{"doi":"10.1101/2023.11.22.568257","title":"Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks","abstract":"The human cerebral cortex is organized into functionally segregated but synchronized regions bridged by the structural connectivity of white matter pathways. While structure-function coupling has been implicated in cognitive development and neuropsychiatric disorders, it remains unclear to what extent the structure-function coupling reflects a group-common characteristic or varies across individuals, at both the global and regional brain levels. By leveraging two independent, high-quality datasets, we found that the graph neural network accurately predicted unseen individuals' functional connectivity from structural connectivity, reflecting a strong structure-function coupling. This coupling was primarily driven by network topology and was substantially stronger than that of the correlation approaches. Moreover, we observed that structure-function coupling was dominated by group-common effects, with subtle yet significant individual-specific effects. The regional group and individual effects of coupling were hierarchically organized across the cortex along a sensorimotor-association axis, with lower group and higher individual effects in association cortices. These findings emphasize the importance of considering both group and individual effects in understanding cortical structure-function coupling, suggesting insights into interpreting individual differences of the coupling and informing connectivity-guided therapeutics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":395402,"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":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9476,"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":1164476,"name":"Hang Yang","orcid":"0000-0002-7349-3366","position":1,"is_corresponding":false},{"id":601016,"name":"Xin Zheng","orcid":"0000-0003-3995-8825","position":2,"is_corresponding":false},{"id":1170665,"name":"Hai Jia","orcid":"0000-0002-0152-5795","position":3,"is_corresponding":false},{"id":1170666,"name":"Jiachang Hao","orcid":"0000-0001-6842-4721","position":4,"is_corresponding":false},{"id":375805,"name":"Xiaoyu Xu","orcid":"0000-0001-6958-6597","position":5,"is_corresponding":false},{"id":1170667,"name":"Chao Li","orcid":"0000-0001-8721-4826","position":6,"is_corresponding":false},{"id":285825,"name":"Xiaosong He","orcid":"0000-0002-7941-2918","position":7,"is_corresponding":false},{"id":373438,"name":"Runsen Chen","orcid":"0000-0002-2145-8630","position":8,"is_corresponding":false},{"id":267521,"name":"Tatsuo S. Okubo","orcid":"0000-0001-7139-0956","position":9,"is_corresponding":false},{"id":235284,"name":"Zaixu Cui","orcid":"0000-0003-4385-8106","position":10,"is_corresponding":false},{"id":1170664,"name":"Peiyu Chen","orcid":"0009-0003-8129-4222","position":0,"is_corresponding":true}],"reference_count":88,"raw_metadata":null,"created_at":"2026-07-19T01:19:22.857556Z","pmid":"38045396","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":[]}