{"id":"W2903950571","doi":"10.1016/j.neuroimage.2019.05.040","title":"A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments","year":2019,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Takeda Pharmaceutical Company; AbbVie; Biogen; Université Côte d’Azur; Bristol-Myers Squibb; Eli Lilly and Company; GE Healthcare; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Agence Nationale de la Recherche; Roche; Alzheimer's Drug Discovery Foundation","keywords":"Brain morphometry; Temporal lobe; Magnetic resonance imaging; Population; Neuroscience; Disease; Generative model; Psychology; Generative grammar; Pathology; Artificial intelligence; Medicine; Computer science; Radiology","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":[0.0008486579,0.0007803385,0.0003771253,0.001088041,0.000302451,0.0005328829,0.001017498,0.0006676542,0.003873004],"category_scores_gemma":[0.0009008225,0.0003334043,0.0007960909,0.0004866718,0.0004750535,0.0006001422,0.000410263,0.0005552912,0.0009032493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005386144,"about_ca_system_score_gemma":0.0006889419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007204711,"about_ca_topic_score_gemma":0.01063417,"domain_scores_codex":[0.9998292,0.00005547834,0.000007216674,0.00007167267,0.00001852841,0.00001787122],"domain_scores_gemma":[0.9997292,0.00009102194,0.00006801228,0.00003411907,0.00004398524,0.00003356109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002973337,0.003424569,0.1511037,0.001199304,0.00216304,0.003114837,0.002077994,0.2547391,0.2528887,0.1364578,0.009734861,0.1801227],"study_design_scores_gemma":[0.0003272521,0.004763731,0.2672789,0.0002138127,0.001268542,0.00426969,0.0005918687,0.5829779,0.0121409,0.1106094,0.01542531,0.000132594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6789367,0.0008428734,0.3019121,0.001232905,0.0001035547,0.000547693,0.00419939,0.000766657,0.01145823],"genre_scores_gemma":[0.9220352,0.0008689041,0.05998939,0.0001795931,0.00002944776,0.0009245375,0.001711298,0.00007386903,0.01418773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007204711,"threshold_uncertainty_score":0.01432556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1014017694669002,"score_gpt":0.3477536081355564,"score_spread":0.2463518386686562,"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."}}