Evidence map›Paper›PMID 33647705›Full record

ArticlePsychiatry research2021

Multifactorial prediction of depression diagnosis and symptom dimensions.

Mary E McNamara, Jason Shumake, Rochelle A Stewart, Jocelyn Labrada, Alexandra Alario, John J B Allen, Rohan Palmer, David M Schnyer, John E McGeary, Christopher G Beevers

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In one paragraph

Article in Psychiatry research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
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  8. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mary E McNamaraDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA. Electronic address: molly.mcnamara@utexas.edu.
Jason ShumakeDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.
Rochelle A StewartDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.
Jocelyn LabradaDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.
Alexandra AlarioDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.
John J B AllenDepartment of Psychology, University of Arizona, Tucson, Arizona, USA.
Rohan PalmerDepartment of Psychology, Emory University, Atlanta, Georgia, USA.
David M SchnyerDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.
John E McGearyVeterans Affairs, Providence RI and Department of Psychiatry and Human Behavior, Brown University School of Medicine, Providence, Rhode Island, USA.
Christopher G BeeversDepartment of Psychology and Institute for Mental Health Research, University of Texas at Austin, Austin, Texas, USA.

Funding

Development of Attention Bias Modification for DepressionR33MH109600 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI BEEVERS, CHRISTOPHER G, SCHNYER, DAVID M · 2017 to 2019
$2.0M
Integrative Prioritization of Alcohol and Drug-Addiction Related Genetic LociR01DA042742 · NIDA · EMORY UNIVERSITY · PI PALMER, ROHAN HUGH CRAIG · 2018 to 2022
$1.7M
Contribution of Genome-Wide Variation to Cognitive Vulnerability to DepressionR56MH108650 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI ALLEN, JOHN J., BEEVERS, CHRISTOPHER G · 2016 to 2016
$767k
Machine Learning and Personalized Prognosis for Depression TreatmentR21MH110758 · NIMH · UNIVERSITY OF TEXAS AT AUSTIN · PI BEEVERS, CHRISTOPHER G · 2016 to 2017
$430k
NIDA NIH HHS R01 DA042742NIMH NIH HHS R21 MH110758NIMH NIH HHS R33 MH109600NIMH NIH HHS R56 MH108650
6 · The paper itself

Abstract

While depression is a leading cause of disability, prior investigations of depression have been limited by studying correlates in isolation. A data-driven approach was applied to identify out-of-sample predictors of current depression from adults (N = 217) sampled on a continuum of no depression to clinical levels. The current study used elastic net regularized regression and predictors from sociodemographic, self-report, polygenic scores, resting electroencephalography, pupillometry, actigraphy, and cognitive tasks to classify individuals into currently depressed (MDE), psychiatric control (PC), and no current psychopathology (NP) groups, as well as predicting symptom severity and lifetime MDE. Cross-validated models explained 20.6% of the out-of-fold deviance for the classification of MDEs versus PC, 33.2% of the deviance for MDE versus NP, but -0.6% of the deviance between PC and NP. Additionally, predictors accounted for 25.7% of the out-of-fold variance in anhedonia severity, 65.7% of the variance in depression severity, and 12.9% of the deviance in lifetime depression (yes/no). Self-referent processing, anhedonia, and psychosocial functioning emerged as important differentiators of MDE and PC groups. Findings highlight the advantages of using psychiatric control groups to isolate factors specific to depression.

Indexed as

DepressionMajor Depressive DisorderAdultAnhedoniaHumansClassificationPsychiatric controlStatistical learning

Identifiers

PMID33647705
PMCPMC8042639

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Registered trials

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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.