Enhancing Neurodegenerative Disease Diagnosis Through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 33; pp. 1715 - 1728
• Main Authors: Yuan, Ning, Guan, Donghai, Li, Shengrong, Zhang, Li, Zhu, Qi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025
• Subjects: Algorithms; Alzheimer Disease - diagnosis; Alzheimer Disease - diagnostic imaging; Blood; Brain - diagnostic imaging; Brain - physiopathology; confidence fusion; Convolution; Data mining; Databases, Factual; Dynamic brain network; Feature extraction; Functional magnetic resonance imaging; Graph convolutional networks; Humans; Magnetic Resonance Imaging; Medical diagnosis; Network topology; Neural Networks, Computer; Neurodegenerative Diseases - diagnosis; Neurodegenerative Diseases - diagnostic imaging; Parkinson Disease - diagnosis; Parkinson Disease - diagnostic imaging; Schizophrenia; spatio-temporal convolution; spatio-temporal feature; Topology
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2025.3564983

Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification methods for dynamic brain networks often rely on sliding windows to extract multi-window features, leading to suboptimal performance due to the spatio-temporal coupling on these windows and limited ability to effectively integrate complex topological features. To address these limitations, we propose a novel method called Confidence-Driven Dynamic Spatio-Temporal Convolutional Network (CD-DSTCN). First, our proposed method employs a spatio-temporal convolutional network integrated with a temporal attention mechanism to extract spatio-temporal features within each window. By propagating information across temporal windows during spatial convolution, the method effectively captures and integrates complex temporal and spatial dependencies. Second, each window generates an output probability, which quantifies prediction confidence based on the true class probability (TCP). This confidence score serves as a weight to assess the relative importance of different time windows. Finally, the confidence-weighted fused features are passed through a multilayer perceptron (MLP) for final classification. Extensive experiments on Alzheimer's and Parkinson's datasets show that the proposed method outperforms the state-of-the-art algorithms and can provide valuable biomarkers for brain disease diagnosis. Our code is publicly available at: https://github.com/YNingCode/CD-DSTCN