{"id":"W3094373193","doi":"10.1016/j.neuroimage.2021.118207","title":"Predictors of real-time fMRI neurofeedback performance and improvement – A machine learning mega-analysis","year":2021,"lang":"en","type":"review","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Montreal Neurological Institute and Hospital; McGill University","funders":"Foundation for Research in Science and the Humanities; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Seventh Framework Programme; Deutsche Forschungsgemeinschaft; Horizon 2020 Framework Programme; Universität Zürich","keywords":"Neurofeedback; Psychology; Sensorimotor rhythm; Psychological intervention; Brain–computer interface; Brain activity and meditation; Neuroimaging; Physical medicine and rehabilitation; Cognitive psychology; Electroencephalography; Medicine; Neuroscience; Psychiatry","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004084923,0.0005904576,0.002491018,0.0005191494,0.0002721883,0.0000820986,0.0003679782,0.000128394,0.000162356],"category_scores_gemma":[0.005154514,0.0005121537,0.0007684174,0.001762128,0.0002470786,0.0001952732,0.0007457999,0.0007716016,0.00003262667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005985193,"about_ca_system_score_gemma":0.0001243668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003923348,"about_ca_topic_score_gemma":0.000005102687,"domain_scores_codex":[0.995941,0.000755798,0.0007958919,0.001431686,0.0006705415,0.0004050426],"domain_scores_gemma":[0.9936075,0.004783607,0.000718202,0.0006839926,0.00009308116,0.0001135732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00009057961,0.0007509048,0.003011917,0.03614639,0.00454146,0.0005999812,0.0003804335,0.0003623033,0.0230069,0.0001599663,0.006474381,0.9244748],"study_design_scores_gemma":[0.0003249121,0.0007421198,0.0006879946,0.001075736,0.007343734,0.00008102151,0.000007561319,0.00178374,0.0007420728,0.000002265669,0.9865115,0.0006973624],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.008591504,0.9846141,0.00001311796,0.0002813663,0.0006028912,0.001120928,0.0003324772,0.0002340263,0.0042096],"genre_scores_gemma":[0.0009501952,0.9943601,0.00003409257,0.000193878,0.0000961455,0.00006396635,0.00004235554,0.0000833577,0.004175851],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9800371,"threshold_uncertainty_score":0.999733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04302923236969472,"score_gpt":0.2906360585653414,"score_spread":0.2476068261956467,"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."}}