通过研究DMN的fMRI时间序列结构差异对阿尔茨海默症痴呆与健康受试者进行分类
Classification of Alzheimer's Dementia vs. Healthy subjects by studying structural disparities in fMRI Time-Series of DMN
AI总结：
本研究提出利用“偏离随机性”（DS）测度结合自编码器模型分析fMRI默认模式网络时间序列的结构差异，使用Gradient Boosting分类器在100名受试者上实现了95%的阿尔茨海默症分类准确率。
摘要：
来自功能磁共振成像（fMRI）默认模式网络（DMN）不同感兴趣区域（ROI）的时间序列，可以揭示健康与非健康人群之间的显著差异。在此，我们提出利用一种现有指标来量化信号中结构缺失/存在的程度，该指标被称为“偏离随机性”（DS）测度，用于表征静息态fMRI时间序列。假设时间序列结构水平的差异能够区分不同的受试者组。本工作采用基于自编码器的模型，通过训练网络重构其输入数据来学习数据的有效表示。所提方法应用于从公开的ADNI数据库获取的50名健康个体和50名阿尔茨海默症（AD）受试者的fMRI时间序列。正如预期，健康fMRI的DS测度与AD患者相比存在差异。使用Gradient Boosting分类器对100名受试者应用DS测度，获得了95%的峰值分类准确率。
英文摘要：
Time series from different regions of interest (ROI) of default mode network (DMN) from Functional Magnetic Resonance Imaging (fMRI) can reveal significant differences between healthy and unhealthy people. Here, we propose the utility of an existing metric quantifying the lack/presence of structure in a signal called, "deviation from stochasticity" (DS) measure to characterize resting-state fMRI time series. The hypothesis is that differences in the level of structure in the time series can lead to discrimination between the subject groups. In this work, an autoencoder-based model is utilized to learn efficient representations of data by training the network to reconstruct its input data. The proposed methodology is applied on fMRI time series of 50 healthy individuals and 50 subjects with Alzheimer's Disease (AD), obtained from publicly available ADNI database. DS measure for healthy fMRI as expected turns out to be different compared to that of AD. Peak classification accuracy of 95% was obtained using Gradient Boosting classifier, using the DS measure applied on 100 subjects.
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