UCLN: Toward the Causal Understanding of Brain Disorders With Temporal Lag Dynamics

• Main Authors: Saqib Mamoon (Author), Zhengwang Xia (Author), Amani Alfakih (Author), Jianfeng Lu (Author)
• Format: Book
• Published: IEEE, 2024-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 1534-4320; 1558-0210; 10.1109/TNSRE.2024.3471646
• Keywords: Deep learning; brain networks; brain disorder identification; causal inference; temporal lag; Medical technology; R855-855.5; Therapeutics. Pharmacology; RM1-950
• Source: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3729-3740 (2024)

Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a powerful tool for exploring interactions among brain regions. A growing body of research is actively investigating various computational approaches for estimating causal effects among brain regions. Compared to traditional methods, causal relationship reveals the causal influences among distinct brain regions, offering a deeper understanding of brain network dynamics. However, existing methods either neglect the concept of temporal lag across brain regions or set the temporal lag value to a fixed value. To address this limitation, we propose a Unified Causal and Temporal Lag Network (termed UCLN) that jointly learns the causal effects and temporal lag values among brain regions. Our method effectively captures variations in temporal lag between distant brain regions by avoiding the predefined lag value across the entire brain. The brain networks obtained are directed and weighted graphs, enabling a more comprehensive disentanglement of complex interactions. In addition, we also introduce three guiding mechanisms for efficient brain network modeling. The proposed method outperforms state-of-the-art approaches in classification accuracy on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Our findings indicate that the method not only achieves superior classification but also successfully identifies crucial neuroimaging biomarkers associated with the disease.