Mild cognitive impairment (MCI) is characterized by subtle changes in cognitive functions, often associated with disruptions in brain connectivity. The present study introduces a novel fine-grained analysis to examine topological alterations in neurodegeneration pertaining to six different brain networks of MCI subjects (Early/Late MCI). To achieve this, fMRI time series from two distinct populations are investigated: (i) the publicly accessible ADNI dataset and (ii) our in-house dataset. The study utilizes sliding window embedding to convert each fMRI time series into a sequence of 3-dimensional vectors, facilitating the assessment of changes in regional brain topology. Distinct persistence diagrams are computed for Betti descriptors of dimension-0, 1, and 2. Wasserstein distance metric is used to quantify differences in topological characteristics. We have examined both (i) ROI-specific inter-subject interactions and (ii) subject-specific inter-ROI interactions. Further, a new deep learning model is proposed for classification, achieving a maximum classification accuracy of 95% for the ADNI dataset and 85% for the in-house dataset. This methodology is further adapted for the differential diagnosis of MCI sub-types, resulting in a peak accuracy of 76.5%, 91.1% and 80% in classifying HC Vs. EMCI, HC Vs. LMCI and EMCI Vs. LMCI, respectively. We showed that the proposed approach surpasses current state-of-the-art techniques designed for classifying MCI and its sub-types using fMRI.

翻译：轻度认知障碍（MCI）的特征是认知功能的细微变化，通常与大脑连接性的中断有关。本研究引入了一种新颖的细粒度分析方法，以研究与MCI受试者（早期/晚期MCI）六个不同脑网络相关的神经退行性变中的拓扑结构改变。为此，研究调查了两个不同人群的功能磁共振成像（fMRI）时间序列：（i）公开可用的ADNI数据集和（ii）我们的内部数据集。该研究利用滑动窗口嵌入将每个fMRI时间序列转换为一系列三维向量，从而便于评估区域大脑拓扑结构的变化。针对维度0、1和2的Betti描述符分别计算了不同的持续性图。采用Wasserstein距离度量来量化拓扑特征的差异。我们既考察了（i）特定感兴趣区（ROI）的受试者间相互作用，也考察了（ii）特定受试者的ROI间相互作用。此外，提出了一种新的深度学习模型用于分类，在ADNI数据集上实现了最高95%的分类准确率，在内部数据集上实现了85%的准确率。该方法进一步适用于MCI亚型的鉴别诊断，在区分健康对照（HC）与早期MCI（EMCI）、HC与晚期MCI（LMCI）以及EMCI与LMCI时，分别达到了76.5%、91.1%和80%的峰值准确率。我们表明，所提出的方法超越了当前利用fMRI对MCI及其亚型进行分类的最先进技术。