TY - JOUR
T1 - Multifactorial prediction of depression diagnosis and symptom dimensions
AU - McNamara, Mary E.
AU - Shumake, Jason
AU - Stewart, Rochelle A.
AU - Labrada, Jocelyn
AU - Alario, Alexandra
AU - Allen, John J.B.
AU - Palmer, Rohan
AU - Schnyer, David M.
AU - McGeary, John E.
AU - Beevers, Christopher G.
N1 - Funding Information:
Funding for this study was provided by National Institute of Health (awards R56MH108650 , R21MH110758 , R33MH109600 , R01DA042742 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Department of Veterans Affairs.
Publisher Copyright:
© 2021
PY - 2021/4
Y1 - 2021/4
N2 - 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.
AB - 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.
KW - Classification
KW - Psychiatric control
KW - Statistical learning
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U2 - 10.1016/j.psychres.2021.113805
DO - 10.1016/j.psychres.2021.113805
M3 - Article
C2 - 33647705
AN - SCOPUS:85101517711
SN - 0165-1781
VL - 298
JO - Psychiatry research
JF - Psychiatry research
M1 - 113805
ER -