{"id":"W4406011773","doi":"10.1162/imag_a_00438","title":"Brain morphology normative modelling platform for abnormality and centile estimation: Brain MoNoCle","year":2025,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"NIHR Newcastle Biomedical Research Centre; National Institute for Health and Care Research; Engineering and Physical Sciences Research Council; UCLH Biomedical Research Centre; Medical Research Council; University College London Hospitals NHS Foundation Trust; UK Research and Innovation","keywords":"Normative; Covariate; Brain morphometry; Abnormality; Computer science; Sample (material); Neuroimaging; Cohort; Psychology; Artificial intelligence; Machine learning; Medicine; Magnetic resonance imaging; Neuroscience; Pathology; Psychiatry; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004795314,0.0002013998,0.0002088086,0.0001661332,0.0009624459,0.0001924129,0.0003323183,0.00003155114,0.000004325266],"category_scores_gemma":[0.006799266,0.0002058798,0.0000575896,0.000620826,0.0006793574,0.001479684,0.0002946694,0.0001822825,0.000006611438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004779019,"about_ca_system_score_gemma":0.00007621497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000172829,"about_ca_topic_score_gemma":0.00000119238,"domain_scores_codex":[0.9980968,0.00008790741,0.000276513,0.0008520007,0.0002290901,0.0004577424],"domain_scores_gemma":[0.9939461,0.005523203,0.000116405,0.0002847266,0.00005744114,0.00007212722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000257486,0.0002694674,0.004717712,0.0002501262,0.000007723313,0.00007692298,0.001156169,0.143773,0.6691328,0.1247886,0.04304035,0.01252972],"study_design_scores_gemma":[0.00049488,0.00004657141,0.002195496,0.0000253127,0.000006688599,0.00009705687,0.0000674248,0.9282531,0.04570197,0.01483977,0.008068837,0.0002028664],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3224998,0.00006182562,0.6083102,0.06388979,0.001520207,0.0007070826,0.00006586451,0.0002492935,0.002695985],"genre_scores_gemma":[0.9543601,0.00001069021,0.003266586,0.04113289,0.00002690382,0.00005503547,0.000001426138,0.00001225561,0.001134123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7844802,"threshold_uncertainty_score":0.8395536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04732947564003205,"score_gpt":0.3109644024817348,"score_spread":0.2636349268417028,"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."}}