{"id":"W4384207068","doi":"10.1016/j.nicl.2023.103472","title":"ComBat Harmonization: Empirical Bayes versus fully Bayes approaches","year":2023,"lang":"en","type":"article","venue":"NeuroImage Clinical","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; U.S. National Library of Medicine; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; University of Southern California; Pennsylvania Department of Health; Meso Scale Diagnostics; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; National Heart, Lung, and Blood Institute; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Science Foundation","keywords":"Computer science; Bayes' theorem; Bayesian probability; Bayesian hierarchical modeling; Artificial intelligence; Machine learning; Bayes factor; Bayesian inference; Posterior probability; Bayesian linear regression; 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":[0.02361127,0.002133564,0.00314427,0.002635459,0.0009808962,0.003580923,0.00487991,0.002291793,0.00783222],"category_scores_gemma":[0.05611445,0.001810015,0.002366474,0.001903187,0.002329853,0.004623962,0.003375165,0.003593107,0.001653643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00154158,"about_ca_system_score_gemma":0.002371699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004118686,"about_ca_topic_score_gemma":0.00408472,"domain_scores_codex":[0.9878091,0.007823062,0.0006482618,0.001425285,0.001934749,0.0003595941],"domain_scores_gemma":[0.9642742,0.0290441,0.001763266,0.002550552,0.002013076,0.0003548208],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008309783,0.0002349121,0.004152152,0.000667442,0.0009289158,0.0002908298,0.001047883,0.3568726,0.001712249,0.2051822,0.009633994,0.4184459],"study_design_scores_gemma":[0.0001027655,0.0001017052,0.0006252007,0.0001326102,0.0001099602,0.0001435631,0.00009493023,0.7889262,0.000982549,0.2035076,0.005218637,0.00005425545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002987324,0.0004760583,0.9941404,0.0003256106,0.00003946655,0.0001506725,0.0001179858,0.0003750007,0.001387342],"genre_scores_gemma":[0.1680956,0.001031388,0.8237322,0.0008081761,0.0004294348,0.0007941829,0.000849697,0.0007379979,0.00352136],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02361127,"threshold_uncertainty_score":0.1248699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5039124505202172,"score_gpt":0.4287254257522735,"score_spread":0.0751870247679437,"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."}}