White matter integrity and cognition in chronic
traumatic braininjury: a diffusion tensor imaging study
Marilyn F.Kraus,1,2,7Teresa Susmaras,2,8Benjamin P.Caughlin,2,8,9Corey J.Walker,2,8John A. Sweeney1,2,3,4,7
and Deborah M. Little2,3,5,6,7 ,8
1Department of Psychiatry,2Department of Neurology,3Department of Psychology,4Department of Bioengineering,
5Department of Anatomy,6Department of Ophthalmology,7Center for Cognitive Medicine,8Center for Stroke
Research,University of Illinois at Chicago Medical Center,Chicago, IL and9Wayne State University School of Medicine,
Correspondence to: Dr Marilyn F. Kraus, MD,Center for Cognitive Medicine and Department of Psychiatry,University of
Illinois College of Medicine,912 South Wood Street, MC 913,USA
T raumatic brain injury (TBI) is a serious public health problem. Even injuries classified as mild, the most
common, can result in persistent neurobehavioural impairment. Diffuse axonal injury is a common finding
after TBI, and is presumed to contribute to outcomes, but may not always be apparent using standard
neuroimaging. Diffusion tensor imaging (DTI) is a more recent method of assessing axonal integrity in vivo.
The primary objective of the current investigation was to characterize white matter integrity utilizing DTI
across the spectrum of chronic TBI of all severities. A secondary objective was to examine the relationship
between white matter integrity and cognition. T wenty mild, 17 moderate to severe TBI and 18 controls
underwent DTI and neuropsychological testing. Fractional anisotropy, axial diffusivity and radial diffusivity
were calculated from the DTI data. Fractional anisotropy was the primary measure of white matter integrity.
Region of interest analysis included anterior and posterior corona radiata, cortico-spinal tracts, cingulum fibre
bundles, external capsule, forceps minor and major, genu, body and splenium of the corpus callosum, inferior
fronto-occipital fasciculus, superior longitudinal fasciculus and sagittal stratum. Cognitive domain scores were
calculated from executive, attention and memory testing. Decreased fractional anisotropy was found in all
13 regions of interest for the moderate to severeTBIgroup, but only in the cortico-spinaltract, sagittal stratum
and superior longitudinal fasciculus for the mild TBI group.White Matter Load (a measure of the total number
of regions with reduced FA) was negatively correlated with all cognitive domains. Analysis of radial and axial
diffusivity values suggested that all severities of TBI can result in a degree of axonal damage, while irreversible
myelin damage was only apparent for moderate to severeTBI.The present data emphasize that white matter
changes existon a spectrum, including mild TBI. Anindexof globalwhite matterneuropathology (White Matter
Load) was related to cognitive function, such that greater white matter pathology predicted greater cognitive
deficits. Mechanistically, mild TBI white matter changes may be primarily due to axonal damage as opposed to
myelin damage. The more severe injuries impact both. DTI provides an objective means for determining
the relationship of cognitive deficits to TBI, even in cases where the injury was sustained years prior to the
Keywords: traumatic brain injury; diffusion tensor imaging; white matter fibre tracts; fractional anisotropy;
diffuse axonal injury; MRI
Abbreviations: DTI¼diffusion tensor imaging; FA¼fractional anisotropy;TBI¼traumatic brain injury; MTBI¼mild
traumatic brain injury; M/STBI¼moderate to severe traumatic brain injury; DAI¼diffuse axonalinjury; ?k¼axial diffusivity;
??¼radial diffusivity; ?¼eigenvalues; ACR¼anterior corona radiata; PCR¼posterior corona radiata; CST¼corticospinal
tracts; Cing, cingulum fibres; fMin¼forceps minor; fMaj¼forceps major; bCC¼body of the corpus callosum; gCC¼genu of
the corpus callosum; sCC¼splenium of the corpus callosum; IFO¼inferior fronto-occipital fasciculus; SLF¼superior long-
itudinal fasciculus; ExCap¼external capsule; SS¼sagittal stratum
Received June 28, 2007 . Revised August14, 2007 . Accepted August16, 2007 . Advance Access publication September14, 2007
doi:10.1093/brain/awm216Brain (2007),130, 2508^2519
? The Author (2007).Publishedby Oxford University Pressonbehalfofthe Guarantorsof Brain. Allrightsreserved.For Permissions, please email: firstname.lastname@example.org
by guest on May 31, 2013
Traumatic brain injury (TBI) of all severities is a significant
public health problem with an incidence between 180 and
500 per 100000 population per year (Bruns and Hauser,
2001, 2003; Bazarian et al., 2005). Recently the numbers of
soldiers returning from military conflicts with TBI has
created a clinical crisis for the United States Veterans
Administration Hospitals (Taber et al., 2006). In addition,
greater public attention is finally being paid to the
problems of athletes with persistent problems secondary
to TBI (Guskiewicz et al., 2000; Pellman et al., 2004). Taken
together, the burden on healthcare systems for both civilian
and military TBI is large.
TBI is clinically rated as mild, moderate or severe
based on acute TBI variables that include duration of loss
of consciousness (LOC), Glasgow Coma Score (GCS)
and post-traumatic amnesia (PTA) (Levin et al., 1979).
Mild TBI (MTBI) is the most common severity, with a
recent WHO task force reporting that 70–90% of all treated
TBI fell into this category (Holm et al., 2005).
Neurobehavioural deficits, especially in cognition, are
often the cause of significant disability after TBI (CDC,
2003). Observed cognitive changes that follow TBI can
include decreased mental flexibility, trouble shifting sets,
impaired attention, poor planning, lack of organization,
problems with sequencing, impaired judgment, deficits in
verbal fluency, problems with working memory, as well
as increased impulsivity (Levin and Kraus, 1994; Miller,
2000; Godefroy, 2003). Determining the extent of clinically
relevant neuropathology (defined as neuropathology asso-
ciated with persistent neurobehavioural deficits) associated
with TBI, particularly in the milder spectrum, is proble-
matic. As such, there is a need for objective and
quantifiable measures of neuropathology that can be
applied to all severities of TBI for the purpose of
determining the relationship between trauma and persistent
disability. This methodology would provide the foundation
for more accurate injury severity grading, prognosis
and treatment planning without having to rely on often
incomplete or inaccurate historical data that has been used
as predictors of outcomes including LOC, PTA and GCS.
Pathophysiology of TBI
There are several significant pathophysiologic sequelae of
TBI that are likely important to neurobehavioural outcome,
including the location and severity of the injury, diffuse
effects and secondary mechanisms of injury. Primary
neurologic injury due to TBI can be direct and/or indirect.
Contusions are common following TBI, and can directly
disrupt function in both cortical and sub-cortical regions.
Certain brain regions may be more vulnerable to contusion
following trauma, such as the frontal and anterior temporal
cortices, due to their position within the skull (Adams
et al., 1980; Levin et al., 1992). Disruption of function can
also result from more diffuse damage to white matter tracts
that are particularly susceptible to the shearing forces that
often occur with TBI (Graham et al., 2002). Such diffuse
axonal injury (DAI) can disrupt critical cortical-subcortical
pathways and lead to widespread cognitive dysfunction
(Gennarelli et al., 1982; Povlishok, 1992). DAI can result
directly from the trauma, or secondary due to ischaemia.
Brain oedema and shift can compromise blood supply
and lead to secondary infarction in the corpus callosum
and deep grey matter, and elevated intracranial pressure
(ICP) can cause damage to the brainstem in TBI (Graham
et al., 1987). And although the diagnosis of DAI can only
be clearly confirmed by microscopic examination, it may be
inferred from specific neuroimaging findings such as
haemorrhages in the corpus callosum or areas of rostral
brainstem (Geddes, 1997; Geddes et al., 1997).
DAI may be the only significant pathology found in
certain cases of TBI, and has been identified via direct
pathological studies as well as neuroimaging in mild
TBI (Povlishock et al., 1983; Graham et al., 1989;
Blumbergs et al., 1994; Goodman, 1994; Mittl et al., 1994;
Aihara et al., 1995; Blumbergs et al., 1995; Gennarelli, 1996;
Inglese et al., 2005b). Changes in white matter, observed as
hyperintense T2 signal, have been observed in mild TBI
(Inglese et al., 2005a, b). These lesions have been reported
primarily in the corpus callosum, internal capsule, and
centrum semiovale (Inglese et al., 2005b). Another issue is
the specificity of lesion type and the clinical relevance of
these lesions found in mild TBI. Kurca and colleagues
reported that mild TBI subjects with defined traumatic
lesions (including both gray and white matter) showed
significantly greater impairment on neuropsychological
evaluations and subjective reports of symptoms consistent
with postconcussion syndrome (Kurca et al., 2006).
As would be expected, as injury severity increases,
the pathophysiology identified on MRI also increases.
For example, chronic moderate to severe TBI has been
related to atrophy in the corpus callosum. The degree
of atrophy in the corpus callosum did appear to be related
not significantly) (Mathias et al., 2004). In chronic
(at least 3 months post injury) severe TBI, increased
atrophy was reported in the corpus callosum, fornix,
anterior limb of the internal capsule, superior frontal
gyrus, para-hippocampal gyrus, optic radiations and optic
chiasma (Tomaiulo et al., 2005). There were only modest
correlations between atrophy of the corpus callosum and
memory function (Tomaiulo et al., 2005).
Although there is some evidence to suggest that standard
T1- or T2-weightedanatomic
promise for quantifying pathophysiology in TBI, it may
not be as sensitive to the neuropathology of milder injuries
(Hughes et al., 2004). The limitation of standard imaging
is highlighted by modest relationships between cognitive
Diffusion tensor imaging is a very promising methodology
in this regard.
MR imaging shows
Diffusion tensor imaging in brain injuryBrain (2007),130, 2508^25192509
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Diffusion tensor imaging
Diffusion tensor imaging (DTI) is a relatively recent tool
developed using MRI technology. DTI allows for the
specific examination of the integrity of white matter
tracts, tracts which are especially vulnerable to the
mechanical trauma of TBI. DTI is a modification of
diffusion-weighted imaging. Standard MRI structural ima-
ging itself is not sensitive enough in identifying impairment
in mild injury (Hughes et al., 2004). Because DTI is more
sensitive to changes in the microstructure of white matter,
it shows considerable promise in the assessment of TBI.
DTI is based upon the diffusivity of water molecules,
which is variably restricted in different tissues. In white
matter, it is more limited in the directions of diffusion.
In healthy tracts, the anisotropy (limited directionality of
diffusion) is higher than in less-organized gray matter. This
difference allows for the calculation of fractional anisotropy
(FA) values for tissue, and the generation of white matter
fibre maps. The values for FA range from 0 to 1 where 0
represents isotropic diffusion,
organization, and 1 represents anisotropic diffusion, or
organized tissues such as in white matter tracts [see
Le Bihan et al. (2001)]. Recently, there has been an increase
in applications of DTI, with previous research demonstrat-
ing its potential utility in qualifying and quantifying
neuropathology in TBI, in which diffuse axonal injury is
common (Huismana et al., 2004). Although the specifics
are still not well understood, FA is believed to reflect many
factors including the degree of myelination and axonal
density and/or integrity (Arfanakis et al., 2002; Song et al.,
2002b, 2003; Harsan et al., 2006). More discrete analysis of
the axial (?k) and radial diffusivity (??) also provide
potential measures of the mechanisms that underlie changes
in white matter following injury (Pierpaoli et al., 2001;
Song et al., 2002a). ?kreflects diffusivity parallel to axonal
fibres. Increases in ?kare thought to reflect pathology of
the axon itself, such as from trauma. ??reflects diffusivity
perpendicular to axonal fibres and appears to be more
dysmyelination or demyelination. Although there is some
preliminary evidence that these measures might be useful
in vivo in trauma (Rugg-Gunn et al., 2001) it is not yet
entirely clear whether ?kand ??are differentially affected
by trauma, and this may be a function of severity as well as
The literature involving the application of DTI in chronic
TBI is limited but shows promise. In chronic moderate to
severe TBI, reduced FA has been reported (Nakayama et al.,
2006; Tisserand et al., 2006; Xu et al., 2007), even in the
absence of observable lesions in standard structural MRI
(Nakayama et al., 2006). Despite general acceptance of this
finding of abnormal FA, the relationship of white matter
integrity to cognitive function in TBI is not yet clear,
and the few studies to assess this in TBI have varied
in outcomes. For example, in a group of chronic severe
or lackof directional
TBI subjects with cognitive impairment there was no
relationship between reduced FA in the corpus callosum
and neuropsychological measures of memory or executive
function, though there was a relationship with performance
on the Mini-Mental State Exam (Nakayama et al., 2006).
However, Salmond and colleagues reported a relationship
between reduced FA and measures of learning and memory
(Salmond et al., 2006) in moderate and severe TBI.
One problem is that the existing
methodology, including placement of regions of interest,
variability in patient populations (such as severity and
acuity/chronicity of TBI subjects), and in the specific
neuropsychological testing used to assess cognition.
Hence, given the potential importance of white matter
pathology to outcome in TBI, and the sensitivity of DTI
in determining the integrity of white matter, further
studies are warranted. A more standardized methodology
is needed that can be used to assess the spectrum of white
matter abnormalities in TBI, at any point after injury, that
would also allow for correlation with clinically relevant
issues such as cognitive function. The current investigation
was designed with these issues in mind.
In this study, a group of chronic TBI subjects of all
severities and a group of demographically matched healthy
controls underwent MRI (anatomical and diffusion tensor
imaging), neuropsychological testing and a neurobeha-
The primary objective of the current investigation was
to test the hypothesis that white matter integrity is reduced
across the spectrum of TBI severity in chronic subjects.
The secondary objective was to examine the relationship
between white matter integrity and cognition assessed with
standard neuropsychological testing across the domains of
executive, attention and memory function.
studies differ in
A total of 39 subjects with a history of TBI, closed head type,
participatedin this study(Table
(13 females, 9 males) had a history of MTBI and 17 (9 females,
8 males) had a history of moderate to severe TBI (M/STBI).
Of these, two subjects with a history of MTBI were excluded for
excessive head motion. The final sample included 20 MTBI
subjects (12 females, 8 males) and 17 (9 females, 8 males) had a
history of M/STBI. All were at least 6 months out from injury;
with the average time out from injury being 107 months for all
TBI subjects. Subjects were recruited from the University of
Illinois Medical Center and via advertisements. Eighteen healthy
controls (11 females, 7 males) were recruited from the commu-
nity. Experimental procedures complied with the code of ethics of
the World Medical Association and the standards of the University
of Illinois Institutional Review Board. All subjects provided
Subjects were excluded if they had a history of psychiatric
disorder before the TBI, substance abuse, current pending
1). Twenty-two subjects
2510 Brain (2007),130, 2508^2519 M.F. Kraus et al.
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litigation or any other neurological or medical condition that
could result in cognitive changes (e.g. severe hypertension,
diabetes). Subjects were not receiving any treatments for cognitive
deficits at the time of the study, pharmacological or otherwise.
The criteria used for defining MTBI, set forth by the American
Congress of Rehabilitation Medicine (Medicine, 1993), are as
follows: MTBI is diagnosed when at least one of the following
criteria is met (1) any period of loss of consciousness; (2) any loss
of memory for events immediately before or after the accident;
(3) any alteration in mental state at the time of the accident
(e.g. feeling dazed, disoriented or confused) and (4) focal
neurological deficit(s) that may or may not be transient
(Medicine, 1993; Cassidy et al., 2004), For this study, subjects
were categorized as moderate or greater severity TBI if the LOC
was greater than 30min and/or the GCS was less than 13
(Levin et al., 1992; Medicine, 1993; Cassidy et al., 2004; Tagliaferri
et al., 2006). These criteria allowed the separation of MTBI from
moderate to severe TBI for the purposes of the present study.
For the MTBI group, the average reported LOC was 0.1h
(range¼0–0.50h), for the M/STBI group average LOC was
213.5h (range¼0.25–1440h). Data on acute TBI variables such
as LOC were collected by medical record when available and by
subject and family report. For the MTBI cases, all except one (who
met criteria for mild TBI by history with positive LOC but did not
seek immediate attention) were seen and diagnosed acutely at an
ER or outpatient setting.
In terms of clinical details concerning the index traumatic
event, for many of the cases the TBI was the primary diagnosis at
the time of their injury. Five MTBI and five M/STBI cases
had associated injuries (traumatic injuries other than the TBI).
Of these, most were fractures of the clavicle or an extremity.
The most common mechanisms of injury were motor vehicle
accidents (17 subjects). The remainder included bicycle accidents,
blunt head trauma and falls. On the neurological exam (exclusive
of cognitive testing) done at the time of evaluation, only eight
TBI subjects (two MTBI, six M/STBI) showed abnormalities,
which were primarily soft signs such as mildy unsteady tandem
gait. Of the MTBI group, all but two were employed or in school
at the time of evaluation; all but three of the M/STBI group were
either employed or in school at the time of evaluation.
Healthy controls were excluded if they had any history of
psychiatric illness or TBI, substance abuse/dependency or a history
of significant medical or neurological illness that would be
associated with significant changes in the brain, such as diabetes,
seizures or stroke. The healthy control group was not significantly
different from the TBI groups in age or years of education
(Table 1). The controls and MTBI groups were not significantly
different in estimates of premorbid IQ (Table 1). The M/STBI
did differ from the controls in terms of premorbid IQ estimates.
The M/STBI group did not differ from MTBI in age at the time
DTI data acquisition
Studies were acquired on a 3.0-Tesla whole body scanner
(Signa VHi, General Electric Medical Systems, Waukesha, WI)
using a customized DTI pulse sequence with a quadrature head
coil. The sequence is based on a single-shot EPI pulse sequence
with the capability of compensating eddy currents induced by
the diffusion gradients via dynamically modifying the imaging
gradient waveforms. The diffusion-weighting orientations are
designed based on the electrostatic repulsion model proposed
by Jones et al. (1999) (TR¼5200ms, TE¼minimum (81ms),
FOV¼22cm, Matrix¼132?132 (reconstructed to 256?256,
NEX¼2, total acquisition time¼5:46).
An additional 3D high-resolution anatomical scan was also
acquired to allow coregistration with the DTI data and normal-
ization to the Montreal Neurological Institute template (MNI)
(3Dinversion recovery fast
IRfSPGR), plane¼axial, TR¼9ms, TE¼2.0ms, flip angle¼25?,
NEX¼1, bandwidth¼15.6kHz, acquisition matrix¼256?256,
750s/mm2, diffusiongradient directions¼27,
Subjects completed a test battery that was assembled to assess
executive function, attention and memory. Since TBI commonly
affects frontal lobe function, the battery was weighed more heavily
on executive measures to heighten sensitivity to deficits in this
area of cognition. Tests included the Tower of London (Shallice,
1982; Culbertson and Zilmer, 2001), Stroop Colour–Word Test
(Stroop, 1935; Jensen and Rohwer, 1966; Golden and Freshwater,
2002), Paced Auditory Serial Addition Test (PASAT) (Gronwall
andSampson, 1974; Gronwall,1977),Trail MakingTest
T able1 Demographic information for traumatic brain injury and control subjects
M SEMM SEMM SEMMTBI M/STBI M/STBI
Number of years of education
Time from injury (in months)
Age at time of injury (years)
Length of LOC (h)
P-values are listed under each contrast and asterisks indicate significant differences between groups (?P<0.05). SEM¼standard error of
the mean; WTAR¼Wechsler test of adult reading; MTBI¼mild traumatic brain injury; M/STBI¼moderate to severe traumatic brain
injury; LOC¼loss of consciousness.
Diffusion tensor imaging in brain injuryBrain (2007),130, 2508^25192511
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(Reitan, 1958), Conners’ Continuous Performance Test (Conners
and Staff, 2000), Controlled
(COWAT) (Benton and Hamsher, 1976; Benton and Hamsher,
1989), Ruff Figural Fluency Test (Ruff, 1988), Wechsler Test of
Adult Reading (WTAR) (Psychological, 2001), California Verbal
Learning Test – Second Edition (CVLT-II) (Delis et al., 2000),
Brief Visual Spatial Memory Test – Revised (BVMT-R) (Benedict,
1997), Digit Span and Spatial Span from the Wechsler Memory
Scales – Third Edition (Wechsler, 1997) and the Grooved
Pegboard (Klove, 1964; Matthews and Klove, 1964). In addition,
subjects had to pass tests for malingering and effort, including the
Test of Memory Malingering (TOMM) and Dot Counting to
ensure that only subjects who performed testing effortfully were
included (Rey, 1941; Tombaugh, 1996, 1997).
Z-scores were calculated for all subjects, with the mean and SD
of data from healthy subjects used to define z-scores for all subject
groups. Negative scores indicate performance below the mean of
healthy subjects. Domain scores for measures of executive
function, attention and memory were generated by averaging the
standardized data from tests assessing these cognitive domains as
presented in Table 2.
DTI data analysis
The 28 diffusion directions, along with the B0 image, were used
to calculate FA as the primary indicator of white matter integrity.
The images were reconstructed and FA, ?1, ?2 and ?3 were
calculated using the program from Johns Hopkins, DTI Studio
(Wakana et al., 2004). The 28 diffusion-weighted image sets were
examined for image quality and head movement. Head movement
was required to be within one voxel across the image acquisition.
Because noise can introduce bias in estimates of the eigenvalues
and because noise decreases the signal-to-noise ratio we applied a
background noise level to all subjects prior to calculation of pixel-
wiseFA andthe eigenvalues
noise¼125). It is important to note that the application of this
criterion and the noise itself can influence calculation of
anisotropy. However, because the analyses focus on differences
between groups the bias introduced by this noise floor should not
influence group differences. The FA, ?1, ?2 and ?3 were then
Parametric Mapping software for analysis (SPM2, Wellcome
Department of Imaging Neuroscience, London, UK). DTI data
from each subject was co-registered with their corresponding
T1-weighted anatomic image set (after skull stripping) using a
normalized mutual information cost function and trilinear
interpolation. Normalization parameters were determined based
upon the high-resolution T1 image relative to the Montreal
Neurological Institute (MNI) template. These normalization
parameters were then applied to the FA and eigenvalue images.
Each image was visually checked for accuracy after both the co-
registration and normalization steps. From these eigenvalue maps,
axial (?k¼?1) and radial [??¼(?2+?3)/2] diffusivity were
calculated. Although no additional smoothing was applied to
and read into Statistical
T able 2 Neuropsychological test results and domain scores for all groups
All groups Control
M SEMM SEMM SEM MTBI M/STBIM/STBI
Executive measures executive domain
Tower of London (total moves)
Stroop color-word [age-corrected (s)]
Trails B (s)
CPT number of errors of commission
RUFF unique designs
Digit span backward scaled score
Spatial Span Backward scaled score
Attention measures attention domain
Digit span forward scaled score
Spatial span forward scaled score
Trails A (s)
CPT number of errors of omission
Memory measures memory domain
CVLT total trials1^5
CVLT long-free recall
BVMT delay recall
CPT Hit reactionTime (ms)
Grooved pegboard [dominant hand (s)]
P-values are listed under each contrast and asterisks indicate significant differences between groups after correction for multiple compar-
isons (?P<0.05;??P<0.01).PASAT¼paced auditory serial addition test;Trails¼trail making test; CPT¼Conners’continuous performance
test; COWAT¼controlled oral word association test; RUFF¼Ruff figural fluency test; CVLT¼California verbal learning test;
BVMT¼brief visual spatial memory test.
2512Brain (2007),130, 2508^2519M.F. Kraus et al.
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the data the magnitude of spatial filtering which occurs during
normalization to standardized space can potentially affect the DTI
data (see Jones et al., 2005; Smith et al., 2006). In some cases,
large smoothing kernels can potentially reduce group differences
(Jones et al., 2005).
All ROI analyses were carried out on data from each individual
subject and hand-drawn in standardized space. ROIs were drawn
individually on the FA maps with respect to the T2 FSE and
colour-coded FA maps.
The specific ROIs included: anterior and posterior corona
radiata (respectively, ACR and PCR), cortico-spinal tracts (CST)
which included parts of the cortico-pontine tract and parts of the
superior thalamic radiation, cingulum (CG) fibres, forceps minor
(fMin), forceps major (fMaj), the body, genu and splenium of the
corpus callosum (bCC, gCC and sCC), the inferior fronto-
occipital (IFO) fasciculus, the superior longitudinal fasciculus
(SLF), external capsule (ExCap) and the sagittal stratum including
the optic radiations (SS). A description of the identification of
these ROIs follows. A representative subject’s FA map with
superimposed ROIs is presented in Fig. 1.
The cingulum was defined firstly as the long association fibre
that is located internal to the cingulate gyrus and running along
its entire length continuing into the parahippocampal gyrus.
It was defined dorsally by the corpus callosum continuing into the
temporal lobe along the ventral/medial wall of the hippocampal
gyri. Some of the cingulum fibres intersect with fibres of the
superior longitudinal fasciculus, inferior longitudinal fasciculus,
superior fronto-occipital fasciculus, inferior fronto-occipital fasci-
culus and uncinate fasciculus. The anterior and posterior corona
radiata are the fibres which run throughout the internal capsule.
The anterior corona radiata was defined as those fibres which run
through limb of the internal capsule and contain nerve tracts
running to and from the anterior areas of the cortex. The
posterior corona radiata was defined by the posterior limb of the
internal capsule. However, the cortico-spinal tract is a large part of
the corona radiata. However, because we wanted to examine the
cortico-spinal tract individually we have excluded these fibres
from our definitions of anterior and posterior corona radiata.
The external capsule contains cortico–cortico association fibres.
The superior longitudinal fasciculus (fibres running from frontal
to parietal to occipital and vice versa), inferior fronto-occipital
fasciculus and the uncinate fasciculus (fibres running from ventral
frontal lobe to pole of temporal lobe) run through the external
capsule. The external capsule was defined as the white matter
tracts located lateral to the lentiform nucleus, most specifically the
putamen of the basal ganglia, and lateral to the extreme capsule is
the claustrum. The external capsule, claustrum and extreme
capsule are very closely associated. We are unable to discriminate
between these tracts. In order to examine the external capsule
separately from the SLF and IFO we excluded any fibre defined
as external capsule from the SLF or IFO. The IFO runs from
the frontal lobe to the occipital and temporal lobes ipsilaterally.
Fig.1 Example region of interestmasks for a single representative subject: (A) forceps minor (green), cortico-spinal tract (purple), inferior
frontal-occipital fasciculus (red), external capsule (yellow), sagittal stratum (blue); (B) anterior corona radiata (green), superior longitudinal
fasciculus (red), posterior corona radiata (blue); (C) cingulum (red), corpus callosum body (blue), splenium (yellow), and genu (green) and
forceps major (purple).
Diffusion tensor imaging in brain injury Brain (2007),130, 2508^25192513
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It is deep within the cerebral hemisphere and runs laterally to the
caudate nucleus. The SLF connects the anterior part of the frontal
lobe to the occipital and temporal lobes. This tract has extensive
branching in the frontal, parietal and temporal lobes. We excluded
fibres associated with the IFO from these masks. Although the
corpus callosum contains fibres which run anterior to posterior we
wanted to investigate differential loss of the genu, splenium, and
body of the corpus callosum as well as in forceps major and
minor. The corpus callosum was first defined as a whole and then
subdivided. The forceps minor were characterized as those fibres
located inferior to the IFO and medially to the anterior portion of
the corona radiata. Forceps major was defined as those fibres
posterior to the posterior corona radiata and medial to the sagittal
stratum. The corticospinal tract was identified by following the
fibre bundle from the brainstem into the cortex. We refer herein
to the corticospinal tract but also include the cortico-bulbar and
cortico-pontine tract in this ROI. Although we define these
regions there is considerable overlap between many of these tracts.
Because of this we inspected each ROI relative to every other ROI
to ensure that the same voxel was not included in more than one
ROI. To ensure that FA was only calculated from white matter
tissue, a threshold of 0.20 was applied prior to extraction of
individual subjects’ FA maps.
White matter load
This was used as an index of global white matter integrity. It was
defined as the number of ROIs that showed significantly decreased
FA values compared to controls. This measure was used as it may
be more sensitive to white matter abnormalities by looking at the
actual number of affected areas across the brain independent of
individual variability in the specific location of these white matter
abnormalities. To measure the White Matter Load, z-scores were
calculated for the FA within each ROI. The control group mean
and SD were treated as zero. We then calculated the number of
ROIs which showed decreased FA for each subject. We used a
conservative criterion of 1 SD below the control mean to define
decreased integrity. White Matter Load was then calculated as the
total number of regions which showed impaired white matter
relative to values from controls. The value for White Matter Load
can range from 0 to 13 (13 ROIs).
Neuropsychological test scores were analysed using a one-way
ANOVA with group membership (controls, MTBI, M/STBI) and
were corrected for multiple comparisons using the least significant
difference post-hoc tests. The primary measures of interest were
three scores which were each a composite of those individual test
results which loaded preferentially on executive, memory and
attention domains, respectively. Because these three domain scores
are more stable than individual tests scores they were also used to
assess relationships between measures of white matter integrity
and cognition using bivariate Pearson correlations.
The primary analyses carried out on the dependent measures
extracted from the DTI data was a two-way mixed design ANOVA
with cerebral hemisphere (right, left) as the within subjects
comparison and group membership (controls, MTBI and M/STBI)
as the between subjects comparison. For those regions where areas
in both hemispheres were assessed together (corpus callosum and
cerebral peduncles) the analysis was a one-way between subjects
ANOVA with group membership (controls, MTBI and M/STBI) as
the between-subjects comparison. The primary dependent measure
was FA. Data were confirmed to have a normal distribution using
the Kolmogorov–Smirnov test.
Group means are presented for the each neuropsychological
test in Table 2. Of note, the only individual measure
which differed significantly between the controls and MTBI
was the number of commissions on the CPT [F(1,37)¼
8.86, P¼0.005], which is a measure associated strongly
with prefrontal function (Miranda et al., in press). Mean
cognitive domain scores are also presented in Fig. 2.
M/STBI differed from the controls on almost all measures.
The trend in means for the individual tests indicate that
the controls have the highest performance, followed by
MTBI, with M/STI showing the most severe and global
impairment. The MTBI group did not differ significantly
from controls in any domain scores when compared to
the controls (P>0.050). The M/STBI group performed
significantly worse than both the controls and MTBI in the
P<0.001; versus MTBI: F(1,35)¼6.39, P¼0.016] and
F(1,34)¼7.83, P¼0.008; versus MTBI: F(1,35)¼6.79,
P¼0.013]. M/STBI performed considerably worse than
the controls on the attention domain [F(1,34)¼9.14,
P¼0.005] but did not differ from MTBI [F(1,34)¼3.194,
Fig. 2 Mean domain scores (normalized z-scores) for the
MTBI (white) and M/STBI (dark gray). Note that the light gray box
around zero indicates1SEM around the control mean.
2514Brain (2007),130, 2508^2519M.F. Kraus et al.
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Fractional anisotropy: symmetry
Although there was a main effect of symmetry across the
CST (P¼0.038) and ACR (P¼0.043) with FA in the right
hemisphere being higher than the left there were no
differential symmetry effects across the three groups. As
such, the remaining analyses are presented collapsed across
Overall, there was a main effect of group membership on
whole brain FA [F(2,54)¼4.52, P¼0.015] relative to
controls. Post-hoc testing demonstrated that the M/STBI
had reduced FA relative to both controls [F(1,34)¼6.47,
P¼0.016] and MTBI [F(1,36)¼5.36, P¼0.027]. In the
ROI analyses, with the exception of fMin [F(2,54)¼2.71,
P¼0.076] and ExCap [F(2,54)¼3.06, P¼0.055], signifi-
cant main effects of group membership were observed for
all other ROIs. As the primary contrast of interest was
comparison between controls and both TBI subject groups,
z-scores were calculated with the controls set to zero. As
can be seen in Fig. 3, MTBI showed reduced FA along the
P¼0.005] and SS [F(1,37)¼6.84, P¼0.013]. FA values
for all ROIs in the M/STBI group were decreased compared
to controls (P<0.05; see Table 3).
Comparisons between MTBI and M/STBI showed that
the M/STBI had reduced FA in the corpus callosum [gCC:
F(1,36)¼8.42, P¼0.006; bCC: F(1,36)¼15.63, P<0.001;
sCC: F(1,36)¼18.76, P<0.001], Cing [F(1,36)¼12.84,
P¼0.042], PCR [F(1,36)¼4.80, P¼0.035] and in the SS
Axial and radial diffusivity
To investigate potential mechanisms for changes in white
matter integrity in chronic TBI, both axial and radial
diffusivity were extracted from a whole brain white matter
mask as well as from the ROIs which showed sensitivity
to all severities of head injury (SS, SLF, CST). As with
the earlier FA analysis, these values were transformed to
z-scores based upon the control group mean. There was an
overall main effect of group for both axial (?k) and radial
(??) diffusivity in the whole brain (P<0.004 for all
comparisons). However, these results were primarily driven
by increased diffusivity in M/STBI. As can be seen in Fig. 4,
M/STBI, relative to controls, showed increased ?kand ??in
all regions [whole brain ?k: F(1,34)¼10.40, P¼0.003;
P40.001; SLF ?k: F(1,34)¼43.56, P<0.001; SLF ??:
P¼0.005; CST ??: F(1,34)¼7.79, P¼0.009). The MTBI
showed increased ?k relative to controls in the SS
P¼0.035] but not in the whole brain or CST. The MTBI
showed no significant increases in radial diffusivity in
Fig. 3 Mean normalized FA for each ROI for the MTBI (green) and
M/STBI (blue). Single asterisks indicate P<0.05, double asterisks
indicate P<0.001for the control group compared to either MTBI
(M) or M/STBI (MS). Note that the light gray box around zero
indicates1SEM around the control mean. Abbreviations: ACR,
PCR: anterior and posterior corona radiata,CST: corticospinal
tracts,Cing: cingulum, fMin, fMaj: forceps minor and major, bCC,
sCC, gCC: body, genu and splenium of the corpus callosum,
IFO: inferior fronto-occipital fasciculus, SLF: superior longitudinal
fasciculus, SS: sagittal stratum,ExtCap: external capsule.Note that
white boxes on the colour-coded direction map indicate the target
fibres but do not indicate the entire region of interest.
Diffusion tensor imaging in brain injuryBrain (2007),130, 2508^25192515
by guest on May 31, 2013
White matter load
The White Matter Load was the total number of regions
with FA 1SD below the control mean (please see the
‘Methods’ section for a complete description).
Each control, on average, had reduced FA in 3.6 out of
13 ROIs (M¼3.61, SEM¼0.55). The load (or number of
regions with reduced FA) increased as the severity of head
injury increased. The MTBI had an average load of about
six ROIs classified as reduced (M¼5.9, SEM¼0.72),
whereas the M/STBI showed reduced FA in 8 out of 14
ROIs (M¼9.06, SEM¼0.89). The controls had signifi-
cantly lower load than the MTBI [F(1,37)¼6.16, P¼0.018]
and M/STBI [F(1,34)¼27.69, P<0.001]. Finally, the
did havea larger loadthanthe MTBI
Relationship between white matter integrity
and neuropsychological function
To examine the relationship between both white matter
integrity and white matter load with neuropsychological
function we conducted a series of correlations for the entire
group of TBI subjects. As is depicted in Fig. 5, there was a
significant correlation between the executive and memory
domains with the composite white matter load [executive:
r(54)¼?0.41, P¼0.002; attention: r(54)¼ ?0.26, P¼0.058;
and memory: r(54)¼?0.40, P¼0.000]. Also depicted in
T able 3 Mean FA for all three groups for each ROI.
Region of interest (ROI) All groups Control
vs. ControlMTBI M/STBI
M SEMM SEMM SEM MTBI M/STBIM/STBI
External capsule (ExCap)
Cortico-spinal tract (CST)
Inf. frontal-occipital (IFO)
Anterior corona radiata (ACR)
Posterior corona radiata (PCR)
Forceps major (fMaj)
Forceps minor (fMin)
Sagittal stratum (SS)
Sup. longitudinal (SLF)
Standard errors of the mean (SEM) are presented in parentheses. P-values are listed under each contrast and asterisks indicate significant
differences between groups (?P<0.05;??P<0.01). Inf¼Inferior, Sup¼Superior.
Fig. 4 Mean normalized axial (?k) andradial (??) diffusivity for the MTBI (white bars) and M/STBI (dark graybars). Single asterisksindicate
P<0.05, double asterisks indicate P<0.01either the MTBI or M/STBI was compared to controls.Note that the light gray box around zero
indicates1SEM around the control mean. Abbreviations: SS: sagittal stratum, SLF: superior longitudinal fasciculus,CST: corticospinal tract.
2516 Brain (2007),130, 2508^2519M.F. Kraus et al.
by guest on May 31, 2013
Fig. 5 is the overlapping distribution of load and neuropsy-
chological function amongst all the three groups.
In terms of correlations between FA in specific ROIs
(r¼?0.368, P¼0.006), sCC (r¼?0.348, P¼0.009), CST
(r¼?0.390, P¼0.003), ExCap (r¼?0.265, P¼0.050),
fMaj (r¼0.563, P<0.001), fMin (r¼.281, P¼0.038),
IFO (r¼?0.346, P¼0.009), ACR (r¼?0.383, P¼0.004),
PCR (r¼?0.407, P¼0.002), SLF (r¼?0.305, P¼0.023),
P¼0.041). Only the fMaj (r¼?0.310, P¼0.022) and
PCR x(r¼0.271, P¼0.046) correlated with the attention
fMin (r¼?0.269, P¼0.047), IFO (r¼?0.314, P¼0.019),
PCR (r¼?0.330, P¼0.014), SS (r¼?0.316, P¼0.019)
and Cing (r¼?0.311, P¼0.021) all corrected with the
memory domain. Although we do not have the statistical
power to examine these correlations within each subject
group the trend is such that these patterns appear
consistent within both the MTBI and M/STBI.
In this study, the moderate to severe TBI subjects
demonstrated reduced white matter integrity, relative to
controls, in all 13 regions of interest. The MTBI showed
reduced white matter integrity in the superior longitudinal
fasciculus, sagittal stratum and corticospinal tract (Fig. 3).
The total number of regions with reduced white matter
integrity (White Matter Load) was greatest in the moderate
to severe group, and least in the controls (Fig. 5). The
MTBI subjects fell between these two groups, being
significantly different than controls (Fig. 5).
In M/STBI increased radial and axial diffusivity is
observed both in the whole brain and in specific regions
of interest (Fig. 4). This finding likely reflects damage to
both myelin and to axons. In MTBI, relatively normal
radial diffusivity and increased axial diffusivity suggests that
irreversible damage to myelin is less common in MTBI as
compared to M/STBI but that axonal damage is present
even 6 months following injury. It could be that the injury
in the MTBI group had less of an effect on myelin due to
trauma acutely or that the less severe injury allowed some
degree of myelin damage that was reversible. Only three
ROIs were assessed in this analysis, and further research is
M/STBI differed from the controls on almost all
measures of cognitive function, being more impaired in
each domain than controls or the MTBI group. Although
there was a trend in executive and attention function to be
more impaired, the MTBI group did not differ significantly
from controls in any domain scores.
The moderate to severe TBI subjects showed reduced
function across all domains. There was a modest negative
correlation between FA in individual regions of interest
with cognitive function. However, the relationship between
overall white matter load was more strongly related to the
domains of executive and memory function than FA in
individual ROIs. This suggests that a global measure such as
white matter load is a useful index, as it appears to relate
more clearly to declines in cognitive functions which rely
on widespread cortical and subcortical networks.
While it is not surprising that moderate and severe
injuries tend to show evidence of white matter changes and
cognitive impairment, acquiring data on all severities in one
study allows for the milder injuries to be assessed in the
context of a spectrum of injury, from the healthy controls
to the more severe injuries. Importantly, the controls were
fairly well matched to the TBI groups in terms of age and
years of education. None of the subjects were actively
involved in litigation. These findings are consistent with
TBI existing on a spectrum of neuropathologic severity and
resulting disability, placing subjects with a history of mild
TBI between controls and more severe injuries. In addition
Fig. 5 Plotsindicatingtherelationshipbetweeneachnormalizeddomainscore(left:executive,middle:attention,right:memory)asafunction
Diffusion tensor imaging in brain injury Brain (2007),130, 2508^2519 2517
by guest on May 31, 2013
to demonstrating that TBI, regardless of severity, results in
chronic changes to the white matter microstructure, the
present findings suggest that injury severity may differen-
tially impact axons and myelin. This finding begins to
address the issue of mechanism in the differential effects of
mild versus more severe TBI on white matter.
In terms of white matter changes, there is some overlap
between amount of pathology and the different clinical
classifications of TBI severity. This is important in under-
standing variation in recovery. Certain injuries classified
acutely as mild based on acute TBI variables such as loss of
consciousness may actually be closer to moderate in the
degree of pathology. Conversely, certain individuals with
moderate or severe TBI may show more intact white matter
than expected based on accepted means of clinical
classification of injury severity. The data presented here
demonstrate that DTI allows for a more sensitive delinea-
tion of severity and mechanism of white matter pathology,
and may help to explain apparent discrepancies between
clinically diagnosed injury severity and cognitive outcomes
across the spectrum of TBI.
This work was supported by National Institute of Health
[K23 MH068787]. The contents of this paper are solely the
responsibility of the authors and do not necessarily
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