{"id":"W3164240626","doi":"10.1523/eneuro.0475-20.2021","title":"Virtual Connectomic Datasets in Alzheimer’s Disease and Aging Using Whole-Brain Network Dynamics Modelling","year":2021,"lang":"en","type":"article","venue":"eNeuro","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; BioClinica; European Commission; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Bristol-Myers Squibb; Eli Lilly and Company; BrightFocus Foundation; University of Southern California; Biogen; National Institute on Aging; Alzheimer's Association; Horizon 2020 Framework Programme; Foundation for the National Institutes of Health","keywords":"Connectome; Computer science; Connectomics; Machine learning; Artificial intelligence; Neuroimaging; Human Connectome Project; Data mining; Functional connectivity; Neuroscience","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":[0.001710724,0.0007420916,0.0003966251,0.001229605,0.0003825998,0.0007258688,0.001011822,0.0009419305,0.0008107917],"category_scores_gemma":[0.004607656,0.0003001081,0.0009063264,0.0009035962,0.0006626049,0.0009086395,0.0009012016,0.0008263612,0.0002220176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007800318,"about_ca_system_score_gemma":0.0005744582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006790961,"about_ca_topic_score_gemma":0.009438523,"domain_scores_codex":[0.9995955,0.0002249847,0.00001668297,0.00009227332,0.00005131392,0.00001928906],"domain_scores_gemma":[0.9986336,0.0007053582,0.0001648015,0.0003036885,0.0001124172,0.00008025821],"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":[0.0005708742,0.0002824471,0.0193268,0.0004217228,0.0005616944,0.0006800249,0.0003148536,0.8988928,0.006739748,0.02073386,0.01419578,0.03727934],"study_design_scores_gemma":[0.00006042932,0.00008085674,0.007533299,0.00003058344,0.0000494077,0.0002094674,0.00006093901,0.9527772,0.00209112,0.03191447,0.005159855,0.00003235218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.633522,0.001449609,0.3390228,0.002067605,0.0002086138,0.0002631409,0.01689168,0.003307442,0.00326717],"genre_scores_gemma":[0.851118,0.0005757032,0.1267481,0.0002086141,0.00007697456,0.0005192598,0.01949605,0.0001772866,0.001080027],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006790961,"threshold_uncertainty_score":0.0135029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06339832233230878,"score_gpt":0.2861223546416394,"score_spread":0.2227240323093306,"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."}}