{"id":"W4401852142","doi":"10.1016/j.media.2024.103309","title":"Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; 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":"Inference; Computer science; Generative model; Artificial intelligence; Machine learning; Statistical inference; Bayesian inference; Bayesian probability; Pattern recognition (psychology); Generative grammar; Mathematics; Statistics","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":[0.000702527,0.0002326958,0.0005152656,0.0004276707,0.0001719892,0.0001671396,0.0002047638,0.0001446961,0.000239857],"category_scores_gemma":[0.000949953,0.0001902466,0.000358846,0.002022303,0.0001805176,0.0003427428,0.0002096068,0.0008379512,0.00002133845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001719388,"about_ca_system_score_gemma":0.000148519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002234016,"about_ca_topic_score_gemma":0.0001128356,"domain_scores_codex":[0.9979028,0.00009810426,0.0004334686,0.0006814869,0.0005442822,0.0003398386],"domain_scores_gemma":[0.9984717,0.0005047375,0.00006268472,0.0004384847,0.0001298283,0.0003925089],"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":[0.0003437458,0.002065989,0.06374292,0.002226933,0.01098096,0.005572902,0.002815608,0.01560373,0.128633,0.2592278,0.02468036,0.4841061],"study_design_scores_gemma":[0.0002560033,0.000025019,0.001878639,0.0001254057,0.0008801253,0.0000662137,0.00007629664,0.9876671,0.0004222233,0.008015208,0.000386807,0.0002008875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2347931,0.0003483201,0.749459,0.01424559,0.00005266083,0.0001599534,0.00001691828,0.0004929451,0.0004314608],"genre_scores_gemma":[0.9629775,0.00002755405,0.03549442,0.0008349929,0.0002512774,0.00003047699,0.0001336407,0.0000413305,0.0002087683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9720634,"threshold_uncertainty_score":0.775803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07956383460373577,"score_gpt":0.3922912596604168,"score_spread":0.3127274250566811,"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."}}