{"id":"W1971938128","doi":"10.1016/j.bbr.2015.02.054","title":"Compensatory motor network connectivity is associated with motor sequence learning after subcortical stroke","year":2015,"lang":"en","type":"article","venue":"Behavioural Brain Research","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia Hospital; BC Mental Health & Substance Use Services; University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; National Institutes of Health; Michael Smith Health Research BC; Alberta Medical Association; Eunice Kennedy Shriver National Institute of Child Health and Human Development; Canada Research Chairs; Heart and Stroke Foundation of Canada","keywords":"Neuroscience; Motor learning; Functional magnetic resonance imaging; Psychology; Stroke (engine); Motor cortex; Supplementary motor area","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":[0.0002931913,0.0002903045,0.0002773918,0.0006841918,0.0002511468,0.0003163666,0.0002869517,0.0003114417,0.003121677],"category_scores_gemma":[0.002750775,0.0001521004,0.0001699888,0.0004379625,0.0003785612,0.0005315986,0.0003082834,0.0003815357,0.0002007714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003250557,"about_ca_system_score_gemma":0.0003161553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002485368,"about_ca_topic_score_gemma":0.005715516,"domain_scores_codex":[0.9998657,0.00002625415,0.00001361125,0.00003369859,0.00002352855,0.00003735633],"domain_scores_gemma":[0.9989575,0.0003057798,0.0004863882,0.00007547338,0.00008322946,0.0000916464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004462827,0.001340582,0.5831648,0.0003686846,0.0008726902,0.002931481,0.001002091,0.007482511,0.2839521,0.003194437,0.002047066,0.1091808],"study_design_scores_gemma":[0.0000191752,0.0002532565,0.9893571,0.00001265705,0.00005631403,0.0008488562,0.0001231756,0.003621011,0.004005263,0.001498612,0.0001959119,0.00000880605],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976694,0.0001039464,0.001079936,0.00009159921,0.000006344738,0.00001736529,0.0001174078,0.00001646477,0.0008975093],"genre_scores_gemma":[0.998928,0.00008694086,0.0002919299,0.00001797999,0.000008847862,0.000020985,0.0001698516,0.000006961782,0.000468427],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003121677,"threshold_uncertainty_score":0.01044303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2284610132504963,"score_gpt":0.3742363392195673,"score_spread":0.145775325969071,"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."}}