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

Abstract

BackgroundA 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.ObjectiveThis 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.MethodsThe 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.ResultsOur 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.ConclusionMultimodal 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.

Novel Alzheimer's disease subtypes based on functional brain connectivity in human connectome project

“…Deep learning has gained significant popularity in brain functional connectivity and network analysis 20 , 21 . It enables the exploration of connection patterns and network structures among various brain regions, thereby providing valuable insights into brain function and cognitive processes.…”

The Combination of a Graph Neural Network Technique and Brain Imaging to Diagnose Neurological Disorders: A Review and Outlook

“…Given its prowess in handling weight adaptation, GAT is frequently employed to explore brain connectivity. Safai et al [100] used GAT to interpret brain connections while extracting structural and functional features from T1-MRI, dMRI, fMRI. Yang et al [51] and Li et al [62] used Pearson correlations as node features and GAT to predict ND.…”

“…The loss function employed not only cross-entropy, but also the local and global decoding loss of edge reconstruction. Safai et al [100] extracted multimodal features from T1-MRI, dMRI, and fMRI, and used GAT to diagnose PD. Kazi et al [21] used non-image data to construct multiple graphs.…”