Brain Connectivity Estimation Network for the Identification of Dementia.

Objectives: The brain network serves as a reliable tool for diagnosing neurological disorders. However, the current modeling algorithms for brain networks often rely on several assumptions regarding the interactions between brain regions, which can be inaccurate. For instance, some studies assume linear relationships among brain regions. Additionally, some research suggests that certain brain regions do not significantly influence outcomes when assessing directional influence between paired regions. Methods: To address this issue, we introduced a novel method for modeling brain connectivity structures that estimates interactions among regions from a different perspective. More importantly, this method considers all the relevant brain regions during evaluation rather than isolating individual relationships. Results: To validate its effectiveness, we conducted extensive experiments using publicly available datasets. The proposed method achieved superior performance across all tasks. Conclusions: The results demonstrate that our method not only excels in identifying various brain disorders but also uncovers new biomarkers, providing fresh insights into neurological disorder research.