{"id":"W2145539542","doi":"10.1109/tmi.2011.2116034","title":"A Model Selection Method for Nonlinear System Identification Based fMRI Effective Connectivity Analysis","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research","keywords":"Akaike information criterion; Nonlinear system; Computer science; Model selection; Selection (genetic algorithm); Artificial intelligence; Cross-validation; Residual; Functional magnetic resonance imaging; Block (permutation group theory); System identification; Identification (biology); Algorithm; Mathematics; Pattern recognition (psychology); Machine learning; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002373681,0.001394374,0.001331025,0.001013383,0.000839912,0.0007327858,0.001070762,0.0009485124,0.002742736],"category_scores_gemma":[0.0042234,0.000604744,0.001365996,0.000672448,0.0006243924,0.0006941321,0.0009142987,0.001664573,0.0007462931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006472222,"about_ca_system_score_gemma":0.001517312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00699244,"about_ca_topic_score_gemma":0.005491103,"domain_scores_codex":[0.9987493,0.000612827,0.00006306918,0.0002459765,0.0002578382,0.00007098636],"domain_scores_gemma":[0.9982314,0.001155886,0.0001429769,0.00007209613,0.0003625603,0.00003508187],"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":[0.0000849687,0.00007212361,0.001327723,0.0001804721,0.0001892688,0.0001948936,0.0001313781,0.8584323,0.005240224,0.01398579,0.001467497,0.1186934],"study_design_scores_gemma":[0.000007115274,0.0000237126,0.000134106,0.000005295288,0.00001169108,0.00001977404,0.000005845496,0.9970061,0.0005650373,0.001655543,0.0005582342,0.000007485342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001472703,0.00004798854,0.9980226,0.00003716654,0.000008573672,0.00002502744,0.00001471218,0.0001633042,0.0002078632],"genre_scores_gemma":[0.2465906,0.0004278904,0.7474069,0.0001815036,0.00009662739,0.0009326369,0.0004345917,0.0003034518,0.003625734],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00699244,"threshold_uncertainty_score":0.01390344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04360683359865434,"score_gpt":0.3181672025048474,"score_spread":0.274560368906193,"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."}}