{"id":"W3116689020","doi":"10.3389/fnins.2020.566876","title":"Network Diffusion Modeling Explains Longitudinal Tau PET Data","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; Division of Civil, Mechanical and Manufacturing Innovation; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Neurodegeneration; Positron emission tomography; Neuroscience; Tau protein; Timeline; Neuroimaging; Pathological; Diffusion MRI; Psychology; Alzheimer's disease; Medicine; Disease; Pathology; Magnetic resonance imaging; Radiology; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001004708,0.000579731,0.0004705835,0.0007741536,0.0002489326,0.0005018495,0.0005371691,0.0008110123,0.001094851],"category_scores_gemma":[0.004717064,0.000334393,0.0005550391,0.0004992302,0.0004125916,0.0009021839,0.0003264389,0.0005161399,0.0002095088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009491394,"about_ca_system_score_gemma":0.0004145202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02058479,"about_ca_topic_score_gemma":0.01403981,"domain_scores_codex":[0.9998324,0.00005970846,0.000006528628,0.00005939139,0.00001563853,0.000026267],"domain_scores_gemma":[0.9989379,0.0006451469,0.0001840275,0.00007754756,0.0001048433,0.00005067161],"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":[0.00008798314,0.00002522972,0.01018988,0.00002995052,0.0000544036,0.0001468009,0.00007857463,0.9777244,0.001639145,0.003360076,0.0005900782,0.006073532],"study_design_scores_gemma":[0.000003438647,0.000004828812,0.001337919,0.000002003025,0.000003958307,0.00001841076,0.000004685711,0.9967908,0.00008242952,0.001664401,0.00008348282,0.000003522775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8333032,0.0006087689,0.1616338,0.0009529656,0.00003477442,0.00006220568,0.001066309,0.0003361925,0.002001886],"genre_scores_gemma":[0.9906734,0.0002327892,0.007162728,0.00003561482,0.00001412759,0.00005836299,0.0004210433,0.00004375757,0.001358267],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02058479,"threshold_uncertainty_score":0.04092997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1396802325779652,"score_gpt":0.2844447889109296,"score_spread":0.1447645563329644,"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."}}