Dissociating Apathy, Depression and Anhedonia: A Machine Learning Analysis of Core Distinguishing Features
This study analyzed data from 4,578 participants across seven datasets to identify core symptoms that differentiate apathy, depression, and anhedonia, which are clinically overlapping conditions. Using machine learning and validated assessment scales, researchers identified a robust five-factor structure revealing three distinct apathy domains (behavioral, social, emotional) separate from depression and anhedonia, with 10 core symptoms achieving >90% accuracy in differentiating pure syndromes.