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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Identifying frequency-dependent imaging genetic associations via hypergraph-structured multi-task sparse canonical

Peilun Song¹, Xue Li², Xiuxia Yuan²

¹School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China.

Computers in Biology and Medicine

|February 9, 2024

Summary

This summary is machine-generated.

This study introduces a novel hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA) to uncover frequency-dependent genetic associations in brain imaging. The method effectively identifies shared and specific genetic and imaging biomarkers for brain disorders.

Keywords:

Brain imaging genetics Hypergraph Schizophrenia Sparse canonical correlation analysis

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Area of Science:

Neuroscience
Genetics
Medical Imaging

Background:

Identifying genetic variations linked to brain imaging phenotypes is crucial for understanding brain disorders.
Neuronal oscillations show frequency-dependent genetic modulation, necessitating exploration of these specific associations.

Purpose of the Study:

To develop a method for exploring frequency-dependent associations between multi-frequency brain imaging phenotypes and single-nucleotide polymorphisms (SNPs).
To identify frequency-shared and frequency-specific imaging genetic biomarkers.

Main Methods:

Developed hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA).
Created hypergraphs for imaging phenotypes and SNPs, incorporating high-order feature relationships.
Employed a multi-task learning framework to distinguish frequency-shared and specific elements.

Main Results:

HS-MTSCCA outperformed competing methods on synthetic data in terms of canonical correlation coefficients, weights, and cosine similarity.
The method demonstrated superior performance on a real schizophrenia dataset.
Identified frequency-shared and frequency-specific biomarkers provided meaningful insights.

Conclusions:

HS-MTSCCA is a powerful tool for brain imaging genetics research.
The approach effectively reveals complex, frequency-dependent genetic associations with brain imaging phenotypes.
The identified biomarkers can advance the understanding of brain disorder pathogenesis.