{"id":"W4400184505","doi":"10.1162/imag_a_00228","title":"A benchmark of individual auto-regressive models in a massive fMRI dataset","year":2024,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Institut Universitaire de Gériatrie de Montréal","funders":"","keywords":"Functional magnetic resonance imaging; Computer science; Autocorrelation; Artificial intelligence; Autoregressive model; Regression; Generalization; Brain activity and meditation; Pattern recognition (psychology); Benchmark (surveying); Neuroimaging; Stimulus (psychology); Convolutional neural network; Correlation; Machine learning; Cognitive psychology; Statistics; Psychology; Electroencephalography; Mathematics; Neuroscience; Cartography","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":[0.003997849,0.001761949,0.0008001705,0.0008089145,0.0004568917,0.0007651614,0.001517831,0.001911421,0.00133496],"category_scores_gemma":[0.009343497,0.0005854397,0.001226331,0.0008362549,0.0007682234,0.001748804,0.00109119,0.002251331,0.0007481595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001090464,"about_ca_system_score_gemma":0.001190364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02230033,"about_ca_topic_score_gemma":0.0235289,"domain_scores_codex":[0.9992242,0.0002599536,0.00004647186,0.0003038009,0.00008982752,0.00007572832],"domain_scores_gemma":[0.9972384,0.001557473,0.0001667523,0.0005456101,0.0003662742,0.0001254322],"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":[0.0004113848,0.0002607338,0.004219204,0.0001982235,0.0004072222,0.0001304406,0.00006486335,0.9299865,0.004107925,0.00146251,0.004413005,0.05433792],"study_design_scores_gemma":[0.00001723629,0.00008426602,0.001537351,0.000009113583,0.00001761391,0.00002313396,0.00001385113,0.9952267,0.001211679,0.001429455,0.0004168885,0.00001260833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7971744,0.002806751,0.1778842,0.00180712,0.0002715682,0.0002435815,0.0070059,0.008129487,0.004676952],"genre_scores_gemma":[0.8954248,0.0005876147,0.08754253,0.0003281765,0.00008265969,0.0002039959,0.01283475,0.0003849152,0.00261059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02230033,"threshold_uncertainty_score":0.04434109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05250717708178675,"score_gpt":0.309298488302708,"score_spread":0.2567913112209212,"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."}}