Building Dynamic Hierarchical Brain Networks and Capturing Transient Meta-states for Early Mild Cognitive Impairment Diagnosis

Abstract

Latest diagnostic studies at the preclinical stage of Alzheimer’s disease focus on dynamic functional connectivity network (dFCN) from resting-state fMRI. However, the existing methods fall short in at least two aspects: 1) Single-scale atlas is generally used for building the dFCN, while functional interactions at and cross multiple spatial scales are largely neglected; 2) Features extracted from dFCN at each time segment are often simply pooled together, whereas the disease related meta-states, i.e., dFCN configurations, appear transiently and may not be sensitively captured. In the presented study, we designed multiscale atlas-based graph convolutional network, and utilized a multiple-instance-learning pooling to tackle these issues. First, we leveraged those previously established multiscale atlases to build hierarchical brain networks, represented by multiscale graphs, which were also applied to different time segments to form dFCNs. At each time segment, we processed these multiscale graphs by our specially designed multiscale graph convolutional networks that were connected based on the inter-scale hierarchy. A long short-term memory (LSTM) architecture was then implemented to process temporal information of the dFCN. The output from the LSTM was pooled with attention-based multiple instance learning to dynamically assign larger weights to disease related (more diagnostic) transient states. Experiments on 481 subjects show that our method achieved 77.78% accuracy (with 75.00% sensitivity and 78.57% specificity) in healthy control vs. early mild cognitive impairment (eMCI) classification, which outperformed the state-of-the-art methods. Our study not only fits the practical needs of eMCI diagnosis with resting-state fMRI but also highlights that the pathological of eMCI could manifest as abnormal transient meta-states of multiscale functional interactions.