Function-structural Interaction with Progressive and Multi-level Feature Fusion for ADHD Classification

Individuals with Attention Deficit Hyperactivity Disorder (ADHD) exhibit intricate structural and functional interconnectivity across multiple brain regions. These patients demonstrate abnormal alterations in both respective modal brain regions and co-occurrent brain regions. Furthermore, there exist multi-level relationships between these abnormal brain structures and functions, encompassing hierarchical interactions between function-structural alterations as well as hierarchical progression from local regions to broader brain networks. However, most existing multi-modal ADHD classification approaches independently embed functional and structural data into separate spaces for information integration, often predominately focusing on uni-modal features. This approaches lead to a significant loss of features related to functiona-structural interaction relationships. Additionally, it is crucial for ADHD classification to accurately identify both uni-modal and co-occurrent abnormal alterations in brain regions which have hierarchical progression relationships. This study proposes a function-structural interaction multi-modal network with progressive and multi-level feature fusion (FSIPM) for ADHD classification. The main contributions are threefold: 1) An innovative function-structural interaction method is proposed to facilitate the mutual regulation of information across modalities, thereby relieving modal feature bias caused by integrated fusion. 2) A multi-level refinement framework is designed to promote the identification of both individual and co-occurrent abnormal brain regions. This progressive approach models the function-structural alterations of abnormal brain regions and the hierarchical relationships from local to brain networks, ensuring a deeper understanding of brain abnormalities. 3) Multi-level feature fusion aims to minimize the loss of details caused by consecutive sampling operations during the progressive process of the network, contributing to a more accurate and nuanced representation of ADHD-related brain alterations. Experimental results on the ADHD-200 and ABIDE I datasets demonstrate that FSIPM achieves competitive performance in ADHD classification while revealing uni-modal and co-occurrent altered brain regions that are consistent with clinical findings.