Multimodal Brain Connectomics-Based Prediction of Parkinson’s Disease Using Graph Attention Networks - Safai, Apoorva; Vakharia, Nirvi; Prasad, Shweta; Saini, Jitender; Shah, Apurva; Lenka, Abhishek; Pal, Pramod Kumar; Ingalhalikar, Madhura (Frontiers in Neuroscience, 2022)
Background: A multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure-function network dynamics involved in complex neuro-degenerative network disorders such as Parkinson’s disease (PD). Deep learning-based graph neural network models generate higher level embeddings, that could capture intricate structural and functional regional interactions related to PD. Objective: This study aimed at investigating the role of structure-function connections in predicting PD, by employing an end-to-end graph attention network (GAT) on multimodal brain connectomes along with an interpretability framework. Methods: The proposed GAT model was implemented to generate node embeddings from the structural connectivity matrix and multimodal feature set containing morphological features and structural and functional network features of PD patients and healthy controls. Graph classification was performed by extracting topmost node embeddings and interpretability framework was implemented using saliency analysis and attention maps. Moreover, we also compared our model with unimodal models as well as other state of the art models. Results: Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and F1 score of 86% and a moderate test accuracy of 73%. Interpretability framework highlighted structural and functional topological influence of motor network and cortico-subcortical brain regions, among which structural features were correlated with onset of PD. The attention maps showed dependency between large scale brain regions based on their structural and functional characteristics. Conclusions: Multimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and a comprehensive interpretability framework.