Accelerated Changes in Cortical Thickness Measurements with Age in Military Service Members with Traumatic Brain Injury

Prediction of chronological age from resting-state EEG power in the first three years of life

The infant brain undergoes rapid and significant developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging data can provide insights into typical and atypical brain development. We utilized longitudinal resting-state EEG data from 457 typically developing infants, comprising 938 recordings, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 of 0.82 and a mean absolute error of 92.4 days. Aperiodic offset and periodic theta, alpha, and beta power were identified as key predictors of age via Shapley values. Application of the model to EEG data from infants later diagnosed with autism spectrum disorder or Down syndrome revealed significant underestimations of chronological age. This study establishes the feasibility of using EEG to assess brain maturation in early childhood and supports its potential as a clinical tool for early identification of alterations in brain development.

Predicting Brain Age and Gender from Brain Volume Data Using Variational Quantum Circuits

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person's brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual's brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

White Matter Organization and Cortical Thickness Differ Among Active Duty Service Members With Chronic Mild, Moderate, and Severe Traumatic Brain Injury

Abstract This study compared findings from whole-brain diffusion tensor imaging (DTI) and volumetric magnetic resonance imaging (MRI) among 90 Active Duty Service Members with chronic mild traumatic brain injury (TBI; n = 52), chronic moderate-to-severe TBI (n = 17), and TBI-negative controls (n = 21). Data were collected on a Philips Ingenia 3T MRI with DTI in 32 directions. Results demonstrated that history of TBI was associated with differences in white matter microstructure, white matter volume, and cortical thickness in both mild TBI and moderate-to-severe TBI groups relative to controls. However, the presence, pattern, and distribution of these findings varied substantially depending on the injury severity. Spatially-defined forms of DTI fractional anisotropy (FA) analyses identified altered white matter organization within the chronic moderate-to-severe TBI group, but they did not provide clear evidence of abnormalities within the chronic mild TBI group. In contrast, DTI FA "pothole" analyses identified widely distributed areas of decreased FA throughout the white matter in both the chronic mild TBI and chronic moderate-to-severe TBI groups. Additionally, decreased white matter volume was found in several brain regions for the chronic moderate-to-severe TBI group compared with the other groups. Greater number of DTI FA potholes and reduced cortical thickness were also related to greater severity of self-reported symptoms. In sum, this study expands upon a growing body of literature using advanced imaging techniques to identify potential effects of brain injury in military Service Members. These findings may differ from work in other TBI populations due to varying mechanisms and frequency of injury, as well as a potentially higher level of functioning in the current sample related to the ability to maintain continued Active Duty status after injury. In conclusion, this study provides DTI and volumetric MRI findings across the spectrum of TBI severity. These results provide support for the use of DTI and volumetric MRI to identify differences in white matter microstructure and volume related to TBI. In particular, DTI FA pothole analysis may provide greater sensitivity for detecting subtle forms of white matter injury than conventional DTI FA analyses.

The effect of processing pipelines, input images and age on automatic cortical morphology estimates

BACKGROUND AND OBJECTIVE

Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age.

METHODS

For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision.

RESULTS

Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction.

CONCLUSIONS

Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.

NeuropsychBrainAge: A biomarker for conversion from mild cognitive impairment to Alzheimer's disease

INTRODUCTION

BrainAge models based on neuroimaging data have diagnostic classification power but have replicability issues due to site and patient variability. BrainAge models trained on neuropsychological tests could help distinguish stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).

METHODS

A linear regressor BrainAge model was trained on healthy controls using neuropsychological tests and neuroimaging features separately. The BrainAge delta, predicted age minus chronological age, was used to distinguish between sMCI and pMCI.

RESULTS

The cross-validated area under the receiver-operating characteristic (ROC) curve for sMCI versus pMCI was 0.91 for neuropsychological features in contrast to 0.68 for neuroimaging features. The BrainAge delta was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.

DISCUSSION

The BrainAge delta from neuropsychological tests is a good biomarker to distinguish between sMCI and pMCI. Other neurological and psychiatric disorders could be studied using this strategy.

Highlights

BrainAge models based on neuropsychological tests outperform models based on neuroimaging features when distinguishing between stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) to Alzheimer's disease (AD).The combination of neuropsychological tests with neuroimaging features does not lead to an improvement in sMCI versus pMCI classification compared to using neuropsychological tests on their own.BrainAge delta of both neuroimaging and neuropsychological models was correlated with the time to conversion, the time taken for a pMCI subject to convert to AD.

Military-related mild traumatic brain injury: clinical characteristics, advanced neuroimaging, and molecular mechanisms

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.

Volumetric MRI Findings in Mild Traumatic Brain Injury (mTBI) and Neuropsychological Outcome

Region of interest (ROI) volumetric assessment has become a standard technique in quantitative neuroimaging. ROI volume is thought to represent a coarse proxy for making inferences about the structural integrity of a brain region when compared to normative values representative of a healthy sample, adjusted for age and various demographic factors. This review focuses on structural volumetric analyses that have been performed in the study of neuropathological effects from mild traumatic brain injury (mTBI) in relation to neuropsychological outcome. From a ROI perspective, the probable candidate structures that are most likely affected in mTBI represent the target regions covered in this review. These include the corpus callosum, cingulate, thalamus, pituitary-hypothalamic area, basal ganglia, amygdala, and hippocampus and associated structures including the fornix and mammillary bodies, as well as whole brain and cerebral cortex along with the cerebellum. Ventricular volumetrics are also reviewed as an indirect assessment of parenchymal change in response to injury. This review demonstrates the potential role and limitations of examining structural changes in the ROIs mentioned above in relation to neuropsychological outcome. There is also discussion and review of the role that post-traumatic stress disorder (PTSD) may play in structural outcome in mTBI. As emphasized in the conclusions, structural volumetric findings in mTBI are likely just a single facet of what should be a multimodality approach to image analysis in mTBI, with an emphasis on how the injury damages or disrupts neural network integrity. The review provides an historical context to quantitative neuroimaging in neuropsychology along with commentary about future directions for volumetric neuroimaging research in mTBI.

A comparison of methods to harmonize cortical thickness measurements across scanners and sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LMEINT), (2) LME that models both site-specific random intercepts and age-related random slopes (LMEINT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2-81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3-85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects.

Brain age in chronic traumatic brain injury

Traumatic brain injury (TBI) is associated with greater 'brain age' that may be caused by atrophy in grey and white matter. Here, we investigated 'brain age' in a chronic TBI (≥10 years) sample. We examined whether 'brain age' increases with years post injury, and whether it is associated with injury severity, cognition and functional outcome. We recruited 102 participants with moderate to severe TBI aged between 40 and 85 years. TBI participants were assessed on average 22 years post-injury. Seventy-seven healthy controls were also recruited. Participants' 'brain age' was determined using T1-weighted MRI images. TBI participants were estimated to have greater 'brain age' compared to healthy controls. 'Brain age' gap was unrelated to time since injury or long-term functional outcome on the Glasgow Outcome Scale-Extended. Greater brain age was associated with greater injury severity measured by post traumatic amnesia duration and Glasgow Coma Scale. 'Brain age' was significantly and inversely associated with verbal memory, but unrelated to visual memory/ability and cognitive flexibility and processing speed. A longitudinal study is required to determine whether TBI leads to a 'one-off' change in 'brain age' or progressive ageing of the brain over time.

Accelerated longitudinal cortical atrophy in OEF / OIF / OND veterans with severe PTSD and the impact of comorbid TBI

Veterans who deployed in support of Operation Enduring Freedom (OEF), Iraqi Freedom (OIF), and New Dawn (OND) commonly experience severe psychological trauma, often accompanied by physical brain trauma resulting in mild traumatic brain injury (mTBI). Prior studies of individuals with posttraumatic stress disorder (PTSD) have revealed alterations in brain structure, accelerated cellular aging, and impacts on cognition following exposure to severe psychological trauma and potential interactive effects of military‐related mTBI. To date, however, little is known how such deployment‐related trauma changes with time and age of injury of the affected veteran. In this study, we explored changes in cortical thickness, volume, and surface area after an average interval of approximately 2 years in a cohort of 254 OEF/OIF/OND Veterans ranging in age from 19 to 67 years. Whole‐brain vertex‐wise analyses revealed that veterans who met criteria for severe PTSD (Clinician‐Administered PTSD Scale ≥60) at baseline showed greater negative longitudinal changes in cortical thickness, volume, and area over time. Analyses also revealed a significant severe‐PTSD by age interaction on cortical measures with severe‐PTSD individuals exhibiting accelerated cortical degeneration with increasing age. Interaction effects of comorbid military‐related mTBI within the severe‐PTSD group were also observed in several cortical regions. These results suggest that those exhibiting severe PTSD symptomatology have accelerated atrophy that is exacerbated with increasing age and history of mTBI.

Advanced brain age in deployment-related traumatic brain injury: A LIMBIC-CENC neuroimaging study

OBJECTIVE

To determine if history of mild traumatic brain injury (mTBI) is associated with advanced or accelerated brain aging among the United States (US) military Service Members and Veterans.

METHODS

Eight hundred and twenty-two participants (mean age = 40.4 years, 714 male/108 female) underwent MRI sessions at eight sites across the US. Two hundred and one participants completed a follow-up scan between five months and four years later. Predicted brain ages were calculated using T1-weighted MRIs and then compared with chronological ages to generate an Age Deviation Score for cross-sectional analyses and an Interval Deviation Score for longitudinal analyses. Participants also completed a neuropsychological battery, including measures of both cognitive functioning and psychological health.

RESULT

In cross-sectional analyses, males with a history of deployment-related mTBI showed advanced brain age compared to those without (t(884) = 2.1, p = .038), while this association was not significant in females. In follow-up analyses of the male participants, severity of posttraumatic stress disorder (PTSD), depression symptoms, and alcohol misuse were also associated with advanced brain age.

CONCLUSION

History of deployment-related mTBI, severity of PTSD and depression symptoms, and alcohol misuse are associated with advanced brain aging in male US military Service Members and Veterans.

Cognitive impairment and World Trade Centre-related exposures

On 11 September 2001 the World Trade Center (WTC) in New York was attacked by terrorists, causing the collapse of multiple buildings including the iconic 110-story 'Twin Towers'. Thousands of people died that day from the collapse of the buildings, fires, falling from the buildings, falling debris, or other related accidents. Survivors of the attacks, those who worked in search and rescue during and after the buildings collapsed, and those working in recovery and clean-up operations were exposed to severe psychological stressors. Concurrently, these 'WTC-affected' individuals breathed and ingested a mixture of organic and particulate neurotoxins and pro-inflammogens generated as a result of the attack and building collapse. Twenty years later, researchers have documented neurocognitive and motor dysfunctions that resemble the typical features of neurodegenerative disease in some WTC responders at midlife. Cortical atrophy, which usually manifests later in life, has also been observed in this population. Evidence indicates that neurocognitive symptoms and corresponding brain atrophy are associated with both physical exposures at the WTC and chronic post-traumatic stress disorder, including regularly re-experiencing traumatic memories of the events while awake or during sleep. Despite these findings, little is understood about the long-term effects of these physical and mental exposures on the brain health of WTC-affected individuals, and the potential for neurocognitive disorders. Here, we review the existing evidence concerning neurological outcomes in WTC-affected individuals, with the aim of contextualizing this research for policymakers, researchers and clinicians and educating WTC-affected individuals and their friends and families. We conclude by providing a rationale and recommendations for monitoring the neurological health of WTC-affected individuals.

Machine learning for brain age prediction: Introduction to methods and clinical applications

The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as ‘brain-age gap’. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.

Aging with Traumatic Brain Injury: Deleterious Effects of Injury Chronicity Are Most Pronounced in Later Life

Understanding the effects of age on longitudinal traumatic brain injury (TBI) outcomes requires attention to both chronic and evolving TBI effects and age-related changes in health and function. The present study examines the independent and interactive effects of aging and chronicity on functional outcomes after TBI. We leveraged a well-defined cohort of individuals who sustained a moderate/severe TBI and received acute inpatient rehabilitation at specialized centers with high follow up rate as part of their involvement in the TBI Model Systems longitudinal study. We selected individuals at one of two levels of TBI chronicity (either 2 or 10 years post-injury) and used an exact matching procedure to obtain balanced chronicity groups based on age and other characteristics (N = 1993). We found that both older age and greater injury chronicity were related to greater disability, reduced functional independence, and less community participation. There was a significant age by chronicity interaction, indicating that the adverse effects of greater time post-injury were most pronounced among survivors who were age 75 or older. The inflection point at roughly 75 years of age was corroborated by post hoc analyses, dividing the sample by age at 75 years and examining the interaction between age group and chronicity. These findings point to a need for provision of rehabilitation services in the chronic injury period, particularly for those who are over 75 years old. Future work should investigate the underlying mechanisms of this interaction towards the goal of developing interventions and models of care to promote healthy aging with TBI.

Accelerated Brain Aging in Mild Traumatic Brain Injury: Longitudinal Pattern Recognition with White Matter Integrity

Mild traumatic brain injury (mTBI) initiating long-term effects on white matter integrity resembles brain-aging changes, implying an aging process accelerated by mTBI. This longitudinal study aims to investigate the mTBI-induced acceleration of the brain-aging process by developing a neuroimaging model to predict brain age. The brain-age prediction model was defined using relevance vector regression based on fractional anisotropy from diffusion tensor imaging of 523 healthy individuals. The model was used to estimate the brain-predicted age difference (brain-PAD) between the chronological and estimated brain age in 116 acute mTBI patients and 63 healthy controls. Fifty patients were followed for 6 ∼ 12 months to evaluate the longitudinal changes in brain-PAD. We investigated whether brain-PAD was greater in patients of older age, post-concussion complaints, and apolipoprotein E (APOE) ɛ4 genotype, and whether it had the potential to predict neuropsychological outcomes. The brain-age prediction model predicted brain age accurately (r = 0.96). The brains of mTBI patients in the acute phase were estimated to be "older," with greater brain-PAD (2.59 ± 5.97 years) than the healthy controls (0.12 ± 3.19 years) (p < 0.05), and remained stable 6-12 month post-injury (2.50 ± 4.54 years). Patients who were older or who had post-concussion complaints, rather than APOE ɛ4 genotype, had greater brain-PADs (p < 0.001, p = 0.024). Additionally, brain-PAD in the acute phase predicted information processing speed at the 6 ∼ 12 month follow-up (r = -0.36, p = 0.01). In conclusion, mTBI accelerates the brain-aging process, and brain-PAD may be capable of evaluating aging-associated issues post-injury, such as increased risks of neurodegeneration.

Network Analysis of Research on Mild Traumatic Brain Injury in US Military Service Members and Veterans During the Past Decade (2010-2019)

OBJECTIVE

To evaluate trends in the extant literature on mild traumatic brain injury (mTBI) in military service members and veterans using network analysis based on a comprehensive search of original, peer-reviewed research articles involving human participants published between January 1, 2010, and December 31, 2019. Specifically, we employed network analysis to evaluate associations in the following areas: (1) peer-reviewed journals, (2) authors, (3) organizations/institutions, and (4) relevant key words.

PARTICIPANTS

Included studies were published in peer-reviewed journals available on Web of Science database, using US military service members or veterans.

DESIGN

Bibliometric network analytical review.

MAIN MEASURES

Outcomes for each analysis included number of articles, citations, total link strength, and clusters.

RESULTS

The top publishing journals were (1) Journal of Head Trauma and Rehabilitation, (2) Military Medicine, (3) Brain Injury, (4) Journal of Neurotrauma, and (5) Journal of Rehabilitation Research and Development. The top publishing authors were (1) French, (2) Lange, (3) Cooper, (4) Vanderploeg, and (5) Brickell. The top research institutions were (1) Defense and Veterans Brain Injury Center, (2) Uniformed Services University of the Health Sciences, (3) University of California San Diego, (4) Walter Reed National Military Medical Center, and (5) Boston University. The top co-occurring key words in this analysis were (1) posttraumatic stress disorder (PTSD), (2) persistent postconcussion symptoms (PPCS), (3) blast injury, (4) postconcussion syndrome (PCS), and (5) Alzheimer's disease.

CONCLUSIONS

The results of this network analysis indicate a clear focus on veteran health, as well as investigations on chronic effects of mTBI. Research in civilian mTBI indicates that delaying treatment for symptoms and impairments related to mTBI may not be the most precise treatment strategy. Increasing the number of early, active, and targeted treatment trials in military personnel could translate to meaningful improvements in clinical practices for managing mTBI in this population.

Factors associated with brain ageing - a systematic review

Background

Brain age is a biomarker that predicts chronological age using neuroimaging features. Deviations of this predicted age from chronological age is considered a sign of age-related brain changes, or commonly referred to as brain ageing. The aim of this systematic review is to identify and synthesize the evidence for an association between lifestyle, health factors and diseases in adult populations, with brain ageing.

Methods

This systematic review was undertaken in accordance with the PRISMA guidelines. A systematic search of Embase and Medline was conducted to identify relevant articles using search terms relating to the prediction of age from neuroimaging data or brain ageing. The tables of two recent review papers on brain ageing were also examined to identify additional articles. Studies were limited to adult humans (aged 18 years and above), from clinical or general populations. Exposures and study design of all types were also considered eligible.

Results

A systematic search identified 52 studies, which examined brain ageing in clinical and community dwelling adults (mean age between 21 to 78 years, ~ 37% were female). Most research came from studies of individuals diagnosed with schizophrenia or Alzheimer’s disease, or healthy populations that were assessed cognitively. From these studies, psychiatric and neurologic diseases were most commonly associated with accelerated brain ageing, though not all studies drew the same conclusions. Evidence for all other exposures is nascent, and relatively inconsistent. Heterogenous methodologies, or methods of outcome ascertainment, were partly accountable.

Conclusion

This systematic review summarised the current evidence for an association between genetic, lifestyle, health, or diseases and brain ageing. Overall there is good evidence to suggest schizophrenia and Alzheimer’s disease are associated with accelerated brain ageing. Evidence for all other exposures was mixed or limited. This was mostly due to a lack of independent replication, and inconsistency across studies that were primarily cross sectional in nature. Future research efforts should focus on replicating current findings, using prospective datasets.

Trial registration

A copy of the review protocol can be accessed through PROSPERO, registration number CRD42020142817.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-021-02331-4.

Cognitive Reserve in Individuals Aging With Traumatic Brain Injury: Independent and Interactive Effects on Cognitive Functioning

OBJECTIVE

To examine the influence of 2 temporal factors-age and injury chronicity-on the relationship between cognitive reserve (CR) and cognitive functioning in older adults with chronic traumatic brain injury (TBI).

SETTING

Outpatient research laboratory.

PARTICIPANTS

Adults, 50 years or older, with a 1- to 45-year history of moderate or severe TBI (N = 108).

DESIGN

Cross-sectional observational study.

MAIN MEASURES

CR was estimated using demographically corrected performance on a word-reading test (an approximation of premorbid IQ). Injury chronicity was operationalized as number of years since the date of injury. Composite cognitive scores were computed from performances on neuropsychological tests of processing speed, executive functioning, and memory.

RESULTS

CR was positively and significantly related to all cognitive performances independent of age, injury chronicity, and injury severity. Greater injury chronicity significantly attenuated the effect of CR on processing speed such that individuals more distal from their injury date evidenced a weaker positive relationship between CR and performance.

CONCLUSION

Temporal factors may modify associations between CR and cognition. Findings suggest that the protective effects of CR are temporally delimited, potentially contending with declines in brain reserve. The prognostic value of traditional outcome determinants should be considered in the context of injury chronicity.

Links between thrombosis and inflammation in traumatic brain injury

Traumatic brain injury (TBI) continues to be a major healthcare problem and there is much to be explored regarding the secondary pathobiology to identify early predictive markers and new therapeutic targets. While documented changes in thrombosis and inflammation in major trauma have been well described, growing evidence suggests that isolated TBI also results in systemic alterations in these mechanisms. Here, we review recent experimental and clinical findings that demonstrate how blood-brain barrier dysfunction, systemic immune response, inflammation, platelet activation, and thrombosis contribute significantly to the pathogenesis of TBI. Despite advances in the links between thrombosis and inflammation, there is a lack of treatment options aimed at both processes and this could be crucial to treating vascular injury, local and systemic inflammation, and secondary ischemic events following TBI. With emerging evidence of newly-identified roles for platelets, leukocytes, the coagulation system and extracellular vesicles in processes of inflammation and thrombosis, there is a growing need to characterize these mechanisms within the context of TBI and whether these changes persist into the chronic phase of injury. Importantly, this review defines areas in need of further research to advance the field and presents a roadmap to identify new diagnostic and treatment options for TBI.

Combat exposure, posttraumatic stress disorder, and head injuries differentially relate to alterations in cortical thickness in military Veterans

Combat-exposed Veterans are at increased risk for developing psychological distress, mood disorders, and trauma and stressor-related disorders. Trauma and mood disorders have been linked to alterations in brain volume, function, and connectivity. However, far less is known about the effects of combat exposure on brain health. The present study examined the relationship between severity of combat exposure and cortical thickness. Post-9/11 Veterans (N = 337; 80% male) were assessed with structural neuroimaging and clinically for combat exposure, depressive symptoms, prior head injury, and posttraumatic stress disorder (PTSD). Vertex-wide cortical thickness was estimated using FreeSurfer autosegmentation. FreeSurfer's Qdec was used to examine relationship between combat exposure, PTSD, and prior head injuries on cortical thickness (Monte Carlo corrected for multiple comparisons, vertex-wise cluster threshold of 1.3, p < 0.01). Covariates included age, sex, education, depressive symptoms, nonmilitary trauma, alcohol use, and prior head injury. Higher combat exposure uniquely related to lower cortical thickness in the left prefrontal lobe and increased cortical thickness in the left middle and inferior temporal lobe; whereas PTSD negatively related to cortical thickness in the right fusiform. Head injuries related to increased cortical thickness in the bilateral medial prefrontal cortex. Combat exposure uniquely contributes to lower cortical thickness in regions implicated in executive functioning, attention, and memory after accounting for the effects of PTSD and prior head injury. Our results highlight the importance of examining effects of stress and trauma exposure on neural health in addition to the circumscribed effects of specific syndromal pathology.

Longitudinal neuroimaging following combat concussion: sub-acute, 1 year and 5 years post-injury

Questions remain regarding the long-term impact of combat concussive blast exposure. While efforts have begun to highlight the clinical impact, less is known about neuroimaging trajectories that may inform underlying pathophysiological changes post-injury. Through collaborative efforts in combat, following medical evacuation, and at universities in the USA, this study followed service members both with and without blast concussion from the sub-acute to 1-year and 5-year outcomes with quantitative neuroimaging. The following two primary and two exploratory groups were examined: combat-deployed controls without blast exposure history ‘non-blast control’ and concussive blast patients (primary) and combat concussion arising not from blast ‘non-blast concussion’ and combat-deployed controls with blast exposure history ‘blast control’ (exploratory). A total of 575 subjects were prospectively enrolled and imaged; 347 subjects completed further neuroimaging examination at 1 year and 342 subjects completed further neuroimaging examination at 5 years post-injury. At each time point, MRI scans were completed that included high-resolution structural as well as diffusion tensor imaging acquisitions processed for quantitative volumetric and diffusion tensor imaging changes. Longitudinal evaluation of the number of abnormal diffusion tensor imaging and volumetric regions in patients with blast concussion revealed distinct trends by imaging modality. While the presence of abnormal volumetric regions remained quite stable comparing our two primary groups of non-blast control to blast concussion, the diffusion tensor imaging abnormalities were observed to have varying trajectories. Most striking was the fractional anisotropy ‘U-shaped’ curve observed for a proportion of those that, if we had only followed them to 1 year, would look like trajectories of recovery. However, by continuing the follow-up to 5 years in these very same patients, a secondary increase in the number of reduced fractional anisotropy regions was identified. Comparing non-blast controls to blast concussion at each time point revealed significant differences in the number of regions with reduced fractional anisotropy at both the sub-acute and 5-year time points, which held after adjustment for age, education, gender, scanner and subsequent head injury exposure followed by correction for multiple comparisons. The secondary increase identified in patients with blast concussion may be the earliest indications of microstructural changes underlying the ‘accelerated brain aging’ theory recently reported from chronic, cross-sectional studies of veterans following brain injury. These varying trajectories also inform potential prognostic neuroimaging biomarkers of progression and recovery.

Structural neuroimaging in mild traumatic brain injury: A chronic effects of neurotrauma consortium study

OBJECTIVES

The chronic effects of neurotrauma consortium (CENC) observational study is a multisite investigation designed to examine the long-term longitudinal effects of mild traumatic brain injury (mTBI). All participants in this initial CENC cohort had a history of deployment in Operation Enduring Freedom (Afghanistan), Operation Iraqi Freedom (Iraq), and/or their follow-on conflicts (Operation Freedom's Sentinel). All participants undergo extensive medical, neuropsychological, and neuroimaging assessments and either meet criteria for any lifetime mTBI or not. These assessments are integrated into six CENC core studies-Biorepository, Biostatistics, Data and Study Management, Neuroimaging, and Neuropathology.

METHODS

The current study outlines the quantitative neuroimaging methods managed by the Neuroimaging Core using FreeSurfer automated software for image quantification.

RESULTS

At this writing, 319 participants from the CENC observational study have completed all baseline assessments including the imaging protocol and tertiary data quality assurance procedures.

CONCLUSIONS/DISCUSSION

The preliminary findings of this initial cohort are reported to describe how the Neuroimaging Core manages neuroimaging quantification for CENC studies.

Mild, moderate and severe: terminology implications for clinical and experimental traumatic brain injury

PURPOSE OF REVIEW

When describing clinical or experimental traumatic brain injury (TBI), the adjectives 'mild,' 'moderate' and 'severe' are misleading. 'Mild' clinical TBI frequently results in long-term disability. 'Severe' rodent TBI actually resembles mild or complicated mild clinical TBI.

RECENT FINDINGS

Many mild TBI patients appear to have recovered completely but have postconcussive symptoms, deficits in cognitive and executive function and reduced cerebral blood flow. After moderate TBI, 31.8% of patients died or were discharged to skilled nursing or hospice. Among survivors of moderate and severe TBI, 44% were unable to return to work. On MRI, 88% of mild TBI patients have evidence of white matter damage, based on measurements of fractional anisotropy and mean diffusivity/apparent diffusion coefficient. After sports concussion, clinically recovered patients have abnormalities in functional connectivity on functional MRI. Methylphenidate improved fatigue and cognitive impairment and, combined with cognitive rehabilitation, improved memory and executive functioning. In comparison to clinical TB, because the entire spectrum of experimental rodent TBI, although defined as moderate or severe, more closely resembles mild or complicated mild clinical TBI.

SUMMARY

Many patients after mild or moderate TBI suffer long-term sequelae and should be considered a major target for translational research. Treatments that improve outcome in rodent TBI, even when the experimental injuries are defined as severe, might be most applicable to mild or moderate TBI.

Neuroimaging of Traumatic Brain Injury

The purpose of this article is to review conventional and advanced neuroimaging techniques performed in the setting of traumatic brain injury (TBI). The primary goal for the treatment of patients with suspected TBI is to prevent secondary injury. In the setting of a moderate to severe TBI, the most appropriate initial neuroimaging examination is a noncontrast head computed tomography (CT), which can reveal life-threatening injuries and direct emergent neurosurgical intervention. We will focus much of the article on advanced neuroimaging techniques including perfusion imaging and diffusion tensor imaging and discuss their potentials and challenges. We believe that advanced neuroimaging techniques may improve the accuracy of diagnosis of TBI and improve management of TBI.

Blast-Exposed Veterans With Mild Traumatic Brain Injury Show Greater Frontal Cortical Thinning and Poorer Executive Functioning

Objective: Blast exposure (BE) and mild traumatic brain injury (mTBI) have been independently linked to pathological brain changes. However, the combined effects of BE and mTBI on brain structure have yet to be characterized. Therefore, we investigated whether regional differences in cortical thickness exist between mTBI Veterans with and without BE while on deployment. We also examined whether cortical thickness (CT) and cognitive performance differed among mTBI Veterans with low vs. high levels of cumulative BE. Methods: 80 Veterans with mTBI underwent neuroimaging and completed neuropsychological testing and self-report symptom rating scales. Analyses of covariance (ANCOVA) were used to compare blast-exposed Veterans (mTBI+BE, n = 51) to those without BE (mTBI-BE, n = 29) on CT of frontal and temporal a priori regions of interest (ROIs). Next, multiple regression analyses were used to examine whether CT and performance on an executive functions composite differed among mTBI Veterans with low (mTBI+BE Low, n = 22) vs. high (mTBI+BE High, n = 26) levels of cumulative BE. Results: Adjusting for age, numer of TBIs, and PTSD symptoms, the mTBI+BE group showed significant cortical thinning in frontal regions (i.e., left orbitofrontal cortex [p = 0.045], left middle frontal gyrus [p = 0.023], and right inferior frontal gyrus [p = 0.034]) compared to the mTBI-BE group. No significant group differences in CT were observed for temporal regions (p's > 0.05). Multiple regression analyses revealed a significant cumulative BE × CT interaction for the left orbitofrontal cortex (p = 0.001) and left middle frontal gyrus (p = 0.020); reduced CT was associated with worse cognitive performance in the mTBI+BE High group but not the mTBI+BE Low group. Conclusions: Findings show that Veterans with mTBI and BE may be at risk for cortical thinning post-deployment. Moreover, our results demonstrate that reductions in CT are associated with worse executive functioning among Veterans with high levels of cumulative BE. Future longitudinal studies are needed to determine whether BE exacerbates mTBI-related cortical thinning or independently negatively influences gray matter structure.