ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2024
Cognitive Signatures of Depressive and Anhedonic Symptoms and Affective States Using Computational Modeling and Neurocognitive Testing.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neurocognitive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Examining effects of effort and reward magnitude on effort-cost decision-making in people with major depressive disorder.Cognitive, affective & behavioral neuroscience · 2026Article
- A novel approach-avoidance task to study decision making under outcome uncertainty.Psychonomic bulletin & review · 2026Article
- Treatment-resistant recurrent unipolar and bipolar depression: associative learning abnormalities.Brain : a journal of neurology · 2025Article
- Optimizing the Color Shapes Task for Ambulatory Assessment and Drift Diffusion Modeling: A Factorial Experiment.JMIR formative research · 2025Article
- Computational Phenotyping of Effort-Based Decision Making in Unmedicated Adults With Remitted Depression.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Article
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundDeeper 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.
methodsSequential 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.
resultsDecision 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).
conclusionsWe demonstrated the utility of multidimensional computational phenotyping, which could be applied to clinical samples to improve characterization and treatment selection.
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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.