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

Autor: Song P; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China., Li X; Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China., Yuan X; Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China., Pang L; Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China., Song X; Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450001, Henan, China; Biological Psychiatry International Joint Laboratory of Henan/Zhengzhou University, Zhengzhou, 450001, Henan, China., Wang Y; School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China. Electronic address: ieypwang@zzu.edu.cn.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2024 Mar; Vol. 171, pp. 108051. Date of Electronic Publication: 2024 Jan 29.
DOI: 10.1016/j.compbiomed.2024.108051
Abstrakt: Identifying complex associations between genetic variations and imaging phenotypes is a challenging task in the research of brain imaging genetics. The previous study has proved that neuronal oscillations within distinct frequency bands are derived from frequency-dependent genetic modulation. Thus it is meaningful to explore frequency-dependent imaging genetic associations, which may give important insights into the pathogenesis of brain disorders. In this work, the hypergraph-structured multi-task sparse canonical correlation analysis (HS-MTSCCA) was developed to explore the associations between multi-frequency imaging phenotypes and single-nucleotide polymorphisms (SNPs). Specifically, we first created a hypergraph for the imaging phenotypes of each frequency and the SNPs, respectively. Then, a new hypergraph-structured constraint was proposed to learn high-order relationships among features in each hypergraph, which can introduce biologically meaningful information into the model. The frequency-shared and frequency-specific imaging phenotypes and SNPs could be identified using the multi-task learning framework. We also proposed a useful strategy to tackle this algorithm and then demonstrated its convergence. The proposed method was evaluated on four simulation datasets and a real schizophrenia dataset. The experimental results on synthetic data showed that HS-MTSCCA outperforms the other competing methods according to canonical correlation coefficients, canonical weights, and cosine similarity. And the results on real data showed that HS-MTSCCA could obtain superior canonical coefficients and canonical weights. Furthermore, the identified frequency-shared and frequency-specific biomarkers could provide more interesting and meaningful information, demonstrating that HS-MTSCCA is a powerful method for brain imaging genetics.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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