{"id":"W4323924205","doi":"10.1016/j.neuroimage.2023.120006","title":"Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study","year":2023,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Conseil National de la Recherche Scientifique; Campus France; Université Libanaise; Centre National de la Recherche Scientifique; Agence Universitaire de la Francophonie; Agence Nationale de la Recherche","keywords":"Resting state fMRI; Electroencephalography; Computer science; Neuroimaging; Pattern recognition (psychology); Diffusion MRI; Inverse problem; Artificial intelligence; Algorithm; Mathematics; Neuroscience; Psychology; Magnetic resonance imaging","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":[0.002269276,0.0005506701,0.0005077585,0.0004985753,0.0003455354,0.0005623946,0.0006219646,0.0009076119,0.0008195477],"category_scores_gemma":[0.01758847,0.0002470211,0.0007896228,0.0003700522,0.000753032,0.0007343167,0.0005364957,0.0007253883,0.00009082101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004924414,"about_ca_system_score_gemma":0.0005181333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0100929,"about_ca_topic_score_gemma":0.005529666,"domain_scores_codex":[0.9994984,0.0002996066,0.00002821005,0.00006257802,0.0000539231,0.0000572148],"domain_scores_gemma":[0.9825948,0.01522481,0.0006381837,0.0005940221,0.0007864383,0.00016167],"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":[0.0002897758,0.0000906961,0.008343341,0.0000772048,0.00007345523,0.0001506673,0.00008122119,0.9842398,0.00145739,0.001270197,0.0002091032,0.003717205],"study_design_scores_gemma":[0.00002766977,0.0001200623,0.001998089,0.00002066676,0.00003583683,0.00004967414,0.00004446133,0.9953603,0.00147189,0.000747272,0.0001100029,0.00001411707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9657521,0.0004463233,0.03192084,0.0003070564,0.00002365016,0.00005221893,0.0002576574,0.0001329101,0.001107326],"genre_scores_gemma":[0.9935321,0.0001125887,0.005965334,0.00003057404,0.000004629825,0.00003443901,0.0001460778,0.00001803217,0.0001562103],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0100929,"threshold_uncertainty_score":0.02006829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07552095675294201,"score_gpt":0.3015397449902746,"score_spread":0.2260187882373326,"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."}}