{"id":"W4362630870","doi":"10.1093/jrsssc/qlad022","title":"Longitudinal canonical correlation analysis","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Canonical correlation; Canonical analysis; Correlation; Multivariate statistics; Longitudinal data; Longitudinal study; Statistics; Multivariate analysis; Mathematics; Latent variable; Applied mathematics; Computer science; Data mining; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007331408,0.0002537821,0.0005748055,0.00008897424,0.0007709074,0.0001236708,0.0004503791,0.000109025,0.0003631631],"category_scores_gemma":[0.007833171,0.0001808107,0.000387052,0.001563243,0.0007047075,0.0001131569,0.0002846545,0.0006640852,0.00009438455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00024753,"about_ca_system_score_gemma":0.0001992594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002401437,"about_ca_topic_score_gemma":0.00005584008,"domain_scores_codex":[0.996995,0.0002012037,0.0007310366,0.000391132,0.001211443,0.0004702187],"domain_scores_gemma":[0.9888771,0.009928978,0.0005193921,0.0002789946,0.0002148631,0.0001806427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0006250701,0.0003017232,0.009538688,0.0000824692,0.001445583,0.000119639,0.0007985442,0.09662062,0.00150711,0.4602841,0.4263236,0.002352883],"study_design_scores_gemma":[0.002015528,0.0009212727,0.7221961,0.00004097427,0.003273352,0.0001008615,0.001094884,0.1256219,0.001630857,0.1152187,0.02688638,0.0009991438],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03227049,0.00003385782,0.9524927,0.007089304,0.002204912,0.0004173727,0.002456557,0.0001493274,0.002885494],"genre_scores_gemma":[0.9810442,0.00004404763,0.01560639,0.001078078,0.0003104196,0.00001416584,0.00002088962,0.00003409856,0.001847664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9487737,"threshold_uncertainty_score":0.9377603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02611802803688286,"score_gpt":0.2698776847131305,"score_spread":0.2437596566762476,"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."}}