{"id":"W4248334202","doi":"10.12688/mniopenres.12767.1","title":"MIST: A multi-resolution parcellation of functional brain networks","year":2017,"lang":"en","type":"article","venue":"MNI Open Research","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Institut Universitaire en Santé Mentale de Québec; McGill University; Institut Universitaire de Gériatrie de Montréal; Montreal Neurological Institute and Hospital","funders":"Fonds de Recherche du Québec - Santé; Azrieli Foundation; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Connectomics; Functional connectivity; Computer science; Cartography; Spatial normalization; Artificial intelligence; Geography; Neuroscience; Connectome; Biology; Voxel","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001093776,0.00132049,0.0007811658,0.002869982,0.001006063,0.002493681,0.001734571,0.001289597,0.04596999],"category_scores_gemma":[0.005104698,0.0008984854,0.001637654,0.002715979,0.0005565417,0.002253113,0.002425148,0.001426098,0.01126632],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008930324,"about_ca_system_score_gemma":0.001008851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007227993,"about_ca_topic_score_gemma":0.01648916,"domain_scores_codex":[0.9994185,0.00008440075,0.00004967668,0.0001629093,0.0002005759,0.00008389773],"domain_scores_gemma":[0.9987714,0.0003931026,0.0001433033,0.0003014544,0.0003099889,0.00008070403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007365842,0.0001072686,0.00650162,0.001534982,0.0004132961,0.0007865902,0.001691024,0.01778263,0.08187357,0.02715931,0.6070528,0.2543603],"study_design_scores_gemma":[0.0002109773,0.0002163933,0.04622996,0.00042238,0.0002676957,0.002196703,0.0005501885,0.233149,0.1277572,0.07219975,0.5163457,0.0004540513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03025848,0.000739429,0.7077796,0.001113205,0.0003945123,0.0004896608,0.1271313,0.1118181,0.02027576],"genre_scores_gemma":[0.1142963,0.0007607769,0.7112852,0.0004065696,0.000163472,0.001856014,0.1186696,0.04262721,0.009934774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04596999,"threshold_uncertainty_score":0.1537849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4633745538975311,"score_gpt":0.4637603814765836,"score_spread":0.0003858275790525334,"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."}}