{"id":"W4318542309","doi":"10.1016/j.neuroimage.2023.119904","title":"Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population","year":2023,"lang":"en","type":"article","venue":"NeuroImage","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hotchkiss Brain Institute; University of Calgary","funders":"","keywords":"Human Connectome Project; Computer science; Convolutional neural network; Functional magnetic resonance imaging; Artificial intelligence; Deep learning; SIGNAL (programming language); Waveform; Resting state fMRI; Pattern recognition (psychology); Speech recognition; Functional connectivity; Neuroscience; Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005291615,0.00009911598,0.0001745591,0.0002003715,0.00007976325,0.0000294546,0.0002015743,0.00002603785,0.00003740364],"category_scores_gemma":[0.00009821809,0.00009873715,0.00003064164,0.0006098764,0.00001727603,0.000429833,0.0002014628,0.0003175247,0.00003188117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003334188,"about_ca_system_score_gemma":0.00008142963,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003578284,"about_ca_topic_score_gemma":0.00003418789,"domain_scores_codex":[0.9987323,0.0001825229,0.0003321566,0.0003374901,0.0002100055,0.0002054749],"domain_scores_gemma":[0.9991797,0.000211321,0.0001874758,0.0003502236,0.00003396883,0.00003732273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002542396,0.00002251267,0.9021308,0.00001432903,0.000008512518,0.000003456796,0.0002823541,0.0009355963,0.006510947,0.00007783456,0.00003151793,0.0899567],"study_design_scores_gemma":[0.0004167512,0.00001127377,0.6817313,0.00001336536,0.000009712856,1.439145e-7,0.00005905052,0.3137722,0.0005311741,0.003315301,0.00003831207,0.0001014363],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963712,0.00001208409,0.001491986,0.00001638608,0.0001003233,0.00013328,0.000165592,0.0000545876,0.001654622],"genre_scores_gemma":[0.9989886,0.00001333802,0.0001061895,0.000004674906,0.0001890501,0.000005712237,0.0005876134,0.00002167825,0.00008310979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3128366,"threshold_uncertainty_score":0.5409319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04569866162769847,"score_gpt":0.3012267443423004,"score_spread":0.2555280827146019,"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."}}