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Background: Deeper phenotyping may improve our understanding of depression. Because depression is heterogeneous, extracting cognitive signatures associated with severity of depressive symptoms, anhedonia, and affective states is a promising approach., Methods: Sequential sampling models decomposed behavior from an adaptive approach-avoidance conflict task into computational parameters quantifying latent cognitive signatures. Fifty unselected participants completed clinical scales and the approach-avoidance conflict task by either approaching or avoiding trials offering monetary rewards and electric shocks., Results: Decision dynamics were best captured by a sequential sampling model with linear collapsing boundaries varying by net offer values, and with drift rates varying by trial-specific reward and aversion, reflecting net evidence accumulation toward approach or avoidance. Unlike conventional behavioral measures, these computational parameters revealed distinct associations with self-reported symptoms. Specifically, passive avoidance tendencies, indexed by starting point biases, were associated with greater severity of depressive symptoms (R = 0.34, p = .019) and anhedonia (R = 0.49, p = .001). Depressive symptoms were also associated with slower encoding and response execution, indexed by nondecision time (R = 0.37, p = .011). Higher reward sensitivity for offers with negative net values, indexed by drift rates, was linked to more sadness (R = 0.29, p = .042) and lower positive affect (R = -0.33, p = .022). Conversely, higher aversion sensitivity was associated with more tension (R = 0.33, p = .025). Finally, less cautious response patterns, indexed by boundary separation, were linked to more negative affect (R = -0.40, p = .005)., Conclusions: We demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Background: Major depressive disorder (MDD) is associated with poor recollection, but the neural mechanisms responsible for this deficit are unclear. Recollection is supported by interactions between the hippocampus and cortex that appear to be mediated by oscillatory activity in the theta band (4-7 Hz) and that are elicited during source memory retrieval. Therefore, we tested the hypothesis that evoked theta power during source memory retrieval would be reduced in MDD, as this would provide a physiological basis for deficient recollection in adults with depression., Methods: Morlet wavelets were applied to event-related potentials collected from 24 unmedicated adults with MDD and 24 healthy control adults during the retrieval of source and semantic memories. Whole-scalp analyses focused on group differences in evoked theta power., Results: There were no group differences in behavior. Nevertheless, from 400 to 799 ms, theta power was broadly reduced in adults with depression versus healthy adults. This reduction was observed during source and semantic retrieval. Parietal midline electrodes showed significantly reduced theta power during source-but not semantic-retrieval in adults with depression versus healthy adults in this interval. Furthermore, theta power over parietal midline sites from 400 to 799 ms was more strongly related to source memory accuracy in healthy adults versus adults with depression., Conclusions: Relative to healthy control adults, adults with depression showed reduced theta power during memory retrieval and a weaker relationship between parietal midline theta power and source memory accuracy. These findings indicate that abnormal theta signals may contribute to memory deficits in adults with MDD., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)