Early Parkinson's Disease Prediction Using rS-fMRI Functional Connectivity and Autoencoder Graph Convolutional Network

Early identification of prodromal Parkinson's disease (PD) is critical, as interventions at this stage can significantly alter its course. We propose a deep learning framework that combines resting-state functional MRI (rs-fMRI) data and a Graph Convolutional Network (GCN) to classify individua...

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Bibliographic Details
Published in:	IEEE access Vol. 13; pp. 178862 - 178875
Main Authors:	Limas, Lesbia Lopez, Manian, Vidya
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Accuracy Artificial neural networks Brain modeling Cluster analysis Correlation Data augmentation Data models Datasets Deep learning Diseases Divergence Euclidean distance Euclidean geometry Feature extraction Functional magnetic resonance imaging graph convolutional network Graph convolutional networks Graph theory Machine learning Measurement Parkinson's disease prodromal Radial basis function rs-fMRI Support vector machines tangent-space functional connectivity
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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