{"id":"W3183311417","doi":"10.1038/s41380-021-01229-4","title":"Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis","year":2021,"lang":"en","type":"article","venue":"Molecular Psychiatry","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":149,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lawson Health Research Institute; Western University","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China-Yunnan Joint Fund; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Schizophrenia (object-oriented programming); Covariance; Neuroscience; Psychology; Network analysis; Computer science; Psychiatry; Mathematics; Statistics","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":[0.001611227,0.0004738385,0.0004492379,0.001241283,0.000483794,0.0008361445,0.0005324594,0.0002936073,0.0008546163],"category_scores_gemma":[0.004472807,0.0002825058,0.0005357197,0.0009037179,0.0004984886,0.00105238,0.0007203632,0.0006262563,0.00006260361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005713152,"about_ca_system_score_gemma":0.000934911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004442216,"about_ca_topic_score_gemma":0.01056524,"domain_scores_codex":[0.9995052,0.0002775055,0.00001832202,0.0001169425,0.00004785485,0.00003417195],"domain_scores_gemma":[0.9988971,0.0007218888,0.0001462926,0.0001407507,0.00006010791,0.00003382608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006685214,0.000387004,0.1369186,0.0003233725,0.00330243,0.0005115708,0.001320243,0.2677349,0.08905242,0.141488,0.003296225,0.3549968],"study_design_scores_gemma":[0.00003211362,0.0001337879,0.07818732,0.00002273083,0.0003441521,0.0002655549,0.0002263667,0.7650419,0.004555411,0.1499777,0.001158246,0.00005471972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3519615,0.000543933,0.6447306,0.0006189612,0.0000201158,0.00006317009,0.0004498689,0.0002472874,0.001364619],"genre_scores_gemma":[0.9239417,0.0001993188,0.07506822,0.00004670796,0.000028007,0.00004946059,0.0002588003,0.00006968027,0.0003380933],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004442216,"threshold_uncertainty_score":0.008832693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259228651788916,"score_gpt":0.2787881233711906,"score_spread":0.252865258192299,"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."}}