Abstract
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.
Recommended Citation
C. L. Cheek et al., "Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods," Behavior Genetics, vol. 54, no. 3, pp. 233 - 251, Springer, May 2024.
The definitive version is available at https://doi.org/10.1007/s10519-024-10177-y
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Brain-imaging genetic studies; Brain-imaging genomics; Deep learning; Machine learning; Methodology; Statistical analysis
International Standard Serial Number (ISSN)
1573-3297; 0001-8244
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Springer, All rights reserved.
Publication Date
01 May 2024
PubMed ID
38336922
Comments
Eunice Kennedy Shriver National Institute of Child Health and Human Development, Grant P50 HD052120