{"doi":"10.1109/embc.2014.6943768","title":"Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches","abstract":null,"journal":"2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","year":2014,"id":624611,"datarank":0.26876392038420827,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.0,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"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":1614705,"name":"Elena A. Allen","orcid":null,"position":1,"is_corresponding":false},{"id":887096,"name":"Hao He","orcid":"0000-0002-4915-310X","position":2,"is_corresponding":false},{"id":353530,"name":"Jing Sui","orcid":"0000-0001-8223-9872","position":3,"is_corresponding":false},{"id":227761,"name":"Vince D. Calhoun","orcid":"0000-0001-9058-0747","position":4,"is_corresponding":false},{"id":227748,"name":"Yuhui Du","orcid":"0000-0002-0079-8177","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Brain functional networks extraction based on fMRI artifact removal: Single subject and group approaches","abstract":"Independent component analysis (ICA) has been widely applied to identify brain functional networks from multiple-subject fMRI. However, the best approach to handle artifacts is not yet clear. In this work, we study and compare two ICA approaches for artifact removal using simulations and real fMRI data. The first approach, recommended by the human connectome project, performs ICA on individual data to remove artifacts, and then applies group ICA on the cleaned data from all subjects. We refer to this approach as Individual ICA artifact Removal Plus Group ICA (TRPG). A second approach, Group Information Guided ICA (GIG-ICA), performs ICA on group data, and then removes the artifact group independent components (ICs), followed by individual subject ICA using the remaining group ICs as spatial references. Experiments demonstrate that GIG-ICA is more accurate in estimation of sources and time courses, more robust to data quality and quantity, and more reliable for identifying networks than IRPG.","is_dataset_classified":null,"base_score":1.791759469228055,"endowment":1.791759469228055,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"25570136","pmcid":null,"openalex_id":"https://openalex.org/W2322398536","authors":[],"funders":[{"funder_name":"NCRR NIH HHS","grant_id":"5P20RR021938","title":null},{"funder_name":"NIBIB NIH HHS","grant_id":"R01EB006841","title":null},{"funder_name":"NIGMS NIH HHS","grant_id":"P20GM103472","title":null}],"total_grants":3,"fwci":0.8649,"citation_percentile":0.71497253,"influential_citations":0,"citation_trend":[{"year":2014,"count":1},{"year":2015,"count":1},{"year":2018,"count":1},{"year":2020,"count":1},{"year":2023,"count":1}],"oa_status":"closed","license":"cc-zero","oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/6923026/6943513/06943768.pdf?arnumber=6943768","host_type":"publisher"},{"url":"https://doi.org/10.1109/embc.2014.6943768","host_type":""},{"url":"https://pubmed.ncbi.nlm.nih.gov/25570136","host_type":"repository"}],"fields_of_study":["Functional Brain Connectivity Studies","Neural dynamics and brain function","Advanced MRI Techniques and Applications"],"mesh_terms":["Data Accuracy","Algorithms","Brain","Humans","Magnetic Resonance Imaging","Radiography","Regression Analysis","Artifacts","Signal-To-Noise Ratio","Connectome","Healthy Volunteers"],"keywords":["Independent component analysis","Artifact (error)","Human Connectome Project","Computer science","Artificial intelligence","Pattern recognition (psychology)","Blind signal separation","Connectome","Functional connectivity","Neuroscience","Psychology","Channel (broadcasting)"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-04T04:19:06.081379Z","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":[]}