Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques.

ABSTRACT:

Background

Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.

Methods

In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the cl

SUBMITTER: Li H 

PROVIDER: S-EPMC7542439 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature