Chen Jiayu, Liu Jingyu, Calhoun Vince D
The Mind Research Network, Albuquerque, NM 87106 USA.
The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA.
Proc IEEE Inst Electr Electron Eng. 2019 May;107(5):912-927. doi: 10.1109/JPROC.2019.2913145. Epub 2019 May 9.
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
影像基因组学专注于刻画基因组对神经生物学特征变异的影响，有望为阐明精神障碍的发病机制、改革诊断系统以及实现精准医疗带来希望。本文旨在全面呈现精神障碍神经影像基因组分析的现状，以及我们如何提高其向临床实践转化的潜力。该综述围绕三个视角展开。（a）朝着可靠性、可推广性和可解释性，我们在此总结多变量模型，并讨论使用这些方法的考量与权衡以及如何得出可靠的研究结果，以此作为进一步阐述的基础。（b）朝着改进诊断，我们在此概述构建维度性跨诊断模型的优势与挑战，以及影像基因组分析如何融入该框架以帮助解构异质性并实现对患者的最佳分层，从而为治疗规划提供更充分的信息。（c）朝着改进治疗。在此我们强调近期在阐明连接基因组风险与神经生物学异常的功能注释、检测基因组易感性和前驱神经发育变化以及识别用于预测治疗反应的影像基因组生物标志物方面所做的努力和取得的进展。通过概述挑战与前景，本综述有望推动采用多变量、维度性和跨诊断设计的影像基因组研究，以获得可推广且可解释的研究结果，从而促进个性化治疗的发展。
