{"id":"W2061588113","doi":"10.3389/fnins.2014.00419","title":"Detection of abnormal resting-state networks in individual patients suffering from focal epilepsy: an initial step toward individual connectivity assessment","year":2014,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University; Université de Montréal; Montreal Neurological Institute and Hospital; Institut Universitaire de Gériatrie de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Resting state fMRI; Blood-oxygen-level dependent; Modularity (biology); Population; Functional connectivity; Psychology; Neuroscience; Computer science; Medicine; Functional magnetic resonance imaging; Biology","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":[0.0004362052,0.0004584617,0.0003392085,0.001256989,0.0001676404,0.0003488122,0.0002762064,0.0003667138,0.0007881566],"category_scores_gemma":[0.001584907,0.0001193199,0.0001932412,0.0004714945,0.0002491869,0.0004465692,0.0003802101,0.0001974385,0.0001223216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001154635,"about_ca_system_score_gemma":0.0001129139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000756395,"about_ca_topic_score_gemma":0.001688694,"domain_scores_codex":[0.9998661,0.00003331083,0.00001682315,0.00004417343,0.00002464528,0.00001489609],"domain_scores_gemma":[0.9996279,0.0001552524,0.00007136445,0.00005321016,0.0000514446,0.00004065638],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001754281,0.0002168963,0.6574256,0.0002976332,0.0003449904,0.002813447,0.001868719,0.009041282,0.163799,0.0007438189,0.000821997,0.1608723],"study_design_scores_gemma":[0.00003225075,0.0006098752,0.9322336,0.0000202547,0.0001283427,0.003488472,0.0007911132,0.04220055,0.0171072,0.002322519,0.001025397,0.0000404458],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845242,0.0001487477,0.01433206,0.00005770049,0.000003861263,0.00005934459,0.0003238852,0.0001026479,0.000447474],"genre_scores_gemma":[0.9934685,0.00006920865,0.00604755,0.000009021952,0.000006495313,0.00002491832,0.0002983031,0.000008972067,0.00006687723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001256989,"threshold_uncertainty_score":0.002636611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04225462667904227,"score_gpt":0.2823490503075443,"score_spread":0.240094423628502,"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."}}