Toward a Quantification of Anhedonia: Unified Matching Law and Signal Detection for Clinical Assessment and Drug Development.

Abstract

Anhedonia, the loss of pleasure from previously rewarding activities, is a core symptom of several neuropsychiatric conditions, including major depressive disorder (MDD). Despite its transdiagnostic relevance, no effective therapeutics exist to treat anhedonia. This is due, in part, to inconsistent assays across clinical populations and laboratory animals, which hamper treatment development. To bridge this gap, recent work has capitalized on two long-standing research domains dedicated to quantifying responsivity to antecedents and consequences across species: the generalized matching law and signal detection theory. This review traces the integration of these quantitative frameworks, which yielded two empirically derived metrics: response bias (log b) and task discriminability (log d). These metrics serve as primary dependent variables in the Probabilistic Reward Task (PRT). In this computerized task, subjects make visual discriminations and probabilistic contingencies are arranged such that correct responses to one alternative are rewarded more often (rich) than correct responses to the other (lean). Under these conditions, healthy participants consistently develop a response bias in favor of the rich alternative, whereas participants with MDD exhibit blunted biases, which correlate with current and predict future anhedonia. Given the correspondence between anhedonic phenotypes and response bias, the PRT has been reverse-translated for rodents and nonhuman primates. Orderly log b and log d values have been observed across diverse clinical populations and laboratory animals. In addition, pharmacological challenges have produced similar outcomes across species. Taken together, this quantitative framework offers a highly translational approach to assaying reward responsiveness to accelerate treatment development for neuropsychiatric disorders involving anhedonia.