A review of magnetic resonance imaging and diffusion tensor
imaging findings in mild traumatic brain injury
M. E. Shenton & H. M. Hamoda & J. S. Schneiderman & S. Bouix &
O. Pasternak & Y. Rathi & M.-A. Vu & M. P. Purohit & K. Helmer &
I. Koerte & A. P. Lin & C.-F. Westin & R. Kikinis & M. Kubicki &
R. A. Stern & R. Zafonte
Published online: 22 March 2012
#Springer Science+Business Media, LLC (outside the USA) 2012
Abstract Mild traumatic brain injury (mTBI), also referred
to as concussion, remains a controversial diagnosis because
the brain often appears quite normal on conventional com-
puted tomography (CT) and magnetic resonance imaging
(MRI) scans. Such conventional tools, however, do not
adequately depict brain injury in mTBI because they are
not sensitive to detecting diffuse axonal injuries (DAI), also
described as traumatic axonal injuries (TAI), the major brain
injuries in mTBI. Furthermore, for the 15 to 30 % of those
diagnosed with mTBI on the basis of cognitive and clinical
symptoms, i.e., the “miserable minority,” the cognitive and
physical symptoms do not resolve following the first
3 months post-injury. Instead, they persist, and in some
cases lead to long-term disability. The explanation given
for these chronic symptoms, i.e., postconcussive syndrome,
particularly in cases where there is no discernible radiological
evidence for brain injury, has led some to posit a psychogenic
origin. Such attributions are made all the easier since both
posttraumatic stress disorder (PTSD) and depression are fre-
quently co-morbid with mTBI. The challenge is thus to use
M. E. Shenton
Clinical Neuroscience Laboratory, Laboratory of Neuroscience,
Department of Psychiatry, VA Boston Healthcare System,
and Harvard Medical School,
Brockton, MA, USA
M. E. Shenton (*):H. M. Hamoda:J. S. Schneiderman:
S. Bouix:O. Pasternak:Y. Rathi:M.-A. Vu:M. P. Purohit:
I. Koerte:M. Kubicki
Psychiatry Neuroimaging Laboratory, Departments of Psychiatry
and Radiology, Brigham and Women’s Hospital,
and Harvard Medical School,
1249 Boylston Street,
Boston, MA 02215, USA
H. M. Hamoda
Department of Psychiatry, Children’s Hospital, and Harvard
Boston, MA, USA
O. Pasternak:C.-F. Westin
Laboratory of Mathematics in Imaging, Department of Radiology,
Brigham and Women’s Hospital, and Harvard Medical School,
Boston, MA, USA
M. P. Purohit:R. Zafonte
Spaulding Rehabilitation Hospital,
Massachusetts General Hospital and Harvard Medical School,
Boston, MA, USA
M. P. Purohit
Division of Medicine, Beth Israel-Deaconness Medical Center,
and Harvard Medical School,
Boston, MA, USA
Department of Radiology, Athinoula A.Martinos Center,
Massachusetts General Hospital, and Harvard Medical School,
Charlestown, MA, USA
A. P. Lin
Center for Clinical Spectroscopy, Department of Radiology,
Brigham and Women’s Hospital, and Harvard Medical School,
Boston, MA, USA
Surgical Planning Laboratory, MRI Division,
Department of Radiology, Brigham and Women’s Hospital,
and Harvard Medical School,
Boston, MA, USA
R. A. Stern
Center for The Study of Traumatic Encephalopathy,
Departments of Neurology and Neurosurgery,
Boston University Medical School,
Boston, MA, USA
Brain Imaging and Behavior (2012) 6:137–192
neuroimaging tools that are sensitive to DAI/TAI, such as
diffusion tensor imaging (DTI), in order to detect brain inju-
ries in mTBI. Of note here, recent advances in neuroimaging
extant brain abnormalities in mTBI. These advances may lead
to the development of biomarkers of injury, as well as to
staging of reorganization and reversal of white matter changes
following injury, and to the ability to track and to characterize
changes in brain injury over time. Such tools will likely be
used in future research to evaluate treatment efficacy, given
their enhanced sensitivity to alterations in the brain. In this
characterizing this patient population using objective radio-
logical measures. Evidence is presented for detecting brain
abnormalities in mTBI based on studies that use advanced
neuroimaging techniques. Taken together, these findings sug-
gest that more sensitive neuroimaging tools improve the de-
tection of brain abnormalities (i.e., diagnosis) in mTBI. These
toolswill likely also provideimportant informationrelevant to
outcome (prognosis), as well as play an important role in
longitudinal studies thatareneeded to understand the dynamic
nature of brain injury in mTBI. Additionally, summary tables
of MRI and DTI findings are included. We believe that the
enhanced sensitivity of newer and more advanced neuroimag-
ing techniques for identifying areas of brain damage in mTBI
will be important for documenting the biological basis of
postconcussive symptoms, which are likely associated with
subtle brain alterations, alterations that have heretofore gone
undetected due to the lack of sensitivity of earlier neuroimag-
ing techniques. Nonetheless, it is noteworthy to point out that
detecting brain abnormalities in mTBI does not mean that
other disorders of a more psychogenic origin are not co-
morbid with mTBI and equally important to treat. They argu-
ably are. The controversy of psychogenic versus physiogenic,
not carefully consider the limitations of conventional neuro-
imaging techniques in detecting subtle brain injuries in mTBI,
and the physiogenic view does not carefully consider the fact
that PTSD and depression, and other co-morbid conditions,
may be present in those suffering from mTBI. Finally, we end
with a discussion of future directions in research that will lead
to the improved care of patients diagnosed with mTBI.
The scope of the problem More than 1.7 million people each
year in the United States experience a traumatic brain injury
(TBI), with 75 to 85 % of these injuries categorized as mild
(mTBI; CDC 2010; Faul et al. 2010; Bazarian et al. 2006).
This number is likely an underestimate because it does not
include those who are seen in private clinics or by primary
care physicians, nor does it include those who do not seek
medical treatment (Langlois et al. 2006). It is estimated, in
fact, that 14 % of mTBI patients are seen in private clinics or
by their own doctors, with an additional 25 % receiving no
medical attention (Sosin et al 1996). Based on the large
number of known and likely unknown cases, traumatic brain
injury has been referred to as the “silent epidemic” (e.g.,
Goldstein 1990). Recently, the public has become more
aware of TBI based on news reports of sports injuries
leading to long-term effects of repetitive trauma to the brain,
as well as news reports about soldiers returning from Iraq
and Afghanistan with TBI. With respect to the latter, the
most frequent combat-related injury incurred by soldiers
returning from Iraq and Afghanistan is TBI, and most par-
ticularly mTBI (Okie 2005). The frequency of these injuries
has led to TBI being called the “signature injury of war”
(Okie 2005). Further, approximately 22 % of the wounded
soldiers arriving at Lundstuhl Regional Medical Center in
Germany have head, neck, or face injuries, with cases of
TBI resulting primarily from improvised explosive devices
(IEDs), landmines, high pressure waves from blasts, blunt
force injury to the head from objects in motion, and motor
vehicle accidents (Okie 2005; Warden 2006). Of particular
note, mTBI characterizes most of the blast-induced traumatic
brain injuries seen in service members returning from Iraq
and Afghanistan, with reports of 300,000 service members
sustaining at least one mTBI as of 2008 (Tanielian and
Jaycox 2008). Mild TBI is thus a major health problem that
affects both civilians and military populations. The estimated
economic cost is also enormous, with mTBI accounting for
44 % of the 56 billion dollars spent annually in the United
States in treating TBI (Thurman 2001).
Lack of radiological evidence Mild TBI is, however, diffi-
cult to diagnose because often the brain appears quite nor-
mal on conventional computed tomography (CT) and
magnetic resonance imaging (MRI) (e.g., Bazarian et al.
2007; Inglese et al. 2005; Hughes et al. 2004; Iverson et
al. 2000; Miller 1996; Mittl et al. 1994; Povlishock and
Coburn 1989; Scheid et al. 2003). This lack of radiological
evidence of brain injury in mTBI has led clinicians typically
to diagnose mTBI on the basis of clinical and cognitive
symptoms, which are generally based on self-report, and
are non-specific as they overlap with other diagnoses (e.g.,
Hoge et al. 2008; Stein and McAllister 2009). To complicate
138Brain Imaging and Behavior (2012) 6:137–192
matters further, while most of the symptoms in mTBI are
transient and resolve within days to weeks, approximately
15 to 30 % of patients evince cognitive, physiological, and
clinical symptoms that do not resolve 3 months post-injury
(e.g., Alexander 1995; Bazarian et al. 1999; Bigler 2008;
Rimel et al. 1981; Vanderploeg et al. 2007). Instead, these
symptoms persist and in some cases lead to permanent
disability (Carroll et al. 2004a and b; Nolin and Heroux
2006), and to what has been referred to as persistent post-
concussive symptoms (PPCS), or postconcussive syndrome
(PCS), although the latter term, “PCS,” is controversial
(e.g., Arciniegas et al. 2005).
This “miserable minority” (Ruff et al. 1996) often
experience persistent postconcussive symptoms (PPCS) that
include dizziness, headache, irritability, fatigue, sleep distur-
bances, nausea, blurred vision, hypersensitivity to light and
noise, depression, anxiety, as well as deficits in attention,
concentration, memory, executive function, and speed of pro-
cessing (e.g., Bigler 2008). Kurtzke and Kurland (1993) esti-
mates the incidence of persistent symptoms as being equal to
the annual incidence of Parkinson’s disease, Multiple Sclero-
sis, Guillain-Barré Syndrome, Motor Neuron Disease, and
Myasthenia Gravis, combined. Moreover, the modal age for
injury is young, in the 20’s and 30’s. Thus mTBI affects a
to date, no consistent or reliable correlations between cogni-
tive/clinical symptoms and radiological evidence of brain
injury based on conventional neuroimaging.
The explanation given for PPCS, particularly when there is
no discernible radiological evidence, has led some to posit a
psychogenic origin (e.g., Belanger et al. 2009; Hoge et al.
2008; Lishman 1988; Machulda et al. 1988). More specifical-
ly, Hoge and colleagues (2008; 2009) suggest that postcon-
cussive symptoms reported by soldiers with mTBI are largely
or entirely mediated by posttraumatic stress disorder (PTSD)
and depression. In their study, after controlling for both PTSD
and depression, the only remaining symptom was headaches.
Headaches, nonetheless, are an important symptom of TBI,
The term “miserable minority,” described above, has been
used to identify those who likely have a more psychogenic
etiology to their symptoms (e.g., Ruff et al. 1996). Such
attributions are easy to make given that the symptoms of
mTBI, as noted above, overlap with other disorders (e.g.,
Hoge et al. 2008). Belanger et al. (2009) also suggest that
most of the symptoms reported by those with mTBI are likely
the result of emotional distress. Others have also argued that
emotional distress and/or psychiatric problems account for
those who continue to experience postconcussive symptoms
(e.g., Belanger et al. 2009; Greiffenstein 2008; Hoge et al.
2008; Lishman 1988; Machulda et al. 1988).
Persistent symptoms, however, may be the result of more
subtle neurological alterations that are beneath the threshold
of what can be detected using conventional neuroimaging
techniques that all too often do not reveal brain pathology in
mTBI (e.g., Hayes and Dixon 1994; Huisman et al. 2004;
Fitzgerald and Crosson 2011; Green et al. 2010; Miller 1996;
Niogi and Mukherjee 2010). This isnot at all surprising, since
conventional techniques are not sensitive to detecting diffuse/
traumatic axonal injuries (DAI/TAI), the major brain injuries
observed in mTBI (e.g., Benson et al. 2007).
There is also evidence from the literature to suggest that
in several cases of mTBI where there was no radiological
evidence of brain injury, autopsy following death from
injuries other than mTBI revealed microscopic diffuse axo-
nal injuries that conventional neuroimaging tools did not
detect, presumably because they were not sufficiently sen-
sitive (e.g., Adams et al. 1989; Bigler 2004; Blumbergs et al.
1994; Oppenheimer 1968).
We would argue that the controversy between mTBI
being psychogenic versus physiogenic in origin is not pro-
ductive because the psychogenic view does not carefully
consider the limitations of conventional neuroimaging tech-
niques in detecting subtle brain injuries in mTBI, and the
physiogenic view does not carefully consider the fact that
PTSD and depression, and other co-morbid conditions, may
be present in those suffering from mTBI. Further, patients
with mTBI may complain more when their symptoms are
not validated. That is, when there is no radiological evi-
dence that explains their symptoms, and yet they still expe-
rience symptoms, these patients may complain more
because of the lack of validation, versus those patients
who have radiological evidence that validates their symp-
toms, leading them to complain less, simply because they
have a medical explanation for their symptoms.
The challenge The challenge then is to use neuroimaging
tools that are sensitive to DAI/TAI, such as Diffusion Tensor
Imaging (DTI), to detect brain injuries in mTBI. Specifical-
ly, with recent advances in imaging such as DTI it will now
be possible to characterize better extant brain injuries in
mTBI. Of note, DTI is a relatively new neuroimaging tech-
nique that is sensitive to subtle changes in white matter fiber
tracts and is capable of revealing microstructural axonal
injuries (Basser et al. 1994; Pierpaoli and Basser 1996;
Pierpaoli and Basser 1996), which are also potentially re-
sponsible for persistent postconcussive symptoms.
Other promising techniques include susceptibility weight-
ed imaging (SWI), which is sensitive to micro-hemorrhages
that may occur in mTBI (e.g., Babikian et al. 2005; Haacke et
al. 2004; Park et al. 2009; Scheid et al. 2007), and Magnetic
Resonance Spectroscopy (MRS), which measures brain
et al. 2005; Lin et al. 2005; Lin et al. 2010; Provencher 2001;
Ross et al. 1998; Ross et al. 2005; Seeger et al. 2003; Shutter
Brain Imaging and Behavior (2012) 6:137–192139
et al. 2004; Vagnozzi et al. 2010). In this review we focus
primarily on MRI and, most particularly, on DTI findings in
mTBI. In a separate article in this special issue, Dr. Alexander
Lin and colleagues review MRS, single photon emission
tomography (SPECT), and positron emission tomography
(PET) findings relevant to brain chemistry alterations in
mTBI, and Dr. Brenna McDonald and colleagues review
functional MRI (fMRI) findings in mTBI. The reader is also
referredtoDr. Robert Sternand colleagues’article,alsointhis
issue, which reviews the evidence for repetitive concussive
encephalopathy in sports-related injuries such as professional
football (see also Stern et al. 2011).
Focus of this review Here we present evidence for brain
abnormalities in mTBI based on studies using advanced
MRI/DTI neuroimaging techniques. Importantly, these
advances make it possible to use more sensitive tools to
investigate the more subtle brain alterations in mTBI. These
advances will likely lead to the development of biomarkers
of injury, as well as to staging of reorganization and reversal
of white matter and gray matter changes following injury,
and to the ability to chart the progression of brain injury
over time. Such tools will also likely be used in future
research to evaluate treatment efficacy, given their enhanced
sensitivity to alterations in the brain.
Taken together, the findings presented below suggest
that more sensitive neuroimaging tools improve the de-
tection of brain injuries in mTBI (i.e., diagnosis). These
tools will, in the near future, likely provide important
information relevant to outcome (prognosis), as well as
play a key role in longitudinal studies that are needed to
understand the dynamic nature of brain injury in mTBI.
We also believe that the enhanced sensitivity of newer
and more advanced neuroimaging techniques for identi-
fying brain pathology in mTBI will be important for
documenting the biological basis of persistent postcon-
cussive symptoms, which are likely associated with
subtle brain alterations, alterations that heretofore have
gone undetected due to the lack of sensitivity of earlier,
conventional neuroimaging techniques.
Below we provide a brief primer of neuroimaging tech-
niques, although the reader is referred to Kou et al. (2010),
Johnston et al. (2001), Le and Gean (2009), and Niogi and
Mukherjee 2010 for more detailed information. For a de-
scription of the molecular pathophysiology of brain injury,
the reader is referred to Barkhoudarian et al. (2011). The
reader is also referred to Dr. Erin Bigler’s article in this
special issue for information regarding post-mortem and
histological findings in mTBI as well as for a discussion
of the physiological mechanisms underlying TBI. Dr. Bigler
emphasizes that neuroimaging abnormalities are “gross indi-
cators” of the underlying cellular damage resulting from
trauma-induced pathology. We concur and believe that we
now have neuroimaging tools that are sufficiently sensitive
to discern both more gross indicators of pathology, as well
as microstructural changes in white matter, and micro-
hemorrhages using newer imaging technologies. The reader
is also referred to Smith et al. (1995) and to several recent and
excellent reviews of neuroimaging findings in mTBI (e.g.,
Belanger et al. 2007; Bruns and Jagoda 2009; Gentry 1994;
Green et al. 2010; Hunter et al. 2011; Kou et al. 2010; Le and
Gean (2009); Maller et al. 2010; Niogi and Mukherjee 2010).
Jang (2011) has also published a recent review of the use of
DTI in evaluating corticospinal tract injuries after TBI.
Following the brief primer, we present MRI and DTI find-
ings relevant to mTBI. We used PUBMED to locate these
articles. The following keywords were used: (MRI or DTI or
Diffusion Tensor) AND (Concussion or Mild TBI or Mild
Traumatic Brain Injury or mTBI). The dates for the articles
selected were inclusive to September 16, 2011. We did not
include articles that were case studies, nor did we include
articles that focused on pediatric and adolescent populations
(see article in this special issue by Wilde and colleagues,
which covers this topic). We also did not include articles that
did not specify the severity of injury, but instead described
only the mechanism of injury, i.e., falls, motor vehicle acci-
dent, hit by tram (e.g., Liu et al. 1999). For the morphometric
MRI empirical studies, we note that most included mild,
moderate, and severe TBI, rather than mTBI alone. Conse-
quently we included all three. This was less the case for the
DTI empirical studies, where many focused only on mTBI.
We were thus able to separate empirical studies that focused
solely on mTBI from those that included several levels of
severity, although we report on both. We include detailed
summary tables of MRI and DTI findings in order to provide
the interested reader with a more in depth and detailed review
of each empirical study included in this review. Following the
review of MRI and DTI findings, we present future directions
for research in mTBI, which include the use of multiple
modalities for imaging the same patients, and the importance
of following patients longitudinally. We also present new
imaging methods that go beyond advanced imaging
approaches reviewed here that, to date, are still as yet not used
routinely in a clinical setting. The potential for developing
biomarkers to identify and to characterize mTBI is also pre-
detect but the injuries to the brain are heterogeneous, and
biomarkers are needed for individualized diagnosis as well
as for early and effective treatment interventions.
Neuroimaging primer and role of neuroimaging in mTBI
Overview TBI is a heterogeneous disorder and there is no
one single imaging modality that is capable of characterizing
140 Brain Imaging and Behavior (2012) 6:137–192
the multifaceted nature of TBI. Advances in neuroimaging
are, nonetheless, unprecedented and we are now able to
visualize and to quantify information about brain alterations
in the living brain in a manner that has previously not been
possible. These advances began with computed axial tomog-
raphy (CT) in the 1970’s, and then with magnetic resonance
imaging (MRI) in the mid-1980’s, with more refined and
advanced MR imaging over the last 25 years, including per-
fusion weighted imaging (useful for measuring abnormal
blood supply and perfusion), susceptibility-weighted imaging
(SWI;usefulfor measuringmicro-hemorrhages– e.g., Haacke
et al. 2004; Park et al. 2009), magnetization transfer MRI
(useful for measuring traumatic lesions – e.g., see review in
Le and Gean 2009), diffusion weighted imaging (DWI; useful
for measuring edema and developed initially for studies of
stroke – see review in Le and Gean (2009)), diffusion tensor
imaging (DTI; useful for measuring white matter integrity –
e.g., Basser et al. 1994), and functional MRI (fMRI; useful
e.g., see article by McDonald et al. in this issue). Other neuro-
imaging tools, although not a complete list, include positron
emission tomography (PET; useful for measuring regional
brain metabolism using 2-fluro-2-deoxy-d-glucose, both
hyper and hypo metabolism observed in TBI – see Le and
Gean (2009) for review), single photon emission tomography
(SPECT; useful for measuring cerebral blood flow but less
sensitive to smaller lesions that are observed on MRI – see
article by Lin et al. in this issue), and magnetic resonance
spectroscopy (MRS; useful for measuring brain metabolites/
altered brain chemistry – see article by Lin et al. in this issue).
The clinical use of such tools lags behind their development,
although the gap between development and clinical applica-
tion is narrowing.
Below, we provide a brief primer for some of the neuro-
imaging tools available today. We include skull films, CT,
and MRI including DWI/DTI, and susceptibility weighted
imaging. This primer is not detailed nor is it comprehensive.
Instead, our intention is to provide the reader who is less
familiar with neuroimaging techniques with a context for
some of the tools available for investigating mTBI. Other
neuroimaging modalities, which will not be described here,
include MRS, PET, SPECT, and fMRI. MRS, PET, and
SPECT, will be reviewed by Dr. Alexander Lin and col-
leagues, and Dr. Brenna McDonald and colleagues will
review fMRI, in separate articles in this issue. Table 1
provides a brief summary of these neuroimaging tools.
Skull-X-ray and CT Skull films, or skull X-rays, while
excellent for detecting skull fracture, are not used routinely
to investigate brain trauma because they provide very limit-
ed information (e.g., Bell and Loop 1971; Hackney 1991).
Figure 1 depicts a normal skull film. Computed Tomogra-
phy or Computed Axial Tomography (CT) supplanted the
use of skull films for evaluating neurotrauma when this
technology became available in the 1970s. CT provides
three-dimensional images of the inside of an object, in this
case the brain, using two-dimensional X-Ray images
obtained around a single axis of rotation. Since CT was
introduced in the 1970s, it has become the imaging modality
of choice for evaluating closed head injury in the emergency
room (ER) (e.g., Johnston et al. 2001).
CT is, in fact, the main imaging modality used in the first
24 h for the management of neurotrauma in the ER (e.g.,
Coles 2007). The reasons for this are because it is widely
available in most hospitals, it is fast, and it is accurate for
detecting emergent conditions such as skull fractures, brain
swelling, intracranial hemorrhage, herniation, and radio-
opaque foreign bodies in the brain (see review in Johnston
et al. 2001; Le and Gean (2009)). The use of thin-volume
CT scanners are also often located in close physical prox-
imity to the ER, thus making it easy to transport neuro-
trauma patients. Additionally, the presence of metallic
objects will not result in possibly dangerous accidents in
the CT suite as would be the case using an MR scan,
depending upon the nature of the trauma, and depending
upon whether or not unknown small pieces of metal are
hidden inside the patient following a car accident or other
type of brain trauma. Of further note, MRI scanners are
generally not in close physical proximity to the ER, and
the scanning time is longer, which is an important consid-
eration for patients who are not medically stable. Moreover,
the CTenvironment is able to accommodate the set up of life
support and monitoring equipment that is, at this time, often
more compatible for the CT than for the MRI environment,
although this is changing. CT thus remains the most impor-
tant neuroimaging tool used in the first 24 h of acute neuro-
trauma in the ER, where the most important question to be
answered quickly is: does this person need immediate neu-
Figure 2 depicts a normal CT scan. Note that the skull
and the brain are visible, although there is no differentiation
between gray and white matter, which is discernible using
MRI. There are also bone artifacts with CT that are not
present with MRI, which means that areas of injury around
bone are easier to detect using MRI. MRI also uses no
ionizing energy, as CT does, which becomes important
when considering pediatric populations. This is also a con-
sideration when several repeat scans are needed over time to
follow the progression of injury.
MRI and SWI Magnetic resonance imaging was introduced
in the mid-1980s with the first images acquired on low-field
magnets, i.e., 0.5 Tesla (T). Originally this type of imaging
was called nuclear magnetic resonance (NMR) imaging but
the name was changed to magnetic resonance (MR) imag-
ing, or MRI. The basic principle behind MRI is that
Brain Imaging and Behavior (2012) 6:137–192141
radiofrequency (RF) pulses are used to excite hydrogen
nuclei (single proton) in water molecules in the human body,
in this case the brain. By modulating the basic magnetic
field, and the timing of a sequence of RF pulses, the scanner
produces a signal that is spatially encoded and results in
images. While NMR can be observed with a number of
nuclei, hydrogen imaging is the only one that is widely used
in the medical use of MRI.
MR images can be produced with different contrasts and
can be optimized to show excellent contrast between gray
and white matter, which CT does not. Early MRI scans had
poor spatial resolution and the time to acquire images was
slow, taking many minutes to acquire even one image. Since
the mid-1980s, however, the field strengths of the magnet
have increased from 0.5 to 1.0, to 1.5 T, and to 3.0 T and
beyond. In combination with advances in the capabilities of
the gradient magnetic fields and the RF equipment available
(parallel imaging), it is now possible to acquire sub-
millimeter morphologic images and rich contrast combina-
tions in clinical settings, in a shorter period of time. More-
over, reconstruction algorithms can recreate images even
when the volume of the pixel elements (voxels) is not
completely isotropic (i.e., the same size in all directions).
Figure 3 depicts MRI scans acquired on a 3 T magnet using
Table 1 Summary of modalities
Imaging Technique/Modality:Function:Advantages Offered:
Computed Tomography (CT)
Imaging of bony structures
3D X-ray imaging of an object (e.g., brain
Primarily used for detecting fractures.
Quick, able to have medical equipment in
scanning area, good for skull fractures or
gross injuries/abnormalities requiring
emergent surgical intervention such as
Better resolution than CT, particularly for
soft tissue, can provide gross delineation
between gray and white matter structures,
better visualization of brain stem areas
compared to CT, can also detect subacute
hemorrhages and macroscopic areas of
white matter damage.
Best imaging technique available for
detecting white matter integrity/damage,
able to detect microscopic white matter
damage and trace specific tracts of the
brain (e.g., corpus callosum, superior
longitudinal fasciculus, uncinate).
Provides increased sensitivity to detect areas
of micro-hemorrhage, particularly at gray-
white matter junctions, that are not de-
tectable on standard MRI.
Provides neurophysiological data that is
related to structural damage/changes,
neuronal health, neurotransmission,
hypoxia, and other brain functions.
Provides information on the concentration
of a chemical or protein in the brain such
as the amount of glucose, which reflects
activity, or the density of a type of protein
such as beta amyloid, a hallmark of
Clinical Magnetic Resonance Imaging
Uses radiofrequency pulses to detect
changes in spin signal of hydrogen atoms.
Diffusion Weighted Imaging (DWI)/
Diffusion Tensor Imaging (DTI)
Special type of MRI sequence that uses the
diffusion properties of water to detect
microstructural tissue architecture.
Susceptibility Weighted Imaging (SWI)Special type of MRI technique that takes
advantage of susceptibility differences
among structures (e.g., oxygenated vs.
deoxygenated blood and iron).
Measures brain chemistry by producing a
spectrum where individual chemicals, or
metabolites can be identified and
concentrations can be measured.
Uses radiotracers labeled with different
isotopes that emit signals indicating areas
of uptake or binding in the brain, most
commonly used is 18-
Fluorodeoxyglucose, an analog of glucose.
Magnetic Resonance Spectroscopy (MRS)
Positron Emission Tomography (PET)
Fig. 1 Lateral (left) and frontal (right) view of normal skull X-ray.
(Courtesy of Amir Arsalan Zamani, M.D.)
142 Brain Imaging and Behavior (2012) 6:137–192
1.5 mm slices. Note the high contrast between gray and
white matter that is not visible on CT (see Figure 2). Cere-
bral spinal fluid (CSF) is also prominent, and one can use
the differences in signal intensity of gray matter, white
matter, and CSF to parcellate automatically the brain into
these three tissue classes (e.g., Fischl et al. 2004; Pohl et al.
2007). Of note here, the different tissue classes, from the
parcellation, include quantitative information such as whole
brain volume for gray matter, white matter, and CSF. This
work is based on research developed over more than a
decade in the field of computer vision.
Due to its superior contrast resolution for soft tissues,
MRI technology is far more sensitive than CT in detecting
small contusions, white matter shearing, small foci of axonal
injury, and small subacute hemorrhages (see review in Niogi
and Mukherjee 2010). That MRI is able to discern these
Fig. 2 CT scan of a normal
brain. Left side is at the level of
the temporal lobe where bone
can be seen as white areas
(see red arrows). Right side is at
the level of the frontal lobe.
(Courtesy of Amir Arsalan
Fig. 3 Structural MRI scans
acquired on a 3 T magnet using
1.5 mm slices: a T1-weighted
image, b T2-weighted image,
c T1-weighted image showing
gray matter, white matter,
and CSF parcellation, and
d T1-weighted image showing
the corpus callosum region
Brain Imaging and Behavior (2012) 6:137–192143
more subtle abnormalities, compared with CT, makes it
particularly well suited for evaluating mTBI. Additionally,
there is higher contrast between brain and CSF, and between
gray and white matter, as well as better detection of edema
with MRI than CT, all important factors in evaluating TBI
(see review in Johnston et al. 2001).
Of further note, and of particular interest to mTBI, Mittl
and coworkers (1994) found that in mTBI, where CT find-
ings were negative, 30 % of these cases showed lesions on
MRI that were compatible with hemorrhagic and non-
hemorrhagic diffuse axonal injuries. The increased sensitiv-
ity of MRI over CT in discerning radiological evidence of
brain injury in mTBI has also been shown, and commented
upon, by a number of other investigators including Jenkins
and coworkers (1986), Levin and coworkers (1984; 1987),
Eisenberg and Levin (1989), and Bazarian and coworkers
(2007). Gentry and coworkers (1988) also observed that in a
prospective study of 40 closed injury patients, MRI was
superior to CT in detecting non-hemorrhagic lesions. These
findings, taken together, suggest that while CT may be
critically important in the first 24 h to assess the immediate
need for neurosurgical intervention, for mTBI, MRI is likely
to be more sensitive for detecting small and subtle abnor-
malities that are not detected using CT (e.g., Gentry et al.
1988; Levin et al. 1987).
There are also several types of MRI sequences that add to
what can be gleaned from conventional MRI, including the
use of T1, T2-weighted FLAIR (FLuid Attenuated Inversion
Recovery) to examine macroscopic white matter lesions and
contusions on the cortical surface, as well as susceptibility-
weighted imaging (SWI), which is a type of gradient-
recalled echo (GRE) MRI that can be performed on conven-
tional scanners. SWI was originally developed for venogra-
phy and called Blood-Oxygen-Level-Dependent (BOLD)
venographic imaging (Ashwal et al. 2006; Haacke et al.
2009; Reichenbach et al. 2000; see also review in Kou et
al. 2010 and Niogi and Mukherjee 2010). SWI takes advan-
tage of susceptibility differences between tissues, resulting
in an enhanced contrast that is sensitive to paramagnetic
properties of intravascular deoxyhemoglobin, i.e., sensitive
to venous blood, to hemorrhage, and to iron in the brain. In
essence, susceptibility differences are detected as phase
differences in the MRI signal. In the image processing stage,
SWI superimposes these phase differences on the usual
(magnitude) MR image, thereby allowing the susceptibility
differences to be accentuated in the final image. Of further
note, SWI shows six times greater ability to detect hemor-
rhagic diffuse axonal injuries than other MRI techniques
(Tong et al. 2003; 2004). This technique is thus particularly
appropriate for discerning micro-hemorrhages in TBI, as it
is sensitive to bleeding in gray/white matter boundaries,
where small and subtle lesions are not discernible using
other MRI techniques, making it particularly useful in the
more acute and subacute stages following brain trauma.
SWI, in conjunction with diffusion measures (e.g., DTI),
will thus likely be important for discerning the subtle nature
of mTBI abnormalities in the future. SWI is offered as a
licensed acquisition and processing package by several ven-
dors, but it can be acquired and processed on any scanners
that are 1.0 T, 1.5 T, 3.0 T, or above. Figure 4 depicts
susceptibility-weighted images, where small black areas
indicate blood vessels.
DWI and DTI Diffusion weighted imaging (DWI), devel-
oped in 1991 for use in humans (e.g., Le Bihan 1991), is
based on the random motion of water molecules (i.e.,
Brownian motion). This motion in the brain is affected by
the intrinsic speed of water displacement depending upon
the tissue properties and type, i.e., gray matter, white matter,
and CSF. DWI was first used to evaluate acute cerebral
ischemia where it was thought that decreased diffusion
was the result of neuronal and glial swelling and likely
related to cytotoxic edema, whereas increased diffusion
was thought to reflect vasogenic edema. The method has
been applied to TBI with mixed results (see Niogi and
Apparent Diffusion Coefficient (ADC) is a measure of
diffusion, on average, and the word “apparent” is used to
emphasize that what is quantified is at the level of the voxel,
and not at the microscopic level. This measure has been
used as an indicator of edema, which, in conjunction with
DTI (see below), can be used to quantify, indirectly, both
edema and damage to the integrity of white matter fiber
bundles in TBI (see review in Assaf and Pasternak 2008;
Niogi and Mukherjee 2010). A measure of free water, how-
ever, derived from DTI (Pasternak et al. 2009; Pasternak et
al. 2010; 2011a; b) may provide a better measure of edema,
and this will be discussed further in the section on future
directions of research.
DTI is a DWI technique that has opened up new possi-
bilities for investigating white matter in vivo as it provides
information about white matter anatomy that is not available
using any other method — either in vivo or in vitro (Basser
et al. 1994; Pierpaoli and Basser 1996; Pierpaoli and Basser
1996; see also review in Assaf and Pasternak 2008). At
today’s image resolution, it does not detect water behavior
within individual axons. Instead it describes local diffusion
properties. In other words, the individual behavior of axons
cannot be described using DTI, but diffusion properties can
be described that are relevant to fiber bundles.
DTI differs from conventional MRI in that it is sensitive
to microstructural changes, particularly in white matter,
whereas CT and conventional MRI (including also FLAIR)
reveal only macroscopic changes in the brain. Thus subtle
changes using DTI can reveal microstructural axonal inju-
ries (Basser et al. 1994; Pierpaoli and Basser 1996; Pierpaoli
144Brain Imaging and Behavior (2012) 6:137–192
and Basser 1996), which are potentially responsible also for
persistent postconcussive symptoms.
The concept underlying DTI is that the local profile of the
diffusion in different directions provides important indirect
information about the microstructure of the underlying tis-
sue. It has been invaluable in investigations of white matter
pathology in multiple sclerosis, stroke, normal aging, Alz-
heimer’s disease, schizophrenia and other psychiatric disor-
ders, as well as in characterizing diffuse axonal injuries in
mTBI (see reviews in Assaf and Pasternak 2008; Kou et al.
2010; Shenton et al. 2010; Whitford et al. 2011).
The latter focus on TBI is relatively recent (see review of
the literature, below). Those investigating mTBI, in partic-
ular, have been disappointed by the lack of information
gleaned from conventional MRI and CT, although, as noted
previously, this is not surprising given that the most com-
mon injuries observed in mTBI are diffuse axonal injury/
traumatic axonal injury (DAI/TAI), which are not easily
detected using conventional MR or CT scans. With the
advent of DTI, however, DAI/TAI have the potential to be
quantified and this information can be used for diagnosis,
prognosis, and for the evaluation of treatment efficacy.
Quantification of pathology using DTI is based on meas-
ures that calculate the amount of restriction of water move-
ment in the brain, which is determined to a large extent by
the tissue being measured. For example, the movement of
water is unrestricted in a medium such as CSF, where it
diffuses equally in all directions (i.e., isotropic). However,
in white matter, the movement of water is more restricted by
axonal membranes, myelin sheaths, microtubules, neurofila-
ments, etc. In white matter, this restriction is dependent on
the directionality of the axons (i.e., diffusion is not equal in
all directions) and is referred to as anisotropic diffusion.
Using tensors, adapted from the field of engineering, the
average shape of the diffusion is characterized as more or
less spherical when there is no impediment to water diffu-
sion, as for example in CSF (i.e., unrestricted water is free to
diffuse in all directions: isotropic). However, the average
shape of the diffusion becomes more elongated, or cigar
shaped, when there is a preferred orientation in which water
is restricted, as for example in white matter. Here, water
diffuses freely in directions parallel to axons but it is re-
stricted in directions that are perpendicular to the axons,
which results in the magnitude of the diffusion along the
axons being larger than the two perpendicular directions,
leading to an elongated ellipsoidal shape of the diffusion
tensor, described as anisotropic. The measurement of the
distance that water diffuses, over a given period of time, for
at least six non-collinear directions, makes it possible to
reconstruct a diffusion tensor (and the associated ellipsoid)
that best describes water diffusion within a given voxel.
Consequently, the volume (size) and shape of the ellipsoid
can be calculated, and this provides important information
about the diffusion properties, and hence about microstruc-
tural aspects of brain tissue.
There are various ways that the shape and size of a
diffusion ellipsoid can be quantified, but the two most
common indices used are Fractional Anisotropy (FA) for
shape, and Mean Diffusivity (MD) for size. FA is a scalar
measure that ranges from 0 to 1, with 0 being completely
isotropic, meaning that water diffuses equally in all direc-
tions, and 1 depicting the most extreme anisotropic scenario
in which molecules are diffusing along a single axis. Ac-
cordingly, in CSF and gray matter, as noted above, the
direction of water is equal in all directions (i.e., isotropic),
and the value is close to 0. In contrast, in white matter, for
example in the corpus callosum, the water is relatively free
along the axons, but restricted perpendicular to the axons,
and therefore more anisotropic, with FA being closer to 1.
Thus in white matter, reduced FA is generally thought to
reflect loss of white matter integrity that may reflect damage
to myelin or axon membrane damage, or perhaps reduced
axonal packing density, and/or reduced axonal coherence
(see review in Kubicki et al. 2007).
Mean diffusivity (MD), the second most common mea-
sure (and proportional to the trace of the diffusion tensor), is
Fig. 4 Sagittal (left) and
axial (right) view of
(SWI) of a normal brain.
Small black areas indicate
blood vessels in the brain
that are enhanced using SWI
Brain Imaging and Behavior (2012) 6:137–192 145
different from FA in that it is a measure of the size of the
ellipsoid, rather than the shape, as is the case for FA. MD is
similar to ADC, described above for DWI, but instead it is
the average ADC along the three principal diffusion direc-
tions, where one axis is in the direction of the largest
magnitude of the diffusion in the voxel, and the other two
are perpendicular to the main diffusion direction. The main
diffusion direction in white matter is referred to as the
longitudinal or axial direction, while the other two direc-
tions are referred to as the radial or tangent axes. FA and
MD are frequently observed as being inversely related. (For
further descriptions of DTI and associated methods of anal-
yses, the reader is referred to Pierpaoli and Basser 1996;
Pierpaoli and Basser 1996; Smith et al. 2006; and the
reviews in Ashwal et al. 2006; Fitzgerald and Crosson
2011; Hunter et al. 2011; Le and Gean 2009; Kou et al.
2010; and Niogi and Mukherjee 2010).
Figure 5, 6, and 7 depict the kind information that can be
extracted from diffusion tensor images. For example,
Figure 5 shows diffusion images that highlight white matter,
along with colored maps that reflect the directions of the
white matter fiber tracts in the brain. Figure 6 shows white
matter tracts superimposed on structural images. Figure 7
shows an area identified as tumor in the frontal lobe, where
white matter fiber tracts can be visualized in relation to the
tumor and in relation to the frontal horn of the lateral
ventricles. These figures reflect important, recent advances
in methodology that are sufficiently robust and sensitive that
they can be used for visualizing and quantifying white
matter pathology in vivo, for the assessment of mTBI clin-
ically. These tools are available now for this purpose and
will be discussed further in the future directions section of
DTI, however, is somewhat non-specific and it is not
known whether disruptions in FA and MD are the result of
disturbances in axonal membranes, myelin sheath, micro-
tubules, neurofilaments, or other factors. More specific
measures, which are being developed (see below), are need-
ed to delineate further the biological meaning of alterations
in white matter integrity (see review in Assaf and Pasternak
2008; Niogi and Mukherjee 2010).
While FA and MD are the two main dependent measures
derived from DTI, there are other measures that have been
developed, including Mode (Ennis and Kindlmann 2006),
which defines more precisely the shape of the diffusion
tensor (useful in distinguishing the anatomy of fiber tracts,
including distinguishing fiber crossings from pathology).
Other measures include Inter-Voxel Coherence (Pfefferbaum
et al. 2000), which measures how similar anisotropic tensors
are in neighboring voxels, useful in measuring anomalies in
macroscopic axonal organization within the tract of interest,
and Axial and Radial Diffusivity, which are purported to
measure axonal and myelin pathology, respectively (Song et
al. 2001; Song et al. 2003; Budde et al. 2007; Budde et al.
2011). These additional measures may provide more specific
information regarding the microstructural abnormalities
discerned using the sensitive, albeit less specific, measures
of FA and MD.
Finally, another relatively new post-processing method is
fiber tractography, which was developed to visualize and to
quantify white matter fiber bundles in the brain (e.g.,
Conturo et al. 1999; Mori et al. 1999; Basser et al. 2000).
This method makes it possible to follow fiber tracts along a
diffusion direction in very small steps so as to create long
fiber tracts that connect distant brain regions. The accuracy
of fiber tractography is dependent upon a number of factors
including image resolution, noise, image distortions and
partial volume effects that result from multiple tracts cross-
ing in a single voxel. The main advantage of DTI tractog-
raphy, from a clinical research perspective, is that the whole
fiber bundle, instead of just a portion of the fiber bundle, can
be evaluated. DTI tractography is thus a promising tool that
can be used not only to understand how specific brain
regions are connected and where damage occurs along fiber
bundles, but it can also be used to understand how this
connectivity may be relevant to functional abnormalities.
Further, tractography methods can be used to both visualize
and to quantify white matter fiber bundle damage in a single
case and thus these methods are potentially important for
diagnosing mTBI based on radiological evidence.
Importantly, many of the measures described above are
just beginning to be applied to investigate brain injuries in
Fig. 5 Diffusion tensor images
acquired on a 3 T magnet. Left:
fractional anisotropy (FA) map.
White areas are areas of high
anisotropy. Right: color by
orientation map. Diffusion in
the left-right direction is
shown in red, diffusion in the
superior-inferior direction is
shown in blue, and diffusion in
the anterior-posterior direction
is shown in green
146Brain Imaging and Behavior (2012) 6:137–192
Fig. 6 Fiber tractography of
commonly damaged tracts in
mild traumatic brain injury,
including: a the anterior corona
radiata and the genu of corpus
callosum, b the uncinate
fasciculus, c the cingulum
bundle in green and the body of
corpus callosum in red, and
d the inferior longitudinal
fasciculus (Niogi and
Mukherjee 2010; reprinted
with permission Wolters
Kluwer Health / Lippincott
Williams & Wilkins)
Fig. 7 Diffusion MRI data for
neurosurgical planning. The
tractography region of interest
(ROI) is a box placed around
the tumor (in green) in the
frontal lobe. The ROI is also
visualized with rectangles in the
slice views below. Tracts are
then created based on the
principal diffusion directions,
which are color-coded (bottom).
Diffusion ellipsoids are shown
along the tract to visualize the
shape of the local diffusion
Brain Imaging and Behavior (2012) 6:137–192 147
mTBI and thus this area is a relatively new frontier for
exploration. The application of DTI, and the measures de-
rived from DTI, will likely contribute enormously to our
understanding of the nature and dynamics of brain injuries
Review of MRI findings in mTBI
Much of the work with MRI has been to investigate the
higher sensitivity of MRI, compared with CT, for detecting
brain abnormalities in mTBI (see previous discussion). Less
attention has been given to investigating morphometric
abnormalities in mTBI using area, cortical thickness, and/
or volume measures. Table 2 lists studies, by first author and
year, which have examined aspects of morphometric abnor-
malities in patients with mTBI. Most of these studies, how-
ever, include a range of TBI, from mild to severe (e.g.,
Anderson et al. 1995; Anderson et al. 1996; Bergeson et
al. 2004; Bigler et al. 1997; Ding et al. 2008; Fujiwara et al.
2008; Gale et al. 2005; Levine et al. 2008; Mackenzie et al.
2002; Schonberger et al. 2009; Strangman et al. 2010; Tate
and Bigler 2000; Trivedi et al. 2007; Warner et al. 2010a; b;
Wilde et al. 2004; Wilde et al. 2006; Yount et al. 2002), with
only a small number of studies that investigate morphometric
abnormalities specifically in mTBI (e.g., Cohen et al. 2007;
Holli et al. 2010). Additionally, while most of the studies
listed in Table 2 categorize severity of TBI (i.e., mild, moder-
ate, or severe) based on scores derived from the Glasgow
Coma Scale (GCS; Teasdale and Jennett 1974), one study
defines severity by posttraumatic amnesia (PTA) duration
(Himanen et al. 2005).
The time of scan post-injury has also varied considerably
from study to study with the least amount of time being a
median of one day (Warner et al., 2010), up to a mean of
30 years (Himanen et al. 2005), with one study that did not
report time of scan post-injury (Yurgelun-Todd et al. 2011).
Additionally, most of these studies were performed using a
1.5 T magnet, with only a small number performed using a
3 T magnet (e.g., Ding et al. 2008; Trivedi et al. 2007;
Warner et al. 2010a; b; Yurgelun-Todd et al. 2011). There
are also different methods used to evaluate brain injuries,
ranging from manual and automated measures of lesion vol-
ume (e.g., Cohen et al. 2007; Ding et al. 2008; Schonberger et
al. 2009), to volume analysis (e.g., Anderson et al. 1995;
Anderson et al. 1996; Bergerson Bergeson et al. 2004; Bigler
et al. 1997; Ding et al. 2008; Gale et al. 1995; Himanen et al.
2005; Mackenzie et al. 2002; Schonberger et al. 2009;
Strangman et al. 2010; Tate and Bigler 2000; Trivedi et al.
2007; Warner et al. 2010a; b; Wilde et al. 2004; Wilde et al.
2006; Yount et al. 2002), to voxel-based-morphometry
(VBM; Gale et al. 2005), to texture analysis (Holli et al.
2010a and b), to semi-automated brain region extraction
based template (SABRE) analysis (Fujiwara et al. 2008;
Levine et al. 2008), to the use of FreeSurfer for volumetric
analysisofmultiplebrain regions (e.g.,Strangmanetal. 2010;
Warner et al. 2010a; b; Yurgelun-Todd et al. 2011).
With all the differences among the studies, the most
important take home message is that MRI can be used to
detect brain abnormalities in patients with TBI. It is also not
surprising that the injuries that are most apparent are ob-
served in more moderate and severe cases of TBI. Further,
the volume of lesions can be detected, although whether or
not these lesions are in frontal or non-frontal regions does
not seem to differentiate between groups on measures of
cognitive function (Anderson et al. 1995). Mild TBI
patients, nonetheless, evince MR lesions in 30% of a sample
of 20 patients (Cohen et al. 2007), and in one study, func-
tional outcome was correlated with lesion volume and
cerebral atrophy, although this study did not analyze,
separately, mild, moderate, and severe cases of TBI
(Ding et al. 2008).
Overall brain volume reduction (atrophy) also seems to
be a common finding in what are likely to be more severe
patients (e.g., Cohen et al. 2007; Ding et al. 2008; Gale et al.
1995; Gale et al. 2005; Levine et al. 2008; Mackenzie et al.
2002; Trivedi et al. 2007; Warner et al. 2010a; Yount et al.
2002), and there are also volume reductions noted in overall
gray matter (e.g., Cohen et al. 2007; Ding et al. 2008;
Fujiwara et al. 2008; Schonberger et al. 2009; Trivedi et
al. 2007), with a finding also of gray matter volume reduc-
tion in the frontal lobe (e.g., Fujiwara et al. 2008; Strangman
et al. 2010; Yurgelun-Todd et al. 2011), and in frontal and
temporal lobes in some cases (e.g., Bergeson et al. 2004;
Gale et al. 2005; Levine et al. 2008). Additionally, Bergeson
et al. (2004) reported a correlation between frontal and
temporal lobe atrophy and deficits in memory and executive
function in patients with a range of severity from mild, to
severe (GCS; 3-14).
Overall reduction in white matter has also been reported
(e.g., Ding et al. 2008; Levine et al. 2008; Schonberger et al.
2009), as well as white matter reduction at the level of the
mesencephalon, corona radiata, centrum semiovale (Holli et
al. 2010a and b), and corpus callosum (Holli et al. 2010a
and b; Warner et al. 2010a; Yount et al. 2002). Ding and
coworkers noted that the changes in white and gray matter
over time were correlated with acute diffuse axonal injuries
and the latter predicted post-injury cerebral atrophy.
More specific reductions in volume in brain regions have
also been observed, including in the hippocampus (Bigler et
al. 1997; Himanen et al. 2005; Strangman et al. 2010; Tate
and Bigler 2000; Warner et al. 2010a), amygdala (e.g.,
Warner et al. 2010a; b), fornix (Gale et al. 1995; Tate and
Bigler 2000), thalamus (e.g., Strangman et al. 2010; Warner
et al. 2010a; Yount et al. 2002), regions involving the
cingulate gyrus (e.g., Gale et al. 2005; Levine et al. 2008;
148Brain Imaging and Behavior (2012) 6:137–192
Table 2 MR morphometry studies
≥ 6 weeks.
Patients: 68 TBI patients (49 M,
19 F), [GCS 3-15]; 38 with frontal
lesions and 29 with no frontallesions.
- No significant differences were
observed between the two groups on
lesion size and ventricle to brain
- No significant differences were
observed between groups on tests ofneuropsychological functioning.
Patients: 88 TBI patients (51 M,
37 F; mean age 28.5), [GCS 3-15], 36 mTBI [GCS 11-15], 22
moderate TBI [GCS 7-10], 29
severe TBI [GCS 3-6].
- Patients had decreased fornix-to-
brain ratio, total brain volume, CC volume; and increased temporalhorn volume, ventricular volume,ventricle-to-brain ratio, and
interpeduncular cistern volume.
Average 21 months.
Controls: 73 controls (36 M, 37 F;
mean age 31).
- Fornix-to-brain ratio correlated with
- Cerebral peduncle and CC volume
correlated with neuropsychological
tests of motor function.
- GCS was correlated with fornix-to-
brain ratio, internal capsule volume, brain volume, and cerebral pedunclevolume.
Subacute and Chronic.
Patients: 63 TBI patients (45 M,
18 F); 35 with lesions (26 M, 9 F;
mean age 29.43), 28 without
lesions (19 M, 9 F; mean age
- Subjects with lesions had
significantly smaller thalami, higher
VBR, and lower GCS.
≥ 6 weeks.
- More severe injuries (GCS<9) had
significantly smaller thalamic
volumes and greater VBR than less
severely injured subjects.
Controls: 33 controls (26 M, 9 F;
mean age 29.24).
- VBR was negatively correlated with
GCS and thalamic volume, and
positively correlated with lesion volume.
- GCS was negatively correlated with
lesion volume, and positively
correlated with thalamic volume.
Subacute and Chronic.
Patients: 94 TBI patients (59 M and
35 F; mean age 27) [GCS 3-15].
- Patients relative to controls showed
reduced hippocampal volume and
44 scans≤100 days post-injury,
55 scans>100 days post-injury.
Brain Imaging and Behavior (2012) 6:137–192149
Table 2 (continued)
temporal horn enlargement. These
measures correlated with cognitive
Controls: 96 controls (37 M, 59 F;
mean age 31).
- In patients >70 days post-injury,
temporal horn volume correlated with
with verbal memory function.
Subacute and Chronic.
≥ 2 months.
Patients: 86 TBI patients (58 M,
28 F; mean age 30.9), [GCS 3-15].
- Decreased fornix and hippocampal
volume in patients versus controls
Controls: 46 controls (31 M, 15 F;
mean age 37.21).
- Fornix and hippocampal volumes
correlated with degree of injury
severity in patients.
- Hippocampal volume correlated with
Subacute and Chronic.
Patients: 14 patients (mean age
36.1) [GCS 9-15]; 11 with mTBI
[GCS 13-15], and 3 with
moderate TBI [GCS 9-12].
- Brain volumes, CSF volumes, and %
Volume of Brain Parenchyma (VBP)were not significantly different
between patients and controls at the
single time point.
14 patients at >3 months after
injury, 7 patients at 2 time
points >3 months apart.
Controls: 10 controls (mean age
34.9) underwent one MR session,
and 4/10 underwent 2 sessions>
3 months apart.
- Rate of decline in %VBP and change
in %VBP between the first and
second time points were significantly greater in patients.
-Change in %VBP was greater in
patients with loss of consciousness
than in those without.
Patients: 27 patients (18 M, 9 F;
mean age 26) with TBI,
[GCS 4-14]. No focal lesions
or infarctions on MRI.
- Patients had significant atrophy,
primarily in the posterior cingulategyrus, and degree of atrophy was
correlated with severity of injury.
Average 22.8 months.
Controls: 12 age and gender-
- Patients also had reduced CC and
thalamic cross-sectional surface
areas, with associated increased lat- eral ventricular volume, as well as
reduced brain volume and increased
- Neuropsychological performance
was not related to changes in
150Brain Imaging and Behavior (2012) 6:137–192
Table 2 (continued) Download full-text
cingulate gyrus cross-sectional
surface area in the TBI patients.
Subacute and Chronic.
Patients: 75 TBI patients (50 M,
25 F; mean age 32.9) [GCS 3-14].
- Patients had significantly increased
atrophy in frontal and temporal lobescompared to controls.
≥ 90 days.
Controls: 75 controls (50 M, 25 F;
mean age 31.4).
- Atrophy in frontal and temporal
lobes correlated with deficits in
memory and executive function.
Subacute and Chronic.
Patients: 77 patients [GCS 3-15];
25 TBI and positive blood alcohol
level (BAL) and 52 TBI with
- Increased general brain atrophy was
observed in patients with a positive
BAL and/or a history of moderate to
heavy alcohol use.
≥ 90 days.
Patients: 9 patients with a history of
TBI (8 M, 1 F; mean age 29.1),
Voxel Based Morphometry.
- Patients showed reduced gray matter
concentration in frontal and
temporal cortices, cingulate gyrus,
subcortical gray matter, and in the
Approximately 1 year
(mean 10.6 months).
Controls: 9 controls: (8 M, 1 F;
mean age 28.8).
- Decreased gray matter concentration
was also correlated with lower
scores on tests of attention and lower
Patients: 61 patients (41 M, 20 F;
mean age at injury 29.4); 17 mTBI [post-traumatic amnesia
(PTA) <1 hr], 12 moderate TBI[PTA 1–24 hrs], 11 severe [PTA
1-7 days], 21 very severe [PTA
>7 days] [GCS not reported].
- Reduced hippocampal and increased
lateral ventricular volumes were
significantly associated with impaired
memory functions, memory
complaints, and executive
Average 30 years.
- Volume of the lateral ventricles was
the best predictor of cognitive
- There was also a modest relationship
between severity of injury and
Suacute and Chronic.
Patients: 60 patients with severe-
to-mild [GCS 3–15] TBI (38 M,
22 F; mean age 28.6).
- Longer Post-Traumatic Amnesia
(PTA) duration predicted increased
≥ 90 days.
- 6 % increase in the odds of
developing later atrophy with each
additional day of PTA.
Brain Imaging and Behavior (2012) 6:137–192151