当前位置: X-MOL 学术Interdiscip. Sci. Comput. Life Sci. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment
Interdisciplinary Sciences: Computational Life Sciences ( IF 4.8 ) Pub Date : 2021-06-09 , DOI: 10.1007/s12539-021-00449-0
Xia-An Bi 1, 2 , Lou Li 1, 2 , Ruihui Xu 1, 2 , Zhaoxu Xing 1, 2
Affiliation  

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer’s disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.



中文翻译:

晚期轻度认知障碍脑影像及基因的致病因素鉴定

轻度认知障碍 (MCI) 是严重认知能力下降的危险信号。它可以分为两个步骤:早期 MCI (EMCI) 和晚期 MCI (LMCI)。作为MCI的后状态和阿尔茨海默病(AD)的前状态,LMCI在脑科学领域的关注度不够,导致LMCI的内在机制尚未得到很好的理解。为了更好地探索LMCI的焦点和病理机制,应用了一种称为遗传进化随机森林(GERF)的方法。从62名受试者(36名LMCI和26名正常对照)获得静息功能磁共振成像(rfMRI)和基因数据,采用Pearson相关分析对两类数据进行多模态融合构建融合特征。我们使用GERF确定了与LMCI高度相关的致病脑区域和基因,并取得了良好的效果。与正常对照(NC)组相比,LMCI的异常脑区为PUT.L、PreCG.L、IFGtriang.R、REC.R、DCG.R、PoCG.L和HES.L,以及致病基因是 FHIT、RF00019、FRMD4A、PTPRD 和 RBFOX1。更重要的是,在以往的研究中,这些风险基因和异常脑区中的大部分已被证实与AD和MCI有关。在这项研究中,我们将它们映射到具有更高准确度的 LMCI,以便对 MCI 的生理机制有更深入的了解。更重要的是,这些风险基因和异常脑区中的大部分已在之前的研究中被证实与 AD 和 MCI 相关。在这项研究中,我们将它们映射到具有更高准确度的 LMCI,以便对 MCI 的生理机制有更深入的了解。更重要的是,这些风险基因和异常脑区中的大部分已在之前的研究中被证实与 AD 和 MCI 相关。在这项研究中,我们将它们映射到具有更高准确度的 LMCI,以便对 MCI 的生理机制有更深入的了解。

更新日期:2021-06-09
down
wechat
bug