Examining the Cognitive Consequences of White Matter Tract Damage in Mild Traumatic Brain Injury

While no marked impairment was found on conventional neuropsychological tests, changes to white matter tract microstructure were apparent 6-10 weeks following mTBI on measures of cogniti

Examining the Cognitive Consequences of White Matter

Tract Damage in Mild Traumatic Brain Injury

Lucy Elisabeth Oehr ORCID: 0000-0002-3144-7580

July 2019

Melbourne School of Psychological Sciences

The University of Melbourne

This thesis was submitted in partial fulfillment of the requirements for Doctor of

Philosophy/Master of Psychology (Clinical Neuropsychology)

Mild traumatic brain injury (mTBI) has been the subject of an enormous body of research spanning back to the 10th century Despite the sheer volume of studies in this area, mTBI is still poorly understood, particularly in terms of cognitive consequences and injury to white matter tracts in the brain The research project central to this thesis employed innovative and novel cognitive and diffusion weighted-imaging (DWI) techniques The overarching aim of this project was to provide a more sensitive and specific examination of cognition and white matter tract damage following mTBI, than currently exists in the literature The thesis also aimed to examine the relationship between cognition and white matter tract damage following mTBI A Systematic Review and Meta-Analysis (Chapter 5) was conducted

to examine the current understanding of this relationship; the findings revealed that this area has received relatively little attention within the broader body of mTBI research

The study central to this thesis included a sample of patients with mTBI and a matched trauma control (TC) group who were recruited from the largest tertiary referral trauma hospital in Australasia, The Alfred hospital (Melbourne, Victoria, Australia) Approximately 6-10 weeks post-injury, participants returned to the hospital to take part in a comprehensive examination involving a neuropsychological

assessment, select questionnaire measures, and a magnetic resonance imaging

(MRI) scan that included DWI sequences

Study One comprised findings of the neuropsychological assessment The assessment was comprehensive in scope and included conventional “paper-and-pen” tests as well as two computer-based tasks designed to place greater load on cognitive systems The design of the neuropsychological assessment addressed key limitations of existing research, such as the use of brief cognitive screens or use

of only a small number of cognitive measures The findings of Study One revealed that the mTBI and TC groups performed similarly on conventional neuropsychological assessment tools; however, novel computerised cognitive measures revealed impairments specifically in the mTBI group

Study Two addressed key limitations of DWI-based research in mTBI, namely

the predominant use of diffusion-tensor imaging (DTI) The data acquisition,

processing, and tractography-based methods were designed to provide a more thorough and specific examination of white matter tract microstructure following

mTBI By use of an additional DWI-based modelling technique, neurite orientation

dispersion and density imaging (NODDI), Study Two revealed more specific

information about white matter tract microstructural changes relative to existing based research Within white matter tracts commonly implicated in mTBI, the combined use of DTI and NODDI revealed alterations to quantitative DWI metrics suggestive of axonal injury and degeneration

DTI-The third study included in this thesis built upon the findings of the Systematic Review and Meta-Analysis and examined the relationship between white matter tract microstructure and cognition in both the mTBI and TC groups Study Three

within the two groups For the TC group, higher cognitive performance was associated with DWI metrics suggestive of greater diffusion of water molecules along fibre bundles, as well as greater density and coherence of white matter tracts These associations were consistent with expectations based on existing research within healthy adult populations In contrast, within the mTBI group, no associations were observed with measures of speed of information processing or attention While there were a small number of significant associations in the mTBI group between DWI metrics and performance on measures of memory and executive function, the relationships were in the opposite direction to those observed in the TC group That

is, higher cognitive performance was associated with reduced diffusion of water molecules along fibre bundles, and both lower density and coherence of fibres within white matter tracts These associations were consistent across white matter tracts examined and cognitive domains

Collectively, the studies included in this thesis make a significant contribution

to the mTBI research literature While no marked impairment was found on conventional neuropsychological tests, changes to white matter tract microstructure were apparent 6-10 weeks following mTBI on measures of cognition that are sensitive to mTBI-related neuropathological change Use of NODDI in conjunction with DTI revealed more specific information about underlying white matter microstructural changes following mTBI, relative to existing research Furthermore, the discrepancy in findings between the mTBI and TC groups indicates that there is an abnormal relationship between white matter microstructure and cognitive function 6-10 weeks following mTBI

This is to certify that:

- this these comprises only my original work in partial fulfilment of the Master

of Psychology/Doctor of Philosophy (Clinical Neuropsychology) except where indicated in the preface;

- due acknowledgement has been made in the text to all other material used;

- thesis is fewer than the maximum word limit in length, exclusive of tables, maps, bibliographies and appendices

Lucy Oehr

The results presented in this thesis represent a sub-sample of a broader research project led by Dr Jacqueline Anderson and based at The Alfred hospital, Melbourne, Victoria, Australia Graduate student researchers enrolled in the Master of Psychology (Clinical Neuropsychology) degree between 2014 and 2019 were involved with recruitment and data collection The neuroimaging component of the study commenced in 2017 and was conducted in collaboration with the Baker IDI Heart and Diabetes Institute, The Alfred Centre, Melbourne, Victoria, Australia MRI technicians at the Baker IDI Heart and Diabetes Institute assisted with neuroimaging data collection Dr Marc Seal (Murdoch Children’s Research Institute) and Dr Benjamin Schmitt (Siemens) both contributed to the development of the MRI scanner protocol Finally, the Developmental Imaging lab of the Murdoch Children’s Research Institute assisted with development of the neuroimaging data pre-processing pipeline for this study

All data included in this thesis—including both cognitive and neuroimaging data—were prepared and processed by me and represent my own original work Statistical analyses were planned and results interpreted in consultation with my supervisors and Dr Sandy Clarke from the Melbourne Statistical Consulting Platform, The University of Melbourne No included work this thesis was carried out prior to enrolment in this current degree No third-party editorial assistance was provided in preparation of this thesis

The included manuscript presented in this thesis was drafted entirely by me The co-author, Dr Jacqueline Anderson, contributed to the initial planning and conception of the manuscript, and was involved with revising and editing drafts She has agreed to use of the manuscript in this thesis and has provided a signed copy of the co-author authorisation form

Finally, I would like to acknowledge funding received including an Australian Government Research Training Program Scholarship, including fee offset scholarship, and the Melbourne Neuroscience Institute STRAPA Award.

Publications and Conference Abstracts During Candidature

Oehr, L., & Anderson, J (2017) Diffusion-Tensor Imaging Findings and Cognitive Function Following Hospitalized Mixed-Mechanism Mild Traumatic Brain Injury: A

Systematic Review and Meta-Analysis Archives of physical medicine and

rehabilitation, 98(11), 2308-2319

Oehr, L.E., Yang, J Y M., Chen, J., Seal, M L., & Anderson, J F I (2018, July) Quantifying the impact of mild traumatic brain injury on the relationship between white matter tract damage and cognitive function Platform presentation at the International Neuropsychological Society 2018 Mid-Year Meeting, Prague, Czech Republic

Oehr, L & Anderson, J (2017, June) Is structural neuropathology associated with

cognitive functioning following mild traumatic brain injury? Poster session

presented at the 40th ASSBI Brain Impairment Conference, Melbourne, Australia

Manuscripts to Arise from this Thesis Currently in Preparation

Oehr, L., & Anderson, J Are conventional neuropsychological tests sufficiently sensitive to detect subtle cognitive changes following mild traumatic brain injury? A comparison of conventional and novel measures

Oehr, L., Yang, J., Chen, J., Maller, J., Seal, M., & Anderson, J Moving beyond DTI: an investigation of sophisticated diffusion-weighted imaging techniques in the study of white matter tract microstructural changes following mild traumatic brain injury

Oehr, L., Seal, M., Chen, J., Maller, J., Yang, J., & Anderson, J Investigating the cognitive consequences of white matter tract microstructural changes two-months following mild traumatic brain injury

Awards

Australian Government Research Training Program Scholarship (2016–2018) Melbourne Neuroscience Institute STRAPA Award (2016–2018)

First and foremost, I would like to thank my primary supervisor, Dr Jacqueline Anderson for your consultation, advice, and guidance throughout my candidature

I am also very grateful for your wise and honest council which both prompted me

to decide to commence my PhD, and to persevere despite the many challenges that I faced along the way During the course of my candidature, I was privileged to work with J in three different roles; you’ve taught me so much beyond this research and been a very important mentor to me I look forward to working with you on future projects

I would like to thank my co-supervisor, Dr Marc Seal, for making the neuroimaging component of this project possible and for welcoming me to the Developmental Imaging lab Not only did you provide unwavering support throughout the thorny process of learning to master the art of diffusion MRI data processing, but you did so with great patience, and you were well attuned to my well-being I am grateful for your wit (and vast supply of gifs) and of course your wise academic council, prompting me to consider the broader issues posed by my research and the question of what it all means

To Dr Joseph Yang, my external supervisor, I would like to thank you firstly for introducing me to the truly amazing potential of DWI and its applications beyond mTBI research I would also like to extend my sincere gratitude for the many hours you spent helping me learn the techniques of processing diffusion MRI data and

tractography I feel very privileged to have been taught by such an experienced neurosurgeon

I would also like to thank Professor Rob Hester, the Chair of my Advisory Committee, for his guidance throughout this project and for his support when things didn’t quite go to plan To Professor Sarah Wilson, for your thoughtful input and contribution, particularly with regards to the broader clinical implications of my area

of research

To the Developmental Imaging team at the Murdoch Children’s Research Institute, thank you all—particularly Jian Chen—for all of your assistance during this project I would also like to thank the MRI technicians at the Baker IDI Heart and Diabetes Institute for your assistance with data acquisition And to my participants, thank you all for your generosity in giving your time and for being a part of this project It wouldn’t have been possible without each and everyone one of you

To my family, I would like to dedicate this thesis My parents, Tarquin and Christine, have been tremendous supports throughout my whole life Without them,

my somewhat luxurious pursuit of academia would not have been possible You’ve both taught me so much about life and I feel very privileged to have you as parents Thank you To my sister Alice, with whom I lived for the majority of this thesis, I owe you a special thanks for being understanding and supportive during the many tense and challenging moments that arose during this project Alice is a great friend and has been an endless source of support and encouragement throughout this thesis,

and my life more generally And of course, Lola, my cat, thank you for sitting patiently on my lap/desk while I typed away and only occasionally walking across the keyboard

While there are many people that I would like to thank for their support throughout this thesis, I will name only a few Christy, you were a truly amazing woman and one of those rare people who truly listens I miss you dearly Theo, I feel very lucky to have you in my life Thank you for always believing in me, supporting me, keeping me sane, and for being hilarious To my other close friends, thank you for sticking with me during my reclusive periods throughout this thesis and for patiently trying to understand what it is about! My colleagues, past and present, for being such great company and for being so accommodating throughout this process To the other notable people in my community, thank you all for encouraging me to keep going! Chris and Isabel for supporting my many sanity-preserving knitting projects, my running friends at AM:PM and VRR for your endless support and excellent company while pounding the pavement All of these people and many more contributed in some way, and I am very grateful

TABLE OF CONTENTS ABSTRACT I DECLARATION IV PREFACE V ACKNOWLEDGEMENTS XII TABLE OF CONTENTS XII LIST OF TABLES XVIII LIST OF FIGURES XXII LIST OF ABBREVIATIONS XXV

CHAPTER 1 OVERVIEW 1

INDIVIDUAL OUTCOME AFTER MTBI AND THE SUBJECTIVE EXPERIENCE OF RECOVERY 2 1.1.1 Psychological and environmental models of persistent symptoms 3

1.1.2 Neuropathological models of persistent symptoms 5

PERSISTENT SUBJECTIVE SYMPTOMS AND OBJECTIVE MEASURES OF OUTCOME 6

OBJECTIVE COGNITIVE OUTCOME FOLLOWING MTBI 8

OBJECTIVE MEASURES OF NEUROPATHOLOGICAL OUTCOME FOLLOWING MTBI 9

TOWARDS A MORE PRECISE EXAMINATION OF OBJECTIVE OUTCOME FOLLOWING MTBI 10

CHAPTER 2 THE CONCEPT OF MILD TRAUMATIC BRAIN INJURY 13

DEFINING MTBI:DIAGNOSIS AND CLINICAL IDENTIFICATION 13

BIOMECHANICS OF INJURY AND THE IMPACT ON THE BRAIN 15

2.2.1 The consequences of acceleration, deceleration and rotational forces 15

2.2.2 Neurochemical changes following mTBI and the neurometabolic cascade 19

2.2.3 Diffuse axonal injury 20

2.2.4 Quantifying microstructural damage in white matter tracts based on DWI models 31

CHAPTER 3 USING DIFFUSION MRI TO INVESTIGATE NEUROPATHOLOGY

FOLLOWING MTBI 32

FROM HISTOLOGY TO BIOMARKERS OF NEUROPATHOLOGY 32

DIFFUSION-WEIGHTED IMAGING 34

3.2.1 Diffusion tensor imaging 34

3.2.2 Limitations of the diffusion tensor model 38

3.2.3 Beyond the tensor model—higher-order DWI models 40

3.2.4 Neurite Orientation Dispersion and Density Imaging 45

RECONSTRUCTING WHITE MATTER PATHWAYS USING TRACTOGRAPHY 47

DWI USED IN MTBI RESEARCH 51

3.4.1 DTI findings following mTBI 52

3.4.2 Advanced DWI-based techniques in mTBI 56

CHAPTER 4 COGNITION AFTER MILD TRAUMATIC BRAIN INJURY 64

COGNITION IN THE EARLY ACUTE TO SEMI-ACUTE PERIOD 64

COGNITIVE FUNCTION IN THE CHRONIC PERIOD 66

4.2.1 Summary statistics and reviews—adding clarity or confusion? 69

4.2.2 The contribution of methodological variation 71

RISK FACTORS FOR POOR COGNITIVE OUTCOME 73

NEUROPSYCHOLOGICAL TOOLS IN THE INVESTIGATION OF MTBI-RELATED COGNITIVE DYSFUNCTION 75

CHAPTER 5 EXAMINING RELATIONSHIPS BETWEEN MEASURES OF WHITE MATTER MICROSTRUCTURE AND COGNITION: 83

HOW NEUROIMAGING CHANGED THE STUDY OF COGNITION 83

5.1.1 Relationships between cognition and white matter tract microstructure within healthy adult population 84

PUBLISHED MANUSCRIPT:DIFFUSION-TENSOR IMAGING FINDINGS AND COGNITIVE FUNCTION FOLLOWING HOSPITALIZED MIXED-MECHANISM MILD TRAUMATIC BRAIN INJURY:ASYSTEMATIC REVIEW AND META-ANALYSIS 88

5.2.1 Abstract 88

5.2.2 Introduction 90

5.2.3 Methods 94

5.2.4 Results 97

5.2.5 Discussion 111

5.2.6 Conclusions 118

5.2.7 Supplier 118

5.2.8 Corresponding author 119

5.2.9 Appendix 1 Full Electronic Search Strategy 119

5.2.10 References 120

AN UPDATE ON COGNITION AND WHITE MATTER TRACT RELATIONSHIPS IN MTBI 128

CHAPTER 6 CURRENT STUDY RATIONALE, RESEARCH QUESTIONS, AND STUDY AIMS 132

RESEARCH QUESTIONS AND AIMS 132

CHAPTER 7 METHOD (PART 1) 136

PARTICIPANTS 136

MEASURES 137

7.2.1 Neuropsychological examination 137

7.2.2 Self-report measures 138

7.2.3 Neuroimaging 140

PROCEDURE 141

7.3.1 Recruitment 141

7.3.2 Neuropsychological assessment and neuroimaging 141

STATISTICAL ANALYSES 142

7.4.1 Analysis for Study One (Chapter 9): investigating group differences in cognitive function 143

7.4.2 Analysis for Study Two (Chapter 10): Investigating group differences in white matter tract microstructural metrics 153

7.4.3 Analysis for Study Three (Chapter 12): Investigating relationships between cognition and neuroimaging findings 156

CHAPTER 8 METHOD (PART 2) 158

NEUROPSYCHOLOGICAL EXAMINATION 158

8.1.1 Wechsler Test of Adult Reading 158

8.1.2 Symbol Digit Modalities Test 160

8.1.3 Wechsler Adult Intelligence Scale-Fourth Edition 162

8.1.4 Trail Making Test 163

8.1.5 Rey Auditory Verbal Learning Test 165

8.1.6 Rey Complex Figure Test 167

8.1.7 Victoria Stroop Test 168

8.1.8 Controlled Oral Word Association Test 170

8.1.9 Ruff Figural Fluency Test 172

8.1.10 N-back task 174

8.1.11 Increasing–Distractors Paradigm 177

SELF-REPORT MEASURES 181

8.2.1 Short-Form McGill Pain Questionnaire–2 182

8.2.2 Multidimensional Fatigue Inventory 183

8.2.3 Pittsburgh Sleep Quality Index 184

8.2.4 RAND 36-Item Health Survey 1.0 185

8.2.5 Inventory of Depressive Symptomatology (IDS) 186

8.2.6 Beck Anxiety Inventory (BAI) 187

8.2.7 PTSD Checklist for DSM-5 (PCL-5) 189

8.2.8 Rivermead Post-Concussive Symptoms Questionnaire 190

NEUROIMAGING 191

8.3.1 Data acquisition 191

8.3.2 DWI pre-processing 192

8.3.3 Fibre orientation distribution estimation 192

8.3.4 Diffusion parametric maps 193

8.3.5 Tractography 194

CHAPTER 9 STUDY ONE: INVESTIGATING GROUP DIFFERENCES IN COGNITIVE FUNCTION 208

DEMOGRAPHIC VARIABLES 208

RESULTS OF BETWEEN-GROUP COGNITIVE TEST RESULTS 210

SUMMARY OF KEY FINDINGS 215

CHAPTER 10 STUDY TWO: INVESTIGATING GROUP DIFFERENCES IN

WHITE MATTER TRACT MICROSTRUCTURAL METRICS 216

NEUROIMAGING VARIABLES 216

10.1.1 Corpus callosum 219

10.1.2 Superior longitudinal fasciculus 221

10.1.3 Inferior Longitudinal Fasciculus 225

10.1.4 Uncinate fasciculus 227

10.1.5 Anterior corona radiata 229

SUMMARY OF KEY FINDINGS 231

CHAPTER 11 STUDY THREE: INVESTIGATING RELATIONSHIPS BETWEEN COGNITIVE AND NEUROIMAGING FINDINGS 232

SYMBOL DIGIT MODALITIES TEST 233

11.1.1 Results of overall regression models 233

11.1.2 SDMT and FA 238

11.1.3 SDMT and ADC 240

11.1.4 SDMT and ICVF 242

ATTENTION INDEX 244

11.2.1 Results of overall regression models 244

11.2.2 Attention Index and ODI 245

MEMORY INDEX 246

11.3.1 Results of overall regression models 246

11.3.2 Memory Index and FA 248

11.3.3 Memory Index and ADC 250

11.3.4 Memory Index and ICVF 252

EXECUTIVE FUNCTION INDEX 254

11.4.1 Results of overall regression models 254

11.4.2 Executive Function Index and FA 256

11.4.3 Executive Function Index scores and ADC 258

11.4.4 Executive Function Index and ODI 259

11.4.5 Executive Function Index and ICVF 260

INCREASING DISTRACTORS PARADIGM 262

11.5.1 Results of overall regression models 262

11.5.2 IDP Level 1 and DWI metrics 264

11.5.3 IDP Level 3 and DWI metrics 266

N-BACK TASK 268

11.6.1 Results of overall regression models 268

11.6.2 1-back performance and DWI metrics 269

SUMMARY OF STUDY 3 272

CHAPTER 12 DISCUSSION 275

SUMMARY OF KEY FINDINGS 275

COGNITIVE OUTCOME 279

NEUROANATOMICAL CORRELATES OF MTBI 284

RELATING NEUROANATOMICAL FINDINGS TO COGNITION 294

STRENGTHS AND LIMITATIONS OF THIS RESEARCH PROJECT 302

FUTURE RESEARCH DIRECTIONS 308

CONCLUSIONS 310

REFERENCES 314

APPENDIX A 399

APPENDIX B 406

APPENDIX C 417

SDMT 418

MEMORY INDEX 427

EXECUTIVE FUNCTION INDEX 432

INCREASING DISTRACTORS PARADIGM (IDP) TASK 437

N-BACK TASK 441

LIST OF TABLESTable 1 List of neuropsychological tests in order of administration for all participants

138 Table 2 List of questionnaire measures in order of administration for all participants

139 Table 3 Pattern matrix for three component solution displaying cognitive variables

arranged by component 149 Table 4 Structure matrix for three component solution displaying cognitive variables

arranged by component 149 Table 5 White matter tracts of interest and corresponding ROIs used for

tractography 196 Table 6 Demographic variables for mTBI and TC groups 209 Table 7 Descriptive statistics for cognitive outcome measures for mTBI and TC

groups 211 Table 8 Summary of ANCOVAs for cognitive outcome measures 212 Table 9 Summary of ANCOVAs for IDP levels controlling for Baseline performance

212 Table 10 Adjusted values for group means and confidence intervals for median

reaction time on IDP levels 1–5 following adjustments for the covariates (Baseline IDP median reaction time; days between injury and assessment) 213 Table 11 Summary of ANCOVAs for the n-back task, controlling for Baseline

reaction time 214 Table 12 Adjusted values for group means and confidence intervals for median

reaction time on n-back task following adjustments for the covariates back level median reaction time; days between injury and assessment)215 Table 13 Descriptive statistics for DWI metrics for all white matter tracts for mTBI

(0-and TC groups 217 Table 14 Summary of ANCOVAs for the corpus callosum 220 Table 15 Summary of ANCOVAs for the right superior longitudinal fasciculus 222 Table 16 Summary of ANCOVAs for the left superior longitudinal fasciculus 224

Table 17 Summary of ANCOVAs for the right inferior longitudinal fasciculus 226

Table 18 Summary of ANCOVAs for the left inferior longitudinal fasciculus 227

Table 19 Summary of ANCOVAs for the right uncinate fasciculus 228

Table 20 Summary of ANCOVAs for the left uncinate fasciculus 229

Table 21 Summary of ANCOVAs for the right anterior corona radiata 230

Table 22 Summary of ANCOVAs for the left anterior corona radiata 231

Table 23 Summary of regression results showing F statistics for group x DWI metric interactions for the SDMT arranged by tract and further divided by DWI model 234

Table 24 Results of multiple regression analyses for FA predicting SDMT performance in TC group 239

Table 25 Results of multiple regression analyses for ADC predicting SDMT performance in the TC group 241

Table 26 Results of multiple regression analyses for ICVF predicting SDMT performance in the TC group 243

Table 27 Results of multiple regression analyses for ODI predicting Attention index scores in the TC group 246

Table 28 Results of multiple regression analyses for ODI predicting Attention index scores in the mTBI group 246

Table 29 Summary of regression results showing F statistics for group x DWI metric interactions for the Memory index scores arranged by tract and further divided by DWI metric 247

Table 30 Results of multiple regression analyses for FA predicting Memory Index scores in the TC group 249

Table 31 Results of multiple regression analyses for FA predicting Memory Index scores in the mTBI group 249

Table 32 Results of multiple regression analyses for ADC predicting Memory Index scores in the TC group 251

Table 33 Results of multiple regression analyses for ADC predicting Memory Index scores in the mTBI group 252

Table 34 Results of multiple regression analyses for ICVF predicting Memory Index scores in the TC group 253

Table 35 Results of multiple regression analyses for ICVF predicting Memory Index

scores in the mTBI group 253 Table 36 Summary of regression results showing F statistics for group x DWI metric

interactions for the Executive Function index scores arranged by tract and further divided by DWI metric 255

Table 37 Results of multiple regression analyses for FA predicting Executive

function index scores in the mTBI group 257

Table 38 Results of multiple regression analyses for ADC predicting Executive

function index scores in the TC group 258 Table 39 Results of multiple regression analyses for ADC predicting Executive

function index scores in the mTBI group 258 Table 40 Results of multiple regression analyses for ODI predicting Executive

Function index scores in the mTBI group 259 Table 41 Results of multiple regression analyses for ICVF predicting Executive

Function index scores in the mTBI group 261 Table 42 Summary of regression results showing F statistics for group x DWI metric

interactions for Level 1 of the IDP task arranged by tract and further divided

by DWI metric 263 Table 43 Results of multiple regression analyses for FA predicting TC group

performance on Level 1 of the IDP 264 Table 44 Results of multiple regression analyses for ADC predicting TC group

performance on Level 1 of the IDP 265 Table 45 Results of multiple regression analyses for ODI predicting TC group

performance on Level 1 of the IDP 265 Table 46 Results of multiple regression analyses for ICVF predicting TC group

performance on Level 1 of the IDP 265 Table 47 Results of multiple regression analyses for FA predicting TC group

performance on Level 3 of the IDP 267 Table 48 Results of multiple regression analyses for FA predicting mTBI group

performance on Level 3 of the IDP 267 Table 49 Results of multiple regression analyses for ICVF predicting TC group

performance on Level 3 of the IDP 267

Table 50 Results of multiple regression analyses for ICVF predicting mTBI group

performance on Level 3 of the IDP 268 Table 51 Results of multiple regression analyses for FA predicting TC group

performance on the 1-back level of the n-back task 270 Table 52 Results of multiple regression analyses for FA predicting mTBI group

performance on the 1-back level of the n-back task 270 Table 53 Results of multiple regression analyses for ODI predicting TC group

performance on the 1-back level of the n-back task 271 Table 54 Results of multiple regression analyses for ODI predicting mTBI group

performance on the 1-back level of the n-back task 271 Table 55 Summary of overall regression models results displaying all significant

interactions between group and DWI metrics for all cognitive outcome measures and white matter tracts 274

LIST OF FIGURES

Figure 1 Virtual reconstruction of the CC based on the DTI model and a

deterministic tractography algorithm (see Chapter 3, Section 3.3.) 24

Figure 2 Virtual reconstruction of the superior longitudinal fasciculus based on the

DTI model and a deterministic tractography algorithm (see Chapter 3, Section 3.3.) 26

Figure 3 Tractography reconstruction of the superior longitudinal fasciculus

demonstrating the anterior indirect (green), posterior indirect (yellow), and direct (red) segments 26

Figure 4 Virtual reconstruction of the inferior longitudinal fasciculus based on the

DTI model and a deterministic tractography algorithm (see Chapter 3, Section 3.3.) 28

Figure 5 Virtual reconstruction of the uncinate fasciculus based on the DTI model

and a deterministic tractography algorithm (see Chapter 3, Section 3.3.) 29

Figure 6 Virtual reconstruction of the corona radiata based on the DTI model and a

deterministic tractography algorithm (see Chapter 3, Section 3.3.) 30

Figure 7 Graphic depiction of the diffusion tensor demonstrating: A) fibre tracts

oriented in relation to the x, y, and z axes of the scanner; B) dimensional representation of the predominant diffusion direction as an ellipsoid, again aligned with the x, y, and z axes of the scanner The orientation of the ellipsoid corresponds to the eigenvectors (E1, E2, E3), while its shape corresponds to the eigenvalues (λ1, λ2, λ3) which represent the diffusivity in each direction 35

three-Figure 8 Axial view of the brain showing the basal ganglia; A) FA map: white areas

indicate high anisotropy (high FA) and dark areas indicate low anisotropy (low FA); B) DEC map using colour-coding to show fibre orientation Red

= left-right; blue = inferior-superior; green = anterior-posterior 37

Figure 9 Comparison of DTI and HARDI-based models of fibre orientations at the

centrum semiovale Top: DTI-based model contains little directional

information in regions of crossing fibres, and the fibre orientation approximates a sphere Bottom: HARDI-based 43

Figure 10 Comparison of deterministic and probabilistic tractography models

showing the CC on coronal view using deterministic tensor-based tractography (left), deterministic FOD-based tractography (middle), and probabilistic FOD-based tractography (right) 48

Figure 11 Graphical depiction of the FACT algorithm showing the streamlines (red)

moving between voxels and following the predominant diffusion direction (arrows) of each voxel 49

Figure 12 Comparison of different tractography models (rows) with and without

ACT (columns, including both back-tracking and no back-tracking) showing tractography reconstructions on a sagittal section 51

Figure 13 Representation of the n-back task showing target stimuli for 0-back,

1-back and 2-1-back levels 175

Figure 14 Placement of IDP stimuli on computer screen 178 Figure 15 Placement of IDP stimuli in 1– to 8–circle levels of IDP 181 Figure 16 Placement of seed, inclusion, and exclusion ROIs for tractography a =

corpus callosum (CC) seed ROI; b–e = exclusion ROIs for CC; f = midline exclusion; g = superior longitudinal fasciculus (SLF) frontal seed/inclusion/exclusion ROI; h = SLF parietal seed/inclusion/exclusion ROI; f = SLF temporal seed/inclusion/exclusion ROI; j–l = SLF exclusion ROIs; m–n = SLF exclusion ROIs; o = inferior fronto-occipital fasciculus (IFOF) inclusion/exclusion ROI; p = SLFai additional exclusion; q–r = SLFpi additional exclusions; s = uncinate fasciculus (UF)/inferior longitudinal fasciculus (ILF) temporal seed ROI; t = UF frontal inclusion/ILF frontal exclusion ROI; u = external capsule (ExC) inclusion/exclusion ROI; v–w = ILF exclusion ROIs; x = UF exclusion ROI;

y = ALIC seed ROI; z = cerebral peduncle inclusion ROI; aa–af = anterior coronal radiata exclusion ROIs 203

Figure 17 Tractography reconstructions of: A) corpus callosum, B) superior

longitudinal fasciculus (all segments), C) inferior longitudinal fasciculus, D) uncinate fasciculus, E) anterior corona radiata 206

Figure 18 Scatterplots depicting significant associations between DTI metrics of

the CC and performance on the SDMT, split by group Top graph displays results for FA; bottom graph displays results for ADC 236

Figure 19 Scatterplots depicting significant associations between NODDI metrics

of the CC and performance on the SDMT, split by group Top graph displays results for ODI (non-significant); bottom graph displays results for ICVF 237

Figure 20 Scatterplot depicting significant association between ODI of white matter

tracts and scores on the Attention index, split by group 245

LIST OF ABBREVIATIONSACT = anatomically-constrained

DAI = diffuse axonal injury

DEC = directionally-encoded colour

(map)

DKI = diffusion kurtosis imaging

DTI = diffusion tensor imaging

DWI = diffusion-weighted imaging

FACT = Fibre Assignment by

Continuous Tracking

fMRI = functional magnetic resonance

imaging

FOD = fibre orientation distribution

fODF = fibre orientation distribution

MD = mean diffusivity msmt-CSD = multi-shell, multi-tissue constrained-spherical deconvolution mTBI = mild traumatic brain injury MRI = magnetic resonance imaging NDI = neurite density index (see ICVF)

SLFpi = SLF posterior indirect segment SWI = susceptibility-weighted imaging TBI = traumatic brain injury

TBSS = tract-based spatial statistics

TC = trauma controls

UF = uncinate fasciculus

CHAPTER 1 Overview

Mild traumatic brain injury (mTBI), often used synonymously with the term concussion, has been the subject of research since the 10th century (McCrory & Berkovic, 2001) More recently, awareness of the significance of concussion has grown dramatically as a result of greater media coverage of sports-related concussions, such as those common to Australian Rules football In 2016, the Australian Football League injury report revealed a 14-fold increase in the number

of concussions (Elkington & Hughes, 2017) In the same year, new guidelines were issued by the Australian Medical Association and the Australian Institute of Sport recommending that children should not return to playing sports for two-weeks following a concussion (Elkington & Hughes, 2017) These recent concerns follow decades of research that has investigated outcome following mTBI; despite this, there remains considerable variation in individual outcome following mTBI and debate continues as to why this is the case

Historically, the subjective experience of ongoing symptomatology has driven research into the consequences of suffering an mTBI This is because patient

“complaint” is typically the primary clinical driver for medical follow up after mTBI, and has also been the most readily measured Recent developments in our ability

to investigate underlying injury-related pathology have enabled researchers to examine structural neuropathological changes associated with recovery following mTBI, thereby providing a means of examining objective recovery after mTBI These

markers of objective recovery, such as objective cognitive performance, in order to understand how these might relate to underlying structural neuropathological

recovery This introductory chapter will provide an overview of individual subjective

outcome following mTBI, focusing specifically on persistent symptoms and current perspectives on their aetiology This overview serves to demonstrate that despite years of research, subjective outcome following mTBI remains variable and very difficult to predict This highlights the importance of investigating objective recovery after mTBI, focussing in particular on structural neuropathology and objective cognition

Individual outcome after mTBI and the subjective experience of recovery

In the early hours, days and weeks following mTBI, individuals commonly experience a variety of physical, affective, and cognitive symptoms that are associated with neurological insult (Barkhoudarian, Hovda, & Giza, 2011) These

symptoms constitute the post-concussive syndrome (PCS) and can include

headaches, nausea, sleep disturbance, irritability, concentration and memory difficulties (Hiploylee et al., 2017) Symptom resolution varies greatly between individuals, from several hours to days and up to weeks (Carroll, Cassidy, Peloso,

et al., 2004) The rate and duration of symptom resolution is influenced by factors including: the head injury itself, such as the presence of intracranial abnormalities

on neuroimaging and the mechanism of injury; physical factors, such as the presence of other trauma-related injuries, pain, and disrupted sleep; stress in the early post-injury period; and medications that can exacerbate existing symptoms

Poppe, Davis, Schmaus, & Hobbs, 2006; Meares et al., 2008) Recovery is also influenced by pre-injury individual factors including age, sex, and levels of physical fitness and education, as well as pre-existing psychiatric disorders (Dougan, Horswill, & Geffen, 2014a; Jacobs et al., 2010; Macciocchi, Barth, Alves, Rimel, & Jane, 1996; Meares et al., 2008) While these variables can influence the nature and duration of the symptomatology, the majority of individuals can expect full symptom resolution by three to six months (Hiploylee et al., 2017) For an estimated 10–30%

of individuals following mTBI, however, PCS symptoms persist well beyond the typical recovery period (Alexander, 1995; Belanger, Barwick, Kip, Kretzmer, & Vanderploeg, 2013) The aetiology of these persistent symptoms is the subject of ongoing debate; the two primary existing models focus on psychological and environmental factors, and injury-related neuropathology as contributing to persistent PCS These two opposing views will be discussed in turn

1.1.1 Psychological and environmental models of persistent symptoms

An abundance of research literature has sought to determine why a minority of individuals report persistent symptoms following mTBI This research has attempted

to clarify the aetiology of persistent PCS and identify associated risk-factors While the lack of an apparent pathological marker of injury led early clinicians and researchers to provide psychosomatic explanations for symptomatology, there is now considerable empirical evidence linking certain pre- and post-injury factors to the development of PCS (Hiploylee et al., 2017)

One of the most consistent findings is that pre-existing psychiatric disorders and early post-injury psychological factors (e.g depression, anxiety, and stress during the early post injury period) are significantly associated with the development and maintenance of PCS symptoms (King, 1996; Lange, Iverson, & Rose, 2011; Massey, Meares, Batchelor, & Bryant, 2015; Meares et al., 2008; Ponsford et al., 2000; Waljas et al., 2015) Second to psychological factors, certain pre-existing psychosocial and environmental factors have been shown to increase susceptibility

to persistent PCS (Carroll, Cassidy, Peloso, et al., 2004; Silverberg & Iverson, 2011) These include increased age, low levels of education, and unemployment in the early recovery period (Carroll, Cassidy, Peloso, et al., 2004; Chiang, Guo, Huang, Lee, & Fan, 2015; Dougan et al., 2014a; Jacobs et al., 2010; King, 2014) Contrary

to early research findings, a recent review revealed that female sex alone is not a reliable risk factor for persistent PCS (Cancelliere, Donovan, & Cassidy, 2016) Other influential pre-existing factors include certain personality traits (e.g narcissistic, histrionic, perfectionistic, compulsive, and borderline traits; Garden, Sullivan, & Lange, 2010; Hibbard et al., 2011; Ruff, Camenzuli, & Mueller, 2009; Wood, McCabe, & Dawkins, 2011), coping styles (Anderson & Fitzgerald, 2018; Belanger

et al., 2013; Snell, Siegert, Hay-Smith, & Surgenor, 2011), and cognitive biases (Gunstad & Suhr, 2001; Iverson, Lange, Brooks, & Rennison, 2010; Lange, Iverson,

& Rose, 2010; Mittenberg, DiGiulio, Perrin, & Bass, 1992; Whittaker, Kemp, & House, 2007) Finally, other post-injury factors can also influence susceptibility to persistent PCS These include how an individual appraises and interprets both the head injury and symptoms (e.g negative appraisal), and strong-held beliefs that

PCS symptoms are due to the injury itself (Anderson & Fitzgerald, 2018; Hou et al., 2012; Snell et al., 2011; Whittaker et al., 2007)

1.1.2 Neuropathological models of persistent symptoms

While the role of psychological and environmental factors in the development and maintenance of persistent PCS is well established, the alternate model posits that injury-related neuropathology contributes to persistent PCS symptoms following mTBI This model was formerly supported by evidence from early histological studies that demonstrated damage to axons and microvasculature well beyond (i.e 7-months) the typical recovery period (e.g., Bigler, 2004), as well as strong theoretical rationale retrospectively linking the nature of PCS symptoms to histological findings at autopsy With recent advances in neuroimaging techniques,

researchers have examined in vivo associations between persistent PCS symptoms

and structural neuropathology following mTBI (Datta, Pillai, Rao, Kovoor, & Chandramouli, 2009) Importantly, this research has revealed subjective symptomatology is significantly related to markers of injury related neuropathology (Bazarian et al., 2007; Hartikainen et al., 2010; Messe et al., 2011; Messe et al., 2012; Niogi, Mukherjee, Ghajar, Johnson, Kolster, Sarkar, et al., 2008; Smits et al., 2011) Alongside structural neuroimaging research, functional neuroimaging studies have also revealed alterations in network connectivity and activity following mTBI that have been associated with the presence and severity of PCS symptoms (Palacios et al., 2017; Pardini et al., 2010; Smits et al., 2009; van der Horn et al., 2015) Collectively, results of both structural and functional neuroimaging studies

provide compelling support for the argument that the presence and extent of related neuropathology contribute to persistent subjective symptoms

Persistent subjective symptoms and objective measures of outcome

As summarised above, the aetiology of persistent symptoms following mTBI remains unresolved The contribution of psychological and environmental factors to the development of persistent PCS has been well documented; however, these factors alone cannot fully explain the variation in experience of subjective symptoms This is further demonstrated by a recent systematic review that examined multivariable prognostic models attempting to predict outcome following mTBI (Silverberg et al., 2015); the authors found that existing models leave a considerable amount of variance unexplained and no single multivariable prognostic model could accurately predict individual recovery Importantly, these models do not include neuropathological factors, which further reinforces that in order to understand individual recovery following mTBI, we need to use objective measures of individual outcome

One of the key reasons subjective outcome has been the focus of such a large body of research is because there is discrepancy between the subjective experience of persistent symptoms and seemingly “normal” performance on objective outcome measures Research consistently finds that a minority of individuals following mTBI continue to report persisted PCS symptoms, and in particular persistent cognitive symptoms, well beyond the expected three-month

and pervasive of all PCS symptoms because they impact on an individual’s capacity

to return to their pre-morbid level of functioning and can themselves result in psychological distress (Broshek et al., 2015) The silent nature of the brain injury makes it difficult for both the individual and those around them to understand these subjective cognitive difficulties because they are incongruous with the physical presentation of the otherwise unscathed or recovered individual Furthermore, they are often unexpected: conventional neuroimaging (e.g computed tomography) is typically normal and performance on formal cognitive assessment beyond the early weeks and months post-injury may reveal only subtle cognitive changes that do not reflect the subjective experience of significant change (Bigler, 2004, 2008; Hiploylee

et al., 2017) Importantly, research has demonstrated that PCS symptoms are not reliably associated with objective performance on formal neuropsychological assessment (Chamelian & Feinstein, 2006; Clarke, Genat, & Anderson, 2012; Datta

et al., 2009; Jamora, Young, & Ruff, 2012; Ngwenya et al., 2018; Oldenburg, Lundin, Edman, Nygren-de Boussard, & Bartfai, 2016) This raises the question as

to whether we are using appropriate methods of objectively examining outcome That is, subjective symptoms may be reflective of underlying injury that current outcome measures are not sufficiently sensitive or specific enough to detect

Objective measures of outcome following mTBI are central to this thesis This

includes objective measures of cognitive outcome, typically by use of formal neuropsychological testing, and objective measures of neuropathological outcome,

by use of structural magnetic resonance imaging (MRI) These topics will be reviewed in detail in subsequent chapters (i.e Chapters 3 and 4), but key concepts

will briefly be introduced in the following section to orient the reader to the overarching research questions and objectives of this thesis

Objective cognitive outcome following mTBI

During the early acute period, which typically refers to the first week post-injury

(Alexander, 1995; Messe et al., 2011), a consistent pattern of cognitive dysfunction has been demonstrated (Carroll et al., 2014) This includes deficits in the cognitive domains of speed of information processing, attention, memory, and executive dysfunction During the first month post-injury, research findings typically reflect a lesser degree of cognitive dysfunction within the same domains (Carroll et al., 2014)

In contrast, during the sub-acute period, which is commonly defined as comprising

the second and third months post-injury (Dodd, Epstein, Ling, & Mayer, 2014; Messe et al., 2011), there is considerable variation in research findings (Carroll et al., 2014) While it is commonly accepted that deficits apparent during the early acute resolve by approximately three months post-injury, there is some support for

persistent and objectively identified cognitive impairments during the chronic period

(i.e beyond three-months post-injury) Yet there is also a considerable body of research that has found no such evidence of persistent cognitive dysfunction following mTBI

The variation between studies with regards to objective cognitive outcome, and the discrepancy between the presence of subjective cognitive symptoms and seemingly “preserved” performance on formal neuropsychological testing have

sensitive enough to detect subtle cognitive changes following mTBI (Bigler, 2008) Indeed, more than 20 years ago Cicerone (1996) proposed that tasks requiring complex and effortful mental processing may enable detection of subtle cognitive changes following mTBI It remains poorly understood, however, whether use of more complex and challenging cognitive measures can indeed reveal subtle cognitive changes following mTBI

Objective measures of neuropathological outcome following mTBI

Advances in magnetic resonance imaging (MRI) techniques have enabled

researchers to examine the neuropathological consequences of mTBI in vivo Most commonly, this research has used an MRI-based technique called diffusion-

weighted imaging (DWI), which has particular sensitivity to diffusion of water

molecules within tissues (Huisman, 2003) A DWI-based modelling technique called

diffusion-tensor imaging (DTI; Basser, Mattiello, & LeBihan, 1994) has frequently

been used to study the neuropathological consequences of mTBI because this technique enables study of white matter tract microstructure in vivo DTI can be used to create virtual three-dimensional (3D) “reconstructions” of white matter

tracts, using a technique known as tractography DTI also provides quantitative

measures that reflect underlying white matter microstructural properties (Alexander, Lee, Lazar, & Field, 2008) Importantly, changes to these quantitative measurements within white matter tracs have been found following mTBI, which changes reflect altered diffusion of water molecules and are commonly interpreted

as markers of axonal injury (Assaf & Pasternak, 2008)

DTI has been applied to the study of structural neuropathology following mTBI because of demonstrated sensitivity to diffuse axonal injury (Assaf & Pasternak, 2008); however, this technique is subject to several significant and well-known limitations Firstly, quantitative measures can be inaccurate in certain regions and are non-specific This means that existing research using DTI has been able to detect change, but is unable to determine pathologically what has caused the change Advancements have since been made in DWI research leading to more sophisticated data acquisition and modelling techniques, as well as more specific quantitative measures of white matter microstructure Due to the improved anatomical specificity relative to DTI, these methods enable us to study changes to white matter tracts microstructure with greater precision, and thereby to disentangle the underlying pathological mechanism associated with change Such advanced DWI-based techniques have not yet been applied to the study of mTBI beyond the early acute period

Towards a more precise examination of objective outcome following mTBI

This introductory chapter serves to provide a brief overview of mTBI and associated recovery There key research questions arise out of this existing research that provide the overarching rationale for this thesis Driven by the discrepancy between subjective and objective measures of outcome within existing research, the research project central to this thesis aims to employ innovative methodologies to examine three specific aspects of outcome following mTBI This includes objective cognitive outcome, injury-related neuropathology (specifically, white matter tract

and white matter tract microstructural changes associated with mTBI These aspects of outcome will be examined at a timepoint that is approaching the end of the typical recovery period (i.e 6-10 weeks post-injury) By establishing whether improved objective measurement tools provide more detailed information about recovery following mTBI, we will be able to better understand factors that may be contributing to the individual’s subjective experience of persistent symptoms

The first research question, and rationale for Study One, builds upon two key aspects of previous research: the discrepancy between subjective reporting of cognitive symptoms and lack of objective evidence of cognitive dysfunction, and the proposal that use of cognitive demanding and complex tasks may facilitate detection of subtle cognitive changes Study One aims to determine whether use of two novel computer-based tasks—designed to place greater demands on cognitive systems—can differentiate between individuals with mTBI and matched trauma controls Study One also employs a more comprehensive selection of traditional neuropsychological tests in order to determine whether the use of the aforementioned novel computer-based tasks can detect differences in cognition that were not apparent using conventional neuropsychological measures

The second research question relates to existing DWI-based research and the predominant use of the DTI model Given the inherent limitations of this model, which are particularly applicable to the study of mTBI-related neuropathology, this research project employed more recently developed, sophisticated DWI-based techniques to examine white matter tract microstructural changes following mTBI

Thus, Study Two aimed to determine whether these sophisticated DWI-based techniques methods can provide more specific information about white matter tract microstructural changes relative to DTI Furthermore, Study Two aimed to identify the underlying pathological mechanism associated with changes to DTI measures currently interpreted as markers of axonal injury

The third and final research question brings together the two previous research questions and seeks to determine whether changes to white matter tract microstructure following mTBI are associated with objective performance on tests

of cognitive function As has been outlined in this chapter, it has been demonstrated

that subjective symptoms following mTBI are associated with markers of white

matter tract damage using DTI Yet in contrast, it remains poorly understood as to

whether objective measures of cognition are also related to markers of white matter

tract damage Empirical evidence directly linking changes to white matter tract microstructure to objective measures of outcome is needed in order to truly understand the role of neuropathology in individual recovery following mTBI Thus, the third aim of this thesis is to determine whether there is indeed a relationship between injury-related white matter tract microstructural changes and objective measures of cognitive function following mTBI

CHAPTER 2 The Concept of Mild Traumatic Brain Injury

This chapter provides an overview of the current understanding of mTBI, including current definitions and “gold standard” criteria for clinical identification The significance of mTBI will be demonstrated through a brief explanation of the biomechanical forces of injury and resultant impact on the brain The neuropathological and neurochemical changes resulting from mTBI will be described, in addition to the nature and distribution of mTBI-related neuroanatomical damage This chapter will end with a brief overview of the anatomical location and characteristics of the long-coursing white matter tracts pertinent to this thesis

Defining mTBI: Diagnosis and Clinical Identification

MTBI is the most prevalent type of traumatic brain injury (TBI), accounting for approximately 80% of the estimated 22, 000 TBI-related hospital admissions that occur in Australia each year (Helps, Henley, & Harrison, 2008) TBI is associated with a significant financial burden that is estimated to exceed $184 million per year

in Australia Additionally, TBI has a considerable impact on society as young adults (aged < 25) who are entering or training to enter the workforce are most likely to incur this type of injury (Helps et al., 2008) This places an additional psychosocial burden on young families as a direct consequence of TBI

Despite its common occurrence, the entity of mTBI remains poorly defined