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Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the ‘FA fine’ metric of the RSI model and ‘orientation dispersion’ (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

Cognitive aging is accompanied by a range of structural and functional differences in the brain, even in the absence of neurodegenerative disease. Functional magnetic resonance imaging (fMRI) studies have reported increased bilateral activation during task performance in elderly participants compared to their younger counterparts, particularly in frontal regions. Alterations have also been observed in the functional architecture of the resting brain, suggesting that aging is associated with changes in the organization of the networks of the brain. However, previous studies have largely focused on the default mode network, and little is known about the effects of age on other resting state-networks (RSNs). The aim of the present study was to investigate age-differences in resting state functional connectivity (RSFC) using fMRI data obtained during rest from 238 healthy participants aged 21–80years. Using independent component analysis (ICA) and dual-regression, the results revealed age-related increases in RSFC across a range of RSNs, including task-positive networks in frontal and parietal regions. In contrast, age-related reductions in the default mode network and occipital visual networks were observed. Furthermore, whereas the effects of age on the various RSNs were found independent of age-related decreases in gray matter volume, sex and subject motion, we report strong positive and widespread effects of estimated subject motion on the RSFC across RSNs. The results provide support for the notion of network-specific effects in aging, manifested as increased tonic activation of task-positive networks, supporting higher-order cognitive functions and cognitive control, along with reduced task-negative default mode network and sensory visual networks during rest. The present results also corroborate recent evidence of strong influence of subject motion on estimated functional connectivity measures and strongly suggest that studies using RSFC measures as imaging phenotypes should adjust for individual differences in in-scanner subject motion. ► We delineated age-variance in resting functional connectivity in 238 adults. ► Increasing co-activation in frontal and parietal networks with increasing age ► Decreasing co-activation in the DMN and two posterior RSNs with increasing age ► Strong positive correlations between connectivity and motion in all tested RSNs ► Results support the notion of network-specific effects of aging.

Oxytocin is a neuropeptide involved in animal and human reproductive and social behavior. Three oxytocin signaling genes have been frequently implicated in human social behavior: OXT (structural gene for oxytocin), OXTR (oxytocin receptor), and CD38 (oxytocin secretion). Here, we characterized the distribution of OXT , OXTR , and CD38 mRNA across the human brain by creating voxel-by-voxel volumetric expression maps, and identified putative gene pathway interactions by comparing gene expression patterns across 20,737 genes. Expression of the three selected oxytocin pathway genes was enriched in subcortical and olfactory regions and there was high co-expression with several dopaminergic and muscarinic acetylcholine genes, reflecting an anatomical basis for critical gene pathway interactions. fMRI meta-analysis revealed that the oxytocin pathway gene maps correspond with the processing of anticipatory, appetitive, and aversive cognitive states. The oxytocin signaling system may interact with dopaminergic and muscarinic acetylcholine signaling to modulate cognitive state processes involved in complex human behaviors. Oxytocin is a hormone and neurotransmitter involved in reproductive and social behavior, but the role of oxytocin-related genes in the human brain remains unclear. Here, the authors map oxytocin pathway gene expression and show that it overlaps with brain regions involved in reward and emotional states.

The substantial individual heterogeneity that characterizes people with mental illness is often ignored by classical case–control research, which relies on group mean comparisons. Here we present a comprehensive, multiscale characterization of the heterogeneity of gray matter volume (GMV) differences in 1,294 cases diagnosed with one of six conditions (attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, depression, obsessive–compulsive disorder and schizophrenia) and 1,465 matched controls. Normative models indicated that person-specific deviations from population expectations for regional GMV were highly heterogeneous, affecting the same area in <7% of people with the same diagnosis. However, these deviations were embedded within common functional circuits and networks in up to 56% of cases. The salience–ventral attention system was implicated transdiagnostically, with other systems selectively involved in depression, bipolar disorder, schizophrenia and attention-deficit/hyperactivity disorder. Phenotypic differences between cases assigned the same diagnosis may thus arise from the heterogeneous localization of specific regional deviations, whereas phenotypic similarities may be attributable to the dysfunction of common functional circuits and networks. A new brain mapping approach tailored to individual people reveals that volume changes in psychiatric illness occur in highly variable locations across individuals, but that these differences often aggregate within common brain systems.

This study on neurodevelopment of functional networks reveals a network tuning process that transforms the human connectome into a stable, individualized wiring pattern. Delay in this tuning was associated with disordered mental health, revealing the detrimental paths that brain plasticity can take during adolescence, when initial symptoms of mental illness occur. The brain functional connectome constitutes a unique fingerprint allowing identification of individuals among a pool of people. Here we establish that the connectome develops into a more stable, individual wiring pattern during adolescence and demonstrate that a delay in this network tuning process is associated with reduced mental health in the formative years of late neurodevelopment.

Maternal brain adaptations have been found across pregnancy and postpartum, but little is known about the long-term effects of parity on the maternal brain. Using neuroimaging and machine learning, we investigated structural brain characteristics in 12,021 middle-aged women from the UK Biobank, demonstrating that parous women showed less evidence of brain aging compared to their nulliparous peers. The relationship between childbirths and a “younger-looking” brain could not be explained by common genetic variation or relevant confounders. Although prospective longitudinal studies are needed, the results suggest that parity may involve neural changes that could influence women’s brain aging later in life.

Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10 −8 , MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation. Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.

Advances in neuroimaging techniques have recently provided glimpse into the neurobiology of complex traits of human personality. Whereas some intriguing findings have connected aspects of personality to variations in brain morphology, the relations are complex and our current understanding is incomplete. Therefore, we aimed to provide a comprehensive investigation of brain–personality relations using a multimodal neuroimaging approach in a large sample comprising 265 healthy individuals. The NEO Personality Inventory was used to provide measures of core aspects of human personality, and imaging phenotypes included measures of total and regional brain volumes, regional cortical thickness and arealization, and diffusion tensor imaging indices of white matter (WM) microstructure. Neuroticism was the trait most clearly linked to brain structure. Higher neuroticism including facets reflecting anxiety, depression and vulnerability to stress was associated with smaller total brain volume, widespread decrease in WM microstructure, and smaller frontotemporal surface area. Higher scores on extraversion were associated with thinner inferior frontal gyrus, and conscientiousness was negatively associated with arealization of the temporoparietal junction. No reliable associations between brain structure and agreeableness and openness, respectively, were found. The results provide novel evidence of the associations between brain structure and variations in human personality, and corroborate previous findings of a consistent neuroanatomical basis of negative emotionality. ► Neuroticism was the NEO-PI trait most clearly linked to brain structure. ► Higher neuroticism was associated with smaller total brain volume. ► Higher neuroticism was associated with reduced frontotemporal surface area. ► Neuroticism was associated with widespread decrease in white matter microstructure. ► Multimodal imaging promising in delineating the neural substrate of personality.

•Cardiovascular risk factors are associated with older brain age.•Blood pressure is more strongly associated with white matter compared to gray matter.•Resting state functional connectivity provides lower brain-age prediction accuracy.•Brain-age prediction accuracy depends on sample size and age range. Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R2 = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R2 = 0.22 [0.16, 0.27] and R2 = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R2 = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.