and reduce risk of falls (Maki, Holliday, &
Topper, 1994). Tightened AP postural sway resulting from increased ML sway has been
reported after the onset of Parkinson’s disease (Mitchell, Collins, De Luca, Burrows, & Lipsitz,
1995), and increased ML sway has been associated with a reduction in occipital and cerebellar
brain volume in individuals with right cerebral artery infarctions (Manor et al., 2010). The
overlap in balance impairments between different neurologically impaired populations
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indicates that reduced balance control in the ML direction may be a common consequence of
neurological injury. The current study found greater AP and ML RMS values than Geurts et al.
(1996), but similar values to that of Brauer et al. (2004) and may be attributable to
comparability of TBI severity used in the studies.
Duration in unipedal stance recovered in individuals after TBI. Time in unipedal stance
increased by approximately 10 seconds from T1 to T2, suggesting improvements in strength
and postural steadiness in both limbs (Jonsson, Seiger, & Hirschfeld, 2004). Unipedal stance is a
common task that is included in various clinical balance assessments and is associated with
increased fall risk (Hurvitz, Richardson, Werner, Ruhl, & Dixon, 2000; Springer, Marin, Cyhan,
Roberts, & Gill, 2007). Previous studies looking at unipedal stance duration after moderate-
severe TBI report reduced duration in patients with chronic TBI (Williams & Morris, 2011) and
similar unipedal stance time to that of T2 of the current study (McFadyen, Swaine, Dumas, &
Durand, 2003). Unipedal stance time is associated with mobility (Williams & Morris, 2011), and
thus may be an important assessment during the course of recovery.
In contrast to our hypotheses, measures of balance control focusing on postural
asymmetry in bipedal stance did not show changes across recovery. Though inter-limb
synchrony in the AP and ML direction did not improve post-injury, significant relationships were
found between inter-limb synchrony and ML RMS. The secondary analyses of AP and ML inter-
limb synchrony and ML COP RMS suggest that inter-limb synchrony was linked to ML postural
sway. Similar findings have been noted in individuals post stroke (Mansfield et al., 2011).
Absolute stance load symmetry also did not improve across recovery. Unlike the previous study
(Mansfield et al., 2011), the present study did not find a relationship between inter-limb
93
synchrony and weight-bearing asymmetry. These variables are sensitive to postural asymmetry
in neurological populations with hemiparesis (Mansfield et al., 2012) and spasticity (Singer et
al., 2013); however, research has shown similar results with initial improvements in the first six
months, followed by plateau at 12 months and a subsequent decline by 24 months post-stroke
(Singer, Nishihara, & Mochizuki, 2016). The commonalities between the current and previous
studies suggest that improvements within the first five months of recovery in overall balance
and unipedal stance time corresponds and may be primarily attributed to inpatient and
outpatient rehabilitation (Wade et al., 1997) and/or spontaneous recovery (Nudo, 2013).
4.5.2 Asymmetry after TBI is evident
This study found that weight-bearing asymmetry exists after TBI, with the least
vertically-loaded foot bearing only 44% of total body weight. In comparison to that of HC (48%),
absolute stance load symmetry was lower in individuals with TBI at all time points, indicating a
subtle but substantial postural asymmetry. Importantly, absolute stance load symmetry is lower
in individuals post-stroke ranging from 40-45% (Mansfield et al., 2011; Mansfield et al., 2012;
Singer et al., 2013; Singer et al., 2016). Weight-bearing asymmetry may reduce postural stability
for dynamic balance control (de Kam, Kamphuis, Weerdesteyn, & Geurts, 2016). Though
previous studies have observed postural asymmetry after TBI prior to postural perturbations
(Newton, 1995), in static balance through a greater lateral COP position (Lehmann et al., 1990),
and during bimanual lifting (Arce, Katz, & Sugarman, 2004), the extent of asymmetry after TBI
has not been quantified. Postural asymmetry after TBI underscores the balance deficits that
exist in this population.
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Cross-correlation analysis (Rxy(0)) revealed poor-to-good inter-limb synchrony. ML inter-
limb synchrony in individuals with TBI fell outside of the HC ranges, similar to observations in
individuals post-stroke (Mansfield et al., 2011; Mansfield et al., 2012; Singer et al., 2013). Arce
et al. (2004) found poor inter-limb vertical force amplitude coupling in the heels and fore-feet
during bimanual load lifting after TBI. The authors suggested that because of an absence of
simultaneous weight shifting from the heel to forefoot and back in both limbs in individuals
with TBI, postural gain control in vertical force is impaired after TBI (Arce et al., 2004). Although
the mean AP inter-limb synchrony fell outside the HC 95% confidence interval, the range of AP
inter-limb synchrony (-0.024 – 0.99) from the TBI sample group was greater, demonstrating
that some TBI participants fall outside the normal range of HC (0.54 – 0.98). Poor inter-limb
synchrony may be related to motor impairment and increased falls, as shown in post-stroke
adults (Mansfield et al., 2012).
The majority of the analyses characterizing postural asymmetry in bipedal conditions did
not reveal unilateral weaknesses or impairments. In contrast, unipedal stance time uniquely
revealed a significant leg effect in TBI participants, indicative of a weaker or impaired right limb
over the left limb. Additionally, increased unipedal stance RMS in the right leg suggests that the
weaker and impaired right limb in TBI participants produces greater postural sway. Initial
dynamic force control within the first five seconds of unipedal stance increases force variability
(Jonsson et al., 2004), and may have contributed to the reduction in unipedal stance time in TBI
participants. We suggest that future studies analyze the dynamic control from bipedal to
unipedal stance to understand postural steadiness after TBI.
95
4.5.3 Study Limitations
Although the present study identified postural asymmetry and an absence of
improvements across recovery after TBI, there are limitations. Sample size was significantly
reduced as a result of the inclusion/exclusion criteria and future studies will benefit from a
greater sample size. Changes in body weight may have influenced the quantitative balance
measures (Hue et al., 2007). However, since premorbid weight was unknown it is unclear
whether weight gain was a return to premorbid weight or related to inactivity post-injury. In
parallel with Christensen et al. (2008), on a group level, both the TMT A and SDMT oral
demonstrated improvements across recovery, indicative of recovery of attention and executive
function, respectively. In contrast, the TMT B did not show a significant change across time,
indicating that task switching did not improve on a group level. These findings may represent a
dissociation between cognitive and balance measures. Alternatively, the absence of
improvement in both cognitive and balance measures reflects heterogeneity in the TBI cohort.
The within group variability also reflects the challenges in examining brain-behaviour
relationships in moderate-to-severe TBI, given the frequent nature of diffuse brain injury.
Different physiological processes that govern a specific behaviour may or may not be
differentially affected by the injury. Thus, while it is possible that balance measures are
influenced by cognitive impairment, analysis of interactions between these factors was beyond
the scope of this study.
96
4.6 Conclusions
The current study is the first to use posturographic measures to examine balance
control and postural asymmetry longitudinally out to one year post-TBI. The observation of
poor ML net COP RMS and ML inter-limb synchrony, absolute stance load asymmetry, and
increased AP COP RMS indicate that poor AP stability and ML control may contribute to the
majority of balance impairments after TBI.
97
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Chapter 5 : Spectral analysis of centre of pressure identifies
altered balance control in individuals with moderate-severe
traumatic brain injury
Habib Perez, O., G. Green, R. E. & Mochizuki, G. Spectral analysis of centre of pressure identifies
altered balance control in individuals with moderate-severe traumatic brain injury. Under
review with Disability and Rehabilitation
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5.1 Abstract
Purpose: To identify impairments and recovery of anticipatory and reactive balance control
after TBI through spectral analyses of static balance tasks and to characterize the contributions
of each limb to balance control.
Methods: Balance was assessed with force platforms at 2, 5 and 12 months post-injury in eyes
open and closed. Secondary analysis of longitudinal balance data from moderate-to-severe TBI
(n = 31) was conducted. Single-visit data from age-matched controls (n = 22) were collected for
visual comparison. Net and individual limb center of pressure (COP) measures and inter-limb
COP coherence were calculated in low (≤ 0.4 Hz) and high (≥ 0.4 Hz) frequencies in the
anteroposterior and mediolateral directions.
Results: Removing vision increased net COP spectral power in low and high frequencies.
Individuals with TBI demonstrated recovery in high frequencies in net COP in the mediolateral
direction over time. Inter-limb coherence in the anteroposterior and mediolateral directions
increased (recovered) over time in high frequencies. Weight-bearing asymmetry was visible in
high frequencies in the anteroposterior and mediolateral direction post-TBI.
Conclusions: Increased amplitude of low and high frequencies suggests that individuals with TBI
have impaired anticipatory and reactive balance mechanisms, which may be driven by weight-
bearing asymmetries and place individuals post-TBI at increased risk of falls.
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5.2 Introduction
Balance impairments after traumatic brain injury (TBI) are common (Walker & Pickett,
2007). In this population, balance impairments lead to increased instability, which elevates fall
risk (Horak, 2006). One method of measuring balance impairment is to assess the systems and
mechanisms that affect balance control; however, only a few studies have analyzed balance
after TBI using quantitative measures that provide characterization of specific impairments
(Basford et al., 2003; Geurts, Ribbers, Knoop, & van Limbeek, 1996; Habib Perez, Green, &
Mochizuki, 2015; Lehmann et al., 1990; Pickett, Radfar-Baublitz, McDonald, Walker, & Cifu,
2007; Wade, Canning, Fowler, Felmingham, & Baguley, 1997). These studies have identified
improvements in balance after injury and have determined that simple balance tasks (i.e.,
standing quietly with eyes open) may not be sufficiently sensitive to reliably detect balance
impairments. Furthermore, while there is evidence indicating that dynamic balance control,
which includes anticipatory and reactive strategies for control, is impaired after injury (Arce,
Katz, & Sugarman, 2004; Ghajar & Ivry, 2008; Newton, 1995), evidence is sparse. Because
anticipatory and reactive mechanisms facilitate preparation and reaction to postural instability,
it is important to advance understanding of changes that occur in the components of balance
that increase risk of falls.
Most studies evaluating balance after TBI focus on clinical balance assessments and
spatial measures of balance (Arce et al., 2004; Basford et al., 2003; Geurts et al., 1996; Habib
Perez et al., 2015; Habib Perez, Green, & Mochizuki, Under Review; Lehmann et al., 1990;
Newton, 1995; Pickett et al., 2007; Wade et al., 1997; Walker & Pickett, 2007). However,
spectral analyses of balance measures have additional utility in understanding the underlying
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mechanisms of balance control and may help to better characterize and predict recovery in the
TBI patients. Postural sway is generally characterized as consisting of low and high frequencies
(Zatsiorsky & Duarte, 1999). Low frequency components (≤ 0.4 Hz) are considered to be
exploratory (Carpenter, Murnaghan, & Inglis, 2010; Zatsiorsky & Duarte, 1999), slow migrations
of the COP to a reference point from the central nervous system (Latash, Ferreira, Wieczorek, &
Duarte, 2003), or error corrections to equilibrium (Kiemel, Oie, & Jeka, 2006) useful for
feedforward (in contrast to feedback) control (Gatev, Thomas, Kepple, & Hallett, 1999).
Feedforward control of movement is linked to predictive models of behaviour (Massion, 1992;
Wolpert & Kawato, 1998) and plays a role in anticipatory control mechanisms. Higher frequency
components (≥ 0.4 Hz) are considered to reflect corrective responses to temporary instability
(Singer & Mochizuki, 2015), representing reactive control mechanisms (Schinkel-Ivy, Singer,
Inness, & Mansfield, 2016). Frequency decomposition of the centre of pressure (COP) have
characterized balance in individuals post-stroke (Paillex & So, 2003; Schinkel-Ivy et al., 2016;
Singer & Mochizuki, 2015; Yanohara et al., 2014) and other neurologic populations (Kanekar,
Lee, & Aruin, 2014) and may provide insight into the impairments in anticipatory and reactive
control mechanisms that may place individuals with TBI at an increased risk of falls.
Typically, balance assessments involve quantifying the overall (net) contributions of
both lower limbs to control. While appropriate in conditions where no asymmetries exist,
individual foot contributions may provide additional information about the underlying control
features in asymmetric conditions (Winter, Prince, Stergiou, & Powell, 1993). Importantly,
previous studies identify persistent unilateral motor weakness and anecdotally report
asymmetrical stance after TBI (Choi et al., 2012; Newton, 1995). In addition, recent work from
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our laboratory demonstrates that balance asymmetry and mediolateral inter-limb synchrony in
TBI closely resembles that of individuals with stroke (Mansfield, Danells, Inness, Mochizuki, &
McIlroy, 2011; Mansfield, Mochizuki, Inness, & McIlroy, 2012; Singer, Mansfield, Danells,
McIlroy, & Mochizuki, 2013), and may be one factor contributing to increased balance
instability after TBI (Habib Perez et al., Under Review). Balance asymmetry after TBI reflects
underlying unilateral motor weakness, and may suggest that the limb with the higher weight-
bearing is driving balance control mechanisms. In the spectral domain, balance asymmetry in
individuals with neurological impairments has been identified by quantifying the extent of COP
coherence, a measurement of correlation of frequency spectra between two time-varying
signals, and is a representation of inter-limb coordination (Myklebust et al., 2009). Examining
the correlation of the COP frequencies of each limb can identify disorders of the postural
system relating to anticipatory and reactive control mechanisms and may be useful in
understanding inter-limb coordination in this population.
The purpose of this study was to investigate whether anticipatory and reactive balance
impairments exist after TBI by carrying out spectral analyses (power spectral density and
coherence) of static balance tasks. To our knowledge, no study has examined how individual
foot contributions affect anticipatory and reactive control mechanisms in quiet standing across
the recovery of TBI. It was hypothesized that spectral measures of net COP would improve
across time in individuals with TBI. Omission of vision was used to increase postural challenge,
thus it was hypothesized that performing tasks with the eyes closed would increase the
amplitude of spectral measures of balance. Despite previous findings where inter-limb
synchrony did not change across time (Habib Perez et al., Under Review), as a result of
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increased sensitivity of spectral measures, it was hypothesized that improvements across time
in inter-limb coherence in individuals with TBI would be detectable. Furthermore, it was
hypothesized that the individual limb applying the most vertical force (i.e., highest weight-
bearing) would demonstrate increased COP power spectral density and decrease across
recovery. Complementary spatial measures of control were analyzed to test the hypothesis that
these would also improve over recovery in individuals with TBI and to determine whether
spatial measures of control across time were affected by postural challenge.
5.3 Methods
5.3.1 Participants
This study involved a secondary analysis of balance data from a larger study with
individuals with clinically-confirmed TBI (The Toronto Rehab Traumatic Brain Injury Recovery
Study). Details of the larger study and its inclusion and exclusion criteria have been reported
previously (Habib Perez et al., Under Review). Additional eligibility criteria for the current study
were completion of three instrumented balance assessments on dual force plates, and the
absence of lower extremity orthopedic injuries.
Seventy-three participants with TBI completed instrumented balance assessments on
dual force plates. Of the 73 TBI participants, 37 participants were ineligible because of missing
data within an assessment, and five were ineligible due to lower extremity orthopedic injuries.
Therefore, 31 participants with TBI were included in the analysis. A group of aged-matched
healthy control (HC) participants were recruited for visual comparison and assessed on one
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occasion. Demographic characteristics of the sample are reported in Table 5-1. All participants
(or a substitute decision maker) provided informed consent. The study was approved by the
Research Ethics Board at the Toronto Rehabilitation Institute.
5.3.2 Data Collection and Procedure
Clinical measures of balance (Community Balance and Mobility (CB&M) scale)(Howe,
Inness, & Wright, 2011) and force plate balance data were undertaken with participants with
TBI at approximately 2-months (T1), 5-months (T2), and 12-months (T3) post injury. Data were
collected using dual force plates (AMTI, Watertown Massachusetts), positioned with the y-axes
in parallel, and separated with a minimal distance (1 mm). Participants stood with each foot on
a separate force plate in two stance conditions: eyes open (EO) and eyes closed (EC). Foot
position was standardized (McIlroy & Maki, 1997). Participants were instructed to stand quietly
for 50 seconds looking straight ahead. Ground-reaction forces and moments from each plate
were sampled at 50 Hz for all TBI participant data. HC participants were collected after all TBI
data was collected and used for visual comparison to TBI participants.
5.3.3 Data Analysis
Ground-reaction forces and moments were low-pass filtered using a 4th ordered dual-
pass Butterworth filter with a 10 Hz cut off frequency prior to processing. The net AP and ML
COP and the respective time series were calculated for all participants. For the spectral analysis,
individual limb and net displacements of the COP were processed by fast Fourier transform. The
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power spectrum density (PSD) estimation was obtained in Matlab using Welch method with
non-overlapping Hanning windows of 5-second data segments. The power spectrum was
divided into two frequency bands: low frequency (0-0.4 Hz) and high frequency (0.4-3 Hz). The
integral for the low and high frequency bands was computed using the trapezoidal method.
Individual limb COP PSD was calculated and grouped based on the leg with the lowest weight-
bearing (LWB) and highest weight-bearing (HWB) limb for comparison in the AP and ML
direction. Net AP and ML COP PSD were also calculated. Inter-limb coherence estimates were
obtained from non-overlapping Hanning windows of 5-second data segments, with a frequency
resolution of 0.04 Hz. Coherence analysis included frequencies in the 0-5 Hz range, with
frequencies below 0.4 Hz and above 0.4 Hz representing low and high frequency bands,
respectively. In the AP and ML direction, the coherence for each frequency bin in the low and
high frequency bands were averaged accordingly if they exceeded the statistical confidence
limit (see below). Inter-limb coherence is expressed as a number between 0 and 1, with 1
indicating a perfect correlation and 0 indicating an absence of a correlation. For each
participant, the average coherence in low and high frequencies were transformed using Fisher
transformation (Mima & Hallett, 1999). Root mean square (RMS) of the net AP and ML COP was
used as the spatial measure of balance control.
5.3.4 Statistical Analysis
Statistical analyses were performed using IBM SPSS 23.0. To test the hypotheses that
spectral and spatial measures would improve across recovery for TBI participants and vision
affects balance stability and, a series of 2 × 3 analyses of variance (ANOVA), with Condition and
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Time as factors, were conducted for AP and ML PSD, AP and ML inter-limb coherence, and net
AP and ML COP RMS. Coherence estimates for the low and high frequencies bands were
considered statistically significant when they exceeded the confidence limit of 0.283 (n =10) at
α = 0.05 (Rosenberg, Amjad, Breeze, Brinllinger, & Halliday, 1989). To test the hypothesis that
the higher weight-bearing limb would demonstrate increased COP power and decrease across
recovery, a 2 × 2 × 3 ANOVA was conducted with Foot (LWB, HWB), Condition and Time as
factors. RMS and PSD measures were log transformed to meet assumptions of normality and
inter-limb coherence was Fisher transformed. In cases where sphericity was not met,
Greenhouse-Geisser values were reported. Statistical significance was set at p<0.05.
5.4 Results
Thirty-one participants with TBI completed balance assessments across three time
points post-injury and data from 22 HC participants were collected prospectively. TBI
participants were assessed on average at 59.8 (±29.0), 150 (±28.9), and 412 (±69.3) days post
injury at T1, T2 and T3. There was no significant difference in age between groups (t(49.8)= 1.3,
p>0.05, d = 0.42; Table 5-1) and no significant difference in mass between the HC group and TBI
group at T1 (t(49)=1.3, p>0.05, d = 0.36). There was a significant mass increase of approximately
8 kg in the TBI participants across the three time points (F1.4,38.9 = 24.1, p<0.001, partial η2 = .46),
with Bonferroni post-hoc tests identifying significant differences from T1 to T2 (p<0.001) and T1
to T3 (p<0.001). Clinical balance measures (CB&M) improved over time (F1.4,38.6 = 26.4, p<0.001,
partial η2 = .49), specifically between T1 and T2 (p<0.001) and T1 and T3 (p<0.001). There was
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no statistically significant difference between T2 and T3 (p>0.05). The HC group completed
more years of education in comparison to TBI participants (t(50) = -2.6, p=0.01, d =0.72).
Table 5-1 – Mean and standard deviation (SD) participant demographics of traumatic brain
injury (TBI) and healthy control (HC) participants. TBI severity was characterized by the lowest
post-traumatic amnesia (PTA) scale and the number of TBI participants in each category is
described below.
TBI HC
Sample size 31 (24 M/7 F) 22 (10 M/12 F)
Age 41.9 (16.9) 36.6 (13.8)
Mass (kg)
T1 73.9 (10.7) 69.6 (12.8)
T2 79.18 (12.3)* --
T3 81.9 (13.9)* --
Severity (PTA)
Moderate 1 --
Severe 6 --
Very Severe 16 --
Extremely Severe 4 --
CB&M Scale
T1 65.0 (18.1) --
T2 78.0 (15.1) * --
T3 78.7 (14.1) * --
Years of education 14.60 (3.27) 17.0 (3.4)
*Denotes significant differences within TBI participants at p<0.05 from T1 value. Note that PTA
severity was obtained from 27 of the 31 TBI participants.
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5.4.1 Spectral Measures of Balance
5.4.1.1 Net COP Power Spectral Density
Omitting vision increased net COP PSD in the AP direction at low frequencies and in the
AP and ML direction at high frequencies. Additionally, changes across recovery were found for
high frequencies in the ML direction. The 2 × 3 ANOVA demonstrated significant effects of
Condition for low frequencies in the AP (F1,27=20.3, p<0.05, partial η2 = .429) direction (Figure 5-
1A). Significant effects of Condition were also identified for high frequencies in the AP
(F1,27=88.9, p<0.05, partial η2 = .77) and ML (F1,27=31.5, p<0.05, partial η2 = .54) direction, with
greater net COP power in EC than EO. A significant effect of Time was observed for ML net COP
PSD in high frequencies (F2,54=7.1, p<0.05, partial η2 = .21). Bonferroni-adjusted post-hoc tests
showed ML net COP PSD in high frequencies was significantly greater in T1 when compared to
T2 (p=0.03) and T3 (p=0.01; Figure 5-1D). No other significant effects of Time were found. In
low frequencies, the mean net COP PSD of TBI participants fell outside the top 95% CI of HC. In
high frequencies, this only occurs in EC. Figure 5-2 demonstrates the mean PSD for the net COP
for HC and a sample of four TBI participants across recovery.
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Figure 5-1 – Net centre of pressure mean integral of the power spectral density (PSD) in low (A)
and high (B) frequencies in the anteroposterior (AP) direction, and the low (C) and high (D)
frequencies in the mediolateral (ML) direction. Group means and standard error for TBI
participants across recovery are displayed for eyes open (EO; black) and eyes closed (EC; grey).
The solid and dashed lines represent mean and 95% confidence intervals for the net COP mean
integral of the PSD in healthy controls in each condition, respectively.
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Figure 5-2 – Mean and standard deviation for the net centre of pressure (COP) power spectral
density (PSD) for healthy controls in the anteroposterior (AP) and mediolateral (ML) direction
for eyes open (EO) and eyes closed (EC) conditions (top row). Individual data net COP PSD from
a sample of four TBI participants across recovery [T1 (black), T2 (grey), and T3 (light grey)] in the
AP and ML direction in both EO and EC conditions (row 2 to 5).
5.4.2 Inter-limb Coordination
5.4.2.1 Inter-limb Coherence
Omitting vision increased inter-limb coherence in low and high frequencies in the AP
direction and in high frequencies in the ML direction. Additionally, inter-limb coherence
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increased across recovery in high frequencies in both the AP and ML direction (see Figure 5-3).
A main effect of Condition was found in the 2 × 3 ANOVA for AP coherence for the low
(F1,27=9.0, p<0.05, partial η2 = .25) and high (F1,27=31.2, p<0.05, partial η2 = .54) frequencies, and
for high frequencies in the ML direction (F1,25=31.9, p<0.05, partial η2 = .56). In addition, high
frequencies demonstrated significant effects of Time for AP coherence (F2,54=5.8, p<0.05, partial
η2 = .17) and ML coherence (F2,50=3.6, p<0.05, partial η2 = .13). Bonferroni-adjusted post-hoc
tests identified that inter-limb coherence in high frequencies increased between T1 and T3 in
the AP (p=0.009) and ML (p=0.03) direction. When visually comparing AP and ML inter-limb
coherence to HC, TBI participants fell within the 95% CI of the HC data.
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Figure 5-3 – Mean and standard error (SE) anteroposterior (AP) (A) and mediolateral ML (B)
inter-limb coherence in low frequencies and AP (C) and ML (D) inter-limb coherence in for high
frequencies. Group means ±SE for TBI participants are displayed for eyes open (EO; black) and
eyes closed (EC; grey) conditions across recovery. The solid and dashed lines represent mean
and 95% confidence intervals for the mean inter-limb coherence in healthy controls in each
condition, respectively. Pound (#) in C and D denotes significant difference between T1 and T3
in TBI participants.
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5.4.2.2 Individual Limb COP
Amplitude of the PSD in the individual limb increased in low and high frequencies with
the omission of vision in the AP and ML direction (see Figure 5-4). The high frequency range in
the ML direction demonstrated a trend towards increased PSD amplitude for the foot with
higher weight-bearing (HWB). Specifically, the 2 × 2 × 3 ANOVA demonstrated a significant main
effect of Condition with EC producing greater power in low frequencies in the AP direction
(F1,27=21.0, p<0.05, partial η2 = .44), and greater power in high frequencies in the AP (F1,27=87.9,
p<0.05, partial η2 = .77) and ML (F1,27=66.1, p<0.05, partial η2 = .71) direction. Low frequency
PSD in the ML direction showed a trend (p=0.08) towards a higher amplitude with the EC.
Differences between LWB and HWB approached statistical significance in high frequencies in
the ML direction (p=0.075, see Figure 5-4D). A Time × Condition interaction approached
statistical significance for high frequencies in the ML direction (p=0.066). With the exception of
a few incidences, PSD in low and high frequencies in both the AP and ML direction in TBI
participants primarily fell outside the 95% CI of HC.
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Figure 5-4 – Individual limb centre of pressure (COP) mean integral of the power spectral
density (PSD) in low (A) and high (B) frequencies in the anteroposterior (AP) direction, and the
low (C) and high (D) frequencies in the mediolateral (ML) direction. Group means and standard
error for TBI participants across recovery are displayed across time. Lower weight-bearing
(LWB; solid) and higher weight-bearing (HWB; open) limb are represented in eyes open (EO;
black) and eyes closed (EC; grey). Group means and 95% confidence intervals (depicted with
error bars) are displayed for healthy control (HC) data.
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5.4.3 Spatial Measures of Balance
Omitting vision increased AP RMS. The 2 × 3 ANOVA for AP RMS demonstrated
significant effects of Condition (F1,27=50.3, p<0.05, partial η2 = .65), with EC producing a greater
AP RMS in comparison to EO. No significant main effect of Time or Condition x Time interaction
was identified for AP RMS. No significant main effect of Condition or Condition x Time
interaction was found for ML RMS. When comparing RMS measures of balance to HC, AP and
ML RMS in TBI participants fell outside the 95% CI of HC participants in EO and EC across time.
(see Figures 5-5A and B).
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Figure 5-5 – Anteroposterior (AP) (A) and mediolateral (ML) (B) centre of pressure root mean
square (COP RMS) group means and standard error for TBI participants across recovery in eyes
open (EO; black) and eyes closed (EC; grey) conditions. The solid and dashed lines represent
mean and 95% confidence intervals for COP RMS in healthy controls in each condition,
respectively.
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5.5 Discussion
This study examined whether anticipatory and reactive control mechanisms are
impaired after TBI and characterized contributions of each limb to these control mechanisms.
Spectral and spatial analyses of the COP during quiet standing with eyes open and closed
measured changes in balance at three time points during recovery of TBI. Consistent with the
hypotheses, removing vision increased net COP measures of balance, inter-limb coherence, and
individual foot COP spectral measures. Recovery changes after TBI were identified in high
frequencies in the ML direction. Differences were visible in the PSD in high frequencies in the
ML direction between the higher and lower weight-bearing limb. Additionally, inter-limb
coherence in the high frequency range increased in both the AP and ML direction over the
course of recovery; however, mean values fell within the boundaries of healthy control
normative data.
5.5.1 Anticipatory and Reactive Balance Control Mechanisms are Impaired after
TBI
This study identified greater low frequency power of net COP in the AP direction and
greater high frequency power in both the AP and ML direction for TBI participants. The only
improvements observed over the course of recovery were in the high frequencies of the net ML
COP. This measure of recovery was primarily driven by performance in the EO condition, as TBI
participants produced greater power in the EC condition at T2 and T3 compared to HC. This
suggests that overall control of anticipatory and reactive balance mechanisms may be impaired
after TBI and do not improve unless visual information is present.
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The effects of removing vision on balance control have been previously studied
(Shumway-Cook & Horak, 1986; Wade & Jones, 1997). This manipulation increases sway,
especially in the AP direction because of the direction in which visual or optic flow information
is processed (Bertenthal, Rose, & Bai, 1997; Dijkstra, Schoner, Giese, & Gielen, 1994; Moraes,
de Freitas, Razuk, & Barela, 2016). Distinct frequency intervals have been linked to sensory
systems used by postural control systems, suggesting that the visual, vestibular, and
somatosensory/proprioceptive system are associated with low (<0.3 Hz), middle (0.3–1 Hz) and
high (> 1 Hz) frequencies of sway, respectively (Bizid et al., 2009; Kanekar et al., 2014; Nagy et
al., 2007; Nagy et al., 2004). Identifying anticipatory and reactive balance control mechanisms
using low and high frequencies in the present study does not completely align to previous
reports. Balance control, both anticipatory and reactive, requires the use of the multiple
sensory systems (Mohapatra & Aruin, 2013; Mohapatra, Krishnan, & Aruin, 2012; Winter, 1995)
and it may be inaccurate to suggest that individual frequency intervals are solely used by the
associated sensory system (Palmieri, Ingersoll, Stone, & Krause, 2002). Some studies suggest
that an impaired vestibular system contributes to balance deficits post-TBI (Basford et al., 2003;
Walker & Pickett, 2007); however, disruption of vestibular system function has been shown to
affect postural sway frequencies that span the 0.25–5 Hz range (Fitzpatrick, Burke, & Gandevia,
1996; Forbes, Siegmund, Schouten, & Blouin, 2014). Given that the majority of observed
differences between HC and TBI participants were found in EC, it is possible that both the
somatosensory and vestibular system were impaired in our sample and individuals post-injury
were thus further reliant on visual information in both low and high frequencies. It may also be
possible that brain regions that integrate multiple sensory modalities (Brang, Taich, Hillyard,
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Grabowecky, & Ramachandran, 2013; Ghazanfar & Schroeder, 2006) were injured, thus
impacting the ability to accurately process incoming sensory information.
The present findings advance understanding of the characteristics of anticipatory and
reactive balance control after TBI. A previous study demonstrated age-appropriate sway
amplitudes and slightly higher latencies in unpredictable feet-in-place reactions after TBI
(Newton, 1995), while poor postural gain control has been found in self-initiated bimanual
load-lifting post TBI (Arce et al., 2004). It has been postulated that impaired anticipatory neural
circuits lead to delayed reaction times and increased variability in movement control (Di Russo,
Incoccia, Formisano, Sabatini, & Zoccolotti, 2005; Ghajar & Ivry, 2008; Segalowitz, Dywan, &
Unsal, 1997). Preparation for balance instability and the latencies and amplitude of postural
reactions are imperative for dynamic balance control (Horak, Diener, & Nashner, 1989; Maki &
McIlroy, 1997; McIlroy & Maki, 1999; Mochizuki, Boe, Marlin, & McIlRoy, 2010). The current
results corroborate findings of previous studies and suggest that anticipatory and reactive
balance mechanisms were impaired. This may place these individuals at an increased risk of
falls. Further studies are needed to understand the components of dynamic balance control to
address balance specific impairments post-TBI.
5.5.2 Inter-limb Coordination after TBI
Inter-limb coordination was analyzed using coherence analysis to identify frequencies of
the COP under each foot that were correlated (Myklebust et al., 2009). Although inter-limb
coherence improved across time points, group means fell within the 95% CI of HC. Inter-limb
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coherence in the AP and ML direction increased at high frequencies, suggesting that the
relationship between limbs in frequencies that correspond to reactive balance control increases
out to one year after injury. Previous work from our laboratory has demonstrated that inter-
limb coordination requires decoupling in preparation for unpredictable perturbations in healthy
adults (Habib Perez, Singer, & Mochizuki, 2016). Although inter-limb coherence in TBI
participants did not differ from HC in either low or high frequencies, the increased coherence at
high frequencies suggests an inappropriately high level of coupling between limbs, with respect
to reactive balance control mechanisms. Poor inter-limb vertical force amplitude coupling
during bimanual load lifting has been found after TBI (Arce et al., 2004). Moreover, poor inter-
limb synchrony in the ML direction has been found post-TBI, resembling the levels of synchrony
observed in stroke patients (Habib Perez et al., Under Review). Inter-limb coherence is a
relatively novel analysis for postural asymmetry and has shown sensitivity for identifying
differences between healthy adults and other neurologically-impaired populations. For
example, individuals with Huntington’s Disease produced reduced inter-limb coherence
compared to healthy young and older adults (Myklebust et al., 2009). The absence of visual
differences found in the present study between TBI and HC may be attributable to differences
in the way in which ‘significant’ coherence was identified. The current study applied a statistical
confidence limit to coherence, similar to what has been used in other physiological studies
(Rosenberg et al., 1989), while Myklebust et al. (2009) did not. The use of confidence limits
eliminated possible low coherence values in TBI participants.
Inter-limb coordination in the current study was also analyzed using weight-bearing
asymmetry categorization. Asymmetry in high frequencies approached statistical significance in
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the ML direction, suggesting that weight-bearing asymmetry may affect reactive balance
mechanisms. Weight-bearing asymmetry reduces postural stability for dynamic balance control
(de Kam, Kamphuis, Weerdesteyn, & Geurts, 2016). Interestingly, the limb with the higher level
of weight-bearing in the present study displayed means much greater than the 95% CI of HC in
both EO and EC at high frequencies. Individuals post-stroke that demonstrate a reduced ability
to recover from balance also display greater COP amplitude of high frequencies in standing
balance (Schinkel-Ivy et al., 2016). The present finding suggests that the imbalance in weight-
bearing symmetry may be driving the amplitude of power of the higher weight-bearing limb in
frequencies that are important for reactive balance control. This may subsequently place
individuals with TBI at a higher risk of falls and renders them less likely to recover from balance
perturbations.
5.5.3 The Role of the Postural Challenge
The current study used an EC condition to increase the postural challenge. In line with
our hypothesis, spectral measures demonstrated significant main effects of Condition with
higher COP power in EC within the TBI group. Additionally, spatial measures of balance (root
mean square of the COP) in the current study were higher in EC in comparison to the EO
condition, but only in the AP direction. Thus, the differences were most notable in all measures
when vision was omitted.
Other studies have demonstrated that performing concurrent cognitive-balance tasks
reveal balance impairments after TBI (McCulloch, Buxton, Hackney, & Lowers, 2010). This
125
suggests that a postural challenge may be more sensitive at detecting balance impairments in
individuals with TBI. As an example, adding a mental arithmetic task increases overall frequency
of the COP in the AP direction (Vuillerme & Vincent, 2006). The findings of the current study
revealed that spectral measures demonstrated that TBI participants fall outside the 95% CI of
healthy controls more frequently. Thus, increasing postural challenge through eyes closed or via
a cognitive task may provide insight into the balance impairments after TBI.
5.6 Conclusion
This study found that individuals with moderate-to-severe TBI produced higher power in
the net COP when compared to normative data of healthy controls, demonstrating that
anticipatory and reactive balance mechanisms may be impaired after TBI. Additionally, high
frequencies, which represent reactive control mechanisms, were largely affected in inter-limb
coordination measures. Lastly, increasing the postural challenge frequently identified visual
differences between TBI and HC. Inferring anticipatory and reactive balance control
mechanisms through spectral analysis of COP in static balance tasks advances knowledge of
balance control after TBI.
126
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Chapter 6 : Discussion
132
The overall purpose of this dissertation was to better understand the role of inter-limb
coordination in postural control and to determine the extent to which inter-limb coordination is
altered in individuals following moderate-to-severe TBI. Across three studies, the following
objectives were examined: (1) to determine whether changes in inter-limb postural
synchronization prior to bouts of postural instability are indicative of modulation in postural
set; (2) to characterize the recovery trajectory of balance control following TBI with a specific
focus on measures of postural asymmetry; and, (3) to investigate whether anticipatory and
reactive balance impairments exist after TBI by carrying out spectral analyses (power spectral
density and coherence) of static balance tasks. This dissertation provides evidence that the
level of inter-limb COP synchrony is dependent on the predictability of perturbation magnitude
prior to postural instability, suggesting that inter-limb synchrony is important during
preparation for postural instability. Additionally, this dissertation demonstrated that although
there are slight improvements in the overall balance five months post injury in individuals with
moderate-to-severe TBI, poor levels of balance control remain and weight-bearing asymmetry
was consistent one year post-injury. Levels of inter-limb synchrony did not change, but inter-
limb coherence increased across time. Weight-bearing asymmetry in TBI was noticeable in
reactive balance measures.
6.1 Summary of findings
The preparation for postural instability across different conditions of perturbation
predictability demonstrated that inter-limb synchrony of the COP in the AP and ML directions
was higher when the magnitude of the perturbation was smaller and more predictable. Larger
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perturbations or perturbations where perturbation magnitude was unpredictable reduced
preparatory ML COP inter-limb synchrony in comparison to when conditions were performed in
blocked (rather than randomized order) and predictable. Most intriguing was how perturbation
force thresholds required less force for individuals to begin moving in a forward direction when
perturbations were randomized and most unpredictable. Inter-limb synchrony of the COP was
not affected by whether the perturbations were internally or externally generated. However,
inter-limb synchrony in the AP direction significantly decreased across blocks of trials (i.e. from
the first five trials to the last five trials) when the perturbation magnitude was larger and
unknown in external perturbations. These results demonstrate that to optimize balance
responses when a stepping response may be required, the CNS prepares for unknown
magnitude of perturbations of instability through the decoupling of inter-limb synchrony.
Although the results from the first study demonstrated functional utility of inter-limb
coupling and decoupling as part of anticipatory postural control, balance control and inter-limb
coordination after moderate-to-severe TBI has not been thoroughly studied. Thus, this was
further investigated in the second study. At five months post-injury, a reduction of the net COP
of RMS in the ML direction was observed in TBI participants as compared to the 2-month time
point. This improvement, however, was not observed in the AP direction for net COP. There
were no changes across the course of recovery in inter-limb synchrony in the AP and ML
direction. However, reduced AP and ML COP inter-limb synchrony was associated with greater
ML RMS across recovery. Individuals post-TBI did not improve their weight-bearing asymmetry
throughout all three time points and vertical force loading ratio was comparable to that of
stroke patients (Mansfield, Danells, Inness, Mochizuki, & McIlroy, 2011). Despite poor and
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unchanged weight-bearing asymmetry in bipedal stances, individuals post-TBI stood in unipedal
stance for 10 s longer within the first few months of recovery in both limbs. Interestingly, it was
unipedal stance that demonstrated a limb difference between the left and right side with
greater unipedal stance duration in the left limb. The visual comparison of balance outcomes to
the healthy control group demonstrated that many of the balance and inter-limb coordination
outcome measures in individuals with TBI at 12 months post-injury did not reach normative
values. Though static balance impairments persist a year after injury, it remains unknown how
this transfers to dynamic balance control.
The use of spectral analyses to the COP measures in static balance provided an
opportunity to examine whether anticipatory and reactive balance control mechanisms were
impaired after TBI and to better understand how these control mechanisms were influenced by
individual limb contributions. ML net COP power spectral density in high frequencies, indicative
of reactive balance control mechanisms, decreased across the span of 12 months post-TBI. High
frequencies were affected by individual limb contributions, as inter-limb coherence increased
across recovery in high frequencies in both the AP and ML direction. The amount of weight-
bearing from each limb demonstrated a trend towards a difference in the amplitude of power
spectral density in the high frequencies. Omitting vision increased net COP power spectral
density in the AP direction at low frequencies, indicative of anticipatory balance control
mechanisms, and in the AP and ML direction at high frequencies. The omission of vision also
increased inter-limb coherence and individual foot COP spectral measures, specifically in high
frequencies. Similar to the previous study, despite initial improvements post-injury, the spectral
and spatial balance measures of the net COP remained above the normative healthy control
135
values. Thus, anticipatory and reactive balance control mechanisms appear to be diminished
after TBI with greater differences to normative healthy control values in reactive balance
control mechanisms.
Through a series of experiments and various methods of analyses of COP data, this
dissertation demonstrates the importance of inter-limb coordination in balance control in
preparing for instability, provides quantitative evidence of diminished balance control after TBI,
and demonstrates that the balance impairments after TBI may be attributable to reduced inter-
limb coordination, as exemplified by reduced weight-bearing symmetry and poor inter-limb
synchrony and inter-limb coherence. Additionally, there is evidence to suggest that both
anticipatory and reactive control mechanisms of balance control may be negatively impacted.
6.2 Inter-limb Coordination in Balance Control
As previously stated, postural control emerges from the interaction of the individual, the
task, and the environment (Shumway-Cook & Woollacott, 2012). Postural control reflects the
ability to maintain equilibrium and requires maintaining the COM within a steady BOS and also
while the BOS is altered (Maki & McIlroy, 1997). During upright quiet standing, healthy
individuals generate equal forces and torque from each limb in order to maintain balance by
keeping the COM within the BOS (Woollacott & Shumway-Cook, 1996). Measuring individual
limb contributions in static balance, which reflects inter-limb coordination, has been
informative to further understanding balance control in healthy individuals and individuals with
balance impairments (Mansfield et al., 2011; Mansfield, Mochizuki, Inness, & McIlroy, 2012;
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Mochizuki, Ivanova, & Garland, 2005; Singer, Mansfield, Danells, McIlroy, & Mochizuki, 2013;
Winter, Prince, Stergiou, & Powell, 1993). Because individual limb contributions are critical for
postural control, inter-limb coordination and postural control are considered interrelated and
have comparable interactions between the individual, the task, and the environment (Figure 6-
1, repeat of Figure 2-2).
Figure 6-1: The theoretical framework of inter-limb coordination. Inter-limb coordination
emerges from the interaction of the individual, task, and the environment. [Adapted from: A.
Shumway-Cook & M. Woollacott, Motor Control: Translating Research into Clinical Practice 4th
Ed. (2012)]
Effective inter-limb coordination in balance control requires individual limbs to produce
relatively symmetrical force output from each limb by temporally synchronizing ankle and hip
moments during upright balance. However, modulating inter-limb coordination to the context
Inter-limb
Coordination
Environment
E
T
Task
I
Individual
137
of the balance task and the environment is also necessary. In addition to overall balance control
measures (i.e. net COP), this dissertation used spatial, temporal, and frequency outcome
measures to advance the knowledge of inter-limb coordination within balance control that
includes weight-bearing asymmetry, inter-limb synchronization, and inter-limb coherence,
respectively. Subsequent sections will discuss inter-limb coordination in balance control in
preparation for balance instability in young healthy adults and inter-limb coordination and
balance control after moderate-to-severe TBI.
6.2.1 The Organization and Control of Inter-limb Coordination in Balance Control
An important consideration in characterizing the impact of TBI on inter-limb
coordination is: how is inter-limb coordination organized in the CNS? The coordination of
homologous and non-homologous limb movements is primarily driven by the activation of
supplementary motor area (SMA), premotor and sensorimotor cortices, cingulate cortex, and
cerebellum as components of a distributed motor network (Debaere et al., 2001). Increased
brain activation in these regions is observed, in particular the SMA, when movement
coordination is less stable during anti-phase or non-isodirectional movements (Debaere et al.,
2001) and during bilateral lower extremity movements (Noble, Eng, & Boyd, 2014). Relatively
equal bilateral force output from the lower extremities is thought to be driven by a central
command and distally modulated by separate controls in each limb for the task (Baldissera &
Tesio, 2017; Mochizuki, Semmler, Ivanova, & Garland, 2006). Additionally, the absence of
temporal delays in muscle activity between limbs and high correlations in the timing and
frequency of oscillations of the muscular activity in the ankle dorsiflexors and plantarflexors
138
while standing (Mochizuki et al., 2006) provide some evidence that between-limb coordination
is important for balance control. In TBI, local or diffuse injury (Gaetz, 2004; Povlishock & Katz,
2005) to the SMA, cingulate motor cortex, and cerebellum or other regions that provide
descending control to parallel spinal circuits involved in co-modulation of muscle activity can
alter the strength of task-dependent co-modulation of bilateral postural muscles (i.e. inter-limb
control) during standing. Bilateral synchronization of single motor units between limbs is
altered post-stroke (Farmer, Bremner, Halliday, Rosenberg, & Stephens, 1993) and
synchronization between motor units of ankle plantarflexor and dorsiflexor muscles in
individuals with stroke demonstrate prolonged motor unit synchronization in comparison to a
control group (Datta, Farmer, & Stephens, 1991), suggesting that bilateral motor unit activation
from descending pathways are impaired after injury and may be more reliant on coordination
of spinal, rather than descending inputs. Thus, altered motor unit synchronization which stems
from cortico-muscular synchronization relationships (Farmer, 1998) may result in poor postural
control. The resulting temporal dissociation or reduction in correlation between limbs when the
task requires high associations provides evidence for the importance of integrity of descending
pathways on inter-limb coordination in balance.
6.2.2 Inter-limb Synchrony in Anticipatory Balance Control
In Study 1 of this dissertation, inter-limb synchronization prior to postural instability in
the AP direction from either self-generated or predictable externally-generated perturbations
were scaled to the magnitude of the perturbation. Specifically, AP perturbations reduced ML
inter-limb synchronization with greater magnitude than AP inter-limb synchronization (Habib
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Perez, Singer, & Mochizuki, 2016). Winter et al. (1993) demonstrated that the unloading and
loading mechanism was critical for control of net COP in the ML direction, controlled by hip
abductors and adductors; however, individual limb COP fluctuations in the ML direction
resulted from ankle invertors and evertors. Conversely, unloading and loading mechanisms
were negligible for net COP and individual limb COP in the AP direction. The significant
reduction in the ML inter-limb synchrony from quiet standing to the other conditions prior to
postural perturbations suggests that ankle invertors and evertors play a large role in the
preparation for instability for early and anticipatory postural adjustments (Klous, Mikulic, &
Latash, 2012; Krishnan, Latash, & Aruin, 2012). Accordingly, the tightly coupled AP inter-limb
synchrony throughout all conditions highlight that the direction of perturbation played a role in
activating the ankle plantarflexors as individuals prepared for postural instability in the forward
direction. Self-generated movements or predictable, externally-generated perturbations
engage in feedforward balance control as a result of the physiological readiness of the CNS,
central set (Prochazka, 1989). Central set is dependent on prior experience and current context,
which enables descending commands of the CNS to prepare and modify contextually
appropriate balance responses and have been shown to be scaled to the magnitude of the
perturbation (Horak, Diener, & Nashner, 1989; Mochizuki, Boe, Marlin, & McIlRoy, 2010;
Prochazka, 1989). The findings of the first study illustrate that inter-limb synchrony was actively
controlled as a result of the physiological readiness for perturbations in the AP direction, and
ankle invertors and evertors actively decoupled ML inter-limb synchrony in order to maintain
overall stability in preparation for instability. On the other hand, reduced ML inter-limb
synchrony in quiet standing in the young healthy group in comparison to ML synchrony
reported in previous studies (Mochizuki et al., 2005; Winter et al., 1993) may suggest that ML
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inter-limb synchrony (i.e. anti-phase relationship of COP under each foot) (Debaere et al.,
2001), may be more difficult to maintain and in turn may display increased variability than AP
inter-limb synchrony. This decoupling in the ML direction may serve as an essential component
of an anticipatory postural adjustment, which would be observed in stepping tasks, but not in
quiet standing. Thus, the high instability or variability of this type of coordination may be
condition-dependent and linked to the requirements of the task.
6.2.3 Inter-limb Coordination in Balance Control after Traumatic Brain Injury
As a part of the overall purpose, this dissertation sought to determine the extent to
which inter-limb coordination was altered in individuals following moderate-to-severe TBI.
Balance control after TBI is commonly impaired and may result from strength imbalances
between limbs and coordination deficits that are prevalent after TBI (Khan et al., 2016; Walker
& Pickett, 2007) as a result of the physiological and structural changes to the body (i.e. brain)
after injury (Laxe et al., 2014). Previous studies have reported asymmetrical upright standing in
static balance (Lehmann et al., 1990) and prior to externally-generated platform perturbations
(Newton, 1995), and poor inter-limb force amplitude coordination in self-generated
perturbations (Arce, Katz, & Sugarman, 2004). Collectively, previous studies underscore that
asymmetry and inter-limb coordination are components of the balance problems that are
present after TBI.
Study 2 of this dissertation demonstrated weight-bearing asymmetry in individuals with
moderate-to-severe TBI with the least vertically loaded limb carrying 44 to 46% of total body
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weight. Normative weight-bearing symmetry ranges between 48 to 52% between the limbs,
and although there was a 2 to 4% difference between normative values and individuals with
TBI, these differences place individuals with TBI within weight-bearing asymmetry ranges
observed in individuals with stroke (Mansfield et al., 2011; Mansfield, Mochizuki, et al., 2012;
Singer et al., 2013; Singer & Mochizuki, 2015). In Study 3 of this dissertation, weight-bearing
asymmetry was used to characterize inter-limb coordination for anticipatory and reactive
balance control mechanisms in the AP and ML direction after TBI. A trend towards differences
in the amplitude of power produced between the higher weight-bearing limb and lower weight-
bearing limb were visible in the ML direction in high frequencies, which are indicative of
reactive balance control mechanisms. In other neurologically impaired populations, such as
stroke, weight-bearing asymmetry led to impaired reactive stepping responses (Inness,
Mansfield, Bayley, & McIlroy, 2016; Inness, Mansfield, Lakhani, & McIlroy, 2014; Mansfield,
Inness, Lakhani, & McIlroy, 2012). Additionally, weight-bearing asymmetry has been shown to
decrease postural stability for dynamic balance control in lateral perturbations towards the
more loaded limb (de Kam, Kamphuis, Weerdesteyn, & Geurts, 2016). These findings suggest
that the imbalance in weight-bearing symmetry may be driving the amplitude of power in the
higher weight-bearing limb in frequencies that are important for reactive balance control. As a
result, this may place individuals with TBI at an increased risk of falls and are less likely to
recover from balance perturbations.
Inter-limb coordination after TBI was also evaluated through temporal and spectral
measures in Studies 2 and 3 of this dissertation: inter-limb COP synchronization and inter-limb
COP coherence, respectively. Individuals with moderate-to-severe TBI revealed moderate to
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strong inter-limb COP synchronization and inter-limb COP coherence in low and high
frequencies. Although individuals with TBI produced inter-limb synchrony and inter-limb
coherence within normative healthy control values, the within-group variability of inter-limb
synchrony was higher in individuals with TBI in both the AP and ML directions. Within-group
variability in inter-limb COP coherence among individuals with TBI was not as large as inter-limb
COP synchronization. Higher within-group variability among individuals with TBI has been
reported in reaction time tasks (Hetherington, Stuss, & Finlayson, 1996; Stuss, Pogue, Buckle, &
Bondar, 1994) and gait spatial and temporal parameters (Niechwiej-Szwedo et al., 2007). Intra-
individual variability has been shown to be an important feature in individuals with TBI in
reaction time tasks (Ghajar & Ivry, 2008; Hetherington et al., 1996). Without repeated
measures from healthy controls, a comparison of intra-individual variability between groups
was not feasible. Postural sway variability, however, can be inferred using the net COP RMS. In
Study 2, inter-limb synchrony in both the AP and ML directions was associated with ML RMS,
suggesting that greater postural sway variability in mechanisms that control loading and
unloading was linked with reduced inter-limb synchronization. Similar findings were also found
in individuals post-stroke (Mansfield et al., 2011), who also demonstrate similar heterogeneity
in these measures and slightly poorer inter-limb synchrony. The relationship between inter-
limb COP coherence and RMS and inter-limb synchronization was not explored. Inter-limb COP
coherence is a relatively novel analysis for inter-limb coordination and has shown to be a
sensitive measure in individuals with Huntington’s Disease (Myklebust et al., 2009). Individuals
with Huntington’s Disease produced reduced inter-limb coherence compared to healthy young
and older adults. This finding may have resulted from the application of a statistical confidence
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limit to the inter-limb COP coherence (Mima & Hallett, 1999; Rosenberg, Amjad, Breeze,
Brinllinger, & Halliday, 1989), which eliminated lower coherence values.
Though mean inter-limb COP coherence in individuals with TBI did not deviate outside
the 95% confidence interval of normative healthy control values at most time points after
injury, the increased coherence at high frequencies above the 95% confidence band at 12
months post-injury may suggest inappropriately high level of coupling between limbs with
respect to reactive balance control mechanisms in both the AP and ML directions. Findings from
Study 1 of this dissertation in younger healthy adults revealed that inter-limb COP
synchronization requires decoupling in preparation for unpredictable postural perturbations
(Habib Perez et al., 2016) and increased coupling in reactive balance mechanism may place
individuals at increased risk of falls. If the same mechanism of postural control from Winter et
al. (1993) are applied to inter-limb COP coherence, one would expect that inter-limb coherence
in the AP and ML directions results from ankle plantarflexors and dorsiflexors and ankle
invertors and evertors, respectively. Myklebust et al. (2009) described inter-limb coherence as
the similarity in frequencies in both limbs of the postural control system. Highly correlated
inter-limb coherence in frequencies below 0.4 Hz and above 0.4 Hz would suggest equal that
anticipatory and reactive control mechanism produced by the central postural control system
are equal in both limbs. Typically, postural control muscle activation patterns that correspond
to anticipatory and reactive control mechanism are reflected in fixed and stepping strategies
(Horak & Nashner, 1996; McIlroy & Maki, 1993), with few studies on the role of ankle
invertors/evertors in anticipatory and reactive control mechanisms (Henry, Fung, & Horak,
1998; Van Deun, Stappaerts, Levin, Janssens, & Staes, 2011). Side-alternating vibration training
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has been shown to increase muscle strength in ankle evertors (i.e. peroneus longus) and reduce
postural sway both directions, but with greater emphasis in the ML direction (Spiliopoulou,
Amiridis, Tsigganos, & Hatzitaki, 2013). As such, Spiliopoulou et al. (2013) suggest that ankle
evertors have a primary role in ML postural control; however, ankle plantarflexors have
demonstrated a role in lateral stability in ankle invertors (Vieira, Minetto, Hodson-Tole, &
Botter, 2013). Asymmetric ankle evertor strength and uncertain contributions from AP postural
control muscles may account for the variability in inter-limb coherence in individuals with TBI
that fall outside the normal range in comparison to healthy controls and the possible
impairments in the inter-limb coherence that are linked to reactive balance control. Alternately,
the increase in inter-limb COP coherence in high frequencies may reflect brain recovery and/or
structural reorganization post-TBI (Sidaros et al., 2008) illustrating greater similarities in the
output from the cortical regions of the central postural control system that are responsible for
ankle and hip postural responses in both the AP and ML direction (Hay & Wachowiak, 2017;
Jacobs & Horak, 2007). Thus, the increase in inter-limb COP coherence outside of the healthy
control range at 12-months post injury may reflect improvements but with greater variability in
this population. Given the mixed findings in the literature and the novelty of inter-limb COP
coherence from one other study (Myklebust et al., 2009), there is uncertainty whether the
increase in inter-limb COP coherence in high frequencies reflects improvements or impairments
in postural responses post-TBI.
One question that remains is whether the differences in balance outcomes observed
between neurologically intact individuals and individuals with TBI are also indicators of balance
impairment. Using the World Health Organization ICF definition, impairment is defined as
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problems in the physiological function or anatomical functions of the body that demonstrate
significant deviation or loss (World Health Organization, 2001). In practice, the absence of
impairment or a recovery process can be defined as a return of balance measures to premorbid
levels. Since premorbid balance measures were not available, Studies 2 and 3 used data from a
healthy control group to infer balance ability prior to injury. In some instances, there were clear
differences between TBI measures and confidence boundaries of the healthy control group;
however, this was not always the case. In addition, unlike many clinical measures, which have
established measurement properties that contribute to identification of minimally clinical
important differences (Wright, Hannon, Hegedus, & Kavchak, 2012), quantitative force platform
measures have identified measures that relate to increased falls risks in aging and
neurologically impaired populations (Maki, Holliday, & Topper, 1994; Mansfield, Mochizuki, et
al., 2012; Mansfield et al., 2015), though these measure do not have established minimal
clinical important differences. Thus, while it is possible to speculate that individuals with TBI
have impaired balance because of similarities in values to other neurological populations where
balance impairments are more clearly defined, it may be premature to say that balance ability
is impaired rather than just reduced.
6.2.4 Inter-limb Coordination and Asymmetry: Does it contribute to balance
impairment in traumatic brain injury?
As previously stated, the rationale for this dissertation was based on the position that
the balance impairments after TBI (Arce et al., 2004; Lehmann et al., 1990; Newton, 1995) may
be attributable to strength imbalances/asymmetries between limbs and coordination deficits
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(Drijkoningen, Caeyenberghs, Vander Linden, et al., 2015; Khan et al., 2016; Walker & Pickett,
2007). The question remains: does inter-limb coordination reflect asymmetry and does it
contribute to balance impairment in TBI? From the perspective that poor inter-limb COP
synchrony was associated with greater postural sway and that individuals with TBI
demonstrated persistent weight-bearing asymmetry across time, the answer to the question is
‘yes’. However, heterogeneity within the TBI group limits generalizability. In spite of this, the
importance and relevance of symmetry in balance control is clear. Under the assumption that
asymmetry and dyscoordination negatively impact balance control by: imposing temporal
delays in reactive control (Lakhani, Mansfield, Inness, & McIlroy, 2011; Mansfield et al., 2011),
inducing inefficient biasing of weight-bearing to the weaker or non-paretic limb (Geurts, de
Haart, van Nes, & Duysens, 2005), or reducing ability to optimize force generation to suit task
demands (Horak, Henry, & Shumway-Cook, 1997), then increasing symmetry and coordination
(within the context of the task) should improve balance and reduce fall risk. Though the risk
factors for falls are multi-factorial in TBI (McKechnie, Pryor, & Fisher, 2015, 2017; Tinetti,
Speechley, & Ginter, 1998) and can be unrelated to coordination, some factors, such as lower
extremity disability, which include reduced strength, sensation, and balance, are risk factors for
falls and can be influenced by symmetry and coordination.
At a physiological level, temporal synchronization of muscle activity is observed in
muscles that span the midline including masseter, diaphragm, and rectus abdominis (Carr,
Harrison, & Stephens, 1994) and to a lesser extent in bilateral soleus muscle during postural
tasks (Mochizuki et al., 2006) in healthy adults. While coordination across segments within a
limb (i.e. intra-limb coordination) is functionally relevant (Kelso, Buchanan, & Wallace, 1991),
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inter-limb coordination is arguably more impactful in bipedal stance and in individuals with
unilateral neurologic injury (Genthon et al., 2008; Wang, Forbes, Croft, Van der Loos, & Blouin,
2015; Wang & Newell, 2012). Importantly, bilateral synchronization of muscle activity is altered
following stroke (Farmer et al., 1993). Thus, it is possible that TBI could similarly disrupt the
processes that facilitate coordinated muscle activity and/or motor behaviour, as motoric
asymmetry in both the upper and lower limbs have been observed in individuals with TBI (Choi
et al., 2012; Hurt, Rice, McIntosh, & Thaut, 1998).
In a general sense, coordination implies organized muscle activity and segmental
movements while performing a task (Enoka & Pearson, 2013). To highlight this point,
individuals with hyperkinetic movement disorders (e.g. ataxia, chorea, dystonia, Huntington’s
Disease) experience disorganized motor control or involuntary movements through neurologic
disease or injury that limits task performance (Bakker et al., 2006; Bastian, 1997; Gibo, Bastian,
& Okamura, 2013). Unlike the relative absence of coordination found in movement disorders,
inter-limb coordination as presented in this dissertation refers to the extent of optimization or
refinement in the control of movement. A reduction in inter-limb coordination does not result
in an inability to stand upright; instead, reduced inter-coordination during upright bipedal quiet
standing cause less efficient balance control (i.e. more impaired), which could increase fall risk.
Therefore, the overall impaired coordination (asymmetry) after TBI that has been highlighted in
previous studies (Campbell & Parry, 2005; Khan, Baguley, & Cameron, 2003; Walker & Pickett,
2007), was noticeable in various inter-limb coordination balance control measures presented in
this dissertation.
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6.3 Balance Recovery after Traumatic Brain Injury
Previous studies examining the longitudinal changes in balance control after TBI have
investigated posturographic changes within the first few weeks post-injury (Wade, Canning,
Fowler, Felmingham, & Baguley, 1997), while other studies analyze overall balance measures
many years post-injury (Ponsford et al., 2014; Walker & Pickett, 2007). Two of the studies that
comprise this dissertation were able to evaluate posturographic balance control measures
across three times points after injury at two, five, and twelve months post-injury. Changes in
balance control measures were observed in net COP ML postural sway, duration in unipedal
stance, and inter-limb COP coherence. Improvements in balance control were observed from
two to five months of injury in ML postural sway, as demonstrated by a reduction of ML RMS
within these two time points. Consistent with previous studies (Wade et al., 1997; Walker &
Pickett, 2007), the greatest improvements in balance control occurred within the first six
months after TBI in previous studies. Mirroring the reduction in net COP ML RMS, a reduction
of the amplitude of the PSD in the high frequencies in ML direction were found from two to five
months post-injury. While there were no further improvements by 12 months post-injury in net
COP ML RMS, the amplitude in the PSD of the net COP in the high frequencies in the ML
direction continued to improve. The additional reduction in the PSD in high frequencies of the
net ML COP suggests that there were improvements in reactive balance control mechanism up
to 12 months post injury. Net ML postural sway is controlled by hip abductors and adductors,
and reflects the ability to load and unload the vertical force from one limb to another (Winter,
1995; Winter et al., 1993). As there were no changes in AP postural sway, the reduction in ML
sway five months after injury suggests that the organization of the CNS may prioritize the
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reduction ML RMS to increase stability and reduce risk of falls (Maki et al., 1994; Manor et al.,
2010; Mitchell, Collins, De Luca, Burrows, & Lipsitz, 1995). Additionally, the changes that were
detected in the spectral measures that were not apparent in the spatial measures of balance
suggest that spatial measures may lack sensitivity in identifying improvements or changes in
balance control in individuals with moderate-to-severe TBI.
Another variable that demonstrated improvements across recovery after TBI is duration
in unipedal stance. In the second study, the average time in unipedal stance increased by
approximately 10 seconds from two to five months post-injury, suggesting improvements in
strength and postural steadiness in both limbs (Jonsson, Seiger, & Hirschfeld, 2004). Unipedal
stance is a common task that is included in various clinical balance assessments and has been
associated with increased fall risk (Hurvitz, Richardson, Werner, Ruhl, & Dixon, 2000; Springer,
Marin, Cyhan, Roberts, & Gill, 2007). Prior studies examining unipedal stance duration after
moderate-to-severe TBI have reported reduced duration in patients with chronic TBI (Williams
& Morris, 2011) and similar unipedal stance time at five months post-injury, which is in
alignment with the months post-injury found in previous studies (McFadyen, Swaine, Dumas, &
Durand, 2003). Unipedal stance time has been associated with mobility (Williams & Morris,
2011), and thus unipedal stance may be an important assessment during the course of recovery
when other net measures of balance may not detect such improvements or when
posturographic measures are not feasible for data collection.
One measure of inter-limb coordination demonstrated changes post-injury, both in the
AP and ML direction. Inter-limb COP coherence increased between two and twelve months
post-injury in high frequencies. Though an increase in inter-limb COP coherence may be
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illustrative of improvements in inter-limb coordination, the increased coherence at high
frequencies in both the AP and ML direction may also suggest an inappropriately high level of
coupling between limbs, specifically, with respect to reactive balance control mechanisms.
Inter-limb coherence is a relatively novel posturographic measure of balance control, and
though the spectral analysis enabled inferences of reactive balance control, there is much need
to investigate the inter-limb coordination prior to reactive balance responses.
It should be noted that some measures of balance control did not identify
improvements over time in Study 2 and 3 of this dissertation. Net COP AP RMS and power
spectral density, inter-limb COP synchronization and inter-limb COP coherence in low
frequencies in both the AP and ML direction, and absolute stance load symmetry did not
improve across time. The absence of improvements in AP RMS, but reductions in ML RMS, in
individuals with TBI suggests that the organization of the CNS after injury may prioritize the
reduction of ML RMS to increase stability and reduce the risk of falls (Maki et al., 1994; Manor
et al., 2010; Mitchell et al., 1995). Though inter-limb COP synchronization did not improve
across time, secondary analyses of the relationship between COP RMS and inter-limb COP
synchrony suggest that reduced inter-limb synchrony was linked to greater ML postural sway.
Although absolute stance load symmetry did not change over time in Study 2, using absolute
stance load symmetry in study 3 provided further information that the higher weight-bearing
limb may be driving the amplitude of the COP oscillations for mechanisms that are important
for reactive balance control. Given the absence of improvement in several balance measures, it
is possible that cognitive and psychiatric factors affect the ability to recover their motor
impairments. Cognitive function after TBI in various cognitive domains has shown
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improvements from the 2-month to 5-month window, while only manual motor (i.e. grip
strength) and visuospatial domains continue to improve from the 5-month to 12-month
window with an upward trajectory (Christensen et al., 2008). There is evidence to suggest
cognitive and motor functions compete for resources throughout the course of recovery,
particularly in the latter half of the year post injury. (Green et al., 2006). The lack of
improvements in balance measures support the notion that motor impairments that affect
balance persist after injury and may result from competing resources to cognitive functions. It is
unclear as to why prioritization in cognitive function over motor function may have occurred in
the Study 2 of this dissertation; however, given that cognitive and motor dysfunction continues
post-injury and both resources are required for postural control, it is likely that individuals with
TBI are at higher risk of falls.
6.4 Limitations and Future Directions
Despite identifying temporal inter-limb measures in the preparation for postural
instability and also spatial, temporal, spectral measures of inter-limb coordination after
moderate-to-severe TBI, there are several limitations from these studies that will be addressed
to improve future investigations. Firstly, the experimenter in Study 1 standing immediately
behind the participant during externally-generated perturbations may have produced different
levels of arousal in participants and this was not measured. Increased levels of arousal may
have contributed to the large between-subject variability observed in inter-limb synchrony. One
method to address any effects of the experimenter (i.e. experimenter bias) in externally-
generated perturbations is to perturb individuals using a moving platform that will induce
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similar feet-in-place and change-in-support responses. Future studies may also benefit from
observing electrodermal activity during externally-generated perturbations, like in previous
studies (Maki & Whitelaw, 1993; Sibley, Mochizuki, Frank, & McIlroy, 2010), and how that may
relate to inter-limb synchrony. Understanding levels of arousal during the preparation for
postural instability would discern the postural strategies used in the coupling and decoupling of
AP and ML inter-limb synchronization, respectively.
Studies 2 and 3 evaluated balance control across time in individuals with moderate-to-
severe TBI. The sample size was significantly reduced as a result of the inclusion/exclusion
criteria to include participants who completed three time points. Though this was the first
study to conduct a longitudinal analysis with quantitative posturographic measures of balance
control in individuals with TBI, future studies may benefit from hierarchical linear modeling
(Gueorguiva & Krystal, 2004) to determine the recovery projection of balance control and the
within-subject variability in participants who missed a testing session. The use of a hierarchical
linear model would also address the variability in days post injury when balance was assessed
and the injury severity of individuals with TBI on the balance outcome measures.
Injury severity is typically calculated from Glasgow Coma Scale or the duration of post
traumatic amnesia; however, the complexity of TBI with respect to the type of brain injury (i.e.
diffuse and/or focal), the region of injury, or depth of injury may inform which type of injury
may account for the balance outcomes throughout recovery. Exploring whether there are
relationships with brain injury location and balance control or inter-limb coordination measures
will be beneficial in future studies, as it may identify the relationship between specific sites of
injury and specific balance impairments. Although, it should be noted that establishing brain-
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behaviour relationships in moderate-severe TBI is challenging. It is uncertain whether
individuals with poor balance control or inter-coordination may have increased injury in the
cortical regions that are required for coordination (Debaere et al., 2001). Furthermore,
although inter-limb COP synchronization and coherence did not differ from healthy control
normative values, the between-subject variability among individuals with TBI was greater than
the healthy controls. Future studies may benefit from identifying participants that lie within and
outside a confidence band of normative values and probe further with intra-individual
variability to establish which participants are at most risk of balance instability and potential
falls risk.
In the longitudinal analysis of balance control in TBI participants, there was significant
body weight gain across the 12 months post-injury. Body weight may have influenced the
quantitative balance measures, as an increase in body weight may decrease postural stability
(Hue et al., 2007). Future studies may profit from obtaining approximate premorbid weight
values, as it was uncertain whether the 8 to 9 kg weight gain was a return to premorbid weight
or related to inactivity following injury, and whether the overall increase in COP RMS resulted
from increased weight. Moreover, given that individuals with TBI were recruited through an
inpatient facility, the recovery of balance during the first five months may have been influenced
by inpatient and outpatient rehabilitation. Future longitudinal studies may benefit from
tracking the types and frequency of rehabilitation provided.
Static balance control is only one aspect of balance control and without testing
individuals in dynamic balance environments the study is limited to understand the falls risks
after TBI. Although this was beyond the scope of this dissertation, it is recommended that
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future studies investigate anticipatory and reactive balance control with quantitative
posturographic measures across the span of recovery after TBI. Individuals with TBI are more
susceptible to higher levels of anxiety post injury (Osborn, Mathias, Fairweather-Schmidt, &
Anstey, 2016) and increased levels of anxiety has been shown to alter balance control strategies
(Adkin, Frank, Carpenter, & Peysar, 2000; Carpenter, Frank, & Silcher, 1999; Carpenter, Frank,
Silcher, & Peysar, 2001; Sturnieks, Delbaere, Brodie, & Lord, 2016). Additionally, increased
levels of anxiety have found to alter muscular and cortical neurophysiological signals in
response to postural perturbations (Adkin, Campbell, Chua, & Carpenter, 2008; Sibley et al.,
2010). Thus, electrodermal activity, which can measure levels of stress or anxiety, during
perturbation testing is highly suggested in future balance studies in individuals with TBI.
In all three studies, where young and age matched healthy controls were assessed,
balance-specific medical history questionnaires were used in the inclusion criteria to exclude
anyone with musculoskeletal injuries or neurological injuries that may affect the findings.
Though this is a standard practice in most studies, it is important to consider that future studies
that include an age-matched control group may benefit from obtaining measures for sensory
systems through additional assessments in both the control and clinical group. High variability
in balance measures in the control group may have been attributed to age-related changes
(Abrahamova & Hlavacka, 2008). Additionally, high variability in individuals with TBI may be
have been attributed to the plantar cutaneous sensation deficits post injury, as balance
impairments post-stroke have been associated with plantar cutaneous sensation deficits
(Parsons, Mansfield, Inness, & Patterson, 2016). Thus, the inclusion of other sensory
assessments such as monofilament assessments of the feet (Collins, Visscher, De Vet,
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Zuurmond, & Perez, 2010) and joint proprioception (Han, Waddington, Adams, Anson, & Liu,
2016) will further inform differences in balance measures found between a control group and
individuals with TBI and within individuals with TBI.
In revisiting the conceptual model of this dissertation (Figure 2-3), the present work
addressed how sensorimotor impairments affect balance control and in turn inter-limb
coordination. Specifically, this dissertation identified how asymmetry affects balance control
and inter-limb coordination in balance control, and how inter-limb coordination with respect to
balance control mechanism was altered after TBI. Though the current study focused primarily
on the balance after TBI, there are many sequelae of TBI that need to be taken into
consideration in future studies. The interplay of cognitive and sensorimotor factors that may be
competing for resources may be further compromised in environments that increase anxiety or
in individuals that are predisposed to psychiatric conditions, such as depression.
6.5 Implications for Rehabilitation
From one study of this dissertation there is considerable evidence demonstrating that
individuals with moderate-to-severe TBI experience balance asymmetry at 2 months post-injury
and that this does not improve significantly across time. These findings support qualitative
observations of balance asymmetry of previous work (Newton, 1995). Given the motor
impairments found in balance control after TBI, it is important to note that rehabilitation
interventions that address balance asymmetry between the 2-month and 5-month window post
injury, when the majority of recovery is visible across time, may benefit individuals with
moderate-to-severe TBI. Unilateral lower-extremity strength training on both limbs (Vinstrup et
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al., 2017) and symmetrical body-weight distribution training (Cheng, Wu, Liaw, Wong, & Tang,
2001) have led to balance asymmetry improvements in individuals with stroke, who typically
experience greater balance asymmetry (Cheng et al., 2001). Other types of rehabilitation that
have been proposed to improve balance in individuals with TBI include ballistic balance training
(Williams et al., 2016) or dynamic balance training that intend to improve specific white matter
regions (Drijkoningen, Caeyenberghs, Leunissen, et al., 2015). The combination of balance
training with a focus on asymmetry presents an attractive option for possible future
interventions.
The current dissertation also identified a high degree of coupling when preparing for
predictable and low magnitude perturbations, while unpredictable perturbations with variable
magnitudes produced lower levels of coupling. In addition, balance impairments corresponding
to both anticipatory and reactive balance control mechanisms demonstrated greater postural
sway amplitudes, which deviated from healthy controls. Identifying dynamic balance control
components that may be impaired, such as stepping reaction time, anticipatory postural
adjustments, muscle onset and magnitude is critical for further understanding balance
impairments after TBI. Furthermore, given the cognitive sequelae post-TBI (Azouvi, Vallat-
Azouvi, & Belmont, 2009; Dockree & Robertson, 2011; Rabinowitz & Levin, 2014), rehabilitation
interventions that increase salience of balance perturbations by pairing them with other
sensory stimuli or cues may benefit the balance performance of individuals with TBI. For
example, bouts of postural perturbations paired with auditory cues reduced reaction times
when postural perturbations were no longer present (Lakhani, Miyasike-Dasilva, Vette, &
McIlroy, 2013). In addition to an increased arousal, the study demonstrated that the CNS can
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adapt to become more prepared with this type of exposure (Lakhani et al., 2013). Additionally,
increased somatosensory stimuli through haptic light touch contact reduced COP variability just
prior to predictive voluntary perturbations and also after predictive voluntary and external
reactive postural perturbations (Johannsen, Wing, & Hatzitaki, 2007). In consideration of the
findings of Study 2 that, on an individual level, improvements in balance measures over time
were at times dissociated from improvements in cognitive measures, the potential utility of
combining cuing with perturbations may only be appropriate for some individuals. Combining
additional sensory stimuli or cues with perturbations may only benefit individuals with TBI who
demonstrate improvements in cognitive measures, as the ability to process additional cognitive
cues or sensory information may positively affect postural timing and magnitude components
of dynamic balance control.
6.6 Summary and Conclusions
The overall purpose of this dissertation was to further understand the role of inter-limb
coordination in postural control and determine the extent to which inter-limb coordination is
altered in individuals following moderate-to-severe TBI. Outcomes from the studies presented
in this dissertation demonstrated that inter-limb synchronization decreased as the magnitude
of balance perturbations became more unpredictable. These results indicated that the CNS
scales the physiological readiness to the magnitude of the largest perturbation magnitude by
decoupling the inter-limb coordination in order to optimize balance responses. Furthermore,
balance control and inter-limb coordination was investigated in individuals with moderate-to-
severe TBI. The outcomes of the latter studies demonstrated balance asymmetry and poor
158
balance control in individuals with TBI. There were few measure of balance control that
demonstrated improvements across recovery including, net COP ML postural sway in both RMS
and power spectral density, duration in unipedal stance, and COP inter-limb coherence. The
majority of improvements in these balance measures were observed between 2-months and 5-
months post-injury, while COP inter-limb coherence demonstrated improvements by 12-
months post-injury. AP postural sway and balance asymmetry did not improve after TBI and the
majority of balance measures were outside the 95% confidence interval of healthy control
values, indicating that balance impairments persist a year after injury. Spectral analyses of the
COP balance data after TBI demonstrated that both anticipatory and reactive balance control
mechanisms, visible in low and high frequencies in the COP spectra, were also impaired after
injury. Two main conclusions can be drawn from these experiments: inter-limb coordination is
important to the preparation of postural instability; and, asymmetry in balance control may be
a driver to poor balance control and inter-limb coordination after TBI that affect both
anticipatory and reactive balance control mechanisms. Future studies examining balance
control after TBI need to incorporate dynamic balance tests, including anticipatory and reactive
balance responses, to further understand the risk of falling post-TBI and how to better provide
interventions for this population.
159
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Appendices
Appendix A – Relationship between inter-limb synchrony and mediolateral postural sway
variability
Appendix A Figure 1 – Scatter plots and lines of best fit displaying the relationship between
inter-limb synchrony and net COP mediolateral (ML) postural sway variability (RMS) for TBI
participants. Relationships between anteroposterior (AP) inter-limb synchrony (Rxy(0)) and net
COP ML RSM at T1 (A), T2 (B) and T3 (C) are plotted. Relationships between ML inter-limb
synchrony and net COP ML RMS at T1 (D), T2 (E), and T3 (F) are also plotted. Statistically
significant correlations were found in the relationships presented in panels A-E.