{"id":"W3187718039","doi":"10.1002/hbm.25566","title":"Evaluation of a personalized functional near<scp>infra‐red</scp>optical tomography workflow using maximum entropy on the mean","year":2021,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital; École de Technologie Supérieure; Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Concordia University; Savoy Foundation; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Functional near-infrared spectroscopy; Diffuse optical imaging; Workflow; Functional magnetic resonance imaging; Computer science; Finger tapping; Magnetoencephalography; Artificial intelligence; Pattern recognition (psychology); Entropy (arrow of time); Computer vision; Iterative reconstruction; Electroencephalography; Physics; Neuroscience; Cognition; Medicine; Psychology","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":[0.001160234,0.0007789136,0.0003834018,0.0005048556,0.0002562469,0.0005637269,0.0007599251,0.0006305946,0.000955132],"category_scores_gemma":[0.001883225,0.000317689,0.0004388221,0.0002621948,0.0003596382,0.0005647783,0.0006113997,0.0003419487,0.0003367348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003426036,"about_ca_system_score_gemma":0.0008132732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001581785,"about_ca_topic_score_gemma":0.002835563,"domain_scores_codex":[0.999616,0.0001213881,0.00002524014,0.00009375479,0.0001129161,0.00003056096],"domain_scores_gemma":[0.9993867,0.0001943359,0.00008102182,0.0001409396,0.0001492085,0.00004783115],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001100442,0.0002555997,0.006560531,0.0004446547,0.0001607493,0.0007113658,0.0003536973,0.1147703,0.5079425,0.002729494,0.001334497,0.3636361],"study_design_scores_gemma":[0.00005549891,0.0005831671,0.009212598,0.00002996412,0.00008404625,0.001371004,0.0001137628,0.7059979,0.2771865,0.002353235,0.002903921,0.0001083594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08209448,0.000165215,0.9156746,0.000107647,0.00002372013,0.0001209207,0.0001386865,0.001197205,0.0004775118],"genre_scores_gemma":[0.3310047,0.0001487139,0.6677833,0.00006182071,0.00001578345,0.0001322217,0.0001712602,0.0001328458,0.0005493828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001581785,"threshold_uncertainty_score":0.006136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07843625004769565,"score_gpt":0.3312424458198057,"score_spread":0.2528061957721101,"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."}}