A genetically informed, group FMRI connectivity modeling approach: application to schizophrenia

Area of Science:

• Neuroimaging
• Genetics
• Computational Neuroscience

Background:

• Neuroimaging provides phenotypic data for genetic studies.
• Genotypes can inform brain connectivity patterns derived from functional magnetic resonance imaging (fMRI).

Purpose of the Study:

• To propose a framework for genetically informed group brain connectivity modeling.
• To integrate genotypic variability directly into brain connectivity analysis.

Main Methods:

• Subjects stratified by genotype.
• Group regularized regression model for brain connectivity using ROI time courses.
• Regression framework incorporates a penalty based on genotypic similarity.

Main Results:

• Simulations supported the approach, showing genetic influences on connectivity differences.
• Applied to resting-state fMRI data from schizophrenia and control subjects using DAOA SNPs.
• DAOA SNPs integration improved modeling of connectivity pattern diversity.
• Connectivity in specific brain regions (left putamen, right posterior cingulate, left middle frontal gyri) jointly modulated by DAOA genotypes and schizophrenia.

Conclusions:

• The proposed framework enables multimodal analysis integrating genotypic variability into brain connectivity.
• This approach enhances the understanding of genetic contributions to brain function and disorders like schizophrenia.