Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA.
Department of Physics, University of Houston, Houston, TX, USA.
Behav Genet. 2024 May;54(3):233-251. doi: 10.1007/s10519-024-10177-y. Epub 2024 Feb 10.
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.
脑影像遗传学分析是一个新兴的研究领域，旨在整合神经影像学模态的数据，这些模态可以描述大脑的结构或功能，以及遗传数据，这些数据可以捕捉基因组的结构和功能，以解释或预测正常（或异常）的大脑表现。脑影像遗传学研究为理解具有遗传病因的复杂脑相关疾病/障碍提供了巨大的潜力。尽管如此，由于典型的数据集融合了多种模态，每种模态都具有高维度、独特的相关性景观，而且通常统计信号与噪声比低，因此很难进行全脑全基因组的联合分析。在这篇综述中，我们从早期的大规模单变量方法开始，概述了脑影像遗传学方法的进展，目前已经发展到深度学习方法，强调了每种方法的优缺点，并随着该领域的发展进行了阐述。最后，我们讨论了该领域的一些遗留挑战和前景。
