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Adults aged 60 and over are most vulnerable to mild traumatic brain injury (mTBI). Nevertheless, the extent to which chronological age (CA) at injury affects TBI-related brain aging is unknown. This study applies Gaussian process regression to T(1)-weighted magnetic resonance images (MRIs) acquired within [Formula: see text] 7 days and again [Formula: see text] 6 months after a single mTBI sustained by 133 participants aged 20–83 (CA [Formula: see text] = 42.6 ± 17 years; 51 females). Brain BAs are estimated, modeled, and compared as a function of sex and CA at injury using a statistical model selection procedure. On average, the brains of older adults age by 15.3 ± 6.9 years after mTBI, whereas those of younger adults age only by 1.8 ± 5.6 years, a significant difference (Welch’s t(32) = − 9.17, p ≃ 9.47 × 10(−11)). For an adult aged [Formula: see text] 30 to [Formula: see text] 60, the expected amount of TBI-related brain aging is [Formula: see text] 3 years greater than in an individual younger by a decade. For an individual over [Formula: see text] 60, the respective amount is [Formula: see text] 7 years. Despite no significant sex differences in brain aging (Welch’s t(108) = 0.78, p > 0.78), the statistical test is underpowered. BAs estimated at acute baseline versus chronic follow-up do not differ significantly (t(264) = 0.41, p > 0.66, power = 80%), suggesting negligible TBI-related brain aging during the chronic stage of TBI despite accelerated aging during the acute stage. Our results indicate that a single mTBI sustained after age [Formula: see text] 60 involves approximately [Formula: see text] 10 years of premature and lasting brain aging, which is MRI detectable as early as [Formula: see text] 7 days post-injury.

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk.

Little is known about brain aging or dementia in nonindustrialized environments that are similar to how humans lived throughout evolutionary history. This paper examines brain volume (BV) in middle and old age among two indigenous South American populations, the Tsimane and Moseten, whose lifestyles and environments diverge from those in high-income nations. With a sample of 1,165 individuals aged 40 to 94, we analyze population differences in cross-sectional rates of decline in BV with age. We also assess the relationships of BV with energy biomarkers and arterial disease and compare them against findings in industrialized contexts. The analyses test three hypotheses derived from an evolutionary model of brain health, which we call the embarrassment of riches (EOR). The model hypothesizes that food energy was positively associated with late life BV in the physically active, food-limited past, but excess body mass and adiposity are now associated with reduced BV in industrialized societies in middle and older ages. We find that the relationship of BV with both non-HDL cholesterol and body mass index is curvilinear, positive from the lowest values to 1.4 to 1.6 SDs above the mean, and negative from that value to the highest values. The more acculturated Moseten exhibit a steeper decrease in BV with age than Tsimane, but still shallower than US and European populations. Lastly, aortic arteriosclerosis is associated with lower BV. Complemented by findings from the United States and Europe, our results are consistent with the EOR model, with implications for interventions to improve brain health.

Traumatic brain injury (TBI), a significant concern in military populations, is associated with alterations in brain structure and function, cognition, as well as physical and psychological dysfunction. Diffusion magnetic resonance imaging (dMRI) is particularly sensitive to changes in brain structure following TBI, as alterations in white matter (WM) microstructure are common. However, dMRI studies in mild TBI (mTBI) are conflicting, likely due to relatively small samples, sample heterogeneity (demographics, pre- and comorbidities) and injury characteristics (mechanism; chronicity). Furthermore, few studies account for brain asymmetry, which may impact cognitive functions subserved by WM tracts. Examining brain asymmetry in large samples may increase sensitivity to detect heterogeneous areas of subtle WM alteration in mTBI.Through the Enhancing Neuroimaging and Genetics through Meta-analysis (ENIGMA) Military-Relevant Brain Injury working group, we conducted a mega-analysis of neuroimaging and clinical data from 16 cohorts of Active Duty Service Members and Veterans (n=2,598; 2,321 males/277 females; age 19-85 years). 1,080 reported a deployment-related TBI, 480 had a history of only non-military-related TBI, 823 reported no history of TBI, and 215 did not differentiate between military and non-military TBI. dMRI data were processed in a harmonized manner along with harmonized demographic, injury, psychiatric, and cognitive measures. Hemispheric asymmetry of fractional anisotropy (FA, a common proxy for myelin organization) was calculated for 19 WM tracts and compared between those with and without TBI history.FA in the cingulum showed greater asymmetry in individuals with a history of deployment-related TBI; this effect was driven by greater left lateralization in the group with TBI. There was a trend towards lower FA of the right cingulum in the TBI group. These results remained significant after accounting for potentially confounding variables including posttraumatic stress disorder, depression, and handedness and were driven primarily by individuals who had sustained their worst TBI before age 40. We further found that alterations in the cingulum were associated with slower processing speed and poorer set shifting.The results indicate an enhancement of the previously reported natural left laterality, possibly due to vulnerability of the non-dominant hemisphere or compensatory mechanisms in the dominant hemisphere. The cingulum is one of the last WM tracts to mature, reaching peak FA around 42 years old. This effect was primarily detected in individuals whose worst injury occurred before age 40, suggesting that the protracted development of the cingulum may lead to increased vulnerability to insults, such as TBI.

Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer’s disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

Graph theoretical analysis of the structural connectome has been employed successfully to characterise brain network alterations in patients with traumatic brain injury (TBI). However, heterogeneity in neuropathology is a well-known issue in the TBI population, such that group comparisons of patients against controls are confounded by within-group variability. Recently, novel single-subject profiling approaches have been developed to capture inter-patient heterogeneity. We present a personalised connectomics approach that examines structural brain alterations in six chronic patients with moderate-to-severe TBI who underwent anatomical and diffusion magnetic resonance imaging (MRI). We generated individualised profiles of lesion characteristics and network measures (including personalised graph metric ‘GraphMe’ plots, and nodal and edge-based brain network alterations) and compared them against healthy reference cases (N=12) to assess brain damage qualitatively and quantitatively at the individual level. Our findings revealed clinically significant alterations of brain networks with high variability between patients. Our profiling can be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalised rehabilitation protocols based on their unique lesion load and connectome.

Traumatic brain injury (TBI) and Alzheimer’s disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute posttraumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory, and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, posttraumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment, and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients’ chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ~6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

Sport-related brain injury is very common, and the potential long-term effects include a wide range of neurological and psychiatric symptoms, and potentially neurodegeneration. Around the globe, researchers are conducting neuroimaging studies on primarily homogenous samples of athletes. However, neuroimaging studies are expensive and time consuming, and thus current findings from studies of sport-related brain injury are often limited by small sample sizes. Further, current studies apply a variety of neuroimaging techniques and analysis tools which limit comparability among studies. The ENIGMA Sports Injury working group aims to provide a platform for data sharing and collaborative data analysis thereby leveraging existing data and expertise. By harmonizing data from a large number of studies from around the globe, we will work towards reproducibility of previously published findings and towards addressing important research questions with regard to diagnosis, prognosis, and efficacy of treatment for sport-related brain injury. Moreover, the ENIGMA Sports Injury working group is committed to providing recommendations for future prospective data acquisition to enhance data quality and scientific rigor.

IntroductionWe evaluated the prevalence of dementia and mild cognitive impairment (MCI) in indigenous Tsimane and Moseten, who lead a subsistence lifestyle.MethodsParticipants from population-based samples ≥60 years of age (n=623) were assessed using adapted versions of the Modified Mini-Mental State Examination, informant interview, longitudinal cognitive testing and brain computed tomography (CT) scans.ResultsTsimane exhibited five cases of dementia (among n=435; crude prevalence=1.2%, 95% confidence interval [CI]: 0.4, 2.7); Moseten exhibited one case (among n=169; crude prevalence=0.6%, 95% CI: 0.0, 3.2), all age≥80 years. Age-standardized MCI prevalence was 7.7% (95% CI: 5.2, 10.3) in Tsimane and 9.8% (95% CI: 4.9, 14.6) in Moseten. Cognitive impairment was associated with visuospatial impairments, parkinsonian symptoms, and vascular calcification in the basal ganglia.DiscussionThe prevalence of dementia in this cohort is among the lowest in the world. Widespread intracranial medial arterial calcifications suggest a previously unrecognized, non-Alzheimer's disease (AD) dementia phenotype.

Background The incidence of blunt-force traumatic brain injury (TBI) is especially prevalent in the military, where the emergency care admission rate has been reported to be 24.6–41.8 per 10,000 soldier-years. Given substantial advancements in modern neuroimaging techniques over the past decade in terms of structural, functional, and connectomic approaches, this mode of exploration can be viewed as best suited for understanding the underlying pathology and for providing proper intervention at effective time-points. Approach Here we survey neuroimaging studies of mild-to-severe TBI in military veterans with the intent to aid the field in the creation of a roadmap for clinicians and researchers whose aim is to understand TBI progression. Discussion Recent advancements on the quantification of neurocognitive dysfunction, cellular dysfunction, intracranial pressure, cerebral blood flow, inflammation, post-traumatic neuropathophysiology, on blood serum biomarkers and on their correlation to neuroimaging findings are reviewed to hypothesize how they can be used in conjunction with one another. This may allow clinicians and scientists to comprehensively study TBI in military service members, leading to new treatment strategies for both currently-serving as well as veteran personnel, and to improve the study of TBI more broadly.

Mild Traumatic brain injury (mTBI) is a signature wound in military personnel, and repetitive mTBI has been linked to age-related neurogenerative disorders that affect white matter (WM) in the brain. However, findings of injury to specific WM tracts have been variable and inconsistent. This may be due to the heterogeneity of mechanisms, etiology, and comorbid disorders related to mTBI. Non-negative matrix factorization (NMF) is a data-driven approach that detects covarying patterns (components) within high-dimensional data. We applied NMF to diffusion imaging data from military Veterans with and without a self-reported TBI history. NMF identified 12 independent components derived from fractional anisotropy (FA) in a large dataset (n= 1,475) gathered through the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) Military Brain Injury working group. Regressions were used to examine TBI- and mTBI-related associations in NMF-derived components while adjusting for age, sex, post-traumatic stress disorder, depression, and data acquisition site/scanner. We found significantly stronger age-dependent effects of lower FA in Veterans with TBI than Veterans without in four components (q&nbsp

Brain atrophy is correlated with risk of cognitive impairment, functional decline, and dementia. Despite a high infectious disease burden, Tsimane forager-horticulturists of Bolivia have the lowest prevalence of coronary atherosclerosis of any studied population and present few cardiovascular disease (CVD) risk factors despite a high burden of infections and therefore inflammation. This study (a) examines the statistical association between brain volume (BV) and age for Tsimane and (b) compares this association to that of 3 industrialized populations in the United States and Europe. This cohort-based panel study enrolled 746 participants aged 40–94 (396 males), from whom computed tomography (CT) head scans were acquired. BV and intracranial volume (ICV) were calculated from automatic head CT segmentations. The linear regression coefficient estimate β^T of the Tsimane (T), describing the relationship between age (predictor) and BV (response, as a percentage of ICV), was calculated for the pooled sample (including both sexes) and for each sex. β^T was compared to the corresponding regression coefficient estimate β^R of samples from the industrialized reference (R) countries. For all comparisons, the null hypothesis β T = β R was rejected both for the combined samples of males and females, as well as separately for each sex. Our results indicate that the Tsimane exhibit a significantly slower decrease in BV with age than populations in the United States and Europe. Such reduced rates of BV decrease, together with a subsistence lifestyle and low CVD risk, may protect brain health despite considerable chronic inflammation related to infectious burden.

White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.

To explore how cerebral microbleeds (CMBs) accompanying mild traumatic brain injury (mTBI) reflect white matter (WM) degradation and cognitive decline, magnetic resonance images were acquired from 62 mTBI adults (imaged ∼7 days and ∼6 months post-injury) and 203 matched healthy controls. On average, mTBI participants had a count of 2.7 ± 2.6 traumatic CMBs in WM, located 6.1 ± 4.4 mm from cortex. At ∼6-month follow-up, 97% of CMBs were associated with significant reductions (34% ± 11%, qlt; 0.05) in the fractional anisotropy of WM streamlines within ∼1 cm of CMB locations. Male sex and older age were significant risk factors for larger reductions (qlt; 0.05). For CMBs in the corpus callosum, cingulum bundle, inferior and middle longitudinal fasciculi, fractional anisotropy changes were significantly and positively associated with changes in cognitive functions mediated by these structures (qlt; 0.05). Our findings distinguish traumatic from non-traumatic CMBs by virtue of surrounding WM alterations and challenge the assumption that traumatic CMBs are neurocognitively silent. Thus, mTBI with CMB findings can be described as a clinical endophenotype warranting longitudinal cognitive assessment.

BackgroundTraumatic brain injury (TBI) can alter brain structure and lead to onset of persistent neuropsychological symptoms. This study investigates the relationship between brain damage and psychological distress after mild TBI (mTBI) using multimodal magnetic resonance imaging (MRI).MethodsEighty-nine mTBI patients from the TRACK-TBI (Transforming Research and Clinical Knowledge in Traumatic Brain Injury) pilot study were included. Subscales of the Brief Symptoms Inventory 18 for depression, anxiety, and somatization were used as outcome measures of psychological distress ∼6 months after the traumatic event. Glasgow Coma Scale scores were used to evaluate recovery. MRIs were acquired within 2 weeks post-injury. Perivascular spaces (PVS) were segmented using an enhanced PVS segmentation method, and the volume fraction was calculated for the whole brain and white matter regions. Cortical thickness and gray matter structures volumes were calculated in Freesurfer; diffusion imaging indices and multi-fiber tracts were extracted using the Quantitative Imaging Toolkit. The analysis was performed considering age, sex, intracranial volume, educational attainment, and improvement level upon discharge as covariates.ResultsPerivascular space fractions in the posterior cingulate, fusiform, and postcentral areas were found to be associated with somatization symptoms. Depression, anxiety, and somatization symptoms were associated with the cortical thickness of the frontal- opercularis and occipital pole, putamen and amygdala volumes, and corticospinal tract and superior thalamic radiation. Analyses were also performed on the two hemispheres separately to explore lateralization.ConclusionsThis study shows how PVS, cortical, and microstructural damages can predict the onset of depression, anxiety, and somatization symptoms in mTBI patients.

Little is known on how mild traumatic brain injury affects white matter based on age at injury, sex, cerebral microbleeds, and time since injury. Here, we study the fractional anisotropy of white matter to study these effects in 109 participants aged 18–77 (46 females, age μ ± σ = 40 ± 17 years) imaged within $$\sim$$ 1 week and $$\sim$$ 6 months post-injury. Age is found to be linearly associated with white matter degradation, likely due not only to injury but also to cumulative effects of other pathologies and to their interactions with injury. Age is associated with mean anisotropy decreases in the corpus callosum, middle longitudinal fasciculi, inferior longitudinal and occipitofrontal fasciculi, and superficial frontal and temporal fasciculi. Over $$\sim$$ 6 months, the mean anisotropies of the corpus callosum, left superficial frontal fasciculi, and left corticospinal tract decrease significantly. Independently of other predictors, age and cerebral microbleeds contribute to anisotropy decrease in the callosal genu. Chronically, the white matter of commissural tracts, left superficial frontal fasciculi, and left corticospinal tract degrade appreciably, independently of other predictors. Our findings suggest that large commissural and intra-hemispheric structures are at high risk for post-traumatic degradation. This study identifies detailed neuroanatomic substrates consistent with brain injury patients’ age-dependent deficits in information processing speed, interhemispheric communication, motor coordination, visual acuity, sensory integration, reading speed/comprehension, executive function, personality, and memory. We also identify neuroanatomic features underlying white matter degradation whose severity is associated with the male sex. Future studies should compare our findings to functional measures and other neurodegenerative processes.

The extent to which brain functional correlations (FCs) are modulated by age and sex is unknown. We studied default mode network (DMN) FC changes in 136 participants with mild traumatic brain injury (mTBI; 52 females, age range: 19 – 79 years, age μ = 42, age σ = 17; 72 participants younger than 40). Structural and functional magnetic resonance images (MRIs) were acquired ~1 week and ~6 months post-injury; the FreeSurfer Functional Analysis STream (FS-FAST) was used for group-level FC comparisons across sexes and age groups (younger vs. older than 40). FC seeds were two sub-networks of the DMN, M1 and M2, defined by the standard Yeo parcellation scheme. For M1, clusters with significant FC differences across sexes were in the right paracentral lobule, central sulcus, postcentral gyrus, superior frontal gyrus, and precentral sulcus (p = 0.0001), and in the left paracentral lobule and central sulcus (p = 0.022). For M2, clusters spanned the right postcentral gyrus, middle occipital gyrus, transverse occipital sulcus, and central sulcus (p = 0.0001), the left precuneus and inferior parietal lobe (p = 0.0096). Females either exhibited no significant FC change or underwent FC increases. Males underwent significant FC decreases within all clusters, suggesting their increased vulnerability to mTBI-related effects. Clusters whose FCs differed significantly across age groups were localized to the left superior temporal gyrus (p = 0.0078), highlighting the vulnerability of temporal regions to age effects. Future studies should explore the age × sex interaction and uncover the mechanisms for these observed findings.

ObjectiveOur study addressed aims (1) to test the hypothesis that moderate-severe traumatic brain injury (TBI) in pediatric patients is associated with widespread white matter (WM) disruption, (2) to test the hypothesis that age and sex affect WM organization after injury, and (3) to examine associations between WM organization and neurobehavioral outcomes.MethodsData from 10 previously enrolled, existing cohorts recruited from local hospitals and clinics were shared with the Enhancing NeuroImaging Genetics Through Meta-Analysis (ENIGMA) Pediatric Moderate/Severe TBI (msTBI) working group. We conducted a coordinated analysis of diffusion MRI (dMRI) data using the ENIGMA dMRI processing pipeline.ResultsFive hundred seven children and adolescents (244 with complicated msTBI and 263 controls) were included. Patients were clustered into 3 postinjury intervals: acute/subacute, ConclusionsWM disruption after msTBI is widespread, persistent, and influenced by demographic and clinical variables. Future work will test techniques for harmonizing neurocognitive data, enabling more advanced analyses to identify symptom clusters and clinically meaningful patient subtypes.

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group’s short-term, intermediate, and long-term goals. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Annually, approximately 3 million children around the world experience traumatic brain injuries (TBIs), of which up to 20% are characterized as moderate to severe (msTBI) and/or have abnormal imaging findings. Affected children are vulnerable to long-term cognitive and behavioral dysfunction, as injury can disrupt or alter ongoing brain maturation. Post-injury outcomes are highly variable, and there is only limited understanding of how inter-individual differences in outcomes arise. Small sample sizes have also complicated efforts to better understand factors influencing the impact of TBI on the developing brain. White matter (WM) disruption is a critical aspect of TBI neuropathology and diffusion MRI (dMRI) is particularly sensitive to microstructural abnormalities. Here we present the results of a coordinated analysis of dMRI data across ten cohorts from three countries. We had three primary aims: (1) to characterize the nature and extent of WM disruption across key post-injury intervals (acute/subacute - within 2 months, post-acute - 2-6 months, chronic - 6+ months); (2) evaluate the impact of age and sex on WM in the context of injury; and (3) to examine associations between WM and neurobehavioral outcomes. Based on data from 507 children and adolescents (244 with complicated mild to severe TBI and 263 control children), we report widespread WM disruption across all post-injury intervals. As expected, injury severity was a significant contributor to the pattern and extent of WM degradation, but explained less variance in dMRI measures with increasing time since injury, supporting other research indicating that other factors contribute increasingly to outcomes over time. The corpus callosum appears to be particularly vulnerable to injury, an effect that persists years post-TBI. We also report sex differences in the effect of TBI on the uncinate fasciculus (UNC), a structure with a key role in emotion regulation. Females with a TBI had significantly lower fractional anisotropy (FA) in the UNC than those with no TBI, and this phenomenon was further associated with more frequent parent-reported behavioral problems as measured by the Child Behavior Checklist (CBCL). These effects were not detected in males. With future harmonization of imaging and neurocognitive data, more complex modeling of factors influencing outcomes will be possible and help to identify clinically-meaningful patient subtypes.

Traumatic brain injury (TBI) and Alzheimer’s disease (AD) are prominent neurological conditions whose neural and cognitive commonalities are poorly understood. The extent of TBI-related neurophysiological abnormalities has been hypothesized to reflect AD-like neurodegeneration because TBI can increase vulnerability to AD. However, it remains challenging to prognosticate AD risk partly because the functional relationship between acute post-traumatic sequelae and chronic AD-like degradation remains elusive. Here, functional magnetic resonance imaging (fMRI), network theory and machine learning (ML) are leveraged to study the extent to which geriatric mild TBI (mTBI) can lead to AD-like alteration of resting-state activity in the default mode network (DMN). This network is found to contain modules whose extent of AD-like, post-traumatic degradation can be accurately prognosticated based on the acute cognitive deficits of geriatric mTBI patients with cerebral microbleeds. Aside from establishing a predictive physiological association between geriatric mTBI, cognitive impairment and AD-like functional degradation, these findings advance the goal of acutely forecasting mTBI patients’ chronic deviations from normality along AD-like functional trajectories. The association of geriatric mTBI with AD-like changes in functional brain connectivity as early as ∼6 months post-injury carries substantial implications for public health because TBI has relatively high prevalence in the elderly.

Although post-traumatic epilepsy (PTE) is a common complication of traumatic brain injury (TBI), the relationship between these conditions is unclear, early PTE detection and prevention being major unmet clinical challenges. This study aims to identify imaging biomarkers that distinguish PTE and non-PTE subjects among TBI survivors based on a magnetic resonance imaging (MRI) dataset. We performed tensor-based morphometry to analyze brain shape changes associated with TBI and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of PTE. Statistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased PTE incidence.

Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer’s disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics—gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)—from typically-aging adults studied cross-sectionally using magnetic resonance imaging (MRI). Drawing from an aggregate sample of 9,473 adults, this study provides (A) regression coefficients β describing the age-dependent trajectories of volumetric measures by sex within the range from 20 to 70 years based on both linear and quadratic models, and (B) average values for BV, GMV, WMV and CSFV at the representative ages of 20 (young age), 45 (middle age) and 70 (old age). The results provided synthesize ∼20 years of brain volumetrics research and allow one to estimate BV at any age between 20 and 70. Importantly, however, such estimates should be used and interpreted with caution because they depend on MRI hardware specifications (e.g. scanner manufacturer, magnetic field strength), data acquisition parameters (e.g. spatial resolution, weighting), and brain segmentation algorithms. Guidelines are proposed to facilitate future meta- and mega-analyses of brain volumetrics.Disclosure statementThe author declares that he has no actual or potential conflicts of interest.