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Misperceiving Momentum: Computational Mechanisms of Biased Striatal Reward Prediction Errors in Bipolar Disorder

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Biol Psychiatry Glob Open Sci. 2024 Apr 30;4(4):100330. doi: 10.1016/j.bpsgos.2024.100330. eCollection 2024 Jul.

ABSTRACT

BACKGROUND: Dysregulated reward processing and mood instability are core features of bipolar disorder that have largely been considered separately, with contradictory findings. We sought to test a mechanistic account that emphasizes an excessive tendency in bipolar disorder to enter recursive cycles in which reward perception is biased by signals that the environment may be changing for the better or worse.

METHODS: Participants completed a probabilistic reward task with functional magnetic resonance imaging. Using an influential computational model, we ascertained whether participants with bipolar disorder (n = 21) showed greater striatal tracking of momentum-biased reward prediction errors (RPEs) than matched control participants (n = 21). We conducted psychophysiological interaction analyses to quantify the degree to which each group modulated functional connectivity between the ventral striatum and left anterior insula in response to fluctuations in momentum.

RESULTS: In participants with bipolar disorder, but not control participants, the momentum-biased RPE model accounted for significant additional variance in striatal activity beyond a standard model of veridical RPEs. Compared with control participants, participants with bipolar disorder exhibited lower insular-striatal functional connectivity modulated by momentum-biased RPEs, an effect that was more pronounced as a function of current manic symptoms.

CONCLUSIONS: Consistent with existing theory, we found evidence that bipolar disorder is associated with a tendency for momentum to excessively bias striatal tracking of RPEs. We identified impaired insular-striatal connectivity as a possible locus for this propensity. We argue that computational psychiatric approaches that examine momentary shifts in reward and mood dynamics have strong potential for yielding new mechanistic insights and intervention targets.

PMID:39132577 | PMC:PMC11313182 | DOI:10.1016/j.bpsgos.2024.100330

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