{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005529627,0.000187298,0.0003388816,0.0001736629,0.0003931046,0.00007651041,0.000206599,0.00008715944,0.001703984],"category_scores_gemma":[0.01848071,0.0001716996,0.0001801731,0.003056867,0.0002072951,0.0003605102,0.0002187008,0.0002832193,0.000159611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006785309,"about_ca_system_score_gemma":0.000157581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002501872,"about_ca_topic_score_gemma":0.0002449828,"domain_scores_codex":[0.9968958,0.0003272627,0.0002978386,0.0008896639,0.00119875,0.0003906188],"domain_scores_gemma":[0.9956656,0.003277191,0.00008141046,0.0003205302,0.0003030896,0.0003522419],"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":[0.00134304,0.001369177,0.3048598,0.0001908525,0.004375486,0.0008347583,0.003189658,0.03206572,0.2431328,0.00282854,0.07103837,0.3347718],"study_design_scores_gemma":[0.001008033,0.0002305031,0.4791206,0.0000271372,0.0008945178,0.00007998241,0.0004116732,0.5038964,0.01008768,0.0004944201,0.00319852,0.0005504987],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8199987,0.00005406241,0.1617953,0.01632635,0.000223964,0.0001557101,0.00003541951,0.0001542979,0.001256246],"genre_scores_gemma":[0.9775134,0.00001682767,0.0003962956,0.02045023,0.000619759,0.0001131175,0.0001328278,0.0000148413,0.0007426927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4718307,"threshold_uncertainty_score":0.9992086,"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."}}