Year: 2025
Type: preprint
Abstract: Background Apathy, depression, and anhedonia are clinically overlapping constructs, which hinders diagnostic clarity and treatment development. This study aimed to comprehensively characterise these syndromes to identify a core set of non-redundant symptoms that maximally dissociate them and to investigate the psychological nature of key distinguishing features.Methods Data from seven datasets (N=4,578) of healthy individuals and patients with major depressive disorder were analysed using the Apathy Motivation Index, Beck Depression Inventory, and Snaith-Hamilton Pleasure Scale. A machine-learning algorithm identified the most informative, non-redundant items for dissociating "pure" apathy, depression, and anhedonia. The nature of emotional apathy was further investigated with follow-up studies.Results Although substantial symptom overlap existed, "pure" syndromes were present. Factor analysis revealed a robust five-factor structure, separating depression, anhedonia, and three distinct apathy domains (behavioural, social, emotional). Machine learning identified 10 core symptoms that differentiated the pure syndromes with high accuracy (AUC >0.90) and could also identify well the presence of each syndrome in individuals suffering from two or more syndromes. Emotional apathy negatively correlated with depression and was specifically associated with reduced affective empathy and a diminished sensitivity to the intensity of negative facial emotions, rather than with alexithymia or antidepressant-induced emotional blunting.Conclusions Apathy, depression, and anhedonia are dissociable constructs with distinct symptom signatures. Emotional apathy is a unique dimension which provides a novel target for research. A 10-item Apathy-Depression-Anhedonia Measure (ADAM) developed here provides a pragmatic tool for rapid, precise phenotyping to guide more personalised therapeutic strategies.
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Funders: Wellcome Trust, UK Research and Innovation, National Institute for Health and Care Research, Leverhulme Trust, Anhui Medical University, Jacobs Foundation, Medical Research Council, Engineering and Physical Sciences Research Council, Natural Science Foundation of Anhui Province, HORIZON EUROPE European Research Council
Awards: G1416707638, Neurocomputational mechanisms of prosocial behaviour in health, development and disorder, The neural and cognitive mechanisms of adaptive and maladaptive emotion-behaviour interactions in naturalistic tasks, PROFUSE: Fusing brain and computational mechanisms of prosocial behaviour across health, development and disorder, Human brain mechanisms underlying motivation and apathy, G58207276, A Neural and Computational Framework of the Effort Paradox, G6601929330
OpenAlex ID: W4412673294