{"id":"W2637774559","doi":"10.1101/152538","title":"A Longitudinal Model for Functional Connectivity Networks Using Resting-State fMRI","year":2017,"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 Alberta","funders":"","keywords":"Functional magnetic resonance imaging; Autocorrelation; Neuroimaging; Set (abstract data type); Permutation (music); Inference; Data set; Longitudinal data; Time series; Variance (accounting)","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":[0.006525318,0.0008093355,0.0009093241,0.001522167,0.0008133333,0.001264251,0.003397575,0.002137508,0.004538596],"category_scores_gemma":[0.01031226,0.0008741449,0.001291252,0.001228249,0.001445651,0.002631923,0.001051546,0.001921236,0.0008113041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002026769,"about_ca_system_score_gemma":0.001669924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02423087,"about_ca_topic_score_gemma":0.02676405,"domain_scores_codex":[0.9985647,0.0007617125,0.00003419591,0.0004066457,0.0001118025,0.0001210736],"domain_scores_gemma":[0.9971216,0.001897654,0.00030608,0.0002090165,0.0003206538,0.0001448405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001682586,0.0001490743,0.009351623,0.00004860927,0.0001593858,0.0002933324,0.0002572436,0.9248486,0.001023102,0.04884389,0.002564361,0.01229248],"study_design_scores_gemma":[0.00001669917,0.00002313838,0.0008939421,0.000006809494,0.00001468993,0.00003268526,0.00001194001,0.9843024,0.00005493288,0.01426456,0.000367834,0.00001028333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.129593,0.0004567137,0.8614952,0.003092168,0.000087821,0.0001783417,0.001942699,0.0005449044,0.002609075],"genre_scores_gemma":[0.8783253,0.0006023193,0.1063331,0.0004255996,0.0001665985,0.001390168,0.001962,0.0001551313,0.01063979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02423087,"threshold_uncertainty_score":0.04817969,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09439499074855749,"score_gpt":0.2789135380970971,"score_spread":0.1845185473485396,"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."}}