Brain disorders are associated with impairments in cognitive and social functioning, placing a substantial burden on families, healthcare systems, and communities. However, accurate diagnosis remains challenging due to complex higher order interactions among brain regions. Existing graph-based methods are largely limited to pairwise connectivity. In addition, these methods often fail to fully exploit well-established neuroscientific prior knowledge, resulting in limited biological interpretability and suboptimal diagnostic performance. Therefore, we propose a prior knowledge-guided adaptive hypergraph learning (PK-AHGL) framework that represents individual-level functional connectivity networks as hypergraphs to capture higher order multiregion interactions while incorporating neuroscientific prior knowledge for brain disorder diagnosis. PK-AHGL consists of three key modules: 1) an adaptive hypergraph convolution module. Unlike traditional hypergraph neural networks that use static hyperedge weights, this module adaptively learns the weights of different hyperedges; 2) a sparse affinity Laplacian module. Key brain regions are extracted from disorder related functional brain networks and used as prior knowledge. Based on these regions, we compute a hyperedge similarity matrix that encourages similar hyperedges to have similar weights; and 3) a proportional margin ranking module. This module further utilizes prior knowledge by guiding hyperedges containing a higher proportion of key brain regions to obtain larger weights. Experiments on autism brain imaging data exchange (ABIDE), Strategic Research Program for the Promotion of Brain Science (SRPBS)-schizophrenia (SCZ), SRPBS-major depressive disorder (MDD), and Alzheimer’s disease neuroimaging initiative (ADNI) show that PK-AHGL achieves accuracies of 75.78%, 82.66%, 74.89%, and 77.22%, respectively, outperforming multiple state-of-the-art methods. These results suggest that PK-AHGL provides an effective auxiliary tool for brain disorder diagnosis and may support community-oriented mental health services.

Survival risk prediction is crucial for personalized treatment of gliomas. Medical image foundational models can explore complex medical features, which are critical for prognosis in gliomas. We propose SAM-Risk, which uses a customized SAM-Med3D with multi-view representation fusion and clinical knowledge-based age-grade stratified loss for glioma survival risk prediction. First, to utilize potential interactions between multiple views at an early stage, we design a 3D representation generation module that transforms 1D handcrafted radiomics and clinical features into 3D representations, which are fused with multimodal MRIs through a multi-view representation fusion module. The fused representation is fed into the customized SAM-Med3D, fine-tuned using LoRA and a disparity function to extract survival risk-related features. We design a feature refinement module to explore the inter-channel relationships among the outputs of the fine-tuned SAM-Med3D. Additionally, we propose an age-grade stratified loss based on glioma prognosis standards to make the predicted risk more consistent with clinical prior knowledge. Validated on two publicly available UCSF-PDGM and BraTS2020 datasets, SAM-Risk achieves a C-index of 75.08% and 73.67%, respectively, outperforming several survival risk prediction methods.