{"id":"W3100333756","doi":"10.1101/2020.01.18.911248","title":"Virtual connectomic datasets in Alzheimer’s Disease and aging using whole-brain network dynamics modelling","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; University of Southern California; Bristol-Myers Squibb; U.S. Department of Defense; Eli Lilly and Company; BrightFocus Foundation; Novartis Pharmaceuticals Corporation; European Commission; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; Horizon 2020 Framework Programme; Foundation for the National Institutes of Health","keywords":"Connectome; Connectomics; Empirical research; Human Connectome Project; Neuroimaging; Obstacle; A priori and a posteriori; Deep learning","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":[0.002207612,0.0007160771,0.000436062,0.001308273,0.0003575346,0.0008340998,0.001100489,0.0009867563,0.0008077194],"category_scores_gemma":[0.005752166,0.0002719406,0.000925904,0.0008109478,0.000655661,0.000884025,0.0009223695,0.0009762708,0.0002113408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007830152,"about_ca_system_score_gemma":0.0005538859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005806115,"about_ca_topic_score_gemma":0.006121945,"domain_scores_codex":[0.999557,0.0002486315,0.000019475,0.00009283527,0.00005952255,0.00002251435],"domain_scores_gemma":[0.9982375,0.0009938861,0.0001801128,0.0003423278,0.000158549,0.00008765684],"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":[0.0004916111,0.0002555019,0.01830409,0.0002421366,0.000375208,0.0004438597,0.0001817571,0.9368465,0.003695173,0.009928281,0.006773832,0.02246198],"study_design_scores_gemma":[0.00003601752,0.00005824261,0.004966111,0.0000177634,0.00002937453,0.00008877271,0.00003715364,0.9771615,0.001293733,0.01472894,0.001563864,0.00001860562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.766344,0.0008735161,0.217769,0.001653219,0.0001560373,0.0002065219,0.009125088,0.002031042,0.001841499],"genre_scores_gemma":[0.9246108,0.000250934,0.06514325,0.0001509113,0.00005923775,0.0002600116,0.008986705,0.00007860685,0.0004596061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005806115,"threshold_uncertainty_score":0.01167506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05231215524171585,"score_gpt":0.2577166063058901,"score_spread":0.2054044510641743,"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."}}