{"id":"W2982604831","doi":"10.1101/826321","title":"Brain structural connectivity predicts brain functional complexity: DTI derived centrality accounts for variance in fractal properties of fMRI signal","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Centrality; Betweenness centrality; Functional magnetic resonance imaging; Resting state fMRI; Psychology; Diffusion MRI; Connectome; Neuroscience; Computer science; Artificial intelligence; Pattern recognition (psychology); Functional connectivity; Mathematics; Magnetic resonance imaging; Medicine; Statistics","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":[0.0009814276,0.0003419694,0.0002595252,0.001382401,0.0002385017,0.0008158319,0.0002705952,0.0003758149,0.001472907],"category_scores_gemma":[0.01093363,0.0001420144,0.000340542,0.0007850291,0.0003519527,0.0006645641,0.0003973197,0.0004573113,0.0001980795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003949337,"about_ca_system_score_gemma":0.0002583409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00412015,"about_ca_topic_score_gemma":0.004858418,"domain_scores_codex":[0.9998118,0.00005968571,0.00001120671,0.00005188449,0.00003825576,0.00002712566],"domain_scores_gemma":[0.9943987,0.003292537,0.001120489,0.0003463144,0.0002736971,0.0005682873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003168058,0.0001529858,0.9495715,0.00004608529,0.0004859619,0.0002281882,0.0003553609,0.01918178,0.005892099,0.002550936,0.0007937163,0.02042455],"study_design_scores_gemma":[0.0000164809,0.00009269706,0.8449588,0.00002018962,0.00009552828,0.0003288961,0.0001353954,0.1462029,0.0007148082,0.007067742,0.0003378243,0.00002857961],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931967,0.0001773288,0.005468443,0.0001256251,0.00001168654,0.00001141222,0.0001746955,0.00004029063,0.0007938066],"genre_scores_gemma":[0.9992999,0.00002885153,0.000479105,0.00000547844,0.000008399228,0.000003376991,0.00007506132,0.000005598199,0.00009412796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00412015,"threshold_uncertainty_score":0.008192301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06256535352897881,"score_gpt":0.2500708087521711,"score_spread":0.1875054552231923,"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."}}