Longitudinal study of early mild cognitive impairment via similarity-constrained group learning and self-attention based SBi-LSTM

Alzheimer's disease (AD) is an incurable neurodegenerative disease. Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer's disease (AD), with approximately 80% of amnestic MCI patients developing AD within 6 years. MCI can be further categorized into two stages (i.e., early MCI (EMCI) and late MCI (LMCI)). To identify EMCI effectively and understand how it changes brain function, the brain functional connectivity network (BFCN) has been widely used. However, the conventional methods mainly focused on detection from a single time-point data, which could not discover the changes during the disease progression without using multi-time points data. Therefore, in this work, we carry out a longitudinal study based on multi-time points data to detect EMCI and validate them on two public datasets. Specifically, we first construct a similarity-constrained group network (SGN) from the resting state functional magnetic resonance imaging (rs-fMRI) data at different time-points, and then use a stacked bidirectional long short term memory (SBi-LSTM) network to extract features for longitudinal analysis. Also, we use a self-attention mechanism to leverage high-level features to further improve the detection accuracy. Evaluated on the public Alzheimer's Disease Neuroimaging Initiative Phase II and III (ADNI-2 and ADNI-3) databases, the proposed method outperforms several state-of-the-art methods.