Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.

ABSTRACT:

Background

Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive models for BD based on data from brain imaging are expanding but have often been limited using only a single modality and the exclusion of the cerebellum, which may be relevant in BD.

Methods

In this study, we sought to improve ML classification of BD by combining information from structural, functional, and diffusion-weighted imaging. Participants (108 BD I, 78 control) with BD type I and matched controls were recruited into an imaging study. This dataset was randomly divided into training and testing sets. For each of the three mod

SUBMITTER: Deng LR 

PROVIDER: S-EPMC11560692 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature