{"id":"W3135100175","doi":"10.1016/j.media.2021.102026","title":"A structural enriched functional network: An application to predict brain cognitive performance","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"NIH Blueprint for Neuroscience Research; National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; U.S. National Library of Medicine; National Institute on Aging; National Research Foundation of Korea; McDonnell Center for Systems Neuroscience; Directorate for Computer and Information Science and Engineering; National Institutes of Health; National Science Foundation","keywords":"Connectome; Computer science; Artificial intelligence; Functional magnetic resonance imaging; Cognition; Diffusion MRI; Network analysis; Default mode network; Cognitive network; Network model; Consistency (knowledge bases); Resting state fMRI; Machine learning; Network architecture; Functional connectivity; Neuroscience; Cognitive radio; Magnetic resonance imaging; Psychology; Computer network","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":[0.0005281277,0.0006435589,0.0003960637,0.002309917,0.0004059714,0.0006104824,0.0003739077,0.0005148737,0.00229485],"category_scores_gemma":[0.001738377,0.00018901,0.0005885333,0.001643472,0.0002254737,0.000423828,0.0004395386,0.0003497224,0.0004103993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003907698,"about_ca_system_score_gemma":0.0004602778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00743791,"about_ca_topic_score_gemma":0.01642234,"domain_scores_codex":[0.9998968,0.00002337099,0.000004910505,0.00004283743,0.00001972792,0.00001231717],"domain_scores_gemma":[0.9996847,0.0001502398,0.00005603584,0.00002294256,0.0000536354,0.0000325583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002414391,0.0008810894,0.1919322,0.000752229,0.002073587,0.001435315,0.0006453279,0.1180576,0.1145205,0.008711179,0.01646949,0.5421072],"study_design_scores_gemma":[0.0001015083,0.0004747993,0.3187191,0.00006817899,0.0007461162,0.001495941,0.0002018894,0.6422654,0.01440156,0.0163455,0.005084784,0.00009508165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7264665,0.001223017,0.2542445,0.0008014038,0.00007990893,0.000296453,0.01109861,0.002307935,0.003481771],"genre_scores_gemma":[0.9061555,0.0006473917,0.08686052,0.00005938848,0.00009909419,0.0002543291,0.003419976,0.0001642794,0.002339532],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00743791,"threshold_uncertainty_score":0.01478922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02227060910104076,"score_gpt":0.2857288497640012,"score_spread":0.2634582406629604,"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."}}