Matthew Rosenblatt

Brain maturation and aging are characterized by widespread changes in structural and functional brain organization. The extent to which these modalities capture shared versus distinct signatures of lifespan development and aging remains unclear. In this study, we applied connectome-based predictive modeling (CPM) to connectomes derived from diffusion MRI and resting-state functional MRI in Human Connectome Project Development, Young Adult, Aging, and combined Lifespan cohorts to predict chronological age. Both structural and functional connectomes significantly predicted age across cohorts, demonstrating that age-related information is distributed throughout whole-brain connectivity patterns. Prediction performance varied across lifespan stages, with generally stronger performance in the Development, Aging, and Lifespan cohorts than in Young Adulthood. Structural and functional models produced significantly correlated predicted ages in several cohorts, indicating shared age-related information across modalities. Structural-functional convergence analyses revealed limited and variable correspondence between modality-specific predictive features, suggesting that structural and functional connectomes capture both shared and complementary aspects of brain maturation and aging. Network-level analyses demonstrated that age-predictive information was distributed across canonical brain networks, including cerebellar, frontoparietal, salience, somatomotor, and subcortical networks, with patterns varying across cohorts and modalities. Multimodal CPM improved prediction relative to unimodal models in several cohorts, supporting the complementary contribution of structural and functional connectivity to age prediction. Cross-cohort, cross-sex, and cross-modality analyses further characterized the generalizability and shared information of predictive models across lifespan stages, sexes, and imaging modalities. This work demonstrates the utility of CPM for characterizing distributed structural and functional connectivity patterns associated with chronological age across the lifespan and provides a foundation for future studies of individual variability in brain maturation and aging.

Neuroimaging studies rarely test whether the variance structure is equivalent across population subgroups. Here, in 4,736 participants from the Adolescent Brain Cognitive Development (ABCD) cohort, we examine racialized heteroscedasticity (i.e., differences in variance across racialized groups) in neuroimaging and behavioral data and test how these differences in variance propagate into predictive modeling. Across neuroimaging modalities, behaviors, and predictive frameworks, variance differences exhibited consistent patterns, indicating that variance structure is a stable property across domains within the dataset. Simulation analyses demonstrated that such differences directly induce subgroup disparities in prediction error and reliability, even in the absence of mean differences. Across neuroimaging modalities, multiple measures demonstrated greater variance in Black participants, particularly in functional imaging modalities. Similar variance patterns were observed in behavioral measures, and predictive models exhibited greater residual dispersion and prediction variance in Black participants even when overall performance metrics were comparable. These findings position variance structure, rather than central tendency, as a critical determinant of model performance, generalizability, and reliability across diverse populations.

Cortical expansion is a defining feature of human evolution and neurodevelopment, involving the tangential growth and gyrification of the cerebral cortex. Although disruptions to the expansion of the cortex are implicated in diverse brain-based disorders, the genetic architecture underlying this process remains undercharacterized. Here, we integrated GWAS data for five neuroanatomical phenotypes measured in up to 73,800 individuals to model a latent genomic factor capturing the shared genetic basis of cortical surface area, folding, curvature, gyrification, and intracranial volume. Using a multivariate framework, we mapped this pleiotropic architecture across biological scales, identifying novel effector genes, neural cell types, and developmental pathways involved in cortical expansion. Functional genomic evidence indicated that a diverse cellular ensemble contributes to the tangential growth and gyrification of the cortex during prenatal neurodevelopment, with progenitor cell lineages prominently involved. Regional analyses further revealed that genetic influences are spatially heterogeneous and organized along canonical anatomical, functional, and developmental gradients. Finally, cortical expansion exhibited substantial genetic overlap with neurodevelopmental, psychiatric, and neurological disorders, with shared genetic influences often concentrated in prefrontal cortical regions. Together, these findings map the genetic architecture of human cortical expansion and implicate it as a common etiological axis linking evolution, neurodevelopment, and health and disease.

Emerging reports suggest that sample sizes commonly used in functional neuroimaging studies may be too small to detect many brain-behavior relationships, posing a major barrier to brain and mental health research. A central challenge is that planning robust studies requires researchers to know what effect sizes to expect, yet this essential information is surprisingly difficult to estimate in practice and thus often omitted from study planning. Critically, standard “mass univariate” procedures for estimating effects across multiple brain areas give an inflated picture of how large effect sizes are. Here, we introduce a method to correct this inflation bias and perform an unprecedented analysis of 63 studies in seven large datasets (n = 100–40,000; 52,979 total participants) to establish effect size benchmarks in functional neuroimaging. We find that between-subjects effects are exceedingly small at the majority of brain areas (Cohen’s |d| < 0.2), requiring consortium-level sample sizes to detect even some of the strongest focal brain effects (n > 500 at 80% statistical power with FDR correction). However, multivariate analyses and within-subject task designs yield substantially larger effect sizes that can be detected at sample sizes within reach of individual labs (n < 50). By establishing data-driven effect size benchmarks, these findings lay the groundwork for more informed study planning in neuroscience while highlighting shared challenges (and the potential for shared solutions) across biomedicine.

Poor generalizability continues to hinder mapping brain activity to behavior using human neuroimaging data. A potential solution is predictive modeling, which evaluates generalizability in unseen data from the same dataset. However, predictive models often fail external validation-a stricter test of generalizability involving evaluation in an independent dataset. In this Perspective, we explain how evaluating generalizability via external validation can improve replicability (illusory generalizability and bias) and reproducibility (data leakage and data manipulations). We also provide advice on statistical power, dataset shift and training models. A model's success in external validation provides evidence for its generalizability, but interpretations depend on the population characteristics of the training and external datasets. Even failed external validation constitutes an opportunity for scientific insight or methodological adjustments. Sharing data and models, combined with standalone external validation studies, will increase the prevalence of external validation. In turn, an increased focus on external validation can drive more generalizable, replicable and reproducible neuroimaging results.

Prevention remains a key strategy to address the growing burden of metabolic dysfunction–associated steatotic liver disease (MASLD), highlighting the importance of exploring modifiable risk factors. Accumulating evidence suggests a close link between physical frailty and MASLD. However, how frailty interacts with metabolic syndrome to affect MASLD and the causality and direction of the association remain largely unknown. Leveraging data from 405,224 UK Biobank participants with a 13.65-year follow-up, we found that physical frailty was associated with an increased risk of clinically diagnosed MASLD and exacerbated the adverse effect of metabolic syndrome on MASLD incidence, implying that frail people may be more vulnerable to this disease because of metabolic syndrome. Mendelian randomization provided evidence for a potential causal effect of physical frailty on MASLD but not the reverse direction. Moreover, the metabolome-wide association analysis revealed widespread associations of plasma metabolites with both frailty and MASLD, suggesting a shared metabolomic foundation between them. Some metabolites, including fatty acids and triglyceride-rich lipoprotein biomarkers, partially explained the frailty-MASLD relationship, indicating a potential metabolomic mechanism. If confirmed in further studies, frailty screening may help identify high-risk individuals and inform early prevention for MASLD, especially for those with metabolic syndrome.

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging. Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied. Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural (diffusion tensor imaging) connectomes, and remain evident in external validation. They suggest that focusing on only the top features may simplify the neurobiological bases of brain–behaviour associations. Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored.

Early life adversity (ELA) is a robust transdiagnostic risk factor for mental health disorders, yet the neurobiological mechanisms mediating its long-term impact remain poorly understood. Network control theory offers a novel framework for capturing how structural brain networks constrain and support brain dynamics. Controllability increases over development, associates with executive function and mental health, and appears sensitive to environmental insults. Thus, it may reflect a neurobiological mediator between ELA and behavioral outcomes. We tested whether alterations in modal controllability mediate the impact of multidimensional ELA on cognitive and behavioral outcomes in youth, and whether these pathways are shaped by genetic risk for neurodevelopmental conditions. Using data from 7,970 children aged 9–11 years in the Adolescent Brain Cognitive Development (ABCD) Study, we derived five latent ELA dimensions from 67 indicators, and computed polygenic risk scores (PRS) for attention-deficit/hyperactivity disorder (ADHD _PRS ) and autism spectrum disorder (ASD _PRS ). Distinct ELA dimensions were associated with increased controllability in medial frontal, frontoparietal, default mode, and motor networks, as well as with externalizing symptoms and impaired crystallized cognition. Controllability partially mediated these associations, and indirect effects were significantly moderated ADHD _PRS and ASD _PRS . Longitudinal analyses further demonstrated that baseline controllability predicted cognitive performance two years later. These findings delineate a neurodevelopmental cascade linking early adversity and genetic vulnerability to transdiagnostic mental health risk, positioning brain controllability as a promising mechanistic marker and potential target for early intervention.

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates—or how much authors cite themselves—have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000–2020. In analyzing over five million citations, we demonstrate four key findings: (1) increasing self-citation rates of Last Authors relative to First Authors, (2) lower self-citation rates in low- and middle-income countries, (3) gender differences in self-citation stemming from differences in the number of previously published papers, and (4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

Effect size estimation is crucial for power analyses and experimental design, but posesunique challenges in fMRI research due to the complexity of the data and analysistechniques. Here, we introduce an interactive web application for exploring fMRI effect maps(neuroprismlab.shinyapps.io/BrainEffeX). We utilized large fMRI datasets to obtain precisevoxel-wise and multivariate effect size estimates from “typical” fMRI study designs:brain-behavior correlation, task vs. rest, and between-group analyses of functionalconnectivity and task-based activation maps. The app is intentionally designed as a growingresource, and we welcome contributions of large (n > 500) datasets.