Associations of MRI-Derived Glymphatic System Impairment With Global White Matter Damage and Cognitive Impairment in Mild Traumatic Brain Injury: A DTI-ALPS Study.
Journal of magnetic resonance imaging : JMRI
BACKGROUND:Assessing the glymphatic function using diffusion tensor image analysis along the perivascular space (DTI-ALPS) may be helpful for mild traumatic brain injury (mTBI) management. PURPOSE:To assess glymphatic function using DTI-ALPS and its associations with global white matter damage and cognitive impairment in mTBI. STUDY TYPE:Prospective. POPULATION:Thirty-four controls (44.1% female, mean age 49.2 years) and 58 mTBI subjects (43.1% female, mean age 48.7 years), including uncomplicated mTBI (N = 32) and complicated mTBI (N = 26). FIELD STRENGTH/SEQUENCE:3-T, single-shot echo-planar imaging sequence. ASSESSMENT:Magnetic resonance imaging (MRI) was done within 1 month since injury. DTI-ALPS was performed to assess glymphatic function, and peak width of skeletonized mean diffusivity (PSMD) was used to assess global white matter damage. Cognitive tests included Auditory Verbal Learning Test and Digit Span Test (forward and backward). STATISTICAL TESTS:Neuroimaging findings comparisons were done between mTBI and control groups. Partial correlation and multivariable linear regression assessed the associations between DTI-ALPS, PSMD, and cognitive impairment. Mediation effects of PSMD on the relationship between DTI-ALPS and cognitive impairment were explored. P-value <0.05 was considered statistically significant, except for cognitive correlational analyses with a Bonferroni-corrected P-value set at 0.05/3 ≈ 0.017. RESULTS:mTBI showed lower DTI-ALPS and higher PSMD, especially in complicated mTBI. DTI-ALPS was significantly correlated with verbal memory (r = 0.566), attention abilities (r = 0.792), executive function (r = 0.618), and PSMD (r = -0.533). DTI-ALPS was associated with verbal memory (β = 8.77, 95% confidence interval [CI] 5.00, 12.54), attention abilities (β = 5.67, 95% CI 4.56, 6.97), executive function (β = 2.34, 95% CI 1.49, 3.20), and PSMD (β = -0.79, 95% CI -1.15, -0.43). PSMD mediated 46.29%, 20.46%, and 24.36% of the effects for the relationship between DTI-ALPS and verbal memory, attention abilities, and executive function. DATA CONCLUSION:Glymphatic function may be impaired in mTBI reflected by DTI-ALPS. Glymphatic dysfunction may cause cognitive impairment related to global white matter damage after mTBI. LEVEL OF EVIDENCE:2 TECHNICAL EFFICACY: Stage 2.
10.1002/jmri.28797
From impact to recovery: tracking mild traumatic brain injury with MRI-a pilot study and case series.
BMJ open sport & exercise medicine
Background:Diagnosis and recovery tracking of mild traumatic brain injury (mTBI) is often challenging due to the lack of clear findings on routine imaging techniques. This also complicates defining safe points for returning to activities. Hypothesis/purpose:Quantitative susceptibility mapping (QSM) can provide information about cerebral venous oxygen saturation (CSvO) in the context of brain injury. We tested the prediction that these imaging modalities would enable the detection of changes and recovery patterns in the brains of patients with mTBI. Study design:In a case-control study, we recruited a cohort of 24 contact sport athletes for baseline QSM and resting-state functional MRI (rs-fMRI) scanning. Two of those who subsequently experienced head impact with significant post-injury symptoms underwent scans at 3, 7, 14 and 28 days post-injury; one had a boxing match without classical mTBI symptoms were also followed-up on. Results:The cohort baseline QSM measurements of the straight sinus were established. The two injured athletes with post-impact symptoms consistent with mTBI had susceptibility results at days 3 and 7 post-impact that fell below the 25th percentile of the baseline values. The per cent amplitude fluctuation quantified from rs-fMRI agreed with the susceptibility trends in the straight sinus. Conclusion:QSM holds promise as a diagnostic tool for tracking mTBI progression or recovery in contact sport head injury.
10.1136/bmjsem-2024-002010
Structural and Volumetric Brain MRI Findings in Mild Traumatic Brain Injury.
Patel J B,Wilson S H,Oakes T R,Santhanam P,Weaver L K
AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE:Routine MR imaging findings are frequently normal following mild traumatic brain injury and have a limited role in diagnosis and management. Advanced MR imaging can assist in detecting pathology and prognostication but is not readily available outside research settings. However, 3D isotropic sequences with ∼1-mm voxel size are available on community MR imaging scanners. Using such sequences, we compared radiologists' findings and quantified regional brain volumes between a mild traumatic brain injury cohort and non-brain-injured controls to describe structural imaging findings associated with mild traumatic brain injury. MATERIALS AND METHODS:Seventy-one military personnel with persistent symptoms and 75 controls underwent 3T MR imaging. Three neuroradiologists interpreted the scans using common data elements. FreeSurfer was used to quantify regional gray and white matter volumes. RESULTS:WM hyperintensities were seen in 81% of the brain-injured group versus 60% of healthy controls. The odds of ≥1 WM hyperintensity in the brain-injured group was about 3.5 times the odds for healthy controls (95% CI, 1.58-7.72; = .002) after adjustment for age. A frontal lobe-only distribution of WM hyperintensities was more commonly seen in the mild traumatic brain injury cohort. Furthermore, 7 gray matter, 1 white matter, and 2 subcortical gray matter regions demonstrated decreased volumes in the brain-injured group after multiple-comparison correction. The mild traumatic brain injury cohort showed regional parenchymal volume loss. CONCLUSIONS:White matter findings are nonspecific and therefore a clinical challenge. Our results suggest that prior trauma should be considered in the differential diagnosis of multifocal white matter abnormalities with a clinical history of mild traumatic brain injury, particularly when a frontal predilection is observed.
10.3174/ajnr.A6346
Predicting functional recovery after mild traumatic brain injury: the SHEFBIT cohort.
Booker James,Sinha Saurabh,Choudhari Kishor,Dawson Jeremy,Singh Rajiv
Brain injury
: Current prognostic models for mild Traumatic Brain Injury (mTBI) are unsatisfactory in identifying patients at risk of an unfavorable outcome following injury. : To identify prognostic indicators of recovery one-year following mTBI. : A large population (n = 596) of patients with mTBI were prospectively recruited following admission to the Emergency Department. Data were collected at brain injury clinics at 8-10 weeks and one-year after injury. Functional recovery at one year was assessed using the Glasgow Outcome Scale-Extended (GOSE). : A follow-up rate of 92% was achieved. The most common aetiologies of mTBI were falls (n = 222) and road traffic collisions (n = 154). Distribution of GCS was 15 (n = 363), 14 (n = 156) and 13 (n = 77). Ordinal regression analysis found that psychiatric history ( < .001), alcohol intoxication ( = .011), assault ( = .022) and GCS < 15 (< 0.001), were associated with worse outcome. An abnormal CT scan was not a predictor of disability. : Patients with a previous psychiatric history, GCS < 15, etiology of assault, and alcohol intoxication result in worse long-term outcomes after mTBI. The predictors identified should be implemented when developing a future-validated a prognostic model for mTBI recovery.
10.1080/02699052.2019.1629626
Return to work after mild traumatic brain injury: association with positive CT and MRI findings.
Acta neurochirurgica
BACKGROUND:Return to work (RTW) might be delayed in patients with complicated mild traumatic brain injury (MTBI), i.e., MTBI patients with associated traumatic intracranial lesions. However, the effect of different types of lesions on RTW has not studied before. We investigated whether traumatic intracranial lesions detected by CT and MRI are associated with return to work and post-concussion symptoms in patients with MTBI. METHODS:We prospectively followed up 113 adult patients with MTBI that underwent a brain MRI within 3-17 days after injury. Return to work was assessed with one-day accuracy up to one year after injury. Rivermead Post-Concussion Symptoms Questionnaire (RPQ) and Glasgow Outcome Scale Extended (GOS-E) were conducted one month after injury. A Kaplan-Meier log-rank analysis was performed to analyze the differences in RTW. RESULTS:Full RTW-% one year after injury was 98%. There were 38 patients with complicated MTBI, who had delayed median RTW compared to uncomplicated MTBI group (17 vs. 6 days), and more post-concussion symptoms (median RPQ 12.0 vs. 6.5). Further, RTW was more delayed in patients with multiple types of traumatic intracranial lesions visible in MRI (31 days, n = 19) and when lesions were detected in the primary CT (31 days, n = 24). There were no significant differences in GOS-E. CONCLUSIONS:The imaging results that were most clearly associated with delayed RTW were positive primary CT and multiple types of lesions in MRI. RTW-% of patients with MTBI was excellent and a single intracranial lesion does not seem to be a predictive factor of disability to work.
10.1007/s00701-022-05244-4
Serum biomarkers and disease progression in CT-negative mild traumatic brain injury.
Cerebral cortex (New York, N.Y. : 1991)
Blood proteins are emerging as potential biomarkers for mild traumatic brain injury (mTBI). Molecular pathology of mTBI underscores the critical roles of neuronal injury, neuroinflammation, and vascular health in disease progression. However, the temporal profile of blood biomarkers associated with the aforementioned molecular pathology after CT-negative mTBI, their diagnostic and prognostic potential, and their utility in monitoring white matter integrity and progressive brain atrophy remain unclear. Thus, we investigated serum biomarkers and neuroimaging in a longitudinal cohort, including 103 CT-negative mTBI patients and 66 matched healthy controls (HCs). Angiogenic biomarker vascular endothelial growth factor (VEGF) exhibited the highest area under the curve of 0.88 in identifying patients from HCs. Inflammatory biomarker interleukin-1β and neuronal cell body injury biomarker ubiquitin carboxyl-terminal hydrolase L1 were elevated in acute-stage patients and associated with deterioration of cognitive function from acute-stage to 6-12 mo post-injury period. Notably, axonal injury biomarker neurofilament light (NfL) was elevated in acute-stage patients, with higher levels associated with impaired white matter integrity in acute-stage and progressive gray and white matter atrophy from 3- to 6-12 mo post-injury period. Collectively, our findings emphasized the potential clinical value of serum biomarkers, particularly NfL and VEGF, in diagnosing mTBI and monitoring disease progression.
10.1093/cercor/bhad405
Clinical performance of a multiparametric MRI-based post concussive syndrome index.
Frontiers in neurology
Introduction:Diffusion Tensor Imaging (DTI) has revealed measurable changes in the brains of patients with persistent post-concussive syndrome (PCS). Because of inconsistent results in univariate DTI metrics among patients with mild traumatic brain injury (mTBI), there is currently no single objective and reliable MRI index for clinical decision-making in patients with PCS. Purpose:This study aimed to evaluate the performance of a newly developed PCS Index (PCSI) derived from machine learning of multiparametric magnetic resonance imaging (MRI) data to classify and differentiate subjects with mTBI and PCS history from those without a history of mTBI. Materials and methods:Data were retrospectively extracted from 139 patients aged between 18 and 60 years with PCS who underwent MRI examinations at 2 weeks to 1-year post-mTBI, as well as from 336 subjects without a history of head trauma. The performance of the PCS Index was assessed by comparing 69 patients with a clinical diagnosis of PCS with 264 control subjects. The PCSI values for patients with PCS were compared based on the mechanism of injury, time interval from injury to MRI examination, sex, history of prior concussion, loss of consciousness, and reported symptoms. Results:Injured patients had a mean PCSI value of 0.57, compared to the control group, which had a mean PCSI value of 0.12 ( = 8.42e-23) with accuracy of 88%, sensitivity of 64%, and specificity of 95%, respectively. No statistically significant differences were found in the PCSI values when comparing the mechanism of injury, sex, or loss of consciousness. Conclusion:The PCSI for individuals aged between 18 and 60 years was able to accurately identify patients with post-concussive injuries from 2 weeks to 1-year post-mTBI and differentiate them from the controls. The results of this study suggest that multiparametric MRI-based PCSI has great potential as an objective clinical tool to support the diagnosis, treatment, and follow-up care of patients with post-concussive syndrome. Further research is required to investigate the replicability of this method using other types of clinical MRI scanners.
10.3389/fneur.2023.1282833
Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis.
Disease markers
OBJECTIVES:To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. METHODS:Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88 ± 13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46 ± 11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. RESULTS:The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. CONCLUSIONS:Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.
10.1155/2021/3015238
Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury.
NeuroImage
BACKGROUND:Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear. METHODS:Here, we leveraged the structural data from a large, heterogeneous dataset (N = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined. RESULTS:Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations. CONCLUSION:We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.
10.1016/j.neuroimage.2024.120751
Deep learning-based multimodality classification of chronic mild traumatic brain injury using resting-state functional MRI and PET imaging.
Frontiers in neuroscience
Mild traumatic brain injury (mTBI) is a public health concern. The present study aimed to develop an automatic classifier to distinguish between patients with chronic mTBI ( = 83) and healthy controls (HCs) ( = 40). Resting-state functional MRI (rs-fMRI) and positron emission tomography (PET) imaging were acquired from the subjects. We proposed a novel deep-learning-based framework, including an autoencoder (AE), to extract high-level latent and rectified linear unit (ReLU) and sigmoid activation functions. Single and multimodality algorithms integrating multiple rs-fMRI metrics and PET data were developed. We hypothesized that combining different imaging modalities provides complementary information and improves classification performance. Additionally, a novel data interpretation approach was utilized to identify top-performing features learned by the AEs. Our method delivered a classification accuracy within the range of 79-91.67% for single neuroimaging modalities. However, the performance of classification improved to 95.83%, thereby employing the multimodality model. The models have identified several brain regions located in the default mode network, sensorimotor network, visual cortex, cerebellum, and limbic system as the most discriminative features. We suggest that this approach could be extended to the objective biomarkers predicting mTBI in clinical settings.
10.3389/fnins.2023.1333725
mTBI-DSANet: A deep self-attention model for diagnosing mild traumatic brain injury using multi-level functional connectivity networks.
Computers in biology and medicine
The main approach for analyzing resting-state functional magnetic resonance imaging (rs-fMRI) is the low-order functional connectivity network (LoFCN) based on the correlation between two brain regions. Based on LoFCN, researchers recently proposed the topographical high-order FCN (tHoFCN) and the associated high-order FCN (aHoFCN) to explore the high-order interactions among brain regions. In this work, we designed a Deep Self-Attention (DSA) framework called mTBI-DSANet to diagnose mild traumatic brain injury (mTBI) using multi-level FCNs, including LoFCN, tHoFCN, and aHoFCN. The multilayer perceptron and self-attention mechanism in mTBI-DSANet were designed to capture important features for the mTBI diagnosis. We evaluated the mTBI-DSANet's performance on the real rs-fMRI dataset, which was collected by Third Xiangya Hospital of Central South University from April 2014 to February 2021. We compared the performance of mTBI-DSANet with distinct FCNs and their combinations under 10-fold cross-validation. Based on the LoFCN+aHoFCN combination, the average performance of mTBI-DSANet achieved the best accuracy of 0.834, which is significantly better than peer methods. The experiments demonstrated the potential of the mTBI-DSANet in assisting mTBI diagnosis.
10.1016/j.compbiomed.2022.106354
The detection of mild traumatic brain injury in paediatrics using artificial neural networks.
Ellethy Hanem,Chandra Shekhar S,Nasrallah Fatima A
Computers in biology and medicine
Head computed tomography (CT) is the gold standard in emergency departments (EDs) to evaluate mild traumatic brain injury (mTBI) patients, especially for paediatrics. Data-driven models for successfully classifying head CT scans that have mTBI will be valuable in terms of timeliness and cost-effectiveness for TBI diagnosis. This study applied two different machine learning (ML) models to diagnose mTBI in a paediatric population collected as part of the paediatric emergency care applied research network (PECARN) study between 2004 and 2006. The models were conducted using 15,271 patients under the age of 18 years with mTBI and had a head CT report. In the conventional model, random forest (RF) ranked the features to reduce data dimensionality and the top ranked features were used to train a shallow artificial neural network (ANN) model. In the second model, a deep ANN applied to classify positive and negative mTBI patients using the entirety of the features available. The dataset was divided into two subsets: 80% for training and 20% for testing using five-fold cross-validation. Accuracy, sensitivity, precision, and specificity were calculated by comparing the model's prediction outcome to the actual diagnosis for each patient. RF ranked ten clinical demographic features and twelve CT-findings; the hybrid RF-ANN model achieved an average specificity of 99.96%, sensitivity of 95.98%, precision of 99.25%, and accuracy of 99.74% in identifying positive mTBI from negative mTBI subjects. The deep ANN proved its ability to carry out the task efficiently with an average specificity of 99.9%, sensitivity of 99.2%, precision of 99.9%, and accuracy of 99.9%. The performance of the two proposed models demonstrated the feasibility of using ANN to diagnose mTBI in a paediatric population. This is the first study to investigate deep ANN in a paediatric cohort with mTBI using clinical and non-imaging data and diagnose mTBI with balanced sensitivity and specificity using shallow and deep ML models. This method, if validated, would have the potential to reduce the burden of TBI evaluation in EDs and aide clinicians in the decision-making process.
10.1016/j.compbiomed.2021.104614
Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.
Huang Ming-Xiong,Huang Charles W,Harrington Deborah L,Robb-Swan Ashley,Angeles-Quinto Annemarie,Nichols Sharon,Huang Jeffrey W,Le Lu,Rimmele Carl,Matthews Scott,Drake Angela,Song Tao,Ji Zhengwei,Cheng Chung-Kuan,Shen Qian,Foote Ericka,Lerman Imanuel,Yurgil Kate A,Hansen Hayden B,Naviaux Robert K,Dynes Robert,Baker Dewleen G,Lee Roland R
Human brain mapping
Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active-duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep-learning neural network method, 3D-MEGNET, and applied it to resting-state magnetoencephalography (rs-MEG) source-magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat-deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All-frequency model, which combined delta-theta (1-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D-MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver-operator-characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta-theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta-theta and gamma-band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta-band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source-imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all-frequency model offered more discriminative power than each frequency-band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.
10.1002/hbm.25340
Neuropathological Mechanisms of Mild Traumatic Brain Injury: A Perspective From Multimodal Magnetic Resonance Imaging.
Frontiers in neuroscience
Mild traumatic brain injury (mTBI) accounts for more than 80% of the total number of TBI cases. The mechanism of injury for patients with mTBI has a variety of neuropathological processes. However, the underlying neurophysiological mechanism of the mTBI is unclear, which affects the early diagnosis, treatment decision-making, and prognosis evaluation. More and more multimodal magnetic resonance imaging (MRI) techniques have been applied for the diagnosis of mTBI, such as functional magnetic resonance imaging (fMRI), arterial spin labeling (ASL) perfusion imaging, susceptibility-weighted imaging (SWI), and diffusion MRI (dMRI). Various imaging techniques require to be used in combination with neuroimaging examinations for patients with mTBI. The understanding of the neuropathological mechanism of mTBI has been improved based on different angles. In this review, we have summarized the application of these aforementioned multimodal MRI techniques in mTBI and evaluated its benefits and drawbacks.
10.3389/fnins.2022.923662
Single Mild Traumatic Brain Injury Deteriorates Progressive Interhemispheric Functional and Structural Connectivity.
Wang Zhuonan,Zhang Ming,Sun Chuanzhu,Wang Shan,Cao Jieli,Wang Kevin K W,Gan Shuoqiu,Huang Wenmin,Niu Xuan,Zhu Yanan,Sun Yingxiang,Bai Lijun
Journal of neurotrauma
The present study examined dynamic interhemispheric structural and functional connectivity in mild traumatic brain injury (mTBI) patients with longitudinal observations from early subacute to chronic stages within 1 year of injury. Forty-two mTBI patients and 42 matched healthy controls underwent clinical and neuropsychological evaluations, diffusion tensor imaging, and resting-state functional magnetic resonance imaging. All mTBI patients were initially evaluated within 14 d post-injury (T-1) and at 3 months (T-2) and 6-12 months (T-3) follow-ups. Separate transcallosal fiber tracts in the corpus callosum (CC) with respect to their specific interhemispheric cortical projections were derived with fiber tracking and voxel-mirrored homotopic connectivity analyses. With diffusion tensor imaging-based tractography, five vertical segments of the CC (I-V) were distinguished. Correlation analyses were performed to evaluate relationships between structural and functional imaging measures as well as imaging indices and neuropsychological measures. The loss of integrity in the CC demonstrated saliently persistent and time-dependent regional specificity after mTBI. The impairment spanned multiple segments from CC II at T-1 and CC I, II, VI, and V at T-2 to all subregions at T-3. Moreover, loss of interhemispheric structural connectivity through the CC corresponded well to regions presenting altered interhemispheric functional connectivity. Decreased functional connectivity in the dorsolateral prefrontal cortex thereafter contributed to poor executive function in mTBI patients. The current study provides further evidence that the CC is a sign to interhemispheric highways underpinning the widespread cerebral pathology typifying mTBI syndrome.
10.1089/neu.2018.6196
Evaluating the state of non-invasive imaging biomarkers for traumatic brain injury.
Neurosurgical review
Non-invasive imaging biomarkers are useful for prognostication in patients with traumatic brain injury (TBI) at high risk for morbidity with invasive procedures. The authors present findings from a scoping review discussing the pertinent biomarkers. Embase, Ovid-MEDLINE, and Scopus were queried for original research on imaging biomarkers for prognostication of TBI in adult patients. Two reviewers independently screened articles, extracted data, and evaluated risk of bias. Data was synthesized and confidence evaluated with the linked evidence according to the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) approach. Our search yielded 3104 unique citations, 44 of which were included in this review. Study populations varied in TBI severity, as defined by Glasgow Coma Scale (GCS), including: mild (n=9), mild and moderate (n=3), moderate and severe (n=7), severe (n=6), and all GCS scores (n=17). Diverse imaging modalities were used for prognostication, predominantly computed tomography (CT) only (n=11), magnetic resonance imaging (MRI) only (n=9), and diffusion tensor imaging (DTI) (N=9). The biomarkers included diffusion coefficient mapping, metabolic characteristics, optic nerve sheath diameter, T1-weighted signal changes, cortical cerebral blood flow, axial versus extra-axial lesions, T2-weighted gradient versus spin echo, translocator protein levels, and trauma imaging of brainstem areas. The majority (93%) of studies identified that the imaging biomarker of interest had a statistically significant prognostic value; however, these are based on a very low to low level of quality of evidence. No study directly compared the effects on specific TBI treatments on the temporal course of imaging biomarkers. The current literature is insufficient to make a strong recommendation about a preferred imaging biomarker for TBI, especially considering GRADE criteria revealing low quality of evidence. Rigorous prospective research of imaging biomarkers of TBI is warranted to improve the understanding of TBI severity.
10.1007/s10143-023-02085-2
Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms.
Translational psychiatry
Mild traumatic brain injury (mTBI) is a significant health burden among military service members. Although mTBI was once considered relatively benign compared to more severe TBIs, a growing body of evidence has demonstrated the devastating neurological consequences of mTBI, including chronic post-concussion symptoms and deficits in cognition, memory, sleep, vision, and hearing. The discovery of reliable biomarkers for mTBI has been challenging due to under-reporting and heterogeneity of military-related mTBI, unpredictability of pathological changes, and delay of post-injury clinical evaluations. Moreover, compared to more severe TBI, mTBI is especially difficult to diagnose due to the lack of overt clinical neuroimaging findings. Yet, advanced neuroimaging techniques using magnetic resonance imaging (MRI) hold promise in detecting microstructural aberrations following mTBI. Using different pulse sequences, MRI enables the evaluation of different tissue characteristics without risks associated with ionizing radiation inherent to other imaging modalities, such as X-ray-based studies or computerized tomography (CT). Accordingly, considering the high morbidity of mTBI in military populations, debilitating post-injury symptoms, and lack of robust neuroimaging biomarkers, this review (1) summarizes the nature and mechanisms of mTBI in military settings, (2) describes clinical characteristics of military-related mTBI and associated comorbidities, such as post-traumatic stress disorder (PTSD), (3) highlights advanced neuroimaging techniques used to study mTBI and the molecular mechanisms that can be inferred, and (4) discusses emerging frontiers in advanced neuroimaging for mTBI. We encourage multi-modal approaches combining neuropsychiatric, blood-based, and genetic data as well as the discovery and employment of new imaging techniques with big data analytics that enable accurate detection of post-injury pathologic aberrations related to tissue microstructure, glymphatic function, and neurodegeneration. Ultimately, this review provides a foundational overview of military-related mTBI and advanced neuroimaging techniques that merit further study for mTBI diagnosis, prognosis, and treatment monitoring.
10.1038/s41398-023-02569-1