Constructing Effective Hyper-Connectivity Networks through Adaptive Directed Hypergraph Embedded Dictionary Learning: Application to Early Mild Cognitive Impairment Detection

The accurate diagnosis of early mild cognitive impairment is crucial for timely intervention and treatment of dementia. But it is challenging to distinguish from normal aging due to its complex pathology and mild symptoms. Recently, effective hyper-connectivity identified through directed hypergraph can be considered as an effective analysis approach for early detection of mild cognitive impairment and exploration of its underlying neural mechanisms, because it captures directional higher-order interactions across multiple brain regions. However, current methods face limitations, including inefficiency in high-dimensional spaces, sensitivity to noise, reliance on manually defined structures, lack of global structural information, and static learning mechanisms. To address these issues, we integrate robust dictionary learning with directed hypergraph structure learning within a unified framework. This approach jointly estimates low-dimensional sparse representations and the directed hypergraph. The integration allows both processes to dynamically reinforce each other, leading to the refinement of the directed hypergraph, which improves the estimation of low-dimensional sparse representations and, in turn, enhances the quality of the directed hypergraph estimation. Experimental analyses on simulated data confirm the positive interplay between these processes, demonstrating the effectiveness of the proposed collaborative learning strategy. Furthermore, results on real-world brain signal data show that the proposed method is highly competitive in early detection of mild cognitive impairment, highlighting its ability to identify effective hyper-connectivity networks with significant differences.