Cortical thickness predicts the first onset of major depression in adolescence
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Given the increasing prevalence of Major Depressive Disorder and recent advances in preventative treatments for this disorder, an important challenge in pediatric neuroimaging is the early identification of individuals at risk for depression. We examined whether machine learning can be used to predict the onset of depression at the individual level. Thirty‐three never‐disordered adolescents (10–15 years old) underwent structural MRI. Participants were followed for 5 years to monitor the emergence of clinically significant depressive symptoms. We used support vector machines (SVMs) to test whether baseline cortical thickness could reliably distinguish adolescents who develop depression from adolescents who remained free of any Axis I disorder. Accuracies from subsampled cross‐validated classification were used to assess classifier performance. Baseline cortical thickness correctly predicted the future onset of depression with an overall accuracy of 70% (69% sensitivity, 70% specificity; p = 0.021). Examination of SVM feature weights indicated that the right medial orbitofrontal, right precentral, left anterior cingulate, and bilateral insular cortex contributed most strongly to this classification. These findings indicate that cortical gray matter structure can predict the subsequent onset of depression. An important direction for future research is to elucidate mechanisms by which these anomalies in gray matter structure increase risk for developing this disorder.
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In a 5-year prospective study of never-depressed daughters aged 10-15 with a mother with depression, lower cortical thickness in the right medial orbitofrontal/ventromedial prefrontal cortex and larger perigenual anterior cingulate cortex cortical thickness emerged as two of the top three neural features predicting the first episode of major depressive disorder.
“Specifically, lower cortical thickness in the right medial OFC/vmPFC (but larger pgACC cortical thickness) emerged as two of the top three neural features predicting the first episode of MDD [87].”
From Prefrontal cortex and depression (2022) · cites this paper as [87]
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- Authors
- Foland‐Ross, L. C., Sacchet, M. D., Prasad, G., Gilbert, B., Thompson, P. M., & Gotlib, I. H.
- Affiliations
- Stanford University (US)
- University of Southern California (US)
- Corresponding author
- Foland‐Ross, Lara C.
- Venue
- International Journal of Developmental Neuroscience
- Publisher
- Wiley
- Published
- 2015-08-24
- Volume
- 46
- Issue
- 1
- Pages
- 125-131
- ISSN
- 0736-5748 (ISSN-L)
- 1873-474X
- PMC
- PMC4604750
- OpenAlex
- W1195446513
- Cited by
- 109
- References
- 69
- Citations by year
- 2026: 2 · 2025: 11 · 2024: 7 · 2023: 9 · 2022: 12 · 2021: 22 · 2020: 8 · 2019: 10 · 2018: 9 · 2017: 11 · 2016: 8
- FWCI
- 5.60
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Developmental Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Mental Health (MH74849, MH090617, MH020016)
- National Alliance for Research on Schizophrenia and Depression
- National Science Foundation (0801700, DGE‐1147470)
- Hope for Depression Research Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- major depressive disorder, adolescent depression, cortical thickness, gray matter structure, medial orbitofrontal cortex, anterior cingulate cortex, insula, precentral cortex, structural MRI, support vector machine, risk prediction, neuroimaging biomarkers
- MeSH terms
- Machine Learning, Adolescent, Cerebral Cortex, Child, Depressive Disorder, Major, Major Depressive Disorder, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Predictive Value of Tests, Psychiatric Status Rating Scales, Sensitivity and Specificity
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