{"id":"W3092384632","doi":"10.1038/s41467-020-18823-9","title":"Reconstructing lost BOLD signal in individual participants using deep machine learning","year":2020,"lang":"en","type":"article","venue":"Nature Communications","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIH Blueprint for Neuroscience Research; National Institute of Neurological Disorders and Stroke; National Institute on Deafness and Other Communication Disorders; National Institute on Drug Abuse; National Institute of Mental Health; McDonnell Center for Systems Neuroscience; Recruitment Program of Global Experts; National Key Research and Development Program of China; Canadian Institutes of Health Research; Peking University; National Natural Science Foundation of China; U.S. Department of Health and Human Services; National Institutes of Health; Government of Canada","keywords":"Computer science; Artificial intelligence; Neuroimaging; SIGNAL (programming language); Deep learning; Frame (networking); Functional connectivity; Pattern recognition (psychology); Blood-oxygen-level dependent; Functional neuroimaging; Neuroscience; Machine learning; Functional magnetic resonance imaging; Computer vision; Psychology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004078,0.0005713541,0.0004440541,0.0004810817,0.000256579,0.0004739643,0.0004481483,0.0008177871,0.0008596198],"category_scores_gemma":[0.004138438,0.0003537244,0.000529695,0.0002875474,0.000541768,0.0005857998,0.000722384,0.0007462921,0.0001796228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002817943,"about_ca_system_score_gemma":0.0004273725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001652468,"about_ca_topic_score_gemma":0.003172956,"domain_scores_codex":[0.9997746,0.00005606125,0.00001630298,0.00008866435,0.00003136452,0.00003293317],"domain_scores_gemma":[0.9995046,0.0001953997,0.00007197279,0.0001404002,0.00005470829,0.00003299852],"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":[0.001754117,0.0002904607,0.05034261,0.0004759855,0.0008500197,0.002941976,0.002062238,0.1848033,0.5473748,0.006763793,0.003020234,0.1993204],"study_design_scores_gemma":[0.00007333634,0.000580696,0.05990104,0.00007104878,0.0002977306,0.002975231,0.0004307397,0.7519737,0.1513383,0.02862536,0.003603846,0.0001289966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7394663,0.000269161,0.2580158,0.0003550193,0.00006168662,0.00005703198,0.0006215672,0.0005960367,0.0005573579],"genre_scores_gemma":[0.9412408,0.0001093914,0.05738687,0.00007734729,0.00001238522,0.00005663006,0.0005502838,0.000138382,0.0004279419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001652468,"threshold_uncertainty_score":0.005310178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1909451783583457,"score_gpt":0.3514378671495706,"score_spread":0.1604926887912249,"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."}}