{"id":"W2964609233","doi":"10.3389/fneur.2019.00904","title":"Identification of the Early Stage of Alzheimer's Disease Using Structural MRI and Resting-State fMRI","year":2019,"lang":"en","type":"article","venue":"Frontiers in Neurology","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"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; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; Eli Lilly and Company; Biogen; University of California, San Diego; BioClinica; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Feature selection; Resting state fMRI; Support vector machine; Prodromal Stage; Pattern recognition (psychology); Artificial intelligence; Correlation; Graph; Psychology; Feature (linguistics); Cognitive impairment; Linear discriminant analysis; Neuroscience; Cognition; Computer science; Mathematics","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":[0.0001476878,0.00008291681,0.0001688798,0.0001037737,0.00005845485,0.000007115517,0.0001581269,0.0000290886,0.000003153221],"category_scores_gemma":[0.00141111,0.00006902645,0.00003368938,0.000209427,0.0003618705,0.0001121002,0.0001467812,0.0001377405,5.862587e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008012998,"about_ca_system_score_gemma":0.00002977553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005124787,"about_ca_topic_score_gemma":0.000006584656,"domain_scores_codex":[0.9988836,0.0002578189,0.0002405523,0.0003209442,0.0001554539,0.0001416375],"domain_scores_gemma":[0.9989828,0.0004638708,0.0002329293,0.0002673382,0.00002927092,0.00002373625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002583237,0.00001284272,0.9097297,0.00003105708,0.000009376127,0.000004589517,0.000261903,0.007310115,0.08183532,0.000276806,0.0001273374,0.0001426831],"study_design_scores_gemma":[0.0003166411,0.0000792125,0.9462911,0.000006291637,0.00001601446,0.000003675106,0.00002267309,0.02929775,0.02132649,0.00249288,0.00007915663,0.00006805465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996851,0.0001259826,0.0001978081,0.00102462,0.001443572,0.0002916132,0.00003182118,0.000008867812,0.00002471969],"genre_scores_gemma":[0.9994177,0.00001089467,0.000100974,0.0003671257,0.00001025518,0.000003360318,1.97663e-7,0.000009060024,0.00008043596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06050883,"threshold_uncertainty_score":0.2814817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178809634492315,"score_gpt":0.2550648439217632,"score_spread":0.2332767475768401,"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."}}