Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

IMPORTANCE Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood.OBJECTIVE To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia (SCZ). DESIGN, SETTING, AND PARTICIPANTSProfiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The number of cases and controls in each of the 6 disorders were as follows:

The members of the collaboration are provided in Appendix A.

Manual tracing of magnetic resonance imaging (MRI) represents the gold standard for segmentation in clinical neuropsychiatric research studies, however automated approaches are increasingly used due to its time limitations. The accuracy of segmentation techniques for subcortical structures has not been systematically investigated in large samples. We compared the accuracy of fully automated [(i) model-based: FSL-FIRST; (ii) patch-based: volBrain], semi-automated (FreeSurfer) and stereological (Measure®) segmentation techniques with manual tracing (ITK-SNAP) for delineating volumes of the caudate (easy-to-segment) and the hippocampus (difficult-to-segment). High resolution 1.5 T T1-weighted MR images were obtained from 177 patients with major psychiatric disorders and 104 healthy participants. The relative consistency (partial correlation), absolute agreement (intraclass correlation coefficient, ICC) and potential technique bias (Bland-Altman plots) of each technique was compared with manual segmentation. Each technique yielded high correlations (0.77-0.87, p < 0.0001) and moderate ICC's (0.28-0.49) relative to manual segmentation for the caudate. For the hippocampus, stereology yielded good consistency (0.52-0.55, p < 0.0001) and ICC (0.47-0.49), whereas automated and semi-automated techniques yielded poor ICC (0.07-0.10) and moderate consistency (0.35-0.62, p < 0.0001). Bias was least using stereology for segmentation of the hippocampus and using FreeSurfer for segmentation of the caudate. In a typical neuropsychiatric MRI dataset, automated segmentation techniques provide good accuracy for an easy-to-segment structure such as the caudate, whereas for the hippocampus, a reasonable correlation with volume but poor absolute agreement was demonstrated. This indicates manual or stereological volume estimation should be considered for studies that require high levels of precision such as those with small sample size.

A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.

Under the influence of genes and a varying environment, human brain structure changes throughout the lifespan. Even in adulthood, when the brain seems relatively stable, individuals differ in the profile and rate of brain changes 1 . Longitudinal studies are crucial to identify genetic and environmental factors that influence the rate of these brain changes throughout development 2 and aging 3 . Inter-individual differences in brain development are associated with general cognitive function 4,5 and risk for psychiatric disorders 6,7 and neurological diseases 8,9 . Genetic factors involved in brain development and aging overlap with those for cognition 10 and risk for neuropsychiatric disorders 11 . A recent cross-sectional study showed brain age to be advanced in several brain disorders. Brain age is an estimate of biological age based on brain structure, which can deviate from chronological age. Several shared loci were found between the genome-wide association study (GWAS) summary statistics for advanced brain age and psychiatric disorders 12 . However, information is still lacking on which genetic variants influence an individual's brain changes throughout life, because this requires longitudinal data. Discovering genetic factors that explain variation between individuals in brain structural changes may reveal key biological pathways that drive normal development and aging and may contribute to identifying disease risk and resilience-a crucial goal given the urgent need for new treatments for aberrant brain development and aging worldwide.As part of the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium 13 , the ENIGMA Plasticity Working Group quantified the overall genetic contribution to longitudinal brain changes by combining evidence from multiple twin cohorts across the world 14 . Most global and subcortical brain measures showed genetic influences on change over time, with a higher genetic contribution in the elderly (heritability, 16-42%). Genetic factors that influence longitudinal changes were partially independent of those that influence baseline volumes of brain structures, suggesting that there might be genetic variants that specifically affect the rate of development or aging. However, the genes involved in these processes are still not known, with only a single, small-scale GWAS performed for longitudinal volume change in gray and white matter of the cerebrum, basal ganglia and cerebellum 15 . In this study, we set out to find genetic variants that may influence rates of brain changes over time, using genome-wide analysis in individuals scanned with magnetic resonance imaging (MRI) on more than one occasion. We also aimed to identify references

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 reso-