Multimodal Brain Connectomics-Based Prediction of Parkinson's Disease Using Graph Attention Networks

Apoorva Safai; Nirvi Vakharia; Shweta Prasad; Jitender Saini; Apurva Shah; Abhishek Lenka; Pramod Kumar Pal; Madhura Ingalhalikar

doi:10.3389/fnins.2021.741489

Multimodal Brain Connectomics-Based Prediction of Parkinson's Disease Using Graph Attention Networks

Front Neurosci. 2022 Feb 23:15:741489. doi: 10.3389/fnins.2021.741489. eCollection 2021.

Authors

Apoorva Safai¹, Nirvi Vakharia¹, Shweta Prasad^{2

3}, Jitender Saini⁴, Apurva Shah¹, Abhishek Lenka², Pramod Kumar Pal², Madhura Ingalhalikar¹

Affiliations

¹ Symbiosis Center for Medical Image Analysis, Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
² Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India.
³ Department of Clinical Neuroscience, National Institute of Mental Health and Neurosciences, Bengaluru, India.
⁴ Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India.

Abstract

Background: A multimodal connectomic analysis using diffusion and functional MRI can provide complementary information on the structure-function network dynamics involved in complex neurodegenerative 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 the 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 proposed GAT model with a multimodal feature set demonstrated superior classification performance over a unimodal feature set. Our model demonstrated superior classification performance over other comparative models, with 10-fold CV accuracy and an F1 score of 86% and a moderate test accuracy of 73%. The interpretability framework highlighted the 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.

Conclusion: Multimodal brain connectomic markers and GAT architecture can facilitate robust prediction of PD pathology and provide an attention mechanism-based interpretability framework that can highlight the pathology-specific relation between brain regions.

Keywords: Parkinson’s disease; graph attention network; interpretability; multimodal brain connectivity; structure-function.