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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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BRIEF RESEARCH REPORT
published: 24 May 2021
doi: 10.3389/fneur.2021.639179
Frontiers in Neurology | www.frontiersin.org 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
*Correspondence:
Paul E. Schulz
paul.e.schulz@uth.tmc.edu
These authors share first authorship
Specialty section:
This article was submitted to
Neurotrauma,
a section of the journal
Frontiers in Neurology
Received: 08 December 2020
Accepted: 20 April 2021
Published: 24 May 2021
Citation:
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
Impairment
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
INTRODUCTION
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.
METHODS
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]
Sex
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 (%)
Behavioral
Sleep difficulties 20 (38%)
Personality changes 24 (45%)
Cognitive
Memory impairment 53 (100%)
Inattention 13 (24%)
Word finding difficulty 14 (26%)
Somatic
Headache 17 (32%)
Vertigo 5 (9%)
Emotional
Depression 3 (6%)
Anxiety 7 (13%)
*Some patients had multiple complaints.
MD values (https://braingps.mricloud.org/) (12). We performed
lesion segmentation on FLAIR sequences through volBrain
software (https://www.volbrain.upv.es/) (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.
Frontiers in Neurology | www.frontiersin.org 2May 2021 | Volume 12 | Article 639179
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.
RESULTS
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.
DISCUSSION
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 (http://surfer.nmr.mgh.harvard.edu/). 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.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author/s.
ETHICS STATEMENT
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.
AUTHOR CONTRIBUTIONS
All authors listed have made a substantial, direct and intellectual
contribution to the work, and approved it for publication.
ACKNOWLEDGMENTS
The content of this manuscript has been presented in part at the
ANA 2020 Conference (42).
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fneur.
2021.639179/full#supplementary-material
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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
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
Frontiers in Neurology | www.frontiersin.org 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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