{"id":"W4288045497","doi":"10.1016/j.neuroimage.2022.119521","title":"A reusable benchmark of brain-age prediction from M/EEG resting-state signals","year":2022,"lang":"en","type":"article","venue":"NeuroImage","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"InteraXon (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Electroencephalography; Benchmark (surveying); Computer science; Artificial intelligence; Machine learning; Magnetoencephalography; Population; Random forest; Set (abstract data type); Deep learning; Brain activity and meditation; Psychology; Medicine; Neuroscience; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00369034,0.002152721,0.0006390955,0.002198107,0.0004773676,0.00136645,0.002088292,0.0009504519,0.00255957],"category_scores_gemma":[0.01585825,0.0003545178,0.000882955,0.001466608,0.0005515022,0.0013836,0.002057881,0.001049763,0.001962752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007806058,"about_ca_system_score_gemma":0.001307415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008645293,"about_ca_topic_score_gemma":0.01034436,"domain_scores_codex":[0.9980994,0.0005432176,0.0002124262,0.0004915617,0.0004940396,0.0001593994],"domain_scores_gemma":[0.9963017,0.001100161,0.0002825295,0.0009363805,0.001166442,0.0002128543],"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":[0.002549824,0.0012042,0.1000786,0.002032773,0.001701247,0.0008723332,0.0003915812,0.2688884,0.02547101,0.008385264,0.110295,0.4781299],"study_design_scores_gemma":[0.0003345573,0.001884261,0.08119381,0.0003159961,0.0002995642,0.001057435,0.0004573615,0.7991881,0.06027886,0.01548605,0.03930017,0.0002038279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6000668,0.003392565,0.2928344,0.001030838,0.001003084,0.0009453234,0.04693187,0.04121232,0.01258285],"genre_scores_gemma":[0.7722199,0.0008086656,0.1297189,0.0003008256,0.0001513192,0.0009587791,0.09065475,0.001602671,0.003584217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008645293,"threshold_uncertainty_score":0.01951665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03373366838768526,"score_gpt":0.2677588123636365,"score_spread":0.2340251439759513,"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."}}