Seeking Optimal Region-Of-Interest (ROI) Single-Value Summary Measures for fMRI Studies in Imaging Genetics

Area of Science:

• Neuroimaging Genetics
• Computational Neuroscience
• Psychiatric Genetics

Background:

• Genome-wide association (GWA) studies in imaging genetics aim to identify genes influencing brain structure, chemistry, and function.
• Whole-brain voxel-wise analysis is computationally intensive and lacks statistical power due to stringent multiple comparisons corrections.
• Functional magnetic resonance imaging (fMRI) studies often involve focal brain activations, allowing for region-of-interest (ROI) based analyses.

Purpose of the Study:

• To evaluate the sensitivity and reliability of different single-value ROI summary measures in imaging genetics.
• To provide guidance for future research in optimizing analysis strategies for genome-wide imaging genetics.

Main Methods:

• Tested four different fMRI databases.
• Compared various single-value ROI summary measures.
• Conducted analyses across patient (schizophrenia), sibling, and control groups, as well as across genotype groups.

Main Results:

• Identified four summary measures that are more powerful than others.
• Measures representing values from the top most-activated voxels within an ROI demonstrated higher reliability in detecting group differences.
• These optimized measures generated greater effect sizes compared to alternative approaches.

Conclusions:

• Single-value summary measures, particularly those from peak-activation voxels within ROIs, offer a more efficient and powerful approach for genome-wide imaging genetics studies.
• This method enhances the ability to detect genetic effects on brain function, especially in the context of psychiatric disorders.
• The findings suggest a refined strategy for analyzing fMRI data in genetic association studies, improving statistical power and reliability.