Rate of abnormalities in quantitative MR neuroimaging of persons with chronic traumatic brain injury.

BACKGROUND: Mild traumatic brain injury (mTBI) can result in lasting brain damage that is often too subtle to detect by qualitative visual inspection on conventional MR imaging. Although a number of FDA-cleared MR neuroimaging tools have demonstrated changes associated with mTBI, they are still under-utilized in clinical practice. METHODS: We investigated a group of 65 individuals with predominantly mTBI (60 mTBI, 48 due to motor-vehicle collision, mean age 47 ± 13 years, 27 men and 38 women) with MR neuroimaging performed in a median of 37 months post-injury. We evaluated abnormalities in brain volumetry including analysis of left-right asymmetry by quantitative volumetric analysis, cerebral perfusion by pseudo-continuous arterial spin labeling (PCASL), white matter microstructure by diffusion tensor imaging (DTI), and neurometabolites via magnetic resonance spectroscopy (MRS). RESULTS: All participants demonstrated atrophy in at least one lobar structure or increased lateral ventricular volume. The globus pallidi and cerebellar grey matter were most likely to demonstrate atrophy and asymmetry. Perfusion imaging revealed significant reductions of cerebral blood flow in both occipital and right frontoparietal regions. Diffusion abnormalities were relatively less common though a subset analysis of participants with higher resolution DTI demonstrated additional abnormalities. All participants showed abnormal levels on at least one brain metabolite, most commonly in choline and N-acetylaspartate. CONCLUSION: We demonstrate the presence of coup-contrecoup perfusion injury patterns, widespread atrophy, regional brain volume asymmetry, and metabolic aberrations as sensitive markers of chronic mTBI sequelae. Our findings expand the historic focus on quantitative imaging of mTBI with DTI by highlighting the complementary importance of volumetry, arterial spin labeling perfusion and magnetic resonance spectroscopy neurometabolite analyses in the evaluation of chronic mTBI.

Lateral frontoparietal effective connectivity differentiates and predicts state of consciousness in a cohort of patients with traumatic disorders of consciousness.

Neuroimaging studies have suggested an important role for the default mode network (DMN) in disorders of consciousness (DoC). However, the extent to which DMN connectivity can discriminate DoC states-unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS)-is less evident. Particularly, it is unclear whether effective DMN connectivity, as measured indirectly with dynamic causal modelling (DCM) of resting EEG can disentangle UWS from healthy controls and from patients considered conscious (MCS+). Crucially, this extends to UWS patients with potentially "covert" awareness (minimally conscious star, MCS*) indexed by voluntary brain activity in conjunction with partially preserved frontoparietal metabolism as measured with positron emission tomography (PET+ diagnosis; in contrast to PET- diagnosis with complete frontoparietal hypometabolism). Here, we address this gap by using DCM of EEG data acquired from patients with traumatic brain injury in 11 UWS (6 PET- and 5 PET+) and in 12 MCS+ (11 PET+ and 1 PET-), alongside with 11 healthy controls. We provide evidence for a key difference in left frontoparietal connectivity when contrasting UWS PET- with MCS+ patients and healthy controls. Next, in a leave-one-subject-out cross-validation, we tested the classification performance of the DCM models demonstrating that connectivity between medial prefrontal and left parietal sources reliably discriminates UWS PET- from MCS+ patients and controls. Finally, we illustrate that these models generalize to an unseen dataset: models trained to discriminate UWS PET- from MCS+ and controls, classify MCS* patients as conscious subjects with high posterior probability (pp > .92). These results identify specific alterations in the DMN after severe brain injury and highlight the clinical utility of EEG-based effective connectivity for identifying patients with potential covert awareness.

Multicenter Cohort Study to Evaluate the Relationship between Radiological Findings and Disability in Moderate and Severe Head Injury Patients.

This was a multicenter cohort study to evaluate the relationship between radiological findings and disability in moderate and severe head injury patients. The study places were the Neurosurgery department of Sylhet M A G Osmani Medical College Hospital, Sylhet Women's Medical College Hospital (SWMCH) and King Faisal Hospital (KFH), Taif, KSA. Sample size was 104 and the study period was 36 months (July 2021 to December 2022). On the basis of radiological findings the participants were divided into three arms. The different arms were diffused traumatic brain injury (arm-1), focal traumatic brain injury (arm-2) and both (diffused and traumatic) types traumatic brain injury (arm-3). Outcome was assessed by modified Rankin Score (mRS). Mean age was significantly higher in female. Overall mean age was 40.28 year. Highest number was in the below 20-year age group followed by the 41-50-year age group. Lowest number of participants was in the above 60-year group. Improved group was significantly higher than 'not improved' and the 'died' group (p<0.00001). Improved participants were significantly higher in the arm-1 and arm-2. Mortality was significantly higher (p<0.00001) in the arm-3 group.

Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury.

In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.

Accelerated Aging after Traumatic Brain Injury: An ENIGMA Multi-Cohort Mega-Analysis.

OBJECTIVE: The long-term consequences of traumatic brain injury (TBI) on brain structure remain uncertain. Given evidence that a single significant brain injury event increases the risk of dementia, brain-age estimation could provide a novel and efficient indexing of the long-term consequences of TBI. Brain-age procedures use predictive modeling to calculate brain-age scores for an individual using structural magnetic resonance imaging (MRI) data. Complicated mild, moderate, and severe TBI (cmsTBI) is associated with a higher predicted age difference (PAD), but the progression of PAD over time remains unclear. We sought to examine whether PAD increases as a function of time since injury (TSI) and if injury severity and sex interacted to influence this progression. METHODS: Through the ENIGMA Adult Moderate and Severe (AMS)-TBI working group, we examine the largest TBI sample to date (n = 343), along with controls, for a total sample size of n = 540, to replicate and extend prior findings in the study of TBI brain age. Cross-sectional T1w-MRI data were aggregated across 7 cohorts, and brain age was established using a similar brain age algorithm to prior work in TBI. RESULTS: Findings show that PAD widens with longer TSI, and there was evidence for differences between sexes in PAD, with men showing more advanced brain age. We did not find strong evidence supporting a link between PAD and cognitive performance. INTERPRETATION: This work provides evidence that changes in brain structure after cmsTBI are dynamic, with an initial period of change, followed by relative stability in brain morphometry, eventually leading to further changes in the decades after a single cmsTBI. ANN NEUROL 2024;96:365-377.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy.

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Head Injury Evaluation and Ambulance Diagnosis (HOME) Study protocol: a feasibility study assessing the implementation of the Canadian CT Head Rule in the prehospital setting.

INTRODUCTION: Traumatic brain injury (TBI) is a common presentation in the prehospital environment. At present, paramedics do not routinely use tools to identify low-risk patients who could be left at scene or taken to a local hospital rather than a major trauma centre. The Canadian CT Head Rule (CCHR) was developed to guide the use of CT imaging in hospital. It has not been evaluated in the prehospital setting. We aim to address this gap by evaluating the feasibility and acceptability of implementing the CCHR to patients and paramedics, and the feasibility of conducting a full-scale clinical trial of its use. METHODS AND ANALYSIS: We will recruit adult patients who are being transported to an emergency department (ED) by ambulance after suffering a mild TBI. Paramedics will prospectively collect data for the CCHR. All patients will be transported to the ED, where deferred consent will be taken and the treating clinician will reassess the CCHR, blinded to paramedic interpretation. The primary clinical outcome will be neurosurgically significant TBI. Feasibility outcomes include recruitment and attrition rates. We will assess acceptability of the CCHR to paramedics using the Ottawa Acceptability of Decision Rules Instrument. Interobserver reliability of the CCHR will be assessed between paramedics and the treating clinician in the ED. Participating paramedics and patients will be invited to participate in semistructured interviews to explore the acceptability of trial processes and facilitators and barriers to the use of the CCHR in practice. Data will be analysed thematically. We anticipate recruiting approximately 100 patients over 6 months. ETHICS AND DISSEMINATION: This study was approved by the Health Research Authority and the Research Ethics Committee (REC reference: 22/NW/0358). The results will be published in a peer-reviewed journal, presented at conferences and will be incorporated into a doctoral thesis. TRIAL REGISTRATION NUMBER: ISRCTN92566288.

[Possibilities of using results of head computed tomography in victims with traumatic brain injuries]. / Vozmozhnosti ispol'zovaniya rezul'tatov komp'yuternoi tomografii golovy u poterpevshikh s cherepno-mozgovoi travmoi.

The constant increase in the number of neurotraumas in the country leads to an increase in forensic examinations of a persons. In Russia, about 600 thousand people receive craniocerebral injuries annually, of which 50 thousand die, others are potentially will be in forensic examination during or after treatment. With an increase in the total number of such examinations, the number of erroneous conclusions is expected to increase. If it is impossible for the radiologist included in the commission to review the results of computed tomography of the head performed in the hospital, the experts are forced to use the data that are recorded in the medical documents. The present study revealed the percentage of erroneous interpretations in such descriptions, systematized typical errors, calculated the sensitivity, specificity and accuracy of computed tomography in craniocerebral injury.

Resting state networks in patients with acute disorders of consciousness after severe traumatic brain injury.

OBJECTIVES: This study aims to describe resting state networks (RSN) in patients with disorders of consciousness (DOC)s after acute severe traumatic brain injury (TBI). METHODS: Adult patients with TBI with a GCS score <8 who remained in a coma, minimally conscious state (MCS), or unresponsive wakefulness syndrome (UWS), between 2017 and 2020 were included. Blood-oxygen-level dependent imaging was performed to compare their RSN with 10 healthy volunteers. RESULTS: Of a total of 293 patients evaluated, only 13 patients were included according to inclusion criteria: 7 in coma (54%), 2 in MCS (15%), and 4 (31%) had an UWS. RSN analysis showed that the default mode network (DMN) was present and symmetric in 6 patients (46%), absent in 1 (8%), and asymmetric in 6 (46%). The executive control network (ECN) was present in all patients but was asymmetric in 3 (23%). The right ECN was absent in 2 patients (15%) and the left ECN in 1 (7%). The medial visual network was present in 11 (85%) patients. Finally, the cerebellar network was symmetric in 8 patients (62%), asymmetric in 1 (8%), and absent in 4 (30%). CONCLUSIONS: A substantial impairment in activation of RSN is demonstrated in patients with DOC after severe TBI in comparison with healthy subjects. Three patterns of activation were found: normal/complete activation, 2) asymmetric activation or partially absent, and 3) absent activation.

Machine learning models for predicting early hemorrhage progression in traumatic brain injury.

This study explores the progression of intracerebral hemorrhage (ICH) in patients with mild to moderate traumatic brain injury (TBI). It aims to predict the risk of ICH progression using initial CT scans and identify clinical factors associated with this progression. A retrospective analysis of TBI patients between January 2010 and December 2021 was performed, focusing on initial CT evaluations and demographic, comorbid, and medical history data. ICH was categorized into intraparenchymal hemorrhage (IPH), petechial hemorrhage (PH), and subarachnoid hemorrhage (SAH). Within our study cohort, we identified a 22.2% progression rate of ICH among 650 TBI patients. The Random Forest algorithm identified variables such as petechial hemorrhage (PH) and countercoup injury as significant predictors of ICH progression. The XGBoost algorithm, incorporating key variables identified through SHAP values, demonstrated robust performance, achieving an AUC of 0.9. Additionally, an individual risk assessment diagram, utilizing significant SHAP values, visually represented the impact of each variable on the risk of ICH progression, providing personalized risk profiles. This approach, highlighted by an AUC of 0.913, underscores the model's precision in predicting ICH progression, marking a significant step towards enhancing TBI patient management through early identification of ICH progression risks.

Emergency Department Evaluation of Young Infants With Head Injury.

OBJECTIVES: We compared the emergency department (ED) evaluation and outcomes of young head-injured infants to older children. METHODS: Using the Pediatric Health Information Systems database, we performed a retrospective, cross-sectional analysis of children <2 years old with isolated head injuries (International Classification of Diseases, 10th Revision, diagnoses) at one of 47 EDs from 2015 to 2019. Our primary outcome was utilization of diagnostic cranial imaging. Secondary outcomes were diagnosis of traumatic brain injury (TBI), clinically important TBI, and mortality. We compared outcomes between the youngest infants (<3 months old) and children 3 to 24 months old. RESULTS: We identified 112 885 ED visits for children <2 years old with isolated head injuries. A total of 62 129 (55%) were by males, and 10 325 (9.1%) were by infants <3 months of age. Compared with older children (12-23 months old), the youngest infants were more likely to: Undergo any diagnostic cranial imaging (50.3% vs 18.3%; difference 31.9%, 95% confidence interval [CI] 35.0-28.9%), be diagnosed with a TBI (17.5% vs 2.7%; difference 14.8%, 95% CI 16.4%-13.2%) or clinically important TBI (4.6% vs 0.5%; difference 4.1%, 95% CI 3.8%-4.5%), and to die (0.3% vs 0.1%; difference 0.2%, 95% CI 0.3%-0.1%). Among those undergoing computed tomography or MRI, TBIs were significantly more common in the youngest infants (26.4% vs 8.8%, difference 17.6%, 95% CI 16.3%-19.0%). CONCLUSIONS: The youngest infants with head injuries are significantly more likely to undergo cranial imaging, be diagnosed with brain injuries, and die, highlighting the need for a specialized approach for this vulnerable population.

Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

Effects of Low-Level Light Therapy on Resting-State Connectivity Following Moderate Traumatic Brain Injury: Secondary Analyses of a Double-blinded Placebo-controlled Study.

Background Low-level light therapy (LLLT) has been shown to modulate recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on the functional connectivity of the brain when at rest has not been well studied. Purpose To use functional MRI to assess the effect of LLLT on whole-brain resting-state functional connectivity (RSFC) in patients with moderate TBI at acute (within 1 week), subacute (2-3 weeks), and late-subacute (3 months) recovery phases. Materials and Methods This is a secondary analysis of a prospective single-site double-blinded sham-controlled study conducted in patients presenting to the emergency department with moderate TBI from November 2015 to July 2019. Participants were randomized for LLLT and sham treatment. The primary outcome of the study was to assess structural connectivity, and RSFC was collected as the secondary outcome. MRI was used to measure RSFC in 82 brain regions in participants during the three recovery phases. Healthy individuals who did not receive treatment were imaged at a single time point to provide control values. The Pearson correlation coefficient was estimated to assess the connectivity strength for each brain region pair, and estimates of the differences in Fisher z-transformed correlation coefficients (hereafter, z differences) were compared between recovery phases and treatment groups using a linear mixed-effects regression model. These analyses were repeated for all brain region pairs. False discovery rate (FDR)-adjusted P values were computed to account for multiple comparisons. Quantile mixed-effects models were constructed to quantify the association between the Rivermead Postconcussion Symptoms Questionnaire (RPQ) score, recovery phase, and treatment group. Results RSFC was evaluated in 17 LLLT-treated participants (median age, 50 years [IQR, 25-67 years]; nine female), 21 sham-treated participants (median age, 50 years [IQR, 43-59 years]; 11 female), and 23 healthy control participants (median age, 42 years [IQR, 32-54 years]; 13 male). Seven brain region pairs exhibited a greater change in connectivity in LLLT-treated participants than in sham-treated participants between the acute and subacute phases (range of z differences, 0.37 [95% CI: 0.20, 0.53] to 0.45 [95% CI: 0.24, 0.67]; FDR-adjusted P value range, .010-.047). Thirteen different brain region pairs showed an increase in connectivity in sham-treated participants between the subacute and late-subacute phases (range of z differences, 0.17 [95% CI: 0.09, 0.25] to 0.26 [95% CI: 0.14, 0.39]; FDR-adjusted P value range, .020-.047). There was no evidence of a difference in clinical outcomes between LLLT-treated and sham-treated participants (range of differences in medians, -3.54 [95% CI: -12.65, 5.57] to -0.59 [95% CI: -7.31, 8.49]; P value range, .44-.99), as measured according to RPQ scores. Conclusion Despite the small sample size, the change in RSFC from the acute to subacute phases of recovery was greater in LLLT-treated than sham-treated participants, suggesting that acute-phase LLLT may have an impact on resting-state neuronal circuits in the early recovery phase of moderate TBI. ClinicalTrials.gov Identifier: NCT02233413 © RSNA, 2024 Supplemental material is available for this article.

Cerebral vasospasm in patients with traumatic subarachnoid hemorrhage, a possible point of intervention?

OBJECTIVE: To determine the incidence of vasospasm in traumatic brain injury patients with traumatic subarachnoid hemorrhage. METHODS: IRB approval was obtained for this retrospective chart review. An institutional trauma database was queried for adult patients with traumatic brain injury (TBI) and traumatic subarachnoid hemorrhage (tSAH) seen on CT head obtained within 20 days. The presence of vasospasm on CTA was determined by radiology report. Association between categorical background characteristics and intracranial vasospasm was assessed by the chi-square test and association between a continuous variables and intracranial vasospasm was assessed by a paired t-test. RESULTS: 1142 patients with traumatic SAH were identified from the trauma database. 792 patients were excluded: 142 for age <18, 632 did not have CT angiography, and 18 had non-traumatic SAH. 350 patients were analyzed, of which 28 (8 %) had vasospasm. Traumatic vasospasm was associated with higher-grade TBI based on Cochran-Armitage trend test (p < 0.05). Vasospasm patients had longer length of stay in the ICU (mean days 13.64 vs 7.24, P < 0.001), and had a higher incidence of death (39.29 % vs 20.81 %), although this did not reach statistical significance. CONCLUSION: Intracranial vasospasm, specifically in patients with tSAH, is associated with more severe TBI and longer stays in the ICU. Our incidence is smaller compared to other studies likely due to the retrospective nature and the infrequency of obtaining CT angiography after initial presentation. Prospective studies are warranted as the incidence is significant and may represent a point of intervention for TBI.

Incidence of Traumatic Brain Injury in a Longitudinal Cohort of Older Adults.

Importance: Traumatic brain injury (TBI) occurs at the highest rate in older adulthood and increases risk for cognitive impairment and dementia. Objectives: To update existing TBI surveillance data to capture nonhospital settings and to explore how social determinants of health (SDOH) are associated with TBI incidence among older adults. Design, Setting, and Participants: This nationally representative longitudinal cohort study assessed participants for 18 years, from August 2000 through December 2018, using data from the Health and Retirement Study (HRS) and linked Medicare claims dates. Analyses were completed August 9 through December 12, 2022. Participants were 65 years of age or older in the HRS with survey data linked to Medicare without a TBI prior to HRS enrollment. They were community dwelling at enrollment but were retained in HRS if they were later institutionalized. Exposures: Baseline demographic, cognitive, medical, and SDOH information from HRS. Main Outcomes and Measures: Incident TBI was defined using inpatient and outpatient International Classification of Diseases, Ninth or Tenth Revision, diagnosis codes received the same day or within 1 day as the emergency department (ED) visit code and the computed tomography (CT) or magnetic resonance imaging (MRI) code, after baseline HRS interview. A cohort with TBI codes but no ED visit or CT or MRI scan was derived to capture diagnoses in nonhospital settings. Descriptive statistics and bivariate associations of TBI with demographic and SDOH characteristics used sample weights. Fine-Gray regression models estimated associations between covariates and TBI, with death as a competing risk. Imputation considering outcome and complex survey design was performed by race and ethnicity, sex, education level, and Area Deprivation Index percentiles 1, 50, and 100. Other exposure variables were fixed at their weighted means. Results: Among 9239 eligible respondents, 5258 (57.7%) were female and 1210 (9.1%) were Black, 574 (4.7%) were Hispanic, and 7297 (84.4%) were White. Mean (SD) baseline age was 75.2 (8.0) years. During follow-up (18 years), 797 (8.9%) of respondents received an incident TBI diagnosis with an ED visit and a CT code within 1 day, 964 (10.2%) received an incident TBI diagnosis and an ED code, and 1148 (12.9%) received a TBI code with or without an ED visit and CT scan code. Compared with respondents without incident TBI, respondents with TBI were more likely to be female (absolute difference, 7.0 [95% CI, 3.3-10.8]; P < .001) and White (absolute difference, 5.1 [95% CI, 2.8-7.4]; P < .001), have normal cognition (vs cognitive impairment or dementia; absolute difference, 6.1 [95% CI, 2.8-9.3]; P = .001), higher education (absolute difference, 3.8 [95% CI, 0.9-6.7]; P < .001), and wealth (absolute difference, 6.5 [95% CI, 2.3-10.7]; P = .01), and be without baseline lung disease (absolute difference, 5.1 [95% CI, 3.0-7.2]; P < .001) or functional impairment (absolute difference, 3.3 [95% CI, 0.4-6.1]; P = .03). In adjusted multivariate models, lower education (subdistribution hazard ratio [SHR], 0.73 [95% CI, 0.57-0.94]; P = .01), Black race (SHR, 0.61 [95% CI, 0.46-0.80]; P < .001), area deprivation index national rank (SHR 1.00 [95% CI 0.99-1.00]; P = .009), and male sex (SHR, 0.73 [95% CI, 0.56-0.94]; P = .02) were associated with membership in the group without TBI. Sensitivity analyses using a broader definition of TBI yielded similar results. Conclusions and Relevance: In this longitudinal cohort study of older adults, almost 13% experienced incident TBI during the 18-year study period. For older adults who seek care for TBI, race and ethnicity, sex, and SDOH factors may be associated with incidence of TBI, seeking medical attention for TBI in older adulthood, or both.

Performance of glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) biomarkers in predicting CT scan results and neurological outcomes in children with traumatic brain injury (BRAINI-2 paediatric study): protocol of a European prospective multicentre study.

INTRODUCTION: In light of the burden of traumatic brain injury (TBI) in children and the excessive number of unnecessary CT scans still being performed, new strategies are needed to limit their use while minimising the risk of delayed diagnosis of intracranial lesions (ICLs). Identifying children at higher risk of poor outcomes would enable them to be better monitored. The use of the blood-based brain biomarkers glial fibrillar acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1) could help clinicians in this decision. The overall aim of this study is to provide new knowledge regarding GFAP and UCH-L1 in order to improve TBI management in the paediatric population. METHODS AND ANALYSIS: We will conduct a European, prospective, multicentre study, the BRAINI-2 paediatric study, in 20 centres in France, Spain and Switzerland with an inclusion period of 30 months for a total of 2880 children and adolescents included. To assess the performance of GFAP and UCH-L1 used separately and in combination to predict ICLs on CT scans (primary objective), 630 children less than 18 years of age with mild TBI, defined by a Glasgow Coma Scale score of 13-15 and with a CT scan will be recruited. To evaluate the potential of GFAP and UCH-L1 in predicting the prognosis after TBI (secondary objective), a further 1720 children with mild TBI but no CT scan as well as 130 children with moderate or severe TBI will be recruited. Finally, to establish age-specific reference values for GFAP and UCH-L1 (secondary objective), we will include 400 children and adolescents with no history of TBI. ETHICS AND DISSEMINATION: This study has received ethics approval in all participating countries. Results from our study will be disseminated in international peer-reviewed journals. All procedures were developed in order to assure data protection and confidentiality. TRIAL REGISTRATION NUMBER: NCT05413499.

History of traumatic brain injury is associated with increased grey-matter loss in patients with mild cognitive impairment.

OBJECTIVES: To investigate whether a history of traumatic brain injury (TBI) is associated with greater long-term grey-matter loss in patients with mild cognitive impairment (MCI). METHODS: 85 patients with MCI were identified, including 26 with a previous history of traumatic brain injury (MCI[TBI-]) and 59 without (MCI[TBI+]). Cortical thickness was evaluated by segmenting T1-weighted MRI scans acquired longitudinally over a 2-year period. Bayesian multilevel modelling was used to evaluate group differences in baseline cortical thickness and longitudinal change, as well as group differences in neuropsychological measures of executive function. RESULTS: At baseline, the MCI[TBI+] group had less grey matter within right entorhinal, left medial orbitofrontal and inferior temporal cortex areas bilaterally. Longitudinally, the MCI[TBI+] group also exhibited greater longitudinal declines in left rostral middle frontal, the left caudal middle frontal and left lateral orbitofrontal areas sover the span of 2 years (median = 1-2%, 90%HDI [-0.01%: -0.001%], probability of direction (PD) = 90-99%). The MCI[TBI+] group also displayed greater longitudinal declines in Trail-Making-Test (TMT)-derived ratio (median: 0.737%, 90%HDI: [0.229%: 1.31%], PD = 98.8%) and differences scores (median: 20.6%, 90%HDI: [-5.17%: 43.2%], PD = 91.7%). CONCLUSIONS: Our findings support the notion that patients with MCI and a history of TBI are at risk of accelerated neurodegeneration, displaying greatest evidence for cortical atrophy within the left middle frontal and lateral orbitofrontal frontal cortex. Importantly, these results suggest that long-term TBI-mediated atrophy is more pronounced in areas vulnerable to TBI-related mechanical injury, highlighting their potential relevance for diagnostic forms of intervention in TBI.

Traumatic Cerebral Venous Sinus Thrombosis: Management and Outcomes.

BACKGROUND: Traumatic brain injury (TBI) with skull fractures parallel to or crossing venous sinuses is a recognized risk factor for traumatic cerebral venous sinus thrombosis (tCVST). Despite the recognition of this traumatic pathology in the literature, no consensus regarding management has been achieved. This study aimed to evaluate the impact of tCVST on TBI outcomes and related complications. METHODS: Patients within a prospective registry at a level I trauma center from 2014 to 2023 were reviewed to identify tCVST cases. The impact of tCVST presence on Glasgow Outcome Scale scores at 6 months, 30-day mortality, and hospital length of stay were evaluated in multivariable-adjusted analyses. RESULTS: Among 607 patients with TBI, 61 patients were identified with skull fractures extending to the vicinity of venous sinuses with dedicated venography. Twenty-eight of these 61 patients (44.3%) had tCVST. The majority (96.4%) of tCVST were located in a unilateral transverse or sigmoid sinus. Complete recanalization was observed in 28% of patients on follow-up imaging (7/25 with follow-up imaging). None of the 28 patients suffered attributable venous infarcts or thrombus propagation. In the adjusted analysis, there was no difference in the 30-day mortality or Glasgow Outcome Scale at 6 months between patients with and without tCVST. CONCLUSIONS: Unilateral tCVST follows a benign clinical course without associated increased mortality or morbidity. The management of tCVST should be distinct as compared to spontaneous CVST, likely without the need for anticoagulation.

Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review.

BACKGROUND: Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. METHODS: The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. RESULTS: A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. CONCLUSIONS: AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.