2023
Suboptimal Sleep Duration Is Associated With Poorer Neuroimaging Brain Health Profiles in Middle‐Aged Individuals Without Stroke or Dementia
Clocchiatti‐Tuozzo S, Rivier C, Renedo D, Lopez V, Geer J, Miner B, Yaggi H, de Havenon A, Payabvash S, Sheth K, Gill T, Falcone G. Suboptimal Sleep Duration Is Associated With Poorer Neuroimaging Brain Health Profiles in Middle‐Aged Individuals Without Stroke or Dementia. Journal Of The American Heart Association 2023, 13: e031514. PMID: 38156552, PMCID: PMC10863828, DOI: 10.1161/jaha.123.031514.Peer-Reviewed Original ResearchConceptsSuboptimal sleep durationWhite matter hyperintensitiesMiddle-aged individualsLong sleep durationLarger WMH volumeSleep durationMiddle-aged adultsHealth profileWMH volumeAmerican Heart Association's LifeAsymptomatic middle-aged adultsWMH presenceVolume of WMHOptimal sleepSelf-reported sleep durationModifiable risk factorsWhite matter tractsProspective magnetic resonanceSimple 7Cardiovascular healthRisk factorsShort sleepMatter hyperintensitiesHigh riskEarly interventionAssociation of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank.
Rivier C, Renedo D, de Havenon A, Sunmonu N, Gill T, Payabvash S, Sheth K, Falcone G. Association of Poor Oral Health With Neuroimaging Markers of White Matter Injury in Middle-Aged Participants in the UK Biobank. Neurology 2023, 102: e208010. PMID: 38165331, PMCID: PMC10870735, DOI: 10.1212/wnl.0000000000208010.Peer-Reviewed Original ResearchConceptsPoor oral healthOral healthBrain healthRisk factorsNeuroimaging markerWMH volumeHealth profileMD scoreSelf-reported poor oral healthCross-sectional neuroimaging studiesFractional anisotropyWhite matter hyperintensity volumeModifiable risk factorsWhite matter injuryPresence of denturesMendelian randomizationMiddle-aged personsFA scoreMiddle-aged participantsLoose teethCardiovascular diseaseHyperintensity volumeBrain MRIModifiable processEarly interventionFuture of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI
Parasuram N, Crawford A, Mazurek M, Chavva I, Beekman R, Gilmore E, Petersen N, Payabvash S, Sze G, Eugenio Iglesias J, Omay S, Matouk C, Longbrake E, de Havenon A, Schiff S, Rosen M, Kimberly W, Sheth K. Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI. Neurology 2023, 100: 1067-1071. PMID: 36720639, PMCID: PMC10259275, DOI: 10.1212/wnl.0000000000207074.Peer-Reviewed Original Research
2022
CGRP, Migraine, and Brain MRI in CADASIL
Goldstein E, Gopal N, Badi M, Hodge D, de Havenon A, Glover P, Durham P, Huang J, Lin M, Baradaran H, Majersik J, Meschia J. CGRP, Migraine, and Brain MRI in CADASIL. The Neurologist 2022, 28: 231-236. PMID: 36729391, PMCID: PMC10277309, DOI: 10.1097/nrl.0000000000000478.Peer-Reviewed Original ResearchConceptsCalcitonin gene-related peptideMigraine-related disabilitySerum calcitonin gene-related peptideSerum CGRP levelsCGRP levelsBrain magnetic resonance imagingCerebral autosomal dominant arteriopathyGene-related peptideAutosomal dominant arteriopathyMagnetic resonance imagingCross-sectional analysisRadiographic associationRadiographic implicationsPrimary outcomeT2 hyperintensityCerebral microbleedsDisability ScaleSubcortical infarctsMigraine pathophysiologyVasoactive peptidesBrain MRIMigraine pathologyMigraineResonance imagingCADASILDeep Learning Applications for Acute Stroke Management
Chavva IR, Crawford AL, Mazurek MH, Yuen MM, Prabhat AM, Payabvash S, Sze G, Falcone GJ, Matouk CC, de Havenon A, Kim JA, Sharma R, Schiff SJ, Rosen MS, Kalpathy‐Cramer J, Gonzalez J, Kimberly WT, Sheth KN. Deep Learning Applications for Acute Stroke Management. Annals Of Neurology 2022, 92: 574-587. PMID: 35689531, DOI: 10.1002/ana.26435.Peer-Reviewed Original ResearchMeSH KeywordsDeep LearningHumansNeural Networks, ComputerNeuroimagingReproducibility of ResultsStrokeConceptsDeep machine learningDeep learning applicationsMedical image analysisDeep neural networksPixel-wise labelingAcute stroke managementReal-world examplesDL applicationsDL approachMachine learningLearning applicationsDL modelsNeural networkStroke managementLesion segmentationMaximal utilityImage analysisElectronic medical record dataInter-rater variabilityCause of disabilityMedical record dataRelevant clinical featuresStroke detectionAdvanced neuroimaging techniquesDecision making
2020
Anticoagulation Type and Early Recurrence in Cardioembolic Stroke
Yaghi S, Mistry E, Liberman AL, Giles J, Asad SD, Liu A, Nagy M, Kaushal A, Azher I, Mac Grory B, Fakhri H, Brown Espaillat K, Pasupuleti H, Martin H, Tan J, Veerasamy M, Esenwa C, Cheng N, Moncrieffe K, Moeini-Naghani I, Siddu M, Scher E, Trivedi T, Lord A, Furie K, Keyrouz S, Nouh A, Leon Guerrero CR, de Havenon A, Khan M, Henninger N. Anticoagulation Type and Early Recurrence in Cardioembolic Stroke. Stroke 2020, 51: 2724-2732. PMID: 32757753, PMCID: PMC7484360, DOI: 10.1161/strokeaha.120.028867.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAnticoagulantsAtrial FibrillationBrain IschemiaEmbolismFemaleHeart DiseasesHeparin, Low-Molecular-WeightHumansIncidenceIntracranial HemorrhagesMaleMiddle AgedNeuroimagingRecurrenceRegistriesRetrospective StudiesRisk AssessmentStrokeTreatment OutcomeUnited StatesWarfarinConceptsSymptomatic intracranial hemorrhageRecurrent ischemic eventsLow molecular weight heparinAcute ischemic strokeMolecular weight heparinIschemic eventsIntracranial hemorrhageIschemic strokeAtrial fibrillationAnalysis inclusion criteriaEarly recurrenceWeight heparinInclusion criteriaSeparate Cox regression analysesComprehensive stroke centerLarge prospective studiesOral anticoagulant therapyCox regression analysisCox regression modelAnticoagulation typeDOAC treatmentStroke RegistryAnticoagulant therapyCardioembolic strokeStroke centers
2017
Advanced imaging in acute ischemic stroke.
Kilburg C, Scott McNally J, de Havenon A, Taussky P, Kalani MY, Park MS. Advanced imaging in acute ischemic stroke. Neurosurgical FOCUS 2017, 42: e10. PMID: 28366054, DOI: 10.3171/2017.1.focus16503.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAged, 80 and overBrain IschemiaFemaleHumansImage Processing, Computer-AssistedMaleNeuroimagingStrokeConceptsAcute ischemic strokeLarge vessel occlusionTranscranial Doppler ultrasonographyCT angiographyVessel wall MRIMR angiographyIschemic strokeDoppler ultrasonographyVessel occlusionMRI techniquesCollateral blood flowImaging modalitiesConventional CTCharacteristics of lesionsFour-dimensional CT angiographyClot burdenCryptogenic strokeAcute settingStroke treatmentPatent foramenOccult lesionsInvasive alternativeBlood flowNew imaging modalityNatural historyDeterminants of the impact of blood pressure variability on neurological outcome after acute ischaemic stroke
de Havenon A, Bennett A, Stoddard GJ, Smith G, Chung L, O'Donnell S, McNally JS, Tirschwell D, Majersik JJ. Determinants of the impact of blood pressure variability on neurological outcome after acute ischaemic stroke. Stroke And Vascular Neurology 2017, 2: 1. PMID: 28959484, PMCID: PMC5435214, DOI: 10.1136/svn-2016-000057.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBlood PressureBlood Pressure DeterminationCerebral AngiographyCerebrovascular CirculationCollateral CirculationComputed Tomography AngiographyDisability EvaluationFemaleHumansIschemic StrokeMaleMiddle AgedNeuroimagingPatient AdmissionPerfusion ImagingPredictive Value of TestsPrognosisRecovery of FunctionRetrospective StudiesRisk FactorsTime FactorsConceptsBlood pressure variabilityProximal vessel occlusionAcute ischemic strokeIschemic strokeVessel occlusionNeurological outcomeBPV indicesPressure variabilityAcute anterior circulation ischemic strokeAnterior circulation ischemic strokeIntravenous tissue plasminogen activatorGood collateral vesselsIschemic core volumeLarger mismatch volumeWorse neurological outcomeAcute stroke therapySubset of patientsSystolic blood pressureTissue plasminogen activatorLogistic regression modelsOrdinal logistic regression modelsEndovascular thrombectomyIschemic penumbraStroke onsetStroke outcome