{"id":"W2768920954","doi":"10.1016/j.neuroimage.2018.02.032","title":"Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":304,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Academy of Finland; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Canada Research Chairs","keywords":"Coupling (piping); Electroencephalography; Space (punctuation); Computer science; Psychology; Statistical physics; Physics; Neuroscience; Materials science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0002261832,0.0001675457,0.0002012575,0.0002692694,0.0001879159,0.00003607263,0.0001772792,0.0000329372,0.00004022945],"category_scores_gemma":[0.00938468,0.0001665904,0.00007356711,0.0004310816,0.000301816,0.0002285191,0.0001388576,0.0003422643,0.0001175623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133839,"about_ca_system_score_gemma":0.00004025134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000231035,"about_ca_topic_score_gemma":0.0007621233,"domain_scores_codex":[0.9985222,0.0001158739,0.0002465357,0.0005260362,0.0003409052,0.0002484688],"domain_scores_gemma":[0.9968394,0.002567855,0.0001289166,0.0003140881,0.0001042653,0.00004545052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003058559,0.0002387523,0.003015764,0.00001901567,0.000005261096,0.00002376314,0.000941631,0.0005459103,0.9894568,0.0005733313,0.002888175,0.0019857],"study_design_scores_gemma":[0.00114739,0.001133857,0.1008219,0.0002381982,0.0000235451,0.00006603949,0.0002995516,0.0239375,0.8303529,0.0003676946,0.04118527,0.0004261431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9846333,0.000007830823,0.002706188,0.004281464,0.001135259,0.000239211,0.00002359415,0.0001136436,0.006859534],"genre_scores_gemma":[0.9978441,0.000008337604,0.00004940779,0.001479802,0.0001403275,0.00001639333,8.797501e-7,0.00002509525,0.0004356281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1591039,"threshold_uncertainty_score":0.9989597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06265404002520929,"score_gpt":0.3205215341009612,"score_spread":0.2578674940757519,"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."}}