{"id":"W2952209243","doi":"10.1016/j.neuroimage.2018.05.028","title":"Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre for Addiction and Mental Health; Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto; Canada Foundation for Innovation; Government of Ontario; Fondation Brain Canada; Ontario Brain Institute","keywords":"Covariance; Similarity (geometry); Brain size; Structural variation; Transcriptome; Biology; Neuroimaging; Structural similarity; Pattern recognition (psychology); Evolutionary biology; Neuroscience; Artificial intelligence; Computer science; Mathematics; Genetics; Gene; Statistics; Gene expression; Genome","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007659339,0.0003062734,0.0003611949,0.001288891,0.0004727003,0.0008742244,0.0003915032,0.0004921143,0.003141259],"category_scores_gemma":[0.004355632,0.0002641797,0.0005826248,0.001190543,0.0006936134,0.0007520134,0.0005640827,0.0005604465,0.0003546975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002539123,"about_ca_system_score_gemma":0.0003316626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00281132,"about_ca_topic_score_gemma":0.004399758,"domain_scores_codex":[0.9994222,0.0001096219,0.00004622213,0.0002446529,0.000104516,0.00007281671],"domain_scores_gemma":[0.9969663,0.0009211627,0.001196819,0.0004359622,0.0002748127,0.0002050368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008851896,0.0001817101,0.789692,0.00008644925,0.001611511,0.0004997248,0.0007629429,0.001025037,0.1874041,0.002171603,0.000848024,0.01483181],"study_design_scores_gemma":[0.000006327251,0.00006418791,0.9945603,0.000004529604,0.00008071287,0.000427007,0.0001094275,0.0008417258,0.002292431,0.001452686,0.0001506473,0.000009968661],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948776,0.0001520901,0.003176204,0.000150309,0.0000110255,0.000007685086,0.0004822116,0.0000475505,0.001095287],"genre_scores_gemma":[0.9983711,0.00006087683,0.0006213365,0.0000407472,0.00002139994,0.000009691849,0.000491216,0.00003381246,0.0003499136],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003141259,"threshold_uncertainty_score":0.01050854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04081281206760667,"score_gpt":0.2630570950262869,"score_spread":0.2222442829586803,"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."}}