Genetic mechanisms underlying the associations between brain structure and mental health

Brain-wide association studies have linked brain structure to complex mental health phenotypes, yet the genetic underpinnings of these effects remain largely unclear. Here, we leverage multimodal neuroimaging, behavioral and genetic data from the UK Biobank to develop an integrative framework combining brain-wide association studies and GWAS to elucidate how genetic variants affect the relationship between brain structure and mental health. Our multivariate brain-wide association study identifies two reproducible brain structural modes: a cerebellar-dominant mode and a cortical-dominant mode that explain individual differences in mental health measures.

Using GWAS, we subsequently identify 30 and 15 independent significant variants respectively for each mode, indicating distinct genetic architectures with no signal overlap. Post-GWAS analyses indicate distinct biological pathways: the cerebellar-dominant mode is enriched for genes involved in chromatin organization and epigenetic regulation, while the cortical-dominant mode is linked to neurotransmitter metabolism. Moreover, we identify shared genetic influences between the brain modes and both mental health and cognitive traits, as well as causal effects of the brain modes on these outcomes.

We validate the framework in the independent HCP cohort, supporting the cross-cohort generalizability of the identified brain–behavior and genetic associations. This research has been conducted using the UK Biobank Resource under application number 85139. B.L. is a registered Lead Collaborator under this application.

This study also used data provided by the Human Connectome Project, WU-Minn Consortium (principal investigators: D. We thank the participants and investigators of the UK Biobank and Human Connectome Project. This work was supported by the Natural Science Foundation of China (grant 82425024 and 32541013), Brain Science and Brain-like Intelligence Technology—National Science and Technology Major Project (2021ZD0200500), and the Fundamental Research Funds for the Central Universities.

These authors contributed equally: Ke Hu, Xiaohan Tian. China Mobile Research Institute, Beijing, China Ke Hu, Tingting Zhang, Hao Zhang & Xiaoguang Zhang State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China Institute of Information and Artificial Intelligence Technology, Beijing Academy of Science and Technology, Beijing, China IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China Beijing Normal University Center for Health and Aging, Beijing, China The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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