Recently, machine learning methods have gained lots of attention from researchers seeking to analyze brain images such as Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to obtain a deeper understanding of the brain and such related diseases, for example, Alzheimer's disease. Finding the common patterns caused by a brain disorder through analysis of the functional connectivity (FC) network along with discriminating brain diseases from normal controls have long been the two principal goals in studying rs-fMRI data. The majority of FC extraction methods calculate the FC matrix for each subject and then use simple techniques to combine them and obtain a general FC matrix. In addition, the state-of-the-art classification techniques for finding subjects with brain disorders also rely on calculating an FC for each subject, vectorizing, and feeding them to the classifier. Considering these problems and based on multi-dimensional nature of the data, we have come up with a novel tensor framework in which a general FC matrix is obtained without the need to construct an FC matrix for each sample. This framework also allows us to reduce the dimensionality and create a novel discriminant function that rather than using FCs works directly with each sample, avoids vectorization in any step, and uses the test data in the training process without forcing any prior knowledge of its label into the classifier. Extensive experiments using the ADNI dataset demonstrate that our proposed framework effectively boosts the fMRI classification performance and reveals novel connectivity patterns in Alzheimer's disease at its early stages.

中文翻译：

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基于张量的 rs-fMRI 分类和功能连接构建框架


最近，机器学习方法受到了研究人员的广泛关注，他们寻求分析静息态功能磁共振成像 (rs-fMRI) 等大脑图像，以更深入地了解大脑和阿尔茨海默病等相关疾病。长期以来，通过分析功能连接（FC）网络来​​发现由脑部疾病引起的常见模式以及区分脑部疾病与正常对照一直是研究 rs-fMRI 数据的两个主要目标。大多数FC提取方法计算每个主题的FC矩阵，然后使用简单的技术将它们组合起来并获得通用的FC矩阵。此外，用于查找患有脑部疾病的受试者的最先进的分类技术还依赖于计算每个受试者的 FC、矢量化并将其输入分类器。考虑到这些问题并基于数据的多维性质，我们提出了一种新颖的张量框架，在该框架中获得通用的FC矩阵，而不需要为每个样本构造FC矩阵。该框架还允许我们降低维度并创建一种新颖的判别函数，该函数不是使用 FC 直接处理每个样本，而是避免在任何步骤中进行向量化，并在训练过程中使用测试数据，而无需将其标签的任何先验知识强加到其中。分类器。使用 ADNI 数据集进行的大量实验表明，我们提出的框架有效提高了 fMRI 分类性能，并揭示了阿尔茨海默病早期阶段的新连接模式。