Detection of Mild Traumatic Brain Injury via Topological Graph Embedding and 2D Convolutional Neural Networks

Early diagnosis of mild traumatic brain injury (mTBI) is challenging, yet significantly important in order to grant the patients with timely treatment and mitigating the risks of possible long-term psychiatric and neurological disorders. To tackle this problem, in this paper, we develop an mTBI detection framework based on graph embedding features combined with convolutional neural networks (CNN). Cortical activity in transgenic calcium reporter mice expressing Thy1-GCaMP6s is recorded in two sessions, prior to and after inducing injury. Functional networks are then constructed for recordings obtained in each session. The Node2vec algorithm is employed to represent nodes of these networks in the node embedding space. Node embedding feature vectors are then aligned, compressed, and represented as three-channel images. A CNN model is used for the classification of brain networks into two categories of normal and mTBI. A maximum classification accuracy of 95.4% is achieved. Our results suggest that functional networks as biomarkers along with the proposed method can effectively be used for detecting mTBI.