  To characterize atypical brain dynamics under diseases, prevalent studies
investigate functional magnetic resonance imaging (fMRI). However, most of the
existing analyses compress rich spatial-temporal information as the brain
functional networks (BFNs) and directly investigate the whole-brain network
without neurological priors about functional subnetworks. We thus propose a
novel graph learning framework to mine fMRI signals with topological priors
from brain parcellation for disease diagnosis. Specifically, we 1) detect
diagnosis-related temporal features using a "Transformer" for a higher-level
BFN construction, and process it with a following graph convolutional network,
and 2) apply an attention-based multiple instance learning strategy to
emphasize the disease-affected subnetworks to further enhance the diagnosis
performance and interpretability. Experiments demonstrate higher effectiveness
of our method than compared methods in the diagnosis of early mild cognitive
impairment. More importantly, our method is capable of localizing crucial brain
subnetworks during the diagnosis, providing insights into the pathogenic source
of mild cognitive impairment.
