2022
A machine‐learning approach for predicting impaired consciousness in absence epilepsy
Springer M, Khalaf A, Vincent P, Ryu JH, Abukhadra Y, Beniczky S, Glauser T, Krestel H, Blumenfeld H. A machine‐learning approach for predicting impaired consciousness in absence epilepsy. Annals Of Clinical And Translational Neurology 2022, 9: 1538-1550. PMID: 36114696, PMCID: PMC9539371, DOI: 10.1002/acn3.51647.Peer-Reviewed Original Research
2016
Impaired consciousness in patients with absence seizures investigated by functional MRI, EEG, and behavioural measures: a cross-sectional study
Guo JN, Kim R, Chen Y, Negishi M, Jhun S, Weiss S, Ryu JH, Bai X, Xiao W, Feeney E, Rodriguez-Fernandez J, Mistry H, Crunelli V, Crowley MJ, Mayes LC, Constable RT, Blumenfeld H. Impaired consciousness in patients with absence seizures investigated by functional MRI, EEG, and behavioural measures: a cross-sectional study. The Lancet Neurology 2016, 15: 1336-1345. PMID: 27839650, PMCID: PMC5504428, DOI: 10.1016/s1474-4422(16)30295-2.Peer-Reviewed Original ResearchConceptsAbsence seizuresCross-sectional studyFMRI amplitudeFunctional MRIBehavioral impairmentsJuvenile absence epilepsyBilateral spike-wave dischargesOnset of seizuresPediatric neurology practiceNational InstituteSpike-wave dischargesPhysiological changesBehavioral responsesDefault mode networkImpaired task performanceSeizure durationImpaired consciousnessElectroencephalography changesNeurology practiceAbsence epilepsyFMRI changesBehavioral deficitsMAIN OUTCOMEBehavioral testingEEG changes