{"id":"W2943663562","doi":"10.1101/626168","title":"Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Douglas Mental Health University Institute; McGill University; Montreal Neurological Institute and Hospital","funders":"Canadian Institutes of Health Research; Lundbeck Canada; Canadian Open Neuroscience Platform; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; McGill University; Health Canada; Canada First Research Excellence Fund; H. Lundbeck A/S; National Science Foundation","keywords":"Grey matter; Schizophrenia (object-oriented programming); Neuroscience; Atrophy; Functional connectivity; Brain size; Diffusion MRI; Psychology; White matter; Magnetic resonance imaging; Anatomy; Biology; Medicine; Pathology; Psychiatry; Radiology","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":[0.0002189405,0.0001667515,0.0001479906,0.0007492797,0.0001578212,0.000273689,0.000119538,0.0001607778,0.001329349],"category_scores_gemma":[0.001019501,0.00009873711,0.0001280506,0.0003596766,0.0003679212,0.0002370921,0.0003410926,0.0001736752,0.00009357862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002497792,"about_ca_system_score_gemma":0.0001623392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003212055,"about_ca_topic_score_gemma":0.00510582,"domain_scores_codex":[0.9999053,0.00001766517,0.000008730243,0.00002815968,0.00002232914,0.00001783235],"domain_scores_gemma":[0.9995422,0.00008718967,0.0002371454,0.0000546397,0.00003620081,0.00004258454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006699808,0.00004005629,0.8496531,0.00005682765,0.0002311147,0.0002667122,0.0005679653,0.00248483,0.1266368,0.0005589276,0.0002221926,0.01861145],"study_design_scores_gemma":[0.000002359585,0.00003131338,0.9965162,0.000003352418,0.0000111567,0.0001972315,0.00008598357,0.001227935,0.001539121,0.0003395645,0.00004277224,0.000002942426],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991665,0.00005043648,0.0004866033,0.00001927717,6.063764e-7,0.00000211254,0.0001041182,0.000007044116,0.0001634502],"genre_scores_gemma":[0.9997314,0.00001965432,0.0001473484,0.000003347452,6.67458e-7,0.000001626244,0.00005385918,0.000001888267,0.00004024199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003212055,"threshold_uncertainty_score":0.006386757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710081205346187,"score_gpt":0.2371978177620572,"score_spread":0.2200970057085953,"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."}}