Evidence map›Paper›PMID 31176387›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2019

Functional and Optogenetic Approaches to Discovering Stable Subtype-Specific Circuit Mechanisms in Depression.

Logan Grosenick, Tracey C Shi, Faith M Gunning, Marc J Dubin, Jonathan Downar, Conor Liston

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Neuroimaging for precision medicine in psychiatry.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024
    Review
  7. Article
  8. Article
  9. Simple and Scalable Algorithms for Cluster-Aware Precision Medicine.Proceedings of machine learning research · 2024
    Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Longitudinal Canonical Correlation Analysis.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023
    Article
  15. Review
  16. Article
  17. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Logan GrosenickFeil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York; Department of Statistics, Columbia University, New York, New York; Simons Foundation, New York, New York.
Tracey C ShiFeil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
Faith M GunningFeil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
Marc J DubinFeil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York.
Jonathan DownarDepartment of Psychiatry, Toronto Western Hospital, Toronto, Ontario, Canada.
Conor ListonFeil Family Brain and Mind Research Institute and Department of Psychiatry, Weill Cornell Medicine, New York, New York. Electronic address: col2004@med.cornell.edu.

Funding

Efficacy of biomarker-guided rTMS for treatment resistant depressionR01MH118388 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI BHATI, MAHENDRA T, GUNNING, FAITH M · 2019 to 2025
$6.2M
Diagnostic and prognostic biomarkers for subtypes of addiction-related circuit dysfunctionR01DA047851 · NIDA · WEILL MEDICAL COLL OF CORNELL UNIV · PI GOLDSTEIN, RITA Z, LISTON, CONOR M · 2019 to 2022
$2.4M
Diagnostic Neuroimaging Biomarkers of Dysfunctional Valence Systems in Depression and AnxietyR56MH114976 · NIMH · WEILL MEDICAL COLL OF CORNELL UNIV · PI GUNNING, FAITH M, LISTON, CONOR M · 2018 to 2018
$724k
NIDA NIH HHS R01 DA047851NIMH NIH HHS R01 MH118388NIMH NIH HHS R56 MH114976
6 · The paper itself

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.

Indexed as

AdultBrainBrain MappingFemaleHumansMagnetic Resonance ImagingMajor Depressive DisorderMaleNeural PathwaysOptogeneticsBiomarkersDepressionDepression subtypesMachine learningNeuroimagingOptogenetic fMRI

Identifiers

PMID31176387
PMCPMC6788795

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.