{"id":"W2010408890","doi":"10.1016/j.neuroimage.2004.05.018","title":"Spatiotemporal analysis of event-related fMRI data using partial least squares","year":2004,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":347,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; James S. McDonnell Foundation","keywords":"Univariate; Partial least squares regression; Resampling; Functional magnetic resonance imaging; Artificial intelligence; Computer science; Multivariate statistics; Neuroimaging; Pattern recognition (psychology); Psychology; Machine learning; Neuroscience","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004546125,0.0005182666,0.0004244231,0.0009026948,0.0003298192,0.000522295,0.0004901796,0.0004016584,0.002620418],"category_scores_gemma":[0.002945696,0.000347419,0.0006003315,0.001448949,0.000193654,0.0007137895,0.0002898095,0.000618484,0.0006886963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403814,"about_ca_system_score_gemma":0.0005808217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001963273,"about_ca_topic_score_gemma":0.003859899,"domain_scores_codex":[0.9998136,0.00004925214,0.000015749,0.00005505735,0.00004896851,0.00001740394],"domain_scores_gemma":[0.999607,0.000193697,0.0000428149,0.00006971436,0.00007436882,0.00001239206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003582461,0.0001644274,0.002993641,0.0004414852,0.0004875709,0.0002686775,0.0001911718,0.03568444,0.3711605,0.006746515,0.003670839,0.5778325],"study_design_scores_gemma":[0.00007610504,0.0003550372,0.040642,0.00002689437,0.0003473886,0.00152194,0.0001538099,0.7460195,0.1737123,0.02526883,0.01176911,0.0001070606],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03097641,0.0002230814,0.9661506,0.00009337408,0.00002679052,0.00008706568,0.0005012002,0.001195032,0.0007464913],"genre_scores_gemma":[0.2742639,0.0005240832,0.7213703,0.00006556322,0.00003819924,0.0003812927,0.001313812,0.0005612987,0.001481548],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002620418,"threshold_uncertainty_score":0.008766234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1167718652115018,"score_gpt":0.3367767946479234,"score_spread":0.2200049294364215,"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."}}