The neuroimaging correlates of depression established across six large-scale population datasets
Issued Date
2026-01-01
Resource Type
eISSN
27316076
Scopus ID
2-s2.0-105044474279
Journal Title
Nature Mental Health
Rights Holder(s)
SCOPUS
Bibliographic Citation
Nature Mental Health (2026)
Suggested Citation
Hamilton K.M., Luo X., Easley T., Ahmad F., Guo T., Jarukasemkit S., Modi H., Naranjo Rincón S., Shelton C., Stahl L., Wang Z., Zhu Y., Lenzini P., Barch D.M., Sheline Y.I., Hannon K., Bijsterbosch J.D. The neuroimaging correlates of depression established across six large-scale population datasets. Nature Mental Health (2026). doi:10.1038/s44220-026-00680-y Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/118044
Title
The neuroimaging correlates of depression established across six large-scale population datasets
Corresponding Author(s)
Other Contributor(s)
Abstract
Depression has been linked to reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks. However, recent meta-analyses have failed to identify significant converging correlates of depression across the literature such that a conclusive mapping of the neuroimaging correlates of depression remains elusive. We leveraged 23,417 participants across 6 population datasets to comprehensively establish the neuroimaging correlates of depression. We found reductions in gray matter volume/cortical surface area associated with depression in the frontal cortex, anterior cingulate and insula, confirming previous studies showing the importance of prefrontal and default mode regions in depression. Our findings demonstrate multiple surprising results, including a lack of depression correlates in subcortical brain regions and significant depression correlates in somatomotor and visual regions. Overall, these results shed new light on key brain regions involved in the pathophysiology of depression, updating our understanding of the neuroimaging correlates of depression symptoms.