Multi-view Brain Networks Construction for Alzheimer’s Disease Diagnosis

Alzheimer’s disease (AD) is a progressive neurodegenerative disease which has a serious impact on patients’ daily lives. Early detection, diagnosis and treatment of AD remain a major challenge in clinical practice. Current methods typically construct brain functionally connectivity networks based on similarities between regions of interest (ROIs) for each individual, neglecting the potential information from identical ROIs. We contend that considering similarities among identical ROIs holds crucial implications for AD classification. To address this issue, we propose a multi-view brain network construction method, which constructs brain network based on the similarity of identical ROIs for AD diagnosis. Firstly, the time series of the same ROIs of all subjects are extracted and recombined into a new set. For the newly composed set the brain connectivity network is obtained based on the similarity between the time series. Next, this constructed network undergoes graph convolutional network (GCN) processing to produce node embeddings. Finally, these embeddings are extracted for all ROIs within each subject, converted into one-dimensional feature vectors, and utilized for disease detection with support vector machine (SVM). To demonstrate the effectiveness of our method, we evaluate the performance of the proposed method in the widely recognized Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, and the results show that our method exhibits higher performance in identifying AD and MCI compared to existing methods.