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34 Neurocomputational Mechanisms of Social Reward Processing in Combat-Exposed Veterans
- Alex F. Skupny, Danielle N. Dun, Katia M. Harle, Alan N. Simmons
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 823-824
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Objective:
Combat exposure is associated with higher rates of depressive symptoms, including anhedonia (i.e., a reduced ability to seek and experience rewards) and feelings of social disconnectedness. While these symptoms are commonly documented in combat-exposed Veterans following deployment, the cognitive mechanisms underlying this pathology is less well understood. Computational modeling can provides detailed mechanistic insights into complex cognition, which may be particularly useful to understand how social reward processing is altered following combat exposure. Here, we use a Bayesian learning model framework to address this question.
Participants and Methods:Thirty-three Operation Enduring Freedom (OEF)/ Operation Iraqi Freedom (OIF)/Operation New Dawn (OND) Veterans (25 Male, 8 Female) between the ages of 18-65 years old (M = 41.61, SD = 10.49) participated in this study. In both classic/monetary and social reward conditions, participants completed a 2-arm bandit task, in which they must choose on each trial between two options (i.e., slot machine vs social partner) with unknown reward rates. While they received monetary outcomes in the classic condition, participants received compliments from different fictitious partners in the social condition. We first compared a learning-independent Win-stay/Lose-shift (WSLS) heuristic and either a Rescorla-Wagner Q-learning or a Bayesian learning model (Dynamic Belief Model/DBM) paired with a Softmax reward maximization policy. DBM+Softmax provided the best fit of the data for most participants (31/33). Individual DBM parameters of prior reward expectation, reward learning (i.e., perceived stability of reward rates), and Softmax reward maximization were estimated and compared across conditions.
Results:Participants did not differ in their reward learning parameters across monetary and social conditions (t(30)= -0.70, p = 0.490), suggesting similar perception of reward stability in both modalities. However, higher Bayesian prior mean (i.e., initial belief of reward rate; t(30)= -2.31, p = 0.028, d=0.42) and greater reward maximization (i.e., Softmax parameter; t(30)= -2.26, p = 0.031, d=0.41) were observed in response to social vs monetary rewards. In the social reward condition, higher self-reported social connectedness was associated with greater model fit of our DBM model (i.e., smaller Bayesian Information Criterion/BIC; r = -0.38, p = 0.041). In this condition, those expecting higher reward rates when initiating reward exploration (those with higher DBM prior mean) endorsed lower self-esteem (Spearman's ρ = -0.43, p = 0.078) and lower positive affect (ρ = -0.32, p = 0.078).
Conclusions:A Bayesian learning modeling framework can characterize mechanistic differences in the processing of social vs non-social reward among combat-exposed Veterans. Individuals with higher social connectedness were more model-based in their performance, consistent with the notion that they are more likely to estimate and anticipate how much social peers have to offer. Combat-exposed individuals with lower self-esteem and positive affect appear to have higher initial expectations of reward from unknown partners, which could reflect greater need for mood and/or self-esteem repair in those individuals. Overall, Bayesian modeling of social reward behavior provides a useful quantitative framework to predict clinically relevant construct of functional outcomes in military populations.
Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans
- Daniel M. Stout, Katia M. Harlé, Sonya B. Norman, Alan N. Simmons, Andrea D. Spadoni
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- Journal:
- Psychological Medicine / Volume 53 / Issue 2 / January 2023
- Published online by Cambridge University Press:
- 30 April 2021, pp. 332-341
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Background
Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.
MethodsIn 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.
ResultsWe identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.
ConclusionsData-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions – setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.