Leveraging stacked classifiers for exploring the role of hedonic processing between major depressive disorder and schizophrenia.

ABSTRACT:

Background

Anhedonia, a transdiagnostic feature common to both Major Depressive Disorder (MDD) and Schizophrenia (SCZ), is characterized by abnormalities in hedonic experience. Previous studies have used machine learning (ML) algorithms without focusing on disorder-specific characteristics to independently classify SCZ and MDD. This study aimed to classify MDD and SCZ using ML models that integrate components of hedonic processing.

Methods

We recruited 99 patients with MDD, 100 patients with SCZ, and 113 healthy controls (HC) from four sites. The patient groups were allocated to distinct training and testing datasets. All participants completed a modified Monetary Incentive Delay (MID) task, which yielded features categorized into five hedonic components, two reward conseque

SUBMITTER: Huang Y 

PROVIDER: S-EPMC12315667 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature