For only reward learning, anhedonia was associated with model-derived learning parameters and neural learning signals.
Computational model-derived learning reinforcement could help reduce negative symptoms associated with most treatments for major depressive disorder (MDD).
A team, led by Vanessa M. Brown, PhD, Department of Psychology, Virginia Tech, determined the associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes following treatment for major depressive disorder.
“According to computational formalizations of reinforcement learning, expectations about the outcomes of choices are updated based on prediction errors,” the authors wrote. “This framework separates learning into computationally derived components associated with behaviorally and neurobiologically distinguishable processes.”
In the mixed cross-sectional-cohort study, the investigators examined 101 particiapnts from a volunteer sample with (n = 69) and without (n = 32) a depression diagnosis. The mean age of the patient population was 34.4 years old.
Each patient performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up between July 2011 and February 2017.
The researchers sought main outcomes of a priori hypotheses about the associations between components of reward-based and loss-based learning with depression symptoms, which were assessed using the computational model-based analyses of particiapnts’ choices assessed
They also assessed changes in both learning parameters and symptoms in a subset of participants who received cognitive behavioral therapy. A total 48 particiapnts—28 with depression and 20 without depression—received CBT and were included at follow-up.
Overall, the investigators were able to identify associations of learning with symptoms during reward learning and loss learning using computational model-based analyses of behavioral choices and nural data, respectively.
For only reward learning, anhedonia was associated with model-derived learning parameters (learning rate: posterior mean regression β = −0.14; 95% CrI, −0.12 to −0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02-0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = −2.10; P = .04).
For only loss learning, negative affect was linked to learning parameters (outcome shift: posterior mean regression β = −0.11; 95% CrI, −0.20 to −0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = −0.28; P = .005).
Another discovery was that CBT resulted in symptom improvement and the normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09-0.77).
“In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets,” the authors wrote.
MDD currently affects about 7% of the US population and treatment can be difficult due to significant symptom getegeneity.
The study, “Reinforcement Learning Disruptions in Individuals With Depression and Sensitivity to Symptom Change Following Cognitive Behavioral Therapy,” was published online in JAMA Psychiatry.