2022
Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy
Chen Y, Li S, Ge W, Jing J, Chen HY, Doherty D, Herman A, Kaleem S, Ding K, Osman G, Swisher CB, Smith C, Maciel CB, Alkhachroum A, Lee JW, Dhakar MB, Gilmore EJ, Sivaraju A, Hirsch LJ, Omay SB, Blumenfeld H, Sheth KN, Struck AF, Edlow BL, Westover MB, Kim JA. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. Journal Of Neurology Neurosurgery & Psychiatry 2022, 94: 245-249. PMID: 36241423, PMCID: PMC9931627, DOI: 10.1136/jnnp-2022-329542.Peer-Reviewed Original ResearchConceptsPost-traumatic epilepsyTraumatic brain injuryCT abnormalitiesElectroencephalography featuresAdmission Glasgow Coma Scale scoreGlasgow Coma Scale scoreRetrospective case-control studyMultivariable logistic regression analysisRisk stratification modelCase-control studyLogistic regression analysisTBI admissionsSevere complicationsFuture trialsBrain injuryCT reportsSeizure diagnosisPredictive valueScale scorePatientsLogistic regressionStratification modelQuantitative electroencephalogramTBI mechanismsRegression analysisEvaluating consciousness and awareness during focal seizures: responsiveness testing versus recall testing
Ramirez V, Litvinov B, Gunawardane NA, Zhao CW, Yotter C, Quraishi IH, Blumenfeld H. Evaluating consciousness and awareness during focal seizures: responsiveness testing versus recall testing. Epileptic Disorders 2022, 24: 899-905. PMID: 35904040, PMCID: PMC10042123, DOI: 10.1684/epd.2022.1472.Peer-Reviewed Original ResearchConceptsFocal seizuresResponsiveness testingCurrent International LeagueClinical practice settingEEG-video monitoringEpilepsy monitoring unitImportant clinical settingsEpilepsy guidelinesPatient groupFocal epilepsyIctal eventsClinical careInternational LeagueSeizuresPatientsClinical settingPractice settingsClassification guidelinesFurther studiesEpilepsyResponsivenessRecall of experiencesPreliminary findingsRecall testingSettingA 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
2014
Chapter 1 Introduction to Neuronal Networks of the Brain
Faingold C, Blumenfeld H. Chapter 1 Introduction to Neuronal Networks of the Brain. 2014, 1-10. DOI: 10.1016/b978-0-12-415804-7.00001-0.Peer-Reviewed Original ResearchNormal brain functionBrain functionBrain disordersNeuronal networksPsychiatric disordersTherapeutic levelsNeuroactive agentsAwake animalsPathological interactionsEpilepsy researchAbnormal interactionsDisordersVivo techniquesBrain mechanismsBrainCritical targetImportant mechanismPatientsTherapyLevels