Wang Ruofan, He Qiguang, Han Chunxiao, Wang Haodong, Shi Lianshuan, Che Yanqiu
School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China.
Front Neurosci. 2023 Aug 8;17:1177424. doi: 10.3389/fnins.2023.1177424. eCollection 2023.
The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.
The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.
First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.
Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.
These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.
卷积神经网络（CNN）是一种主流的深度学习（DL）算法，在解决临床检查和诊断问题（如阿尔茨海默病（AD））方面声名远扬。AD是一种退行性疾病，由于其潜在病理机制不明，临床诊断困难。以往的研究主要集中在调查与AD相关的大脑功能网络中的结构异常，或提出不同的深度学习方法用于AD分类。
本研究旨在利用从功能网络探索中提取的脑拓扑特征与CNN提取的深度特征相结合的优势。我们建立了一个基于功能磁共振成像（fMRI）的新型分类框架，该框架利用静息态功能磁共振成像（rs-fMRI）结合相位同步指数（PSI）和二维卷积神经网络（2D-CNN）来检测AD患者大脑功能连接异常。
首先，应用PSI通过数据预处理阶段获得的感兴趣区域（ROI）信号构建脑网络，并提取八个拓扑特征。随后，将2D-CNN应用于PSI矩阵，通过从2D-CNN卷积层提取八个深度特征来探索网络连接的局部和全局模式。
最后，对结合PSI和2D-CNN的方法进行分类分析，采用支持向量机（SVM）和五折交叉验证策略识别AD。结果发现，联合方法的分类准确率达到98.869%。
这些结果表明，我们的框架可以自适应地结合最佳的脑网络特征来探索网络同步、功能连接，并表征脑功能异常，通过提取的特征可以有效检测AD异常，这可能为探索AD的潜在发病机制提供新的见解。
