Learning Asynchronous Common and Individual Functional Brain Network for AD Diagnosis

Construction and analysis of functional brain network (FBN) with rs-fMRI is a promising method to diagnose functional brain diseases. Traditional methods usually construct FBNs at the individual level for feature extraction and classification. There are several issues with these approaches. Firstly, due to the unpredictable interferences of noises and artifacts in rs-fMRI, these individual-level FBNs have large variability, leading to instability and unsatisfactory diagnosis accuracy. Secondly, the construction and analysis of FBNs are conducted in two successive steps without negotiation with or joint alignment for the target task. In this case, the two steps may not cooperate well. To address these issues, we propose to learn common and individual FBNs adaptively within the Transformer framework. The common FBN is shared, and it would regularize the FBN construction as prior knowledge, alleviating the variability and enabling the network to focus on these disease-specific individual functional connectivities (FCs). Both the common and individual FBNs are built by specially designed modules, whose parameters are jointly optimized with the rest of the network for FBN analysis in an end-to-end manner, improving the flexibility and discriminability of the model. Another limitation of the current methods is that the FCs are only measured with synchronous rs-fMRI signals of brain regions and ignore their possible asynchronous functional interactions. To better capture the actual FCs, the rs-fMRI signals are divided into short segments to enable modeling cross-spatiotemporal interactions. The superior performance of the proposed method is consistently demonstrated in early AD diagnosis tasks on ADNI2 and ADNI3 data sets.