Dynamic Spectral Graph Convolution Networks with Assistant Task Training for Early MCI Diagnosis

Functional brain connectome, also known as inter-regional functional connectivity (FC) matrix, is recently considered providing decisive markers for early mild cognitive impairment (eMCI). However, in most existing methods, vectorized static FC matrices and some “off-the-shelf” classifiers were used, which may lead to a deprecation of both spatial and temporal information and thus compromise the diagnosis performance. In this paper, we propose dynamic spectral graph convolution networks (DS-GCNs) for early MCI diagnosis using functional MRI (fMRI). First, a dynamic brain graph is constructed so that the connectivity strengths (edges) are derived by time-varying correlations of fMRI signals, and the node signals are computed from T1 MR images. Then, the spectral graph convolution (GC) based long short term memory (LSTM) network is employed to process long range temporal information from the dynamic graphs. Finally, instead of directly using demographic information as additional inputs as in the conventional methods, we proposed to predict gender and age of each subject as assistant tasks, which in turn captures useful network features and facilitates the main task of eMCI classification; we refer this strategy as assistant task training. Experiments on 294 training and 74 testing subjects show that eMCI classification results achieved \(79.7\%\) accuracy (with \(86.5\%\) sensitivity and \(73.0\%\) specificity) and outperformed the state-of-the-art methods. Notably, the proposed method could be further extended to other Connectomics studies, where the graphs are computed through white matter fiber connections or gray matter characteristics.