Increases in regional brain volume across two native South American male populations.

Industrialized environments, despite benefits such as higher levels of formal education and lower rates of infections, can also have pernicious impacts upon brain atrophy. Partly for this reason, comparing age-related brain volume trajectories between industrialized and non-industrialized populations can help to suggest lifestyle correlates of brain health. The Tsimane, indigenous to the Bolivian Amazon, derive their subsistence from foraging and horticulture and are physically active. The Moseten, a mixed-ethnicity farming population, are physically active but less than the Tsimane. Within both populations (N = 1024; age range = 46-83), we calculated regional brain volumes from computed tomography and compared their cross-sectional trends with age to those of UK Biobank (UKBB) participants (N = 19,973; same age range). Surprisingly among Tsimane and Moseten (T/M) males, some parietal and occipital structures mediating visuospatial abilities exhibit small but significant increases in regional volume with age. UKBB males exhibit a steeper negative trend of regional volume with age in frontal and temporal structures compared to T/M males. However, T/M females exhibit significantly steeper rates of brain volume decrease with age compared to UKBB females, particularly for some cerebro-cortical structures (e.g., left subparietal cortex). Across the three populations, observed trends exhibit no interhemispheric asymmetry. In conclusion, the age-related rate of regional brain volume change may differ by lifestyle and sex. The lack of brain volume reduction with age is not known to exist in other human population, highlighting the putative role of lifestyle in constraining regional brain atrophy and promoting elements of non-industrialized lifestyle like higher physical activity.

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age µ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age µ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age µ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age µ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury.

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Prediction of cognitive outcome after mild traumatic brain injury from acute measures of communication within brain networks.

A considerable but ill-defined proportion of patients with mild traumatic brain injury (mTBI) experience persistent cognitive sequelae; the ability to identify such individuals early can help their neurorehabilitation. Here we tested the hypothesis that acute measures of efficient communication within brain networks are associated with patients' risk for unfavorable cognitive outcome six months after mTBI. Diffusion and T1-weighted magnetic resonance imaging, alongside cognitive measures, were obtained to map connectomes both one week and six months post injury in 113 adult patients with mTBI (71 males). For task-related brain networks, communication measures (characteristic path length, global efficiency, navigation efficiency) were moderately correlated with changes in cognition. Taking into account the covariance of age and sex, more unfavorable communication within networks were associated with worse outcomes within cognitive domains frequently impacted by mTBI (episodic and working memory, verbal fluency, inductive reasoning, and processing speed). Individuals with more unfavorable outcomes had significantly longer and less efficient pathways within networks supporting verbal fluency (all t > 2.786, p < .006), highlighting the vulnerability of language to mTBI. Participants in whom a task-related network was relatively inefficient one week post injury were up to eight times more likely to have unfavorable cognitive outcome pertaining to that task. Our findings suggest that communication measures within task-related networks identify mTBI patients who are unlikely to develop persistent cognitive deficits after mTBI. Our approach and findings can help to stratify mTBI patients according to their expected need for follow-up and/or neurorehabilitation.

Exploring personalized structural connectomics for moderate to severe traumatic brain injury.

Graph theoretical analysis of the structural connectome has been employed successfully to characterize 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 personalized connectomics approach that examines structural brain alterations in five chronic patients with moderate to severe TBI who underwent anatomical and diffusion magnetic resonance imaging. We generated individualized profiles of lesion characteristics and network measures (including personalized 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 alterations of brain networks with high variability between patients. With validation and comparison to stratified, normative healthy control comparison cohorts, this approach could be used by clinicians to formulate a neuroscience-guided integrative rehabilitation program for TBI patients, and for designing personalized rehabilitation protocols based on their unique lesion load and connectome.

Significant Acceleration of Regional Brain Aging and Atrophy After Mild Traumatic Brain Injury.

Brain regions' rates of age-related volumetric change after traumatic brain injury (TBI) are unknown. Here, we quantify these rates cross-sectionally in 113 persons with recent mild TBI (mTBI), whom we compare against 3 418 healthy controls (HCs). Regional gray matter (GM) volumes were extracted from magnetic resonance images. Linear regression yielded regional brain ages and the annualized average rates of regional GM volume loss. These results were compared across groups after accounting for sex and intracranial volume. In HCs, the steepest rates of volume loss were recorded in the nucleus accumbens, amygdala, and lateral orbital sulcus. In mTBI, approximately 80% of GM structures had significantly steeper rates of annual volume loss than in HCs. The largest group differences involved the short gyri of the insula and both the long gyrus and central sulcus of the insula. No significant sex differences were found in the mTBI group, regional brain ages being the oldest in prefrontal and temporal structures. Thus, mTBI involves significantly steeper regional GM loss rates than in HCs, reflecting older-than-expected regional brain ages.

Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients.

BACKGROUND AND PURPOSE: Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. MATERIALS AND METHODS: Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 - 64y). RESULTS: Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted. CONCLUSIONS: Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.

Anatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.

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.

Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression.

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.

Mild Traumatic Brain Injury Results in Significant and Lasting Cortical Demyelination.

Despite contributing to neurocognitive deficits, intracortical demyelination after traumatic brain injury (TBI) is understudied. This study uses magnetic resonance imaging (MRI) to map intracortical myelin and its change in healthy controls and after mild TBI (mTBI). Acute mTBI involves reductions in relative myelin content primarily in lateral occipital regions. Demyelination mapped ~6 months post-injury is significantly more severe than that observed in typical aging (p < 0.05), with temporal, cingulate, and insular regions losing more myelin (30%, 20%, and 16%, respectively) than most other areas, although occipital regions experience 22% less demyelination. Thus, occipital regions may be more susceptible to primary injury, whereas temporal, cingulate and insular regions may be more susceptible to later manifestations of injury sequelae. The spatial profiles of aging- and mTBI-related chronic demyelination overlap substantially; exceptions include primary motor and somatosensory cortices, where myelin is relatively spared post-mTBI. These features resemble those of white matter demyelination and cortical thinning during Alzheimer's disease, whose risk increases after mTBI.

Functional Connectome Dynamics After Mild Traumatic Brain Injury According to Age and Sex.

Neural and cognitive deficits after mild traumatic brain injury (mTBI) are paralleled by changes in resting state functional correlation (FC) networks that mirror post-traumatic pathophysiology effects on functional outcomes. Using functional magnetic resonance images acquired both acutely and chronically after injury (∼1 week and ∼6 months post-injury, respectively), we map post-traumatic FC changes across 136 participants aged 19-79 (52 females), both within and between the brain's seven canonical FC networks: default mode, dorsal attention, frontoparietal, limbic, somatomotor, ventral attention, and visual. Significant sex-dependent FC changes are identified between (A) visual and limbic, and between (B) default mode and somatomotor networks. These changes are significantly associated with specific functional recovery patterns across all cognitive domains (p < 0.05, corrected). Changes in FC between default mode, somatomotor, and ventral attention networks, on the one hand, and both temporal and occipital regions, on the other hand, differ significantly by age group (p < 0.05, corrected), and are paralleled by significant sex differences in cognitive recovery independently of age at injury (p < 0.05, corrected). Whereas females' networks typically feature both significant (p < 0.036, corrected) and insignificant FC changes, males more often exhibit significant FC decreases between networks (e.g., between dorsal attention and limbic, visual and limbic, default-mode and somatomotor networks, p < 0.0001, corrected), all such changes being accompanied by significantly weaker recovery of cognitive function in males, particularly older ones (p < 0.05, corrected). No significant FC changes were found across 35 healthy controls aged 66-92 (20 females). Thus, male sex and older age at injury are risk factors for significant FC alterations whose patterns underlie post-traumatic cognitive deficits. This is the first study to map, systematically, how mTBI impacts FC between major human functional networks.

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

Individual differences in attentional lapses are associated with fiber-specific white matter microstructure in healthy adults.

Attentional lapses interfere with goal-directed behaviors, which may result in harmless (e.g., not hearing instructions) or severe (e.g., fatal car accident) consequences. Task-related functional MRI (fMRI) studies have shown a link between attentional lapses and activity in the frontoparietal network. Activity in this network is likely to be mediated by the organization of the white matter fiber pathways that connect the regions implicated in the network, such as the superior longitudinal fasciculus I (SLF-I). In the present study, we investigate the relationship between susceptibility to attentional lapses and relevant white matter pathways in 36 healthy adults (23 females, Mage  = 31.56 years). Participants underwent a diffusion MRI (dMRI) scan and completed the global-local task to measure attentional lapses, similar to previous fMRI studies. Applying the fixel-based analysis framework for fiber-specific analysis of dMRI data, we investigated the association between attentional lapses and variability in microstructural fiber density (FD) and macrostructural (morphological) fiber-bundle cross section (FC) in the SLF-I. Our results revealed a significant negative association between higher total number of attentional lapses and lower FD in the left SLF-I. This finding indicates that the variation in the microstructure of a key frontoparietal white matter tract is associated with attentional lapses and may provide a trait-like biomarker in the general population. However, SLF-I microstructure alone does not explain propensity for attentional lapses, as other factors such as sleep deprivation or underlying psychological conditions (e.g., sleep disorders) may also lead to higher susceptibility in both healthy people and those with neurological disorders.

Navigating the link between processing speed and network communication in the human brain.

Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.

The structural connectome in traumatic brain injury: A meta-analysis of graph metrics.

Although recent structural connectivity studies of traumatic brain injury (TBI) have used graph theory to evaluate alterations in global integration and functional segregation, pooled analysis is needed to examine the robust patterns of change in graph metrics across studies. Following a systematic search, 15 studies met the inclusion criteria for review. Of these, ten studies were included in a random-effects meta-analysis of global graph metrics, and subgroup analyses examined the confounding effects of severity and time since injury. The meta-analysis revealed significantly higher values of normalised clustering coefficient (gö=ö1.445, CI=[0.512, 2.378], pö=ö0.002) and longer characteristic path length (gö=ö0.514, CI=[0.190, 0.838], pö=ö0.002) in TBI patients compared with healthy controls. Our findings suggest that the TBI structural network has shifted away from the balanced small-world network towards a regular lattice. Therefore, these graph metrics may be useful markers of neurocognitive dysfunction in TBI. We conclude that the pattern of change revealed by our analysis should be used to guide hypothesis-driven research into the role of graph metrics as diagnostic and prognostic biomarkers.

Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review.

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)-based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.