{"doi":"10.1016/j.dcn.2022.101087","title":"Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI","abstract":null,"journal":"Developmental Cognitive Neuroscience","year":2022,"id":654643,"datarank":0.5244761342199721,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"self_citation_contribution":0.5244761342199721,"citation_network_contribution":0.0,"self_endowment_contribution":0.5244761342199721,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"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":1708528,"name":"Ryann Tansey","orcid":null,"position":1,"is_corresponding":false},{"id":1708530,"name":"Amanda Ip","orcid":null,"position":2,"is_corresponding":false},{"id":1708532,"name":"Christiane Rohr","orcid":null,"position":3,"is_corresponding":false},{"id":1708533,"name":"Dennis Dimond","orcid":null,"position":4,"is_corresponding":false},{"id":460931,"name":"Deborah Dewey","orcid":"0000-0002-1323-5832","position":5,"is_corresponding":false},{"id":460930,"name":"Signe Bray","orcid":"0000-0002-7626-2600","position":6,"is_corresponding":false},{"id":1708525,"name":"Kirk Graff","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Benchmarking common preprocessing strategies in early childhood functional connectivity and intersubject correlation fMRI","abstract":"Preprocessing choices present a particular challenge for researchers working with functional magnetic resonance imaging (fMRI) data from young children. Steps which have been shown to be important for mitigating head motion, such as censoring and global signal regression (GSR), remain controversial, and benchmarking studies comparing preprocessing pipelines have been conducted using resting data from older participants who tend to move less than young children. Here, we conducted benchmarking of fMRI preprocessing steps in a population with high head-motion, children aged 4-8 years, leveraging a unique longitudinal, passive viewing fMRI dataset. We systematically investigated combinations of global signal regression (GSR), volume censoring, and ICA-AROMA. Pipelines were compared using previously established metrics of noise removal as well as metrics sensitive to recovery of individual differences (i.e., connectome fingerprinting), and stimulus-evoked responses (i.e., intersubject correlations; ISC). We found that: 1) the most efficacious pipeline for both noise removal and information recovery included censoring, GSR, bandpass filtering, and head motion parameter (HMP) regression, 2) ICA-AROMA performed similarly to HMP regression and did not obviate the need for censoring, 3) GSR had a minimal impact on connectome fingerprinting but improved ISC, and 4) the strictest censoring approaches reduced motion correlated edges but negatively impacted identifiability.","is_dataset_classified":null,"base_score":3.4965075614664802,"endowment":3.4965075614664802,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35196611","pmcid":"PMC8866685","openalex_id":"https://openalex.org/W4213174632","authors":[],"funders":[{"funder_name":"Natural Sciences and Engineering Research Council of Canada","grant_id":"unidentified","title":"unidentified"}],"total_grants":1,"fwci":2.8899,"citation_percentile":0.91664627,"influential_citations":0,"citation_trend":[{"year":2022,"count":6},{"year":2023,"count":3},{"year":2024,"count":11},{"year":2025,"count":8},{"year":2026,"count":4}],"oa_status":"gold","license":"cc-by-nc-nd","oa_locations":[{"url":"https://www.sciencedirect.com/science/article/pii/S1878929322000317/pdf","host_type":"journal"},{"url":"https://www.sciencedirect.com/science/article/pii/S1878929322000317/pdf","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1878929322000317?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S1878929322000317?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.dcn.2022.101087","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35196611","host_type":"repository"},{"url":"https://doaj.org/article/a6a4c969049240a8a57891e31cd8a376","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8866685","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC8866685","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8866685?pdf=render","host_type":"Europe_PMC"},{"url":"http://dx.doi.org/10.1016/j.dcn.2022.101087","host_type":""}],"fields_of_study":["Functional Brain Connectivity Studies","Advanced Neuroimaging Techniques and Applications","Advanced MRI Techniques and Applications","0301 basic medicine","03 medical and health sciences","0302 clinical medicine"],"mesh_terms":["Brain","Child","Child, Preschool","Humans","Image Processing, Computer-Assisted","Magnetic Resonance Imaging","Artifacts","Benchmarking","Connectome"],"keywords":["Censoring (clinical trials)","Functional magnetic resonance imaging","Artificial intelligence","Preprocessor","Psychology","Pattern recognition (psychology)","Regression","Connectome","Regression analysis","Computer science","Statistics","Neuroscience","Mathematics","Machine learning","Functional connectivity","fMRI","Motion","Early Childhood","Preprocessing","Connectome Fingerprinting","Neurophysiology and neuropsychology","QP351-495","Brain","Magnetic Resonance Imaging","Benchmarking","Child, Preschool","Image Processing, Computer-Assisted","Humans","Artifacts","Child","Original Research"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Peace, Justice and strong institutions"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-11T07:38:05.038521Z","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":[]}