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Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association

Jin-Xing Liu¹, Shuang-Qing Wang², Cui-Na Jiao²

¹School of Computer Science, Qufu Normal University, Rizhao 276826, China; School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, 266113, China.

Methods (San Diego, Calif.)

|January 21, 2025

Summary

This summary is machine-generated.

This study introduces a new method, DHRSAA, to find links between genes and brain imaging data. It improves understanding of complex relationships for better biomarker discovery in neuroimaging genetics.

Keywords:

Brain Imaging Genetics Deep neural network Hyper-Laplacian regularized self-representation Sparse canonical correlation analysis

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Area of Science:

Neuroimaging Genetics
Computational Biology
Biostatistics

Background:

Brain imaging genetics investigates associations between genetic factors (e.g., single nucleotide polymorphisms/SNPs) and brain imaging quantitative traits (QTs).
Existing methods often overlook nonlinear genotype-phenotype correlations and higher-order relationships among subjects.
This limits the comprehensive understanding of complex genetic influences on brain structure and function.

Purpose of the Study:

To propose a novel method, deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA), for identifying genotype-phenotype associations.
To enhance the discovery of relevant biomarkers by considering nonlinear and higher-order relationships.
To improve the interpretability and biological significance of findings in brain imaging genetics.

Main Methods:

Utilized a deep neural network to capture nonlinear relationships among samples.
Employed self-representation learning with hyper-Laplacian regularization to reconstruct data and preserve local structure in high-dimensional embeddings.
Incorporated structural regularization to uncover relationships among SNPs and imaging QTs.

Main Results:

DHRSAA demonstrated superior canonical correlation coefficients compared to state-of-the-art methods.
The method identified clearer canonical weight patterns, indicating improved association detection.
Validated performance on real neuroimaging genetics data, highlighting its effectiveness.

Conclusions:

DHRSAA effectively identifies genotype-phenotype associations and relevant biomarkers in neuroimaging genetics.
The method's ability to model complex relationships enhances interpretability and biological significance.
DHRSAA represents a significant advancement for biomarker discovery in the field.