Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.

ABSTRACT:

Background

Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomarkers might support diagnostic decisions. This study used machine learning on multimodal biobehavioral data from a sample of GAD, MD and healthy subjects to differentiate subjects with a disorder from healthy subjects (case-classification) and to differentiate GAD from MD (disorder-classification).

Methods

Subjects with GAD (n = 19), MD without GAD (n = 14), and healthy comparison subjects (n = 24) were included. The sample was matched regarding age, sex, handedness and education and free of psychopharmacological medication. Binary support vector machines were used within a nested leave-one-out cross-validatio

SUBMITTER: Hilbert K 

PROVIDER: S-EPMC5346520 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature