{"doi":"10.1007/978-3-031-47425-5_2","title":"A Groupwise Method for the Reconstruction of Hypergraph Representation of Resting-State Functional Networks","abstract":"Functional MRI (fMRI) is an important modality for exploring the brain state and characterizing connectivity across brain regions, but the application of fMRI for disease diagnosis remains limited in clinical practice. To enhance the reliability in modeling functional brain connectivity, we propose a novel method to construct a hypergraph representation of brain networks from resting-state fMRI. Each edge in a hypergraph can connect an arbitrary number of brain regions instead of just two regions as in conventional graph-based networks, allowing for measuring high-order relationships between multiple regions. Existing hypergraph reconstruction methods in fMRI studies typically have a central node in each hyperedge, which limits the edge set by the number of brain regions. However, this hypergraph still needs a high-dimensional space to represent. In addition, only positive weights were previously allowed for the hypergraph incident matrix. In our proposed method, we remove those constraints and develop a novel computational framework to reconstruct general hypergraph representations from resting-state fMRI with consistent topology across groups. In our proposed method, the number of hyperedges does not need to be the same as the number of regions, which decreases the feature dimension space. To validate our method, we classify the brain state using hypergraph-based features and demonstrate superior performance over competing methods on two datasets.","journal":"Lecture notes in computer science","year":2023,"id":415567,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9508,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":328019,"name":"Yonggang Shi","orcid":"0000-0002-3466-4302","position":1,"is_corresponding":false},{"id":1197517,"name":"Mingyang Xia","orcid":"0009-0001-7359-0393","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:22:12.933438Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}