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从临床和遗传角度揭示精神分裂症有前景的神经影像学生物标志物

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives.

机构信息

Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.

Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China.

出版信息

Neurosci Bull. 2024 Sep;40(9):1333-1352. doi: 10.1007/s12264-024-01214-1. Epub 2024 May 4.

Abstract

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

摘要

精神分裂症是一种复杂而严重的脑部疾病。神经科学家越来越有兴趣使用基于磁共振的脑成像衍生表型 (IDP) 来研究精神疾病的病因。IDP 具有宝贵的临床优势,并在识别大脑异常方面具有生物学意义。在这篇综述中,我们旨在讨论使用临床多模态神经影像学和影像学遗传学来识别精神分裂症潜在生物标志物的当前和前瞻性方法。我们首先通过表型分类和神经影像学基因组学描述了 IDP。其次,我们讨论了多模态神经影像学在观察性研究和随机对照试验中的临床证据的应用。第三,考虑到 IDP 的遗传证据,我们讨论了如何利用神经影像学数据作为中介表型,通过多基因风险评分和孟德尔随机化进行关联推断。最后,我们讨论了机器学习作为验证生物标志物的最佳方法。总之,未来专注于神经影像学生物标志物的研究工作旨在增进我们对精神分裂症的理解。

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Towards a biologically annotated brain connectome.迈向具有生物学注释的脑连接组学。
Nat Rev Neurosci. 2023 Dec;24(12):747-760. doi: 10.1038/s41583-023-00752-3. Epub 2023 Oct 17.

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