Linking Gene Sequences to Brain Connectivity Alterations in Schizophrenia

Abstract

Schizophrenia is a complex neuropsychiatric disorder characterized by altered brain connectivity patterns and strong genetic underpinnings. This study develops an intelligent computational system that integrates genomic data with functional magnetic resonance imaging to establish meaningful correlations between genetic variations in key schizophrenia-associated genes (DISC1, LRRTM1, DRD2) and altered brain connectivity patterns. The proposed methodology uses gene expression analysis combined with graph-based brain network analysis from fMRI data. Feature extraction involves constructing connectivity matrices and computing topological network measures such as clustering coefficients and global efficiency, while machine learning algorithms are trained to classify schizophrenia patients and identify at-risk individuals using integrated genomic and neuroimaging features. This research expects to deliver a comprehensive gene-brain analysis system capable of predicting schizophrenia risk based on these integrated profiles. It aims to identify novel biomarkers linking specific genetic variants to connectivity alterations in brain regions such as the prefrontal cortex and hippocampus. Anticipated outcomes include the development of interpretable visualizations, like brain maps and connectivity networks, offering insights into how key genes impact neural function. Ultimately, this interdisciplinary approach is designed to advance precision psychiatry by supporting earlier diagnosis and enabling more personalized treatment strategies.