DIFFUSION TENSOR IMAGING INVESTIGATIONS OF

MILD BRAIN DAMAGE

by

Yuko Koshimori

A thesis submitted in conformity with the requirements

for the degree of Master‟s of Science

Graduate Department of Rehabilitation Science

University of Toronto

© Copyright by Yuko Koshimori 2011

ii

Diffusion Tensor Imaging Investigations of Mild Brain Damage

Yuko Koshimori

Master‟s of Science

Graduate Department of Rehabilitation Science

University of Toronto

2011

Abstract

Diffusion tensor imaging (DTI) is a powerful tool to investigate subtle pathological changes in

the brain as it can provide microstructural and physiological information about white matter

tracts. Using this technology, we examined white matter changes secondary to traumatic brain

injury (TBI) and spinal cord injury (SCI) in two separate studies. Objective: (1) To examine the

utility of DTI for single case diagnosis of mild TBI. (2) To examine the sub-acute effects of SCI

on white matter tissue in the brains of individuals without direct injury to the brain. Methods:

DTI-derived fractional anisotropy (FA) in regions of interest (ROIs) were used as measures of

brain white matter integrity. Results: (1) The anterior limb of the internal capsule, and the genu

of the corpus callosum were sensitive and specific to mTBI. (2) SCI was associated with

changes in brain white matter: SCI patients showed significantly greater degree of hemispheric

FA asymmetry than control subjects in the superior corona radiata and posterior corona radiata.

Conclusions: (1) Our findings provide preliminary proof of principal evidence that DTI can be

used to diagnose mTBI in individual cases. (2) White matter changes are observed in the brains

of SCI patients without TBI; degree of hemispheric asymmetry may be a useful biomarker for

detecting subtle white matter changes.

iii

Acknowledgments

This thesis would not have been possible without the guidance and the help of people who

contributed and extended their valuable assistance in the preparation and completion of these

studies. First and foremost, I would like to thank Dr. Robin Green, my primary supervisor for

the invaluable learning opportunity, for the completions of enormous work as well as for her

encouragement and patient. I am also grateful for all the efforts provided by my committee

members. Thanks to Dr. Gary Turner for his tremendous support at each and every step of this

academic process, to Dr. Adrian Crawley for his generous help for the data analysis, and to Dr.

David Mikulis for his invaluable advice and expertise. I would also like to thank Dr. Cheryl

Bradbury for her assistance and cooperation for the projects. I am indebted to my current and

former lab members for their advice and kind support. Thanks to Diana Frasca, Danielle,

DeSouza, Areeba Adnan, Alexandra Arnold-Oatley, Kate Dupuis, Kadeen Jones, and Brenda

Colella. Last, but not least, I greatly appreciated the support, encouragement, and love of my

family in Japan and friends that allowed me to go through this process.

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Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

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

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vi

Chapter 1: General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter2: Diffusion tensor imaging for clinical diagnosis of mild traumatic brain injury . . . 13

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . 13

Introduction . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 26

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . 32

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Chapter 3: Reduced white matter integrity of the brain measured by diffusion tensor

imaging in patients with spinal cord injury . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . 35

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 36

Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . 39

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . 46

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . 47

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

General Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. 51

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List of Tables

Chapter 1

Table 1: Demographic and clinical characteristics of subjects . . . . . . . . . . . . . . . . . . . . . . . . . 22

Table 2: Mean FA values in right and left sides of 12 ROIs for younger and older control groups

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 29

Table 3-1: The number of ROIs with FA values of > 3.0 SDs, 2.5 – 2.99 SDs, 2.0 - 2.49 SDs,

1.5 - 1.99 and 1.0 – 1.49 SDs below the age-stratified control mean for confirmed TBI patients .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Table 3-2: The number of ROIs with FA values of > 3.0 SDs, 2.5 – 2.99 SDs, 2.0 - 2.49 SDs,

1.5 - 1.99 and 1.0 – 1.49 SDs below the age-stratified control mean for TBI patients with normal

MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . 31

Table 3-3: The number of ROIs with FA values of > 3.0 SDs, 2.5 – 2.99 SDs, 2.0 - 2.49 SDs,

1.5 - 1.99 and 1.0 – 1.49 SDs below the age-stratified control mean for non-TBI patients . . .. .31

Chapter 2

Table 1: Demographic and clinical characteristics of subjects . . . . . . . . . . . . . . . . . . . .. . . . . . 42

Table 2: Mean asymmetric scores of controls as well as individual patients‟ asymmetric scores

and the standard deviations relative to control mean in the superior corona radiata and posterior

corona radiata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 47

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List of Figures

Chapter 1

Figure 1: A schematic image of myelinated axons . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 7

Figure 2: Diffusion ellipsoid with its principal axes along the eigenvectors . . . . . . . .. . . . . . . . 8

Chapter 2

Figure1: White matter parcellation maps for FA analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Chapter 3

Figure 1: White matter parcellation maps for FA analysis . . . . . . . . . . . . . . . . .. . . . . . . . . . . . 45

1

CHAPTER 1: GENERAL INTRODUCTION

Clinical Diagnosis of Mild Traumatic Brain injury

Definition and Prevalence of Traumatic Brain Injury

Traumatic brain injury (TBI) refers to a sudden external insult to the brain, including

contact damage or the exertion of acceleration and deceleration forces, including rotational ones

(Alexander, 1995). Mild TBI (mTBI), representing 80% of all traumatic brain injuries

(Arciniegas, Held, & Wagner, 2002), is a brain injury for which the duration of lost or altered

consciousness is at the briefest end of the continuum.

The severity of TBI is typically classified by clinical indices that measure either

alterations to consciousness or the depth of unconsciousness. These include the Glasgow Coma

Scale score, duration of loss of consciousness, or duration of posttraumatic amnesia (Hannay,

Howieson, Loring, Fischer, & Lezak, 2004; Nuwer, Hovda, Schrader, & Vespa, 2005;

Povlishock & Katz, 2005; Saatman, et al., 2008; Smith, Meaney, & Shull, 2003). The Glasgow

Coma Scale is the most widely used measure (Moppett, 2007) and consists of an independent

assessment of motor responsiveness, verbal performance and eye opening, ranging from 3 (most

severe) to 15 (normal; Teasdale & Jennett, 1974). It is obtained readily at the scene of the

accident or in acute care. Posttraumatic amnesia refers to the period of time between trauma and

regaining normal continuous memory functioning (King, 1997). During this period, a patient is

unable to lay down new memories effectively, even though they may be fully conscious.

Posttraumatic amnesia is commonly measured with the Galveston Orientation and Amnesia

Test, which measures orientation and continuous memory (Bode, Heinemann, & Semik, 2000).

Loss of consciousness is defined as the absence of spontaneous or non-spontaneous (i.e., in

response to noxious stimuli) eye opening without ocular injury or oculomotor palsy (Gennarelli,

2

et al., 1982). Using these scales, mTBI is defined by the Glasgow Coma Scale scores of 13 to

15, loss of consciousness of 30 minutes or less, and/or posttraumatic amnesia of 24 hours or less

(American Congress of Rehabilitation Medicine, 1993), though definitions vary.

The prevalence of mTBI is high, with an annual worldwide incidence varying from 100

to 600 per 100,000 people (Cassidy, et al., 2004) and mTBI now widely recognized as a major

public health concern. MTBIs are most commonly sustained in contact sports, such as hockey,

football and boxing, moving vehicle accidents, falls, assaults and, exposure to bomb blasts

(Cassidy, et al., 2004; French & Parkinson, 2008; Mendez, Hurley, Lassonde, Zhang & Taber,

2005; Sosin, Sniezek, & Thurman, 1996; Stiell, et al., 2001). The incidence of mTBI shows a

bimodal distribution, with an elevation in younger adults, aged 15 to 24 years for which the

male to female ratio is 2:1 (Sosin, et al., 1996). A second peak occurs in older adults, due in

large part to falls and injuries, with a roughly even distribution between the sexes.

MTBI gives rise to a range of cognitive, somatic and emotional symptoms including

attention and memory problems; headache and dizziness; and symptoms of anxiety and

depression (Carroll, et al., 2004; Kelly & Rosenberg, 1997; King, 1997). Symptoms typically

resolve within a few weeks (Alexander, 1995; Iverson, 2005; Nuwer, et al., 2005). However,

approximately 30% of mTBI patients (Hofman, et al., 2001) have persisting symptoms several

months post-injury (Alexander, 1995; Kushner, 1998; Ingebrigsten, Waterloo, Marup-Jensen,

Attner, & Rommeret, 1998; Sterr, Herron, Hayward, & Montaldi, 2006) and after a year, 10 to

15% of mTBI patients still show significant symptoms (Alexander, 1995; Cierone & Kalmar,

1995; Dikmen, Machamer, Winn, & Temkin, 1995; Kushner, 1998; Nuwer, et al., 2005).

Better Diagnosis of MTBI: the Clinical Need

The exceedingly high rates of injury reported above are still thought to be an

underestimate of the true number of mTBI (Belanger, Vanderploeg, Curtiss, & Warden, 2007).

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This is partly because conventional behavioural and neuroimaging tools used to assess mTBI,

such as neuropsychological assessment, CT and MRI, are not sufficiently sensitive to detect the

subtle neuropathological changes associated with mTBI. Thus, many mild traumatic brain

injuries are missed (Garnett, Blamire, Rajagopalan, Styles, & Cadoux-Hudson, 2000),

particularly when concomitant injuries have been sustained that consume the focus of medical

attention, such as spinal cord injury (SCI; Saboe, Reid, Davis, Warren, & Grace, 1991; Tolone,

Turkka, Salonen, Ahoniemi, & Alaranta, 2007) and crush injuries (Duhaime, Eppley, Margulies,

Heher, & Barlette, 1995).

The problem of missed diagnosis has important clinical implications. First, along with

headache, dizziness, and symptoms of anxiety and depression, mTBI gives rise to cognitive

symptoms such as attention and memory problems (Carroll, et al., 2004; Kelly & Posenberg,

1997; King, 1997). While these cognitive symptoms may be mild, they can still impede

functioning. Thus, a diagnosis of mTBI is needed to ensure withdrawal from activities that pose

a risk of harm to self or others, such as the operation of heavy machinery or participation in

contact sports. Diagnosis is also needed to ensure increased vigilance against a subsequent brain

injury, both in the short term - because of risk of second impact syndrome (Saunders &

Harbaugh, 1984) - and in the longer term, because of the putative cumulative impact of multiple

injuries (Nuwer, et al., 2005) including chronic traumatic encephalopathy (Le & Gean, 2009). A

diagnosis is also needed to ensure that clinical intervention ensues, where appropriate, given

evidence of improved outcomes with early intervention (Cicerone, et al., 2005; Miles,

Grossman, Johnson, Babb, Diller, et al., 2008; Till, Colella, Verwegen, & Green, 2008) and the

known deleterious impact of cognitive impairment on recovery from co-morbidities, such as

spinal cord injury (Bradbury, et al., 2008).

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Neuropathology of MTBI

Postmortem and animal studies have shown that the primary neuropathology of mTBI is

microscopic axonal injury (Oppenheimer, 1968; Povlishock, Becker, Cheng, & Vaughan, 1983).

The microscopic axonal injury can occur in the absence of focal contusions, hemorrhage,

herniation or macroscopic axonal tearing (Povlishock, et al., 1983) or vascular injury

(Blumbergs, Scott, Mnanavis, Wainwright, et al., 1995; Oppenheimer, 1968; Povlishock, et al.,

1983).

Microscopic axonal damage of a traumatic nature (a.k.a. traumatic axonal injury;

Geddes, Whitwell, & Graham, 2000) is the result of shear, tensile and compressive strains

within the tissue that stretch and distort axon fibres (Gulani and Sundgren, 2006; Smith, et al.,

2003) rendering axons temporarily undulated and misaligned (Wolf, Stys, Lusardi, Meaney, &

Smith, 2001). The majority of them regain most of their pre-stretched orientation (Wolf, et al.,

2001), but calcium dysregulation can occur due to influx of extracellular calcium (Maxwell, et

al., 1999), activation of sodium channels (Wolf, et al., 2001) and/or mechanical damage to

channels (Buki & Povlishock, 2006). As a result, enzymes that degrade key structural proteins

become activated, destroying microtubules and neurofilaments, leading to focal axonal swelling

with accumulated proteins and organelles; axonal disconnection can occur under these

conditions, but not invariably (Buli & Povlishock, 2006; Maxwell, et al., 1991; Park, Bell, &

Baker, 2008).

Limits of Current Diagnostic Tools: CT and Conventional MRI Are Insensitive to the

Microscopic Neuropathology of MTBI

These microscopic changes, some of them biochemical, are notoriously difficult to

detect. Indeed, the limits of conventional imaging for mTBI have been widely reported (Hayes

& Dixon, 1994; Huisman, Sorensen, Hergan, Gonzalez, & Schaefer, 2003; Saatman, et al.,

5

2008). CT can reliably detect skull fractures and life-threatening intracranial hemorrhage

(Besenski, 2002; Hurley, McGowan, Arfanakis, & Taber, 2004; Le & Gean, 2009; Povlishock

& Katz, 2005), is relatively inexpensive (Hurley, et al., 2004) and can accommodate life-support

and monitoring equipment (Hurley, et al., 2004; Le & Gean, et al., 2009). However, the majority

of mTBI patients (with estimates ranging from 60 to 95%) show no visible abnormality on

intracranial CT (Borg, et al., 2004).

MRI is more sensitive to the neuropathology of mTBI than CT (Hurley, et al., 2004; Lee,

Wintermark, Gean, Ghajar, Manley, Mukherjee, et al., 2008; Mittl, et al., 1994; Saatman, et al.,

2008). In particular, T2*-weighted gradient recalled echo is highly sensitive to hemorrhagic

traumatic axonal injury while T2-weighted fluid attenuated inversion recovery is useful to detect

nonhemorrhagic traumatic axonal injury (Huisman, et al., 2003; Kinoshita, et al., 2005; Niogi &

Hukherjee, 2010). Nonetheless, the white matter lesions that are visualizable on fluid attenuated

inversion recovery are macroscopic (Niogi & Mukherjee, 2010) and thus mTBI lesions may not

be visualized.

Diffusion Tensor Imaging (DTI): A Promising Approach for MTBI Diagnosis

A relatively new magnetic resonance imaging technique, DTI, is showing promise for

the diagnosis of mTBI. There is a burgeoning literature demonstrating DTI differences between

patients with mTBI and healthy controls (Arafanakis, Haughton, Carew, Rogers, Dempsey, et

al., 2002; Inglese, et al., 2005; Kraus, Susmaras, Caughlin, Walker, Sweeney, et al., 2007;

Mayer, et al., 2010; Rutgers, Fillard, Parado, Tadie, Lasjaunias, et al., 2008; Rutgers, Toulgoat,

Cazejust, Fillard, Lasjaunias, et al., 2008). Some of these studies have also shown correlations

with neuropsychological assessments (Lipton, et al., 2009; Miles, et al., 2008).

Several studies have directly demonstrated the enhanced sensitivity of DTI to brain

injury relative to conventional MRI. For example, Niogi, et al. (2008b) demonstrated that the

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number of white matter structures with microstructural injury detected by DTI was correlated

with impaired reaction time in mild TBI whereas the number of traumatic microbleeds detected

by conventional MRI sequences was unrelated to the cognitive function.

Brief overview of DTI

DTI is an MRI technique that can measure the preferred displacement of water

molecules in brain white matter (Le Bihan, 2006) allowing us to infer microstructual and

physiological information about white matter tracts (Mukherjee, Berman, Chung, Hess, &

Henry, 2008). The properties of DTI have been described in a number of reviews (Assaf &

Pasternak, 2008; Alexander, Lee, Lazar, & Field, 2007; Bammer, 2003; Le Bihan, et al., 2001;

Mukherjee, et al., 2008). In brief, water mobility in brain white matter is rather anisotropic,

reflecting non-equal, directional diffusivities (Basser, 1995; Pierpaoli & Basser, 1996; Watts,

Liston, Niogi, & Ulug, 2003). As most or all of the axons of the brain are tightly aligned in a

parallel fashion in white matter tracts, water diffuses over the long axis of the axons greater

compared to its displacement perpendicular to the axon. (See Figure 1; Chenevert, Pipe,

Williams, & Brungerg, 1999; Mac Donald, Dikranian, Song, Bayly, Holtzman, et al., 2007).

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Figure 1. A schematic image of myelinated axons. Yellow dots represent water molecules. To

move perpendicular to the axons, water molecules must cross myelin sheath.

A diffusion tensor is a mathematical model of the three-dimensional pattern of diffusion

anisotropy (Alexander, et al., 2007; Mukherjee, et al., 2008). From the measured tensor, one can

calculate the principal diffusivities or eigenvalues, annotated 1, 2, and 3 in order of largest to

smallest magnitude of diffusivities (Neil, 2008; Nucifora, Verma, Lee, & Mehhem, 2007) as

well as eigenvectors (i.e., orientation; Pierpaoli & Basser, 1996) of the eigenvalues. The

eigenvector associated with the largest eigenvalue is oriented parallel to the fiber direction while

the two other eigenvectors are oriented perpendicular to it (Basser, Mattiello, & Le Bihan, 1994;

Neil, 2008). As illustrated below, diffusion ellipsoids are often used to visualize the concept of

the anisotropic diffusion (see Figure. 2; Mukherjee, et al., 2008; Neil, 2008).

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Figure 2. Diffusion ellipsoid with its principal axes along the eigenvectors.

Quantifying Diffusion: Fractional Anisotropy (FA)

In DTI, the most common metric of diffusion (or anisotropy) is fractional anisotropy

(FA), representing the fraction of overall diffusion in a brain region that is anisotropic. Other

metrics include directionally-averaged mean diffusivity or apparent diffusion coefficient. FA is

a scalar rotationally invariant metric with values ranging from zero to one (Le Bihan, et al.,

2001). Rotationally invariant measures have values independent of the laboratory frame of

reference, of the direction of the applied diffusion gradients, and of the orientation of the tissue

structures within each voxel (Basser, et al., 1994). In general, FA has shown greater sensitivity

to mTBI than other diffusion metrics (but see Messe, et al., 2010; Zhang, Johnson, Pennell, Ray,

Sebastianelli, et al., 2010). Several studies have now reported abnormal FA values where

apparent diffusion coefficient and mean diffusivity values were in the normal range (Arfanakis,

et al., 2002; Bendlin, et al., 2008; Inglese, et al., 2005). Perhaps more importantly, FA has

shown greater correlations with the clinical sequelae of mTBI than other indices (Niogi,

Mukherjee, Ghajar, Johnson, Kolster, et al., 2008; Miles, et al., 2008; Wilde, et al., 2008). Taken

together, these findings suggest that DTI, and FA as a proxy measure for changes in white

matter integrity, may hold considerable potential for diagnosis of mTBI.

It should be noted that despite its potential, FA has limitations that must be taken into

consideration. Most studies have shown that mTBI subjects have reduced FA values relative to

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uninjured controls. However, a few studies have reported higher FA mean values in patients.

Mechanisms of reduced FA include focal misalignments of the cytoskeletal network, change in

axolemmal permeability, axonal swelling and disconnection of axons (Arafanakis, et al., 2002;

Buki & Povlishock, 2006) followed by Wallerian degeneration (Griffin, et al., 1992; Povlishock

& Katz, 2005). Proposed mechanisms for increased FA include compressed intracellular spaces

due to cytotoxic edema (Mayer, et al., 2010; Wilde, et al., 2008) early injury or enhanced linear

arrangement of unaffected tracts late injury by a loss of crossing affected white matter tract or

compensatory alteration related to injury in other region (Lo, et al., 2009; Pierpaoli, et al., 2001).

Gaps in Research

Despite excellent promise, further research is needed before DTI will be ready for

clinical use. Most of the research to date employs group comparisons (Rutgers, et al., 2008a;

Lipton, et al., 2008; Lo, et al., 2009; Mayer, et al., 2010). However, for DTI to be used for

clinical purposes, we must be able to diagnose at the single case level. DTI has yet to be

validated for single case diagnosis.

Spinal Cord Injury and Effects on the Brain

Definition and Prevalence of Spinal Cord Injury

Spinal cord injury (SCI) results from mechanical injury to the spinal cord that disrupts

reciprocal connections from brain to body, giving rise to motor and sensory impairments

(Baptiste, Tighe, & Fehlings, 2009). These deficits can manifest in varying severities depending

on the anatomical levels (i.e., cervical, thoracic, lumbar or sacral). Loss of motor and/or sensory

function in the cervical segments of the spinal cord results in quadriplegia (tetraplegia). Damage

10

at the level of thoracic, lumbar or sacral segments of the spinal cord result in paraplegia

(Maynard, et al., 1997).

Injury severity can be classified as incomplete and complete injury (Maynard, et al.,

1997). This is typically determined by neurological examination of sensory and motor

functioning (Maynard, et al., 1997). The American Spinal Cord Injury Association impairment

scale is the most widely used scale with five categories ranging from complete injury (A), to

normal sensory and motor function (E). Incomplete injury is characterized by partially preserved

sensory and/or motor functions, while complete injury is defined by an absence of sensory and

motor function in the lowest sacral segment (Waters, Adkins & Yakura, 1991). However even in

complete injuries, nerve fibers that traverse the lesion epicenter may be spared at the time of the

primary mechanical traumatic event (Baptiste, et al., 2009).

The estimated incidence of traumatic SCI worldwide is approximately 13 to 52 cases per

million (Dryden, et al., 2003; O‟Connor & Murray, 2006). The mean age is in the third to fourth

decade and men are at three or four times greater risk of sustaining a traumatic SCI (Chiu, Lin,

Lam, Chu, Chiang, et al., 2010). The primary causes of traumatic SCI are the same as those for

TBI, namely traffic accidents and falls, accounting for the majority of cases. Injury at the

cervical level is most common, ranging from 41.6 % to 75% of injuries, followed by injury at

the thoracic level (16% to 36%) and injury at the lumbar level (9% to 17.6%).

Pathology within the spinal cord

Trauma to the vertebral column can cause injury to the cord from transient or fixed bone

displacement into the spinal canal compressing the spinal cord. Non-boney elements including

disc material and hematomas produced by the trauma can also compress the spinal cord (Sekhon

& Fehlings, 2001). Following the initial injury, the spinal cord undergoes a delayed cascade of

biochemical and cellular processes that involve a larger area of damage than the initial injury

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(Baptiste, et al., 2009). SCI can cause two types of cell death, namely necrosis and apoptosis

(Hainx, Black, & Waxman, 2003; Koda, et al., 2002; Lee, et al., 2004; Shifman, Zhang, &

Selzer, 2008). Necrosis represents immediate cell death (Majno & Joris, 1995) while apoptosis

is a delayed process of cellular dysregulation ultimately leading to cell death. Cell death occurs

not only at lesion sites, but also remotely in neurons (Bradbury, King, Lucinda, Priestley, &

McMahon, 1998; Himes, Goldberger, & Tessler, 1994; Shibayama, Hattori, Himes, Murray &

Tessler, 1998), oligodendrocytes (Crowe, Bresnahan, Shuman, Masters, & Beattie, 1997; Koda,

et al., 2002), and microglia (Koda, et al., 2002).

Apoptotic cell death including oligodentrocytes was observed preferentially in the region

of the distal axonal segment of white matter tracts undergoing Wallerian degeneration in the

corticospinal tract as well as dorsal ascending tract (Warden, et al., 2001). Oligodendrocytes

appear to be particularly susceptible (Casha, Yu, & Fehlings, 2001; Emery, et al., 1998; Koda, et

al., 2002). Loss of oligodendrocytes due to apoptosis could result in demyelination of spared

axons, interfering with signal transduction (Shuman, Bresnahan, & Beattie, 1997), which

contributes to functional impairment (Crowe, et al., 1997; Li, Farooque, Holtz, & Olsson, 1999;

Warden, et al., 2001).

Pathology of the Brain Secondary to Spinal Cord Injury

Pathological changes are not confined to the spinal cord. The structural and functional

changes in the brain following SCI have been reported in both animal and human studies. For

example, animal models have demonstrated altered synaptic structures (Kim, Dai, McAtee,

Vicini, & Bregman, 2006), cell atrophy (Wannier, Schmidlin, Bloch, & Rouiller, 2005),

apoptotic cell death in the primary motor cortex (Hains, Black, & Waxman, 2003) as well as

functional reorganization in the primary somatosensory cortex (Endo, Spenger, Tominaga,

Brene, & Olson, 2007). In the human brain, reduced gray matter volume has been demonstrated

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in the primary motor cortex and frontal areas (Wrigley, et al., 2009) and somatosensory and

posterior parietal cortices (Jurkiewicz, Crawley, Verrier, Fehlkings, & Mikulis, 2006) and

alterations in brain function have been reported in the motor and somatosensory cortices

(Jurkiewicz, et al., 2007; Mikulis, et al., 2002). To date, changes in gray matter structures have

been well studied.

On the contrary, axonal or white matter changes have been relatively unexplored. To our

knowledge, there have been six human studies that investigated white matter following SCI.

Two studies using the voxel-based morphometry methods failed to find significant changes in

white matter volume between healthy control and SCI patient groups. There are four studies that

used DTI to investigate changes in white matter integrity. Two studies found no significant

group differences between SCI patients and controls (Gustin, et al., 2009; Wei, et al., 2008)

while two other studies found abnormal FA values along the corticospinal tracts in the patient

group (Guleria, et al., 2008; Wrigley, et al., 2009). Thus, the effects of SCI on white matter are

equivocal and to be elucidated.

13

CHAPTER 2: DIFFUSION TENSOR IMAGING FOR

CLINICAL DIAGNOSIS OF MILD TBI

Abstract

Prevalence of mild TBI (mTBI) has reached epidemic proportions. However, reliable diagnosis

is limited by insensitivity of conventional neuroimaging to the neuropathology of mTBI:

microscopic white matter changes. Diffusion tensor imaging (DTI) has been shown to be more

sensitive to mTBI than conventional MRI in group studies. For clinical diagnosis it will be

important to determine whether DTI shows sensitivity and specificity for mTBI in individual

cases. To assess sensitivity we first examined a common DTI measure, fractional anisotropy

(FA), in mTBI predilection sites derived from the literature in two SCI patient groups: (1) five

with definitive TBI on neuroradiological report and (2) four whose TBI clinical indices (i.e.,

Glasglow Coma Sclae, posttraumatic amnesia, and loss of consciouness) suggest the presence of

mTBI, but whose conventional MRI findings were normal. To assess specificity, five additional

SCI patients who had no evidence of TBI on neuroradiological or clinical report were examined

for FA abnormalities. For each patient FA values were compared to a closely-matched, age-

stratified control group. We reported FA values of greater than 1.0 standard deviation below the

control mean in each ROI. The anterior limb of the internal capsule, and the genu of the corpus

callosum were sensitive and specific to mTBI. These preliminary data suggest that DTI may

have clinical relevance as a tool for diagnosing mTBI in SCI patients. Given the clinical

significance of mTBI, enhanced diagnosis will aid with prognosis and rehabilitation planning.

14

Introduction

Traumatic brain injury (TBI) refers to a blow to the head or the exertion of acceleration

and deceleration forces to the brain, with or without external skull impact. Approximately 80%

of TBIs are mild (Arciniegas, et al., 2002), which is defined typically as a Glasgow Coma Scale

score of 13 to 15, loss of consciousness of 30 minutes or less and posttraumatic amnesia of 24

hours or less (American Congress of Rehabilitation Medicine, 1993). MTBI has been associated

with a combination of cognitive, somatic, motor and emotional symptoms (Carroll, et al., 2004;

Kelly & Posenber, 1997; King, 1997). Increasingly referred to as an “epidemic” (Ellemberg,

Henry, Macciocchi, Guskiewicz, & Broglio, 2009), mTBI has a worldwide annual incidence

estimated at up to 600 per 100,000 people (Cassidy, et al., 2004).

The clinical manifestations of mTBI are not reliably detected using traditional

behavioural approaches, such as neuropsychological or clinical motor testing (Fait, McFadyen,

Swaine, & Cantin, 2009) or conventional neuroimaging (Hurley, et al., 2004: Lee, et al., 2008;

Mittl, et al., 1994; Saatman, et al., 2008). Consequently, clinicians tend to rely on subjective,

self-report measures of symptoms (King, Crawford, Wenden, Moss, & Wade, 1995; Randolph,

Millis, Barr, McCrea, Guskiewicz, et al., 2009). However, the objective verification of mTBI is

an important clinical gap, and represents a long-standing, and sometimes contentious area of

study (see Green, Koshimori, & Turner, 2010.)

One of the key diagnostic challenges is arguably the microscopic neuropathology of

mTBI. MTBI is associated with microscopic axonal (Oppenheimer, 1968; Povlishock, et, al.,

1983) and vascular injury (Bigler, 2004) that is well documented in post-mortem and animal

studies (Adams, Koyle, Ford, Gennarelli, Graham et al., 1989; Buki & Povlishock, 2006;

Maxwell, et al., 1999; Povlishock, Erb, & Astruc, 1992; Wolf, et al., 2001). Microscopic axonal

injury, in particular, can occur in the absence of macroscopic white matter tears (Povlishock, et

15

al 1983) or vascular damage (Blumbergs, et al, 1995) including petechial hemorrhage

(Oppenheimer, 1968; Povlishock, et al., 1983).

To overcome the issue of poor sensitivity with conventional neuroimaging methods, one

technique that is used increasingly in experimental settings is MRI diffusion tensor imaging

(DTI). DTI shows sensitivity to subtle white matter abnormalities by measuring the diffusion of

hydrogen nuclei contained in water molecules over time (Le Bihan, et al., 2006). Depending

upon the properties of the tissue environment, water diffusion will occur in an isotropic or

anisotropic fashion (i.e., with a preferred direction). Since most or all axons are tightly aligned

in a parallel fashion in white matter tracts, water shows a preferred diffusion pattern, moving

predominantly along the direction of the axons rather than perpendicular to them (Chenevert,

Brunberg, & Pipe, 1999; Mac Donald, Dikranian, Song, Bayly, Holtzman, et al., 2007;

Mukherjee, et al., 2008). This allows us to infer microstructural and physiological information

about the integrity of axonal fiber bundles (Basser, 1995).

Fractional anisotropy (FA), a scalar value between zero and one (A quantity which has

magnitude only, and has the same value in every coordinate system), is one of the most widely

used DTI indices of anisotropy (Alexander, Lee, Lazar, & Field, 2007). An FA value of zero

represents completely isotropic diffusion (i.e., random diffusion) whereas a value of one

represents completely anisotropic diffusion (Le Bihan, et al., 2001). When the integrity of white

matter is compromised, as may be the case in mTBI, FA values are reduced (Arfanakis et al.,

2002). Reduced FA values secondary to mTBI have been observed both acutely and during the

chronic phases of injury in a number of studies (Lipton, et al., 2009; Niogi, et al., 2008b; Rutger,

et al., 2008a; Rutgers, et al., 2008b). For examples of increased FA both early and later post-

injury, see Wilde et al. (2008), Mayer et al. (2010), and Lo, et al. (2009). Here, it is thought that

cytotoxic edema [early] and loss of damaged crossing tracts [later] can result in increased

anisotropic movement (Bhagat, et al., 2006; Field, Hasan, Jellison, Arfanakis, & Alexander,

16

2003; Green, et al., 2002; Lo, et al., 2009; Pierpaoli, et al, 2001). A comprehensive survey of the

diffusion imaging literature revealed that there are a number of brain regions that are

particularly susceptible to mTBI. These studies revealed abnormal FA values in the anterior

corona radiata (Maruta, Suh, Niogi, Mukherjee, & Ghajar, 2010; Niogi, et al., 2008b) and

superior corona radiata (Kraus, et al., 2007; Mayer, et al., 2010; Niogi, et al., 2008b); the genu

(Kumar, et al., 2009; Lipton, et al., 2008; Lo, et al., 2009; Niogi, et al., 2008b; Rutgers, et al.,

2008a) and splenium of the corpus callosum (Arfanakis, et al., 2002; Inglese, et al., 2005);

anterior and posterior limbs of the internal capsule (Arfanakis, et al., 2002; Inglese, et al., 2005;

Lipton, et al., 2008; Lo, et al., 2009); cingulum (Maruta, et al., 2010; Niogi, et al., 2008b,

Rutgers, et al., 2008b), superior longitudinal fasciculus (Kraus, et al., 2007); and uncinate

fascilus (Geary, Kraus, Pliskin, & Little, 2010; Mayor, et al., 2010; Niogi, et al., 2008b).

To date, the large majority of these DTI studies of mTBI have been group studies.

However, to objectively verify the presence of mTBI for clinical purposes, reliable identification

of mTBI in individual cases is needed. One study has shown promising findings in this regard

(Niogi, et al., 2008b). This study examined 34 chronic mTBI patients based on Glasgow Coma

Scale, loss of consciousness and posttraumatic amnesia findings who also reported at least one

postconcussive symptom. FA values of each patient were compared with the mean of 26 healthy

controls in 20 white matter structures using the ROI analysis. FA values with greater than 2.5

standard deviations below the control mean were considered abnormal. The study demonstrated

that (1) 85% of the patients had at least one injured ROI, (ten of whom had normal findings on

MRI sequences that are particularly sensitive to TBI, namely fluid-attenuated inversion recovery

and T2*-weighted gradient-recalled echo sequences; (2) none of the control subjects showed

deviations of 2.5 SDs or greater below the mean, and (3) the number of damaged white matter

structures, but not microbleeds was correlated with reaction times on an attention task.

17

Additionally, if DTI is to be a useful clinical tool, the sensitivity and specificity of the

method must be determined. Sensitivity refers to the ability of a diagnostic test to correctly

detect actual positives (i.e., diagnosing an mTBI when it is present). Specificity refers to the

ability of a test to correctly identify true negatives (i.e., a negative diagnosis of mTBI in an

uninjured person). The current proof-of-principal study aimed to identify those brain areas that

are maximally sensitive and specific to mTBI as a step towards the implementation of DTI for

single case diagnosis. We employed FA values given the ubiquity of their use and their

demonstrated sensitivity to mTBI.

The current study comprised three sets of examinations to investigate sensitivity and

specificity of DTI for mTBI. First, after identifying those brain regions that were most

susceptible to mTBI from prior research (see above), we examined the sensitivity of FA to

mTBI in these regions. The aim of this examination was to verify the sensitivity of mTBI

predilection sites to DTI using a sample of patients with unequivocal TBI (based on

microbleeds) with milder range of TBI indices (i.e., Glasgow Coma Scale, posttraumatic

amnesia, or loss of consciousness), and a very clean medical history; this was to ensure that

positive findings could not be attributed to other factors. This examination entailed the

individual comparison of patients with a definitive diagnosis of TBI to a normative sample of

age and education-matched controls.

The next step was to compare the FA values of four patients with mTBI to a normative

control sample. These patients had positive TBI clinical indices in the mild range, but showed

fully normal MRI scans (i.e., no microbleeds or no hyperintensities) on a protocol specifically

designed for TBI. Again, the group was carefully screened for co-morbidities that might

contaminate findings. The purpose of this examination was to identify those ROIs from the

previous examination that most robustly discriminated patients from controls.

18

Lastly, we assessed the specificity of the ROIs for mTBI. This was achieved by

examining the above ROIs in patients with fully normal MRI findings (neither microbleeds,

hyperintensities nor other spurious findings) and fully normal Glasgow Coma Scale, loss of

consciousness or post-traumatic amnesia. Here, the objective was to identify those ROIs that

would not show any (spurious) FA reductions compared to controls, that is, the ROIs that were

not only sensitive to mTBI, but also specific to it.

The patients examined in this study were drawn from a large sample of patients with

traumatic spinal cord injury who were therefore at elevated risk of mTBI. Therefore, we

excluded ROIs that might be affected by lost afferent projections from the spinal cord, including

the corticospinal or corticopontine tracts (Guleria, et al., 2008; Wrigley, et al., 2009).

Method

Subjects

Patients

Patients for the three examinations were selected from a sample of 82 patients

participating in a larger on-going study on the Spinal Cord Rehabilitation Program at Toronto

Rehabilitation Institute examining the prevalence of TBI in SCI between June 2006 and May

2009. Inclusion criteria for the current study were as follows: (1) 18 to 55 years of age; (2)

between two and six months post-injury; and, (3) able to provide informed consent. Exclusion

criteria were: (1) prior history of SCI, neurologic disorder, psychiatric illness or systemic illness

potentially affecting the brain (e.g., diabetes) and (2) drug dependence. Table 1 shows the

clinical and demographic information for participants in each of the three studies.

(I) Examining ROI sensitivity using patients with definitive TBI: microbleed patients.

This sub-group comprised five patients with microbleeds based on gradient-recalled echo T2*-

weighted images. Overall, patients in this group ranged in severity from an estimated moderate

19

to normal. A careful examination of the medical records and discussion with clinicians familiar

with the patients‟ medical histories indicated that some of the TBI clinical scores and findings

were tenuous due to post-injury medication and intubation, as well as alcohol at the time of

injury. Thus, we have reported here the clinical data along with some contextual details.

Patient One‟s clinical indices for TBI are ambiguous due to alcohol at scene of accident

and post-operative medication and surgeries. Nonetheless, there appears to be some degree of

posttraumatic amnesia and retrograde memory loss, the latter estimated at 5-15 minutes. She

also had positive CT of the brain. Patient Two was sleeping at the time of the accident, a motor

vehicle accident. He had a Glasgow Coma Scale of 15 in Emergency, but this was several hours

post-surgery and may be an underestimate of the severity of injury. There was loss of

consciousness at the scene of the accident. Patient Three had a Glasgow Coma Scale of 11 at the

scene of the motor vehicle accident; this rose quickly to 13 and then 15 at Emergency.

Posttraumatic amnesia was present, though of uncertain duration. In Emergency, CT and

conventional MRI were normal and he was diagnosed with mTBI in acute care. Patient four was

fully conscious at the scene of the accident and Glasgow Coma Scale was therefore not

completed. However, he had positive CT of the brain. In addition, there is no memory of the

accident, and therefore at least a brief period of post traumatic amnesia. Patient five had 5

minutes of loss of consciousness, a Glasgow Coma Scale of 15 at the hospital and some

retrograde memory loss. Posttraumatic amnesia could not be estimated due to post-surgical

medications. She had positive CT of the brain.

(II) Examining ROI sensitivity using patients with normal MRI and positive TBI indices.

This sub-group included four patients with normal conventional MRI (i.e., T1-weighted images,

T2-weighted fluid-attenuated inversion recovery images, and gradient-recalled echo T2*-

weighted images) and positive TBI indices. Again, medical records were carefully examined

and a discussion with a clinician familiar with patients‟ medical histories was carried out. By

20

design, this group was a milder TBI group than the previous group. All of the patients had

sustained a blow to the head in the context of their traumatic SCI. Patient Six was intoxicated at

the scene of the accident. However, some loss of consciousness is probable. At the scene of the

accident, Glasgow Coma Scale was 9, but this is invalidated by alcohol. At Emergency,

Glasgow Coma Scale was 14. Patient Seven had loss of consciousness of uncertain duration, but

a Glasgow Coma Scale of 15 by the time Emergency Services arrived. Admission diagnosis was

query mTBI. Patient Eight had a Glasgow Coma Scale score of 15, with one to two hours of

reported loss of consciousness; however, she had consumed alcohol prior to injury, which

confounds the estimate of loss of consciousness due to mTBI. Patient Nine had a Glasgow

Coma Scale score of 15, no posttraumatic amnesia and self-reported momentary loss of

consciousness.

(III) Examining ROI specificity using MRI and clinically negative patients. This sub-

group comprised five patients with no evidence of concomitant TBI. All had normal Glasgow

Coma Scale, and no loss of consciousness or posttraumatic amnesia. Other direct and indirect

indicators of TBI were also negative. These included: (1) evidence of blow to the head, (2)

positive findings on MRI T1-weighted images; T2-weighted fluid-attenuated inversion recovery

images and gradient-recalled echo T2*-weighted images; and, (3) circumstances of injury

compatible with acquired brain injury (e.g., hypoxia, acceleration/deceleration forces).

Control Subjects

Fifteen healthy control subjects were drawn from the local community of students, staff,

and family and friends of students and staff. Inclusion criteria and exclusion criteria were the

same as for patients, except for the injury criteria.

Control sub-groups. In order to match patients and controls as closely as possible, two

control groups were drawn from the 15 controls, a younger group (N=8) and an older group

21

(N=6). (See Table 1 for further details.) Each subject was compared to one of these two groups.

The gap in age between any patient and his/her respective normative group was no more than

eight years, and the gap in education was no more than six years.

22

Table 1

Demographic and Clinical Characteristics of Subjects

Groups Sex Age Years

of

Education

Injury

Type

Days

between

Injury and

MRI

Presence of TBI

indices (+/-)

Number of

microbleeds

Patient group

Microbleed

group

1 F 21 12 MVA 134 + 14

2 M 23 12 MVA 101 + N/A

3 M 51 12 MVA 147 + 2

4 M 51 15 Fall 62 + 2

5 F 41 14 Fall 68 + 3

Mean ± SD 37.4 ± 14.7 13 ± 1.4

Normal MRI/+

TBI indices

group

6 M 21 12 Fall 66 + 0

7 M 22 14 Sports 101 + 0

8 F 47 14 Fall 153 + 0

9 M 28 16 Sports 90 + 0

Mean ± SD 29.5 ± 12 14 ± 1.6

No TBI group

10 M 20 12 MVA 86 - 0

11 M 31 12 Fall 54 - 0

12 M 39 14 MVA 74 - 0

13 M 29 10 Fall 85 - 0

14 M 19 12 Fall 68 - 0

Mean ± SD 27.6 ± 8.3 12 ± 1.4

Control groups

Younger

controls

Mean ± SD (2F) 26.3 ± 4.5 15.1 ± 2

Older controls

Mean ± SD (1F) 43.3 ± 9 18.2 ± 2.1

F: female, M: male, MVA: motor vehicle accident

23

Materials

MRI protocol

Acquisition. Whole brain conventional MRI and DTI were acquired on a 3Tesla (3T)

General Electric Signa-EXCITE scanner (GE Healthcare, Waukesha, WI, USA) equipped with

an eight-channel head coil. DTI was acquired with diffusion-weighted spin-echo single-shot

echo planar imaging with diffusion encoding in 15 noncollinear directions. The sequence

parameters were as follows: TR = 10,000 ms; TE = 84 ms; field of view = 240 mm2; matrix =

128 x 128 mm; the number of slices = 45, slice thickness = 3 mm without gaps; b value = 1000

s/mm2. Parallel imaging was employed using the Array Spatial Sensitivity Encoding Technique

with an acceleration factor of 2. DTI for all subjects were acquired in the axial plane. One

additional image with no diffusion weighting was also acquired. The DTI scans were repeated

three times to increase signal to noise ratio. The total DTI acquisition time was approximately 8

minutes. The following conventional 3T MR sequences were acquired: (1) axial three-

dimensional (3D) inversion recovery fast spoiled gradient-recalled echo T1-weighted images

(TR = 7.9 ms, TE = 3.1 ms, TI = 450 ms, flip angle = 12º) with 220 mm2 field of view, 166 1.0-

mm contiguous partitions at a 256 x 256 matrix; (2) axial T2-weighted fluid-attenuated

inversion recovery images (TR = 8652, TE = 136.1 to 146, TI = 2250 ms) with 220 mm2 field of

view, 4 mm slice thickness, 28 slices, and 384 x 224 matrix (3) axial gradient-recalled echo

T2*-weighted images (TR = 600, TE =25, flip angle of 45º) with 220 mm2 filed of view, 45 or

46 slices, 3 mm slice thickness, and 384 x 224 matrix.

\

MRI Data Analysis

Evaluation of conventional MRI

The conventional MRI images were qualitatively evaluated by an experienced

neuroradiologist for TBI and other neurological findings. The radiologist was blinded to DTI

24

findings and clinical scores. The evaluation included classification of all visible lesions based on

their signal characteristics. Additionally, microbleeds in the five patients were located by

another experienced neuroradiologist (D.M.) who was blind to their initial MRI reports.

DTI processing

Imaging data were processed offline using FSL tools from the FMRIB software library

(FSL, http://www.fmrib.ox.ac.uk/; Smith, et al., 2004; Woolrich, et al., 2009). FMRIB‟s

diffusion toolbox was used to correct motion and eddy current distortion with b = 0 as reference.

An average set of DTI images was generated from the three sets of DTI data. Non-brain tissue

of the averaged DTI set was removed using brain extraction tool (Smith, 2002), and FA maps

were derived using DTIfit. The images from all subjects were nonlinearly transformed to a

standard space (Andersson, Jenkinson, & Smith, 2007a, 2007b) by normalizing them to a 1x1x1

mm common template, the International Consortium of Brain Mapping-DTI-81 (ICBM-DTI-81;

Mori, Wakana, Nagae-Poetscher, & van Zijl, 2005). ICMB-DTI-81 is white matter atlas in

sterotaxic coordinates based on DTI data from 81 healthy subjects (42 males and 39 females

with the mean age of 38.63 raging from 18 to 59 years; right-handed) and its white matter

parcellation maps include 50 manually segmented white matter structures. Consistent with

previous studies on the current topic (Arfanakis, et al., 2002; Bendlin, et al., 2008; Inglese, et

al., 2005), we chose FA as our primary diffusion measure.

Region of interest (ROI) analysis

The white matter parcellation maps were superimposed on normalized subject data

where 3D ROI analyses were performed for the calculation of FA values. The following white

matter structures were selected based on the literature review: genu of the corpus callosum,

splenium of the corpus callosum; anterior corona radiata; anterior limb of the internal capsule;

uncinate fasciculus; cingulum and superior longitudinal fasciculus. ROIs identified as possible

25

predilection sites in the literature, but which contained the corticospinal tracts were removed.

These were the posterior limb of the internal capsule and superior corona radiata due to possible

effects of SCI on the brain (Guleria, et al., 2008; Ramu, Herrera, Grill, Bockhorst, & Narayana,

2008; Wrigley, et al, 2009). The FA values in the right and left hemispheres of all the structures

were examined separately. Therefore, a total of 12 ROIs were investigated in Study 1 (Fig.1).

Design and Procedures

The design was retrospective, and utilized a series of single case analyses. Outcome

measures were FA values in ROI predilection sites. Of eighty-two patients eligible from the

larger study, the medical histories were carefully screened to identify factors that could

influence FA values (e.g., neurologic, psychotic, and systemic illnesses and drug dependence).

This reduced the sample to the 14 employed in the study.

Data Analysis

A case report analysis was conducted. Each case was compared with either the younger

or older control group depending on the age of the patient. FA values for each ROI for each

patient were classified relative to the normative controls: 1.0 to 1.49 standard deviations (SDs)

below the mean; 1.5 to 1.99 SDs below the mean; 2.0 to 2.49 SDs below the mean; and 2.5 to

2.99 SDs below the mean or 3.0 or more SDs below the mean. In addition, a chi square analysis

was run. Here, the frequencies of patients who had FA values of greater than 1.5 SDs below the

mean, which has an experimental false positive rate of 0.09, were compared between two

groups. The first group was the combined TBI groups, that is, the patient group with TBI

confirmed by structural lesions plus the patient group with no structural lesions, but positive

clinical indices for TBI. The second group was the “no TBI” group. The chi square analysis was

undertaken for all ROIs combined.

26

Figure 1. White matter parcellation maps for FA analysis. A. genu of corpus callosum; B.

splenium of corpus callosum; C. anterior limb of internal capsule; D. anterior corona radiata; E.

uncinate fasciculus; F. superior longitudinal fasciculus; G. cingulum

Results

Conventional MRI Findings

All subjects except those in the positive TBI group had normal findings on conventional

MRI. The second reading of microbleeds was in a good agreement with initial MRI reports in

terms of the number and locations of the microbleeds.

Control groups. FA values for each ROI for the control groups are presented in Table 2.

Individual Case Analyses

(I) Examining ROI sensitivity using patients with definitive TBI (microbleed patients).

Table 3-1 illustrates that in all of the predilection ROIs, FA values of greater than 1.0 SD below

A B C D

E F G

27

the control mean were observed in at least one patient with the exception of the splenium of the

corpus callosum.

Several ROIs showed robust findings. The left anterior limb of the internal capsule was

the most affected ROI, with two patients showing FA values of greater than 2.5 SDs below the

control mean and another with FA values between -1.5 and -1.99 SDs. The two patients with

severely reduced FA values in the left anterior limb of internal capsule also showed reduced FA

values in the right anterior limb of the internal capsule. The genu of the corpus callosum and the

right uncinate fasciculus were also robustly affected, with one patient showing FA values of

between -2.0 and -2.49 SDs, one showing FA values between -1.5 and -1.99 SDs, and another

showing FA values between -1.0 and -1.49 SDs. In the right cingulum, one patient had an FA

value of greater than 3.0 SDs below the control mean and two others had FA values of between

-1.0 and -1.49 SDs.

Table 3-1 also illustrates a number of areas that were less robustly affected. These

included the bilateral anterior corona radiata, bilateral superior longitudinal fasciculus, left

uncinate fasciculus and left cingulum.

This analysis enabled us to confirm the sensitivity of DTI to TBI in all areas identified in

the literature as mTBI predilection sites (some more sensitive than others) with the exception of

the splenium of the corpus callosum. As the splenium displayed no sensitivity to mTBI in the

above examination, it was not included in the next examination.

(II) Examining ROI sensitivity using patients with normal MRI and positive TBI indices.

Using this more mildly injured group of patients, we were able to identify those ROIs that

remained sensitive using DTI analysis. Table 3-2 illustrates that the genu of the corpus

callsoum, bilateral anterior limb of the internal capsule, anterior corona radiata, and uncinate

fasciculus, left superior longitudinal fasciculus and right cingulum all showed reduced FA

values in at least one patient from this group.

28

Again, the left anterior limb of the internal capsule was most robustly affected, with one

patient showing FA values of greater than 3 SDs below the control mean and two additional

patients showing FA values of between -2.0 and -2.49 SDs. These three patients also had FA

values of between -1.0 and -2.49 SDs in the right anterior limb of the internal capsule.

Regions also showing fairly marked sensitivity were the left uncinate fasciculus (in

which two patients had FA values between -2.0 and -2.49 SDs, and one patient had an FA value

of between -1.0 and -1.49 SDs), followed by the genu of the corpus callosum (with one patient

with an FA value of -2.0 and -2.49 SDs and another with a value in the -1.5 and -1.99 SDs

range). In the right anterior coronal radiata, one patient had an FA value of between -2.0 and -

2.49 SDs (this patient also had an equally reduced FA value in the left anterior corona radiata)

and a second patient had a FA value of -1.0 and -1.49 SDs. Regions more mildly affected were

the left superior longitudinal fasciculus, the right cingulum, and the right uncinate fasciculus.

(III) Examining ROI specificity using MRI and clinically negative patients. Table 3-3

illustrates that the genu of the corpus callosum as well as the right anterior limb of the internal

capsule and right anterior coronal radiata showed no FA reductions in any of the non-TBI

patients. Therefore, these regions showed not only sensitivity to mTBI, but also specificity.

Mild FA reductions (-1.0 and -1.49 SDs) were observed in the left anterior limb of the

internal capsule, left anterior corona radiata, and right cingulum in one patient, suggesting that

these areas might be somewhat less specific and that a higher threshold of FA value would be

needed to discriminate patients from controls in these regions. Even more substantive reductions

were observed in the left superior longitudinal fasciculus (-1.5 and -1.99 SDs) and the uncinate

fasciculus, including a reduction at greater than 3 SDs below the control mean in this region

bilaterally.

29

Chi-square Analysis

The combined TBI groups and the “no TBI” group were significantly different for the

frequencies of patients who had FA values of greater than 1.5 SDs below the control mean (p =

0.012).

Table 2

Mean FA values in Right and Left Sides of 12 ROIs for Younger and Older Control Groups

ROI Younger (N = 8) Older (N = 6)

Genu of corpus callosum 0.571 ± 0.022 0.552 ± 0.019

Splenium of corpus callosum 0.624 ± 0.013 0.604 ± 0.015

Left anterior limb of internal capsule 0.516 ± 0.013 0.505 ± 0.019

Right anterior limb of internal

capsule

0.526 ± 0.016 0.515 ± 0.022

Left anterior corona radiata 0.456 ± 0.031 0.423 ± 0.022

Right anterior corona radiata 0.458 ± 0.027 0.427 ± 0.025

Left cingulum 0.369 ± 0.037 0.378 ± 0.025

Right cingulum 0.388 ± 0.021 0.360 ± 0.016

Left uncinate fasciculus 0.400 ± 0.015 0.425 ± 0.023

Right uncinate fasciculus 0.427 ± 0.027 0.428 ± 0.034

Left superior longitudinal fasciculus 0.420 ± 0.017 0.400 ± 0.026

Right superior longitudinal fasciculus 0.435 ± 0.023 0.403 ± 0.026

30

Table 3-1

The Number of ROIs with FA Values of > 3.0 SDs, 2.5 - 2.99 SDs, 2.0 - 2.49 SDs, and 1.5 - 1.99 SDs, and 1.0 - 1.49 SDs Below the

Age-stratified Control Mean for Confirmed TBI Patients

TBI

Patie

nt

Genu

of CC

Spleniu

m of CC

ALIC_

R

ALIC_L ACR_

R

ACR_

L

UNC_

R

UNC_

L

SLF_R SLF_L CG_R CG_L

P1 2.0-

2.49

N/A 2.5-

2.99

> 3.0 N/A 2.0-

2.49

2.0-

2.49

2.5-

2.99

N/A 1.0-

1.49

P2 N/A N/A 1.5-1.99 1.0-

1.49

2.0-

2.49

> 3 1.5-

1.99

P3 1.5-

1.99

1.0-

1.49

1.5-

1.99

1.0-

1.49

P4

P5 1.5-

1.99

2.5-2.99 1.0-

1.49

Note. N/A: ROIs with microbleeds were excluded

31

Table 3-2

The Number of ROIs with FA Values of > 3.0 SDs, 2.5 - 2.99 SDs, 2.0 - 2.49 SDs, and 1.5 - 1.99 SDs, and 1.0 - 1.49 SDs Below the

Age-stratified Control Mean for TBI Patients with Normal MRI

TBI

Patient

Genu of

CC

ALIC_R ALIC_L ACR_R ACR_L UNC_R UNC_L SLF_R SLF_L CG_R CG_L

P6 1.5-1.99 2.0-2.49 > 3 1.0-1.49 2.0-2.49 1.0-1.49 1.0-1.49

P7 1.0-1.49 1.0-1.49

P8 2.0-2.49 1.0-1.49 2.0-2.49 2.0-2.49 2.0-2.49 1.0-1.49 1.0-1.49 1.0-1.49

P9 2.0-2.49 2.0-2.49 1.0-1.49 1.5-1.99

Table 3-3

The Number of ROIs with FA Values of > 3.0 SDs, 2.5 - 2.99 SDs, 2.0 - 2.49 SDs, and 1.5 - 1.99 SDs, and 1.0 - 1.49 SDs Below the

Age-stratified Control Mean for Non-TBI Patients

TBI

Patient

Genu of

CC

ALIC_R ALIC_L ACR_R ACR_L UNC_R UNC_L SLF_L CG_R

P10 1.5-1.99 1.5-1.99 1.0-1.49

P11 > 3 > 3

P12

P13 1.0-1.49 1.0-1.49

P14

32

Discussion

MTBI is a major public health concern given its high incidence and its clinical

consequences. However, the diagnosis of mTBI is challenging, in large part due to the

insensitivity of the current diagnostic neuroimaging tools for traumatic axonal injury, a

predominant neuropathology of mTBI. Several recent studies suggest that DTI holds promise

for the detection of microscopic axonal injury common in mTBI (Oppenheimer, 1968;

Povlishock, et al, 1983), illustrating its potential as clinical diagnostic tool. The current study

aimed to further examine the utility of DTI for the clinical diagnosis of mTBI.

Sensitivity

In the TBI groups, examinations I and II, FA values were reduced in the genu of the

corpus callosum, anterior limb of the internal capsule, anterior coronal radiate, uncinate

fasciculus, superior longitudinal fasciculus and cingulum. Among these regions, the anterior

limb of the internal capsule demonstrated highest sensitivity, with six of nine patients showing

reduced FA values. This finding is consistent with a small study examining five individual

mTBI cases (Arfanakis, et al., 2002) in which all of the five patients showed reduced FA values.

One group study that included 17 mTBI patients also found that the anterior limb of the internal

capsule was one of the most affected ROIs. This study used the whole brain analysis, and

patients in this study were normal on conventional MRI including T2-weighted fluid-attenuated

inversion recovery images, and gradient-recalled echo T2*-weighted images (Lipton, et al.,

2008).

Our findings of very reduced FA in the genu of the corpus callosum are also consistent

with previous group and frequency studies (Kumar, et al., 2009; Lipton, et al., 2008; Lo, et al.,

2009; Niogi, et al., 2008b; Rutgers, et al., 2008a). Consistent with other studies (Geary, et al.,

33

2010; Mayer, et al., 2010; Niogi, et al., 2008b), the uncinate fasciculus was also highly affected,

although as we discuss below, this region had low specificity for mTBI as well.

The anterior corona radiata was less affected in our sample. Previously, the anterior

corona radiata has shown mixed results. Niogi, et al (2008b) has shown that the anterior corona

radiata was the most affected ROI, with 41 % of the patients showing significantly reduced FA

values, including patients with normal conventional MRI. However, subsequent group studies

failed to find significant group differences in this region (Geary, et al., 2010; Little, et al., 2010).

As with the anterior corona radiata, the superior longitudinal fasciculus and cingulum were also

less sensitive to mTBI in our sample. Although an early group study reported that superior

longitudinal fasciculus was significantly affected by mTBI (Kraus, et al., 2007), subsequent

studies have failed to find significant group differences in the superior longitudinal fasciculus

(Little, et al., 2010; Mayer, et al., 2010). Previous studies showed that the cingulum was less

affected by mTBI compared with the ROIs mentioned above (Niogi, et al., 2008b, Rutgers, et

al., 2008b). Thus, our results were also consistent with these findings.

Specificity

Importantly, we demonstrated specificity of DTI findings in areas that demonstrated

good sensitivity. Using a threshold of 1.0 SD below controls, this included the genu of the

corpus callosum, the right anterior limb of the internal capsule and the right anterior coronal

radiata. For clinical purposes, a threshold of 1.0 SD may not effectively discriminate patients

from controls and a larger SD may be needed for greater specificity. Indeed, when we increased

the threshold to 1.5 SDs below controls, this increased the specificity of the left anterior limb of

the internal capsule and left anterior coronal radiata in addition to that of the three ROIs

mentioned above with only minimal effects on sensitivity in the main ROI predilection sites,

that is the genu of the corpus callosum and anterior limb of the internal capsule bilaterally.

34

Another factor of high importance is the variability of FA within ROIs. It is likely that

those predilection sites for mTBI with the smallest normative control standard deviations will

offer the maximal clinical utility for mTBI diagnosis.

Taken together, these results suggest that for clinical purposes, using an SD of 1.5 in the

genu of the corpus callosum and the anterior limb of the internal capsule

would provide optimum sensitivity and specificity. Ideally, we would employ an algorithm that

would allow us to combine ROIs, for example, one that could permit a higher threshold if only

one ROI was affected, but a lower threshold when two or three ROIs were affected.

Conclusion

The current study, employing a small sample of very carefully selected patients,

demonstrated robust sensitivity and specificity of DTI to mTBI for single case diagnosis of

mTBI. These preliminary data provide support for the clinical potential of DTI. Future research

including a larger sample size and controlling for other demographic variables that may impact

DTI values (such as sex and handedness) is needed to confirm the clinical utility of DTI for

mTBI.

35

CHAPTER 3: REDUCED WHITE MATTER INTEGRITY OF THE

BRIAN MEASURED BY DIFFUSION TENSOR IMAGING IN

PATIENTS WITH SPINAL CORD INJURY

Abstract

The primary objective of this study was to examine whether patients with traumatic spinal cord

injury (SCI) without concomitant TBI (TBI) showed changes in white matter integrity in the

brain at the sub-acute stage using diffusion tensor imaging (DTI). Eight traumatic SCI patients

without concomitant TBI and 15 age- and education-matched healthy control subjects were

included. DTI-derived absolute fractional anisotropy (FA) values as well as computed FA

hemispheric asymmetry scores were used as measures of brain white matter integrity. FA data in

the ROIs placed in the motor and sensory pathways were compared between healthy controls

and patients. FA absolute values did not show significant differences between the two groups

for any ROI. However, the patients demonstrated significantly greater degree of FA asymmetry

in the posterior corona radiata, F = 4.123, p = 0.032, and in the superior corona radiata, F =

3.991, p = 0.035, compared with age matched controls. These results were observed after

controlling for pre-morbid and co-existing medical conditions. Traumatic SCI patients without

concomitant TBI display altered white matter integrity in the brain at the sub-acute stage. FA

asymmetry scores are more sensitive than FA absolute values. Further research will be

necessary to confirm whether the FA asymmetry scores can serve as a potential biomarker of

brain abnormalities secondary to SCI.

36

Introduction

A number of studies over the past decade have documented the pathophysiologic

consequences of spinal cord injury (SCI) in the brain. Animal models have demonstrated altered

synaptic structures (Kim, et al., 2006), cell atrophy (Wannier, et al., 2005) apoptotic cell death

in the primary motor cortex (Hains, et al., 2003) as well as functional reorganization in the

primary somatosensory cortex (Endo, et al., 2007). In the human brain, structural and

functioning imaging studies have also revealed brain changes following SCI. Reduced gray

matter volume has been demonstrated in the primary motor cortex and frontal areas (Wrigley, et

al., 2009) and somatosensory and posterior parietal cortices (Jurkiewicz, et al., 2006), and

alterations in brain function have been reported in the motor and somatosensory cortices

(Jurkiewicz, et al., 2007; Mikulis, et al., 2002).

Most of the studies investigating the effects of SCI on the brain have examined gray

matter structures. Relatively few studies have investigated axonal or white matter changes, and

of these, findings have been equivocal. The earliest studies used voxel-based morphometry

methods and examined both gray and white matter; here, the authors observed no changes in

white (or gray) matter (Crawely, et al., 2004; Jurkiewicz, et al., 2006). A further four human

studies examined white matter changes after SCI using DTI (Guleria, et al., 2008; Gustin, et al.,

2010; Wei, et al., 2008; Wrigley, et al., 2009).

DTI methods can detect subtle changes in white matter such as axonal and myelin

damage (Buki & Povlishock, 2006) as well as changes in the extracellular matrix (Pierpaoli, et

al., 2001) and thus may be more sensitive than voxel-based morphometory to white matter

changes following SCI. Gustin et al. (2009) examined mean diffusivity in the entire brain, and

found no significant group differences between SCI patients and controls in white matter. Wei et

al. (2008) examined FA in three groups of patients: those with SCI and confirmed TBI, those

with SCI and no TBI, and a group of uninjured controls. Again, they found no differences

37

between the SCI-only group and controls.

In contrast, the studies of Guleria et al. (2008) and Wrigley et al. (2009) obtained

positive findings in white matter. Both studies observed changes in the corticospinal tract,

including the corona radiata, the posterior limb of the internal capsule, the pons and the

pyramids in addition to the cerebral peduncle (Guleria et al., 2008). Wrigley et al. (2009)

additionally found changes in frontal regions, including the primary motor cortex, and the

medial prefrontal and anterior cingulate cortices; parietal regions such as SI and precuneus

cortices, as well as the superior cerebellar cortex.

An alternative explanation for these positive findings, however, is the presence of TBI

(TBI). In studies examining brain changes in traumatic SCI patients, whereby an external insult

causes damage to the spinal cord (LaPlaca, Simon, Prado, & Cullen, 2007), one methodological

challenge is the presence of concomitant TBI (Iida, et al., 1999; Richards, Brown, Hagglund, et

al., 1988). For example, in the study examining the incidence of TBI in SCI, Tolonen et al.

(2007) found that 23 of 31 SCI patients had evidence of TBI. In some studies, TBI has been

associated in particular with cervical SCI (Go, Devivo, & Richards, 1995; Watanabe, Zafonte, &

Lairson, 1999).

The causes of SCI were not reported in the study by Wrigley et al. (2008). The patients

in the Guleria et al. (2008) study were traumatic SCI patients. The authors reported that their

patients showed no evidence of TBI on MRI. However, conventional MRI is known to display

limited sensitivity to traumatic axonal injury after brain injury (Hayes & Dixon, 1994; Saatman,

et al., 2008), and the study did not include T2* gradient-recalled echo or fluid attenuated

inversion recovery sequences, which are considered to be the most sensitive of the conventional

MRI protocols for traumatic axonal injury (Huisman, et al., 2003; Niogi & Mukherjee, 2010).

As well, patients in the study were at elevated risk of TBI because SCIs were at the cervical

level, and injuries were caused by motor vehicle accidents and falls, which are associated with

38

acceleration and deceleration forces, including rotational ones that are known to induce

traumatic axonal injury (Geddes, et al., 2000).

Considered together, it is difficult to conclusively rule out the presence of TBI in either

of the above studies, and as these studies obtained positive findings in regions that have also

shown positive findings in TBI studies, including the corona radiata (Guleria, et al., 2008;

Mayer, et al., 2010; Wrigley, et al., 2009), the posterior limb of the internal capsule (Guleria, et

al., 2008; Inglese, et al., 2005; Wrigley, et al., 2009), and the cerebral peduncle (Guleria, et al.,

2008; Rutgers, et al., 2008b), it is possible that the positive findings observed in these two

studies are attributable to TBI. Thus, further research into the question of white matter changes

following SCI is warranted.

The current study was designed to examine changes in white matter integrity after

traumatic SCI where the combination of clinical and neuroradiological information would

enable us to rule out concomitant TBI with some degree of confidence: We examined clinical

records detailing the precise mechanism and circumstances of injury to exclude patients in

which brain injury was possible. We also employed a conventional MRI protocol designed to

detect white matter pathology after TBI: T2*-weighted gradient-recalled echo, along with T1-

weighted spin echo and T2-weighted fluid-attenuated inversion recovery sequences.

We included in our study a somewhat novel approach to measuring FA abnormalities.

Previous studies have typically reported group-wise absolute differences in FA in a priori ROIs.

However, Guleria et al. (2008) also noted significant hemispheric differences in FA values in

SCI patients that were not present in controls. In another recent study, abnormal left-right

hippocampal volume differences were observed in a patient with TBI, even though each volume

on its own fell within the normal range (MacDonald, et al., 2008). These findings suggest that

the degree of asymmetry may be a useful index of neuropathology, particularly where brain

changes are subtle.

39

Thus, the study tested whether SCI would affect the white matter tracts of the brain in

sub-acute SCI patients. We hypothesized that (1) patients would show significantly reduced FA

values relative to controls in the selected ROIs, and (2) patients would show significantly

greater degree of asymmetries in the selected homologous ROIs. We compared absolute FA

values in a series of ROIs used in past studies and their degree of hemispheric asymmetry

between patients and matched controls and statistically controlled for potentially confounding

medical co-morbidities.

Methods

Subjects

SCI group

Patients were selected from a larger on-going study on the Spinal Cord Rehabilitation

Program at Toronto Rehabilitation Institute examining prevalence of TBI in SCI between June

2006 and May 2009. Inclusion criteria for the larger study were as follows: (1) 18 to 55 years of

age; (2) between about two and six months post-injury; and (3) able to provide informed

consent. There were 82 patients in the larger study. The following inclusion criteria were

additionally applied for the current study: no previous history of neurologic disease including

TBI and SCI; no extensive history of contact sports with high concussion risk, such as hockey,

football and boxing; and no evidence of concomitant TBI, operationalized as (1) normal

findings on MRI T2*-weighted gradient recalled echo and T2-weighted fluid-attenuated

inversion recovery imaging; (2) no loss of consciousness or posttraumatic amnesia at the time of

injury or thereafter (note that a recent study of mild TBI demonstrated that patients without loss

of consciousness or post-traumatic amnesia showed no differences from controls in FA at one-

month post-injury (Zhang, et al., 2010); and, (3) after a thorough chart review for each patient, a

consensus by two experienced neuropsychologists that circumstances of injury were not

40

compatible with acquired brain injury (e.g., no evidence of hypoxia, no injury with strong

acceleration/deceleration forces, with the exception of one patient who fell from 18 feet and

landed initially on his back, and subsequently hit his head). On the basis of these criteria, eight

patients (seven males) were included in the current study. Their demographic and clinical

characteristics are shown in Table 1.

Control group

A control group consisting of 15 healthy subjects (12 males) was drawn from the local

community (see Table 1). Inclusion criteria and exclusion criteria for the control subjects were

the same as those for the current study. Healthy control subjects with incidental signal

abnormalities on MRI were excluded from the study (N=4).

Materials

MRI Protocol

Acquisition. Whole brain conventional MRI and DTI were acquired on a 3Tesla (3T)

General Electric Signa-EXCITE scanner (GE Healthcare, Waukesha, WI, USA) equipped with

an eight-channel head coil. DTI was acquired with diffusion-weighted spin-echo single-shot

echo planar imaging with diffusion encoding in 15 noncolinear directions. The sequence

parameters were as follows: TR = 10,000 ms; TE = 84 ms; field of view = 240 mm2; matrix =

128 x 128 mm; the number of slices = 45, slice thickness = 3 mm without gaps; b value = 1000

s/mm2. Parallel imaging was employed using the Array Spatial Sensitivity Encoding Technique

with an acceleration factor of 2. DTI for all subjects were acquired in the axial plane. One

additional image with no diffusion weighting was also acquired. The DTI scans were repeated

three times to increase signal to noise ratio. The total DTI acquisition time was approximately 8

minutes. The following conventional 3T MR sequences were acquired: (1) axial three-

41

dimensional (3D) inversion recovery fast spoiled gradient-recalled echo T1-weighted images

(TR = 7.9 ms, TE = 3.1 ms, TI = 450 ms, flip angle = 12º) with 220 mm2 field of view, 166 1.0-

mm contiguous partitions at a 256 x 256 matrix; (2) axial T2-weighted fluid-attenuated

inversion recovery images (TR = 8652, TE = 136.1 to 146, TI = 2250 ms) with 220 mm2 field of

view, 4 mm slice thickness, 28 slices, and 384 x 224 matrix (3) axial gradient-recalled echo

T2*-weighted images (TR = 600, TE =25, flip angle of 45º) with 220 mm2 field of view, 45 or

46 slices, 3 mm slice thickness, and 384 x 224 matrix.

42

Table 1

Demographic and Clinical Characteristics of Subjects

Groups Sex Age Years

of

Education

Injury Type SCI

Level/

Severity

Days between

Injury and MRI

Medical Conditions Blow to

Head

Patients

Full SCI group

1 F 41 16 Fall T12/Inc 59 Bipolar disorder No

2 M 20 12 MVA C7/Inc 86 No

3 M 31 12 Fall T12/Inc 54 No

4 M 39 14 MVA T6/Inc 74 No

5 M 33 13 MVA C4,5/C 147 Migraine,

Depression,

Drug dependence

No

6 M 19 12 Fall T12/Inc 68 No

7 M 50 18 Fall T12/Inc 88 Impaired glucose

tolerance

Yes

8 M 29 10 Sports T12/Inc 85 No

Mean ± SD 30.6 ± 10.4 13.2 ± 2.3 87.6 ± 33.8

Controls

Mean ± SD 14 (3F) 32.5 ± 11.2 16.1 ± 2.6

F: female, M: male, MVA: motor vehicle accident, T: thoracic, C: cervical

43

MRI Data Analysis

Evaluation of conventional MRI

Conventional MRI images were qualitatively evaluated by an experienced

neuroradiologist (D. M.) blinded to DTI findings and clinical ratings. The evaluation included

classification of all visible lesions based on their signal characteristics.

DTI processing

Imaging data were processed offline using FSL tools from the FMRIB software library

(FSL, http://www.fmrib.ox.ac.uk/; Smith, et al., 2004; Woolrich, et al., 2009). FMRIB‟s

diffusion toolbox was used to correct motion and eddy current distortion with b = 0 as reference.

An average set of DTI images was generated from the three sets of DTI data. Non-brain tissue

of the averaged DTI set was removed using brain extraction tool (Smith, 2002), and FA maps

were derived using DTIfit. The images from all subjects were nonlinearly transformed to a

standard space (Andersson, et al., 2007a, 2007b) by normalizing them to a 1x1x1 mm common

template, the International Consortium of Brain Mapping-DTI-81 (ICBM-DTI-81; Mori, et al.,

2005). ICMB-DTI-81 is white matter atlas in sterotaxic coordinates based on DTI data from 81

healthy subjects (42 males and 39 females with the mean age of 38.63 raging from 18 to 59

years; right-handed) and its white matter parcellation maps include 50 manually segmented

white matter structures. Consistent with previous studies on the current topic (Guleria, et al.,

2008; Wrigley, et al., 2009), we chose FA as our primary diffusion measure.

Region of interest (ROI) analysis

The white matter parcellation maps were superimposed on normalized subject data

where 3D ROI analyses were performed for the calculation of FA values. The following white

matter structures were selected based on previous research investigating brain damage

44

secondary to SCI (Glueria, et al., 2008; Wrigley, et al., 2009): corona radiata including superior

corona radiata and posterior corona radiata; internal capsule including anterior limb of internal

capsule and posterior limb of internal capsule; and cerebral peduncle. The selected white matter

parcellation maps are displayed in Fig 1. Because we used the white matter parcellation maps

whose divisions of structures are arbitrary (Mori, et al., 2008), and there is no white matter

parcellation maps particularly for the genu of the internal capsule, we included the anterior limb

of the internal capsule. The FA values in the right and left hemispheres of all the structures were

examined separately, resulting in absolute FA scores in each of the 10 ROIs and 5 asymmetry

scores.

45

Figure 1. White matter parcellation maps for FA analysis. A. cerebral peduncle; B. anterior limb

of internal capsule; C. posterior limb of internal capsule; D. superior corona radiata; E. superior

corona radiata; F. posterior corona radiata; G. posterior corona radiata.

Statistical Analysis

Mean absolute FA values and mean computed FA asymmetry scores for each group for

each ROI were analyzed using multivariate analysis of covariance (MANCOVA), controlling

for the effects medical co-morbidities of the patients (see Table 1). FA hemispheric asymmetry

scores between the right and left homologous ROIs were calculated for each subject as follows:

FA asymmetry = (higher FA – lower FA)/[(higher FA + lower FA)/2]. This allowed us to test

for differences in FA asymmetry between our groups without regard for lateralization

differences (i.e. left greater than right or vice-versa).

46

Results

The two groups were matched on age (30.6 ± 10.4 vs 32.5 ± 11.2 years, p = 0.95).

However, they were significantly different on years of education (13 ± 2.3 vs 16 ± 2.6 years, p =

0.02). Note that post -hoc analyses showed that were no correlations between years of education

and FA values for the controls in any of the ROIs, with the exception of the left anterior limb of

internal capsule, r = -0.637, p = 0.048 in the controls, which indicated that higher education was

associated with lower FA values.

There was no significant difference in mean FA values observed between control and

patient groups for any ROI. Significant between-group differences were observed in the

hemispheric asymmetry scores in the superior corona radiata, F = 3.991, p = 0.035, and

posterior corona radiata, F = 4.123, p = 0.032. We additionally presented individual SCI

asymmetric data and their SDs relative to control mean in the superior corona ratidata and

posterior corona radiata (Table 2). Seven of the eight patients had asymmetry of greater than 1.0

SD relative to the control mean in either structure or both structures.

47

Table 2

Mean Asymmetric Scores (%) of Controls as well as Individual Patients’ Asymmetric Scores

(%) and the Standard Deviations Relative to Control Mean in the Superior Corona Radiata and

Posterior Corona Radiata

Superior corona radiata Posterior corona radiata

Control mean (%)

± SD 2.08 ± 1.65 3.51 ± 2.22

Patient Asymmetric

scores (%) SD relative to

control mean Asymmetric

scores (%) SD relative to

control mean

1 4.17 1 – 1.49 6.49 1 -1.49

2 6.30 2.5 – 2.99 11.18 > 3

3 1.55 3.11 4 4.63 1.5 – 1.99 4.86 5 9.45 > 3 3.59 6 2.27 6.57 1 – 1.49

7 2.31 10.43 > 3

8 3.84 1 – 1.49 7.10 1.5 – 1.99

Discussion

Brain FA is altered following SCI in patients without TBI

To our knowledge, this is the first study to demonstrate structural brain changes using

DTI methods in a group of traumatic SCI patients who were carefully screened for concomitant

TBI on clinical report and conventional neuroimaging. Our findings are consistent with previous

reports (Guleria, et al., 2008; Wrigley, et al., 2009), suggesting that SCI is associated with

changes in white matter integrity in the brain as measured using diffusion imaging methods. We

have extended previous research by using extensive clinical and imaging information to rule out

concomitant TBI and previous or co-morbid head injury as an alternative explanation for the

DTI changes.

48

FA asymmetry is a sensitive marker of brain changes following SCI

For group-wise comparisons of FA in a priori ROIs, we utilized a novel diffusion

imaging marker of brain damage following SCI: FA asymmetry. FA asymmetry was used to

characterize the relative differences between SCI and control groups in homologous ROIs.

Guleria and colleagues (2008) had previously observed FA asymmetry in a group of SCI

patients. In a related approach, hemispheric asymmetry in hippocampal volumes was

considered to be a marker of neuropathological change in TBI (MacDonald, et al., 2008). Here,

FA asymmetry proved more sensitive than absolute FA values for identifying brain changes in

SCI.

Contrary to the reports of Guleria et al. (2008) and Wrigley et al. (2009), we did not

observe differences on standard group-wise comparisons of FA in a priori ROIs. This is likely

attributable to differences in the composition of the SCI groups. While these previous studies

used only complete injury patients, our group was comprised of a majority (seven of eight) of

patients with incomplete injury. Thus variability in the localization and extent of FA reductions

would be greater in our sample, potentially obscuring group-wise differences. In contrast, a

relative measure of lateralized change in FA, as we have derived here, should be more sensitive

in this population. Indeed this was the case. These data suggest that FA asymmetry may be a

more sensitive marker of white matter changes in the brain following incomplete SCI injury.

Further large-scale, prospective studies will be needed to correlate FA asymmetry scores with

behavioral and functional data to confirm whether this marker has potential clinical utility as a

marker for brain changes in incomplete SCI.

We found significant group differences in the superior corona radiata and posterior

corona radiata. The significant finding in the superior corona radiata was consistent with both of

the previous studies (Guleria et al., 2008; Wrigley, et al., 2009). ROIs in the sensory pathway

including the posterior corona radiata were not examined by Guleria et al. (2008). Although

49

Wrigley, et al. (2009) did not report significant group difference in the posterior corona radiata

in particular, they reported abnormal FA values in primary sensory cortex and the precuneus,

suggesting that parietal regions adjacent to posterior corona radiata were affected. Consistent

with these data, gray matter atrophy was also reported by Jurkiewicz, et al. (2006) in primary

sensory and posterior parietal cortices. These findings suggest that posterior parietal cortices as

well as adjacent and underlying white matter may be impacted by SCI.

It was surprising that we did not observe any changes in the lower white matter

structures (i.e., posterior limb of the internal capsule and cerebral peduncle). This might be due

to the analysis method that we used. The FA values in smaller ROIs such as the posterior limb

of the internal capsule might have been confounded by the partial volume effect using the white

matter parcellation maps. Another possible explanation was that MRI data in patients tend to be

noisy in general resulting in greater asymmetries in those regions.

Conclusion

We have demonstrated white matter in the brain is altered following SCI, even in the

absence of clinical or conventional neuroimaging evidence of a TBI. Greater hemispheric

asymmetry using a standard measure of diffusion (FA) within the posterior corona radiata and

the superior corona radiata differentiated our „clean‟ SCI sample from a group of closely

matched control subjects. FA asymmetry appears to be a promising neural marker of structural

brain changes following incomplete SCI, even in the absence of standard clinical and

neuroradiological evidence of brain injury.

Future studies with larger samples of SCI patients, carefully screened for TBI, will be

necessary to characterize the behavioural and functional impact of structural brain changes

following SCI. Also, analysis methods such as the ROI analysis by manually placing

standardized circles, squares, or ovals over specific white matter (Nucifora, et al., 2007) or the

50

voxel-based analysis using the tract-based spatial statistics (Smith, Jenkinso, Johansen-Berg, et

al., 2006) may be used to reduce the partial volume effect.

51

CHAPTER 4: GENERAL DISCUSSION

DTI is a powerful tool to investigate subtle pathological changes in the brain as it can

provide microstructual and physiological information about white matter tracts (Mukherjee, et

al., 2008). Using this technology, we examined white matter changes secondary to TBI and SCI

in two separate studies. The purpose of the first study was to examine the utility of DTI for

single case diagnosis of mild TBI. This study employed people with SCI as a convenience

population, given the high prevalence of TBI in this clinical population. The second study

sought to examine the sub-acute effects of SCI on white matter tissue in the brains of individuals

without direct injury to the brain.

In the first study, we examined two groups of TBI patients at the mild end of the severity

spectrum (a total of nine patients), one group with TBI confirmed on imaging, the other – a

milder group – with normal MRI but positive TBI clinical indices (i.e., Glasgow Coma Scale,

posttraumatic amnesia, or loss of consciousness). The aim was to determine the brain regions

where DTI was maximally sensitive. We then examined a group of five patients from the same

larger sample, without any evidence of TBI (i.e., normal imaging and no positive TBI indices)

in order to demonstrate specificity of the DTI for the detection of mTBI in the maximally

sensitive ROIs. In an effort to ensure that any observed differences between groups were

attributable to mTBI and not the effects of aging, we included two age-stratified control groups

and each patient was examined using his/her closely age-matched group. We found that the

anterior limb of the internal capsule was highly sensitive and specific to mild TBI, followed by

the genu of the corpus callosum and to a lesser extent, the anterior coronal radiata. While the

uncinate fasciculus was sensitive to mTBI in our sample, this ROI was not specific. Our

findings provide preliminary proof of principal evidence that DTI can be used to sensitively and

specifically diagnose mTBI in individual cases.

52

In the second study, we examined brain white matter in eight patients with incomplete

sub-acute SCI who were carefully screened for concomitant TBI. We found that SCI was

associated with changes in brain white matter. More specifically, SCI patients showed

significantly greater hemispheric FA asymmetry than control subjects even though separate

examinations of right and left homologous structures showed no between-group differences. The

changes in white matter were observed in the superior corona radiata and posterior corona

radiata. This study was the first study to show white matter changes using the degree of

hemispheric FA asymmetry as an index in this population. The findings suggest that

hemispheric asymmetry may be a useful biomarker for detecting subtle white matter changes.

Key Limitations

MTBI Study

Niogi and Mukherjee (2010) have recently argued against the clinical utility of ROI-

based approach. Nonetheless, we argue that maximal sensitivity and specificity are most likely

to be attained by identifying patterns of FA change in a single region (i.e., a univariate

approach) or a constrained number of brain regions (i.e., a multi-variate approach) with known

susceptibility to damage from the mechanical forces in TBI (Besenski, 2002). However, before

DTI can be introduced clinically using this approach, its sensitivity and specificity will need to

be verified on a larger population than the one employed in this study.

Furthermore, as individual case analysis will require normative data, issues such as

feasibility, cost effectiveness, reproducibility (Bonekamp, et al., 2003; Mukherjee, Chung,

Berman, Hess, & Henry, 2008; Ozturk, et al., 2008; Pfefferbaum, Adalsteinsson, & Sullivan,

2003) as well as other technical challenges (Assaf & Pasternak, 2008) will need to be addressed.

It is likely that normative data will need to be collected separately for each platform using this

approach.

53

SCI Study

We carefully screened our patients to exclude concomitant TBI. However, there is no

gold standard for determining absence of mTBI. Further study may be required to replicate our

findings in SCI patients whose injury causes were unrelated to trauma or trauma that does not

cause TBI (e.g., blunt force to the spinal cord). In addition, functional data such as the American

Spinal Cord Injury Association scores coinciding with the time of brain scans were not obtained,

as this was not part of the larger study from which our patients were drawn. Prospective studies

will be needed to associate DTI findings and functional data.

Clinical Implications

Clinical diagnosis of mild TBI is needed in order to ensure that treatment or intervention

ensues, where necessary, as mTBI gives rise to a range of cognitive, somatic and emotional

symptoms. These include attention and memory problems; headache and dizziness; and

symptoms of anxiety and depression (Carroll, et al. 2004; Kelly & Rosenberg, 1997; King,

1997), which can be chronic in some patients (Alexander, 1995; Cicerone & Kalmar, 1995;

Dikmen, et al., 1986; Kushner, 1998; Nuwer, et al., 2005). Improved diagnosis can ensure that

patients withdraw from activities where cognitive impairment would elevate the risk of further

injury to self (Nuwer, et al., 2005; Saunders & Harbaugh, 1984) or others. In addition, objective

evidence of mTBI using DTI imaging can provide valuable data to adjudicate legal and

insurance issues for the patients.

In the SCI study, we observed only loss of white matter integrity in the brain after SCI.

Further research is needed to ascertain whether the changes to the white matter of the brain in

the current study have behavioural consequences. If negative behavioural consequences are

observed, then interventions to avert these secondary brain effects will need to be undertaken.

54

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