Neuroanatomy Reflects Individual Variability in Impulsivity in Youth.

Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity at both normative and clinically significant levels. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences coupled with difficulty inhibiting responses.

Subtyping psychotic disorders using a data-driven approach reveals divergent cortical and cellular signatures.

Structural brain abnormalities in psychosis are well-replicated but heterogenous posing a barrier to uncovering the pathophysiology, etiology, and treatment of psychosis. To parse neurostructural heterogeneity and assess for the presence of anatomically-derived subtypes, we applied a data-driven method, similarity network fusion (SNF), to structural neuroimaging data in a broad cohort of individuals with psychosis (schizophrenia spectrum disorders (SSD) n=280; bipolar disorder with psychotic features (BD) n=101). SNF identified two transdiagnostic subtypes in psychosis (subtype 1: n=158 SSD, n=75 BD; subtype 2: n=122 SSD, n=26 BD) that exhibited divergent patterns of abnormal cortical surface area and subcortical volumes.

Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort.

As an increasing realization, many behavioral relationships are interwoven with inherent variations in human populations. Presently, there is no clarity in the biomedical community on which sources of population variation are most dominant. The recent advent of population-scale cohorts like the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®) are now offering unprecedented depth and width of phenotype profiling that potentially explains interfamily differences.

Multilayer meta-matching: Translating phenotypic prediction models from multiple datasets to small data.

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity.

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching.

Individualized phenotypic prediction based on structural magnetic resonance imaging (MRI) is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts.

Longer scans boost prediction and cut costs in brain-wide association studies.

A pervasive dilemma in brain-wide association studies (BWAS) is whether to prioritize functional magnetic resonance imaging (fMRI) scan time or sample size. We derive a theoretical model showing that individual-level phenotypic prediction accuracy increases with sample size and total scan duration (sample size × scan time per participant). The model explains empirical prediction accuracies well across 76 phenotypes from nine resting-fMRI and task-fMRI datasets (R = 0.

Convergent and Divergent Brain-Cognition Development.

How brain networks and cognition co-evolve during development remains poorly understood. Here, we use resting-state functional magnetic resonance imaging (rs-fMRI) and cognitive data at baseline and Year 2 of 2,949 individuals in the Adolescent Brain Cognitive Development (ABCD) Study to examine how stable and changing features of brain network organization predict cognitive development during early adolescence. We find that baseline resting-state functional connectivity (FC) more strongly predicts future cognitive ability than baseline cognitive ability.

Multidimensional components of impulsivity during early adolescence: Relationships with brain networks and future substance-use in the Adolescent Brian and Cognitive Development (ABCD) study.

Impulsivity is a multifaceted construct that typically increases during adolescence and is implicated in risk for substance use disorders that develop later in life. Here, we take a multivariate approach to identify latent dimensions of impulsivity, broadly defined, among youth enrolled in the Adolescent Brain and Cognitive Development (ABCD) study and explore associations with individual differences in demographics, substance-use initiation and canonical resting state networks (N=11,872, ages ~9-10). Using principal component analysis, we identified eight latent impulsivity dimensions, the top three of which together accounted for the majority of the variance across all impulsivity assessments.

Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease.

The network organization of the human brain dynamically reconfigures in response to changing environmental demands, an adaptive process that may be disrupted in a symptom-relevant manner across psychiatric illnesses. Here, in a transdiagnostic sample of participants with (n=134) and without (n=85) psychiatric diagnoses, functional connectomes from intrinsic (resting-state) and task-evoked fMRI were decomposed to identify constraints on brain network dynamics across six cognitive states. Hierarchical clustering of 110 clinical, behavioral, and cognitive measures identified participant-specific symptom profiles, revealing four core dimensions of functioning: internalizing, externalizing, cognitive, and social/reward.

The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry.

An important aim in psychiatry is to establish valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations. To advance progress in this area, we introduce an open dataset containing behavioral and neuroimaging data from 241 individuals aged 18 to 70, comprising 148 individuals meeting diagnostic criteria for a broad range of psychiatric illnesses and a healthy comparison group of 93 individuals. These data include high-resolution anatomical scans, multiple resting-state, and task-based functional MRI runs.

Governance for anti-racist AI in healthcare: integrating racism-related stress in psychiatric algorithms for Black Americans.

While the world is aware of America's history of enslavement, the ongoing impact of anti-Black racism in the United States remains underemphasized in health intervention modeling. This Perspective argues that algorithmic bias-manifested in the worsened performance of clinical algorithms for Black vs. white patients-is significantly driven by the failure to model the cumulative impacts of racism-related stress, particularly racial heteroscedasticity.

Optimizing Biophysical Large-Scale Brain Circuit Models With Deep Neural Networks.

Biophysical modeling provides mechanistic insights into brain function, from single-neuron dynamics to large-scale circuit models bridging macro-scale brain activity with microscale measurements. Biophysical models are governed by biologically meaningful parameters, many of which can be experimentally measured. Some parameters are unknown, and optimizing their values can dramatically improve adherence to experimental data, significantly enhancing biological plausibility.

Network-based Molecular Constraints on Synaptic Density Alterations in Schizophrenia.

Converging neuroimaging, genetic, and post-mortem evidence show a fundamental role of synaptic deficits in schizophrenia pathogenesis. However, the underlying molecular and cellular mechanisms that drive the onset and progression of synaptic pathology remain to be established. Here, we used synaptic density positron emission tomography (PET) imaging using the [C]UCB-J radiotracer to reveal a prominent widespread pattern ( < 0.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results.

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases.

Quantifying associations between socio-spatial factors and cognitive development in the ABCD cohort.

Despite the mounting demand for generative population models, their limited generalizability to underrepresented demographic groups hinders widespread adoption in real-world applications. Here we propose a diversity-aware population modeling framework that can guide targeted strategies in public health and education, by estimating subgroup-level effects and stratifying predictions to capture sociodemographic variability. We leverage Bayesian multilevel regression and post-stratification to systematically quantify inter-individual differences in the relationship between socioeconomic status and cognitive development.

Protective role of parenthood on age-related brain function in mid- to late-life.

The experience of human parenthood is near ubiquitous and can profoundly alter one's body, mind, and environment. However, we know very little about the long-term neural effects of parenthood for parents themselves, or the implications of pregnancy and caregiving experience on the aging adult brain. Here, we investigate the link between the number of children parented and age on brain function in 19,964 females and 17,607 males from the UK Biobank.

Considering the interconnected nature of social identities in neuroimaging research.

Considerable heterogeneity exists in the expression of complex human behaviors across the cognitive, personality and mental health domains. It is increasingly evident that individual variability in behavioral expression is substantially affected by sociodemographic factors that often interact with life experiences. Here, we formally address the urgent need to incorporate intersectional identities in neuroimaging studies of behavior, with a focus on research in mental health.

The cell-type underpinnings of the human functional cortical connectome.

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging.

Embracing variability in the search for biological mechanisms of psychiatric illness.

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations.

Shared and unique lifetime stressor characteristics and network connectivity predict adolescent anxiety and depression.

Background: Exposure to major life stressors and aberrant functional connectivity have been linked to anxiety and depression, especially during adolescence. However, whether specific characteristics of life stressors and functional network connectivity act together to differentially predict anxiety and depression symptoms remains unclear.

Methods: We utilized baseline lifetime stressor exposure and resting-state functional magnetic resonance imaging data in a longitudinal sample of 107 adolescents enriched for anxiety and depressive disorders.