{"id":"W4392927049","doi":"10.1371/journal.pcbi.1011942","title":"Continuous evaluation of denoising strategies in resting-state fMRI connectivity using fMRIPrep and Nilearn","year":2024,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Institut Universitaire de Gériatrie de Montréal","funders":"National Institute of Mental Health; National Institute on Deafness and Other Communication Disorders; Consortium canadien en neurodégénérescence associée au vieillissement; Courtois Foundation; Institut national de recherche en informatique et en automatique (INRIA); Institut de Valorisation des Données; Fonds de Recherche du Québec - Santé; Foundation for the National Institutes of Health","keywords":"Benchmark (surveying); Computer science; Noise reduction; Functional magnetic resonance imaging; Software; Noise (video); Artificial intelligence; Machine learning; Communication noise; Resting state fMRI; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01057066,0.002679967,0.001357972,0.002449142,0.0009623638,0.002353535,0.002269384,0.002492704,0.00244498],"category_scores_gemma":[0.04882136,0.0006739618,0.001572355,0.001606202,0.00125995,0.002279009,0.002110958,0.002096697,0.001481253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000894827,"about_ca_system_score_gemma":0.00130423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004806228,"about_ca_topic_score_gemma":0.007277993,"domain_scores_codex":[0.9969049,0.001110599,0.0003704268,0.0006196079,0.0008076919,0.00018677],"domain_scores_gemma":[0.9883886,0.007198851,0.0005693022,0.001429795,0.002116892,0.0002966346],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004716359,0.0009896755,0.01679719,0.004904301,0.002502883,0.001153066,0.001477584,0.3739684,0.08059867,0.01893171,0.05013469,0.4438254],"study_design_scores_gemma":[0.0003016595,0.001149029,0.0110781,0.0004045958,0.0003923265,0.0009214975,0.0003900694,0.875484,0.07395174,0.02379512,0.01186752,0.0002642898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2201751,0.004988851,0.7318193,0.00133776,0.0008630888,0.0005233975,0.005462083,0.02943856,0.005391937],"genre_scores_gemma":[0.3946047,0.001811657,0.5700835,0.0008463668,0.000207495,0.001245516,0.01813014,0.01021421,0.00285634],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01057066,"threshold_uncertainty_score":0.05590367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09935050165288456,"score_gpt":0.3444353841617565,"score_spread":0.2450848825088719,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}