{"id":"W2017436068","doi":"10.1016/j.neuroimage.2015.01.032","title":"BrainPrint: A discriminative characterization of brain morphology","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":182,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; National Center for Complementary and Integrative Health; National Institute on Aging; National Cancer Institute, Cairo University; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital; National Institutes of Health; National Cancer Institute; Servier; NIH Blueprint for Neuroscience Research; Fujirebio Europe; Eisai; Biogen; Pfizer; Canadian HIV Trials Network, Canadian Institutes of Health Research; BioClinica; Synarc; Association France Alzheimer; National Institute of Neurological Disorders and Stroke; IXICO; Takeda Pharmaceutical Company; Medpace; National Center for Research Resources; F. Hoffmann-La Roche; Massachusetts General Hospital; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; National Center for Complementary and Alternative Medicine; Meso Scale Diagnostics; Johnson and Johnson Pharmaceutical Research and Development; Harvard NeuroDiscovery Center; Alzheimer's Drug Discovery Foundation; Merck; Alzheimer's Association; Foundation for the National Institutes of Health; Genentech; Alexander von Humboldt-Stiftung; Centre d'Imagerie BioMédicale; Ellison Medical Foundation","keywords":"Discriminative model; Artificial intelligence; Brain morphometry; Computer science; Pattern recognition (psychology); Representation (politics); Similarity (geometry); Characterization (materials science); Polygon mesh; Magnetic resonance imaging; Image (mathematics); Medicine; Physics","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":[0.0002783055,0.00009256465,0.000187996,0.00007139676,0.00002400392,0.00001116354,0.0001331481,0.00005484101,0.000131881],"category_scores_gemma":[0.001501972,0.00007646497,0.0000468604,0.0001824176,0.00006935595,0.00009460531,0.00008472765,0.0001043228,0.00003197722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001337755,"about_ca_system_score_gemma":0.0000211832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006734771,"about_ca_topic_score_gemma":7.074922e-7,"domain_scores_codex":[0.9991799,0.000148478,0.0002367164,0.0001820269,0.000121342,0.0001315238],"domain_scores_gemma":[0.9991854,0.0002434825,0.0001589887,0.0002409822,0.0001024679,0.0000686668],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003322119,0.0004183331,0.00103545,0.00004098041,0.00001456217,0.00004385726,0.0006345013,8.979473e-7,0.7507524,0.2371904,0.007519634,0.002315762],"study_design_scores_gemma":[0.005452712,0.001927065,0.4385445,0.00009602019,0.0001679097,0.0002650736,0.000825701,0.002559747,0.187628,0.3308606,0.03049074,0.001182065],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937841,0.00000218334,0.0516137,0.002008674,0.0001255337,0.0001745312,0.00003910898,0.00005674319,0.008138507],"genre_scores_gemma":[0.9916353,0.000001262724,0.006725112,0.0005224575,0.00004656382,0.00001062964,0.00002437455,0.00001533718,0.001018996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5631244,"threshold_uncertainty_score":0.3118151,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028673799353285,"score_gpt":0.3295487545443496,"score_spread":0.2266813746090212,"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."}}