{"id":"W2056941957","doi":"10.1016/j.jneumeth.2011.11.016","title":"Nonlinear hemodynamic responses in human epilepsy: A multimodal analysis with fNIRS-EEG and fMRI-EEG","year":2011,"lang":"en","type":"article","venue":"Journal of Neuroscience Methods","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hôpital Notre-Dame; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal; Montreal Heart Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Electroencephalography; Functional magnetic resonance imaging; EEG-fMRI; Epilepsy; Neuroimaging; Haemodynamic response; Psychology; Neuroscience; Artificial intelligence; Computer science; Medicine; Radiology","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":[],"consensus_categories":[],"category_scores_codex":[0.002217427,0.0001600085,0.0005999696,0.0009428218,0.00008497136,0.00004373917,0.0001977344,0.00005639151,0.00001271404],"category_scores_gemma":[0.0008226923,0.0001075785,0.0001325748,0.001214963,0.0004725983,0.0002382802,0.00005018908,0.0005496874,2.114383e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005113956,"about_ca_system_score_gemma":0.0001062744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005662752,"about_ca_topic_score_gemma":0.000007555846,"domain_scores_codex":[0.9981705,0.0004108131,0.0004857723,0.0002956883,0.0003483913,0.0002888688],"domain_scores_gemma":[0.9989152,0.0001963617,0.0002557206,0.0002617567,0.0001408935,0.0002300971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000582746,0.0004084555,0.13938,0.00001917058,0.00003011543,0.0005676316,0.0003620379,0.000006596489,0.8553197,0.00008764165,0.000005331036,0.003230557],"study_design_scores_gemma":[0.0009469529,0.003304739,0.898178,0.0001190385,0.0004618159,0.0009873131,0.00009885962,0.03283454,0.06235049,0.0004437273,0.00009720567,0.0001772547],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8606412,0.0000518469,0.1382988,0.0004064558,0.00005144855,0.00009649162,0.000001200236,0.00002603446,0.0004265425],"genre_scores_gemma":[0.5194921,0.00003437569,0.4801458,0.0002254331,0.00001314127,0.000001354718,1.19382e-7,0.000008756071,0.0000788816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7929692,"threshold_uncertainty_score":0.4386922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05309492176206429,"score_gpt":0.4238752299901258,"score_spread":0.3707803082280615,"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."}}