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Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder
Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM)...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136861/ https://www.ncbi.nlm.nih.gov/pubmed/33970903 http://dx.doi.org/10.1371/journal.pcbi.1008955 |
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author | Pedersen, Mads L. Ironside, Maria Amemori, Ken-ichi McGrath, Callie L. Kang, Min S. Graybiel, Ann M. Pizzagalli, Diego A. Frank, Michael J. |
author_facet | Pedersen, Mads L. Ironside, Maria Amemori, Ken-ichi McGrath, Callie L. Kang, Min S. Graybiel, Ann M. Pizzagalli, Diego A. Frank, Michael J. |
author_sort | Pedersen, Mads L. |
collection | PubMed |
description | Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders. |
format | Online Article Text |
id | pubmed-8136861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81368612021-06-02 Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder Pedersen, Mads L. Ironside, Maria Amemori, Ken-ichi McGrath, Callie L. Kang, Min S. Graybiel, Ann M. Pizzagalli, Diego A. Frank, Michael J. PLoS Comput Biol Research Article Adaptive behavior requires balancing approach and avoidance based on the rewarding and aversive consequences of actions. Imbalances in this evaluation are thought to characterize mood disorders such as major depressive disorder (MDD). We present a novel application of the drift diffusion model (DDM) suited to quantify how offers of reward and aversiveness, and neural correlates thereof, are dynamically integrated to form decisions, and how such processes are altered in MDD. Hierarchical parameter estimation from the DDM demonstrated that the MDD group differed in three distinct reward-related parameters driving approach-based decision making. First, MDD was associated with reduced reward sensitivity, measured as the impact of offered reward on evidence accumulation. Notably, this effect was replicated in a follow-up study. Second, the MDD group showed lower starting point bias towards approaching offers. Third, this starting point was influenced in opposite directions by Pavlovian effects and by nucleus accumbens activity across the groups: greater accumbens activity was related to approach bias in controls but avoid bias in MDD. Cross-validation revealed that the combination of these computational biomarkers were diagnostic of patient status, with accumbens influences being particularly diagnostic. Finally, within the MDD group, reward sensitivity and nucleus accumbens parameters were differentially related to symptoms of perceived stress and depression. Collectively, these findings establish the promise of computational psychiatry approaches to dissecting approach-avoidance decision dynamics relevant for affective disorders. Public Library of Science 2021-05-10 /pmc/articles/PMC8136861/ /pubmed/33970903 http://dx.doi.org/10.1371/journal.pcbi.1008955 Text en © 2021 Pedersen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Pedersen, Mads L. Ironside, Maria Amemori, Ken-ichi McGrath, Callie L. Kang, Min S. Graybiel, Ann M. Pizzagalli, Diego A. Frank, Michael J. Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title | Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title_full | Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title_fullStr | Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title_full_unstemmed | Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title_short | Computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
title_sort | computational phenotyping of brain-behavior dynamics underlying approach-avoidance conflict in major depressive disorder |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136861/ https://www.ncbi.nlm.nih.gov/pubmed/33970903 http://dx.doi.org/10.1371/journal.pcbi.1008955 |
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