Diffusion-Based Causality-Preserving Neural Network for Dementia Recognition

Abstract

Analyzing large-scale functional brain networks for brain disorders often relies on undirected correlations in activation signals between brain regions. While focusing on co-occurring activations, this approach overlooks the potential for directionality inherent in brain connectivity. Established research indicates the causal nature of brain networks, suggesting that activation patterns co-occur and potentially influence one another. To this end, we propose a novel dffusion vector auto-regressive (Diff-VAR) method, enabling the assessment of whole-brain effective connectivity (EC) as a directed and weighted network by integrating the search objectives into the deep neural network model as learnable parameters. The EC learned by our method identifies widespread differences in flow of influence within the brain network for individuals with impaired brain function compared to those with normal brain function. Moreover, we introduce an adaptive smoothing mechanism to enhance the stability and reliability of inferred EC. We evaluated the results of our proposed method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The model's performance is compared with existing correlation-based and causality-based methods. The results revealed that the brain networks constructed by our method achieve high classification accuracy and exhibit features consistent with physiological mechanisms. The code is available at https://github.com/SaqibMamoon/Diff-VAR.