Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods

被引:0
作者
Connor L. Cheek
Peggy Lindner
Elena L. Grigorenko
机构
[1] University of Houston,Texas Institute for Evaluation, Measurement, and Statistics
[2] University of Houston,Department of Physics
[3] University of Houston,Department of Information Science Technology
[4] University of Houston,Department of Psychology
[5] Baylor College of Medicine,undefined
[6] Sirius University of Science and Technology,undefined
来源
Behavior Genetics | 2024年 / 54卷
关键词
Statistical analysis; Machine learning; Deep learning; Brain-imaging genomics; Brain-imaging genetic studies; Methodology;
D O I
暂无
中图分类号
学科分类号
摘要
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach’s strengths and weaknesses and elongating it with the field’s development. We conclude by discussing selected remaining challenges and prospects for the field.
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页码:233 / 251
页数:18
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