  Alzheimer's Disease (AD) is a currently incurable neurodegeneartive disease.
Accurately detecting AD, especially in the early stage, represents a high
research priority. AD is characterized by progressive cognitive impairments
that are related to alterations in brain functional connectivity (FC). Based on
this association, many studies have been published over the decades using FC
and machine learning to differentiate AD from healthy aging. The most recent
development in this detection method highlights the use of graph neural network
(GNN) as the brain functionality analysis. In this paper, we proposed a stack
of spatio-temporal feature extraction and graph generation based AD
classification model using resting state fMRI. The proposed multi-level
generated connectome (MLC) based graph convolutional network (GCN) (MLC-GCN)
contains a multi-graph generation block and a GCN prediction block. The
multi-graph generation block consists of a hierarchy of spatio-temporal feature
extraction layers for extracting spatio-temporal rsfMRI features at different
depths and building the corresponding connectomes. The GCN prediction block
takes the learned multi-level connectomes to build and optimize GCNs at each
level and concatenates the learned graphical features as the final predicting
features for AD classification. Through independent cohort validations, MLC-GCN
shows better performance for differentiating MCI, AD, and normal aging than
state-of-art GCN and rsfMRI based AD classifiers. The proposed MLC-GCN also
showed high explainability in terms of learning clinically reasonable
connectome node and connectivity features from two independent datasets. While
we only tested MLC-GCN on AD, the basic rsfMRI-based multi-level learned GCN
based outcome prediction strategy is valid for other diseases or clinical
outcomes.
