Talk title: A Computational Approach to Understanding Motivational Symptoms in Depression

Motivational symptoms of depression are debilitating and associated with poor clinical outcome, but the mechanisms underlying them are poorly understood. This talk will present data examining how cognitive processes related to effort-based decision making for reward are associated with depressive symptoms, using a computational approach. Results from two cross-sectional studies, including 250 participants (healthy volunteers, unmedicated depressed patients, first degree relatives and remitted depressed patients), will be presented. Participants completed a rewarded physical effort task using a grip squeeze device, and motivational symptoms were assessed through questionnaires. Data were analyzed using a hierarchical computational approach, with model parameters estimated in a Bayesian framework using sampling. In the non-clinical study (N=90), general depressive symptoms were associated with lower reward sensitivity (P<0.001), while anhedonia was related to a lack of willingness to engage in effortful responding (P<0.05). In the clinical study (N=180), current or past depression was associated with lower propensity to accept challenges, independent of reward or effort level. Preliminary results from a study examining the effect of L-Dopa administration on effort-based decision making (N=80) will be presented, suggesting a surprising but robust moderation of the pharmacological intervention by weight. These studies illuminate the cognitive mechanisms contributing to depressive symptoms relating to disrupted motivational processing.

Read more here:

Husain and Roiser 2018 https://www.nature.com/articles/s41583-018-0029-9

Halahakoon et al 2020 https://pubmed.ncbi.nlm.nih.gov/32725180/