{"id":"W2728707994","doi":"10.7554/elife.28927","title":"Inferring multi-scale neural mechanisms with brain network modelling","year":2018,"lang":"en","type":"article","venue":"eLife","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":202,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Horizon 2020; Bundesministerium für Bildung und Forschung; European Commission; Berlin Institute of Health; James S. McDonnell Foundation","keywords":"Neurophysiology; Functional magnetic resonance imaging; Connectome; Resting state fMRI; Neuroscience; Electroencephalography; Computer science; Inference; EEG-fMRI; Brain activity and meditation; Artificial intelligence; Novelty; Population; Psychology; Functional connectivity; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0002312113,0.0001607979,0.0001549989,0.000044483,0.0005083793,0.0000520441,0.0001674439,0.00004119258,0.00003045866],"category_scores_gemma":[0.0008433264,0.0001357476,0.00003955849,0.0002919128,0.000157957,0.000208429,0.0001434457,0.0001636961,0.0001366414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003249749,"about_ca_system_score_gemma":0.00002530475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003125524,"about_ca_topic_score_gemma":0.0002204001,"domain_scores_codex":[0.9985605,0.00008090946,0.0001367396,0.0004772033,0.000357075,0.0003875948],"domain_scores_gemma":[0.9983197,0.001228247,0.00005998252,0.0002397129,0.0000742364,0.00007811634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006031411,0.0003565041,0.009230881,0.000048913,0.00007578388,0.00009607356,0.003457309,0.7102926,0.2048213,0.03890263,0.02910198,0.003012881],"study_design_scores_gemma":[0.0008070309,0.0004677239,0.001395756,0.00004646869,0.00001181225,0.00003997401,0.00008462942,0.8951417,0.09213015,0.002163632,0.007298775,0.0004123107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2841354,0.00002777322,0.7086177,0.003599541,0.0009080316,0.0002337199,0.000004736617,0.0003107957,0.002162217],"genre_scores_gemma":[0.9673465,0.000002333941,0.01984724,0.01141027,0.0006957495,0.00002338457,5.275639e-7,0.00003156294,0.0006423664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6887705,"threshold_uncertainty_score":0.5535626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06684019509728883,"score_gpt":0.2714940986117285,"score_spread":0.2046539035144397,"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."}}