{"id":"W2892218220","doi":"10.1016/j.compbiomed.2018.09.004","title":"Predicting conversion from MCI to AD by integrating rs-fMRI and structural MRI","year":2018,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":156,"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; Novartis Pharmaceuticals Corporation; Servier; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; BioClinica; Eli Lilly and Company; Northern California Institute for Research and Education; Alzheimer's Drug Discovery Foundation","keywords":"Modality (human–computer interaction); Neuroimaging; Cognitive impairment; Computer science; Artificial intelligence; Cognition; Neuroscience; Psychology","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":[0.0001556589,0.0001032827,0.0001840028,0.00007098292,0.0001838897,0.000005240216,0.00007956177,0.00005846195,0.00001391671],"category_scores_gemma":[0.002045994,0.00007509446,0.000006936485,0.000108771,0.0006304223,0.00004791725,0.0001785388,0.0001355907,0.000002139318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001993521,"about_ca_system_score_gemma":0.000006228978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001661415,"about_ca_topic_score_gemma":0.00006563306,"domain_scores_codex":[0.9991685,0.00009476477,0.0001302849,0.0004033022,0.00005214473,0.0001510474],"domain_scores_gemma":[0.9970301,0.002767265,0.00004106463,0.00007947545,0.00002127935,0.0000608163],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003721933,0.00002139918,0.4865376,0.00003125606,0.00003454771,0.00001858344,0.01037394,0.000003815826,0.4044971,0.001580714,0.04322388,0.05330496],"study_design_scores_gemma":[0.01516795,0.01437731,0.600381,0.002174108,0.0001047721,0.0003238521,0.008164865,0.147824,0.09753122,0.03589387,0.07629925,0.001757746],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817635,0.0003698508,0.004473817,0.01162441,0.001425442,0.0001179034,0.00001110247,0.0000329568,0.0001810196],"genre_scores_gemma":[0.9894123,0.00006770919,0.001437844,0.008774983,0.0002808585,0.000002973532,0.000005554792,0.000003766097,0.00001402977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3069659,"threshold_uncertainty_score":0.3062263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02130939070299175,"score_gpt":0.2992604517642707,"score_spread":0.2779510610612789,"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."}}