{"id":"W4210463906","doi":"10.1016/j.compbiomed.2022.105285","title":"Brain age estimation using multi-feature-based networks","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; Natural Science Foundation of Jilin Province","keywords":"Gyrification; Feature (linguistics); Metric (unit); Pattern recognition (psychology); Mean absolute error; Human Connectome Project; Computer science; Artificial intelligence; Estimation; Neuroimaging; Brain morphometry; Index (typography); Curvature; Magnetic resonance imaging; Statistics; Mathematics; Mean squared error; Cerebral cortex; Neuroscience; Biology; Medicine; Functional connectivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008990772,0.0006640656,0.0005936153,0.002419963,0.0003354122,0.0006319131,0.0006135695,0.0008536961,0.001075611],"category_scores_gemma":[0.00335906,0.0002717786,0.000766901,0.001332119,0.0002225532,0.001565332,0.0005533514,0.0005869218,0.0005630911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004274246,"about_ca_system_score_gemma":0.0003037254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003898147,"about_ca_topic_score_gemma":0.0056987,"domain_scores_codex":[0.9996693,0.00007707624,0.00001827872,0.0001377129,0.00005978617,0.00003779538],"domain_scores_gemma":[0.998874,0.0005010535,0.000197516,0.0001044344,0.0002767084,0.00004621828],"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":[0.0007272075,0.000247074,0.05308474,0.0002549546,0.0005589936,0.0006684824,0.0002092405,0.1615348,0.03897787,0.005329464,0.006726889,0.7316803],"study_design_scores_gemma":[0.000009398947,0.00007485376,0.02384744,0.00002494805,0.0001021625,0.0007169126,0.00003651914,0.958797,0.007142743,0.007747528,0.001470332,0.00003008831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.149962,0.001667518,0.844621,0.0002931951,0.0001437549,0.00006925487,0.0009560508,0.0008442307,0.001443045],"genre_scores_gemma":[0.8679861,0.0007677505,0.1280152,0.00006315846,0.0001764291,0.00007090182,0.0009533747,0.00008827906,0.001878782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003898147,"threshold_uncertainty_score":0.007750928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05947883706733691,"score_gpt":0.3389799971025317,"score_spread":0.2795011600351948,"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."}}