{"id":"W3137238909","doi":"10.1038/s41598-021-85386-0","title":"Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; Montreal Neurological Institute and Hospital; Hôpital du Sacré-Cœur de Montréal; Concordia University; École de Technologie Supérieure; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Functional near-infrared spectroscopy; Deconvolution; Computer science; Blind deconvolution; Haemodynamic response; Artificial intelligence; Temporal resolution; SIGNAL (programming language); Computer vision; Biological system; Pattern recognition (psychology); Neuroscience; Algorithm; Optics; Physics; Medicine; Psychology; Cognition; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264767,0.0001260682,0.0002794171,0.00005930655,0.000400987,0.0001546187,0.00005199141,0.00006795332,0.0004432581],"category_scores_gemma":[0.0009681477,0.00009440446,0.0001661404,0.0005232401,0.001089216,0.0001054767,0.00005955112,0.000252844,0.000005669287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000148926,"about_ca_system_score_gemma":0.00105129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004340009,"about_ca_topic_score_gemma":0.000007499313,"domain_scores_codex":[0.9979468,0.0001290041,0.0004991692,0.0005056513,0.0006312495,0.0002881255],"domain_scores_gemma":[0.9984393,0.0001343709,0.0001724767,0.0006935066,0.000448105,0.0001122168],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001523576,0.0001326349,0.03595861,0.00003375395,0.0000355374,0.0004266231,0.0001228941,0.00004030499,0.959768,0.0004764257,0.002827818,0.00002505842],"study_design_scores_gemma":[0.0004790658,0.0001169947,0.05199405,0.000230927,0.0002411707,0.00461476,0.0003453826,0.03539487,0.8961492,0.006154175,0.004059628,0.000219831],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842445,0.0004637125,0.01179924,0.0005828664,0.001348119,0.0002057438,0.000003096866,0.00009904491,0.001253696],"genre_scores_gemma":[0.9521072,0.000007520863,0.04159851,0.00007649329,0.00004530849,0.000003344379,0.00004500231,0.00001600312,0.006100613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06361883,"threshold_uncertainty_score":0.4853365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0266209343691466,"score_gpt":0.3196990936293349,"score_spread":0.2930781592601883,"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."}}