Adaptive High-Order Fusion Learning for Brain Disorder Detection.

The functional brain network (FBN) serves as an important tool for investigating neurological and mental disorders. Unlike many traditional networks whose structures are often known in advance, FBNs need to be estimated from neuroimaging or electrophysiological data, and their quality generally determines the performance of downstream tasks, particularly in disorder detection. Recent studies have shown that high-order FBNs tend to achieve better discriminative performance, while some other work indicates that increasing the order of FBN does not necessarily bring additional gains in discriminative performance and may even lead to a rapid decline in discriminability. To fully leverage information across different-order FBNs and identify optimal order for downstream tasks, we design an adaptive high-order FBN fusion learning framework (AHFL) with attention mechanism for brain disorder detection. Specifically, we first construct a series of FBNs with continuously increasing orders and propose a data-driven approach to evaluate each order's contribution to the classification performance. The self-attention mechanism is employed to capture contextual dependencies during the sequential generation of multi-order FBNs, thereby offering a natural fusion approach. Experimental evidence shows that the proposed method achieves superior performance compared to the baseline. In particular, we find that third-order FBN is achieved the highest weights, playing a crucial role for the detection of autism spectrum disorder (ASD), whereas second-order FBN is the most effective for identifying patients with major depressive disorder (MDD). Our findings advance the identification of discriminative high-order FBNs and establish a generalizable diagnostic framework for brain disorders.