Multimodal data integration for precision brain health

Breakthroughs in human neuroscience have enabled the generation of diverse and high-dimensional data from genomics and epigenomics, advanced neuroimaging, fluid and digital biomarkers, as well as cognitive and behavioral assessments. Despite these advances, single-modality studies often fall short in capturing the complexity of brain health and disease, leaving gaps in our understanding of nervous system development, mechanisms underlying neuropsychiatric and neurological conditions, and factors affecting brain aging. Recent research has demonstrated the potential of integrating data across multiple biological and behavioral domains to uncover novel biomarkers, patient subtypes, and mechanistic insights. However, challenges remain around analytic integration, clinical translation, and ensuring reproducibility and equity in findings.This Research Topic aims to catalyze the advancement of precision brain health through the integration of multimodal data. The primary objective is to gather contributions that leverage complex heterogeneous datasets, spanning biological, cognitive, environmental, and digital domains, to develop individualized models of risk, resilience, progression, and treatment response. We seek to answer key questions:How can multimodal integration improve early diagnosis and prognosis? What approaches best capture the gene–brain–behavior–environment interplay?Which computational and analytic strategies ensure robust, clinically actionable insights while accounting for cohort differences and population-level diversity?The Research Topic encompasses all areas where multimodal data integration can meaningfully advance understanding and clinical application in brain health but excludes work irrelevant to neurological basis, such as non-cognitive pain management. To gather further insights in the integration of multimodal data for brain health, we welcome articles addressing, but not limited to, the following themes:-Novel analytic approaches for multimodal data integration in neuroscience-Multimodal biomarkers of brain development and aging-Gene–environment–brain interactions and their clinical implications-Precision psychiatry and individualized treatment response prediction-Environmental, digital, and social determinants of brain health-Early detection and risk stratification of neurodevelopmental and neuropsychiatric conditions-Reproducibility, cohort harmonization, fairness, and interpretability in multimodal researchWe encourage article types including original research, reviews, perspectives, and methodological contributions that contribute to a more predictive, precise, and personalized approach to human brain health. Topic editors: Charisse N Winston, Xinglin Zeng, Yuwen He, Lin Hua Source: https://www.frontiersin.org/research-topics/83656/multimodal-data-integration-for-precision-brain-health