ArticleBiological psychiatry2024
Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample.
Article in Biological psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 23 citations in OpenAlex.
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry.Biological psychiatry · 2026Review
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- Identifying transdiagnostic neurobiomarkers for precision major psychiatric disordersJournal of translational internal medicine · 2026Article
- Associations Between Thalamic Gray Matter Volume and Psychomotor Symptoms in Major Depressive Disorder: A Voxel-Based Morphometry Study.Neuropsychiatric disease and treatment · 2026Article
- From Monoamines to Systems Psychiatry: Rewiring Depression Science and Care (1960s-2025).Biomedicines · 2025Article
- Heterogeneity Matters: Parsing One-to-One and Many-to-One Brain-Symptom Mappings in Depression.Biological psychiatry · 2025Article
- Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.Cell reports. Medicine · 2025Article
- Major depressive disorder on a neuromorphic continuum.Nature communications · 2025Article
- Structural neural plasticity evoked by rapid-acting antidepressant interventions.Nature reviews. Neuroscience · 2025Review
- The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research.Biological psychiatry global open science · 2025Review
- Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Neuroimaging for precision medicine in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder.EBioMedicine · 2024Article
- Article
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundRecent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.
methodsWe used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.
resultsThe optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.
conclusionsOur results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.
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