The neuroimaging correlates of depression established across six large-scale population datasets
8 affiliations
- 1Department of Radiology, Washington University School of Medicine, Saint Louis, MO USA
- 2Department of Mathematics, Washington University, Saint Louis, MO USA
- 3Cognitive Clinical and Computational Neuroscience (CCCN) Center of Excellence, Chulalongkorn University, Bangkok, Thailand
- 4Division of Neurology, Department of Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
- 5Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO USA
- 6Department of Psychological and Brain Sciences, Washington University, Saint Louis, MO USA
- 7Departments of Psychiatry, Neurology and Radiology Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA
- 8McLean Imaging Center, Harvard Medical School, Boston, MA USA
Abstract
Neuroimaging data offers noninvasive insights into the structural and functional organization of the brain and is therefore commonly used to study the neuroimaging correlates of depression. To date, a substantial body of literature has suggested reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks linked to depression. 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. Here we leveraged 23,417 participants across six 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 prior 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, significant depression correlates in somatomotor and visual regions, and limited functional connectivity findings. 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. We anticipate that these insights will inform further research into the role of sensorimotor and visual regions in depression and into the impact of heterogeneity on functional connectivity correlates of depression.
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BibTeX
@article{hamilton2026neuroimaging,
title = {{The neuroimaging correlates of depression established across six large-scale population datasets}},
author = {Kassandra Miyoko Hamilton and Xiaoke Luo and Ty O. Easley and Fyzeen Ahmad and Thomas Guo and Setthanan Jarukasemkit and Hailey Modi and Samuel Naranjo Rincon and Cabria Shelton and Lyn Stahl and Zijian Wang and Zhu Yuling and Petra A. Lenzini and Deanna M. Barch and Yvette I. Sheline and Kayla Hannon and Janine Diane Bijsterbosch},
journal = {Nature Mental Health},
year = {2026},
month = jul,
doi = {10.1038/s44220-026-00680-y},
url = {https://doi.org/10.1038/s44220-026-00680-y},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - The neuroimaging correlates of depression established across six large-scale population datasets
AU - Kassandra Miyoko Hamilton
AU - Xiaoke Luo
AU - Ty O. Easley
AU - Fyzeen Ahmad
AU - Thomas Guo
AU - Setthanan Jarukasemkit
AU - Hailey Modi
AU - Samuel Naranjo Rincon
AU - Cabria Shelton
AU - Lyn Stahl
AU - Zijian Wang
AU - Zhu Yuling
AU - Petra A. Lenzini
AU - Deanna M. Barch
AU - Yvette I. Sheline
AU - Kayla Hannon
AU - Janine Diane Bijsterbosch
PY - 2026
DA - 2026/07/13
JO - Nature Mental Health
DO - 10.1038/s44220-026-00680-y
UR - https://doi.org/10.1038/s44220-026-00680-y
AB - Neuroimaging data offers noninvasive insights into the structural and functional organization of the brain and is therefore commonly used to study the neuroimaging correlates of depression. To date, a substantial body of literature has suggested reduced size of subcortical regions and abnormal functional connectivity in frontal and default mode networks linked to depression. 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. Here we leveraged 23,417 participants across six 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 prior 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, significant depression correlates in somatomotor and visual regions, and limited functional connectivity findings. 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. We anticipate that these insights will inform further research into the role of sensorimotor and visual regions in depression and into the impact of heterogeneity on functional connectivity correlates of depression.
SN - 2731-6076
ER -