{"id":"W2995571684","doi":"10.1016/j.celrep.2019.11.081","title":"The Noisy Brain: Power of Resting-State Fluctuations Predicts Individual Recognition Performance","year":2019,"lang":"en","type":"article","venue":"Cell Reports","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Krembil Foundation","funders":"United States-Israel Binational Science Foundation; Canadian Institute for Advanced Research","keywords":"Neurophysiology; Resting state fMRI; Noise (video); Trait; Electroencephalography; Cognition; Task (project management); Neuroscience; Psychology; Statistical power; Functional connectivity; Neuroimaging; Brain activity and meditation; Cognitive psychology; Audiology; Computer science; Artificial intelligence; Mathematics; Medicine; Statistics","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":[0.0002172354,0.0003043915,0.0002284098,0.0002831719,0.0000982997,0.0004580664,0.0001226769,0.0002840534,0.0007772686],"category_scores_gemma":[0.002218694,0.00009137873,0.00008813618,0.0001606064,0.0003064031,0.0002201275,0.0001877272,0.000235084,0.000180973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006448801,"about_ca_system_score_gemma":0.00005464798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003134108,"about_ca_topic_score_gemma":0.0004572096,"domain_scores_codex":[0.9998865,0.00002231903,0.00001398681,0.00004110419,0.00002093987,0.00001511866],"domain_scores_gemma":[0.9989946,0.0004733542,0.0002994422,0.00008333071,0.00004474235,0.0001044971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008314177,0.0001144656,0.8728566,0.00004646417,0.0001589818,0.0006196247,0.0004421696,0.001603856,0.1008814,0.0001189159,0.0001256968,0.02220047],"study_design_scores_gemma":[0.000002513712,0.0001448429,0.9941494,0.000002316699,0.00001733798,0.0006153026,0.00007043834,0.001638916,0.003148678,0.0001614644,0.00004253284,0.00000625875],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989998,0.00004417558,0.0006655658,0.0000151172,0.000001567066,0.000001544076,0.00005669931,0.000008798455,0.000206702],"genre_scores_gemma":[0.9997689,0.00001322982,0.000123172,0.000003267568,0.000002117447,0.000001073579,0.00004815281,0.000002066364,0.00003785102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007772686,"threshold_uncertainty_score":0.002600253,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214986419364699,"score_gpt":0.2274366366354181,"score_spread":0.2052867724417712,"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."}}