ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019
Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A technical review of canonical correlation analysis for neuroscience applications.Human brain mapping · 2020Pooled it
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Developing Clinically Interpretable Neuroimaging Biotypes in Psychiatry.Biological psychiatry · 2026Review
- Resolving heterogeneity in first-episode and drug-naive major depressive disorder based on individualized structural covariance network: evidence from the REST-meta-MDD consortium.Psychological medicine · 2025Article
- Comparing Data-Driven Subtypes of Depression Informed by Clinical and Neuroimaging Data: A Registered Report.Biological psychiatry global open science · 2025Article
- Neuroimaging for precision medicine in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample.Biological psychiatry · 2024Article
- Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety.Nature medicine · 2024Article
- Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.Proceedings of machine learning research · 2024Article
- Methods for and Use of Functional Magnetic Resonance Imaging in Psychiatry.Advances in neurobiology · 2024Review
- Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.Nature. Mental health · 2024Article
- Weakened effective connectivity between salience network and default mode network during resting state in adolescent depression.Frontiers in psychiatry · 2024Article
- Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.Molecular psychiatry · 2023Article
- Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes.Biological psychiatry · 2023Review
- Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder.Nature neuroscience · 2023Article
- New and emerging approaches to treat psychiatric disorders.Nature medicine · 2023Review
- Contribution of the Opioid System to the Antidepressant Effects of Fluoxetine.Biological psychiatry · 2022Article
- Differential Impact of Anxious Misery Psychopathology on Multiple Representations of the Functional Connectome.Biological psychiatry global open science · 2022Article
- Regional gene expression signatures are associated with sex-specific functional connectivity changes in depression.Nature communications · 2022Article
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Abstract
backgroundPreviously, we identified four depression subtypes defined by distinct functional connectivity alterations in depression-related brain networks, which in turn predicted clinical symptoms and treatment response. Optogenetic functional magnetic resonance imaging offers a promising approach for testing how dysfunction in specific circuits gives rise to subtype-specific, depression-related behaviors. However, this approach assumes that there are robust, reproducible correlations between functional connectivity and depressive symptoms-an assumption that was not extensively tested in previous work.
methodsFirst, we comprehensively reevaluated the stability of canonical correlations between functional connectivity and symptoms (N = 220 subjects) using optimized approaches for large-scale statistical hypothesis testing, and we validated methods for improving estimation of latent variables driving brain-behavior correlations. Having confirmed this necessary condition, we reviewed recent advances in optogenetic functional magnetic resonance imaging and illustrated one approach to formulating hypotheses regarding latent subtype-specific circuit mechanisms and testing them in animal models.
resultsCorrelations between connectivity features and clinical symptoms were robustly significant, and canonical correlation analysis solutions tested repeatedly on held-out data generalized. However, they were sensitive to data quality, preprocessing, and clinical heterogeneity, which can reduce effect sizes. Generalization could be markedly improved by adding L2 regularization, which decreased estimator variance, increased canonical correlations in left-out data, and stabilized feature selection. These improvements were useful for identifying candidate circuits for optogenetic interrogation in animal models.
conclusionsMultiview, latent-variable approaches such as canonical correlation analysis offer a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic functional magnetic resonance imaging holds promise for testing hypotheses regarding latent, subtype-specific mechanisms driving depressive symptoms and behaviors.
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