Investigating the Impact of Diffuse Axonal Injury on Working

INVESTIGATING THE IMPACT OF DIFFUSE AXONAL INJURY ON WORKING

MEMORY PERFORMANCE FOLLOWING TRAUMATIC BRAIN INJURY USING

FUNCTIONAL AND DIFFUSION NEUROIMAGING METHODS.

Gary R. Turner

A thesis submitted in conformity with the requirements

for the degree of Doctorate of Philosophy,

Graduate Department of Psychology

in the University of Toronto

Investigating the impact of diffuse axonal injury on working memory performance following

traumatic brain injury using functional and diffusion neuroimaging methods.

Gary R. Turner, Ph.D., 2008

Department of Psychology, University of Toronto

Abstract

Traumatic brain injury (TBI) is a leading cause of disability globally. Cognitive deficits represent the primary source of on-going disability in this population, yet the mechanisms of these deficits remain poorly understood. Here functional and diffusion-weighted imaging techniques were employed to characterize the mechanisms of neurofunctional change following TBI and their relationship to cognitive function. TBI subjects who had sustained moderate to severe brain injury, demonstrated good functional and neuropsychological recovery, and screened positive for diffuse axonal injury but negative for focal brain lesions were recruited for the project. TBI subjects and matched controls underwent structural, diffusion-weighted and functional MRI. The functional scanning paradigm consisted of a complex working memory task with both load and executive control manipulations. Study one demonstrated augmented functional engagement for TBI subjects relative to healthy controls associated with executive control processing but not maintenance operations within working memory. In study two, multivariate neuroimaging analyses demonstrated that activity within a network of bilateral prefrontal cortex (PFC) and posterior parietal regions was compensatory for task performance in the TBI sample. Functional connectivity analyses revealed that a common network of bilateral PFC regions was active in both groups during working memory performance, although this activity was behaviourally relevant at lower

iii levels of task demand in TBI subjects relative to healthy controls. In study three, diffusion- imaging was used to characterize the impact of diffuse white matter pathology on these neurofunctional changes. Unexpectedly, decreased white matter integrity was not correlated with working memory performance following TBI. However, markers of white matter pathology did inversely correlate with the compensatory functional changes observed previously. These results implicate diffuse white matter pathology as a primary mechanism of functional brain change following TBI. Moreover, reactive neurofunctional changes appear to mediate the impact of diffuse injury following brain trauma, suggesting new avenues for neurorehabilitation in this population.

Acknowledgements

This work would not have been possible without the generous support and guidance of my supervisor, Brian Levine. His intellectual mentorship and unwavering support over the course of my doctoral studies has been invaluable. I consider our collaborations on this project merely the prelude to what I hope will be an enduring friendship and research partnership. I also thank my committee members, Morris Moscovitch and Randall

McIntosh for their guidance and support throughout the project. I owe an enormous debt of gratitude as well to Robin Green, who initially set me on course for this wondrous adventure and who has become a mentor, life coach and close personal friend over these last few years.

I am deeply grateful for the enormous support of the Levine lab research team who provided invaluable assistance with this project and others throughout my tenure in the lab.

Specifically, for their help on this dissertation project, I offer my heartfelt thanks to Adriana

Restagno and Marina Mandic. Also for their friendship and support I especially thank

Charlene O’Connor as well as Nathan Spreng, Eva Svoboda, Nadine Richard, and, for always keeping me grounded, Asaf Gilboa.

My family has continued to be an unending source of strength, support, encouragement and love through all of my endeavours and I owe them everything.

All I have been able to accomplish over the tenure of my dissertation studies I owe first and foremost to my life partner Marcelo Martins. His support, patience and generosity has provided me with the strength to weather the hard spots and the confidence to celebrate the successes. I could not imagine this journey without him.

Table of Contents

Abstract ...... ii

Acknowledgements ...... iv

Table of Contents ...... v

List of Tables ...... vii

List of Figures ...... viii

General Introduction ...... 1

Chapter 1: Augmented neural activity during working memory following diffuse axonal injury ………...... 13

Abstract ...... 13

Introduction ...... 14

Method ...... 15

Results ...... 21

Discussion ...... 28

Chapter 2: Compensatory neural recruitment during verbal working memory performance after TBI: evidence for an altered functional engagement hypothesis ...... 32

Abstract ...... 32

Introduction ...... 33

Method ...... 36

Results ...... 40

Discussion ...... 57

Conclusion ...... 63

Chapter 3: Diffuse axonal injury as a mechanism for functional brain changes following traumatic brain injury: an integrated diffusion-weighted and functional imaging study...... 65

Abstract ...... 65

Introduction ...... 67

Method ...... 71

Results ...... 78

Discussion...... 91

Conclusion ...... 97

General Discussion ...... 98

vii

List of Tables

Chapter 1

Table 1.1: TBI patient demographics, acute injury characteristics, structural neuroimaging data, and neuropsychological test data...... 16

Table 1.2: Activation cluster maxima corresponding to maximal BOLD signal changes during the Alphabetize vs. Maintain conditions ...... 26

Chapter 2

Table 2.1: Cluster maxima from the behaviour PLS (ST-bPLS) analysis for the control group (See figure 2.1a)...... 45

Table 2.2: Cluster maxima from the behaviour PLS (ST-bPLS) analysis for the TBI group (See figure 2.1b)...... 46

Table 2.3: Cluster maxima from the combined behaviour and seed (ST-bPLS & ST-sPLS) analysis for the left inferior frontal gyrus seed (see figure 2.3) ...... 53

Table 2.4: Cluster maxima from the combined behaviour and seed (ST-bPLS & ST-sPLS) analysis for the right posterior middle frontal gyrus (BA 46 / 44) seed ...... 55

Chapter 3

Table 3.1: Voxel cluster coordinate and maxima for the control versus TBI whole brain FA comparison ...... 80

Table 3.2: Suprathreshold activation cluster maxima from LV 1 (p < .001) in the Seed and Genu PLS analysis (see Figure 3.4)...... ….. .86

Table 3.3: Suprathreshold activation cluster maxima from LV 1 (p < .001) in the Behaviour, Seed and Genu PLS analysis (TBI group only, see Figure 3.5). Seed was placed in right lateral middle frontal gyrus (BA 46/44). All abbreviations as in Table 3.2...... ……88

Table 3.4: Regions where white matter FA is predicted by BOLD response in right GFm during Alphabetize 5 task (TBI participants only). Letters correspond to Figure 3.6 ...... …. .90

viii

List of Figures

Chapter 1

Figure 1.1: Schematic of fMRI behavioural paradigm...... ……………18

Figure 1.2: Behavioural data for Alphaspan task during fMRI scanning...... …23

Figure 1.3: Areas of maximal BOLD signal change in the Executive Demand contrast ...... 25

Figure 1.4: Region of interest (ROI) analysis indicating mean % change difference between Alphabetize and Maintain conditions for the control group and individual TBI participants ...... 28

Chapter 2

Figure 2.1.a: Brain regions demonstrating significant correlations between brain response and task accuracy in control participants...... 44

Figure 2.1.b: Brain regions demonstrating significant correlations between brain response and task accuracy in TBI participants...... 44

Figure 2.2: Correlations between brain activity in anterior middle frontal gyrus seed regions (highlighted in Figure 2.1)...... …47

Figure 2.3: Brain regions demonstrating reliable and positive correlations with left inferior frontal gyrus (BA 44/6) activity and task accuracy during Alphabetize 5 trials in the control group ...... 52

Figure 2.4: Brain regions demonstrating reliable and positive correlations with left inferior frontal gyrus (BA 44/6) activity and task accuracy during Alphabetize 5 trials in the control group...... 54

Figure 2.5: Conceptual representation of combined behavioural and seed partial least squares analysis ...... … .57

Chapter 3

Figure 3.1: Group differences in voxel-wise distribution of white matter FA values ...... 79

Figure 3.2: Regions demonstrating significant differences in white matter FA between control and TBI participants...... 80

Figure 3.3: Correlations between callosal FA and Accuracy on the Alphabetize 5 letter working memory task ...... 82

Figure 3.4: Brain regions demonstrating reliable and positive correlations with right GFm seed (A5, F3, F5) and negative correlations with Genu FA (all tasks) in TBI subjects ...... …85

Figure 3.5: Brain regions demonstrating reliable and positive correlations with right GFm seed (A5, F3, F5) and negative correlations with Genu FA (all tasks) in TBI subjects...... 87

Figure 3.6: Regions where activity (i.e. BOLD response) in right middle frontal gyrus during Alphabetize 5 condition predicted lower white matter FA...... 89

INTRODUCTION

Dissertation objectives and key research questions

Traumatic brain injury (TBI) represents a significant and growing public health concern. World-wide incidence rates for TBI range from 180 to 500 cases per 100,000 population per year (CDC, 2001), with trauma-related brain injury poised to become the third leading cause of death and disability globally by 2020 (Povlishock & Katz, 2005). TBI survivors often experience chronic disability resulting in significant personal and societal costs and cognitive deficits have been identified as the primary source of lingering disability in this population (CDC, 2001). While the neural mechanisms underlying these deficits are not well characterized, diffuse pathology, such as diffuse axonal injury (DAI), is suspected to play a significant role and has been shown to be the primary determinant of overall recovery in more severe cases (Ross, Temkin, Newell, & Dikmen, 1994). Diffuse neuropathology is ubiquitous in TBI. Incidence has been estimated at 40-50% of hospitalizations and rises to

100% in motor vehicle accidents where there was loss of consciousness (Meythaler, Peduzzi,

Eleftheriou, & Novack, 2001). Our increasing reliance upon motorized transport and advances in acute care treatment of brain trauma suggests that the number of people living with TBI and, more specifically, DAI will continue to grow over the coming decades. Thus, investigating the relationship between DAI and cognition in this population is emerging as a priority in health care research.

There have been numerous attempts to correlate measures of diffuse neuropathology and cognitive performance. Early investigations relied on gross measures of diffuse injury,

1 2 such as ventricular volume (Meyers, Levin, Eisenberg, & Guinto, 1983; Wilson, 1990; Vilkki,

Holst, Ohman, Servo, & Heiskanen, 1992). More recently, emerging techniques, such as diffusion-weighted imaging and magnetic resonance spectroscopy, have facilitated greater precision in the measurement of diffuse neuropathology in vivo, yet the correlation between measures of white matter volume and/or structural integrity and cognitive performance is equivocal. In an earlier report, Tomaiuolo and colleagues (2004) identified a negative relationship between corpus callosum and fornix volume and neuropsychological test performance, however many of the observed correlations were found to be insignificant in a follow-up report (Tomaiuolo et al., 2005). Salmond et al. (2006) reported a significant relationship between diffusivity (a measure of white matter integrity) and a measure of learning and memory. Yet they failed to identify similar correlations with indices of motor or attentional functioning, nor did they observe significant correlations between a second scalar measure of diffusion (fractional anisotropy – FA) and any of their cognitive measures.

Kraus et al. (2007) recently reported correlations between white matter FA and indices of executive functioning, attention and memory across a range of TBI severity. However, they did not characterize the incidence of focal lesions in their sample, making it difficult to attribute their findings to diffuse injury. Nakayama et al. (2006) failed to identify a relationship between memory performance and white matter integrity in the corpus callosum in a sample of DAI participants without focal lesions.

As evident from this brief review, efforts to characterize the relationship between diffuse axonal injury and cognitive functioning following TBI are complicated by myriad factors which likely contribute to the equivocality of the findings. These include task-related differences, overlapping contributions of focal and diffuse injury and methodological differences (e.g. region of interest versus whole-brain analysis, or the choice of a diffusion

3 metric). Another potential source of variability, which heretofore has not been considered in this population, is the role of neural or cognitive reorganization following TBI in mediating the relationship between DAI and cognitive performance. There is a growing body of literature involving the utilization of fMRI methods to examine functional brain changes during cognitive task performance following TBI (see Levine et al., 2006 for a review).

While similar interpretive challenges appear in this literature (i.e. task-, injury- and analysis- related variability), there is converging evidence that the functional neural correlates of cognition are altered following TBI. Moreover, advances in our understanding of cognition as an emergent property of activity in widely distributed neural networks (McIntosh, 1999;

Postle, 2006) raise important questions with respect to the potential interactions amongst diffuse axonal injury, functional reorganization and cognitive performance in this population.

The goal of my dissertation research is to investigate the interaction of traumatically- induced DAI, functional brain activity and cognition using functional and structural neuroimaging methods. Specifically, I will address three research questions:

(i) Does TBI alter the functional neuroanatomy of cognitive task performance?

(ii) Does cognitive performance depend on functional reorganization following TBI?

(iii) How do structural and functional brain changes interact following TBI?

As the overarching theme of my dissertation project involves the application of neuroimaging methods to investigate the impact of diffuse axonal injury on the neural and behavioural correlates of cognitive functioning, the following section provides a brief review of the pathobiology of TBI, with a particular emphasis on the mechanisms of DAI. This is followed by a short primer on the benefits of adopting neuroimaging methods in neuropsychological investigations. In the final section, I review general methods for the

project and briefly review the rationale, research hypotheses and methods for each of the

three accompanying papers.

Pathobiology of traumatic brain injury

Early models of TBI identified oedema as the primary, if not the sole, source of

neuropathology following brain trauma. These ideas have since been supplanted with a

more sophisticated model of neuropathological change associated with TBI (see Povlishock

& Christman, 1995; Povlishock & Katz, 2005 for comprehensive reviews of TBI-related

neuropathology). Pathological reports (e.g. Adams, Gennarelli, & Graham, 1982; Strich,

1956), animal models (e.g. Gennarelli et al., 1982) and, increasingly, neuroimaging methods

(Arfanakis et al., 2002; Huisman et al., 2004; Kraus et al., 2007) have elucidated the cellular

mechanisms and structural consequences associated with a complex series of

neuropathological cascades involving molecular, metabolic and ischemic changes that are

initiated when the brain is rapidly displaced within the cranium. Traumatically-induced

neuropathology may be characterized along two principal dimensions: focal versus diffuse

injury and primary versus secondary processes (Povlishock & Katz, 2005). Focal injury, or

contusions, result from haemorrhagic lesions occuring at grey-white matter junctions or

along the cortical surface which may be abraded by contact with bony ridges of the cranium.

Contusions of the latter type typically occur in regions of the orbital frontal cortex and

anterior temporal lobes adjacent to the bony ridges of the skull surrounding the orbits.

Haemorrhagic lesions may also occur at (or opposite to) the location of impact, a process

known as coup-contra-coup injury. While cortical contusions represent the primary form of focal brain injury, secondary focal damage may occur as a result of microvascular changes

attributable to physical shear and tensile forces or due to ischemic injury secondary to

metabolic disturbances.

Diffuse injury, the focus of the current research, was initially presumed to occur

immediately upon impact as a result of shear and strain forces. These mechanical forces

were assumed to produce physical disconnection of proximal and distal segments of the

axon, a model of DAI which was derived from early pathologist reports of ‘retraction balls’

appearing at the ends of the disconnected axonal segments. Subsequent research revealed a more complex process involving both primary and secondary neuropathological cascades.

Initially, as the axolemma is displaced by the physical force of the trauma, its flow disrupts intra-axonal neurofillaments which, in turn, impedes afferent and efferent transit along the length of the axon. Accumulating organelles at the site of the disruption produce axonal swelling and eventual degradation of fibre integrity leading, over the course of several days, weeks and perhaps months, to axotomy or disconnection of the distal segment from the neuronal soma. While axonal disconnection due to mechanical shear forces is still presumed to occur in cases of severe injury, the discovery of these secondary neuropathological

processes suggests that DAI may be considerably more prevalent, and at lower severity

levels, than earlier pathological reports suggested. This possibility is further supported by

emerging data suggesting that even small focal alterations in membrane permeability,

secondary to mechanical strain and shear forces, may trigger auto-destructive cascades which

also lead to eventual axotomy. Moreover, a recent report by Smith and Meany (2000)

identified these auto-destructive processes in both myelinated and unmyelinated neurons

where the axonal membrane remained intact. In these cases, physical strain forces on

unmyelinated axons appeared to disrupt the sodium calcium pumps, disrupting ionic

homeostasis within the axon fibre and thereby initiating the autodestructive cascades that

break down neurofillaments. Povlishock and Katz (2005), in their recent review, suggested

that similar pathological mechanisms may not be limited to the axolemma but may also

occur at the site of the neuronal soma.

In sum, diffuse axonal injury is a primary consequence of TBI. While early reports

of DAI implicated mechanical shearing and tensile forces as the sole mechanism of injury,

more recent data has demonstrated that the effect of these physical forces is mediated

through a complex series of secondary intracellular cascades. Physical forces disrupt ionic

homeostasis and result in delayed axotomy that occurs over days, weeks or months following

the initial injury. Moreover, these autodestructive secondary cascades may or may not

involve axonal swelling (often used as an acute marker of injury severity). Axonal damage

may also occur as the result of focal alterations in membrane permeability or altered

functioning of ionic pumps which regulate homeostasis within the axolemma and may

impact either myelinated or unmyelinated fibres or both. These data suggest that DAI following TBI may more extensive than previously understood at all levels of TBI severity, implicating this neuropathological process as a potential mechanism for the patterns of

cognitive dysfunction observed following brain trauma.

Functional neuroimaging methods in TBI

Price and Friston (2002) have argued that functional neuroimaging may complement

structural neuroimaging and behavioural assessments of patients in a number of ways, two of

which are relevant here. First, the authors argue that functional imaging may reveal cognitive responses not evident through behavioural measures alone. As discussed above, the variability reported in investigations of DAI and cognition may be attributable to the instantiation of novel or supplementary cognitive processes following TBI, thereby

7 compensating for diffuse structural damage. Price and Friston (1999) have labelled this

‘cognitive reorganization’. Second, they argue that functional imaging may reveal neural mechanisms of recovery following injury through processes such as reverse diaschisis, perilesional recruitment, ‘unmasking’ of normally inhibited systems or learning-dependent plasticity. These processes fall into the category of ‘neural reorganization’ under the authors’ schema of reorganization following brain damage. With respect to the present research objective, evidence for neural or cognitive reorganization following DAI would suggest that cognitive deficits attributable to DAI may be the result of absent or functionally inadequate reorganization, opening potential avenues for rehabilitation interventions. This possibility of using functional neuroimaging methods to observe such functional recruitment has been identified as an important tool in the armamentarium of rehabilitationists in a recent review

(Chen, Abrams, & D'Esposito, 2006). A final point raised by Price and Friston (2002) is worth reviewing briefly here. While counter-intuitive with respect to standard neuropsychological investigations, determination of cognitive or neural reorganization in a patient population using fMRI methods is critically dependent upon their capacity to perform the task at an equivalent level to controls. Performance differences on the behavioural measure of interest raise the possibility that brain differences may simply be reflecting these performance differences and not ‘reorganization’ per se. I return to this issue in the ‘General Methods’ section below.

So far, in this general introduction, I have presented data that diffuse injury following

TBI may be more extensive than previously understood. In this context, the correlations between DAI and cognitive functioning that have been reported to date in the literature are surprisingly tenuous. Heterogeneity in methods, cognitive measures, and patient sample

characteristics undoubtedly contribute to this paradox. Here I propose that variability in

cognitive and/or neural reorganization is a significant contributor to these divergent

findings, and if so, may offer a window of intervention for remediation or compensation of

cognitive deficits following TBI. In the final section, I propose a plan of research using

functional and diffusion neuroimaging techniques to interrogate this proposition.

General Methods

Participants

One of the primary goals of the current research was to reduce heterogeneity within

the TBI patient cohort recruited into this study, thus allowing us to attribute functional brain

changes to specific neural mechanisms following TBI. There has been considerable

heterogeneity in participant injury characteristics in previous reports investigating the role of

DAI in cognitive functioning, thus limiting the study of brain structure, function and

behavioural interactions. I address this issue directly in a number of ways. First, the TBI

patients recruited for this research (characterized in detail in paper 1) are free of significant

premorbid or secondary neurological illness or psychiatric diagnosis. Furthermore, selection

criteria were devised to maximize homogeneity and minimize variability in this notoriously

heterogeneous patient population. Specifically, we selected participants who had sustained

TBI as a result of a motor-vehicle accident where there was documented loss of consciousness and indication of significant post-traumatic amnesia (i.e. moderate to severe

TBI). Incidence of DAI has been estimated near 100% when these criteria are met

(Meythaler et al., 2001). Participants were also excluded if there was evidence of macroscopic focal lesions or if there was no indication of DAI-lesions on neuroradiological report. Finally from this pool of ‘pure’ DAI subjects, we selected only those who were in

the chronic recovery period and who had demonstrated good functional and

neuropsychological recovery. While these criteria may limit the generalizability of these data in a clinical sense, they are critical to parsing the impact of DAI on the neural basis of cognitive recovery following TBI.

Functional Imaging Paradigm

Diffuse neuropathology such as DAI is pervasive following brain trauma, thus one might predict that TBI would be associated with relative deficits in more integrative cognitive domains, where processing demands are distributed across the cerebrum. This is indeed the case. Deficits in executive functions, which are mediated by prefrontal cortical regions and their connections to posterior and subcortical brain structures, are a hallmark of

TBI (see Turner & Levine, 2004 for a review). Working memory, or the capacity to hold information ‘in mind’ when it is no longer present in the environment, and to manipulate, monitor and update it in the service of behaviour (Goldman-Rakic, 1987), is considered to rely upon these prefrontally-mediated neural networks. Working memory is perhaps the most clearly characterized domain of higher cognition both in terms of its behavioural and neural correlates (Postle, 2006). Recently, Vallat-Azouvi, Weber, Legrand and Azouvi (2007) convincingly demonstrated that working memory is disrupted following TBI and that deficits are specific to executive control processes as opposed to simple maintenance operations (see

D'Esposito, Cooney, Gazzaley, Gibbs, & Postle, 2006 for further evidence). This is consistent with a theoretical dissociation first proposed by Baddeley (1986) and allows for clear predictions with respect to the preferential impact of DAI on executive control processes in working memory following TBI. Here I have selected a working memory paradigm where both executive and storage demands could be explicitly manipulated in an

effort to optimally challenge mechanisms of neural or cognitive reorganization in our TBI

sample. Moreover, to maximize sensitivity to potential brain changes following TBI, I

selected a paradigm for which the functional neuroanatomy has been well characterized in

healthy control participants (Alphaspan, Craik, 1990; Postle, Berger, & D'Esposito, 1999 and

see Chapter 1 for a complete task description).

In the final section of this general introduction I provide a brief synopsis of the three

research papers that comprise my dissertation thesis project. I present the general research

question being addressed, the methodological approach and a brief review of the methods

and the rationale for each study vis-a-vis the overall objective of the dissertation research.

Research study overview

Chapter 1. Does TBI alter the functional neuroanatomy of cognitive task performance? This question

has been examined previously (e.g. McAllister et al., 1999; McAllister et al., 2001; Perlstein et

al., 2004). In this study, I expand upon these earlier reports in three ways: (i) by isolating

the contribution of DAI (see ‘Participants’ above) (ii) matching performance between TBI

and healthy controls (see ‘Functional Imaging’ above) and (iii) by parsing the impacts of TBI

on executive control and storage processes in working memory. These have been

confounded in previous reports, potentially masking neurofunctional changes attributable to

either.

The principal objective of this first study is to demonstrate that altered neural

recruitment could be observed during executive control processing in a sample of ‘pure’

DAI subjects who were multiple years post-injury, who had demonstrated good functional and neuropsychological recovery, and whose behavioural performance was equivalent to a

non-injured comparison group. Study one is foundational in that it replicates and advances

the findings of these previous reports and sets the stage for examining whether the presence

of functional brain changes correspond to behaviour and thus mediate the impact of DAI

following brain trauma.

Chapter 2. Does cognitive performance depend on functional reorganization following TBI? Having

investigated the functional correlates of higher cognitive functioning (i.e. executive

processing in working memory) in Chapter 1, in this series of analyses I explore whether

evidence of altered functional recruitment, secondary to DAI, may be related to behavioural

performance. Given the distributed nature of DAI, I employed multivariate analysis

methods to identify behaviourally relevant functional brain networks engaged at differing

levels of working memory task demand following DAI. The principal goal of the second study was to explicitly correlate measures of functional reorganization and cognitive task performance following DAI.

Chapter 3. How do structural and functional brain changes interact following TBI? The goal of the first two studies was to investigate whether DAI was specifically associated with altered functional brain activity during performance of a working memory task and whether this pattern of neural reorganization was behaviourally relevant. Interpretation of these reports depends on the assumption that DAI is present. While this assumption is likely valid given the fact that all patients had a significant TBI, there was no quantification of DAI. In this final report I asked whether quantitative measures of DAI would directly predict performance on a working memory task and to what extent this relationship was dependent upon the neural reorganization investigated in studies 1 and 2. Here I integrated functional

neuroimaging, diffusion-weighted imaging and working memory behavioural data for the

first time in this population. The principal objective of this study was to document the

extent to which altered functional brain activity mediated the impact of diffuse injury

following TBI.

In summary, my dissertation research combines functional and diffusion-weighted

neuroimaging methods to investigate the impact of DAI on the functional neuroanatomy of

higher cognitive functioning, specifically executive control within working memory. To

assess this relationship, I ask three questions: Does DAI specifically result in altered

functional recruitment following TBI? Are these changes behaviourally relevant? How does

DAI interact with neurofunctional changes to influence behavioural and functional

outcomes following TBI? Increasing evidence points to DAI as a primary source of cognitive deficit following TBI. In the three Chapters that follow1, I have employed

functional and structural neuroimaging methods to investigate why this may be the case and

what the implications may be for functional recovery.

1 The three chapters that follow this General Introduction are presented here in manuscript format. At the time of writing, report 1 is in submission at Neurology, reports 2 and 3 are in preparation for submission. The three reports have been reformatted into standardized APA format for consistency within this thesis document.

CHAPTER 1

Augmented Neural Activity During Working Memory

Following Diffuse Axonal Injury

Abstract

Deficits in working memory are commonly observed following traumatic brain injury (TBI), with executive

control processes preferentially impacted relative to storage and rehearsal. Previous activation functional

neuroimaging investigations of working memory in patients with TBI have reported altered functional

recruitment, but methodological issues including sample heterogeneity (e.g. variability in injury mechanism,

severity, neuropathology or chronicity), under-specified definitions of ‘working memory’, and behavioural

differences between TBI and control groups have hindered interpretation of these changes. Here, executive

control processing in working memory was explicitly engaged during fMRI in a sample of carefully selected

chronic stage, moderate to severe TBI patients with diffuse axonal injury (DAI), but without focal lesions.

Despite equivalent task performance, we observed a pattern of greater recruitment of inter- and intra-

hemispheric regions of prefrontal cortex (PFC) and posterior cortices in our DAI sample. Enhanced activations were recorded in left dorsolateral PFC (middle frontal gyrus), right ventrolateral PFC (inferior frontal gyrus), as well as bilateral posterior parietal cortices and left temporal-occipital junction. Region of interest analyses confirmed that these effects were robust across individual patients and could not be attributed to load factors or slowed speed of processing. These data suggest that augmented functional recruitment in the context of normal behavioural performance may be a neural marker of capacity or efficiency limits that can affect functional outcome following TBI with diffuse injury.

Introduction

Deficits in working memory are commonly observed in patients with traumatic brain injury (TBI, McAllister, Flashman, Sparling, & Saykin, 2004). Working memory, however, is not a unitary process; it can be separated into storage and executive control components

(Baddeley, 1986). There is substantial evidence that these components can be dissociated at the neural as well as at the behavioural level (D'Esposito, Cooney, Gazzaley, Gibbs, &

Postle, 2006; Goldman-Rakic, 1987), with the executive control component strongly associated with dorsolateral prefrontal cortex (dlPFC, Curtis & D'Esposito, 2003).

This functional localization creates a puzzle when considering patients with TBI, where ventral, not dorsal frontal regions, are vulnerable to focal lesions (Courville, 1937), and where focal lesions may be absent even in the presence of marked cognitive deficits

(Scheid, Walther, Guthke, Preul, & von Cramon, 2006). This raises the possible influence of diffuse axonal injury (DAI). DAI is a ubiquitous consequence of rapid deceleration of the head, followed by disrupted ionic homeostasis, cytoskeleton compromise, and ultimately, disconnection of the distal axonal segment from the soma (Povlishock & Katz, 2005). dlPFC functions may be preferentially impacted by DAI as a result of its extensive reciprocal connections with almost all cortical and subcortical structures in the brain (Petrides &

Pandya, 1999).

While DAI is likely to substantially contribute to the executive control deficits in

TBI, extant evidence to support this hypothesis is weak. Investigations of the functional neuroanatomy of working memory following significant TBI have suggested increased recruitment of frontal regions (Christodoulou et al., 2001; McAllister et al., 1999; Perlstein et al., 2004), but these are complicated by methodological factors such as task complexity (in particular, confounding working memory load effects with the critical executive control

processes), performance impairments in patients, and the inclusion of patients with focal and

diffuse injuries (for a review see Levine et al., 2006). More evidence is found in mild TBI

(Chen et al., 2004; McAllister et al., 1999), but these studies were conducted at the acute phase, with little expected long-term functional consequences relative to patients with more significant TBI.

In this study, we assessed patients with severe TBI and documented DAI but without large focal lesions. We have adopted a working memory paradigm with well- established functional neuroanatomy that is capable of separating executive control and load factors, with good convergent validity with other measures of executive control in working

memory (Kane et al., 2004). Finally, we have used a standardized pre-scan training regimen

to equate task performance between TBI and control subjects during fMRI scanning.

Methods

Participants

Eight patients (6 males) with moderate to severe TBI were recruited along with

twelve (12) healthy control subjects (8 males). TBI patients and control subjects were

matched on age [t(18) = .785 (p > .05; NS)] and education [t(18) = -1.99 (p > .05; NS)]. All

subjects were right-handed, native English speakers and were screened for previous

neurological injury, major medical conditions affecting cognition, history of psychiatric

illness, and the use of medications affecting cognition (Levine et al., in press). Two other sets

of control subjects were used for the purposes of characterizing TBI patients’

neuropsychological status and structural neuroimaging data (see Table 1.1).

Table 1.1: TBI patient demographics, acute injury characteristics, structural neuroimaging data, and neuropsychological test data.

Subject # 1133 2930 3639 3649 1054 3645 3653 3646 Mean (SD) Control Demographics Age (years) 332632302743362832 (6)28 (8) Education (years) 141415171715141615 (1)15 (2) Injury Characteristics Glascow Coma Score 4.5 15 9 10.5 9 9 9 6 9 (8.3-9.4) Loss of Consciousness (hrs) 336 0.25 5 168 26 < 0.25 6 96 26 (5.5-132) Post Traumatic Amnesia (hrs) 1008 336 504 n/a 240 n/a 4 1080 420 (264-882) Severity Classification Sev. Mod. Mod. Mod. Sev. Mod. Mod. Sev. Quantitative Structural Imaging (μl x 103) Gray Matter 61.5 61.8 62 61.4 55.9 60 60.5 (2.3)* 63.1 (1.6) White Matter 39.8 41.5 41.5 43.3 44.4 42.4 42.2 (1.6)* 46.8 (1.8) Cerebral Spinal Fluid 23.1 20.7 20.6 19.2 23.9 21.2 21.5 (1.7)* 16.3 (1.9) Select Neuropsychological Tests Shipley Institute of Living Scale (verbal) 34 26 34 29 28 28 36 31 (4) 30 (4) Verbal Fluency (# words generated) 48 44 35 45 49 38 33 41 (6) 40 (10) Symbol- Digit, written (# correct) 54 47 51 46 69 55 45 52 (8) 59 (10) Symbol-Digit, oral (# correct) 64 58 64 67 88 65 49 65 (12) 72 (12) Trail-making test (B-A) 243312332535 2527 (8)31 (15) Wisconsin Card Test (perseverations) 11 10 13 17 11 16 14 13 (3) 20 (9) Self-Ordered Pointing (total errors) 4 10 6 13 4 10 8 8 (3) 6 (4)

Notes. Three separate control groups were used. Demographics are compared to the functional imaging control subjects (N=12); structural neuroimaging data are compared to age-matched healthy adults (N=12); neuropsychological data are compared to age-, education-, and socioeconomic-matched healthy adults (N=27). Data are means and standard deviations, except for acute injury characteristics, which are medians and intraquartile ranges. * Indicate significant differences from controls. Bold text in Select Neuropsychological Tests signify impaired performance.

TBI patients were recruited from consecutive admissions as part of the Toronto TBI

study (Fujiwara, Schwartz, Gao, Black, & Levine, in press; Levine et al., in press). All patients

had sustained a TBI as a result of a motor vehicle accident and were in the chronic stage of recovery at the time of study participation. Despite their significant TBIs, all patients demonstrated good functional recovery as evidenced by a return to pre-injury employment or academic status. Injury severity was determined by GCS as documented by the trauma team leader’s score at the time of discharge from the Trauma Unit, corresponding to the recommended 6-hour GCS score (Teasdale & Jennett, 1974). Severity in two cases (1054,

2930) was upgraded from that indicated by the GCS due to extended loss of consciousness, extended post-traumatic amnesia, or both. Seven of eight patients underwent a separate structural MRI TBI protocol (for details, see Levine et al., in press). Radiological interpretation indicated evidence of DAI-related neuropathology (hemosiderin deposits)

localized to the frontal lobes in all seven patients, with additional pathology visualized in the

parietal lobe (2 patients), the temporal lobe (1 patient), the corpus callosum (1 patient), the

basal ganglia (2 patients), and the thalamus (1 patient). No patient had lesions greater than 3

mm in diameter. Whole-brain volumetric measures of gray matter, white matter, and

cerebrospinal fluid (CSF) following our published image analysis methods (Levine et al., in

press) were available for six patients, all of whom showed evidence of atrophy relative to

age- and education-matched control subjects. Taken together, the radiological interpretation

and significant volume loss in the TBI patients are consistent with DAI.

Neuropsychological test data were compared with a local normative sample of age-,

education-, and socioeconomically-matched control subjects. TBI patients demonstrated

average to high average performance on the verbal subtask of the Shipley Institute of Living

Scale (Zachary, 1986). This premorbid estimate, combined with matched education levels

between our TBI and fMRI control subjects, minimize group differences in native verbal

capacity limitations, an important criterion for a study of verbal working memory. TBI

patients also performed normally on other neuropsychological tests of attention and

executive functioning, including (with one exception) a task explicitly measuring executive

control within working memory (Petrides & Milner, 1982).

Behavioural Task

We employed a modified version of the Alphaspan task (Craik, 1986), previously used in neuroimaging studies to separate executive control processes from storage and

rehearsal in verbal working memory (D'Esposito, Postle, Ballard, & Lease, 1999; Postle et al.,

1999). On each trial, subjects studied a letter set consisting of 3 or 5 consonant letter strings

(set size or ‘load’ manipulation) and were asked to ‘MAINTAIN’ the letter set over a brief

delay or ‘ALPHABETIZE’ the letters into the correct alphabetical position during the delay

(executive demand manipulation). At the end of the delay, a probe was presented consisting

of a letter and an ordinal position (e.g. L-4? – ‘Was ‘L’ the 4th letter in the set?’). On

Maintain trials, the probe referred to the ordinal position in the original letter set while on

Alphabetize trials the probe referred to the letter position following alphabetization of the

list. Probability of a correct probe was set at 0.5 for all trials in all conditions (see Figure 1.1

for a schematic of the task design). Executive control was operationalized as the difference

between Alphabetize and Maintain conditions (Postle et al., 1999).

Figure 1.1: Schematic of fMRI behavioural paradigm. Warning cue (“Alphabetize” or “Maintain”) was presented at the beginning of each trial block. ITI = intertribal interval.

3 / 5 letters (1.5 / 2.5 s) DelayDelay ( 7 sec ) Probe (2s on + 2s off) ITI

avg of 5 sec D R H C M + M – 4 ?

Time(s) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Prior to scanning, each participant was provided with step by step instructions as to how to perform the tasks, followed by 20 trials (5 trials in each of the 4 conditions). During scanning, subjects completed 28 trials of each of the four task conditions, Alphabetize 3 and

5 letter sets (A3, A5) and Maintain 3 and 5 letter sets (M3, M5) in a single scan session.

Within each session four 12-min scans were acquired. Trials were grouped by executive demand with 2 blocks of seven trials at each level of executive demand presented during a single scan acquisition. Set size was randomized within each block. At the beginning of each trial block, subjects were presented with a warning screen to alert them as to the

upcoming condition (i.e. either “Maintain” or “Alphabetize”). To alert the participant to the

upcoming letter set, a grey rectangle was presented at the center of the screen for 500 ms.

Letter strings were presented at a standard timing rate of 500 ms per letter (1500 ms for

three item set sizes, 2500 ms for five items). The letter set was then replaced by a fixation cross presented at the center of the screen for 7000 ms. At the end of this delay period, the probe was presented for 2 seconds, with an additional 2 second period of blank screen during which responses were allowed. Trials were separated by a variable inter trial interval averaging 5 seconds across all trials in a single scan session. Total stimulus onset asynchrony

was 18000 ms (3 letter trials) or 19000 ms (5 letter trials).

fMRI scanning and analyses

Scanning was performed on a whole-body 3.0 T MRI system (Signa 3T94 hardware,

VH3M3 software; General Electric Healthcare, Waukesha, WI) with a standard quadrature

bird-cage head coil. Subjects were placed in the scanner in supine position, with their head

firmly placed in a vacuum pillow to minimize head movement. Earplugs were provided to

reduce the noise from the scanner, and sensors were placed on subjects’ right index finger

and around the chest to monitor heart rate and respiration. A volumetric anatomical MRI

was performed before functional scanning, using standard high-resolution 3D T1-weighted

fast spoiled gradient echo (FSPGR) images (TR/TE=7.2/3.1 ms, inversion-recovery

prepared T1=300 ms, flip angle 15°, 256×192 acquisition matrix, 124 axial slices 1.4 mm

thick, voxel size=0.86×0.86 cm, FOV=22×16.5 cm). Functional imaging was performed to

measure the blood oxygenation level dependent (BOLD) effect (Ogawa, Lee, Kay, & Tank,

1990). Scans were obtained using a single-shot T2*-weighted pulse with spiral in-out,

achieving 26 slices, each 5 mm thick (TR/TE=2000/30 ms, flip angle 70°, 64×64 acquisition

matrix, voxel size=3.125×3.125, slice spacing=0, FOV=20×20 cm). Data processing and

analyses were performed using Analysis of Functional NeuroImages software (Cox & Hyde,

1997). The initial 10 time points from each functional image volume were excluded from

the analyses to allow for stabilization of the magnetic field gradients. Time-series data were

spatially co-registered (aligned volumetrically to a reference image within the run, using the

3dvolreg program in AFNI) to correct for small head motion using a 3-D Fourier transform

interpolation, and the linear trends were removed. Uncorrected head motion (spikes) was

identified through visual inspection and reduced through averaging the two surrounding

time points. Physiological motion (respiration and heart beat) was also removed through

linear filtering. Finally, slice timing correction was carried out to account for the time

dependent discrepancy between the initial and final slice acquisitions. Following

preprocessing, the data were submitted to a deconvolution analysis using the AFNI plugin

3dDeconvolve. The functional data were modeled using a general linear model to derive

parameter estimates and corresponding t-statistics for the impulse response functions

corresponding to the four primary working memory conditions (Alphabetize 3/5, Maintain

3/5) as well as the two types of probe trials (probe 3-letter, probe 5-letter). We constrained

the shape of the haemodynamic response function by convolving the stimulus time-series

with a gamma function to obtain ideal waveform time-series. The resulting whole brain,

voxel-based maps of the parameter estimates and their statistical (T-statistic) assessments

(i.e. the within subject, within condition activation maps) were transformed into stereotaxic

space (Cox & Hyde, 1997; Talairach & Tournoux, 1988) by aligning the anterior–posterior-

commissure, inferior–superior and left-right maxima, as well as mid-saggital reference points

of the structural scans and scaling them to fit the Talairach–Tournoux Atlas brain.

Activation maps were spatially smoothed with a Gaussian filter with a full width at half

21 maximum value of 8.0 mm to minimize individual variation of the anatomical landmarks.

These steps were performed to facilitate the subsequent group analysis, which consisted of voxel-wise, mixed effects (groups fixed, conditions fixed, subjects random), three-factor

ANOVA for unbalanced designs. This program is included as a MATLAB script with the

AFNI software package. The three fixed factors each had two levels: Group (TBI, Control subjects), Executive Demand (Alphabetize, Maintain) and Set Size (3 letters, 5 letters). The fourth random factor, subject, was nested within Group. Task effects within groups were thresholded at an individual voxel probability of p<0.0001, with clusters >150 μl (3 acquisition voxels), and having a connectivity radius of 2 mm (i.e. two clusters need to be separated by at least 2 mm to be considered different). For the interaction of Group x

Condition (GxED), we report clusters of activation thresholded at p < .001, cluster size >

150 μl. Region-of-interest (ROI) analyses were drawn at these cluster maxima to assess the impact of inter-individual variability and potential speed of processing effects. Parameter estimates from each voxel for each condition were extracted from these ROIs and outliers defined as having scores more than 1.5 times the inter-quartile range of their respective groups for each condition were trimmed using a Winsorizing procedure to 2 SD above or below the true mean (i.e. exclusive of the outlier). This procedure allows the maintenance of extreme observations without unduly influencing statistical estimates.

Results

Behaviour

One TBI patient was identified as an outlier for accuracy (TBI # 3646). Trial-by-trial analysis for this patient revealed a consistent pattern of timing out on Alphabetize trials, greater for 5 than for 3 letter sets sizes. This pattern is indicative of a task-specific

decrement at response as opposed to disengagement or poor arousal. On those trials where

responses were recorded, performance was within the average range of the TBI group for

both 3- and 5-letter sets (78% and 67% respectively). As we were interested in neural

response during encoding and delay trial epochs, and not at probe, we did not exclude this

patient’s data. For the purposes of statistically assessing group effects on task performance,

Alphabetize accuracy values for this subject were trimmed to 2 SD below the true mean

using a Winsorizing procedure.

A two group (control vs. TBI) x 2 (executive demand) x 2 (set size) repeated

measures ANOVA revealed a significant main effect of executive demand (ED) and set size

(Set) [F(1) = 18.51 (ED); F(1) = 11.42 (Set), p < .01 for both comparisons] with poorer

performance observed during the Alphabetize and set size 5 conditions. The interaction

between ED and Set was non-significant [F(1) = 0.02, NS]. Importantly, there was no main

effect of group [F(1) = 2.54; NS] and no group x ED [F(1) = 0.094, NS] or group x Set [F(1)

= 0.182, NS] interactions, indicating that the main effects of condition were equivalent across both the control and TBI groups (Figure 1.2, top panel). Post-hoc analyses revealed

that there were no group differences on any of the tasks (p > .05, all comparisons). Analyses

including the uncorrected outlier data did not alter these results.

Figure 1.2: Behavioural data for Alphaspan task during fMRI scanning. Panel A: task accuracy (% correct) for TBI and control subjects. Panel B: reaction time (msec). Error bars represent 95% confidence intervals.

A. 1.00 0.90

0.80 Control 0.70 TBI Accuracy 0.60 0.50 Alpha 3 Alpha 5 Maintain 3 Maintain 5 Task

B. 2500 2000

1500 Control 1000 TBI RT (msecs) 500 0 Alpha 3 Alpha 5 Maintain 3 Maintain 5 Task

On measures of reaction time, there was a main effect of condition [F(3) = 43.81, p

< .001], and group [F(1) = 6.41; p < .05] but no group by task interaction [F(3) = 1.54; NS].

Post-hoc testing revealed that control subjects exhibited faster responding at probe for all tasks with the exception of Maintain 3 (p < .05, all comparisons). Task main effects were driven by longer reaction times at probe on 5-letter versus 3-letter set size trials, irrespective of executive demand (p < .001 for all comparisons; See Figure 1.2).

Neuroimaging Analyses

Executive control was associated with increased activity in left lateral frontal,

posterior parietal and insular cortices, replicating prior work with this paradigm (Postle et al.,

1999; Figure 1.3, panel A; Table 1.2). The same contrast in TBI patients revealed a more

extensive pattern of suprathreshold activation (Figure 1.3, panel B; Table 1.2). Brain voxels

demonstrating greater Alphabetization versus Maintain activity (t > 4.96, p < 10-4 ) in this contrast represented .004% of total brain volume in the TBI sample as compared to .0008% in control subjects. Specific areas of activation included the lateral PFC bilaterally, the insular and superior parietal cortices bilaterally, and right temporal-occipital junction (TOJ).

Group (TBI vs. Control) x Executive Demand (Alphabetize vs. Maintain) interactions were observed in left middle frontal gyrus (GFm), right inferior frontal gyrus

(GFi), and left posterior regions including inferior and superior parietal cortices. Activity in these areas was significantly greater during Alphabetize than Maintain trials and this difference was significantly greater in TBI patients relative to control subjects (Figure 1.3, panel C; Table 1.2). An area of left angular gyrus, Brodmann Area (BA) 39 exhibited a significant Group X Executive Demand interaction associated with greater activation during

Maintain trials in the control subjects. We used ROI analyses to confirm that these interactions were reliable (see Methods). Across ROIs an average of 77% of the TBI sample showed an increase in BOLD signal during Alphabetize trials that was greater than the 95th percentile for controls (Figure 1.4). A small sub-cluster threshold activation in the

right medial frontal polar cortex (BA 10) demonstrated a significant Group x Executive

Demand x Set size interaction. Post-hoc analyses revealed that this interaction was driven by

increased activity during Alphabetize 5 relative to Maintain 5 trials in TBI patients over and

above the control sample; a similar, albeit attenuated, pattern was observed in the 3-letter set

25 conditions. No similar interactions were observed within lateral PFC, suggesting that group differences in executive control processes mediated by this region were not impacted by load as defined in this study.

Figure 1.3: Areas of maximal BOLD signal change in the Executive Demand contrast (i.e. Alphabetize > Maintain conditions), collapsed across set size.

t=7 A D. Right IFG (BA 44)

TBI Control 0.2 0.15 0.1 0.05

t=-7 chg) (% 0 -0.05

B t=7 Alpha 5 - Maintain5 -0.1 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 Time (seconds)

Left MFG (BA 46)

TBI 0.4 Control t=-7 0.35 F=16 0.3 C 1 0.25 2 0.2 0.15 0.1 (% chg) (% 0.05 0 Alpha 5 - Maintain 5 -0.05 -0.1 Z=6 Z=16 Z=26 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 F=0 Time (seconds)

Notes: Panel A = Control subjects; Panel B = TBI patients. Panel C. Areas demonstrating a significant Group by Executive Demand interaction. All suprathreshold cluster maxima for each group and the interaction are reported in Table 1.2.. Panel D.1 & D.2. Temporal differences (% change in BOLD signal) between Alphabetize and Maintain set size 5 conditions for control subjects and TBI patients, cross-referenced to numbers as specified in panel C. Green and red bars below the x-axis represent the onset of stimulus presentation and onset of probe respectively.

Table 1.2. Activation cluster maxima corresponding to maximal BOLD signal changes during the Alphabetize vs. Maintain conditions (i.e. main effect of Executive Demand).

Region Control TBI Group x ED Lat. BA x y zxy zxy z Frontal Cortex Frontopolar Medial Frontal Gyrus R 10 {1 55 11} Dorsolateral Middle Frontal Gyrus L 46 -46 43 14 -43 46 11 L 46 -40 28 20 -40 26 20 L8 -492844 -492547 Ventrolateral Inferior Frontal Gyrus R 44 55 13 14 R 46 462811 L44 -37429 Premotor Superior Frontal Gyrus R 6 4 16 53 L6 -19462 -19462 Middle Frontal Gyrus L 6 -40 10 62 -34 10 56 Insula R 13 31 22 2 31 19 2 L 13 -31 22 5 -34 19 5 Posterior/Subcortical Parietal Cortex Inferior lobule L 40 -55 -40 50 Superior lobule L 7 -16 -64 53 -19 -70 56 -22 -70 56 R7 34-7350 22 -67 56 Angular Gyrus L 39 {-52 -64 35} -46 -70 38 Occipital Cortex Superior Occ. Gyrus R 39 31 -73 29 Thalamus Anterior Nucleus R 7 -13 17

Notes: Maxima are presented for each group and for the Group by Executive Demand interaction. Coordinates are reported in standard space according to the stereotaxic map of Talairach and Tournoux (1988). Individual voxel probability was set at p < 10-4 for each group and 10-3 for the interaction, cluster size > 150ul. Coordinates in parentheses indicate negative activations.

Although TBI subjects did not show significant evidence of slowing on neuropsychological tasks (see Table 1.1), the reaction time differences between our groups on 3 of the 4 experimental tasks suggested that slowed processing speed during alphabetization may have contributed to a cumulative enhancement of the haemodynamic

27 response. To test this hypothesis, we extracted ROI time series data for the 5 letter set sizes for the Alphabetize and Maintain conditions (where we expected speed of processing differences to be most prominent) in two anterior lateral PFC ROIs demonstrating reliable

Group by ED interactions. In right GFi, differences in BOLD signal between the two conditions peaked between 8-10 seconds after stimulus onset in both groups (Figure 1.3, panel D.1), whereas time to peak occurred earlier in our TBI sample in the left GFm ROI

(6-8 seconds after stimulus onset; Figure 1.3, panel D.2). Neither of these anterior lateral

PFC regions demonstrated a significant interaction of group (TBI, Control) X trial time

(TR1- TR8) [RGFi: F(1,7) = 1.83, p < .05; LGFm: F(1,7) = 1.67, p < .05]. Thus, we could find no evidence that ‘time on task’ significantly altered the haemodynamic response in our

TBI patients relative to the control group. In sum, our group-level and single-subject ROI analyses confirm that the neural signature of executive control in working memory is altered following TBI and that these brain changes cannot be accounted for by differences in behavioural performance, variability within our patient sample or time on task differences.

Figure 1.4: Region of interest (ROI) analysis indicating mean % change difference between Alphabetize and Maintain conditions for the control group and individual TBI participants.

Right IFG (BA 44) Left MFG (BA 46) Left MFG (BA 8)

2.5 3 2

2 2.5 1. 5

2 1.5 1 1. 5 1 1 0.5

0.5

0.5 0 0 0

-0.5 -0.5 -0.5

-1 Set 3 Set 5 -1 Set 3 Set 5 -1 Set 3 Set 5

Left MFG (BA 6) Left iPL (BA 40) Left AG (BA 39)

3 2 2.5

2.5 2 1. 5 2 1.5 Alphabetize vs. Maintain (% chg) (% Maintain vs. Alphabetize 1 1.5 1 1 0.5 0.5

0.5 0 0

0 -0.5

-0.5 -0.5 -1

-1 Set 3 Set 5 -1 Set 3 Set 5 -1.5 Set 3 Set 5

Notes: Scatter plots for each ROI include (i) group mean (filled squares) and 95% confidence interval of the mean (error bars) for the control sample and (ii) individual scores for TBI participants (empty squares). Data is presented for three and five letter sets separately.

Discussion

Our results demonstrate that DAI following TBI is associated with altered functional brain response during executive control processing in working memory. Previous studies investigating the functional neuroanatomy of working memory in TBI have been confounded by heterogeneity in patient selection or task characteristics (e.g. Christodoulou et al., 2001; McAllister et al., 1999). Our patients had evidence of DAI by both clinical radiological and volumetric criteria. None had neuroradiological evidence of significant

focal lesions. We were thus able to specifically examine the impact of DAI on executive

control processes to a greater degree than heretofore possible. While the patients had

sustained moderate to severe brain injury, all had returned to pre-injury employment or

academic status and were several years post-injury, thus avoiding confounds related to

recovery phase or performance differences. Finally, the functional neuroanatomy of our task

is well characterized, has good convergent validity with other executive measures, and the

design allowed us to independently manipulate load and executive control demands. By

eliminating these sources of patient- and task-related variance, we showed that the functional

neuroanatomy of executive control processing is specifically altered by DAI following

moderate to severe brain injury. Follow-up analyses indicated that our results were reliable

across the TBI sample and could not be attributed to slowing.

These data are consistent with the characterization of working memory as an

emergent property of coordinated activity within a distributed network of brain regions

(Postle, 2006; Wager & Smith, 2003). We have demonstrated that diffuse injury, in the absence of focal brain pathology, is sufficient to alter brain networks subserving working memory processing. Our findings replicate earlier work demonstrating the involvement of

PFC and left perisylvian regions during performance of healthy subjects on this task (Postle et al., 1999). However, we observed a significant group by executive demand interaction within lateral PFC regions bilaterally, as well as in left posterior parietal cortices. These data demonstrate that DAI is associated with augmented functional recruitment within this network of brain regions, particularly during tasks requiring high executive control.

Moreover, equivalent group performance on our tasks suggests that executive control is facilitated by supplemental recruitment within these regions following DAI.

Altered functional recruitment following brain injury has been characterized as a

decline in the efficiency of neural processing operations (Chen et al., 2006). TBI patients

often report subjective changes associated with cognitively demanding tasks in spite of

normal task performance, a dissociation that presents significant challenges with respect to

diagnostics and treatment planning in the absence of measurable signs of cognitive

dysfunction in patients with TBI, as well as patients with other neuropathologies causing diffuse or multifocal damage (e.g., dementia, multiple sclerosis). Reduced cognitive efficiency, as indexed by altered functional recruitment during neuropsychologically normal task performance, may serve as a novel metric, reflecting the costs of normal behavioural performance at the neural level. Indeed, these measures may correlate well with subjective complaints of patients with diffuse damage, but without demonstrable neuropsychological deficits.

Two further clinical implications emerge from these findings. First, although augmented recruitment was observed here in a sample of well recovered patients without focal injury, we predict that patients with more severe behavioural or functional deficits would similarly demonstrate this pattern of enhanced neural recruitment during cognitively demanding tasks, albeit with diminishing behavioural gains. Such a pattern has recently been reported by Newsome and colleagues (2007) and suggests that these data may have clinical relevance across the spectrum of TBI severity. Second, TBI is commonly associated with deficits in arousal, chronic pain, anxiety and depressive symptomology, each of which has been associated with similar patterns of augmented functional neural recruitment to those reported here (see Hillary, Genova, Chiaravalloti, Rypma, & DeLuca, 2006 for a review).

While the cumulative impact of such co-morbidity on higher cognitive functions following

TBI has yet to be fully investigated, it is plausible that any reduction in neural processing

efficiency resulting from brain injury may be exacerbated by secondary impacts of co-morbid

physical or psychological impairments. The impact of such interactions may impact the

pace or extent of recovery in these patients and presents a challenge both for clinicians and

researchers in assessing the implications of such co-morbidity.

In sum, DAI is associated with augmented functional recruitment during high-level

executive control tasks. Efforts to diagnose and remediate executive control deficits

following TBI have been hampered by a dearth of assessment tools with sufficient sensitivity

to detect executive deficits in a structured laboratory setting (Levine, Katz, Black, & Dade,

2002). We propose that functional brain data provides a potential neural biomarker of altered executive control functions that may be discernable even in patients without frank

neuropsychological or functional deficits (see Chen et al., 2006 for a further discussion).

Such neural biomarkers may be particularly beneficial in the armamentarium of clinicians

working with TBI, where executive control deficits are often reported as a primary

complaint but which remain frustratingly impervious to standard neuropsychological or

structural neuroimaging investigations.

CHAPTER 2

Compensatory neural recruitment during verbal working memory performance after

TBI: evidence for an altered functional engagement hypothesis.

Abstract

There is increasingly convergent evidence that traumatic brain injury is associated with altered patterns of neural recruitment during performance of working memory tasks. Typically, this has manifest as increased recruitment of homologous regions of prefrontal cortex (e.g. right ventrolateral PFC during performance of a verbal working memory task). However the behavioural correlates of these functional changes are poorly understood. We used fMRI to examine neural activity in a sample of TBI patients and matched healthy controls performing working memory tasks with varying memory load and executive demand. In part one of this report, we correlated brain responses and task performance using multi-variate techniques. We identified networks within left and right PFC that uniquely and positively correlated with performance in our control and TBI samples respectively, demonstrating, for the first time in a TBI sample, evidence of compensatory functional recruitment. In part 2 of the report we investigated whether these compensatory brain changes were facilitated by functional reorganization (i.e. recruitment of novel brain regions not engaged by our control sample on any of our tasks) or altered functional engagement (i.e. differential recruitment of similar brain regions between our groups). This combined functional connectivity and brain-behaviour analysis provide strong support for the altered functional engagement hypothesis. Implications for our understanding of neuroplastic change and the design of rehabilitation interventions following brain injury are considered.

Introduction

There is increasingly convergent evidence that traumatic brain injury (TBI) is associated with altered patterns of neural recruitment during performance of working memory tasks (McAllister et al., 1999; Perlstein et al., 2004), typically manifested as increased activity in homologous regions of prefrontal cortex (e.g. right ventrolateral PFC during performance of a verbal working memory task: Christodoulou et al., 2001; McAllister et al.,

1999) and more dispersed activity adjacent to areas implicated in task performance in non- injured controls (e.g. more extensive recruitment within left inferior and middle frontal gyri:

Christodoulou et al., 2001). However, the functional implications of this altered recruitment are less well understood. Only two previous studies have directly investigated brain and behaviour correlations during working memory performance in TBI with conflicting results.

McAllister et al. (1999) reported a positive correlation between activity in left frontal PFC regions and accuracy on a verbal working memory task (N-back) across all participants in their study (they did not report group specific correlations). In contrast, Newsome et al.

(2007), using a similar paradigm, observed brain-behaviour correlations only in posterior cortices and only during the lowest working memory load. Several other reports investigating the neural correlates of working memory following TBI did not directly correlate brain and behavioural measures, although a review of the brain and behaviour data from these reports suggests similarly equivocal findings. Christodoulou and colleagues

(2001) observed decreased lateralization (i.e. increased right PFC activity) and increased dispersion (i.e. increased activity in areas adjacent to those observed in healthy controls) during performance on a modified N-back task in a sample of moderate to severe TBI patients. However performance in the TBI group was significantly lower than that of

healthy matched controls, suggesting that this increased brain activation was not sufficient to

support normal task performance in the TBI group. A similar result was observed by

Perlstein et al. (2004), with increased right PFC activity insufficient to elicit comparable N- back performance between the control and TBI samples. In contrast, McAllister et al.

(1999, 2001), observed increased right PFC activity with increasing working memory load in

the context of equivalent group performance, suggesting that this additional recruitment may

have been compensatory.

Although there have been several consistently reported findings across the few

studies that have examined functional brain activity in TBI (e.g. reduced hemispheric

lateralization, see Hillary et al., 2006 for a review), the heterogeneity in range of TBI severity

and chronicity likely affects interpretation of brain-behaviour patterns in these studies,

irrespective of whether these were directly assessed. Moreover, performance variability is

now considered a hallmark of neurocognitive functioning in TBI patients (Levine &

Downey-Lamb, 2002; Stuss & Levine, 2002). Such variability, whether in brain or

behavioural measures or both, serves to significantly dampen signal to noise ratios in

correlational analyses and presents a significant barrier to the detection of consistent brain-

behavior patterns in this population.

In the current report, we investigate these correlations in a carefully screened sample

of moderate to severe, chronic-phase TBI survivors, each of whom demonstrated good

neuropsychological and functional recovery after their injury. All had evidence of diffuse

axonal injury on neuroradiological report but were screened for focal lesions greater than

3mm. The patients were scanned with fMRI using a working memory paradigm that varied

load and executive demands independently, permitting the examination of brain-behaviour

correlations across multiple levels of working memory task demand. In Research Study 1

above, we replicated others’ findings of more widespread activation, including increased

right frontal activity, associated with increased executive demands in the TBI patients. In

this study, we adopted multivariate network analyses methods to identify networks of

behaviourally relevant brain regions in our TBI group, as these methods are well-suited to

the detection of functional neuroanatomical changes in distributed systems.

Here we report the results of two multivariate analyses. The first directly examined

the functional correlations between brain activity and performance on a verbal working memory task using spatial-temporal behavioural partial least squares (ST-bPLS, McIntosh,

Chau, & Protzner, 2004). This technique has demonstrated enhanced statistical power

relative to univariate approaches and is robust to outlier influences, thus allowing us to

extract correlational data that might otherwise be obscured by sample heterogeneity using

more traditional univariate analysis methods (McIntosh et al., 2004). The second analysis

examined the functional connectivity of those brain regions demonstrating robust brain and

behaviour correlations in study one using seed-based PLS analysis techniques. Our interest

in this interaction of functional connectivity and brain-behavioural correlations emerged

from observing the striking overlap between patterns of altered functional recruitment

subserving working memory functioning after brain injury and in healthy subjects under

conditions of high cognitive demand (Curtis & D'Esposito, 2003; Rypma, Prabhakaran,

Desmond, Glover, & Gabrieli, 1999). These data suggest that TBI and cerebral challenge in

the healthy brain may result in the engagement of similar brain regions to support task

performance. To our knowledge, there exists no direct empirical data to support this (but

see Hillary et al., 2006 for a conceptual review).

General Method

Participants

Eight participants (6 males) with moderate to severe traumatic brain injury were

recruited for this study. All subjects were part of the Toronto TBI study and were recruited

based upon consecutive admissions to a level 1 trauma center. Further details with respect

to the patient demographics, injury characteristics and recruitment inclusion and exclusion

criteria of the larger sample have been reported elsewhere (Levine et al., in press).

Demographic and injury characteristics for the subset of patients included in the present

report have been described in Research Study 1 and are only briefly reviewed here. TBI

participants had sustained closed head injury as a result of a motor vehicle accident and were

in the chronic stage of recovery at the time of study participation. The injury severity profile

of the group included 5 moderate and 3 severe TBI participants as determined by trauma

GCS, loss of consciousness and extent of post-traumatic amnesia. Exclusion criteria

included previous head injury, significant psychiatric history or evidence of current or recent alcohol and drug abuse. TBI patients were also excluded from participation if they had evidence of focal lesions greater than 3mm in diameter based on high resolution structural

MRI. All TBI participants in the current study had evidence of DAI-related neuropathology

(hemosiderin deposits) on neuroradiological report and each had demonstrated good functional recovery (return to work or school). Seven of the eight TBI participants underwent extensive behavioural testing as part of the larger Toronto TBI Study and data are reported in Chapter 1. Twelve (12) neurologically normal participants (8 males) were recruited for the current study. TBI and control participants were matched on age [t(18) =

.785, p > .05; NS] and education [t(18) = -1.99, p > .05; NS]. All control participants were

right-handed and were native English speakers and were screened for previous neurological

injury, history of psychiatric illness, or drug use.

Behavioural Task

In the present study we employed a modified version of the Alphaspan protocol

described by Postle and colleagues (1999) and based on earlier work by Craik (1986). For

each task trial, participants were required to study a letter set consisting of either 3 or 5

consonant letter strings (set size or ‘load’ manipulation) and asked to either ‘MAINTAIN’

the letter set over a brief delay or ‘ALPHABETIZE’ the letters into their correct alphabetical

position during the delay period (executive demand manipulation). At the end of the delay, a

probe was presented consisting of a letter and an ordinal position (e.g. L-4? – ‘Was ‘L’ the 4th letter in the set?’). On Maintain trials, the probe referred to the ordinal position in the original letter set while on Alphabetize trials the probe referred to the letter position following alphabetization of the list. Probability of a correct probe was set at 0.5 for all trials in all conditions. Prior to scanning, each participant completed a training session consisting of step by step instructions for each task condition. Once there were no further questions for the administrator, all subjects completed 20 further trials (5 trials in each of the 4 conditions) immediately prior to entering the MR scanner. During scanning, participants completed 28 trials of each of the four task conditions (Alphabetize 3 & 5 letter sets,

Maintain 3 & 5 letter sets) during a single scanning session. Within each session a total of four individual scans were acquired. Trials were grouped by executive demand with 2 blocks of seven trials at each level of executive demand presented during a single scan acquisition. Total stimulus onset asynchrony was 18000 ms (3 letter trials) or 19000 ms (5

38 letter trials). Each individual scan acquisition was 12 minutes in duration (see Chapter 1,

Figure 1.1 for a full trial schematic).

fMRI scanning and analyses

Scanning was performed at Sunnybrook Health Sciences Centre on a research- dedicated whole-body 3.0 T MRI system (Signa 3T94 hardware, VH3M3 software; General

Electric Healthcare, Waukesha, WI) with a standard quadrature bird-cage head coil.

Participants were placed in the scanner in supine position, with their head firmly placed in a vacuum pillow to minimize head movement. Earplugs were provided to reduce the noise from the scanner, and sensors were placed on participants’ right index finger and around the chest, to monitor heart rate and respiration. A volumetric anatomical MRI was performed before functional scanning, using standard high-resolution 3D T1-weighted fast spoiled gradient echo (FSPGR) images (TR/TE=7.2/3.1 ms, inversion-recovery prepared T1=300 ms, flip angle 15°, 256×192 acquisition matrix, 124 axial slices 1.4 mm thick, voxel size=0.86×0.86 cm, FOV=22×16.5 cm). Functional imaging was performed to measure the blood oxygenation level dependent (BOLD) effect (Ogawa et al., 1990). Scans were obtained using a single-shot T2*-weighted pulse with spiral in-out, achieving 26 slices, each 5 mm thick (TR/TE=2000/30 ms, flip angle 70°, 64×64 acquisition matrix, 26 axial slices 5 mm thick, voxel size=3.125×3.125, slice spacing=0, FOV=20×20 cm). Data processing was performed using Analysis of Functional NeuroImaging (AFNI) software

(http://afni.nimh.nih.gov/, Cox, 1996). Time series data were spatially coregistered to correct for head motion by using a 3D Fourier transform interpolation. Motion-corrected images were then spatially transformed to an fMRI spiral scan template generated from 30 subjects scanned locally. This template was registered to the MNI305 template. The

transformation of each subject to the spiral template was achieved using a 12-parameter

affine transform with sinc interpolation as implemented in SPM99

(http://www.fil.ion.ucl.ac.uk/spm/, Friston et al., 1995). Images were smoothed with an 8-

mm isotropic Gaussian filter before analysis. For each subject, “brain” voxels in a specific

image were defined as voxels with an intensity greater than 15% of the maximum value in

that image. The union of masks was used for group analyses as described below.

Partial Least Squares (PLS) Analyses

The primary image analysis was done with spatio-temporal partial least squares (ST-

PLS) (McIntosh, Bookstein, Haxby, & Grady, 1996). ST-PLS operates on the entire data structure at once, which requires that the data be in matrix form. One data matrix is made for each group. Within group, the rows of the data matrix are arranged as follows: condition blocks are stacked, and each subject has a row of data within each condition block. With n subjects and k conditions, there are n_k rows in the matrix. The columns of the data matrix contain the signal intensity measure at each voxel at each time point. The first column has intensity for the first voxel at the first time point, the second column has the intensity for the first voxel at the second time point. With m voxels and t time points, there are m _ t columns in the matrix. The haemodynamic response function (HRF) for any given condition normally lasts for several scans; thus a “lag window” is defined for a short signal segment within a trial that represents the response of each voxel. In the current experiment, the lag window is 6 (TR = 2; 12 s), beginning at trial onset minus 1 TR. The HRF for each trial is expressed as the intensity difference from trial onset. Two ST-PLS analyses were conducted.

The first, behavioural ST-PLS (ST-bPLS), was the primary analysis to test the hypothesis of an interaction between task conditions and group in terms of brain–behaviour correlations.

This addressed the fundamental question as to whether those regions demonstrating

functional relevance differed between our two groups. From this analysis we identified seed

voxels to be used in the combined behaviour and functional connectivity analysis described

in part two of this report. This second analysis used a behaviour-seed PLS (ST-sPLS)

technique to address two questions: (i) does the behavioural relevance of brain regions

demonstrating reliable brain-behaviour correlations depend upon their functional

connectivity both inter- and intra-hemispherically and (ii) are these relationships altered by

TBI?

General Results

Behavioural Results

We chose to examine accuracy as our behavioural measure for these analyses as we

were principally interested in examining the functional brain correlates of executive

processes (i.e. manipulation) within working memory. We maintain that accuracy provides a

purer measure of the outcome of these processes and is less susceptible than reaction time

measures to extraneous processes known to be active at probe (e.g. scanning, retrieval, motor speed, but see Hillary et al., 2006 for a discussion of this issue). Full behavioural analyses are presented in Chapter 1 and only the between group results relative to the

current study are described here. These behavioural data were used for both study phases

described below.

One TBI patient was identified as an outlier for accuracy (TBI # 3646). We used a

winsorizing procedure by which the outlier values for this subject’s accuracy on Alphabetize

3/5 trials were trimmed to 2 SD below the true mean of the sample (i.e. exclusive of the

outlier). To confirm that our results were not unduly influenced by this outlier correction we

ran all analyses with this subject’s original data. There was a marginal but insignificant

impact of the correction on the robustness of the behavioural PLS output (see ST-bPLS

Results below). There was no impact on the behavioural results. Statistical analysis was

carried out using a two group (control vs. TBI) x 2 (executive demand) x 2 (set size) repeated

measures ANOVA. There was no main effect of group [F(1) = 2.54; NS] and no group x

ED [F(1)=0.094, NS] or group x Set [F(1)=0.182, NS] interactions, indicating that the main effects of condition were stable across both the control and TBI groups. Post-hoc analyses revealed that there were no group differences on any of the tasks (p > .05, all comparisons).

fMRI analyses I: brain and behaviour correlations (behavioural PLS)

In Chapter 1, we documented evidence of increased neural response during executive control processing in working memory in patients with ‘pure’ DAI (Chapter 1,

Figure 1.3, Table 1.2). This was the case even though performance was matched between

our two groups. However, few previous reports have directly investigated the link between

these altered patterns of brain response and working memory behaviour in a TBI sample,

leaving open the question as to whether these brain changes are compensatory, deleterious

or incidental to task performance following TBI. The presence of brain changes in the

context of preserved behavioural performance in our earlier report suggested that such

changes are unlikely to be deleterious to task performance. However, it remains possible

that different patterns of neural recruitment, for example within versus between cerebral

hemispheres, may not be equivalently related to behaviour. Moreover, given that DAI has

also been associated with widespread disruptions in cerebral microvasculature and metabolic

changes (Povlishock & Katz, 2005), one might predict that the altered pattern of functional recruitment observed in our previous report may reflect physiological or systemic brain

changes that, while co-occuring with working memory processing, are nonetheless incidental

to behavioural performance. To directly test the hypothesis that the altered functional

recruitment observed during executive control processing in working memory following

DAI is compensatory, we used ST-bPLS, a multivariate technique to assess brain-behaviour

correlations simultaneously across the whole-brain. By adopting this technique, we were

able to identify those brain regions that were correlated with behaviour in healthy control

subjects and assess whether these brain-behaviour correlations are altered by DAI.

Spatial-temporal behavioural partial least squares is a variant of ST-PLS that

identifies latent variables (LVs) that capture task- and group-dependent patterns of brain–

behaviour correlations (McIntosh et al., 2004). The correlation of behaviour measures and

the fMRI signal is computed across subjects within each task, producing within-task brain–

behaviour correlations for each of the four task conditions and the two groups. Singular

value decomposition of the brain–behaviour correlation matrix produces three new matrices:

voxel saliences, singular values, and task saliences. The variation across the task saliences

indicates whether a given LV represents a similarity or difference in the brain–behaviour

correlation across tasks and between our two groups. This can also be shown by calculation of correlation between the brain scores (dot product of the voxel salience and fMRI data) and behaviour data for each task. The voxel saliences give the corresponding spatiotemporal activity pattern. They are displayed as a singular image, which shows voxels that are weighted in proportion to the strength and direction (positive or negative) of their brain– behaviour correlation. Statistical assessment for ST-bPLS is done using permutation tests for the LVs and bootstrap estimation of standard errors for the voxel saliences. The permutation test assesses whether the effect represented in a given LV is sufficiently strong, in a statistical sense, to be different from random noise. The SE estimates of the saliences

from the bootstrap tests are used to assess the reliability of the nonzero saliences on

significant LVs.

fMRI results I

A two-group ST-bPLS with accuracy as the behavioural measure identified one

significant LV (LV 1, p < .01) and a second that fell just below the standard threshold for

statistical significance (p < .064). When we ran these analyses using the uncorrected data for

TBI patient # 8, LV 2 fell just within the significance threshold (p < .05). However, we

believe that the outlier corrected data represents a truer sampling of the subject’s task

performance. For this reason, and to protect against a spurious inflation of the brain-

behaviour correlations due to an extreme score for this subject, we report the more

conservative, outlier corrected data throughout the remainder of this report.

LV 1 identified regions of reliable and positive correlations between brain response

and task accuracy across all tasks in the TBI sample. In our control sample, only

performance on theA5 and M3 conditions was reliably correlated with activity in these

regions. While the A5 correlation was robust and reliable (r=0.85), the M3 correlation was

considerably smaller (r=0.48) and only marginally reliable based on bootstrap estimates of standard error, potentially reflecting near ceiling performance for controls on this task

[X=0.96(0.03)] and will not be addressed further. Positive brain weights for LV 1 were observed in bilateral dlPFC, middle frontal gyrus (GFm), BA 46; right ventrolateral

prefrontal cortex (vlPFC), inferior frontal gyrus (GFi), BA 44; anterior and posterior

cingulate gyrus (GC), BA 32 & 30 respectively; left superior parietal cortex and bilateral

regions of the TOJ, BA 18/19 near the fusiform (GF) and lingual gyri (GL). The second LV

identified a main effect of group for the A3, A5 and M5 tasks. Dominant positive weights

(positive correlations with behaviour in the control group) were observed in left vlPFC,

inferior frontal gyrus, BA 44 as well as in the supra-marginal gyrus (Gsm) of the left inferior

parietal lobule (LPi), BA 40. In contrast, dominant negative weights (positive correlations

with behaviour in the TBI group) were observed in right GFm, BA 46; right Gsm (BA 40) as

well as right frontal polar cortex, BA 10, left insula, left precuneus (BA 7) and posterior

cingulate gyrus. Single group analyses yielded similar results confirming these findings of a

hemispheric dissociation in brain behaviour correlates between our two groups (Figure 2.1,

Tables 2.1 & 2.2).

Figure 2.1a. Brain regions demonstrating significant correlations between brain response and task accuracy in control participants (yellow clusters). (BSR = 4, p < 10-4; cluster = 15). Figure 2.1b. Brain regions demonstrating significant correlations between brain response and task accuracy in the TBI group. (BSR = 4, p < 10-4; cluster = 10).

4-6s

6-8s

8-10s

-8 10 12 14 16 18 20 22 24 26 28 10 12 14 16 18 20 22 24 26 28 Notes: Arrows indicate cluster maxima that differentiated our two groups with respect to brain behaviour correlations during the Alphabetize tasks. These include left inferior and middle frontal gyri (controls) and two regions of right anterior middle frontal gyrus (TBI). These voxels were used as seeds to explore changes in functional connectivity in the subsequent series of analyses. Colour bar indicates bootstrap ratio range (BSR), a measure conceptually similar to a Z-score, that is used to determine reliability of the brain /behaviour correlation pattern.

Table 2.1: Cluster maxima from the behaviour PLS (ST-bPLS) analysis for the control group (See figure 2.1a).

LagxyzAnat BA BSR Clust. 0 -36 -78 -10 GL 18 7.8919 58 0 24 -67 -13 GF 19 7.1583 54

1 -67 9 29 GFi 44 8.0633 20 1 -40 48 23 GFm 46/9 6.5961 22 1 -63 -46 10 GTs 22 6.3414 18 1 -51 -30 31 Gsm 40 6.6677 44 1 -24 -55 58 PCu 7 5.4227 27 1 44 -28 53 LPi 40 5.273 30 1 -24 -78 -13 GF 18 7.4925 108 1 28 -78 -13 GF 18 6.4417 75

2 -44 26 -18 GFi 47 6.7831 21 2 -24 -59 62 PCu 7 8.1431 17 2 -51 -29 35 LPi 40 5.9971 24 2 28 -90 -16 GF 18 6.8112 91 2 -44 -84 23 GO 19 6.4364 26 2 -48 -63 -10 GOm 19 6.1715 17

3 48 -64 -27 Cereb 7.4412 58

LagxyzAnat BA BSR Clust. 0 -8 70 -7 GFs 10 -6.9307 40 0 4 70 -7 GFs 10 -5.4135 18 0 55 -61 33 Gsm 40 -6.1099 16

3 -20 -36 50 PCu 7 -8.9998 30 3 -4 -84 37 CU 19 -7.0375 16

Notes: Cluster threshold was set by a bootsrap ratio > 4, p < .0001, and a minimum cluster size of 15 voxels. Lag refers to the temporal window with lag 0 corresponding to 2-4 seconds after stimulus onset. Each lag represents a 2 second time window. Voxel coordinates are reported in accordance with the stereotaxic atlas of Talairach and Tournoux (1988). Anat = anatomical region, abbreviations are consistent with those found in the atlas. BA=Brodmann area, BSR=bootstrap ratio, Clust.= cluster size (in voxels). Top section of the table indicates those brain regions that demonstrated a positive correlation with task accuracy. Bottom portion indicates those regions that were inversely correlated with accuracy in the control sample.

Table 2.2. Cluster maxima from the behaviour PLS (ST-bPLS) analysis for the TBI group (see figure 2.1b).

LagxyzAnat BA BSR Clust. 0 24 -72 29 GO 19 6.9401 11

1 24 -73 26 GO 19 10.2363 19

2 24 -73 26 GO 19 9.1954 12 2 40 12 3 Insula 7.3828 23

4 -59 -9 -20 GTi 20 6.3222 19 4 -67 -42 9 GTs 22 6.1611 15

LagxyzAnat BA BSR Clust. 0 -20 -82 -13 GFm 18 -6.2519 12 0 8 -82 -13 GL 18 -5.7971 18 0 -20 -25 -29 BS -6.978 15 0 0 -35 2 B. Stem -5.4666 24

1 20 -86 -13 GFm 18 -7.1859 22 1 0 -25 -32 B. Stem -10.132 16 1 -20 -86 -19 Cereb -6.4683 19

2 40 47 16 GFm 46 -11.9796 28 2 36 21 25 GFm 46 -7.8931 12 2 -28 -57 25 PCu 18 -7.6894 14 2 24 -86 -13 GFm 18 -6.4147 13 2 -20 -29 -36 BS -7.5555 16 2 12 -29 -39 BS -6.2537 19

3 44 47 16 GFm 46 -9.9943 16 3 36 21 25 GFm 46 -6.0106 13 3 -28 -50 17 GTm 39 -5.0793 14 3 -24 -56 54 LPs 7 -7.1324 18

4 -16 -83 -26 Cereb -5.2311 14

Notes: Cluster threshold was set by a bootstrap ratio > 4, p < .0001, and a minimum cluster size of 15 voxels. Lag refers to the temporal window with lag 0 corresponding to 2-4 seconds after stimulus onset. Each lag represents a 2 second time window. Voxel coordinates are reported in accordance with the stereotaxic atlas of Talairach and Tournoux (1988). Anat = anatomical region, abbreviations are consistent with those found in the atlas. BA=Brodmann area, BSR=bootstrap ratio, Clust.= cluster size (in voxels). Top section of the table indicates those brain regions that demonstrated a negative correlation with task accuracy. The bottom portion indicates those regions that were positively correlated with accuracy in the TBI sample.

To better represent this group X lateralization interaction within PFC, we extracted the brain-behaviour correlations for each task from those PFC regions demonstrating

maximal positive correlations in the ST-bPLS analysis for each group. This resulted in 4

regions of interest (ROI). For the TBI group, maximal saliency was observed in right PFC

in an area bordering right GFm and GFi (46/45) and a more anterior GFm region bordering

BA 46/10. For the control group, maximal saliency was observed in the left lateral PFC in a

region bordering the inferior and pre-central frontal gyri (BA 44/6) and a more anterior

segment of middle frontal gyrus (BA 9/46), which appeared homologous to the right

anterior PFC region observed for the TBI sample. Figure 2.2 presents the correlational data

for these bilateral anterior PFC regions for each task and group.

Figure 2.2. Correlations between brain activity in anterior middle frontal gyrus seed regions (highlighted in Figure 2.1). Talairach coordinate of left middle frontal gyrus (GFm): -40, 48, 23 ; right GFm: 40, 47, 16.

1 0.9 0.8 0.7 0.6 Left GFm 0.5 Right GFm 0.4 0.3

(r-values) 0.2 0.1 0 A3 A5 M3 M5 A3 A5 M3 M5

Controls TBI Brain / Behaviour (accuracy) correlations correlations (accuracy) / Behaviour Brain

Interim discussion I: evidence for compensatory functional recruitment following TBI

We used ST-bPLS to investigate whether the altered functional recruitment of brain

regions observed during working memory performance was related to task accuracy. We

hypothesized, based on previous reports from the functional neuroimaging literature (e.g.

McAllister et al., 1999; Perlstein et al., 2004), that the recruitment of areas within right PFC, which we had reported previously in this TBI sample, would be positively correlated with task accuracy (i.e. compensatory). These data are consistent with our hypothesis. Activity in a right lateralized network, including two distinct regions of right lateral PFC in the vicinity of the anterior middle frontal gyrus on the border of BA 10/46 and a more dorsal region of

GFm on the border of BA 44/45/46 was highly correlated with working memory task accuracy in our TBI group. Similar regions of right lateral PFC were observed by Perlstein et al. (2004) to track positively with working memory load in their TBI sample. This region has also been implicated in supraspan working memory performance in non-injured participants (e.g. Rypma et al., 1999) and during working memory performance in healthy aging (Erickson et al., 2007). However, these data represent the first time that activity in this region has been directly and positively correlated with performance on a working memory task in a TBI sample. This finding contrasts with the recent report of Newsome et al.

(2007), who failed to find brain behaviour correlations in any PFC region during a demanding 2-back working memory task in a severe TBI sample. This is an unexpected finding given that PFC activity has often been associated with N-back performance, particularly during the 2-back condition (McAllister et al., 1999; McAllister et al., 2001).

While the exact cause of this discrepancy between our two reports is unclear, it is notable that their TBI sample included only severe TBI participants who had extensive focal and diffuse brain damage and who were scanned in the acute injury phase (12-18 weeks post- injury). Both the severity and recency of the injuries in their sample would be expected to produce significant variability in both brain and behavioural responses, which may have attenuated correlations in their analyses. Moreover, the authors did report brain-behaviour correlations in posterior brain regions during the less demanding 1-back condition, where

overall variability would be expected to be lower, thus providing some support for this

interpretation.

In contrast to the right lateralized brain and behaviour correlations observed in our

TBI sample, task accuracy in our control sample during the Alphabetize tasks and Maintain 5

task was predominantly associated with activity in a network of left lateralized brain regions,

including dorsal and ventral aspects of PFC (BA 46/44), although this network expanded to

include right lateral PFC during Alphabetize 5 trials. This pattern replicates many previous

reports implicating vlPFC and dlPFC in working memory tasks involving a significant

executive control component (Curtis & D'Esposito, 2003 and see Cabeza and Nyberg, 2000

for a review}. Figure 2.2 illustrates the striking group by hemisphere interaction evident in

our part one results. These data are consistent with previous reports investigating working

memory performance after TBI and extend those findings by demonstrating the functional

relevance of this altered functional recruitment.

fMRI analyses II: brain, behaviour and functional connectivity (behaviour & seed PLS).

The results of study one supported our prediction that right hemisphere recruitment during working memory performance was compensatory for TBI participants and, consistent with previous report, this compensatory recruitment was also evident in healthy controls under conditions of high working memory demand (i.e. Alphabetize 5). This overlapping pattern of recruitment between our two groups suggested the possibility that the compensatory functional recruitment identified within PFC following TBI (Part I) does not represent the instantiation of novel PFC networks to support working memory performance after injury but rather reflects the unmasking of extant but behaviourally latent functional connectivity within PFC. To our knowledge, the behavioural correlates of altered functional connectivity following TBI have not been investigated previously.

To examine this question, we entered the 4 PFC ROIs identified in our first series of

analyses as seeds into a combined behaviour and functional connectivity analysis. Each of

these regions (left inferior frontal gyrus, left middle frontal gyrus, right anterior middle

frontal gyrus, right posterior middle frontal gyrus) demonstrated highly reliable and significant correlations with behaviour that differentiated our two groups. Given the

common overlap of right PFC regions associated with brain damage (or the highest level of

working memory demand in our control sample), we predicted that the supplementary

recruitment of right PFC that has been commonly reported following TBI represents the

activation of existing, but behaviourally latent, bilateral PFC networks (i.e. functional engagement) as opposed to the instantiation of novel neural networks (i.e. functional reorganization). To test this prediction, we used combined behaviour and seed analysis

(Grady et al., 2003 and see below) to characterize brain areas that are concurrently behaviourally relevant and functionally connected to each of these seed regions, thereby distinguishing behaviourally latent from behaviourally relevant brain networks in our two groups across a range of working memory task demands.

We conducted spatial-temporal seed PLS (ST-sPLS, McIntosh et al., 2004) analyses to identify latent variables (LV) that capture task- and group- dependent changes in functional connectivity between the seed ROI and the rest of the brain (i.e. brain-seed correlations). The correlation of the fMRI signal for the seed and for the rest of the brain is computed across subjects within groups within each task, resulting in a matrix of within group and within-task brain-seed correlation maps. Singular value decomposition of the brain-seed correlation matrix produces three new matrices: the singular image of voxel saliences, singular values, and task saliences. The variation across the task saliences indicates whether a given LV represents a similarity or difference in the brain-seed correlation across

51 tasks. This can also be shown by calculation of correlation between the brain scores (dot product of the voxel salience and fMRI data) and seed fMRI signal for each task. The voxel saliences give the corresponding spatiotemporal activity pattern. Statistical assessment is similar to that used for ST-bPLS. We combined this seed analysis with a behavioural PLS

(ST-bPLS, described above) in a single analysis in order to simultaneously identify patterns of functional connectivity and behavioural correlations that were both common to and divergent between our two groups.

fMRI results II

We entered the 4 behaviourally relevant seeds within PFC into the combined seed and behavioural analysis. Specifically, we were interested in identifying those patterns of brain activity demonstrating a reliable main effect of group or group by task interaction for both behaviour and connectivity with the seed region. We present the results from each of the four seeds individually below followed by a summary of the findings for these analyses.

Left inferior frontal gyrus (GFi, BA 44/6). LV 1 (p < .001) reflected behaviour and seed correlations for the Alphabetize and Maintain 3 tasks in the control sample (Figure 2.3,

Table 2.3). For the TBI group, no task demonstrated both behavioural and seed correlations; however seed correlations alone were stable for all tasks. Positive correlations with both task accuracy and left GFi activity for controls but only GFi activity in TBIs were observed in left GFm and right inferior parietal lobule as well in left occipital cortex and bilateral cerebellar cortices (Figure 2.3, Table 2.3).

Figure 2.3. Brain regions demonstrating reliable and positive correlations with left inferior frontal gyrus (BA 44/6) activity and task accuracy during Alphabetize and Maintain 3 trials in the control group but only with seed activity in the TBI group (LV 1).

2-4

4-6

Controls TBI

50 50

0 0 -50 -50 -100 r =-0.86 -100 r =-0.19 Brain Scores Brain 0.7 0.8 0.9 1.0 0.6 0.7 0.8 0.9 Accuracy (Alphabetize 5)

50 50 0 0 -50 -50

Brain Scores Brain -100 r =-0.85 -100 r =-0.64 0 0.5 1.0 0 0.5 1.0 L.GFi activity (Alphabetize 5)

Notes: Only the task and seed activity correlation was reliable in TBI participants. (BSR = 6, p < 10-4; cluster = 5). Image volumes range from Z=10, Z=28 (step=2).

Table 2.3. Cluster maxima from the combined behaviour and seed (ST-bPLS & ST-sPLS) analysis for the left inferior frontal gyrus seed (see figure 2.3) (LV 1).

Lag x y z Anat BA BSR Clust. 0 -40 44 24 GFm 46/9 -7.7347 10 0 -63 9 22 GFi 44 -10.0308 10 0 44 -25 45 LPi 40 -8.5795 7

1 -36 48 27 GFm 46/9 -8.7742 9 1 -67 9 29 GFi 44 -21.0005 19 1 40 -21 42 LPi 40 -7.3339 17 1 -24 -48 -18 Cereb -7.4733 9 1 16 -59 -17 Cereb -8.1558 10

2 -67 9 29 GFi 44 -16.6289 12 2 -51 -63 -10 GOm 19 -9.0696 8 2 12 -67 -17 Cereb -7.7589 11

4 -67 9 29 GFi 44 -9.3991 8

Notes: Cluster threshold was set by a bootstrap ratio of 6, p < 10-4; cluster = 5. Lag refers to the temporal window with lag 0 corresponding to 2-4 seconds after stimulus onset. Each lag represents a 2 second time window. Voxel coordinates are reported in accordance with the stereotaxic atlas of Talairach and Tournoux (1988). Anat. = anatomical region, abbreviations are consistent with those found in the atlas. BA=Brodmann area, BSR=bootstrap ratio, Clust.= cluster size (in voxels). Coordinates in bold type represent areas immediately adjacent to the seed voxel.

Left middle frontal gyrus (GFm, BA 46). LV 1 (p < .001) demonstrated reliable

behaviour and seed correlations for both Alphabetize 3 and 5 tasks in the control group. As in the left GFi ROI, TBIs demonstrated reliable correlations only with seed activity for all

tasks. Brain regions demonstrating positive correlations with both task accuracy and left

GFm activity for controls but only left GFm activity in TBIs were observed in left inferior

frontal gyrus, left inferior parietal lobule, bilateral superior parietal lobule and bilateral TOJ.

Right posterior middle frontal gyrus (BA 46/44). LV 1 (p < .001) demonstrated a

reliable pattern of behaviour and seed correlations for the Alphabetize and Maintain 5

conditions for the TBI group. Only the seed correlations were reliable for controls.

Regions demonstrating positive correlations with both Alphabetize task accuracy and seed

activity in TBI but only seed activity in controls were observed in bilateral anterior middle

54 frontal gyrus (BA 46/10), left posterior cingulate gyrus, left precuneus (BA 7) and left posterior middle temporal gyrus (BA 39; Figure 2.4, Table 2.4).

Figure 2.4: Brain regions demonstrating reliable and positive correlations with right posterior middle frontal gyrus (BA 46/44) and task accuracy (LV 1).

2-4

4-6

Controls TBI

50 50 0 0

-50 -50 -100 r = -0.21 -100 r =-0.50 Scores Brain 0.7 0.8 0.9 1.0 0.6 0.7 0.8 0.9

Accuracy: Alphabetize 3

50 50

0 0

-50 -50

-100 r = -0.59 -100 r =-0.59 Brain Scores Brain 0.7 0.8 0.9 1.0 0.6 0.7 0.8 0.9

Accuracy: Alphabetize 5

Notes: Seed activity and accuracy correlations were reliable during Alphabetize and Maintain 5 tasks for TBI participants. For controls, only the seed correlations were reliable for the Alphabetize tasks. An inverse task/behaviour correlation was also observed for controls during Maintain 5 trials. (BSR = 6, p < 10-4; cluster = 5). Image volumes range from Z=10, Z=28 (step=2).

Table 2.4. Cluster maxima from the combined behaviour and seed (ST-bPLS & ST-sPLS) analysis for the right posterior middle frontal gyrus (BA 46/44) seed (LV 1). See figure 2.4.

Lag x y z Anat BA BSR Clust. 0 32 21 25 GFm 46 -9.3272 13 0 -8 -33 -42 B. Stem -7.2393 17

1 36 21 25 GFm 46 -14.6958 19 1 -20 -57 21 GC 30 -9.4378 18

2 40 51 16 GFm 46 -10.6319 11 2 -48 47 12 GFi 46 -8.9892 13 2 36 21 25 GFm 46 -38.2769 33 2 -24 -48 50 PCu 7 -11.8536 20 2 -24 -57 25 GC 31 -14.2909 37

3 36 20 21 GFi 45 -10.2296 17 3 -32 -61 21 GTm 39 -8.88 17 3 -16 -60 47 PCu 7 -10.3049 30

4 -20 -67 59 LPs 7 -7.7757 13

Right anterior middle frontal gyrus (BA 46/10). LV 1 (p < .001) expressed reliable behaviour and seed correlations for both groups. For the control group, correlations with behaviour and activity in the seed region were reliable only for the Alphabetize 5 task whereas this pattern was reliable for both Alphabetize 3 and 5 as well as the Maintain 5 tasks in the TBI group. Positive brain weights (i.e. regions demonstrating positive correlations with behaviour and seed) were observed in right posterior middle frontal gyrus, left anterior middle frontal gyrus, posterior cingulate gyrus, left superior parietal lobule and the inferior occipital cortex and TOJ bilaterally.

Interim discussion II. Evidence for altered functional engagement within right lateral PFC as

a mechanism of compensatory reorganization.

We used a combined behaviour and seed PLS analysis to test our hypothesis that the

compensatory functional recruitment identified within PFC following TBI in study 1 did not

represent true reorganization (i.e. the instantiation of novel PFC networks to support

working memory performance) but rather reflected the engagement of extant but

behaviourally latent functional connectivity within PFC. The results of these four analyses

using task activity from highly behaviourally salient seed regions in left and right PFC are

consistent with this functional engagement hypothesis. Moreover, these data suggest a

differential pattern of step-wise functional recruitment of right PFC regions between our

two groups as working memory task demands increased (see Figure 2.5 for a schematic representation of these results). Engagement of left lateral PFC regions (GFi and GFm) was associated with baseline working memory demands (i.e. Maintain 3 in our protocol) in both groups. However, when working memory demands were increased, either by increasing load

(Maintain 5) or executive demand (Alphabetize 3), right anterior GFm became functionally connected to this baseline network in TBI and controls. However, the addition of this right hemisphere node was only behaviourally relevant in our control group. For the TBI sample, it is only when an additional region of right middle frontal gyrus at the border of BA 46/44 was incorporated into the network that activity in these regions was positively associated with performance on the higher demand working memory tasks. Importantly, this expanded network of right PFC regions is functionally connected in the control group during these tasks but is only behaviourally salient when both executive demand and load are increased

from baseline levels (i.e. Alphabetize 5). These data suggest that bilateral functional

connectivity within PFC is evident during working memory performance whenever load is

57 increased or when executive demands are present. However, the threshold at which this expanded right PFC network is functionally necessary for working memory performance, is reduced following TBI.

Figure 2.5. Conceptual representation of combined behavioural and seed partial least squares analysis (study 2).

bc bc a. L. GFi (-67, 9, 29) bc b. L GFm (-40, 48 23) c. R. GFm (40, 47, 16) d. R. GFm (36, 21, 25) a dad a d Control

L R

bc bc bc

a d a dad TBI Baseline + Load or + Load and (Maintain + Alpha + Alpha

Notes: Dashed lines connecting seed regions represent functional connectivity between these regions. Solid lines signify both functional connectivity and correlations between activity in the network and task accuracy. + Load or + Alpha corresponds to Maintain 5 letter and Alphabetize 3 letter conditions, respectively. + Load and + Alpha corresponds to Alphabetize 5 letter condition. a. Left inferior frontal gyrus (BA 44/6), b. left anterior middle frontal gyrus, c. right anterior middle frontal gyrus, d. right posterior middle frotnal gyrus. Coordinates in parentheses are in the space of Talairach and Tourneaux (1988).

General Discussion

There is increasingly convergent evidence that traumatic brain injury is associated with altered functional recruitment during the performance of cognitively demanding tasks such as those engaging executive control processes in working memory. However, the functional implications of this altered neural activity have been poorly characterized. To

address this issue directly, we conducted two analyses to investigate: (i) whether altered

patterns of neural recruitment observed following TBI are functionally adaptive (i.e.

compensatory) and if so, (ii) do these functional brain changes represent the instantiation of

novel neural networks following brain trauma (i.e. true functional reorganization) or the

activation of existing networks that are behaviourally inert in the undamaged or under-

challenged brain (i.e. altered functional engagement).

To address the first question we used multivariate network analysis (ST-bPLS) to

identify patterns of brain activity that correlated with task accuracy. We identified two

dominant patterns of brain activity that corresponded with task accuracy. The first

described a network comprised of bilateral anterior GFm regions, an area of right GFm

posterior and superior to the first, anterior cingulate and regions of the posterior, inferior

temporal cortex bilaterally. Activity in this network of brain regions was highly positively

correlated with accuracy on the Alphabetize 5-letter task for controls and all tasks in our TBI

sample. A second significant pattern differentiated our two groups with respect to brain-

behaviour correlations on the Alphabetize and Maintain 5-letter set size conditions. Areas of

left inferior frontal gyrus and left posterior parietal cortex were positively correlated with

activity in our control group. In contrast, performance on these same tasks were positively

correlated with activity in right lateral PFC along the border of GFM and GFi, right posterior parietal and left lingual gyrus in our TBI group.

These results provide strong support for our original hypothesis that recruitment of right lateral PFC during cognitively demanding tasks, as has now been reported in several studies, is compensatory. Few reports to date have directly correlated brain and behaviour measures in a TBI population. Our findings are consistent with those of McAllister et al.

(1999) who observed positive correlations between activity in left GFi and accuracy on a

59 working memory task; a pattern we also observed in our healthy controls (they did not report group-specific correlations). However, our data contrast with those reported more recently by Newsome and colleagues (2007), who failed to observe significant correlations between brain response and performance during the 2-back condition of an N-back working memory task. This task is similar to that used by McAllister et al. (1999), where maximal brain response during the 2-back condition was observed in their sample of mild TBI patients. We suggest that patient heterogeneity in their sample may have limited the ability of Newsome et al. (2007) to detect significant correlations. While other reports examining functional brain changes during working memory performance following TBI did not report direct brain and behaviour correlations (e.g. Christodoulou et al., 2001; Perlstein et al., 2004), each of these studies reported recruitment of right lateral PFC regions during working memory task performance, consistent with our findings. Moreover, consistent with the strong group by laterality interaction we observed in our own data (Figure 2.2), Perlstein et al. (2004) also reported a group by laterality interaction, whereby increased working memory load was associated with increased activity in left lateral PFC for controls and right lateral

PFC for TBI.

To our knowledge, no previous report has investigated the impact of altered functional connectivity on working memory performance following TBI. We were particularly interested in these questions of functional connectivity, as our TBI sample was carefully screened for evidence of focal brain pathology, leaving us with a relatively ‘pure’ sample of DAI patients. Higher cognition is increasingly understood to be emergent from patterns of activity within large scale neural networks anchored by nodes within PFC (Grady et al., 2003; McIntosh, 1999). Given extensive reports of working memory deficits following

TBI (e.g. D'Esposito, Cooney, Gazzaley, Gibbs, & Postle, 2006; and see Turner & Levine,

60 submitted), we surmised that such networks subserving working memory might be particularly sensitive to DAI. To test this hypothesis, we again employed multivariate methods (a combined behaviour and seed PLS) to examine the functional connectivity and behavioural relevance of networks anchored by four PFC ‘seed’ regions, identified in study

1 as being highly correlated with performance on our working memory tasks. Here our question was whether the compensatory recruitment of right PFC regions represented instantiation of novel neural networks or the engagement of existing functional connections that are behaviourally latent in the undamaged or under-challenged brain.

The results from part two of this report are consistent with this latter hypothesis.

We have demonstrated that lateral regions of PFC, including anterior middle frontal gyri bilaterally, left GFi and the border zone of right inferior and middle frontal gyri, are functionally connected in both groups, though their behavioural relevance is differentially altered both by task demands and by brain injury (see Figure 2.5 for an overview of these results). Specifically, left lateral PFC activity was common to both groups during performance of our low load, low executive demand working memory task (Maintain 3).

Increases in either load (moving from 3 to 5 letter set size) or executive demand

(alphabetizing the letter set), resulted in the engagement of right PFC in both groups to support task performance. However, this additional recruitment of right PFC was more extensive in the TBI group, and included both anterior and posterior regions of right GFm.

This same network of regions was also functionally connected in the control group, but the network was only behaviourally relevant when both load and executive demand increased (i.e.

Alphabetize 5 trials).

Results from study 2 demonstrate that increasing either load or executive demand resulted in recruitment of right anterior PFC in addition to more commonly reported left

PFC regions. But this expanded network was sufficient to support behavioural performance only in our control group. TBI patients required additional recruitment of right posterior lateral PFC to support performance on these tasks. However, when both executive demand and load increased (i.e. during Alphabetize 5 trials), control performance was correlated with activity in this broader PFC network. Thus the stepwise pattern of behaviourally relevant functional recruitment observed in our controls as working memory demands increased was truncated for TBIs, where maximal right PFC recruitment was observed with any increase in task demand.

A recent review by Hillary and colleagues (2006), addressed this question of altered functional recruitment during working memory performance under conditions of ‘cerebral challenge’ and their conclusions provide important context for our findings. Their review included studies examining both individual differences related to organic brain damage or psychological states such as fatigue or stress, and task-based differences such as increased working memory load or executive demands. As with the current report, the authors observed a consistently reported pattern of altered functional recruitment under all challenging conditions with increased recruitment of right PFC being the dominant feature.

In contrast to the present findings, however, they concluded that this altered functional recruitment was not compensatory but rather was consistently associated with poorer performance on working memory tasks. They argue that this additional recruitment is therefore not directly compensatory but rather represents the engagement of cognitive control mechanisms to facilitate the establishment of new task subroutines. Our own data strongly suggest that the right hemisphere recruitment is directly compensatory, consistent with reports by McAllister et al. (1999, 2001). While it is outside the scope of this report to review data from other neurological populations, we would argue that, in the TBI literature,

increased right dlPFC activity represents functional recruitment that is fundamentally

compensatory in nature. In two studies of TBI across the severity spectrum (McAllister et

al., 1999; Current results, see Chapter 1), when task performance was equated, there

remained clear evidence of increased right lateral PFC activity in the TBI groups. Moreover,

the data reported here provide direct evidence that right lateral PFC activity is positively

correlated with task performance (Figure 2.2). For those reports where performance was

not equivalent, the indirect observation of an inverse relationship between performance and

brain activity does not rule out a facilitative contribution to task performance from increased

right PFC engagement. Indeed, Hillary and colleagues (2006) argue that the right PFC may

compensate for altered task performance under conditions of cerebral challenge by

implementing cognitive control processes to support novel task processing routines. Our findings are not inconsistent with this overall proposal. However, there remains a fundamental discrepancy between the two reports as to whether right PFC recruitment is associated with better or poorer working memory performance. While the publication of further data directly correlating brain and behaviour measures would be helpful in resolving this discrepancy, in the final section of the report we propose a tentative explanation that would serve to unify these accounts and provide some insight into the potential clinical ramifications of these data.

As described above, our patient group represented a highly selective sample of TBI survivors. There was no evidence of macroscopic focal brain lesions, all participants were many years post-injury, and all had demonstrated good functional recovery. However, all of our TBI patients had evidence of diffuse axonal injury on neuroradiological report and we have previously reported quantitative structural MRI data demonstrating highly significant decreases in total volumes of white and gray matter in our TBI group compared to an age-

and education-matched sample (Chapter 1). This neuropathological profile is similar to that

reported in studies of healthy aging, where diffuse white matter pathology and generalized

reductions in gray and white matter have been observed (e.g. Raz et al., 2005; Tisserand et

al., 2002). Interestingly, studies examining the functional neuroanatomy of working memory

in healthy aging (e.g. Erickson et al., 2007; e.g. Rypma & D'Esposito, 2000) also report

similar patterns of compensatory right prefrontal recruitment as we observed in our TBI

sample. This convergence of neuropathology and functional recruitment patterns suggests

that diffuse axonal injury may mimic healthy aging, at least with respect to the functional

neuroanatomy of higher cognition. Hillary et al. (2006) propose that the compensatory nature of right prefrontal activity in healthy aging may reflect gradual alterations in neural

networks more consistent with true functional reorganization over the life-span. This may

also be the case in our TBI patients, each of whom was many years post-injury and, as is

often the case in healthy aging, had few functional or neuropsychological impairments.

Although we raise this possibility by way of resolving a conflict between our findings and the conclusions of Hillary et al. (2006), if this convergence truly reflects similar mechanisms of neuroplastic change in TBI and healthy aging, our results raise important questions with respect to the neurobehavioural sequelae and potential limitations of neuroplastic change as

TBI and aging processes interact in long-term brain injury survivors.

Conclusion

In this report, we used multivariate methods to demonstrate that the altered functional recruitment we had reported in a previous investigation of executive control in working memory following TBI was compensatory. Increased activity in a network of PFC

64 regions, including right (TBI) and left (Controls) lateral PFC in the vicinity of the middle frontal gyrus, was positively correlated with accuracy on our working memory tasks, suggesting a significant group by task asymmetry in brain-behaviour correlations following

TBI. Moreover, we were able to show, for the first time in this population, that this altered pattern of functional recruitment may not represent true cortical reorganization after brain injury but rather engagement of existing neural networks within PFC that are similarly engaged during increased working memory demands in healthy subjects (albeit at a lower threshold of task demand for the TBI participants). This characterization of altered functional recruitment following TBI as engagement rather than reorganization may have important therapeutic implications. Benefits of rehabilitation interventions based on a principle of effortful functional engagement have recently been demonstrated in healthy aging (Mahncke et al., 2006). Moreover, the correspondence of findings in our TBI sample with that reported in studies of normal aging raise intriguing questions with respect to the nature of, and capacity for, neuroplastic change in aging following diffuse axonal injury.

CHAPTER 3

Diffuse axonal injury as a mechanism for functional brain changes following

traumatic brain injury: an integrated diffusion and functional imaging study.

Abstract

Functional neuroimaging studies of patients with traumatic brain injury (TBI) have demonstrated compensatory functional recruitment during working memory performance, particularly on tasks requiring executive control processing. As diffuse axonal injury (DAI) is a primary pathology in TBI, we hypothesized that such alterations in functional recruitment occur in response to DAI. In this report, we use diffusion tensor imaging methods to quantify DAI and to in turn relate this to compensatory functional brain changes following brain trauma. Diffusion-weighted imaging data were acquired in a sample of moderate to severe

TBI subjects with DAI and without large focal lesions and age- and education-matched, healthy controls.

Fractional anisotropy (FA) values were derived from each subject’s diffusion-weighted images and compared between groups at the whole brain level and within regions of interest (ROI) placed in the genu and splenium of the corpus callosum for each subject. Interactions amongst FA, functional brain activity and working memory performance were investigated using both univariate and multivariate methods. Although FA was reduced in TBI patients in the genu and splenium of the corpus callosum and corresponding fibres in the forceps minor and major, these changes were uncorrelated with working memory performance. However, genu

FA was negatively correlated with functional activation in a network of regions previously identified as compensatory, including right lateral prefrontal cortex. Activation in this region was also negatively correlated with FA in long-fibre tracts in the left hemisphere but positively correlated with FA in the right anterior limb

66 of the internal capsule. These results suggest that FA is reduced following TBI, even in the absence of focal brain pathology, and these changes in white matter integrity are related to altered functional brain response following TBI. Specifically, compensatory activity within right lateral PFC during working memory performance was associated with reduced FA in anterior callosal fibre tracts and increased FA in the right internal capsule, suggesting that functional engagement of this region may be facilitated through two complementary processes: (i) reduced trans-callosal inhibition and (ii) attentional control facilitated by relatively preserved thalamo-cortical fibre pathways.

Introduction

Although traumatic brain injury (TBI) is among the most common causes of

neurologic disability, the precise relationships between brain injury characteristics and

behavioural changes are unclear. One approach to this issue has been to challenge TBI

patients with cognitive tasks while monitoring their brain activity with functional

neuroimaging, such as fMRI. Recent studies of this sort have documented changes in activity

associated with TBI, especially increased prefrontal activity during performance of cognitive

tasks (for review see Levine et al., 2006 and see Chapters 1, 2).

It is hypothesized that changes in functional brain activity following TBI are due to

diffuse axonal injury (DAI), a primary neuropathology of TBI affecting brain interconnectivity. Accordingly, we observed increased working memory-related activation in

a sample of TBI patients with significant DAI that were carefully screened for focal lesions

that can confound such analyses (Chapters 1, 2). As the patients performed normally on the

working memory task, we suggested that the altered functional brain activity may reflect a

compensatory response to DAI. However, direct evidence linking DAI and functional brain

changes in TBI is lacking.

In this study, we use diffusion tensor imaging (DTI) methods, combined with

functional neuroimaging methods, to probe the mechanisms underlying cognitive recovery

in TBI patients with DAI. By integrating these imaging methods in a single set of analyses

we aimed to characterize the contribution of DAI to both functional brain changes and

behavioural performance following TBI.

DTI is emerging as an important technique for investigating changes in white matter

integrity following TBI (Arfanakis et al., 2002; Huisman et al., 2004; Xu, Rasmussen,

Lagopoulos, & Haberg, 2007). DTI uses magnetic resonance methods to create images based on the characteristics of water diffusion in the brain (Basser & Pierpaoli, 1996). The

technique, based on the principles of Brownian motion of water through tissues, takes

advantage of the fact that diffusion displacement distances are comparable with cell dimensions, allowing for the characterization of cell integrity or pathology. This is particularly true in myelinated axonal fibres where diffusion across the axon is smaller than along its length, meaning that water diffusion in white matter pathways of the brain is anisotropic (i.e. unequal in all dimensions). Thus DTI may be particularly suited to identifying and characterizing DAI resulting from TBI where rotational and acceleration/deceleration forces often lead to disruptions in axonal membrane integrity and permeability (Salmond et al., 2006).

Diffusion-weighted imaging has provided important insights into the distribution and extent of DAI in vivo, often displaying better sensitivity to white matter abnormalities than traditional neuroimaging methods, particularly in the acute injury phase (Huisman et al.,

2004). Diffusion indices also correlate well with measures of injury severity (Ptak et al.,

2003) and outcome (Huisman et al., 2004; Wilde et al., 2006), suggesting that these methods may be important for characterizing neural and functional recovery both acutely and in chronic TBI. Similar correlations have been shown in other populations where white matter changes have been observed, such as multiple sclerosis (Audoin et al., 2007) and healthy aging (Madden et al., 2007).

Fractional anisotropy (FA), a reliable, rotationally invariant scalar metric for measuring diffusion anisotropy (Alexander, Lee, Lazar, & Field, 2007; Basser & Pierpaoli,

1996, 1998), is the most commonly reported index of diffusion following brain injury and it has been shown to be the most discriminating scalar index of diffusion in TBI (Benson et al.,

2007). Reduced FA (i.e. poorer diffusion along white matter fibre tracts) has been most commonly reported in the corpus callosum, brain stem, fornix and sub-cortical white matter following TBI (Huisman et al., 2004; Salmond et al., 2006). This pattern of neuropathological change shows good concordance with data from experimental models of

TBI and post-mortem investigations examining DAI–related injury throughout the brain

(Povlishock & Jenkins, 1995).

In the first section of this report we assess the sensitivity of DTI to white matter changes in a chronic TBI sample without focal lesions or gross neuropsychological or functional impairment. There are now several published reports investigating TBI-related neuropathology using diffusion-weighted imaging (Kraus et al., 2007; Nakayama et al., 2006;

Xu et al., 2007). However, to our knowledge, no previous studies have explicitly investigated this question in a well recovered, chronic phase, ‘pure’ DAI sample.

Next we investigate the relationship of white matter FA to performance on a working memory task that has served as a behavioural assay for investigating functional brain changes in our earlier reports (Chapter 1). FA has previously been shown to positively correlate with TBI patients’ overall mental status (e.g. Folstein Mental Status Exam,

Nakayama et al., 2006) as well as with performance in specific domains of cognitive function, such as learning and memory (Salmond et al., 2006), inhibitory control and speed of processing (Wilde et al., 2006) and executive functioning and attention (Kraus et al.,

2007). However, other reports have failed to find significant correlations between FA and standardized intelligence and memory batteries or with performance on a complex measure of working memory and attention (Nakayama et al., 2006). In this study, we used a verbally- based paradigm known as Alphaspan (Craik, 1990) that enables independent manipulation of working memory load and executive control demands. Performance on this task has been

70 associated with functional brain response in a network of regions distributed both within and across hemispheres (Postle et al., 1999), thus allowing us to probe the contribution of

FA in anterior-posterior long-fibre tracts and interhemispheric callosal fibres to working memory performance following TBI.

In the final section of this investigation we examine the relationship of compensatory functional brain changes to white matter FA following TBI. Numerous studies have now reported functional brain changes during cognitive task performance following TBI (see

Levine et al., 2006 for a review). The most commonly observed pattern involves reduced lateralization of functional brain response relative to healthy controls during performance of tasks typically associated with a highly lateralized brain response, particularly in regions of

PFC (e.g. greater functional activity within right lateral PFC during an episodic memory task,

Levine, Cabeza et al., 2002). In our prior study, the right lateral PFC was engaged by TBI patients with pure DAI at lower working memory task demands than controls; this regional activation was positively associated with working memory performance in the TBI patients

(Chapter 2). As TBI patients’ task performance was equivalent to that of controls, we hypothesized that their activation patterns reflected the engagement of alternate or additional cortical resources in order to compensate for connectivity changes. We assessed this hypothesis by integrating FA into our brain-behavior analyses. While FA and fMRI datasets have been integrated with performance in some studies (Baird, Colvin, VanHorn,

Inati, & Gazzaniga, 2005; Olesen, Nagy, Westerberg, & Klingberg, 2003), this has not been done in TBI (but for case study evidence, see Werring et al., 1998).

Method

Participants

Eight participants (6 males) with moderate to severe traumatic brain injury were recruited from consecutive admissions to a major urban trauma center as part of the

Toronto TBI study. Further details with respect to the patient demographics, injury characteristics and recruitment inclusion and exclusion criteria of the larger sample have been reported elsewhere (Levine et al., in press). Demographic and injury characteristics for the subset of patients included in the present report have been described in Chapter 1 and are only briefly reviewed here. TBI participants had sustained closed head injury as a result of a motor vehicle accident and were in the chronic stage of recovery at the time of study participation. The injury severity profile of the group included 5 moderate and 3 severe TBI participants as determined by trauma GCS, loss of consciousness and extent of post- traumatic amnesia. Exclusion criteria included previous head injury, significant psychiatric history or evidence of current or recent alcohol and drug abuse. TBI patients were also excluded from participation if they had evidence of focal lesions greater than 3mm in diameter based on high resolution structural MRI. All TBI participants in the current study had evidence of DAI-related neuropathology (hemosiderin deposits) on neuroradiological report and each had demonstrated good functional recovery (return to work or school).

Seven of the eight TBI participants underwent extensive behavioural testing as part of the larger Toronto TBI Study; these data are reported in the earlier paper (Chapter 1). Twelve

(12) neurologically normal participants (8 males) were recruited as a comparison sample.

Diffusion-weighted imaging data was unavailable for one of these participants. The absence of this participant did not alter our previously reported matching of age and education between our two groups [age: t(17) = .785, p > .05; NS; education: t(17) = -1.99, p > .05;

NS]. All comparison participants were right-handed, native English speakers and were

screened for previous neurological injury, history of psychiatric illness, or drug use.

Behavioural Task

We employed a modified version of the Alphaspan protocol described by Postle and colleagues (Postle et al., 1999) and based on earlier work by Craik (Craik, 1986). For each

task trial, participants were required to study a letter set consisting of either 3 or 5 consonant letter strings (set size or ‘load’ manipulation) and asked to either ‘MAINTAIN’ the letter set over a brief delay or ‘ALPHABETIZE’ the letters into their correct alphabetical position during the delay period (executive demand manipulation). At the end of the delay, a probe was presented consisting of a letter and an ordinal position (e.g. L-4? – ‘Was ‘L’ the 4th letter

in the set?’). On Maintain trials, the probe referred to the ordinal position in the original

letter set while on Alphabetize trials the probe referred to the letter position following

alphabetization of the list. Probability of a correct probe was set at 0.5 for all trials in all

conditions. Prior to scanning, each participant completed a training session consisting of

step-by-step instructions for each task condition. Once there were no further questions for

the administrator, all subjects completed 20 further trials (5 trials in each of the 4 conditions)

immediately prior to entering the MR scanner. During scanning, participants completed 28

trials of each of the four task conditions (Alphabetize 3 & 5 letter sets, Maintain 3 & 5 letter

sets) during a single scanning session. Within each session a total of four individual scans

were acquired. Trials were grouped by executive demand with 2 blocks of seven trials at

each level of executive demand presented during a single scan acquisition. Total stimulus

onset asynchrony was 18000 ms (3 letter trials) or 19000 ms (5 letter trials). Each individual

scan acquisition was 12 minutes in duration.

Neuroimaging methods fMRI.

Functional imaging scans were acquired at Sunnybrook Health Sciences Centre on a

research-dedicated whole-body 3.0 T MRI system (Signa 3T94 hardware, VH3M3 software;

General Electric Healthcare, Waukesha, WI) with a standard quadrature bird-cage head coil.

Participants were placed in the scanner in supine position, with their head firmly placed in a

vacuum pillow to minimize head movement. Earplugs were provided to reduce the noise

from the scanner, and sensors were placed on participants’ right index finger and around the

chest, to monitor heart rate and respiration. A volumetric anatomical MRI was performed

before functional scanning, using standard high-resolution 3D T1-weighted fast spoiled

gradient echo (FSPGR) images (TR/TE=7.2/3.1 ms, inversion-recovery prepared T1=300

ms, flip angle 15°, 256×192 acquisition matrix, 124 axial slices 1.4 mm thick, voxel

size=0.86×0.86 cm, FOV=22×16.5 cm). Functional imaging was performed to measure the

blood oxygenation level dependent (BOLD) effect (Ogawa et al., 1990). Scans were obtained

using a single-shot T2*-weighted pulse with spiral in-out, achieving 26 slices, each 5 mm

thick (TR/TE=2000/30 ms, flip angle 70°, 64×64 acquisition matrix, 26 axial slices 5 mm

thick, voxel size=3.125×3.125, slice spacing=0, FOV=20×20 cm). Data processing was

performed using Analysis of Functional NeuroImaging (AFNI) software

(http://afni.nimh.nih.gov, Cox, 1996). Time series data were spatially coregistered to correct

for head motion by using a 3D Fourier transform interpolation. Motion-corrected images

were then spatially transformed to an fMRI spiral scan template generated from 30 subjects

scanned locally. This template was registered to the MNI305 template. The transformation

of each subject to the spiral template was achieved using a 12-parameter affine transform

with sinc interpolation as implemented in SPM99 (http://www.fil.ion.ucl.ac.uk/spm, Friston

et al., 1995). Images were smoothed with an 8-mm isotropic Gaussian filter before analysis.

For each subject, “brain” voxels in a specific image were defined as voxels with an intensity

greater than 15% of the maximum value in that image. The union of masks was used for

group analyses as described below.

Diffusion-Weighted Imaging.

We used a single-shot spin echo planar sequence (TR/TE, 7400/87.1 ms; slice thickness, 4 mm; field of view, 25 cm2; number of experiments, 5; pixel matrix: 256x256) for

diffusion tensor analysis. Diffusion gradients (b=1000 s/mm2) were always applied on two

axes simultaneously around the 180 degree pulses. Diffusion properties were measured along

six non-collinear directions. Diffusion-weighted magnetic resonance images were transferred

to a workstation supplied by the manufacturer (Advantage Workstation, GE Medical

Systems); structural distortion induced by large diffusion gradients was corrected on the

basis of T2-weighted echo–planar images (b=0 s/mm2). The six elements of the diffusion

tensor were estimated in each voxel, assuming a monoexponential relationship between

signal intensity and the b matrix. The eigenvectors and eigenvalues of the diffusion tensor were determined by using multivariate analysis. Fractional anisotropy maps were generated on a voxel-by-voxel basis using DTI Studio software (Johns Hopkins Medical Imaging

Center, Baltimore, Md.). For voxel based analyses (i.e. using whole-brain FA maps), spatial normalisation is an essential pre-processing step. As the contrast of the fractional anisotropy map is different from that of T1-weighted and T2-weighted and other template images in the statistical parametric mapping software package (SPM99; Wellcome, Department of

Cognitive Neurology, London, UK), it is necessary to make a fractional anisotropy template

75 to normalise the individual fractional anisotropy maps correctly. We employed a method described by Nakayama et al. (2006) and Xu et al. (2007) to transform fractional anisotropy

(FA) maps for all of our participants into a common template space, thus allowing us to examine voxel-wise group differences in FA. Importantly, we extended the work of

Nakayama et al. (2006) by limiting our analysis to only those voxels that had been classified as white matter. By including only white matter voxels, we avoided confounding differences in white matter integrity (FA) with differences in white matter volume. A fractional anisotropy template was created based on FA maps from all of our participants (patients and controls). Individual fractional anisotropy maps were made by the Advantage Workstation

(GE Medical Systems) and transferred to a Windows workstation with SPM99 running on

MatlabV.6.5 (Mathworks, Natic, Massachusetts, USA). T2-weighted echo–planar images of all participants were transformed to the T2-weighted template. The parameter of the transformation was applied to the fractional anisotropy maps. Normalised individual fractional anisotropy maps were smoothed with an 8-mm full-width at half-maximum isotropic Gaussian kernel. Individual fractional anisotropy maps were normalised to the fractional anisotropy template. The normalised fractional anisotropy images were smoothed with a 4-mm full-width at half-maximum isotropic Gaussian kernel. Once the images had been spatially normalised and smoothed, group comparisons (two samples t test) were applied to calculate the statistical significance between the control and patient group on

SPM99 (section 1). These whole-brain white matter FA maps were also used in our combined behavioural, DTI and fMRI analyses in sections 2 and 3.

We also drew regions of interest (ROI) within the corpus callosum on each participant’s FA map using callosal boundaries defined by the schema of Witelson (1989).

To mitigate against partial volume effects and avoid conflating callosal volume loss with

76 changes in white matter fibre tract integrity, ROIs were selectively drawn over the genu and splenium of the corpus callosum where white matter voxels are most easily and reliably identified. We adopted a standard ROI volume (54 voxels: 3 in y- and z-planes; 3 on either side of the midline in the x-plane), centered in these two regions of the callosum using neuroanatomical markers defined by Witelson (1989). We confirmed that all ROIs fit within the boundaries of these callosal structures by overlaying the ROI masks onto each participant’s T2-weighted image.

Analysis methods

1. Changes in white matter diffusivity following TBI. We examined this question in three ways.

First, we plotted a histogram of average white matter voxel counts at each FA value (from 0 to 1, binned in units of .01) for both of our groups (see Benson et al., 2007). Voxel counts in each bin were normalized by total white matter voxels for each subject and averaged across subjects in each group. The between group contrast in the proportion of voxels occuring at each FA value is plotted in Figure 3.1. A higher proportion of voxels at lower

FA values relative to controls may be considered a gross marker of white matter damage.

Next, we contrasted average whole-brain, white matter masked, FA maps for our two groups using the contrast function included with the statistical parametric mapping analysis software

(SPM 2, Friston et al., 1995). Finally, to compare FA between groups using ROI methods, the FA values for each ROI were extracted and averaged for each subject within our two groups.

2. FA and working memory performance. We directly correlated average ROI FA values for each subject with performance (% correct) on each of our working memory tasks for both TBI and comparison groups. We probed this brain and behaviour relationship further in our

TBI sample by entering task accuracy during high load working memory tasks (i.e. M5 and

A5) into a simple regression analysis with the whole-brain, white matter masked FA maps

using SPM 2. Here we were interested in identifying areas where task performance predicted

white matter FA on a whole brain basis.

3.0 FA and fMRI

We used two complementary approaches to relate the FA data to the fMRI data. The first examined regional FA in relation to whole-brain fMRI data using multivariate methods.

The second examined regional fMRI data in relation to whole-brain FA data using univariate

regression.

3.1 Multivariate integration of regional FA and whole-brain fMRI using multivariate methods. We used

multivariate analysis techniques (partial least squares – PLS, see McIntosh et al., 2004) to

integrate the results from our previous functional imaging investigation with the diffusion-

weighted data from the current study. PLS analysis methods have been fully described

elsewhere (McIntosh et al., 1996; McIntosh et al., 2004) and are only briefly reviewed here.

Using singular value decomposition, PLS generates latent variables (LVs) that express

relationships between patterns of whole brain activation and one or more of the following:

a) task manipulations, b) behavioural performance, or c) other individual difference measures

such as seed voxel activity (to examine whole brain co-activation with activation in a specific

region) or, in the case of this paper, FA. The statistical significance of each LV was assessed

by permutation testing. The stability of each brain region’s contribution to the LV was

determined through bootstrap resampling (subjects were resampled 500 times). Brain regions

were considered reliable if they had a ratio of salience to standard error (hereafter referred to

as the bootstrap ratio, interpreted similar to a Z-score (34)) greater than 4, corresponding to

99.9% confidence limits.

In our previous study with this sample, a combined seed voxel and behavioural PLS

demonstrated compensatory recruitment of activity in a right prefrontal ROI in the vicinity

of BA 46/44 (i.e. the seed voxel). In the present study, we asked whether this compensatory

brain activity was related to callosal FA by including FA values from our manually-drawn

ROIs in the multivariate model.

3.2. Relating fMRI to whole-brain FA. As in section 2, we were interested in expanding from

our ROI analyses to explore the relationship between compensatory brain activity and FA at

the whole-brain level. Following methods initially described by Baird et al. (2005), we

extracted a measure of functional brain response from the right dlPFC seed region during

performance of the most challenging condition (Alphabetize 5) for each TBI subject and

entered it into a simple regression analysis with whole brain white matter FA maps using

SPM 2. The goal of this analysis was to reveal regions where the level of functional brain

response (that we had previously identified as compensatory during the A5 task), was

correlated, either positively or negatively, with white matter FA across the whole brain.

Results

Based upon the analyses outlined above, we asked the following three questions to

determine whether white matter changes were related to compensatory functional

recruitment following TBI.

1. Is white matter FA reduced in well-recovered, chronic-phase TBI?

We compared FA values between our two groups in three ways: (i) globally, by

examining the relative distribution of white matter FA values; (ii) through whole-brain

analysis, by examining regional differences in white matter using voxel-based methods and

(iii) using ROI methods to compare average FA between our two groups in ROIs drawn in the splenium and genu of the corpus callosum.

In the first analysis, we observed global differences between the TBI and control groups in the whole brain distribution of voxel-wise FA values (Figure 3.1). Voxel counts peak at marginally significantly lower FA values in TBI patients as compared to healthy controls [TBI peak = 0.37; Control peak = .39. Difference = .02: t(16) = 1.404, p = 0.089, single tailed]. As seen in Figure 3.1, the proportion of white matter voxels is consistently higher for TBI than comparison subjects at lower FA values. However, this pattern is reversed as FA values increase. These results are consistent with findings recently reported by Benson et al. (2007) and demonstrates that global FA is reduced in our TBI sample, although detection of statistically reliable effects may have been hampered by low power.

Figure 3.1: Group differences in voxel-wise distribution of white matter FA values.

FA values (Bin = 0-1.0; width =.01)

Notes: Voxel counts are normalized by total number of voxels for each subject after white matter masking. Bars represent differences between control and TBI groups with respect to the proportion of voxels occuring at each FA value (binned from .01 to 1.0).

Second, by directly comparing whole brain, white matter FA maps across groups, we identified significant differences at the juncture of callosal and anterior-posterior fibre pathways adjacent to the splenium (left) and genu (right). Significant differences were also

80 observed in the isthmus of the callosum, cingulum bundle, right cerebellum and in white matter underlying left posterior parietal cortex (t > 2.58, cluster size = 25 voxels; p < .05, cluster size corrected for multiple comparisons, Figure 3.2, Table 3.1). Importantly, the reverse contrast (i.e. Control FA > TBI FA) did not identify any suprathreshold voxels, suggesting that deceased FA in these regions is specific to the TBI participants.

Figure 3. 2: Regions demonstrating significant differences in white matter FA between control and TBI participants.

c d b b a

No suprathreshold clusters were identified in the reverse contrast (i.e. Control > TBI). a=right cerebellum; b=right genu; c= right isthmus; d=left splenium. (t-contrast of parameter estimates, p < .01, 25 voxels; p < .05 corrected).

Table 3.1. Voxel cluster coordinate and maxima for the control versus TBI whole brain FA comparison (p < .01, 25 voxels; p < .01 corrected). No suprathreshold clusters were identified in the reverse contrast (i.e. Control > TBI).

Cluster X Y Z Size Anatomical Location a. 14 -74 -28 35 Cerebellum b. 14 38 10 104 Genu (forceps minor) c. -12 -30 36 67 Isthmus / post.body c. 16 -4 50 27 Cingulum d. -22 -56 14 25 Splenium (forceps major) Not seen -32 -20 42 80 Sub-gyral (PPC)

Finally, we directly contrasted FA values extracted from the genu and splenium ROIs across groups. As expected, splenium FA was significantly lower in the TBI group relative to controls [t(17)= -2.895, p < .01] and genu FA was also reduced in TBI, although the

81 difference fell within the marginally significant range [t(17)= -1.958, p < .067]. To confirm that both the whole brain and ROI-based group results were convergent, we contrasted FA values from the whole brain analysis for voxels from the medial aspects of the genu and splenium corresponding to the ROI placements. Contrast t-values for these voxels in both genu and splenium regions were significant and negative [p < .05]. However, their spatial extent did not survive cluster thresholds for multiple comparison corrections and thus are not reported in Table 3.1.

2. Do changes in white matter FA predict working memory performance?

To assess the relationship of FA to working memory performance, we examined correlations between FA values derived from the ROIs with working memory accuracy in the TBI participants and controls. No significant correlations with accuracy were observed in either anterior or posterior callosal ROIs for any of the working memory tasks (p > .05, all comparisons; see Figure 3.3 for ROI FA and Alphabetize 5 accuracy correlations).

Figure 3.3. Correlations between callosal FA and Accuracy on the Alphabetize 5 letter working memory task. No correlation (R-squared) was significant (p > 0.05).

Genu FA Splenium FA

TBI

R2 = -0.045 R2 = 0.055

Control

R2 = -0.192 R2 = 0.027

Accuracy (Alphabetize 5)

We further investigated whether whole brain FA was related to task performance by

regressing task accuracy during Alphabetize and Maintain 5 tasks (A5, M5) onto whole brain

FA. Only a single cluster of voxels demonstrated a significant and positive correlation

between FA and accuracy and only for A5. This cluster was centered in an area of right medial posterior parietal cortex through which the posterior aspect of the cingulum bundle traverses (p < .01, cluster size = 25; p < .05, cluster size corrected for multiple comparisons).

3. Is reduced FA related to functional (compensatory) brain changes following TBI?

In section 2 we established that there was no significant relationship between callosal

or whole brain FA and working memory task performance. While one possible conclusion

of this analysis is that DAI as indexed by FA does not relate to working memory

performance, it is also possible that such a relationship is mediated by altered functional

brain response. In other words, as a result of DAI, TBI patients engage alternate brain

networks in order to maintain working memory task performance.

We tested this hypothesis by correlating FA values in the anterior callosal ROI with working memory-related activation in a right posterior lateral prefrontal seed region (BA

44/46), previously shown to be correlated with working memory performance in our TBI

sample (Chapter 2). As our previous functional connectivity and behavioural analysis

emphasized patterns of altered functional recruitment within PFC, we focused the final

series of analyses on the relationship between FA in the anterior corpus callosum and

patterns of compensatory functional we had identified previously. Specifically, we

conducted PLS analyses to examine the covariance structure between genu FA and seed-

voxel network co-activation. To confirm that working memory performance did not

significantly change this co-activation pattern (i.e. that behaviour and compensatory

activation in right lateral PFC explained a common portion of the variance in brain

response) we entered task accuracy into our model. This addition did not change the model

results significantly. We therefore report results from the Genu—seed-voxel PLS that does

not include the behavioural data.

These analyses were extended in a second correlational analysis of whole brain FA

and right prefrontal seed voxel activity. We predicted that activity in right lateral prefrontal

cortex (and functionally connected regions in anterior middle frontal gyrus and posterior

parietal cortices) would be negatively correlated with callosal FA. If confirmed, this would

provide evidence in support of a relationship between white matter DAI and altered

functional response during working memory performance in TBI.

3.1 Partial least squares (PLS) analyses.

Consistent with our prediction, the PLS analysis identified a network of brain regions that were functionally connected to our seed region in right lateral PFC and negatively correlated with FA in the callosal ROI. Importantly, in the TBI group, this pattern explained the largest portion of the variance (Genu-seed PLS: LV 1 = 32.21% of model variance, p <

.001). Correlations with genu FA and seed activity were maximally differentiated during the

high load working memory tasks (A5, M5 and marginally with M3) in the vicinity of the seed

region as well as right anterior middle frontal gyrus, bilateral inferior parietal lobule and left

temporal-occipital junction – a functional network that we had previously identified as

compensatory for our TBI participants (Figure 3.4, Table 3.2).

Figure 3.4. Brain regions demonstrating reliable and positive correlations with right GFm seed (A5, M3, M5) and negative correlations with Genu FA (all tasks) in TBI subjects.

2-4

4-6

Brain 18 19 20 21 22 23 24 Score

50 50

0 0

-50 -50

-100 r = - 0.56 -100 r = - 0.74 -.70 .74 .76 -.70 .74 .76 Controls: Genu FA TBI: Genu FA

Notes: No reliable correlations were observed for any factor or task in controls. BSR = 4, p < .0001. (Min. cluster size = 15). Correlation plots present correlations between overall brain scores for each subject and group and genu FA during the Alphabetize 5 condition. Correlation was reliable for TBI but not controls.

Table 3.2. Suprathreshold activation cluster maxima from LV 1 (p < .001) in the Seed and Genu PLS analysis (see Figure 3.4).

Gyrus BA Lat.xyzBSR Clust Size Lag GFd 10 R 4 63 19 8.2685 64 2 GFd 10R 4 63195.4935213 GFd 10 R 4 63 19 5.321 17 4 GFm 10 R 36 51 20 7.8056 85 2 GFs 10 R 28 56 23 5.5089 16 1 GFd 9 R 12 52 31 6.0768 36 1 GFm 46 / 44 R 32 20 21 9.3871 24 2 GFm 46 R 40 21 25 9.0666 18 1 GPrC 4 L -16 18 54 5.2195 23 2 GFs 8 L -24 22 50 6.4578 19 1 LPi 40 R 55 -22 23 5.8864 37 1 LPi 40 R 55 -18 23 5.4297 19 2 LPi 40 R 55 -22 20 6.0659 16 3 LPi 40 L -32 -36 50 5.4958 34 1 GC 24 R 4 -2 37 6.1236 43 2 GC 31 R 20 -33 42 6.5077 24 1 GTs 22 R 28 -30 16 5.8414 42 0 PCu 7 R 12 -25 45 8.2193 249 2 PCu 7 L -8 -40 50 9.2118 129 3 PCu 7 L -12 -40 50 5.7872 62 1 PCu 7 L -8 -40 50 5.9306 29 4 GTm / PCu 7 / 39 L -20 -57 25 8.195 70 2 GTm 19 / 39 L -32 -61 21 7.6538 74 1 GOm 19 L -28 -62 10 7.4981 33 3 GOm 19 L -24 -73 15 6.0536 72 0 GTm 39 R 36 -57 18 5.646 18 2 GTm 19 L -44 -85 15 6.9206 47 3 GL 18 R 8 -58 3 5.3186 16 0 Fusiform 37 R 40 -51 -14 6.7572 68 3 Cerebellum L -32 -36 -28 6.6985 34 3 Cerebellum L -20 -51 -14 5.6629 31 1 Cerebellum R 32 -82 -16 5.6185 16 2

Notes: Seed was placed in right lateral middle frontal gyrus (BA 46/44). Light grey font represents similar brain regions at different lag intervals. (GFd=medial frontal gyrus, GFm=middle frontal gyrus; GFs=superior frontal gyrus, GPrC=precentral gyrus; LPi=inferior parietal lobule; GC=cingulate gyrus; GTs=superior temporal gyrus; PCu=precuneus; GTm=middle temporal gyrus; GOm=middle occipital gyrus, GL=lingual gyrus).

When included in this model, working memory accuracy did not alter these results

(Figure 3.5, Table 3.3), implying that neurofunctional changes were indeed mediating the impact of callosal FA loss on behaviour.

Figure 3.5. Brain regions demonstrating reliable and positive correlations with right GFm seed (A5, M3, M5) and negative correlations with Genu FA (all tasks) in TBI subjects.

2-4

4-6

Brain 18 19 20 21 22 23 24 Score 50 50 50

0 0 0

-50 -50 -50 r = .42 r = 67 r = - .75 0.6 0.7 0.8 0.9 -.4 -.2 0 .2 .4 .6 0.6 0.7 0.75 0.8 Genu Accuracy Seed activity

Notes: Correlations with accuracy did not contribute to this pattern. BSR = 4, p < .0001. (Min. cluster size = 15). Correlation plots present correlations between overall brain scores and accuracy, seed activity and genu FA for each TBI subject. Only seed and genu FA correlations were significant. Accuracy did not contribute to this pattern.

Table 3.3. Suprathreshold activation cluster maxima from LV 1 (p < .001) in the Behaviour, Seed and Genu PLS analysis (TBI group only, see Figure 3.5). Seed was placed in right lateral middle frontal gyrus (BA 46/44). All abbreviations as in Table 3.2.

Gyrus BA Lat. x y z BSR Clust Size Lag GFm 46 R 36 45 27 6.8949 43 2 GFm 9 R 32 14 31 7.8619 16 2 PCu 7 R 9 -32 45 6.1183 25 3 GC 31 L -2 -35 33 5.8776 34 2 PCu / CG 7 L -13 -43 40 4.8122 16 2 LPs 7 L -24 -59 49 7.7053 19 3 GTm 19 L -24 -60 20 6.599 21 2 GTm 19 / 39 L -24 -67 16 7.8367 25 1 Cerebellum L -30 -52 -23 5.4126 22 1 Cerebellum R 21 -82 -25 5.8913 23 3

To summarize, activity in prefrontal and posterior areas functionally connected to a right lateral prefrontal seed region during working memory performance were associated with FA reductions in the anterior corpus callosum. These functional brain changes may therefore mediate the impact of structural brain changes on working memory performance following TBI, identifying a potential mechanism for behaviourally-relevant, altered functional recruitment in this population.

3.2. Correlating right lateral prefrontal activity with whole-brain, white matter FA

The results reported in section 3.1 suggest that working memory task performance

may be mediated by altered brain connectivity patterns in the presence of callosal DAI, as

indexed by reduced FA. In this final set of analyses, we examined whether FA changes in

other white matter pathways were associated with compensatory functional brain activity.

Specifically, we correlated BOLD response with right lateral prefrontal seed voxel activity

during the high load working memory tasks in our TBI sample with whole brain, white

matter FA (Baird et al., 2005 and see Methods). The goal of this analysis was to (i) confirm

our result from section 3.1 that anterior callosal FA was related to compensatory functional

89 recruitment in our TBI sample and (ii) explore whether FA in white matter outside of these callosal regions was related to this compensatory brain response.

Consistent with our multivariate findings, seed voxel activity was significantly negatively correlated with white matter FA in the genu as well as in the splenium, intra- hemispheric long-fibre tracts bilaterally, white matter underlying the left dorsolateral prefrontal cortex, right inferior parietal lobule, left temporal-occipital junction and the cerebellum. Positive correlations were observed in the anterior limb of the right internal capsule (A5; Figure 3.6, Table 3.4) as well as in external capsule bilaterally and in white matter underlying left superior parietal cortex (M5).

Figure 3.6. Top panel . Regions where activity (i.e. BOLD response) in right middle frontal gyrus during Alphabetize 5 condition predicted lower white matter FA. Bottom panel. Activity in this same region predicted higher FA in ‘c’, anterior limb of internal capsule (t-contrasts of parameter estimates; p < .01, 25 voxels, corrected to p <.05). Labels correspond to coordinates in Table. 3.4.

6.1 10

a b a

6.2

c c

Table 3.4: Regions where white matter FA is predicted by BOLD response in right GFm during Alphabetize 5 task (TBI participants only). Letters correspond to Figure 3.6.

Lat.xyz

Right GFm activity predicts lower FA in ...

Corpus callosum (splenium) -2 -34 16 (a) Inferior Longitudinal Fasc. L -38 -37 6 (b) Frontal-Occipital Fasc. R 24 -11 21 Posterior/inferior temporal lobe L -44 -26 -16 Posterior limb of internal capsule L -30 -29 12

Right GFm activity predicts higher FA in ...

Anterior limb of internal capsule R 14 16 1 (c)

Results Summary

In section one we demonstrated that white matter integrity is reduced in chronic phase TBI in anterior, posterior and isthmus/posterior body regions of the corpus callosum, right cerebellum and white matter underlying left posterior parietal cortices. However, these reductions did not correlate significantly with behavioural performance on our working memory tasks with high load and executive demands (section 2). In section 3 we integrated functional, structural and behavioural data to examine the interaction of reduced FA and altered functional brain response during working memory. Using both multivariate and univariate methods we demonstrated that increased activity in right lateral PFC, which we had previously identified as compensatory, was associated with reduced FA in the corpus callosum as well as in white matter underlying left dlPFC and regions of left inferior parietal and temporal-occipital cortex. Positive associations between FA and activity in right lateral

PFC were observed in the right anterior limb of the internal capsule as well as left superior parietal white matter.

Discussion

DAI as a mechanism of altered functional recruitment

Task-related changes in functional recruitment of brain activity has been frequently associated with TBI (Christodoulou et al., 2001; McAllister et al., 1999; Perlstein et al., 2004;

Chapter 1). Recently we demonstrated that this altered recruitment is positively associated with performance on working memory tasks with high load and executive control demands in patients with pure DAI (see Chapter 2), a pattern we have described as altered functional engagement. A common pattern emerging from these reports is one of reduced lateralization in functional brain activity during cognitive task performance following TBI, particularly in lateral prefrontal regions. The mechanisms underlying these functional brain changes have yet to be characterized. Here we investigated the hypothesis that neurofunctional recruitment is altered in response to DAI. We assessed this hypothesis in

TBI participants without focal injury, thus allowing us to specifically examine the impact of

DAI on functional brain changes during working memory performance. We used diffusion- weighted imaging measures (i.e. FA) to quantify white matter changes and related these to both behavioural and functional brain data. To our knowledge, no previous studies have directly related functional, behavioural and diffusion data in a TBI sample.

In section one we provided evidence of decreased FA in white matter in our TBI sample. Similar findings have been reported previously (Kraus et al., 2007; Nakayama et al.,

2006; Xu et al., 2007), however, this report is the first to demonstrate reduced white matter

FA in a sample of functionally and neuropsychologically-recovered, chronic-phase TBI patients without evidence of focal injury.

Having demonstrated structural brain changes in our sample we next investigated whether these reductions in FA were predictive of performance on our working memory

92 tasks. There was little correlation between white matter FA and task accuracy, leading us to examine whether compensatory functional changes may be mediating this relationship. A similar pattern has recently been associated with DAI resulting from multiple sclerosis

(Audoin et al., 2007; Reddy et al., 2000). Our results in section 3 were consistent with such a mediation hypothesis. By combining behavioural, functional and structural analyses, we demonstrated that right lateral prefrontal activity and a network of functionally connected brain regions (see figure 3.4), which was highly correlated with working memory performance in our TBI sample but not in controls, was correlated with reduced FA in the anterior corpus callosum. This inverse relationship was also observed in regions of white matter in left inferior parietal cortex and temporal-occipital junction through which frontal- posterior fibre pathways are known to course. These findings suggest that altered functional network activity including right lateral prefrontal cortex mediates working memory in the presence of DAI, allowing our patients to perform normally in spite of severe

TBI with significant DAI.

Neural versus cognitive reorganization

Taken together, this series of analyses suggests that altered functional recruitment during working memory performance following TBI reported by us and others (see Levine et al., 2006) may be at least in part attributable to diffuse white matter changes. Price and

Friston (1999) distinguished ‘neural reorganization’ from ‘cognitive reorganization’, wherein functional brain changes reflect a different cognitive approach to the task following brain injury rather than true neural reorganization per se. While we cannot rule out the possibility that our TBI subjects utilized alternative cognitive strategies from those employed by the healthy controls in our study, data from the current and previous reports with this patient sample conducted in our laboratory argue against this interpretation. An altered cognitive

approach to task performance should be reflected in the recruitment of novel brain regions

outside of those commonly implicated in a particular cognitive processing domain (Price &

Friston, 1999). This was not the case in our TBI sample. In fact, our previous data

supported an ‘altered functional engagement hypothesis’, wherein TBI subjects showed

recruitment of a highly overlapping pattern of brain regions with control subjects, albeit at lower levels of task demand. Thus our data are consistent with an altered neural reorganization hypothesis. In this paper, white matter damage in both inter- and intra- hemispheric pathways are identified as structural correlates of these functional brain changes.

Neural versus functional reorganization

Altered functional recruitment has been reported following focal brain damage resulting from stroke (e.g. Winhuisen et al., 2007) and neoplasm (e.g. Thiel et al., 2005)

during cognitive task performance in highly lateralized domains (e.g. language and motor

functions). Typical patterns of neural reorganization include reduced hemispheric

lateralization and increased activity in regions adjacent to those normally engaged by non-

injured control subjects. Candidate neural mechanisms include loss of transcallosal

inhibition secondary to focal damage occuring at the origins of these interhemispheric fibres

or recruitment of functionally intact neural tissue adjacent to that damaged by focal injury

(see Price & Crinion, 2005; Thiel et al., 2005 for reviews). While the neural mechanisms

underlying functional reorganization after focal brain injury are becoming increasingly

characterized, those associated with more diffuse pathology are not as well understood.

Audoin and colleagues (2007) recently proposed a model of neural reorganization associated

with diffuse axonal injury in early stage multiple sclerosis. They reported that a rightward

shift in functional brain activity observed in lateral PFC during working memory task

performance in this population was inversely correlated with FA in pathways connecting

lateral PFC regions bilaterally and positively correlated with FA values in fibres connecting

right thalamus and right lateral PFC (Audoin et al., 2005; Duong et al., 2005). Based on

these findings, they argued that recruitment of right lateral PFC following axonal injury

represents a rebalancing of functional activity in response to structural disruptions within left

lateralized working memory networks and/or callosal pathways.

Our data are consistent with this model. We observed inverse correlations between

right lateral prefrontal activity and FA in both the genu and fibres linking regions commonly

implicated in a left-mediated verbal working memory network. Additionally, we report a

positive correlation between activity in right lateral prefrontal cortex and FA in the anterior limb of the internal capsule, a fibre bundle containing forward projections of the anterior thalamic tracts into lateral PFC regions. Audoin and colleagues (2007) have demonstrated that increased connections between right thalamus and right dlPFC (Brodmann area 45/46) were correlated with increased activity in this PFC region and inversely correlated with functional connectivity between this region and its left homologue in early multiple sclerosis

(MS) patients performing a working memory task. Consistent with our own data, these

findings suggest that fronto-thalamic interactions may modulate the impact of damaged

cortico-cortico fibre pathways by engaging degenerate brain systems to support

performance, a process Audoin et al.(2007) have described as reactive neuroplastic change.

We would argue for a more parsimonious dynamic functional reorganization account as our

data suggest that reciprocal thalamo-cortical loops bias processing towards subsidiary or

degenerate neural networks in response to either endogenous alterations in functional

connectivity due to injury or to exogenous, task-related challenges. Put another way, our data

are ambivalent with respect to the requirement for structural neuroplastic change as we and

others (e.g. Rypma et al., 1999) have demonstrated altered functional recruitment in healthy

control subjects with increasing task demands that are highly overlapping with our TBI

findings but presumably do not reflect structural brain changes. Nonetheless, concordance

of our TBI findings with those of Audoin et al.(2007) with respect to the mapping of functional and structural changes in the absence of observable behavioural differences

argues in favour of a role for frontal-subcortical circuits in the mediation of neurofunctional

changes associated with white matter dysfunction.

DAI, neurofunctional change and recovery from TBI

These data demonstrate that altered functional engagement may mediate brain-

behaviour relationships following DAI. As discussed previously, similar patterns have been

associated with white matter neuropathology in multiple sclerosis (Reddy et al., 2000) and

healthy aging (see Buckner, 2004, Greenwood, 2007 for reviews). Yet, within these

populations, there is considerable variability in the expression of cognitive deficits both

between individuals and within individuals over time. For TBI, this manifests as highly

variable individual recovery profiles, begging the question as to why functional

reorganization may facilitate task performance in some individuals more than others.

Although the presence and location of large focal lesions is one likely source of variability

(Povlishock & Katz, 2005), this does not account for the range of variability in patients

without large focal lesions. Moreover, uneven rates of cognitive decline evident in MS

(Hoffmann, Tittgemeyer, & von Cramon, 2007) and normal aging (Buckner, 2004), suggest

that white matter changes may independently contribute to variable recovery patterns

following brain damage. In this respect the present results suggest that good

neuropsychological recovery following TBI with DAI may depend upon functional

reorganization facilitated by relatively spared ascending subcortical-PFC projection fibres.

We have tentatively labelled this a ‘recalibration’ account, as the neurofunctional change we

have characterized reflects a threshold change vis-à-vis healthy controls who show similar

patterns of altered recruitment at higher levels of cognitive demand. These data implicate

frontal-subcortical connectivity as a mechanism for these functional alterations, thus

implicating the integrity of these pathways in the variability of recovery following TBI.

Support for this hypothesis is found in a recent study by Kraus and colleagues (2007)

who measured the degree of FA loss occurring in 28 areas within the cerebrum following

either mild or moderate-to-severe TBI. In their data, moderate to severe TBI resulted in a

disproportionate loss of FA in callosal fibre tracts relative to mildly injured subjects. Yet, as

in the present study, these significant neuropathological differences between the mild and moderate/severe TBI groups were not associated with significant differences in executive

control or attention performance. Although Kraus and colleagues (2007) do not report

functional MRI data, there is a suggestion that preserved anterior projection fibres may be

mediating against more significant behavioural impairment in this group. While callosal and

cingulum fibre integrity were significantly impacted with moderate/severe injury vis-à-vis

mild TBI, no such differences were observed in the projection fibres running through the

interior capsule to anterior cortex. Moreover, FA in this region was significantly greater on

the right than the left and was positively correlated with behavioural performance, but only

in the executive function domain, consistent with our report that compensatory functional

brain activity during executive control in working memory was associated with right anterior

capsular FA.

Conclusion

While the time course and extent of recovery following TBI are subject to myriad

influences, our data and convergent findings from multiple sclerosis (Audoin et al., 2007;

Duong et al., 2005) and healthy aging (Buckner, 2004) suggest that the pattern, and not

simply the extent, of white matter changes across the cerebrum will impact the nature of

cognitive changes over time in these populations. By combining region-of-interest with

whole brain analysis, we were able to examine microstructural-functional relationships

implicating ascending-descending projections, callosal or commissural fibres, and short and long cortico-cortico tracts. Maps of fibre class alterations and their respective cognitive impacts following DAI could provide a model for predicting cognitive decline in other populations (e.g. MS, aging, ischemic cerebral vascular disease), where disease progression may not preferentially impact particular fibre classes.

Finally, we have proposed that relative preservation of ascending fibre pathways from subcortical structures into PFC, particularly in the right hemisphere, may facilitate compensatory functional reorganization following damage to interhemispheric tracts. Thus, interventions targeting ascending neurotransmitter systems either pharmacologically (see

Tenovuo, 2006 for a review) or through behavioural interventions (Mahncke, Bronstone, &

Merzenich, 2006), may hold some promise in facilitating the compensatory recruitment we have characterized here.

DISCUSSION

The application of neuroimaging methods to study the neural and behavioural

impacts of traumatic brain injury has advanced significantly since the earliest in vivo reports of structural changes using CT methods (e.g. Levin, Meyers, Grossman, & Sarwar, 1981;

Meyers et al., 1983). Over this period, novel techniques for identifying and quantifying

structural brain damage (e.g. MRI: Ogawa et al., 1992) or alterations in white matter

connectivity (e.g. DTI: Arfanakis et al., 2002) have advanced our understanding of the

neuropathological sequelae of TBI, providing convergent evidence for earlier human

pathological (Adams, Graham, Murray, & Scott, 1982) and experimental animal (Gennarelli

et al., 1982) reports. While these structural imaging investigations represent an important

advance in mapping the distribution of trauma-induced neuropathology in vivo, static images

of a functional neural system are of limited utility in fully understanding the impact of

trauma on the neural basis of behaviour. Over the last decade this limitation has begun to

be addressed by the utilization of functional neuroimaging techniques to study alterations in

brain activity patterns in this population. However, in many respects this research has

remained in its adolescence. Variability in experimental methods, complexity in the

neuropathological profiles of TBI patients, and between group differences in task

performance has hindered interpretation of these functional brain data. As such, the impact

of TBI on the functional anatomy of cognition and the potential structural mechanisms

underlying these changes remains underspecified.

In my dissertation research project I have endeavoured to narrow this gap by

addressing some of the limitations evident in previous reports while employing novel

techniques to investigate the relationship between structural and functional brain data and

cognitive performance following TBI. As outlined in the General Introduction, I have

framed the project around three primary research questions, each of which was addressed in a separate series of analyses:

(i) Does TBI alter the functional neuroanatomy of cognitive task performance?

(ii) Does cognitive performance depend on functional reorganization following TBI?

(iii) How do structural and functional brain changes interact following TBI?

In the next section I summarize the results of each analysis individually. I conclude with a brief discussion of common themes, clinical implications and proposals for future research.

Results Summary

Chapter 1: Augmented neural recruitment following diffuse axonal injury.

It was necessary to confirm the presence of neurofunctional changes in our sample of rigorously screened ‘pure’ DAI patients before proceeding to examine the potential mechanisms and behavioural consequences of such changes. Based upon previous works

(e.g. McAllister et al., 1999; Perlstein et al., 2004), I hypothesized that DAI would be associated with altered functional recruitment during executive control processing in working memory and that regions of right lateral PFC and expanded regions of left PFC and posterior parietal cortices would be recruited in our TBI patients relative to healthy, matched controls. This was indeed the case. This series of analyses demonstrated, for the first time in a ‘pure’ DAI sample, increased functional brain response, isolated to executive control

100

processing in verbal working memory, in patients relative to healthy age- and education-

matched controls. Identification of this altered functional recruitment was remarkable in a

number of ways. First, our subjects had sustained a moderate to severe brain injury without

evidence of focal brain lesions on neuroradiological report, thus implicating diffuse

pathology in these functional brain changes. This is the first time in the literature that diffuse

injury has been isolated as a primary neuropathological mechanism underlying altered

neurofunctional status during working memory performance following brain injury (but see

Maruishi, Miyatani, Nakao, & Muranaka, 2007 for a similar result using a complex attention

measure). Second, we report these functional brain changes in a sample of patients who were multiple years post-injury and who had demonstrated good neuropsychological and

functional recovery, suggesting that these neurofunctional changes were not reflected in

general cognitive or functional domains.

Finally, task performance differences are a significant confound in functional

neuroimaging reports involving patient populations. Brain differences are inexorably

confounded with performance differences, such that it is impossible to discern whether the

neurofunctional differences are a cause or a consequence of the behavioural differences

between patients and healthy controls (Price & Friston, 2002). By equating group

performance on the working memory task during scanning, the functional brain changes

reported in paper 1 provided preliminary evidence of compensatory functional recruitment

in our TBI sample that could not be attributed to slowed cognitive processing speed or

variability within our TBI sample.

In summary, consistent with my general hypothesis, the results of paper 1 confirmed

that TBI, and more specifically, DAI, altered the functional neuroanatomy of executive

control processing in working memory. These changes could not be attributed to

101 performance differences, cognitive slowing or variability within the TBI group and were evident in a sample of well-recovered, chronic stage survivors of moderate to severe head trauma. Having established this pattern of neurofunctional change, in paper 2, I directly investigated whether these functional brain changes were truly compensatory and whether they reflected neural reorganization or altered functional engagement of latent functional networks vis-à-vis our healthy control participants.

Chapter 2: Compensatory neural recruitment: evidence for an altered functional engagement hypothesis.

Paper one provided preliminary evidence that altered functional recruitment was compensatory, as augmented neural response was recorded in TBI patients in the context of equivalent scan task performance. This series of analyses investigated whether cognitive performance depended on these neurofunctional changes in chronic-phase TBI and to what extent these brain and behaviour relationships differed across groups. Previous studies of brain and behaviour correlations following TBI have been equivocal (Levine et al., 2006), however, increased compensatory recruitment of right lateral PFC during working memory tasks has been reported in other patient populations with dominant white matter pathology

(e.g. MS, Audoin et al., 2005; healthy aging, Erickson et al., 2007). On this basis, a similar pattern of compensatory recruitment within regions of right lateral PFC was predicted for the subjects with DAI pathology in this study.

The results of this second report were consistent with this hypothesis. Moreover, the pattern of brain and behavioural correlations for the TBI and healthy control groups were strikingly different. For the control participants, accuracy was maximally correlated with brain response in left lateral PFC and posterior parietal cortices - areas typically

102 reported in studies of verbal working memory. For the TBI group, brain and behavioural correlations were maximal in right PFC. These differences in the lateralization of behaviourally relevant functional brain responses were particularly striking given the absence of focal lesions and normal functional and neuropsychological recovery in this patient sample.

However, these results and those reported earlier also revealed substantial overlap in functional brain responses between our groups, particularly at higher memory load or executive control demands. This observation suggested that laterality differences may not represent neural reorganization so much as differential functional recruitment within PFC as a function of task demand. To investigate this possibility, we extracted functional brain response measures in each of four regions of interest (ROIs) corresponding to the PFC cluster maxima from the earlier brain and behaviour analysis. Partial least squares analysis methods were used to express patterns of whole brain activation that systematically co- varied with activity in these seed voxels and working memory task performance across our two groups. Our working hypothesis, based upon our earlier findings, predicted equivalent functional connectivity amongst PFC ROIs in both groups. However, the behavioural relevance of activity in this network would systematically vary as a function of task demand and group.

The results presented in paper 2 were consistent with this altered functional engagement hypothesis. A step-wise pattern of functional recruitment of right PFC regions differentiated our two groups as working memory task demands increased. Regions of right lateral PFC were functionally connected to homologous regions of left PFC in both groups.

However, this functional connectivity was behaviourally relevant only for the control group when both executive demand and load were increased (i.e. Alphabetize 5 task). In our TBI

103 group a similar pattern of bilateral functional connectivity was observed, however, this pattern was behaviourally relevant when either executive demand or load factors were increased. These data suggest that bilateral functional connectivity within PFC is evident during working memory performance whenever load is increased or executive control demands are high. However, the threshold at which this expanded right PFC network is functionally necessary for working memory performance is reduced following TBI. These data provide early evidence for an altered functional engagement hypothesis of functional brain change following TBI. In the third and final report I use diffusion imaging methods to identify possible structural correlates of these functional brain changes.

Chapter 3: Diffuse axonal injury as a mechanism for functional brain changes following traumatic brain injury.

Building upon evidence of altered functional engagement during working memory performance following TBI observed in the first two papers, this final report utilized diffusion tensor imaging to investigate the neural mechanisms of these functional brain changes. A number of previous studies have demonstrated changes in fractional anisotropy following TBI both with and without focal brain lesions (e.g. Benson et al., 2007; Kraus et al., 2007; Nakayama et al., 2006). However, evidence of correlations between altered diffusion and cognitive performance has been equivocal (e.g. Kraus et al., 2007; Nakayama et al., 2006; Salmond et al., 2006). While a portion of this variability may be attributable to the methodological challenges discussed earlier, I hypothesized that the neurofunctional changes observed in the first two papers may be modulating the behavioural impact of structural brain changes following TBI. Evidence of functional modulation has been reported in healthy (Greenwood, 2007) and pathological (Grady et al., 2003) aging and multiple sclerosis

104

(Duong et al., 2005; Reddy et al., 2000). Here I integrated functional and structural

neuroimaging methods to test this hypothesis in a brain injured sample.

There were three principal findings. First, reductions in white matter fractional

anisotropy were observed in the corpus callosum, cingulum bundle, posterior parietal white

matter and the cerebellum. Second, with the exception of a small region of the posterior cingulum, whole brain FA was not reliably correlated with working memory performance.

Moreover, there were no significant correlations between splenium and genu FA and working memory accuracy. However, consistent with our hypothesis, genu FA was inversely correlated with the pattern of compensatory functional brain changes reported earlier. Put another way, loss of white matter anisotropy in this region predicted compensatory functional brain response during working memory task performance. Using whole-brain analysis methods, similar structure-function relationships were observed in long fibre tracts bilaterally and with white matter underlying posterior parietal cortices.

Unexpectedly, a single cluster of voxels in the anterior limb of the internal capsule demonstrated positive correlations (i.e. increased seed activity predicted increased white matter FA).

In summary, this final series of analyses demonstrated that changes in white matter

FA are measurable multiple years post-injury although the behavioural correlates of these neuropathological changes may be masked by compensatory functional brain response.

However, it remains unclear whether these functional brain changes represent structural neuroplastic reorganization, cognitive reorganization or task-specific functional reorganization of existing brain networks. While we provided evidence for the latter in papers 2 and 3, contrasting these accounts directly was beyond the scope of this project.

105

However, clarification of the relationship between structural brain damage, neurofunctional

change and behavioural outcomes would seem to be a crucial area of future research.

General Conclusions

In the final section of this dissertation report, I review the overall experimental

approach I adopted for investigating neurofunctional change following TBI. Next I

integrate the conclusions of the three component papers and relate them to two overarching

goals of the research. First, how do these data inform the literature with respect to models

of altered functional recruitment following TBI? Second, what is the clinical relevance of

these data with respect to diagnosis, prognosis and neurorehabilitation following brain

injury. In these concluding sections, I integrate the conclusions from the three papers,

discuss their overall theoretical or clinical relevance and suggest future research directions.

A novel approach to investigating neurofunctional changes in TBI: improving signal to noise

An overarching goal of this project was to advance understanding of the impact of

TBI on cognitive function using novel neuroimaging techniques and analysis methods.

Numerous reports applying structural, functional and, more recently, diffusion imaging have

now appeared in the TBI literature (for a review see, Levine et al., 2006). As is the case with

any emerging field of inquiry, these pioneering studies have been plagued with methodological and experimental design deficiencies. The complex neuropathological sequelae following brain trauma as well as conceptual and practical challenges involved in acquiring and analyzing data from a damaged brain in vivo has led to an uneven body of

106

research. Here my objective was to build upon lessons learned from these early studies to

implement a research protocol that served to (i) maximize ‘signal’ by utilizing multiple

neuroimaging methods and image analysis techniques and (ii) minimize ‘noise’ by setting

stringent selection criteria for TBI recruitment and employing a cognitive task paradigm for which the functional and behavioural correlates have been well characterized in non-injured subjects. While these criteria may limit the immediate clinical relevance of these data, the goal of this project was to clarify, to the extent possible, the relationship between brain function, structure and behaviour following TBI. In this respect, these data may serve as a baseline for future investigations using more representative patient samples and increasingly complex ‘real-world’ measures of cognitive performance.

Altered functional engagement: A new model of neurofunctional change following TBI.

Price and Friston (2002) have argued that neurofunctional changes following brain damage may reflect either neural (i.e. structural neuroplastic change) or cognitive (i.e. an alternate cognitive strategy) reorganization. While much of their work has focused on patients with focal brain lesions, there has been recent evidence for both models of compensatory brain change in populations with principally diffuse neuropathology. In a recent paper investigating functional brain changes and altered white matter FA in early, asymptomatic MS, Audoin and colleages (2007) argued that increased recruitment of right

PFC regions was related to neuroplastic change in subcortical and fronto-subcortical white matter fibre bundles. Their data is consistent with the neuroplastic change account put forth by Price and Friston (2002). However, in a recent review of neurofunctional change in healthy aging, Greenwood (2007) argued that functional brain changes associated with white

107

matter atrophy reflect altered processing strategies. This account is consistent with a recent

study by Erickson et al. (2007) who reported reduced engagement of right frontal regions in

a cohort of healthy aging subjects following strategy training. The authors argued that

training-induced functional changes reflected reduced reliance on strategic processing

(mediated by right lateral PFC), consistent with a cognitive reorganization account. While I

did not set out to test these accounts of functional brain change following TBI, my data are

not fully consistent with either. Our cohort of TBI subjects recruited a similar network of

brain regions within right lateral PFC as healthy controls but at a lower level of working

memory task demand. In report 2, I argued that this was preliminary evidence for altered

functional engagement of latent neural networks in this population. Report 3 identified

integrity of thalamo-cortical pathways as a potential neural mechanism underlying altered

functional recruitment of right lateral PFC during working memory. These results were

consistent with the work of Audoin et al. (2007) who suggested that structural neuroplastic

change in fronto-subcortical connectivity was the primary mechanism for compensatory functional brain change. In report 3, I argued that an altered functional engagement account is more parsimonious in that it is consistent with observations of augmented neural

recruitment associated with both endogenous (including structural brain changes) and

environmental (e.g. task demand) factors. In other words, an altered functional engagement

account does not imply that structural brain damage is necessary to observe augmented

functional recruitment of these compensatory brain networks. Moreover, our data are not

consistent with an information processing or strategy-shift account of neurofunctional brain

changes. Indeed, our functional engagement hypothesis is in part based upon the

recognition that altered functional recruitment following brain damage or under conditions

of cerebral challenge are highly overlapping. This, according to the schema of Price and

108

Friston (2002), is inconsistent with a cognitive reorganization account, where strategic shifts

would presumably recruit substantially different neural networks.

While speculative, the altered functional engagement account of neurofunctional

changes following TBI is consistent with extant data in both brain injured and healthy

populations. Moreover these data suggest that the relative integrity of white matter fibre bundles projecting through the right anterior internal capsule linking thalamic and prefrontal cortical structures may serve to mediate these functional brain changes. Such changes may occur in response to either brain damage, systemic endogenous (such as fatigue) or environmental factors (including task demands). Although the current data cannot parse the relative merits of these models of functional brain change, this question represents an urgent area of future research as each model would suggest differing approaches to rehabilitation or remediation of behavioural deficits when functional reorganization is insufficient. In the final section, I discuss these clinical implications of the neurofunctional changes associated with TBI in more depth.

TBI and neurofunctional change: clinical implications.

Here we have demonstrated that TBI is associated with altered functional

recruitment of neural resources during executive control processing in working memory. As

discussed in the previous section, these compensatory brain changes were observed in a

sample of participants who underwent a rigorous screening process before entering the

study, thus minimizing confounds owing to injury or individual difference characteristics.

These data demonstrate, for the first time, that neurofunctional changes are a

consequence of diffuse axonal injury and may occur in the absence of significant

109 neuropsychological or functional deficits. Moreover, these changes are measurable multiple years post-injury and are not attributable to co-morbid physical or neuropsychiatric impairments. The pattern of augmented neural recruitment observed here mirrors that reported in healthy aging (Grady et al., 2003) and observed under conditions of cerebral challenge, whether task related (Rypma & D'Esposito, 2002) or endogenously-derived (e.g. sleep loss, Habeck et al., 2004). The recruitment of common neural mechanisms to support cognitive task performance following brain injury and under conditions of environmental or personal challenge begs the question of how these factors interact following brain injury.

These data suggest that the convergence of TBI with factors such as aging or fatigue may have additive (perhaps even multiplicative) effects on cognitive and functional recovery or post-recovery adaptation. Moreover, identification of these functional brain changes in such a selective cohort of TBI patients raises the possibility that such interactions may be a source of variability in outcome and/ or functional status across the broader spectrum of TBI survivors. Results from Chapter 3 suggest that white matter changes in interhemispheric and long-fibre tracts were inversely related to the pattern of altered functional engagement reported earlier, with positive correlations identified for FA in thalamo-cortical projection fibres. This finding, observed in a sample of well-recovered TBI patients, implicates subcortical-cortical connectivity as a potentially important mechanism of variability in recovery and hence a potentially useful prognostic marker or a target for rehabilitation interventions following TBI.

Ultimately, these results demonstrate that complex cognition (operationalized here as executive control in verbal working memory) is facilitated by functional brain changes following moderate to severe TBI. While this is a good news story in terms of preservation of higher cognitive capacity in at least a subset of brain injury survivors, evidence of

110

neurofunctional compensation also suggests two potentially important clinical implications

and potential avenues of future research.

The first of these reiterates a question I discussed briefly in Chapter 2 and concerns

whether or not functional reorganization incurs a cost in terms of cognitive capacity. If, as the data presented in Chapter 2 suggest, there is a step-wise shift to earlier recruitment of supplemental neural resources (i.e. right dorsolateral prefrontal cortex in the current paradigm) as task demands increase following diffuse brain injury, how durable are these changes over time? Recent research has suggested that continuous engagement of executive control processes results in declining performance upon repeated challenge over time in healthy adults (Inzlicht & Gutsell, 2007; Vohs & Heatherton, 2000). This evidence for such a capacity-limited system suggests that recruitment of supplemental neural resources at lower levels of task demand following TBI might exacerbate performance declines under cognitively demanding conditions. While I did not set out to test this question directly in the current research protocol, anecdotal evidence from the clinic would appear to be consistent with such a hypothesis. A common complaint following TBI is one of ‘mental fatigue’, often described as a felt sense of increased mental effort to complete high level cognitive tasks relative to premorbid capacity. The current results identify functional reorganization as a candidate neural mechanism that might underlie this phenomenological experience.

Moreover, executive control capacity has been shown to decline across the lifespan, potentially expanding the temporal relevance of these questions from situational- or task- specific occurrences of mental fatigue to longer-term implications for capacity changes in executive control across the lifespan following TBI.

Based on these findings, further research into the kinetics of changes in executive control capacity, both in healthy and brain damaged individuals, is warranted to more

111 directly address the question of how time and TBI interact to alter executive control capacity. Of equal importance, the identification of a neural basis for this phenomenological experience of mental fatigue reported by TBI survivors would be of considerable value clinically in improving understanding by the patients, their families and health care providers of what heretofore has been a poorly understood, but often functionally debilitating, consequence of brain injury.

A second and concluding implication of these data concerns the design and implementation of rehabilitation interventions. While these findings must be considered preliminary, the identification of a candidate structural basis for functional reorganization

(i.e. cortical-subcortical connections, Chapter 3), which is, in turn, related to improved executive control (Chapter 2), suggests that interventions targeting these connections and their neurochemical bases may demonstrate efficacy in remediating deficits in this domain following TBI. Recent work by Fegen et al. (2007) has provided early evidence of the efficacy of such an approach. By administering a cholinergic agonist the authors demonstrated changes in a neural marker of executive control of processing within posterior visual association cortex. While there are few behavioural interventions specifically designed to target executive control processes based on functional and structural brain data, further identification, validation and refinement of such neural biomarkers may aid in the development and assessment of behavioural strategies designed to engage and challenge executive control processing and potentially complement pharmacological interventions for

TBI rehabilitation – a line of research I will pursue upon completion of my doctoral studies.

112

References

Adams, J. H., Gennarelli, T. A., & Graham, D. I. (1982). Brain damage in non-missile head

injury: Observations in man and subhuman primates. In W. T. Smith & J. B.

Cavanagh (Eds.), Recent Advances in Neuropathology (2nd ed., pp. 241). Edinburgh:

Churchill Livingstone.

Adams, J. H., Graham, D. I., Murray, L. S., & Scott, G. (1982). Diffuse axonal injury due to

nonmissile head injury in humans: An analysis of 45 cases. Annals of Neurology, 12,

557-563.

Alexander, A. L., Lee, J. E., Lazar, M., & Field, A. S. (2007). Diffusion tensor imaging of the

brain. Neurotherapeutics, 4, 316-329.

Arfanakis, K., Haughton, V. M., Carew, J. D., Rogers, B. P., Dempsey, R. J., & Meyerand, M.

E. (2002). Diffusion tensor MR imaging in diffuse axonal injury. American Journal of

Neuroradiology, 23, 794-802.

Audoin, B., Duong, M. V., Ranjeva, J. P., Ibarrola, D., Malikova, I., Confort-Gouny, S., et al.

(2005). Magnetic resonance study of the influence of tissue damage and cortical

reorganization on PASAT performance at the earliest stage of multiple sclerosis.

Human Brain Mapping, 24, 216-228.

Audoin, B., Guye, M., Reuter, F., Duong, M. V. A., Confort-Gouny, S., Malikova, I., et al.

(2007). Structure of WM bundles constituting the working memory system in early

multiple sclerosis: A quantitative DTI tractography study. Neuroimage, 36, 1324-1330.

Baddeley, A. (1986). Working Memory. New York: Oxford University Press.

113

Baird, A. A., Colvin, M. K., VanHorn, J. D., Inati, S., & Gazzaniga, M. S. (2005). Functional

connectivity: Integrating behavioral, diffusion tensor imaging, and functional

magnetic resonance imaging data sets. Journal of Cognitive Neuroscience, 17, 687-693.

Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues

elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance Series B,

111, 209-219.

Basser, P. J., & Pierpaoli, C. (1998). A simplified method to measure the diffusion tensor

from seven MR images. Magnetic Resonance in Medicine, 39, 928-934.

Benson, R. R., Meda, S. A., Vasudevan, S., Kou, Z. F., Govindarajan, K. A., Hanks, R. A., et

al. (2007). Global white matter analysis of diffusion tensor images is predictive of

injury severity in traumatic brain injury. Journal of Neurotrauma, 24, 446-459.

Buckner, R. L. (2004). Memory and executive function in aging and AD: Multiple factors

that cause decline and reserve factors that compensate. Neuron, 44, 195-208.

CDC. (2001). Traumatic brain injury in the Unied States: A report to Congress. Atlanta, GA: Centers

for Disease Control.

Cabeza, R., & Nyberg, L. (2000). Imaging cognition II: An empirical review of 275 PET

and fMRI studies. Journal of Cognitive Neuroscience, 12, 1-47.

Chen, A. J. W., Abrams, G. M., & D'Esposito, M. (2006). Functional reintegration of

prefrontal neural networks for enhancing recovery after brain injury. Journal of Head

Trauma Rehabilitation, 21, 107-118.

Chen, J. K., Johnston, K. M., Frey, S., Petrides, M., Worsley, K., & Ptito, A. (2004).

Functional abnormalities in symptomatic concussed athletes: An fMRI study.

Neuroimage, 22, 68-82.

114

Christodoulou, C., DeLuca, J., Ricker, J. H., Madigan, N. K., Bly, B. M., Lange, G., et al.

(2001). Functional magnetic resonance imaging of working memory impairment after

traumatic brain injury. Journal of Neurology Neurosurgery and Psychiatry, 71, 161-168.

Courville, C. B. (1937). Pathology of the central nervous system, part 4. Mountain View, CA: Pacific

Press Publishing.

Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic

resonance neuroimages. Computers and Biomedical Research, 29, 162-173.

Cox, R. W., & Hyde, J. S. (1997). Software tools for analysis and visualization of fMRI data.

NMR in Biomedicine, 10, 171-178.

Craik, F. I. M. (1986). A functional account of age differences in memory. In F. Klix & H.

Hagendorf (Eds.), Human memory and cognitive capabilities, mechanisms and performances

(pp. 409-422). New York: Elsevier Science.

Craik, F. I. M. (1990). Changes in memory with normal aging. In R. J. Wurtman, S. Corkin, J.

H. Gromden & E. Ritter-Walker (Eds.), Advances in neurology: Alzheimer's disease (Vol.

51, pp. 201-205). New York: Raven Press Ltd.

Curtis, C. E., & D'Esposito, M. (2003). Persistent activity in the prefrontal cortex during

working memory. Trends in Cognitive Sciences, 7, 415-423.

D'Esposito, M., Cooney, J. W., Gazzaley, A., Gibbs, S. E. B., & Postle, B. R. (2006). Is the

prefrontal cortex necessary for delay task performance? Evidence from lesion and

fMRI data. Journal of the International Neuropsychological Society, 12, 248-260.

D'Esposito, M., Postle, B. R., Ballard, D., & Lease, J. (1999). Maintenance versus

manipulation of information held in working memory: An event-related fMRI study.

Brain and Cognition, 41, 66-86.

115

Duong, M. V. A., Audoin, B., Boulanouar, K., Ibarrola, D., Malikova, I., Confort-Gouny, S.,

et al. (2005). Altered functional connectivity related to white matter changes inside

the working memory network at the very early stage of MS. Journal of Cerebral Blood

Flow and Metabolism, 25, 1245-1253.

Erickson, K. I., Colcombe, S. J., Wadhwa, R., Bherer, L., Peterson, M. S., Scalf, P. E., et al.

(2007). Training-induced plasticity in older adults: Effects of training on hemispheric

asymmetry. Neurobiology of Aging, 28, 272-283.

Fegen, D.A., Buchsbaum, B., Chen, A.J.-W., Rissman, J., Cooney, J., & D'Esposito, M.

Acetylcholine increases the distinctiveness of distributed cortical representations.

Society for Neuroscience Annual Meeting, San Diego, CA (November, 2007).

Friston, K. J., Holmes, K. J., Worsley, J.-P., Poline, C. D., Frith, C. D., & Frackowiak, R. S. J.

(1995). Statistical parametric maps in functional neuroimaging: A general linear

approach. Human Brain Mapping, 2, 189-210.

Fujiwara, E., Schwartz, M. L., Gao, F., Black, S. E., & Levine, B. (In press). Ventral frontal

cortex functions and quantified MRI in traumatic brain injury. Neuropsychologia.

Gennarelli, T. A., Thibault, L. E., Adams, J. H., Graham, D. I., Thompson, C. J., &

Marcincin, R. P. (1982). Diffuse axonal injury and traumatic coma in the primate.

Annals of Neurology, 1982, 564-572.

Goldman-Rakic, P. S. (1987). Circuitry of primate prefrontal cortex and regulation of

behavior by representational memory. In F. Plum & V. Mountcastle (Eds.), Handbook

of physiology: The nervous system (Vol. 5, pp. 373-417). Bethesda, MD: American

Physiological Society.

116

Grady, C. L., McIntosh, A. R., Bieg, S., Keightley, M. L., Burian, H., & Black, S. E. (2003).

Evidence from Functional Neuroimaging of a Compensatory Prefrontal Network in

Alzheimer's Disease. Journal of Neuroscience, 23, 986-993.

Greenwood, P. M. (2007). Functional plasticity in cognitive aging: Review and hypothesis.

Neuropsychology, 21, 657-673.

Habeck, C., Rakitin, B. C., Moeller, J., Scarmeas, N., Zarahn, E., Brown, T., et al. (2004). An

event-related fMRI study of the neurobehavioral impact of sleep deprivation on

performance of a delayed-match-to-sample task. Cognitive Brain Research, 18, 306-321.

Hillary, F. G., Genova, H. M., Chiaravalloti, N. D., Rypma, B., & DeLuca, J. (2006).

Prefrontal modulation of working memory performance in brain injury and disease.

Human Brain Mapping, 27, 837-847.

Hoffmann, S., Tittgemeyer, M., & von Cramon, D. Y. (2007). Cognitive impairment in

multiple sclerosis. Current Opinion in Neurology, 20, 275-280.

Huisman, T. A., Schwamm, L. H., Schaefer, P. W., Koroshetz, W. J., Shetty-Alva, N.,

Ozsunar, Y., et al. (2004). Diffusion tensor imaging as potential biomarker of white

matter injury in diffuse axonal injury. American Journal of Neuroradiology, 25, 370-376.

Inzlicht, M., & Gutsell, J. N. (2007). Running on empty - Neural signals for self-control

failure. Psychological Science, 18, 933-937.

Kane, M. J., Hambrick, D. Z., Tuholski, S. W., Wilhelm, O., Payne, T. W., & Engle, R. W.

(2004). The generality of working memory capacity: A latent-variable approach to

verbal and visuospatial memory span and reasoning. Journal of Experimental Psychology-

General, 133, 189-217.

117

Kraus, M. F., Susmaras, T., Caughlin, B. P., Walker, C. J., Sweeney, J. A., & Little, D. M.

(2007). White matter integrity and cognition in chronic traumatic brain injury: A

diffusion tensor imaging study. Brain, 130, 2508-2519.

Levin, H. S., Meyers, C. A., Grossman, R. G., & Sarwar, M. (1981). Ventricular enlargement

after closed head injury. Archives of Neurology, 38, 623-629.

Levine, B., Cabeza, R., McIntosh, A. R., Black, S. E., Grady, C. L., & Stuss, D. T. (2002).

Functional reorganisation of memory after traumatic brain injury: A study with

H(2)(15)0 positron emission tomography. Journal of Neurology, Neurosurgery &

Psychiatry, 73, 173-181.

Levine, B., & Downey-Lamb, M. (2002). Design and evaluation of intervention experiments.

In P. J. Eslinger (Ed.), Neuropsychological interventions: Emerging treatment and management

models for neuropsychological impairments (pp. 80-104). New York: Guilford Publications.

Levine, B., Fujiwara, E., O'Connor, C., Richard, N., Kovasevic, N., Mandic, M., et al. (2006).

In vivo characterization of traumatic brain injury neuropathology with structural and

functional neuroimaging. Journal of Neurotrauma, 23, 1396-1411.

Levine, B., Katz, D., Black, S. E., & Dade, L. (2002). New approaches to brain-behavior

assessment in traumatic brain injury. In D. T. Stuss & R. Knight (Eds.), Principles of

frontal lobe function (pp. 448-465). New York: Oxford University Press.

Levine, B., Kovacevic, N., Nica, I., Cheung, G., Schwartz, M. L., & Black, S. E. (in press).

Toronto traumatic brain injury study: Injury severity and quantified MRI. Neurology.

Madden, D. J., Spaniol, J., Whiting, W. L., Bucur, B., Provenzale, J. M., Cabeza, R., et al.

(2007). Adult age differences in the functional neuroanatomy of visual attention: A

combined fMRI and DTI study. Neurobiology of Aging, 28, 459-476.

118

Mahncke, H. W., Bronstone, A., & Merzenich, M. M. (2006). Brain plasticity and functional

losses in the aged: scientific bases for a novel intervention. In Reprogramming the Brain

(Vol. 157, pp. 81-109).

Mahncke, H. W., Connor, B. B., Appelman, J., Ahsanuddin, O. N., Hardy, J. L., Wood, R.

A., et al. (2006). Memory enhancement in healthy older adults using a brain plasticity-

based training program: A randomized, controlled study. Proceedings of the National

Academy of Sciences of the United States of America, 103, 12523-12528.

Maruishi, M., Miyatani, M., Nakao, T., & Muranaka, H. (2007). Compensatory cortical

activation during performance of an attention task by patients with diffuse axonal

injury: A functional magnetic rsonance imaging study. Journal of Neurology, Neurosurgery

and Psychiatry, 78, 168-173.

McAllister, T. W., Flashman, L. A., Sparling, M. B., & Saykin, A. J. (2004). Working memory

deficits after traumatic brain injury: Catecholaminergic mechanisms and prospects

for treatment - a review. Brain Injury, 18, 331-350.

McAllister, T. W., Saykin, A. J., Flashman, L. A., Sparling, M. B., Johnson, S. C., Guerin, S.

J., et al. (1999). Brain activation during working memory 1 month after mild

traumatic brain injury: A functional MRI study. Neurology, 53, 1300-1308.

McAllister, T. W., Sparling, M. B., Flashman, L. A., Guerin, S. J., Mamourian, A. C., &

Saykin, A. J. (2001). Differential working memory load effects after mild traumatic

brain injury. Neuroimage, 14, 1004-1012.

McIntosh, A. R. (1999). Mapping cognition to the brain through neural interactions. Memory,

7, 523-548.

McIntosh, A. R., Bookstein, F. L., Haxby, J. V., & Grady, C. L. (1996). Spatial pattern

analysis of functional brain images using partial least squares. Neuroimage, 3, 143-157.

119

McIntosh, A. R., Chau, W., & Protzner, A. B. (2004). Spatiotemporal analysis of event-

related fMRI data using partial least squares. Neuroimage, 23, 764-775.

Meyers, C. A., Levin, H. S., Eisenberg, H. M., & Guinto, F. C. (1983). Early versus late

lateral ventricular enlargement following closed head-injury. Journal of Neurology

Neurosurgery and Psychiatry, 46, 1092-1097.

Meythaler, J. M., Peduzzi, J. D., Eleftheriou, E., & Novack, T. A. (2001). Current concepts:

Diffuse axonal injury-associated traumatic brain injury. Archives of Physical Medicine and

Rehabilitation, 82, 1461-1471.

Nakayama, N., Okumura, J. S., Shinoda, J., Yasokawa, Y., Kazuhiro, M., Yoshimura, S., et al.

(2006). Evidence for white matter disruption in traumatic brain injury without

macroscopic lesions. Journal of Neurology Neurosurgery and Psychiatry, 77, 850-855.

Newsome, M. R., Scheibel, R. S., Steinberg, J. L., Troyanskaya, M., Sharma, R. G., Rauch, R.

A., et al. (2007). Working memory brain activation following severe traumatic brain

injury. Cortex, 43, 95-111.

Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic-resonance-imaging

with contrast dependent on blood oxygenation. Proceedings of the National Academy of

Sciences of the United States of America, 87, 9868-9872.

Ogawa, T., Sekino, H., Uzura, M., Sakamoto, T., Taguchi, Y., Yamaguchi, Y., et al. (1992).

Comparative study of magnetic resonance and CT scan imaging in cases of severe

head injury. Acta Neurochirurgica - Supplementum, 55, 8-10.

Olesen, P. J., Nagy, Z., Westerberg, H., & Klingberg, T. (2003). Combined analysis of DTI

and fMRI data reveals a joint maturation of white and grey matter in a fronto-parietal

network. Cognitive Brain Research, 18, 48-57.

120

Perlstein, W. M., Cole, M. A., Demery, J. A., Seignourel, P. J., Dixit, N. K., Larson, M. J., et

al. (2004). Parametric manipulation of working memory load in traumatic brain

injury: Behavioral and neural correlates. Journal of the International Neuropsychological

Society, 10, 724-741.

Petrides, M., & Milner, B. (1982). Deficits on subject-ordered tasks after frontal- and

temporal-lobe lesions in man. Neuropsychologia, 20, 249-262.

Petrides, M., & Pandya, D. N. (1999). Dorsolateral prefrontal cortex: Comparative

cytoarchitectonic analysis in the human and the macaque brain and corticocortical

connection patterns. European Journal of Neuroscience, 11, 1011-1036.

Postle, B. R. (2006). Working memory as an emergent property of the mind and brain.

Neuroscience, 139, 23-38.

Postle, B. R., Berger, J. S., & D'Esposito, M. (1999). Functional neuroanatomical double

dissociation of mnemonic and executive control processes contributing to working

memory performance. Proceedings of the National Academy of Sciences of the United States of

America, 96, 12959-12964.

Povlishock, J. T., & Christman, C. W. (1995). The pathobiology of traumatically induced

axonal injury in animals and humans: A review of current thoughts. Journal of

Neurotrauma, 12, 555-564.

Povlishock, J. T., & Jenkins, L. W. (1995). Are the pathobiological changes evoked by

traumatic brain injury immediate and irreversible? Brain Pathology, 5, 415-426.

Povlishock, J. T., & Katz, D. I. (2005). Update of neuropathology and neurological recovery

after traumatic brain injury. Journal of Head Trauma Rehabilitation, 20, 76-94.

Price, C. J., & Crinion, J. (2005). The latest on functional imaging studies of aphasic stroke.

Current Opinion in Neurology, 18, 429-434.

121

Price, C. J., & Friston, K. J. (1999). Scanning patients with tasks they can perform. Human

Brain Mapping, 8, 102-108.

Price, C. J., & Friston, K. J. (2002). Functional imaging studies of neuropsychological

patients: Applications and limitations. Neurocase, 8, 345-354.

Ptak, T., Sheridan, R. L., Rhea, J. T., Gervasini, A. A., Yun, J. H., Curran, M. A., et al. (2003).

Cerebral fractional anisotropy score in trauma patients: A new indicator of white

matter injury after trauma. American Journal of Roentgenology, 181, 1401-1407.

Raz, N., Lindenberger, U., Rodrigue, K. M., Kennedy, K. M., Head, D., Williamson, A., et al.

(2005). Regional brain changes in aging healthy adults: General trends, individual

differences and modifiers. Cerebral Cortex, 15, 1676-1689.

Reddy, H., Narayanan, S., Arnoutelis, R., Jenkinson, M., Antel, J., Matthews, P. M., et al.

(2000). Evidence for adaptive functional changes in the cerebral cortex with axonal

injury from multiple sclerosis. Brain, 123, 2314-2320.

Ross, B. L., Temkin, N. R., Newell, D., & Dikmen, S. S. (1994). Neuropsychological

outcome in relation to head injury severity. Contributions of coma length and focal

abnormalities. American Journal of Physical Medicine & Rehabilitation, 73, 341-347.

Rypma, B., & D'Esposito, M. (2000). Isolating the neural mechanisms of age-related changes

in human working memory. Nature Neuroscience, 3, 509-515.

Rypma, B., & D'Esposito, M. (2002). Influence of working memory demand and subject

performance on prefrontal cortical activity. Journal of Cognitive Neuroscience, 14, 721-

731.

Rypma, B., Prabhakaran, V., Desmond, J. E., Glover, G. H., & Gabrieli, J. D. (1999). Load-

dependent roles of frontal brain regions in the maintenance of working memory.

Neuroimage, 9, 216-226.

122

Salmond, C. H., Menon, D. K., Chatfield, D. A., Williams, G. B., Pena, A., Sahakian, B. J., et

al. (2006). Diffusion tensor imaging in chronic head injury survivors: Correlations

with learning and memory indices. Neuroimage, 29, 117-124.

Scheid, R., Walther, K. R., Guthke, T., Preul, C., & von Cramon, D. Y. (2006). Cognitive

sequelae of diffuse axonal injury. Archives of Neurology, 63, 418-424.

Smith, D. H., & Meaney, D. F. (2000). Axonal damage in traumatic brain injury. The

Neuroscientist, 6, 483-495.

Strich, S. (1956). Diffuse degeneration of the cerebral white matter in severe dementia

following head injury. Journal of Neurology Neurosurgery and Psychiatry, 19, 163-185.

Stuss, D. T., & Levine, B. (2002). Adult clinical neuropsychology: lessons from studies of the

frontal lobes. Annual Review of Psychology, 53, 401-433.

Talairach, J., & Tournoux, P. (1988). Co-Planar Stereotaxic Atlas of the Human Brain (M.

Rayport, Trans.). New York: Thieme Medical Publishers, Inc.

Teasdale, G., & Jennett, B. (1974). Assessment of coma and impaired consciousness. A

practical scale. Lancet, 2, 81-84.

Tenovuo, O. (2006). Pharmacological enhancement of cognitive and behavioral deficits after

traumatic brain injury. Current Opinion in Neurology, 19, 528-533.

Thiel, A., Habedank, B., Winhuisen, L., Herholz, K., Kessler, J., Haupt, W. F., et al. (2005).

Essential language function of the right hemisphere in brain tumor patients. Annals of

Neurology, 57, 128-131.

Tisserand, D. J., Pruessner, J. C., Sanz Arigita, E. J., van Boxtel, M. P., Evans, A. C., Jolles, J.,

et al. (2002). Regional frontal cortical volumes decrease differentially in aging: an

MRI study to compare volumetric approaches and voxel-based morphometry.

Neuroimage, 17, 657-669.

123

Tomaiuolo, F., Carlesimo, G. A., Di Paola, M., Petrides, M., Fera, F., Bonanni, R., et al.

(2004). Gross morphology and morphometric sequelae in the hippocampus, fornix,

and corpus callosum of patients with severe non-missile traumatic brain injury

without macroscopically detectable lesions: A T1 weighted MRI study. Journal of

Neurology Neurosurgery and Psychiatry, 75, 1314-1322.

Tomaiuolo, F., Worsley, K. J., Lerch, J., Di Paola, M., Carlesimo, G. A., Bonanni, R., et al.

(2005). Changes in white matter in long-term survivors of severe non-missile

traumatic brain injury: A computational analysis of magnetic resonance images.

Journal of Neurotrauma, 22, 76-82.

Turner, G. R., & Levine, B. (2004). Disorders of executive function and self-awareness. In J.

Ponsford (Ed.), Rehabilitation of neurobehavioral disorders (pp. 224-268). New York:

Guilford Publications.

Turner, G. R., & Levine, B. (submitted). Augmented neural activity during executive control

processing following diffuse axonal injury.

Vallat-Azouvi, C., Weber, T., Legrand, L., & Azouvi, P. (2007). Working memory after

severe traumatic brain injury. Journal of the International Neuropsychological Society, 13,

770-780.

Vohs, K. D., & Heatherton, T. F. (2000). Self-regulatory failure: A resource-depletion

approach. Psychological Science, 11, 249-254.

Vilkki, J., Holst, P., Ohman, J., Servo, A., & Heiskanen, O. (1992). Cognitive test

performances related to early and late computed tomography findings after closed-

head injury. Journal of Clinical and Experimental Neuropsychology, 14, 518-532.

Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: A meta-

analysis. Cognitive, Affective & Behavioral Neuroscience, 3, 255-274.

124

Werring, D. J., Clark, C. A., Barker, G. J., Miller, D. H., Parker, G. J., Brammer, M. J., et al.

(1998). The structural and functional mechanisms of motor recovery:

Complementary use of diffusion tensor and functional magnetic resonance imaging

in a traumatic injury of the internal capsule. Journal of Neurology Neurosurgery and

Psychiatry, 65, 863-869.

Wilde, E., Chu, Z., Bigler, E. D., Hunter, J. V., Fearing, M. A., Hanten, G., et al. (2006).

Diffusion tensor imaging in the corpus callosum in children after moderate to severe

brain injury. Journal of Neurotrauma, 23, 1412-1426.

Wilson, J. (1990). The relationship between neuropsychological function and brain damage

detected by neuroimaging after closed head injury. Brain Injury, 4, 349-363.

Winhuisen, L., Thiel, A., Schumacher, B., Kessler, J., Rudolf, J., Haupt, W. F., et al. (2007).

The right inferior frontal gyrus and poststroke aphasia - A follow-up investigation.

Stroke, 38, 1286-1292.

Witelson, S. F. (1989). Hand and sex-differences in the isthmus and genu of the human

corpus-callosum - A postmortem morphological-study. Brain, 112, 799-835.

Xu, J., Rasmussen, I. A., Lagopoulos, J., & Haberg, A. (2007). Diffuse axonal injury in severe

traumatic brain injury visualized using high-resolution diffusion tensor imaging.

Journal of Neurotrauma, 24, 753-765.

Zachary, R. A. (1986). Shipley Institute of Living Scale. Revised Manual. Los Angeles: Western

Psychological Services.