基于通路的脑活动分类用于阿尔茨海默病分析

Jongan Lee, Younghoon Kim, Y. Jeong, D. Na, Kwang-H. Lee, Doheon Lee
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引用次数: 2

摘要

静息状态(RS)功能磁共振成像(fMRI)技术的出现,使得基于脑区定量活动指标对阿尔茨海默病(AD)状态进行分类成为可能。目前基于连接的分类技术的可重复性有限,因为需要预先了解区分脑区域和阿尔茨海默病进展过程中的内在异质性。实际上,类似的挑战已经在分子生物信息学领域得到了解决。他们已经实现了更高的和可重复的分类精度,并已确定了可解释的标记通过纳入分子途径信息在他们的分类。我们采用了与基于rs - fmri的AD分类问题类似的策略。在从文献中收集各种脑功能通路后,我们量化了哪些通路在AD患者和健康受试者之间表现出显著不同的活动水平。此外,阿尔茨海默病患者和健康受试者之间的差异通路可能有助于解释阿尔茨海默病进展过程中的功能改变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pathway-based classification of brain activities for alzheimer's disease analysis
The advent of resting-state (RS) functional magnetic resonance imaging (fMRI) technology has made it possible to classify Alzheimer's disease (AD) states based on the quantitative activity indices of brain regions. Current connectivity-based classification techniques suffer from limited reproducibility due to the need for prior knowledge on discriminative brain regions and intrinsic heterogeneity in the course of AD progression. Actually, similar challenges have been already addressed in molecular bioinformatics communities. They have achieved higher and reproducible classification accuracy and have identified interpretable markers by incorporating molecular pathway information in their classification. We have adopted a similar strategy to the RS-fMRI-based AD classification problem. After collecting various functional brain pathways from literature, we have quantified which pathways show significantly different activity levels between AD patients and healthy subjects. Moreover, discriminatory pathways between AD patients and healthy subjects may facilitate the interpretation of functional alterations in the course of AD progression.
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