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Diffusion Tensor Imaging Correlates of Concussion Related Cognitive Impairment


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Introduction: Cognitive impairment after concussion has been widely reported, but there is no reliable imaging biomarker that predicts the severity of cognitive decline post-concussion. This study tests the hypothesis that patients with a history of concussion and persistent cognitive impairment have fractional anisotropy (FA) and mean diffusivity (MD) values from diffusion tensor imaging (DTI) that are specifically associated with poor performance on the Montreal Cognitive Assessment (MoCA). Methods: Fifty-three subjects (19 females) with concussions and persistent cognitive symptoms had MR imaging and the MoCA. Imaging was analyzed by atlas-based, whole-brain DTI segmentation and FLAIR lesion segmentation. Then, we conducted a random forest-based recursive feature elimination (RFE) with 10-fold cross-validation on the entire dataset, and with partial correlation adjustment for age and lesion load. Results: RFE showed that 11 DTI variables were found to be important predictors of MoCA scores. Partial correlation analyses, corrected for age and lesion load, showed significant correlations between MoCA scores and right fronto-temporal regions: inferior temporal gyrus MD ( r = −0.62, p = 0.00001), middle temporal gyrus MD ( r = −0.54, p = 0.0001), angular gyrus MD ( r = −0.48, p = 0.0008), and inferior frontal gyrus FA ( r = 0.44, p = 0.002). Discussion: This is the first study to demonstrate a correlation between MoCA scores and DTI variables in patients with a history of concussion and persistent cognitive impairment. This kind of research will significantly increase our understanding of why certain persons have persistent cognitive changes after concussion which, in turn, may allow us to predict persistent impairment after concussion and suggest new interventions.
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published: 24 May 2021
doi: 10.3389/fneur.2021.639179
Frontiers in Neurology | 1May 2021 | Volume 12 | Article 639179
Edited by:
Niklas Marklund,
Lund University, Sweden
Reviewed by:
Ralph George Depalma,
United States Department of Veterans
Affairs, United States
Sarah C. Hellewell,
Curtin University, Australia
Paul E. Schulz
These authors share first authorship
Specialty section:
This article was submitted to
a section of the journal
Frontiers in Neurology
Received: 08 December 2020
Accepted: 20 April 2021
Published: 24 May 2021
Gonzalez AC, Kim M, Keser Z,
Ibrahim L, Singh SK, Ahmad MJ,
Hasan O, Kamali A, Hasan KM and
Schulz PE (2021) Diffusion Tensor
Imaging Correlates of Concussion
Related Cognitive Impairment.
Front. Neurol. 12:639179.
doi: 10.3389/fneur.2021.639179
Diffusion Tensor Imaging Correlates
of Concussion Related Cognitive
Angelica C. Gonzalez 1†, Minseon Kim 1† , Zafer Keser 1, Lamya Ibrahim 1, Sonia K. Singh 1,
Mohammed J. Ahmad 1, Omar Hasan 1, Arash Kamali 2, Khader M. Hasan 2and
Paul E. Schulz 1
1Department of Neurology, University of Texas McGovern Medical School, Houston, TX, United States, 2Department of
Diagnostic and Interventional Radiology, University of Texas McGovern Medical School, Houston, TX, United States
Introduction: Cognitive impairment after concussion has been widely reported, but
there is no reliable imaging biomarker that predicts the severity of cognitive decline
post-concussion. This study tests the hypothesis that patients with a history of
concussion and persistent cognitive impairment have fractional anisotropy (FA) and mean
diffusivity (MD) values from diffusion tensor imaging (DTI) that are specifically associated
with poor performance on the Montreal Cognitive Assessment (MoCA).
Methods: Fifty-three subjects (19 females) with concussions and persistent cognitive
symptoms had MR imaging and the MoCA. Imaging was analyzed by atlas-based,
whole-brain DTI segmentation and FLAIR lesion segmentation. Then, we conducted a
random forest-based recursive feature elimination (RFE) with 10-fold cross-validation on
the entire dataset, and with partial correlation adjustment for age and lesion load.
Results: RFE showed that 11 DTI variables were found to be important predictors of
MoCA scores. Partial correlation analyses, corrected for age and lesion load, showed
significant correlations between MoCA scores and right fronto-temporal regions: inferior
temporal gyrus MD (r= −0.62, p=0.00001), middle temporal gyrus MD (r= −0.54, p
=0.0001), angular gyrus MD (r= −0.48, p=0.0008), and inferior frontal gyrus FA (r=
0.44, p=0.002).
Discussion: This is the first study to demonstrate a correlation between MoCA
scores and DTI variables in patients with a history of concussion and persistent
cognitive impairment. This kind of research will significantly increase our understanding
of why certain persons have persistent cognitive changes after concussion which,
in turn, may allow us to predict persistent impairment after concussion and suggest
new interventions.
Keywords: diffusion tensor imaging, concussion, mild traumatic brain injury, cognitive impairment, prognosis
Concussion, which is interchangeably used with “mild traumatic brain injury” (mTBI), is defined
as a clinical syndrome of biomechanically induced alteration of brain function, which may involve
loss of consciousness (1). As a sequela of concussion, people can develop cognitive impairment,
behavioral abnormalities, and mood disorders. The number of TBI-related Emergency Department
Gonzalez et al. DTI Correlates of Concussion-Related Cognitive Impairment
visits in 2014 was reported as 2.87 million, with 56,800 deaths in
the United States (2,3). Despite its prevalence and severity, no
diagnostic or prognostic biomarkers unique to concussion have
been validated, which has greatly hindered our ability to test early
interventions (4).
Previous studies have revealed that diffuse axonal injury
(DAI) is a critical pathologic finding in concussion that cannot
be detected by CT or conventional MRI (58). Diffusion
tensor imaging (DTI) has been widely used in the study of
concussion because it can reliably detect the microstructural
white matter changes found in DAI. The two most commonly
used DTI parameters are fractional anisotropy (FA) and mean
diffusivity (MD) (9,10). FA quantifies the directionality of water
diffusion, which ranges from 0 (isotropic) to 1 (anisotropic).
MD measures the total diffusion rate in all directions within a
voxel. White matter damage, as seen in DAI, results in fewer
microstructural elements that limit diffusion, thereby decreasing
the FA and increasing the MD. In addition, the Montreal
Cognitive Assessment (MoCA) has proven to be a promising
tool due to its ability to screen for occult memory impairment
in patients with post-concussive syndrome and mTBI with
high sensitivity.
The purpose of this study was to test the hypothesis that
patients with cognitive impairment post-concussion have DTI-
derived neuroimaging biomarkers that are specifically associated
with poorer MoCA scores.
Fifty-three subjects (19 females) with a history of concussion
were evaluated at UTHealth Neurosciences Neurocognitive
Disorders Center in Houston, Texas. The concussion was
secondary to various etiologies, including sports-related, car
accidents, and falls, that lead to varying degrees of persistent
cognitive impairment and neuropsychologic symptoms
were included in this study (Tables 1,2for demographics,
characteristics, and symptoms). The subjects had a MoCA and
MR imaging, including T1w, fluid-attenuated inversion recovery
(FLAIR), and diffusion-weighted imaging (DWI) sequences.
Image Acquisitions and Analyses
Whole-brain MRI data were acquired on a Philips 3.0 T Intera
scanner using a SENSE receive head coil. Both T1-weighted
and FLAIR sequences had a spatial resolution of 1 mm ×
1 mm ×1 mm, and field-of-view was 256 ×256 mm. Diffusion-
weighted image (DWI) data were acquired axially using a
single-shot multi-slice 2-D spin-echo diffusion sensitized and
fat-suppressed echo-planar imaging (EPI) sequence, with the
balanced Icosa21 tensor encoding scheme (11). The b-factor was
1,000 s mm2, TR/TE 7,100/65 ms, FOV 256 ×256 mm, and
slice thickness/gap/#slices =3 mm/0 mm/44. The EPI phase
encoding used a SENSE k-space undersampling factor of two,
with an effective k-space matrix of 128 ×128, and an image
matrix after zero-filling of 256 ×256. The constructed image
spatial resolution for the DWI data was =1×1×3 mm.
We performed whole-brain atlas-based DTI segmentation
through MRICloud software (168 regions) and obtained FA and
TABLE 1 | Demographic characteristics and description of subjects with
post-concussive symptoms.
Characteristics (n=53) Frequency (%)
Age, Median [IQR*] 55 [36–68]
Female 19 (35%)
Male 34 (64%)
Number of Trauma
Single 20 (38%)
Multiple 33 (62%)
Mechanism of Traumaa
Sports-related 27 (51%)
Falls 9 (17%)
Motor vehicle accident 14 (26%)
Hit head against surface 7 (13%)
Physical abuse 1 (2%)
Suicidal attempt 1 (2%)
Loss of Consciousness 25 (47%)
MoCA Score, Median [IQR*] 26 [20–27]
Cognitive Risk Factors other than TBI
Family history of dementia 15 (28%)
Cardiovascularb27 (51%)
Depression/anxiety 23 (43%)
*IQR: inter-quartile range.
aSome patients had multiple machanisms of trauma and multiple cognitive risk factors.
bCardiovascular risk factors include tobacco use, BMI >30, hypertension, hyperlipidemia,
and diabetes mellitus.
TABLE 2 | Description of symptoms.
Complaints* (n=53) Frequency (%)
Sleep difficulties 20 (38%)
Personality changes 24 (45%)
Memory impairment 53 (100%)
Inattention 13 (24%)
Word finding difficulty 14 (26%)
Headache 17 (32%)
Vertigo 5 (9%)
Depression 3 (6%)
Anxiety 7 (13%)
*Some patients had multiple complaints.
MD values ( (12). We performed
lesion segmentation on FLAIR sequences through volBrain
software ( (13). Lesion load was
then converted to the percentage of total intracranial volume
(ICV) [formula =lesion volume (ml)/intracranial volume (ICV)
×100]. Both DTI and lesion segmentations were inspected on a
case-by-case basis for anatomical accuracy.
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Gonzalez et al. DTI Correlates of Concussion-Related Cognitive Impairment
Statistical Analyses
Histogram plots were utilized to identify distribution patterns.
Descriptive statistics were used to compute the means and
standard deviations (SD) or the medians and the range between
first and third quantiles if not normally distributed. We then
conducted a random forest-based recursive feature elimination
(RFE) with 10-fold cross-validation in the entire dataset for
all the regions FA and MD values to compute the importance
for each predictor and remove redundant predictors of MoCA
scores. The central premise using a feature selection technique
is that data contains some features that are either redundant or
irrelevant; thus, they can be removed without incurring much
loss of information (14). In practice, the backward elimination
regression model method that calculates the importance of
each independent variable and removes the ones with the least
importance based on root mean square error (RMSE) metric. As
it is not a machine-learning algorithm, training or test dataset
was not used. After the removal of the redundant independent
variables, the partial correlation adjusted for age and lesion
load was performed to identify the correlations between MoCA
scores and remaining DTI scores. We corrected our analysis for
white lesion load as leukoaraiosis is independently associated
with cognitive decline (15). After obtaining p-values, the false
discovery rate (FDR) analysis of 5% was also conducted for
multiple comparison analyses and only corrected p-values were
reported. R statistical package was used for the statistical analyses.
Study Design and Participants
Fifty-three subjects who suffered from a concussion and had
persistent symptoms were included in this study (34 males and
19 females). Twenty-seven patients had a history of concussion
related to sports, with the majority being football players. Other
mechanisms of concussion included: motor vehicle accident (14),
falls (9), hitting the head against a surface (7), suicide attempts
(1), and physical abuse (1). Thirty-three patients reported
multiple concussions, and 25 reported a loss of consciousness
from their trauma. The last concussion before they presented
to the clinic varied from 1 month to 45 years. Most patients
experienced symptoms months or years before they consulted.
A summary of the patient’s demographics and description can be
found in Table 1, and a more detail information can be found in
Supplementary Material.
Forty-eight patients were experiencing memory problems
as their main reason for consult, and it was associated with
symptoms such as headache, changes in their sleep, personality
and or behavioral changes, difficulty finding words, new onset
of mood disorder, and vertigo. Five other patients reported
headache (2), inattention (2), and vertigo (1) as their chief
complaints, with memory problems as an associated symptom.
Table 2 shows patient’s self-reported symptoms. Forty-two
patients had other cognitive risk factors, such as obesity,
hypertension, hyperlipidemia, hypertriglyceridemia, diabetes,
cardiovascular disease, depression, anxiety, family history of
dementia or tobacco use (Table 1).
Age, MoCA scores, and lesion load were not normally
distributed, whereas DTI values were normally distributed per
histogram plots. Median age was 55 (1st quantile =36- 3rd
quantile =68), median MoCA was 26 (20–27), and median lesion
load was 0.06 (0.02–0.25).
RFE showed that 12 DTI variables were found to be important
predictors of MoCA scores and were included in the correlation
analyses; the right inferior temporal gyrus (ITG) MD (0.90 ±0.13
×10-3 mmmm/s), the right middle temporal gyrus (MTG) MD
(0.91 ±0.11 ×10-3) and FA (0.20 ±0.01), the right angular gyrus
(AG) MD (1.05 ±0.17 ×10-3 mmmm/s), the right inferior
frontal gyrus (IFG) FA (0.22 ±0.02), the right entorhinal cortex
MD (0.90 ±0.13 ×10-3), the left fornix FA (0.39 ±0.09), the left
nucleus accumbens MD (1.13 ±0.22 ×10-3), right splenium of
the corpus callosum FA (0.61 ±0.04), and bilateral tapetum of
corpus callosum FAs (right =0.45 ±0.08, left =0.52 ±0.09).
For these values, the partial correlation analyses corrected for
age and lesion showed a significant correlation between MoCA
scores and right fronto-temporal regions; right ITG MD (r=
0.62, p=0.00001), right MTG MD (r= −0.54, p=0.0001),
right AG MD (r= −0.48, p=0.0008), right IFG FA (r=
0.44, p=0.002) whereas the remaining values did not show
significant correlations with MoCA after FDR corrections (p>
0.05). Significant correlations were highlighted in Figure 1.
To our knowledge, this is the first study to investigate
the correlation between MoCA scores and DTI variables
using atlas-based methods in patients with a history of
concussion and persistent cognitive impairment. The MoCA is
a quick, convenient, and sensitive screening tool for cognitive
impairment. Its administration consists of 12 individual tasks
grouped into seven cognitive domains: (1) visuospatial/executive;
(2) naming; (3) attention; (4) language; (5) abstraction; (6)
memory and (7) orientation. Memory, attention, and visuospatial
functions are the most frequently affected domains in TBI (3). We
analyzed 53 patients’ data from medical records in an outpatient
setting. The average time between the evaluation of the patients
and the last concussion was approximately 2.8 years. Hence, our
analysis reflects the relationship between chronic brain changes
after TBI and the subject’s performance on the MOCA.
Concussion or mTBI is a clinical diagnosis due to the absence
of validated diagnostic biomarkers (4). Predicting cognitive
outcomes is vital for early rehabilitation, medical management,
and experimental therapies designed to improve long-term
prognosis. There is a dearth of standardized techniques for the
detection and prediction of cognitive outcomes after mTBI.
It is important to note that several studies have applied
other methods to have a better understanding of the anatomical
changes post TBI and how these alterations can affect cognitive
performance (16). It has been well established that a reduction
of total brain volume and cerebral atrophy are common
sequelae of TBI (1720). Prior publications have assessed these
subtle volumetric changes to predict a clinical outcome post
TBI. Most of the morphometric measures that have been
published are based on the segmentation techniques available in
FreeSurfer ( Warner et al.
assessed the relationship between the cognitive outcomes in 24
patients post traumatic axonal injury (TAI) with white matter
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Gonzalez et al. DTI Correlates of Concussion-Related Cognitive Impairment
FIGURE 1 | Scatter plots illustrating significant partial correlations (adjusted for age and lesion load) between Montreal cognitive assessment (MoCA) and right (R) (A)
Inferior temporal (ITG), (B) Middle temporal (MTG), (C) Angular (AG), and (D) Inferior frontal gyri (IFG).
integrity and regional brain volumes (21). Their work concluded
that regional brain volumes were correlated with deficits in
neuropsychological outcomes and that volumes of some gray
matter structures were more strongly associated with damage
to related white matter tracts in the chronic phase than in
the acute phase. Numerous other studies have identified that
certain brain regions such as the thalamus and hippocampus are
selectively vulnerable to atrophy after trauma and have significant
value when predicting functional outcome (21,22). Therefore,
brain volume and cortical track integrity are useful tools when
assessing cognitive prognosis in post TBI patients. In this study,
we focused on microstructural changes and its correlation with
MOCA scores.
Numerous studies have investigated the role of DTI in mTBI.
In 2002, Arfanakis et al. (6) described five patients with mTBI
who underwent DTI in the first 24 h of presentation to the
Emergency Department and identified regions of diffuse axonal
injury that appear normal with conventional neuroimaging.
However, DTI in the acute phase can show changes due to
vasogenic edema that can be reversible and therefore does not
reflect the chronic brain changes related to cognitive impairment.
In the last decade, over a 100 publications have demonstrated
the value of DTI at detecting microstructural disruption in
concussion (23). However, this study represents an advance
over previous studies because it investigated patients who are
in the chronic phase post-concussion, which eliminates possible
misleading findings in the acute and subacute phases. Also, our
DTI analysis reflects white as well as gray matter changes, as
opposed to most previous studies that have analyzed only white
matter changes. Finally, an important advance here is that the
MoCA was performed around the same time as the brain DTI
so that the correlations between them are more reliable.
Our findings demonstrated a negative correlation between
MoCA scores and MD values in the right inferior temporal
gyri, middle temporal gyri, and angular gyri. We found a
positive correlation between MoCA scores and FA values in
the right inferior frontal gyri. Therefore, areas exhibiting loss
of integrity reflected by abnormal FA and MD values, which
significantly correlated with MOCA scores, were mostly in the
right frontotemporal area. Notably, this result also indicates that
the changes in MOCA scores after concussion are not due to
impaired language because the right hemisphere typically does
not affect language function, outside of left handers.
The temporal lobe of the brain has several brain structures
that are critical for cognitive functions. It subdivides into the
superior, middle, and inferior temporal gyrus (STG, MTG, ITG).
Between these subdivisions and between different parcellations of
the frontal, parietal and occipital lobes, there are functional white
matter connections (structural connectivity) that are essential for
memory and visuospatial performance (24). The ITG contains
the temporal area 2 anterior (TE2a) and the temporal area 2
posterior (TE2p) that appear to function in vision. The MTG
contains the perirhinal cortex, which contributes to declarative
memories and semantic knowledge (25). Declarative memories
are those that can be consciously thought of and verbalized. Some
studies speculate that the medial temporal lobe is crucial for
semantic memory—the ability to recall general facts about the
world (26). On the other hand, the role of the right inferior frontal
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Gonzalez et al. DTI Correlates of Concussion-Related Cognitive Impairment
gyrus (IFG) has been strongly associated with switching attention
from one object to another by inhibiting the previously attended
locus (2729). As above, memory, attention, and visuospatial
functions are the most frequently affected domains in TBI (3),
and here we identified a clear correlation between those areas
of the brain that exhibited significant DTI findings and the most
commonly affected cognitive domains in TBI.
Our results suggest that lower MoCA scores are associated
with higher MD in the right temporal regions and decreased FA
in the right frontal region. Our study supports previous findings
that have established an association between post-concussion
syndrome and increased MD and decreased FA in DTI. However,
these findings have been inconsistent when trying to identify the
areas of the brain that are affected, and our results differ from
some previous studies. For example, there have been suggestions
of increased MD in the corpus callosum (3032); the left
uncinate fasciculus (33); the inferior fronto-occipital fasciculus,
the inferior longitudinal fasciculus, the superior longitudinal
fasciculus, the corticospinal tract and the left anterior thalamic
radiation (31).
Decreased FA in different brain areas has also been
inconsistent. Studies have suggested decreased FA in the whole
brain (34); the corpus callosum (32,33,35,36); the right
anterior corona radiata, internal capsule (anterior limb), fornix,
and medial superior frontal gyrus (35); the pontine tegmentum
(37); and the left uncinate fasciculus and bilateral superior
thalamic radiations (33). These discrepant results could be
attributed to variations in the time interval between injury and
imaging and differences in study design and analytic techniques.
Consequently, no standard DTI biomarker is identified for PCS
diagnosis and prediction (38).
Our findings extend previous ones and present strong
evidence for right frontotemporal changes underlying persistent
cognitive symptoms after concussion because (1) there is
a clinico-pathologic correlation between our cognitive and
imaging data, (2) the affected regions identified in DTI match
the symptoms reported by the patients and (3) the cognitive
weakness domains on testing correspond to the function of
the affected brain areas exhibiting DTI changes. We would
suggest, then, that increased MD and decreased FA in the right
frontotemporal regions may predict low MoCA scores in people
who have suffered from mTBI. These results may be helpful when
assessing patients complaining of cognitive impairment who have
a history of concussion.
There are some limitations to this study. For example,
diffusion tensor metrics are sensitive, but non-specific markers
for microstructural changes of the brain parenchyma, which
can be altered in many brain pathologies including infection,
inflammation or trauma. Our study included a heterogeneous
population, and subjects had other cognitive risk factors such as
hypertension, hyperlipidemia and diabetes. These comorbidities
are also known to cause white matter changes (39). Therefore,
we cannot rule out a contribution by those risks factors to
white matter damage and poor MoCA performance. Although
we have not controlled our analysis for all the risk factors
for cognitive impairment, we qualitatively presented them in a
detailed manner in Table 1. On the other hand, many patients
were young, and lacked these risk factors, and still had the
changes we noted on DTI imaging. Other limitations of our
study include small sample size, differences in the interval from
injury to imaging, and lack of a control group. The small sample
size meant that our study lacked the power to perform in-
depth analyses for MOCA subscores and their DTI correlates;
hence, we only investigated global cognitive impairment and it’s
DTI correlates. A larger, future study would allow for important
subscore analyses.
Going forward, it will be important to determine which
components of concussion are potentially reversible, and
which may be irreversible. Moreover, additional, large-scale,
longitudinal studies and translational research are needed to
further explore DTI as a reliable prognostic indicator. Functional
imaging, such as PET scans, SPECT scans, and evoked potentials,
have shown inconsistent results across studies, while a limited
number of studies have found promise in the application of
MR spectroscopy in detecting diffuse axonal injury and post-
concussion syndrome (40,41). Further research into potential
biochemical markers, such as neurofilaments, neuron specific
enolase, S100B, and ferritin (39), which are correlated with
imaging and cognitive assessment results, would also broaden
our insight into diagnostic, prognostic, and therapeutic options
for mTBI.
The original contributions presented in the study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author/s.
The studies involving human participants were reviewed
and approved by Committee for the Protection of Human
Subjects. Written informed consent from the participants’ legal
guardian/next of kin was not required to participate in this
study in accordance with the national legislation and the
institutional requirements.
All authors listed have made a substantial, direct and intellectual
contribution to the work, and approved it for publication.
The content of this manuscript has been presented in part at the
ANA 2020 Conference (42).
The Supplementary Material for this article can be found
online at:
Frontiers in Neurology | 5May 2021 | Volume 12 | Article 639179
Gonzalez et al. DTI Correlates of Concussion-Related Cognitive Impairment
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Conflict of Interest: The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a
potential conflict of interest.
Copyright © 2021 Gonzalez, Kim, Keser, Ibrahim, Singh, Ahmad, Hasan, Kamali,
Hasan and Schulz. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
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Frontiers in Neurology | 7May 2021 | Volume 12 | Article 639179
... Again, it seems odd that perfusion SPECT imaging is held to an unrealistic standard that fMRI, diffusion tensor imaging, FDG-PET, amyloid PET and other forms of neuroimaging do not meet. For example, numerous diffusion tensor imaging studies of mild TBI lack the rigorous criteria established by Davalos and Bennett (54) but these studies were still funded and published after these criteria were set (106,(111)(112)(113)(114)(115)(116)(117)(118). For example, several diffusion tensor imaging studies lack control groups (115,118), several have small sample sizes (111)(112)(113)(114)(115), several examined limited neuropsychological testing (112,113,(115)(116)(117)(118)(119), and all lacked randomization. ...
... For example, numerous diffusion tensor imaging studies of mild TBI lack the rigorous criteria established by Davalos and Bennett (54) but these studies were still funded and published after these criteria were set (106,(111)(112)(113)(114)(115)(116)(117)(118). For example, several diffusion tensor imaging studies lack control groups (115,118), several have small sample sizes (111)(112)(113)(114)(115), several examined limited neuropsychological testing (112,113,(115)(116)(117)(118)(119), and all lacked randomization. ...
... For example, numerous diffusion tensor imaging studies of mild TBI lack the rigorous criteria established by Davalos and Bennett (54) but these studies were still funded and published after these criteria were set (106,(111)(112)(113)(114)(115)(116)(117)(118). For example, several diffusion tensor imaging studies lack control groups (115,118), several have small sample sizes (111)(112)(113)(114)(115), several examined limited neuropsychological testing (112,113,(115)(116)(117)(118)(119), and all lacked randomization. ...
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Brain perfusion single photon emission computed tomography (SPECT) scans were initially developed in 1970's. A key radiopharmaceutical, hexamethylpropyleneamine oxime (HMPAO), was originally approved in 1988, but was unstable. As a result, the quality of SPECT images varied greatly based on technique until 1993, when a method of stabilizing HMPAO was developed. In addition, most SPECT perfusion studies pre-1996 were performed on single-head gamma cameras. In 1996, the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (TTASAAN) issued a report regarding the use of SPECT in the evaluation of neurological disorders. Although the TTASAAN report was published in January 1996, it was approved for publication in October 1994. Consequently, the reported brain SPECT studies relied upon to derive the conclusions of the TTASAAN report largely pre-date the introduction of stabilized HMPAO. While only 12% of the studies on traumatic brain injury (TBI) in the TTASAAN report utilized stable tracers and multi-head cameras, 69 subsequent studies with more than 23,000 subjects describe the utility of perfusion SPECT scans in the evaluation of TBI. Similarly, dementia SPECT imaging has improved. Modern SPECT utilizing multi-headed gamma cameras and quantitative analysis has a sensitivity of 86% and a specificity of 89% for the diagnosis of mild to moderate Alzheimer's disease—comparable to fluorodeoxyglucose positron emission tomography. Advances also have occurred in seizure neuroimaging. Lastly, developments in SPECT imaging of neurotoxicity and neuropsychiatric disorders have been striking. At the 25-year anniversary of the publication of the TTASAAN report, it is time to re-examine the utility of perfusion SPECT brain imaging. Herein, we review studies cited by the TTASAAN report vs. current brain SPECT imaging research literature for the major indications addressed in the report, as well as for emerging indications. In Part II, we elaborate technical aspects of SPECT neuroimaging and discuss scan interpretation for the clinician.
... The most commonly used DTI parameters are fractional anisotropy (FA) and mean diffusivity (MD), which measure the directionality of water diffusion and the total diffusion rate, respectively, at particular points in space known as voxels. When there has been microstructural or diffuse axonal damage, FA decreases and MD increases as a result of fewer structural elements in the tissue to limit omnidirectional water diffusion [80]. In DTI, the anisotropic diffusion measurements, taken over a period of milliseconds, are synthesized into a mathematical construct called a tensor, which can be combined over an area of study to create highly detailed graphical constructs of tissues [81]. ...
... This is an important observation because it demonstrates the feasibility of DTI for monitoring post-concussion recovery. Another analysis of 53 subjects with a history of concussion and persistent cognitive symptoms identified correlations between cognitive assessment scores and DTI variable measurements (FA and MD) in frontotemporal regions of the brain [80]. Further, a third study of 11 pediatric subjects who underwent DTI within 3 days of concussion found significant increases in thalamic water diffusion anisotropy in subjects relative to controls [83]. ...
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Mitigating the substantial public health impact of concussion is a particularly difficult challenge. This is partly because concussion is a highly prevalent condition, and diagnosis is predominantly symptom-based. Much of contemporary concussion management relies on symptom interpretation and accurate reporting by patients. These types of reports may be influenced by a variety of factors for each individual, such as preexisting mental health conditions, headache disorders, and sleep conditions, among other factors. This can all be contributory to non-specific and potentially misleading clinical manifestations in the aftermath of a concussion. This review aimed to conduct an examination of the existing literature on emerging approaches for objectively evaluating potential concussion, as well as to highlight current gaps in understanding where further research is necessary. Objective assessments of visual and ocular motor concussion symptoms, specialized imaging techniques, and tissue-based concentrations of specific biomarkers have all shown promise for specifically characterizing diffuse brain injuries, and will be important to the future of concussion diagnosis and management. The consolidation of these approaches into a comprehensive examination progression will be the next horizon for increased precision in concussion diagnosis and treatment.
... Sport-related concussion (SRC) has become a widely investigated area in recent years, with many sport-related activities involving high energy events that can expose athletes to direct and indirect traumas and increase their risk of injury. 1,2 Consequences of SRC have been identified, with poorer mental health, 3,4 impaired cognition, 5,6 and reduced quality of life, 7,8 among the most prevalent outcomes. Although physical pain often accompanies SRC, 9,10 surprisingly few studies incorporate this factor into SRC research. ...
... 28 Despite the literature unclear on the role that physical pain has on cognitive flexibility, there are studies linking physical pain with impairments to other areas of cognition. 5,6 As these studies did not account for SRC, it may be that impaired cognition was misattributed to physical pain, and therefore assessing both simultaneously is warranted. ...
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Sport-related concussion (SRC) and physical pain are both associated with poor mental health, impaired cognition, and reduced quality of life. Despite SRC and physical pain often co-occurring, there is little research that investigates these two factors together, and therefore it is difficult to conclude which of these contributes to the negative outcomes associated with them. Therefore, the present study aimed to investigate the effect of SRC and physical pain on mental health, cognitive ability, and quality of life. Depression was measured using the Center for Epidemiological Studies Depression Scale, anxiety was assessed using the State-Trait Anxiety Inventory while the SF-12 recorded health-related quality of life. A trail making task (TMT) assessed cognitive flexibility of participants. Analysis of 83 participants (43 concussed) revealed that SRC led to reduced accuracy on TMT(A) and (B), whereas physical pain was responsible for poorer mental health and reduced quality of life. This study highlights the influence that SRC has on cognitive ability and the impact that physical pain has on mental health and quality of life. With this information, we are better placed to predict the negative consequences of SRC and physical pain and therefore tailor support accordingly.
... Cognitive functions are a group of brain superior functions, including memory, visuospatial orientation, attention, learning, information processing capacity, and reaction time. The most frequently affected domains after a concussion are memory, attention, and visuospatial functions [90,91]. ...
... Cognitive impairments are due to the well-known energy crisis of the brain, which is incapable of providing good interaction between complex neural circuits from different brain areas. In literature, there is also evidence in support of a diffuse axonal injury as an anatomical substrate underlying cognitive dysfunction [90], and depending on the extent and distribution of damage multiple sensory, motor, emotional, and cognitive systems can be affected [92]. ...
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Concussion represents one of modern medicine’s biggest challenges. As we are gaining more and more information on pathophysiology, diagnosis, and treatment, a lot is still to be cleared. On the side of pharmacology, rehabilitation is the leading treatment for concussion signs and symptoms. From acute to the chronic phase of brain dysfunction, rehabilitation is nowadays providing help to people recover faster and better. In this chapter, we will analyze in depth the key information and evidence supporting current concussion rehabilitation methods and protocols. Through this chapter, we are exploring how aerobic training, vestibular rehabilitation, and oculomotor exercises are working together with the treatment of migraine and neck pain. We also aim to provide the basis and relevance of cognitive rehabilitation and double-task-multifunctional training and the importance of fatigue and mood problem management.
... Conventionally, while brain MRI is an imaging method showing the brain activity based on increased blood flow, DTI is a special MRI based on the water molecules flow through the neuronal fibers, mainly used to monitor the white matter pathways [37,38]. Due to its specificity to white matter imaging, DTI is currently preferred in mTBI study, diagnosis, and outcome prognosis [39,40]. Despite that, several studies investigated the volumetric changes of brain grey matter using T1 MRI. ...
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Traumatic brain injury (TBI) is currently a problematic issue of public health due to its frequency, and many of the mild cases often remain undiagnosed despite the possible predisposition to prolonged or persistent post-concussive symptomatology. It was shown here that the severity and persistence of grey matter (GM) changes following TBI could predict disease outcomes. Our aim was to conduct a voxel-wise meta-analysis to detect significant GM changes following mild TBI (mTBI) and to investigate whether these changes are associated with the duration and severity of post-concussion syndrome (PCS). A voxel-wise meta-analysis was conducted regarding the GM and white matter (WM) changes in mTBI adult patients versus healthy controls, and Seed-based d Mapping was used to correlate the data. Standard meta-analysis statistical processing was used to assess heterogeneity and publication bias. Our analysis showed significant GM volume increases in the left medial cingulate/paracingulate gyri, the middle frontal gyrus, and the right caudate nucleus of the mTBI patients and significant volume loss in the thalamus, the frontal lobe, and the temporal lobe. These changes could potentially be associated with PCS that some mTBI later patients develop as a result to the injury or other compensatory changes. Additional studies considering long-term GM changes in mTBI patients and their potential relationship to PCS could provide further insight into the pathophysiological similarities and correlations between mTBI and PCS.
Background: Mild traumatic brain injury (mTBI) is characterized as brain microstructural damage, which may cause a wide range of brain functional disturbances and emotional problems. Brain network analysis based on machine learning is an important means of neuroimaging research. Obtaining the most discriminating functional connection is of great significance to analyze the pathological mechanism of mTBI. Methods: To better obtain the most discriminating features of functional connection networks, this study proposes a hierarchical feature selection pipeline (HFSP) composed of Variance Filtering (VF), Lasso, and Principal Component Analysis (PCA). Ablation experiments indicate that each module plays a positive role in classification, validating the robustness and reliability of the HFSP. Furthermore, the HFSP is compared with recursive feature elimination (RFE), elastic net (EN), and locally linear embedding (LLE), verifying its superiority. In addition, this study also utilizes random forest (RF), SVM, Bayesian, linear discriminant analysis (LDA), and logistic regression (LR) as classifiers to evaluate the generalizability of HFSP. Results: The results show that the indexes obtained from RF are the highest, with accuracy = 89.74%, precision = 91.26%, recall = 89.74%, and F1 score = 89.42%. The HFSP selects 25 pairs of the most discriminating functional connections, mainly distributed in the frontal lobe, occipital lobe, and cerebellum. Nine brain regions show the largest node degree. Limitations: The number of samples is small. This study only includes acute mTBI. Conclusions: The HFSP is a useful tool for extracting discriminating functional connections and may contribute to the diagnostic processes..
This brain imaging study examined subjects with a history of repetitive concussive and sub-concussive forces sustained over the course of their careers in the Canadian Football League (CFL). We hypothesized that microstructural and functional abnormalities, assessed using DTI and rsfMRI, respectively, would be present in these retired athletes, that are not present in matched controls. Seventeen aging, retired CFL players (rCFL, aged 58.5±6.2y, ranged 45-66) completed three neuropsychological tests, and had anatomical, diffusion and functional MRI scans performed. Healthy age- and sex-matched control data (n=2117) were used to develop a subject-specific and region-wise Z-scoring approach. Regional DTI fractional anisotropy (FA) and rsfMRI signal complexity (fractal dimension; FD) Z-score data was further analyzed as a subject-specific total, left, and right injury burden (IB) value for each MRI metric. Microstructural abnormality was detected in 6 of 17 subjects based on DTI FA. The rsfMRI data showed 4 subjects with higher total FDIB, and several regions had Z-score outliers detected in multiple subjects. The right pre-motor cortex, right hippocampus dentate gyrus, and right visual cortex were the most abnormally functioning grey matter brain regions. Total FAIB was negatively correlated with career length, social functioning, and significantly with emotional well-being, and positively correlated with physical health. Total FDIB was negatively correlated with energy and fatigue and general health, and positively correlated with age, career length, and education. This study provides evidence of brain changes years after professional athletes have retired.
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In this supplement, we build on work previously published under the Human Connectome Project. Specifically, we show a comprehensive anatomic atlas of the human cerebrum demonstrating all 180 distinct regions comprising the cerebral cortex. The location, functional connectivity, and structural connectivity of these regions are outlined, and where possible a discussion is included of the functional significance of these areas. In part 6, we specifically address regions relevant to the temporal lobe.
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Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).
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This review seeks to summarize diffusion tensor imaging (DTI) studies that have evaluated structural changes attributed to the mechanisms of mild traumatic brain injury (mTBI) in adult civilian, military, and athlete populations. Articles from 2002 to 2016 were retrieved from PubMed/MEDLINE, EBSCOhost, and Google Scholar, using a Boolean search string containing the following terms: “diffusion tensor imaging”, “diffusion imaging”, “DTI”, “white matter”, “concussion”, “mild traumatic brain injury”, “mTBI”, “traumatic brain injury”, and “TBI”. We added studies not identified by this method that were found via manually-searched reference lists. We identified 86 eligible studies from English-language journals using, adult, human samples. Studies were evaluated based on duration between injury and DTI assessment, categorized as acute, subacute/chronic, remote mTBI, and repetitive brain trauma considerations. Since changes in brain structure after mTBI can also be affected by other co-occurring medical and demographic factors, we also briefly review DTI studies that have addressed socioeconomic status factors (SES), major depressive disorder (MDD), and attention-deficit hyperactivity disorder (ADHD). The review describes population-specific risks and the complications of clinical versus pathophysiological outcomes of mTBI. We had anticipated that the distinct population groups (civilian, military, and athlete) would require separate consideration, and various aspects of the study characteristics supported this. In general, study results suggested widespread but inconsistent differences in white matter diffusion metrics (primarily fractional anisotropy [FA], mean diffusivity [MD], radial diffusivity [RD], and axial diffusivity [AD]) following mTBI/concussion. Inspection of study designs and results revealed potential explanations for discrepant DTI findings, such as control group variability, analytic techniques, the manner in which regional differences were reported, and the presence or absence of persistent functional disturbances. DTI research in adult mTBI would benefit from more standardized imaging and analytic approaches. We also found significant overlap in white matter abnormalities reported in mTBI with those commonly affected by SES or the presence of MDD and ADHD. We conclude that DTI is sensitive to a wide range of group differences in diffusion metrics, but that it currently lacks the specificity necessary for meaningful clinical application. Properly controlled longitudinal studies with consistent and standardized functional outcomes are needed before establishing the utility of DTI in the clinical management of mTBI and concussion.
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Problem/condition: Traumatic brain injury (TBI) has short- and long-term adverse clinical outcomes, including death and disability. TBI can be caused by a number of principal mechanisms, including motor-vehicle crashes, falls, and assaults. This report describes the estimated incidence of TBI-related emergency department (ED) visits, hospitalizations, and deaths during 2013 and makes comparisons to similar estimates from 2007. Reporting period: 2007 and 2013. Description of system: State-based administrative health care data were used to calculate estimates of TBI-related ED visits and hospitalizations by principal mechanism of injury, age group, sex, and injury intent. Categories of injury intent included unintentional (motor-vehicle crashes, falls, being struck by or against an object, mechanism unspecified), intentional (self-harm and assault/homicide), and undetermined intent. These health records come from the Healthcare Cost and Utilization Project's National Emergency Department Sample and National Inpatient Sample. TBI-related death analyses used CDC multiple-cause-of-death public-use data files, which contain death certificate data from all 50 states and the District of Columbia. Results: In 2013, a total of approximately 2.8 million TBI-related ED visits, hospitalizations, and deaths (TBI-EDHDs) occurred in the United States. This consisted of approximately 2.5 million TBI-related ED visits, approximately 282,000 TBI-related hospitalizations, and approximately 56,000 TBI-related deaths. TBIs were diagnosed in nearly 2.8 million (1.9%) of the approximately 149 million total injury- and noninjury-related EDHDs that occurred in the United States during 2013. Rates of TBI-EDHDs varied by age, with the highest rates observed among persons aged ≥75 years (2,232.2 per 100,000 population), 0-4 years (1,591.5), and 15-24 years (1,080.7). Overall, males had higher age-adjusted rates of TBI-EDHDs (959.0) compared with females (810.8) and the most common principal mechanisms of injury for all age groups included falls (413.2, age-adjusted), being struck by or against an object (142.1, age-adjusted), and motor-vehicle crashes (121.7, age-adjusted). The age-adjusted rate of ED visits was higher in 2013 (787.1) versus 2007 (534.4), with fall-related TBIs among persons aged ≥75 years accounting for 17.9% of the increase in the number of TBI-related ED visits. The number and rate of TBI-related hospitalizations also increased among persons aged ≥75 years (from 356.9 in 2007 to 454.4 in 2013), primarily because of falls. Whereas motor-vehicle crashes were the leading cause of TBI-related deaths in 2007 in both number and rate, in 2013, intentional self-harm was the leading cause in number and rate. The overall age-adjusted rate of TBI-related deaths for all ages decreased from 17.9 in 2007 to 17.0 in 2013; however, age-adjusted TBI-related death rates attributable to falls increased from 3.8 in 2007 to 4.5 in 2013, primarily among older adults. Although the age-adjusted rate of TBI-related deaths attributable to motor-vehicle crashes decreased from 5.0 in 2007 to 3.4 in 2013, the age-adjusted rate of TBI-related ED visits attributable to motor-vehicle crashes increased from 83.8 in 2007 to 99.5 in 2013. The age-adjusted rate of TBI-related hospitalizations attributable to motor-vehicle crashes decreased from 23.5 in 2007 to 18.8 in 2013. Interpretation: Progress has been made to prevent motor-vehicle crashes, resulting in a decrease in the number of TBI-related hospitalizations and deaths from 2007 to 2013. However, during the same time, the number and rate of older adult fall-related TBIs have increased substantially. Although considerable public interest has focused on sports-related concussion in youth, the findings in this report suggest that TBIs attributable to older adult falls, many of which result in hospitalization and death, should receive public health attention. Public health actions: The increase in the number of fall-related TBIs in older adults suggests an urgent need to enhance fall-prevention efforts in that population. Multiple effective interventions have been identified, and CDC has developed the STEADI initiative (Stopping Elderly Accidents Deaths and Injuries) as a comprehensive strategy that incorporates empirically supported clinical guidelines and scientifically tested interventions to help primary care providers address their patients' fall risk through the identification of modifiable risk factors and implementation of effective interventions (e.g., exercise, medication management, and Vitamin D supplementation).
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Objectives To review the evidence for the use of diffusion tensor imaging (DTI) parameters in the human brain as a diagnostic tool for and predictor of post-concussion syndrome (PCS) after a mild traumatic brain injury (mTBI). Design Systematic review. Data sources All relevant studies in AMED, Embase, MEDLINE, Ovid, PubMed, Scopus, and Web of Science through 20 May, 2016. Study selection Studies that analyze traditional DTI measures [fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD)] and the severity of PCS symptoms or the development of PCS in humans after an mTBI. Data extraction Population studied, patient source, mTBI diagnosis method, PCS diagnosis method, DTI values measured, significant findings, and correlation between DTI findings and PCS. Data synthesis Ten studies investigated correlations between DTI values and PCS symptom severity or between DTI values and the development of PCS in mTBI patients. Decreased FA and increased MD and RD were associated with the development and severity of PCS. AD was not found to change significantly. Brain regions found to have significant changes in DTI parameters varied from study to study, although the corpus callosum was most frequently cited as having abnormal DTI parameters in PCS patients. Conclusion DTI abnormalities correlate with PCS incidence and symptom severity, as well as indicate an increased risk of developing PCS after mTBI. Abnormal DTI findings should prompt investigation of the syndrome to ensure optimal symptom management at the earliest stages. Currently, there is no consensus in the literature about the use of one DTI parameter in a specific region of the brain as a biomarker for PCS because no definite trends for DTI parameters in PCS subjects have been identified. Further research is required to establish a standard biomarker for PCS.
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Aim: The aim of the present study is to evaluate diffusion tensor tractography (DTT) as a tool for detecting diffuse axonal injury in patients of acute, mild, and moderate traumatic brain injury (TBI), using two diffusion variables: Fractional anisotropy (FA) and mean diffusivity (MD). The correlation of these indices with the severity of post-concussive symptoms was also assessed. Materials and methods: Nineteen patients with acute, mild, or moderate TBI and twelve age- and sex-matched healthy controls were recruited. Following Magnetic Resonance Imaging (MRI) on a 3.0-T scanner, DTT was performed using the 'fiber assignment by continuous tracking' (FACT) algorithm for fiber reconstruction. Appropriate statistical tools were used to see the difference in FA and MD values between the control and patient groups. In the latter group, the severity of post-concussive symptoms was assessed six months following trauma, using the Rivermead Postconcussion Symptoms Questionnaire (RPSQ). Results: The patients displayed significant reduction in FA compared to the controls (P < 0.05) in several tracts, notably the corpus callosum, fornix, bilateral uncinate fasciculus, and bilateral superior thalamic radiations. Changes in MD were statistically significant in the left uncinate, inferior longitudinal fasciculus, and left posterior thalamic radiation. A strong correlation between these indices and the RPSQ scores was observed in several white matter tracts. Conclusion: Diffusion tensor imaging (DTI)-based quantitative analysis in acute, mild, and moderate TBI can identify axonal injury neuropathology, over and above that visualized on conventional MRI scans. Furthermore, the significant correlation observed between FA and MD indices and the severity of post-concussive symptoms could make it a useful predictor of the long-term outcome.
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The amount of medical image data produced in clinical and research settings is rapidly growing resulting in vast amount of data to analyze. Automatic and reliable quantitative analysis tools, including segmentation, allow to analyze brain development and to understand specific patterns of many neurological diseases. This field has recently experienced many advances with successful techniques based on non-linear warping and label fusion. In this work we present a novel and fully automatic pipeline for volumetric brain analysis based on multi-atlas label fusion technology that is able to provide accurate volumetric information at different levels of detail in a short time. This method is available through the volBrain online web interface (, which is publically and freely accessible to the scientific community. Our new framework has been compared with current state-of-the-art methods showing very competitive results.
Introduction Cognitive impairment is one of the most important culprit influencing the long-term neurological outcome commonlyobserved in TBI survivors. Aims To examine the performance of patients with Mild and Moderate traumatic brain injury (TBI) on the Montreal Cognitive Assessment (MoCA) using as a screening tool. Results Total 228 (127 Mild TBI & 101 Moderate TBI) patients were recruited in this study. Results showed that patients with moderate TBI had lower score on the MoCA as compared to patients with mild TBI (p Value = 0.031). This difference was observed statistically significant among mild and moderate TBI for the cube copy (p = 0.039) and clock (p = 0.017) i.e. visuospatial/executive function, Digit span test (p value = 0.040) i.e. concentration and recall memory (p = 0.04). MoCA Score were higher for patients with higher GCS score at admission. Education status was also correlated with MoCA scores; those patients with higher level of education had significant association with higher MoCA scores (p value = 0.012). This study showed that age and gender were insignificant variables to determine cognitive function. Conclusion Assessment of cognitive impairment should be considered as a mandatory protocol while evaluating post TBI patients, even in cases of mild TBI. Visuospatial/Executive function, memory and attention are the most commonly impaired cognitive functions in patients of TBI, and these are the main domain of cognition which differentiates mild impairment from moderate impairment. This information enables us and provides insight to our experience to predict the burdens of problem and plan to develop post TBI dedicated rehabilitating programme.
Image analysis tools for brain magnetic resonance imaging (MRI) have become increasingly important for computer-aided diagnosis that involves large amounts of medical image data. The authors of this article have endeavored to develop software tools to serve the clinical research community, starting with a stand-alone executable, hybrid local computation model for today's modern architecture of cloud services, which they call MRICloud. MRICloud provides a high-throughput neuroinformatics platform for automated brain MRI segmentation and analytical tools for quantification via distributed remote computation and Web-based user interfaces. There are several key, inherent advantages to a cloud-based software as a service - in particular, how it improves the efficiency of software implementation, upgrades, and maintenance. The client-server model is also ideal for high-performance computing, allowing for distribution of computational servers across the world. This article introduces the basic functions and utilities of MRICloud, its developmental history and future perspectives, its infrastructures, and the benefits of this cloud service framework.
The hippocampus is one of the most thoroughly investigated structures in the brain. Ever since the 1957 report of the case study H.M., who famously lost the ability to form new, declarative memories after surgical removal of the hippocampus and nearby temporal lobe structures to treat intractable epilepsy, the hippocampus has been at the forefront of research into the neurobiological bases of memory. This research led to the discovery in the hippocampus of long-term potentiation, the pre-eminent model of the cellular basis of memory. Furthermore, the discovery of place cells, head direction cells, and grid cells in the rodent hippocampal formation established a firm foundation for the notion that the hippocampus plays a critical role in memory formation by providing the brain with a spatiotemporal framework within which the various sensory, emotional, and cognitive components of an experience are bound together. This framework allows the experience to be stored in such a way that it can be later retrieved as a conscious recollection of that experience.