{"id":"W3097634862","doi":"10.1016/j.bpsc.2020.10.006","title":"Cloud-Based Functional Magnetic Resonance Imaging Neurofeedback to Reduce the Negative Attentional Bias in Depression: A Proof-of-Concept Study","year":2020,"lang":"en","type":"article","venue":"Biological Psychiatry Cognitive Neuroscience and Neuroimaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Advanced Research; National Institute of Mental Health; National Institutes of Health; Intel Corporation; National Center for Advancing Translational Sciences; John Templeton Foundation","keywords":"Neurofeedback; Functional magnetic resonance imaging; Psychology; Major depressive disorder; Stimulus (psychology); Attentional bias; Cognitive psychology; Audiology; Attentional control; Depression (economics); Proof of concept; Clinical psychology; Physical medicine and rehabilitation; Neuroscience; Mood; Electroencephalography; Cognition; Medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000390207,0.0003632763,0.000346047,0.0001567192,0.0006170691,0.0001089987,0.0004663803,0.00003513882,0.0000271661],"category_scores_gemma":[0.01687143,0.0002561913,0.00010744,0.00177263,0.001204432,0.0002843784,0.0004826299,0.0005654187,0.000007668495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633474,"about_ca_system_score_gemma":0.000136744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001113399,"about_ca_topic_score_gemma":0.00000656995,"domain_scores_codex":[0.9955715,0.0009696073,0.0004979107,0.001816296,0.0006295775,0.0005151276],"domain_scores_gemma":[0.994234,0.005029891,0.0001930173,0.0002132039,0.0001326437,0.0001972097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00204557,0.001176098,0.882225,0.00002186027,0.0000021646,0.0001152651,0.0007955524,0.0005486791,0.09034035,0.0002867738,0.001275062,0.02116758],"study_design_scores_gemma":[0.001691219,0.001804998,0.9763297,0.00008058017,0.00001438406,0.00003911459,0.000958488,0.003868398,0.01397376,0.0004206087,0.0004808372,0.0003379189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621026,0.0004666956,0.0007105563,0.03371139,0.00123043,0.001456883,0.00006026074,0.00007873236,0.0001824622],"genre_scores_gemma":[0.9421477,0.000007755178,0.00005385682,0.05740473,0.0002267165,0.0001247402,8.55393e-7,0.00001892335,0.00001477331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09410464,"threshold_uncertainty_score":0.999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1379748406321105,"score_gpt":0.3196071363453377,"score_spread":0.1816322957132272,"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."}}