{"doi":"10.1016/j.jneumeth.2010.11.029","title":"Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis","abstract":null,"journal":"Journal of Neuroscience Methods","year":2011,"id":626692,"datarank":0.5606504427425053,"base_score":3.7376696182833684,"endowment":3.7376696182833684,"self_citation_contribution":0.5606504427425053,"citation_network_contribution":0.0,"self_endowment_contribution":0.5606504427425053,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":41,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1189888,"name":"Marc M. 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The eigenvector in the maxvar approach gives an indication of the relative importance of each ROI in obtaining a maximal overall correlation, and hence, can be interpreted as a functional connectivity pattern of the ROIs. The successive canonical correlations define subsequent functional connectivity patterns, in decreasing order of importance. We apply our method on synthetic data and real fMRI data and show its advantages compared to unconstrained CCA and to PCA. Furthermore, since the representative signals for the ROIs are optimized for maximal correlation they are also ideally suited for further effective connectivity analyses, to assess the information flows between the ROIs in the brain.","is_dataset_classified":null,"base_score":3.7376696182833684,"endowment":3.7376696182833684,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"21277327","pmcid":null,"openalex_id":"https://openalex.org/W1984946761","authors":[],"funders":[{"funder_name":"Flemish Regional Ministry of Education","grant_id":"GOA 10/019","title":null},{"funder_name":"Belgian Fund for Scientific Research Flanders","grant_id":"G.0588.09","title":null},{"funder_name":"Interuniversity Attraction Poles Programme – Belgian Science Policy","grant_id":"IUAP P6/054","title":null},{"funder_name":"European Commission","grant_id":"IST-2007-217077","title":null},{"funder_name":"European Commission FP7","grant_id":"FP7_217077","title":null},{"funder_name":"European Commission","grant_id":"217077","title":"Heterogeneous 3-D Perception Across Visual Fragments"}],"total_grants":6,"fwci":1.5323,"citation_percentile":0.80851064,"influential_citations":0,"citation_trend":[{"year":2013,"count":6},{"year":2014,"count":4},{"year":2015,"count":2},{"year":2016,"count":3},{"year":2017,"count":5},{"year":2018,"count":2},{"year":2019,"count":3},{"year":2020,"count":3},{"year":2021,"count":4},{"year":2022,"count":1},{"year":2023,"count":3},{"year":2024,"count":3},{"year":2025,"count":1}],"oa_status":"green","license":"other-oa","oa_locations":[{"url":"https://zenodo.org/record/3419949","host_type":"repository"},{"url":"https://zenodo.org/record/3419949","host_type":"repository"},{"url":"https://api.elsevier.com/content/article/PII:S0165027011000458?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0165027011000458?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.jneumeth.2010.11.029","host_type":"journal"},{"url":"http://www.osti.gov/scitech/biblio/4548081-isolating-true-fluctuations-diurnal-variation","host_type":"repository"},{"url":"https://pubmed.ncbi.nlm.nih.gov/21277327","host_type":"repository"},{"url":"https://lirias.kuleuven.be/bitstream/123456789/311923/1/version_2011%20Journal%20of%20NeuroscienceMethods_Van%20Hulle.pdf","host_type":"repository"},{"url":"https://dx.doi.org/10.1016/j.jneumeth.2010.11.029","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Advanced Neuroimaging Techniques and Applications","Neural dynamics and brain function","03 medical and health sciences","0302 clinical medicine","Algorithms","Brain","Brain Mapping","Humans","Image Processing, Computer-Assisted","Magnetic Resonance Imaging","Neural Pathways","Signal Processing, Computer-Assisted","Software"],"mesh_terms":["Algorithms","Brain","Brain Mapping","Humans","Image Processing, Computer-Assisted","Magnetic Resonance Imaging","Neural Pathways","Signal Processing, Computer-Assisted","Software"],"keywords":["Canonical correlation","Multiset","Pairwise comparison","Pattern recognition (psychology)","Correlation","Mathematics","Artificial intelligence","Eigenvalues and eigenvectors","Computer science","Algorithm","Combinatorics","Brain Mapping","Neural Pathways","Image Processing, Computer-Assisted","Brain","Humans","Signal Processing, Computer-Assisted","Magnetic Resonance Imaging","Algorithms","Software"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T14:52:00.213004Z","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":[]}