{"id":"W3132920621","doi":"10.1002/cjs.11604","title":"Multivariate functional response low‐rank regression with an application to brain imaging data","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multivariate statistics; Covariate; Human Connectome Project; Functional magnetic resonance imaging; Regression; Neuroimaging; Computer science; Regularization (linguistics); Functional data analysis; Multivariate analysis; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Machine learning; Psychology; Neuroscience; Functional connectivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01105514,0.001190975,0.001634656,0.0009565344,0.0005265822,0.001095627,0.001637224,0.001762401,0.002967858],"category_scores_gemma":[0.02375317,0.0007302761,0.001378531,0.001552076,0.001589228,0.001317354,0.001626437,0.002869257,0.0009310595],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008594667,"about_ca_system_score_gemma":0.001559559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008183354,"about_ca_topic_score_gemma":0.008015417,"domain_scores_codex":[0.9963112,0.002839698,0.0000815192,0.0002944653,0.0003261032,0.0001469139],"domain_scores_gemma":[0.9857459,0.01096066,0.0009240952,0.001040761,0.001066257,0.0002622746],"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":[0.0001737822,0.00009678112,0.001596057,0.0001243548,0.000103954,0.0002401481,0.0001158496,0.9016247,0.002888801,0.05070376,0.001864573,0.04046736],"study_design_scores_gemma":[0.000009022275,0.00001675373,0.0001416201,0.000003631082,0.000004329748,0.00001657808,0.000004255594,0.9934121,0.0002432663,0.005839277,0.0002986066,0.00001052403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008059153,0.0001101111,0.9908954,0.0003145361,0.00001080957,0.00002743891,0.00008721724,0.0003320801,0.0001632761],"genre_scores_gemma":[0.3181967,0.0006367877,0.6745821,0.0003111849,0.0001385542,0.0005193651,0.0006280071,0.0004408063,0.004546637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01105514,"threshold_uncertainty_score":0.0584659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06789917346270974,"score_gpt":0.293176718489014,"score_spread":0.2252775450263043,"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."}}