Alzheimer's disease classification using mutual information generated graph convolutional network for functional MRI

Abstract

Background

High-order cognitive functions depend on collaborative actions and information exchange between multiple brain regions. These inter-regional interactions can be characterized by mutual information (MI). Alzheimer's disease (AD) is known to affect many high-order cognitive functions, suggesting an alteration to inter-regional MI, which remains unstudied.

Objective

To examine whether inter-regional MI can effectively distinguish different stages of AD from normal control (NC) through a connectome-based graph convolutional network (GCN).

Methods

MI was calculated between the mean time series of each pair of brain regions, forming the connectome which was input to a multi-level connectome based GCN (MLC-GCN) to predict the different stages of AD and NC. The spatio-temporal feature extraction in MLC-GCN was used to capture multi-level functional connectivity patterns generating connectomes. The GCN predictor learns and optimizes graph representations at each level, concatenating the representations for final classification. We validated our model on 552 subjects from ADNI and OASIS3. The MI-based model was compared to models with several different connectomes defined by Kullback-Leibler divergence, cross-entropy, cross-sample entropy, and correlation coefficient. Model performance was evaluated using 5-fold cross-validation.

Results

The MI-based connectome achieved the highest prediction performance for both ADNI2 and OASIS3 where it's accuracy/Area Under the Curve/F1 were 87.72%/0.96/0.88 and 84.11%/0.96/0.91 respectively. Model visualization revealed that prominent MI features located in temporal, prefrontal, and parietal cortices.

Conclusions

MI-based connectomes can reliably differentiate NC, mild cognitive impairment and AD. Compared to other four measures, MI demonstrated the best performance. The model should be further tested with other independent datasets.