Assessing social anhedonia in a transdiagnostic sample: Insights from a computational psychiatry lens.

Anhedonia, a reduced ability to experience positive affect and seek rewards, is present across many psychiatric disorders, notably among individuals who experienced trauma. Within the social domain, anhedonia manifests as an altered sense of belonging and social isolation and is associated with poorer clinical outcomes. Yet, mechanistic operationalizations of social anhedonia are lacking, limiting our understanding of the relationship between these mechanisms and affective symptoms. To address these questions, we developed a social reward exploration task which was administered to a transdiagnostic sample of trauma-exposed Veterans (N = 33) while they underwent functional magnetic resonance imaging. The goal was to maximize compliments from two unknown partners, as participants were told these partners selected compliments based on seeing their picture. A Bayesian reinforcement learning modeling approach was used to extract cognitive and neural markers of compliment (reward) exploration. To address these questions, we developed a social reward exploration task which was administered to a transdiagnostic sample of trauma-exposed Veterans (N = 33) while they underwent functional magnetic resonance imaging. The goal was to maximize compliments from two unknown partners, as participants were told these partners selected compliments based on seeing their picture. A Bayesian reinforcement learning modeling approach was used to extract cognitive and neural markers of compliment (reward) exploration. Higher social connectedness (β = 0.51; 95 % CI=[0.11,0.94]) and anxiety (β = 0.57; 95 % CI=[0.13,1.00]) were independently associated with more model-based choices of the partner they anticipated to be most complimenting. In the dorsal anterior cingulate cortex (ACC; z = 3.89, <i>p</i> .001) and left inferior parietal lobule (z = 3.96, <i>p</i> .001), neural responses to reward prediction errors (RPE) were more positive in response to compliment relative to non-compliment outcomes. Greater positive RPE ACC activation was associated with lower anxiety (β = -0.51; 95 % CI=[-0.99,-0.10]. Computational approaches to social reinforcement learning can help identify important neurocognitive differences in social reward sensitivity among individuals with complex affective profiles, such as trauma-exposed individuals. Understanding these differences may help develop new prediction and treatment tools for social anhedonia.

Authors

Institutions

Publication Details

Journal
Journal of Mood and Anxiety Disorders
Published
2024-09-17
DOI
https://doi.org/10.1016/j.xjmad.2024.100088
Primary Topic
Mental Health Research Topics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Assessing social anhedonia in a transdiagnostic sample: Insights from a computational psychiatry lens.

Katia M. Harlé, Alan N. Simmons, Andrea D. Spadoni, Jonathon R. Howlett et al.
Journal of Mood and Anxiety Disorders
Mental Health Research Topics
article

Assessing social anhedonia in a transdiagnostic sample: Insights from a computational psychiatry lens.

Katia M. Harlé, Alan N. Simmons, Andrea D. Spadoni, Jonathon R. Howlett, Danielle N. Dun
article en

Abstract

Anhedonia, a reduced ability to experience positive affect and seek rewards, is present across many psychiatric disorders, notably among individuals who experienced trauma. Within the social domain, anhedonia manifests as an altered sense of belonging and social isolation and is associated with poorer clinical outcomes. Yet, mechanistic operationalizations of social anhedonia are lacking, limiting our understanding of the relationship between these mechanisms and affective symptoms. To address these questions, we developed a social reward exploration task which was administered to a transdiagnostic sample of trauma-exposed Veterans (N = 33) while they underwent functional magnetic resonance imaging. The goal was to maximize compliments from two unknown partners, as participants were told these partners selected compliments based on seeing their picture. A Bayesian reinforcement learning modeling approach was used to extract cognitive and neural markers of compliment (reward) exploration. To address these questions, we developed a social reward exploration task which was administered to a transdiagnostic sample of trauma-exposed Veterans (N = 33) while they underwent functional magnetic resonance imaging. The goal was to maximize compliments from two unknown partners, as participants were told these partners selected compliments based on seeing their picture. A Bayesian reinforcement learning modeling approach was used to extract cognitive and neural markers of compliment (reward) exploration. Higher social connectedness (β = 0.51; 95 % CI=[0.11,0.94]) and anxiety (β = 0.57; 95 % CI=[0.13,1.00]) were independently associated with more model-based choices of the partner they anticipated to be most complimenting. In the dorsal anterior cingulate cortex (ACC; z = 3.89, <i>p</i> .001) and left inferior parietal lobule (z = 3.96, <i>p</i> .001), neural responses to reward prediction errors (RPE) were more positive in response to compliment relative to non-compliment outcomes. Greater positive RPE ACC activation was associated with lower anxiety (β = -0.51; 95 % CI=[-0.99,-0.10]. Computational approaches to social reinforcement learning can help identify important neurocognitive differences in social reward sensitivity among individuals with complex affective profiles, such as trauma-exposed individuals. Understanding these differences may help develop new prediction and treatment tools for social anhedonia.

Journal of Mood and Anxiety DisordersVol. 8
University of California San Diego (US), VA San Diego Healthcare System (US)
U.S. Department of Veterans Affairs, Center of Excellence for Stress and Mental Health, Veterans Affairs San Diego Healthcare System
Reduced inequalities
Openalex Percentile: Top 81%
Mental Health Research Topics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.