Machine learning framework for depression subtype grouping

Integrating high-resolution imaging and clinical symptom analysis via correlation and clustering

Bibliographic Data

ID15528836
AuthorsGaurav Verma (0000-0002-1393-1053, Icahn School of Medicine at Mount Sinai, corresponding author), Yael Jacob (0000-0003-3278-9264, Icahn School of Medicine at Mount Sinai), Laurel S Morris (Icahn School of Medicine at Mount Sinai), Xin Xing (0000-0002-4766-4926, University of Nebraska at Omaha), Bradley N Delman (0000-0002-9792-8942, Icahn School of Medicine at Mount Sinai), James W Murrough (0000-0001-6286-1242, James J. Peters VA Medical Center), Ai-Ling Lin (Institute of Informatics of the Slovak Academy of Sciences), Priti Balchandani (0000-0001-7709-5835, Icahn School of Medicine at Mount Sinai)
Year2026
Volume17
Publication date2026-03-24
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueFrontiers in Psychiatry (JOURNAL)
Journal identifiersISSN: 1664-0640 • E-ISSN: 1664-0640
PublisherFrontiers Media (PUBLISHER • CH)
DOI10.3389/fpsyt.2026.1747824
OpenAlexW7140213357
LanguageEN
References cited37

Introduction Major depressive disorder (MDD) affects approximately one in six individuals over their lifetime, with many patients experiencing treatment-resistant depression, characterized by inadequate or insufficient response from at least one antidepressant treatment. Current classification strategies for depression rely primarily on clinical assessment of symptom severity, which are prone to reader bias and test-retest variability. Moreover, these symptom-based subtypes have shown limited utility in predicting treatment response. This study introduces a data-driven, non-biased classification framework that integrates clinical features with high-resolution magnetic resonance imaging (MRI)-derived features. Using canonical correlation analysis (CCA) and hierarchical clustering, the approach identifies distinct subtypes of MDD, offering a more objective and potentially predictive alternative to traditional methods. Materials & methods Sixty-four participants with MDD currently experiencing a major depressive episode and not currently undergoing treatment completed a battery of 11 clinical symptom severity assessments and scanned with 7T T1-weighted MRI with parameters: TE/TR = 3.62/6000 ms, 320x240x240 array size with 224x168 mm 2 field-of-view (FOV) for voxel dimensions of 0.7 mm 3 isotropic. The images were automatically segmented using the FreeSurfer 6.0 package and 87 resulting imaging features, along with 11 clinical measures were processed through CCA to derive highly-correlated clinical-imaging phenotypes. An analysis using the Sillhouette and other methods determined an optimal number of clusters for this dataset. Participants with MDD were plotted on axes consisting of highly correlated clinical-imaging phenotypes derived from CCA and subsequently grouped through hierarchical clustering. Results CCA identified three highly correlated (r > 0.9) clinical-imaging variable pairs. The first, an anhedonia-related phenotype, showed high loadings from anhedonia severity and brainstem features. The second phenotype was associated with childhood trauma and anhedonia, with the right frontal pole as the primary imaging feature. The third phenotype linked general distress and perceived stress with the right superior temporal lobe. Hierarchical clustering along these canonical axes revealed two distinct clusters: one characterized by high childhood trauma scores and the other showing scores comparable to healthy controls. Conclusion Taken together, this study presents a novel ML framework for classifying depression using CCA and clustering

Canonical correlation · Cluster analysis · Correlation · Depression (economics · Functional magnetic resonance imaging · Major depressive disorder · Voxel · Digital Mental Health Interventions · Emotion and Mood Recognition · Treatment of Major Depression

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Open DOIOpen Access