Transcriptomic and neuroimaging data integration enhances machine learning classification of schizophrenia.

ABSTRACT:

Background

Schizophrenia is a polygenic disorder associated with changes in brain structure and function. Integrating macroscale brain features with microscale genetic data may provide a more complete overview of the disease etiology and may serve as potential diagnostic markers for schizophrenia.

Objective

We aim to systematically evaluate the impact of multi-scale neuroimaging and transcriptomic data fusion in schizophrenia classification models.

Methods

We collected brain imaging data and blood RNA sequencing data from 43 patients with schizophrenia and 60 age- and gender-matched healthy controls, and we extracted multi-omics features of macroscale brain morphology, brain structural and functional connectivity, and gene transcription of schizophrenia risk genes. Mu

SUBMITTER: Wang M 

PROVIDER: S-EPMC11061866 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature