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Journal Article

Citation

Cha J, Speaker S, Hu B, Altinay M, Koirala P, Karne H, Spielberg J, Kuceyeski A, Dhamala E, Anand A. J. Affect. Disord. 2020; 281: 303-311.

Copyright

(Copyright © 2020, Elsevier Publishing)

DOI

10.1016/j.jad.2020.12.037

PMID

33341013

Abstract

BACKGROUND: Many subjects with major depression (MDD) exhibit subthreshold mania symptoms (MDD+). This study investigated, for the first time, using emotional inhibition tasks, whether the neural organization of MDD+ subjects is more similar to bipolar depression (BDD) or to MDD subjects without subthreshold bipolar symptoms (MDD-).

METHOD: This study included 118 medication-free young adults (15 - 30 yrs.): 20 BDD, 28 MDD+, 41 MDD- and 29 HC subjects. Participants underwent fMRI during emotional and non-emotional Go/No-go tasks during which they responded for Go stimuli and inhibited response for happy, fear, and non-emotional (gender) faces No-go stimuli. Univariate linear mixed-effects (LME) analysis for group effects and multivariate Gaussian Process Classifier (GPC) analyses were conducted.

RESULTS: MDD- group compared to both the BDD and MDD+ groups, exhibited significantly lower activation in parietal, temporal and frontal regions (cluster-wise corrected p <0.05) for emotional inhibition conditions vs. non-emotional condition. GPC classification of emotional (happy + fear) vs. non-emotional response-inhibition activation pattern showed good discrimination between BDD and MDD- subjects (AUC: 0.70; balanced accuracy: 70% (corrected p = 0.018)) as well as between MDD+ and MDD- subjects (AUC: 0.72; balanced accuracy: 67% (corrected p = 0.045)) but less efficient discrimination between BDD and MDD+ groups (AUC: 0.68; balanced accuracy: 61% (corrected p = 0.273)). Notably, classification of the MDD- group was weighted for left amygdala activation pattern.

LIMITATIONS: Results also need to be tested in a different independent dataset.

CONCLUSION: Using an fMRI emotional Go-Nogo task, MDD- subjects can be discriminated from BDD and MDD+ subjects.


Language: en

Keywords

Machine learning; Bipolar Depression; Emotional response inhibition; fMRI; Subthreshold bipolar disorder Young adults

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