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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.

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.

The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region’s contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.

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.

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.

With the advent of susceptibility-weighted imaging (SWI), the ability to identify cerebral microbleeds (CMBs) associated with mild traumatic brain injury (mTBI) has become increasingly commonplace. Nevertheless, the clinical significance of post-traumatic CMBs remains controversial partly because it is unclear whether mTBI-related CMBs entail brain circuitry disruptions which, although structurally subtle, are functionally significant. This study combines magnetic resonance and diffusion tensor imaging (MRI and DTI) to map white matter (WM) circuitry differences across 6 months in 26 healthy control volunteers and in 26 older mTBI victims with acute CMBs of traumatic etiology. Six months post-mTBI, significant changes (p < 0.001) in the mean fractional anisotropy of perilesional WM bundles were identified in 21 volunteers, and an average of 47% (σ = 21%) of TBI-related CMBs were associated with such changes. These results suggest that CMBs can be associated with lasting changes in perilesional WM properties, even relatively far from CMB locations. Future strategies for mTBI care will likely rely on the ability to assess how subtle circuitry changes impact neural/cognitive function. Thus, assessing CMB effects upon the structural connectome can play a useful role when studying CMB sequelae and their potential impact upon the clinical outcome of individuals with concussion.

Connectomics alterations associated with subtle forms of cerebrovascular neuropathology-such as cerebral microbleeds (CMBs)-can result in substantial neurological and/or cognitive deficits in victims of traumatic brain injury (TBI). Quantifying CMB-related connectome changes in mild TBI (mTBI) patients requires ingenious neuroinformatics to integrate structural magnetic resonance imaging (sMRI) with diffusion-weighted imaging (DWI) for patient-tailored profiling while preserving the data scientist's ability to implement population studies. Such solutions, however, can assist the refinement of rehabilitation protocols and streamline large-scale analysis while accommodating the heterogeneity of mTBI. This study describes a pipeline for the multimodal integration of sMRI/DWI/DTI to quantify white matter (WM) neural network circuitry alterations associated with mTBI-related CMBs. The approach incorporates WM streamline matching, topology-compliant streamline prototyping and along-tract analysis within a unified framework. When applied to the analysis of neuroimaging data acquired from both mTBI and healthy control volunteers, the approach facilitates the identification of patient-specific CMB-related connectomic changes while incorporating the ability to perform group analyses. This pipeline for the identification and profiling of connectopathies can assist the adaptation of clinical rehabilitation protocols to patients' individual needs.

Although broadly utilized, computed tomography (CT) has been superseded by magnetic resonance imaging (MRI) for the volumetric assessment of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) in the aging human brain. Nevertheless, many scenarios remain where MRI is unavailable or discouraged; furthermore, in developing countries, CT can often be the only accessible imaging method for assessing brain structure in older patients with mild cognitive impairment or Alzheimer’s disease. Thus, there is merit in developing effective approaches for the estimation of brain volumetrics using CT. Here, MRI and CT scans were acquired from 10 older adults [mean (µ) ± standard deviation (σ) of age = 65 ± 7 yrs; 5 females]. To demarcate WM, GM and CSF from head CT, we developed a brain segmentation method based upon probabilistic, atlas-dependent classification. MRI-only segmentation was compared to CT-only segmentation; similarity was calculated through the Dice coefficient (DC). A normal distribution of DCs was found after contrasting the methods [µ ± σ across participants: 85.5% ± 4.6% (WM), 86.7% ± 5.6% (GM) and 91.3% ± 2.8% (CSF)], suggesting a satisfactory capacity of CT to assess brain volumetrics. Sensitivity was adequate: WM, GM and CSF volumes were estimated within ~5%, ~4% and ~3% of their MRI-based values. There was no indication of volume over- or under-estimation with CT [t (9) = 0.89, p > 0.80]. These results facilitate the integration of CT-based brain volumetrics with MRI, thereby offering a wider range of methods for quantifying macroscale brain changes in neurodegenerative diseases.

Recent studies utilizing susceptibility-weighted imaging (SWI) suggest a higher incidence of cerebral microbleeds (CMBs) in geriatric patients with mild traumatic brain injury (mTBI) compared to mTBI victims in the second, third and fourth decades of life. The clinical implications of acute CMB findings on neuroradiological examinations remain unclear, partly because older adults’ TBI-related CMBs can be difficult to distinguish non-invasively from CMBs associated with other conditions, including cerebral amyloid angiopathy (CAA). In this longitudinal study, we combined magnetic resonance and diffusion tensor imaging (MRI and DTI, respectively) to assess the nature and extent of white matter (WM) connectivity alterations due to CMBs after mTBI. A total of 26 geriatric mTBI victims were scanned both acutely (

In recent decades, the joint use of computed tomography (CT) and magnetic resonance imaging (MRI) to study geriatric mild traumatic brain injury (mTBI) has increased considerably. Although MRI is the gold standard for the noninvasive evaluation of white matter (WM) and gray matter (GM) volumes in the living human brain, CT continues to be used widely during treatment of geriatric mTBI, particularly when MRI is unavailable or contraindicated. Furthermore, in clinical settings, CT scans are frequently acquired from geriatric mTBI patients even though MRI scans are not. Partly for this reason, there has been renewed interest in the development of CT-only approaches to the calculation of brain structure changes after geriatric mTBI. In this study, we introduce a novel, CT-only brain segmentation method and validate it using both MRI and CT volumes acquired from ten geriatric mTBI volunteers (age: µ = 65 yrs; σ = 7 yrs). Segmentation accuracy is quantified using the Sørensen-Dice coefficient (SDC), a robust measure of inter-modality tissue classification agreement between CT and MRI. Across all cases, the mean and standard deviation of the SCD is found to be 85.5% ± 4.6% for WM and 86.7% ± 5.6% for GM, indicating excellent ability to calculate GM/WM volumes from CT in geriatric mTBI patients. Since mTBI has higher incidence after age 65 than in any other adult age group, our contribution is important because it broadens the ability to integrate CT-based findings into research studies of geriatric mTBI and of its effects upon brain aging and neurodegeneration.

Introduction/Background The rehabilitation of older victims with traumatic brain injury (TBI) presents substantial challenges due to the limited mobility and poor capacity for neurological recovery of older adults. In this study we present a novel approach for identifying TBI-affected white matter (WM) connections whose functional role likely involves them in modulating clinically-observed neurological deficits being targeted by rehabilitation routines for the elderly. Material and method This study was undertaken according to US federal law (45 CFR 46) and with IRB approval. Informed written consent was obtained from all volunteers. This study included 26 older victims of mild TBI (age: μ = 66.8 years, σ = 5.93 years) and 26 age- and sex-matched healthy control (HC) volunteers. MRI volumes were acquired using T1, T2, GRE/SWI and DWI sequences. Cerebral lesions were identified in each volunteer using a robust supervised classifier and deterministic tractography was implemented. Peri-lesional white matter (WM) streamlines were identified and changes in their functional anisotropy (FA) were assessed using a novel approach, which involved DTI streamline matching, streamline prototyping and along-tract analysis. WM streamlines found to experience substantial FA changes were integrated with a search engine which queries the scientific literature to identify aspects of brain function deficits modulated by TBI-affected connectivity. Results In all patients where CMBs had been identified, the WM connections identified as peri-lesional were found to be involved in specific circuits which modulates memory retrieval, processing speed, speech and executive control. The patient-specific insights gained can suggest how rehabilitation routines could be modified to benefit the individual needs of each patient. Conclusion Because next-generation rehabilitation protocols for TBI victims will likely rely on the ability to understand which brain network alterations lead to neurological deficits in specific patients, personalized methods such as ours could one day be valuable to TBI rehabilitation professionals.