Convolutional Neural Network with an Element-wise Filter to Classify Dynamic Functional Connectivity

Zhihui Chen, Junzhong Ji, Yin Liang

Abstract: The dynamic nature of the brain functional connectivity (FC) is well accepted in recent years. However, most of the current FC classification methods are based on the static estimation of FC. In this paper, we propose a novel convolutional neural network with an element-wise filter for classifying dynamic functional connectivity (DFC-CNN). First, a DFC matrix is estimated to quantify the DFC. Then, taking the DFC matrix as input, the DFC-CNN model employs one-dimensional convolutional kernels to extract the high-level features of DFC. Moreover, an element-wise filter is specially designed for the DFC matrix, which further improves the classification performance. The experimental results on the autism brain imaging data exchange I (ABIDE I) indicate that the proposed model can distinguish subject groups more accurately, and also can be used to identify the abnormal brain regions.