{"id":"W2593212236","doi":"10.1016/j.jneumeth.2017.03.006","title":"Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM","year":2017,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":195,"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; Le Bonheur Children's Hospital; National Institutes of Health; Alzheimer's Disease Neuroimaging Initiative; U.S. Department of Defense","keywords":"Support vector machine; Feature selection; Graph; Artificial intelligence; Receiver operating characteristic; Resting state fMRI; Pattern recognition (psychology); Cognitive impairment; Computer science; Machine learning; Psychology; Cognition; Neuroscience","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":[0.0006054773,0.0006628977,0.0005844388,0.001656771,0.0002430374,0.0007907595,0.0004227311,0.0008317244,0.0009333889],"category_scores_gemma":[0.001740485,0.0001628578,0.0007820901,0.0005415724,0.0002179239,0.0005255806,0.0002769217,0.0007936683,0.0003013814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003091316,"about_ca_system_score_gemma":0.0003581811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004976742,"about_ca_topic_score_gemma":0.004669393,"domain_scores_codex":[0.999873,0.00003257083,0.000009233119,0.00004601738,0.00001500172,0.00002413387],"domain_scores_gemma":[0.9995162,0.0003115445,0.00004171151,0.00003009466,0.00006361525,0.00003684273],"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":[0.00279315,0.001688136,0.215442,0.0003379174,0.001282619,0.001107663,0.0001719879,0.2134809,0.01669267,0.005005447,0.01403952,0.5279582],"study_design_scores_gemma":[0.00003056282,0.0001616964,0.03920908,0.00002268104,0.0001458192,0.0003053985,0.00008023668,0.9502001,0.001304886,0.008111903,0.0004077636,0.00001975851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8410199,0.001463029,0.1516119,0.0009656101,0.0001418667,0.00009124795,0.00167815,0.0009533361,0.002075062],"genre_scores_gemma":[0.9847723,0.0001992982,0.01329419,0.00006128057,0.00004666931,0.00002302468,0.001059105,0.00002215909,0.0005219086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004976742,"threshold_uncertainty_score":0.009895563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1419211466240455,"score_gpt":0.3994692548903988,"score_spread":0.2575481082663533,"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."}}