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Aberrant brain network development represents a putative aetiological component in mental disorders, which typically emerge during childhood and adolescence. Previous studies have identified resting-state functional connectivity (RSFC) patterns reflecting psychopathology, but the generalisability to other samples and politico-cultural contexts has not been established. We investigated whether a previously identified cross-diagnostic case-control and autism spectrum disorder (ASD)-specific pattern of RSFC (discovery sample; aged 5-21 from New York City, USA; n = 1666) could be validated in a Norwegian convenience-based youth sample (validation sample; aged 9-25 from Oslo, Norway; n = 531). As a test of generalisability, we investigated if these diagnosis-derived RSFC patterns were sensitive to levels of symptom burden in both samples, based on an independent measure of symptom burden. Both the cross-diagnostic and ASD-specific RSFC pattern were validated across samples. Connectivity patterns were significantly associated with thematically appropriate symptom dimensions in the discovery sample. In the validation sample, the ASD-specific RSFC pattern showed a weak, inverse relationship with symptoms of conduct problems, hyperactivity and prosociality, while the cross-diagnostic pattern was not significantly linked to symptoms. Diagnosis-derived connectivity patterns in a developmental clinical US sample were validated in a convenience sample of Norwegian youth, however, they were not associated with mental health symptoms., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Cerebral blood flow (CBF) is critical for brain metabolism and function. Age-related changes in CBF are associated with increased risk of neurocognitive disorders and vascular events such as stroke. Identifying correlates and positive modifiers of age-related changes in CBF before the emergence of incipient clinical decline may inform public health advice and clinical practice. Former research has been inconclusive regarding the association between regular physical activity and CBF, and there is a lack of studies on the association between level of everyday activities and CBF, in older adults. To investigate these relationships, 118 healthy community-dwelling adults (65-89 years) underwent pseudo-continuous arterial spin labeling (ASL) MRI, neurocognitive, physical, and activity assessments at baseline. Eighty-six participants completed a follow-up ASL MRI, on average 506 (SD = 113) days after the baseline scan. Cross-sectional analysis revealed credible evidence for positive associations between time spent on low intensity physical activity and CBF in multiple cortical and subcortical regions, time spent on moderate to vigorous intensity physical activity and accumbens CBF, participation in social activity and CBF in multiple cortical regions, and between reading and thalamic CBF, indicating higher regional CBF in more active adults. Longitudinal analysis revealed anecdotal evidence for an interaction between time and baseline level of gardening on occipital and parietal CBF, and baseline reading on pallidum CBF, indicating more change in CBF in adults with lower level of activity. The findings support that malleable lifestyle factors contribute to healthy brain aging, with relevance for public health guidelines., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Estimating age based on neuroimaging-derived data has become a popular approach to developing markers for brain integrity and health. While a variety of machine-learning algorithms can provide accurate predictions of age based on brain characteristics, there is significant variation in model accuracy reported across studies. We predicted age in two population-based datasets, and assessed the effects of age range, sample size and age-bias correction on the model performance metrics Pearson's correlation coefficient (r), the coefficient of determination (R 2 ), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results showed that these metrics vary considerably depending on cohort age range; r and R 2 values are lower when measured in samples with a narrower age range. RMSE and MAE are also lower in samples with a narrower age range due to smaller errors/brain age delta values when predictions are closer to the mean age of the group. Across subsets with different age ranges, performance metrics improve with increasing sample size. Performance metrics further vary depending on prediction variance as well as mean age difference between training and test sets, and age-bias corrected metrics indicate high accuracy-also for models showing poor initial performance. In conclusion, performance metrics used for evaluating age prediction models depend on cohort and study-specific data characteristics, and cannot be directly compared across different studies. Since age-bias corrected metrics generally indicate high accuracy, even for poorly performing models, inspection of uncorrected model results provides important information about underlying model attributes such as prediction variance., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The structure and integrity of the ageing brain is interchangeably linked to physical health, and cardiometabolic risk factors (CMRs) are associated with dementia and other brain disorders. In this mixed cross-sectional and longitudinal study (interval mean = 19.7 months), including 790 healthy individuals (mean age = 46.7 years, 53% women), we investigated CMRs and health indicators including anthropometric measures, lifestyle factors, and blood biomarkers in relation to brain structure using MRI-based morphometry and diffusion tensor imaging (DTI). We performed tissue specific brain age prediction using machine learning and performed Bayesian multilevel modeling to assess changes in each CMR over time, their respective association with brain age gap (BAG), and their interaction effects with time and age on the tissue-specific BAGs. The results showed credible associations between DTI-based BAG and blood levels of phosphate and mean cell volume (MCV), and between T1-based BAG and systolic blood pressure, smoking, pulse, and C-reactive protein (CRP), indicating older-appearing brains in people with higher cardiometabolic risk (smoking, higher blood pressure and pulse, low-grade inflammation). Longitudinal evidence supported interactions between both BAGs and waist-to-hip ratio (WHR), and between DTI-based BAG and systolic blood pressure and smoking, indicating accelerated ageing in people with higher cardiometabolic risk (smoking, higher blood pressure, and WHR). The results demonstrate that cardiometabolic risk factors are associated with brain ageing. While randomized controlled trials are needed to establish causality, our results indicate that public health initiatives and treatment strategies targeting modifiable cardiometabolic risk factors may also improve risk trajectories and delay brain ageing., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

MRI-derived brain measures offer a link between genes, the environment and behavior and have been widely studied in bipolar disorder (BD). However, many neuroimaging studies of BD have been underpowered, leading to varied results and uncertainty regarding effects. The Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Bipolar Disorder Working Group was formed in 2012 to empower discoveries, generate consensus findings and inform future hypothesis-driven studies of BD. Through this effort, over 150 researchers from 20 countries and 55 institutions pool data and resources to produce the largest neuroimaging studies of BD ever conducted. The ENIGMA Bipolar Disorder Working Group applies standardized processing and analysis techniques to empower large-scale meta- and mega-analyses of multimodal brain MRI and improve the replicability of studies relating brain variation to clinical and genetic data. Initial BD Working Group studies reveal widespread patterns of lower cortical thickness, subcortical volume and disrupted white matter integrity associated with BD. Findings also include mapping brain alterations of common medications like lithium, symptom patterns and clinical risk profiles and have provided further insights into the pathophysiological mechanisms of BD. Here we discuss key findings from the BD working group, its ongoing projects and future directions for large-scale, collaborative studies of mental illness., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Identifying brain processes involved in the risk and development of mental disorders is a major aim. We recently reported substantial interindividual heterogeneity in brain structural aberrations among patients with schizophrenia and bipolar disorder. Estimating the normative range of voxel-based morphometry (VBM) data among healthy individuals using a Gaussian process regression (GPR) enables us to map individual deviations from the healthy range in unseen datasets. Here, we aim to replicate our previous results in two independent samples of patients with schizophrenia (n1 = 94; n2 = 105), bipolar disorder (n1 = 116; n2 = 61), and healthy individuals (n1 = 400; n2 = 312). In line with previous findings with exception of the cerebellum our results revealed robust group level differences between patients and healthy individuals, yet only a small proportion of patients with schizophrenia or bipolar disorder exhibited extreme negative deviations from normality in the same brain regions. These direct replications support that group level-differences in brain structure disguise considerable individual differences in brain aberrations, with important implications for the interpretation and generalization of group-level brain imaging findings to the individual with a mental disorder., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub-cortical volumes, cortical and subcortical T1/T2-weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age-matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two-group case-control classifications revealed highest accuracy for AD using global T1-weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF-based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain-based mapping of overlapping and distinct pathophysiology in common disorders., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Computerized cognitive training (CCT) combined with transcranial direct current stimulation (tDCS) has showed some promise in alleviating cognitive impairments in patients with brain disorders, but the robustness and possible mechanisms are unclear. In this prospective double-blind randomized clinical trial, we investigated the feasibility and effectiveness of combining CCT and tDCS, and tested the predictive value of and training-related changes in fMRI-based brain activation during attentive performance (multiple object tracking) obtained at inclusion, before initiating training, and after the three-weeks intervention in chronic stroke patients (>6 months since hospital admission). Patients were randomized to one of two groups, receiving CCT and either (a) tDCS targeting left dorsolateral prefrontal cortex (1 mA), or (b) sham tDCS, with 40s active stimulation (1 mA) before fade out of the current. Of note, 77 patients were enrolled in the study, 54 completed the cognitive training, and 48 completed all training and MRI sessions. We found significant improvement in performance across all trained tasks, but no additional gain of tDCS. fMRI-based brain activation showed high reliability, and higher cognitive performance was associated with increased tracking-related activation in the dorsal attention network and default mode network as well as anterior cingulate after compared to before the intervention. We found no significant associations between cognitive gain and brain activation measured before training or in the difference in activation after intervention. Combined, these results show significant training effects on trained cognitive tasks in stroke survivors, with no clear evidence of additional gain of concurrent tDCS., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

The restructuring and optimization of the cerebral cortex from early childhood and through adolescence is an essential feature of human brain development, underlying immense cognitive improvements. Beyond established morphometric cortical assessments, the T1w/T2w ratio quantifies partly separate biological processes, and might inform models of typical neurocognitive development and developmental psychopathology. In the present study, we computed vertex-wise T1w/T2w ratio across the cortical surface in 621 youths (3-21 years) sampled from the Pediatric Imaging, Neurocognition, and Genetics (PING) study and tested for associations with individual differences in age, sex, and both general and specific cognitive abilities. The results showed a near global linear age-related increase in T1w/T2w ratio across the brain surface, with a general posterior to anterior increasing gradient in association strength. Moreover, results indicated that boys in late adolescence had regionally higher T1w/T2w ratio as compared to girls. Across individuals, T1w/T2w ratio was negatively associated with general and several specific cognitive abilities mainly within anterior cortical regions. Our study indicates age-related differences in T1w/T2w ratio throughout childhood, adolescence, and young adulthood, in line with the known protracted myelination of the cortex. Moreover, the study supports T1w/T2w ratio as a promising surrogate measure of individual differences in intracortical brain structure in neurodevelopment., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Functional interconnections between brain regions define the "connectome" which is of central interest for understanding human brain function. Resting-state functional magnetic resonance (rsfMRI) work has revealed changes in static connectivity related to age, sex, cognitive abilities and psychiatric symptoms, yet little is known how these factors may alter the information flow. The commonly used approach infers functional brain connectivity using stationary coefficients yielding static estimates of the undirected connection strength between brain regions. Dynamic graphical models (DGMs) are a multivariate model with dynamic coefficients reflecting directed temporal associations between nodes, and can yield novel insight into directed functional connectivity. Here, we leveraged this approach to test for associations between edge-wise estimates of direction flow across the functional connectome and age, sex, intellectual abilities and mental health. We applied DGM to investigate patterns of information flow in data from 984 individuals from the Human Connectome Project (HCP) and 10,249 individuals from the UK Biobank. Our analysis yielded patterns of directed connectivity in independent HCP and UK Biobank data similar to those previously reported, including that the cerebellum consistently receives information from other networks. We show robust associations between information flow and age and sex for several connections, with strongest effects of age observed in the sensorimotor network. Visual, auditory and sensorimotor nodes were also linked to mental health. Our findings support the use of DGM as a measure of directed connectivity in rsfMRI data and provide new insight into the shaping of the connectome during aging., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Increasing interest in the structural and functional organisation of the human brain encourages the acquisition of big data sets comprising multiple neuroimaging modalities, often accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates cross-study harmonisation and reduces pipeline-related variability. Using diffusion magnetic resonance imaging (MRI) data from 218 individuals in the UK Biobank, we evaluate the effects of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. In lack of a ground truth, we compared diffusion metric sensitivity to age between pipelines. By comparing distributions and age sensitivity of the resulting diffusion metrics based on different approaches (diffusion tensor imaging, diffusion kurtosis imaging and white matter tract integrity), we evaluate a general pipeline comprising seven postprocessing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing and final diffusion metric estimations. Based on this evaluation, we suggest an optimised processing pipeline for diffusion weighted MRI data., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)