{"id":"W4308463461","doi":"10.1016/j.nicl.2022.103262","title":"Advancing brain network models to reconcile functional neuroimaging and clinical research","year":2022,"lang":"en","type":"review","venue":"NeuroImage Clinical","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Deutsche Forschungsgemeinschaft; European Commission; Joachim Herz Stiftung","keywords":"Interpretability; Predictability; Neuroimaging; Context (archaeology); Computer science; Functional magnetic resonance imaging; Strengths and weaknesses; Artificial intelligence; Machine learning; Relevance (law); Functional neuroimaging; Data science; Neuroscience; Psychology","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":[0.003172907,0.001944337,0.00162394,0.002785071,0.0002333642,0.001841945,0.002261714,0.00221839,0.002248526],"category_scores_gemma":[0.006059288,0.0004803584,0.0009742086,0.001739846,0.001422604,0.003733772,0.001207539,0.003649446,0.001310587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001830574,"about_ca_system_score_gemma":0.002013013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002449611,"about_ca_topic_score_gemma":0.002674485,"domain_scores_codex":[0.9994373,0.0002488409,0.00004543925,0.0001025472,0.0001392504,0.00002659766],"domain_scores_gemma":[0.9973851,0.002010453,0.0001369552,0.000104566,0.0003020117,0.00006094334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005210673,0.00005256782,0.000617446,0.01239651,0.0005452175,0.000288115,0.0001977685,0.02186248,0.001528099,0.1891828,0.02330044,0.7499764],"study_design_scores_gemma":[0.00003118906,0.0001232464,0.001637462,0.01044212,0.0004531923,0.001235106,0.0001735838,0.02862073,0.00124998,0.4239827,0.5319057,0.0001450027],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0005895954,0.9415488,0.04744804,0.006244469,0.0008517458,0.00003187389,0.0001308567,0.0001274839,0.003027048],"genre_scores_gemma":[0.009027451,0.9685592,0.01916059,0.001041326,0.001010812,0.0000909883,0.0001942823,0.00003518462,0.0008801396],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003172907,"threshold_uncertainty_score":0.01678014,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6076680174027896,"score_gpt":0.5226547092174607,"score_spread":0.08501330818532893,"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."}}