Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.

Autor: Ging-Jehli NR; Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island. Electronic address: nadja@gingjehli.com., Kuhn M; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts., Blank JM; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts., Chanthrakumar P; Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island; Warren Alpert Medical School of Brown University, Providence, Rhode Island., Steinberger DC; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts., Yu Z; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Herrington TM; Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts., Dillon DG; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts., Pizzagalli DA; Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts., Frank MJ; Carney Institute for Brain Science, Department of Cognitive, Linguistic, & Psychological Sciences, Brown University, Providence, Rhode Island.
Jazyk: angličtina
Zdroj: Biological psychiatry. Cognitive neuroscience and neuroimaging [Biol Psychiatry Cogn Neurosci Neuroimaging] 2024 Jul; Vol. 9 (7), pp. 726-736. Date of Electronic Publication: 2024 Feb 23.
DOI: 10.1016/j.bpsc.2024.02.005
Abstrakt: 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.)
Databáze: MEDLINE
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