{"id":"W2330364112","doi":"10.3389/fnins.2016.00085","title":"Approaches to Capture Variance Differences in Rest fMRI Networks in the Spatial Geometric Features: Application to Schizophrenia","year":2016,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; National Science Foundation","keywords":"Voxel; Schizophrenia (object-oriented programming); Pattern recognition (psychology); Resting state fMRI; Centroid; Independent component analysis; Artificial intelligence; Correlation; Set (abstract data type); Computer science; Psychology; Neuroscience; Mathematics","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":[0.002162978,0.0007725076,0.0006315837,0.001426756,0.0004702599,0.0006203948,0.0006757739,0.0005708974,0.0007456032],"category_scores_gemma":[0.00401731,0.0003643741,0.001207651,0.0008870528,0.0005228524,0.000491089,0.0009550375,0.0007928949,0.0001472785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007140256,"about_ca_system_score_gemma":0.001245225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01050062,"about_ca_topic_score_gemma":0.01233523,"domain_scores_codex":[0.9996684,0.0001642185,0.00001968146,0.00006404188,0.00005435713,0.00002937352],"domain_scores_gemma":[0.9987817,0.000771559,0.0001559662,0.000113435,0.0001178121,0.00005952087],"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":[0.001005951,0.0004928351,0.03928819,0.0003146258,0.0008119601,0.000632118,0.0009087552,0.4122308,0.07999386,0.01133987,0.0009571486,0.452024],"study_design_scores_gemma":[0.00003936202,0.000172693,0.03271014,0.0000169448,0.00006817244,0.0002434725,0.0001223709,0.951786,0.005613783,0.008436061,0.0007253478,0.00006569192],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3203258,0.0005588076,0.6773245,0.0002448835,0.00001859254,0.0001540454,0.0002438393,0.0006664047,0.0004631111],"genre_scores_gemma":[0.6394762,0.0003148539,0.3586372,0.00004708757,0.00002463637,0.0002109425,0.0003303552,0.0001237605,0.0008349927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01050062,"threshold_uncertainty_score":0.02087897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05158066061768775,"score_gpt":0.2321210260939142,"score_spread":0.1805403654762264,"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."}}