{"id":"W2949875748","doi":"10.1101/157263","title":"Inferring multi-scale neural mechanisms with brain network modelling","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Neurophysiology; Resting state fMRI; Functional magnetic resonance imaging; Connectome; Electroencephalography; Computer science; Neuroscience; Inference; Network topology; Novelty; Artificial intelligence; EEG-fMRI; Brain activity and meditation; Population; Functional connectivity; Psychology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001037651,0.0008740291,0.0007604199,0.0008985825,0.0004196075,0.001112728,0.001317051,0.00174592,0.0009792976],"category_scores_gemma":[0.006056572,0.0008390357,0.001028137,0.0006243444,0.001006029,0.0020593,0.001214201,0.00127953,0.000189525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032064,"about_ca_system_score_gemma":0.0007658795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006481406,"about_ca_topic_score_gemma":0.007453044,"domain_scores_codex":[0.9996549,0.0001706972,0.00001408818,0.00009234992,0.00004312766,0.00002491251],"domain_scores_gemma":[0.9979845,0.001590601,0.0001673946,0.0001260111,0.00006100777,0.00007041398],"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":[0.000007676593,0.000005784139,0.0003644172,0.00001068832,0.00002680255,0.00002875371,0.000016338,0.9936132,0.0005006612,0.003925332,0.00006721211,0.001433175],"study_design_scores_gemma":[0.000001811619,0.000001702072,0.00006268117,8.675074e-7,0.000001535748,0.000004143919,0.000001843977,0.9933133,0.00004832308,0.006520491,0.0000415531,0.000001885998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06773619,0.0002202746,0.9300849,0.0004297995,0.00002140597,0.00002927208,0.0001591228,0.0003130286,0.001006011],"genre_scores_gemma":[0.8656241,0.0003465451,0.1320318,0.0001254869,0.00004872484,0.000237543,0.0002652786,0.0001278152,0.001192747],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006481406,"threshold_uncertainty_score":0.01288736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04771913100736744,"score_gpt":0.2447669307030951,"score_spread":0.1970477996957276,"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."}}