Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression Article Swipe
Logan Grosenick
,
Tracey C. Shi
,
Faith M. Gunning
,
Marc J. Dubin
,
Jonathan Downar
,
Conor Liston
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1101/498964
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1101/498964
Background: Using canonical correlation analysis (CCA), hierarchical clustering, and machine learning methods, we recently identified four subtypes of depression defined by distinct patterns of abnormal functional connectivity in depression-related brain networks, which in turn predicted differing clinical symptom profiles and individual differences in treatment response. However, whether and how dysfunction in specific circuits may give rise to specific depressive symptoms and behaviors remains unclear. Furthermore, this approach assumes that there are robust and stable canonical correlations between functional connectivity and depressive symptoms--an assumption that was not extensively tested in our earlier work. Methods: First, we comprehensively re-evaluate the stability of canonical correlations between functional connectivity and symptoms, using optimized approaches for large-scale statistical testing, and we validate methods for improving stability. Next, we illustrate one approach to formulating hypotheses regarding subtype-specific circuit mechanisms driving depressive symptoms and behaviors and then testing them in animal models using optogenetic fMRI. We review recent work in this field and describe one example of this approach. Results: Correlations between connectivity features and clinical symptoms are robustly significant, and CCA solutions tested repeatedly on held-out data generalize, but they are sensitive to data quality, preprocessing decisions, and clinical sample heterogeneity, which can reduce effect sizes. Generalization can be markedly improved by adding L2-regularization to CCA, which decreases variance, increases canonical correlations in left-out data, and stabilizes feature selection. This approach, in turn, can be used to identify candidate circuits for optogenetic interrogation in rodent models. Conclusions: Multi-view approaches like CCA are a conceptually useful framework for discovering stable patient subtypes by synthesizing multiple clinical and functional measures. Optogenetic fMRI holds substantial promise for testing hypotheses regarding subtype-specific mechanisms driving specific symptoms and behaviors in depression.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/498964
- https://www.biorxiv.org/content/biorxiv/early/2018/12/17/498964.full.pdf
- OA Status
- green
- Cited By
- 16
- References
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2903893930
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2903893930Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/498964Digital Object Identifier
- Title
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Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in DepressionWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-12-17Full publication date if available
- Authors
-
Logan Grosenick, Tracey C. Shi, Faith M. Gunning, Marc J. Dubin, Jonathan Downar, Conor ListonList of authors in order
- Landing page
-
https://doi.org/10.1101/498964Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2018/12/17/498964.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2018/12/17/498964.full.pdfDirect OA link when available
- Concepts
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Optogenetics, Canonical correlation, Computer science, Generalization, Correlation, Psychology, Machine learning, Artificial intelligence, Cognitive psychology, Neuroscience, Mathematics, Geometry, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
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2024: 1, 2023: 3, 2022: 5, 2021: 5, 2020: 1Per-year citation counts (last 5 years)
- References (count)
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1Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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