{"id":"W2310684638","doi":"10.1523/eneuro.0158-15.2016","title":"Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain","year":2016,"lang":"en","type":"article","venue":"eNeuro","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"FP7 Information and Communication Technologies; Seventh Framework Programme; James S. McDonnell Foundation; National Institutes of Health; European Commission; National Institute of Neurological Disorders and Stroke","keywords":"Stroke (engine); Diffusion MRI; Neuroimaging; Stroke recovery; Neuroscience; Brain activity and meditation; Motor system; Computer science; Electroencephalography; Physical medicine and rehabilitation; Medicine; Psychology; Physics; Magnetic resonance imaging; Rehabilitation","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":[0.0003681714,0.000667526,0.000523148,0.0003857064,0.0002785412,0.0007797141,0.00084509,0.0009814519,0.000923068],"category_scores_gemma":[0.001140262,0.00042112,0.0007743252,0.0002233167,0.0008613327,0.00047047,0.0008250072,0.0005078396,0.0001288005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005330596,"about_ca_system_score_gemma":0.0007457848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009905427,"about_ca_topic_score_gemma":0.004719444,"domain_scores_codex":[0.9999084,0.00004057087,0.000004055905,0.0000182512,0.00001508312,0.00001352863],"domain_scores_gemma":[0.9997289,0.0001684273,0.00003735692,0.00002117902,0.00002038176,0.00002384289],"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":[0.00003167309,0.00001790655,0.0004294492,0.0000135461,0.00001819367,0.00004082908,0.0000411984,0.9957799,0.001044093,0.00130486,0.00008242811,0.001195934],"study_design_scores_gemma":[0.00000987893,0.000027057,0.0002146605,0.000002584296,0.000005349483,0.00001345054,0.000008665166,0.9978595,0.0001642603,0.00151285,0.0001774992,0.000004275715],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6151773,0.0005176674,0.3757955,0.0009179725,0.00007831381,0.0001540097,0.0004740639,0.0005674752,0.006317728],"genre_scores_gemma":[0.9684473,0.0003669795,0.02853001,0.00009164587,0.00002104007,0.0002640246,0.0001563053,0.00005585593,0.002067035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009905427,"threshold_uncertainty_score":0.01969552,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02229480573123988,"score_gpt":0.2123606645041065,"score_spread":0.1900658587728666,"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."}}