Guido J. Falcone, MD, ScD, MPH
Associate Professor of NeurologyCards
About
Titles
Associate Professor of Neurology
Academic Chief, Division of Neurocritical Care, Neurology; Director of Clinical Research in Neurocritical Care, Neurology; Training Director, Yale/AHA Bugher Center for Intracerebral Hemorrhage Research, Neurology; Staff Neurointensivist, Neurology
Biography
I am a Neurologist with subspecialty training in Neurocritical Care and Stroke, and an Epidemiologist with expertise in Population Genetics and Big Data. While on clinical duties, I treat critically ill patients that have sustained a significant neurological injury due to ischemic stroke, subarachnoid hemorrhage, intraparenchymal hemorrhage, traumatic brain injury, seizures, recent neurosurgery, decompensated neuromuscular diseases, and several others.
My research lies at the interphase of clinical neurology, neuroimaging, population genetics and genomic medicine. I am interested in understanding how common and rare genetic variation influences the occurrence, severity, functional outcome and recurrence of stroke, both hemorrhagic and ischemic. Genetic variants influencing these phenotypes can be used for numerous applications, including: (1) identification of novel biological mechanisms involved in causing stroke and determining its severity and outcome, (2) answering non-genetic epidemiological questions using gene mutations as instruments (in the statistical sense of the word), and (3) risk stratification of patients according to their genetic profile. Through the International Stroke Genetics Consortium, I work in close collaboration with numerous investigators interested in stroke genomics from around the world.
Appointments
Neurology
Associate Professor on TermPrimary
Other Departments & Organizations
Education & Training
- Neurocritical Care Fellowship
- Harvard Medical School / Massachusetts General Hospital / Brigham and Women's Hospital
- ScD
- Harvard School of Public Health, Department of Epidemiology
- MPH
- Harvard School of Public Health, Quantitative Methods
- Neurology Residency
- F.L.E.N.I.
- MD
- University of Buenos Aires School of Medicine
Research
Publications
2025
Voxel-Wise Map of Intracerebral Hemorrhage Locations Associated With Worse Outcomes
Karam G, Chen M, Zeevi D, Harms B, Berson E, Torres-Lopez V, Rivier C, Malhotra A, Qureshi A, Falcone G, Sheth K, Payabvash S. Voxel-Wise Map of Intracerebral Hemorrhage Locations Associated With Worse Outcomes. Stroke 2025, 56: 868-877. PMID: 40052269, DOI: 10.1161/strokeaha.124.048453.Peer-Reviewed Original ResearchConceptsImpact of intracerebral hemorrhageAssociated with worse outcomesClinical risk factorsVoxel-wise mapsMapping of brain regionsBaseline hematomaVoxel-wise analysisHematoma volumeICH locationBrain regionsValidation cohortWorse outcomesPresence of intraventricular hemorrhageRisk factorsBaseline hematoma volumeModified Rankin Scale scorePoor scan qualityMulticenter clinical trialRankin Scale scoreDeep white matterLow-risk categoryComprehensive stroke centerIntraventricular hemorrhageConsecutive patientsPatient ageSpatial Correlates of Dementia and Disability After Intracerebral Hemorrhage.
Chen Y, Rivier C, Mora S, Torres Lopez V, Payabvash S, Sheth K, Harloff A, Falcone G, Rosand J, Mayerhofer E, Anderson C. Spatial Correlates of Dementia and Disability After Intracerebral Hemorrhage. Journal Of The American Heart Association 2025, 14: e037930. PMID: 39921496, DOI: 10.1161/jaha.124.037930.Peer-Reviewed Original ResearchConceptsFollow-up telephone interviewsElectronic health recordsAssociated with disabilityAssociated with dementiaDevelopment of dementiaDevelopment of disabilityIncident dementiaAssociated with intracerebral hemorrhageIntracerebral hemorrhageHealth recordsTelephone interviewsDementia conversionCohort studyDementiaDisabilityLobar intracerebral hemorrhageIntracerebral hemorrhage locationIntracerebral hemorrhagic lesionsFunctional outcomesNoncontrast computed tomography scanCohortMedian follow-up timeRight posterior limbOutcomesLeft temporo-occipital regionOptimal Magnitude of Blood Pressure Reduction and Hematoma Growth and Functional Outcomes in Intracerebral Hemorrhage
Li Q, Lv X, Morotti A, Qureshi A, Dowlatshahi D, Falcone G, Sheth K, Shoamanesh A, Murthy S, Viswanathan A, Goldstein J. Optimal Magnitude of Blood Pressure Reduction and Hematoma Growth and Functional Outcomes in Intracerebral Hemorrhage. Neurology 2025, 104: e213412. PMID: 39913881, PMCID: PMC11803522, DOI: 10.1212/wnl.0000000000213412.Peer-Reviewed Original ResearchConceptsSystolic blood pressure reductionSystolic blood pressureMagnitude of blood pressure reductionATACH-2 trialHematoma expansionMagnitude of SBP reductionAcute kidney injuryATACH-2Blood pressure reductionIntracerebral hemorrhageMm HgSBP reductionPost hoc analysisFunctional outcomesHematoma growthAntihypertensive Treatment of Acute Cerebral Hemorrhage 2Poor outcomeHoc analysisMedian hematoma volumeModified Rankin Scale scoreSevere intracerebral hemorrhagePressure reductionMultivariable logistic regression assessed associationsRisk of poor outcomesAdmission systolic blood pressureBidirectional relationship between epigenetic age and stroke, dementia, and late-life depression
Rivier C, Szejko N, Renedo D, Clocchiatti-Tuozzo S, Huo S, de Havenon A, Zhao H, Gill T, Sheth K, Falcone G. Bidirectional relationship between epigenetic age and stroke, dementia, and late-life depression. Nature Communications 2025, 16: 1261. PMID: 39893209, PMCID: PMC11787333, DOI: 10.1038/s41467-024-54721-0.Peer-Reviewed Original ResearchThis study shows a bidirectional link between accelerated epigenetic aging and brain health events like stroke, dementia, and depression, supporting new prevention strategies for aging-related conditions.Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors
Demarais Z, Conlon C, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Torres-Lopez V, Sheth K, Meeker D, Zhao H, Ohno-Machado L, Acosta J, Huo S, Falcone G. Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors. Neurology 2025, 104: e210276. PMID: 39889253, DOI: 10.1212/wnl.0000000000210276.Peer-Reviewed Original ResearchConceptsStroke survivorsWorse glycemic controlPoor glycemic controlStroke patientsAssociated with worse glycemic controlGlycemic controlPolygenic risk scoresClinical management of stroke patientsAssociated with poor glycemic controlManagement of stroke patientsCross-sectional designGenetic association studiesUncontrolled diabetesSusceptibility to T2DMUK BiobankType 2 diabetes mellitusAdverse vascular outcomesRisk scoreAssociation studiesHemoglobin A1cSurvivorsVascular outcomesSusceptibility to diabetesStrokeDiabetesHealth-Related Behaviors and Risk of Common Age-Related Brain Diseases Across Severities of Genetic Risk
Marini S, Kimball T, Mayerhofer E, Tack R, Senff J, Prapiadou S, Rivier C, Duskin J, Kourkoulis C, Falcone G, Yechoor N, Tanzi R, Rosand J, Singh S, Parodi L, Anderson C. Health-Related Behaviors and Risk of Common Age-Related Brain Diseases Across Severities of Genetic Risk. Neurology 2025, 104: e210297. PMID: 39869844, DOI: 10.1212/wnl.0000000000210297.Peer-Reviewed Original ResearchImproving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT.
Tran A, Karam G, Zeevi D, Qureshi A, Malhotra A, Majidi S, Murthy S, Park S, Kontos D, Falcone G, Sheth K, Payabvash S. Improving the Robustness of Deep-Learning Models in Predicting Hematoma Expansion from Admission Head CT. American Journal Of Neuroradiology 2025, ajnr.a8650. PMID: 39794133, DOI: 10.3174/ajnr.a8650.Peer-Reviewed Original ResearchFast Gradient Sign MethodDeep learning modelsRobustness of deep learning modelsAdversarial attacksAdversarial imagesAdversarial trainingSign MethodModel robustnessDeploying deep learning modelsDeep learning model performanceConvolutional neural networkImprove model robustnessAcute intracerebral hemorrhageHematoma expansionMulti-threshold segmentationReceiver operating characteristicIntracerebral hemorrhageGradient descentType attacksData perturbationNeural networkProjected GradientTraining setAntihypertensive Treatment of Acute Cerebral HemorrhageThreshold-based segmentation
2024
Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography
Tran A, Desser D, Zeevi T, Karam G, Zietz J, Dell’Orco A, Chen M, Malhotra A, Qureshi A, Murthy S, Majidi S, Falcone G, Sheth K, Nawabi J, Payabvash S. Optimizing Automated Hematoma Expansion Classification from Baseline and Follow-Up Head Computed Tomography. Applied Sciences 2024, 15: 111. PMID: 40046237, PMCID: PMC11882137, DOI: 10.3390/app15010111.Peer-Reviewed Original ResearchIntracerebral hemorrhageHematoma expansionFollow-up CT scansFollow-up head computed tomographyPredictors of poor outcomeDeep learning classification modelFollow-up scansHead computed tomographyFalse-negative resultsHematoma segmentationAutomated segmentationMulticentre cohortCT scanValidation cohortPoor outcomeComputed tomographyFollow-upClassification modelOptimizational methodHematomaAnnotationGenal: a Python toolkit for genetic risk scoring and Mendelian randomization
Rivier C, Clocchiatti-Tuozzo S, Huo S, Torres-Lopez V, Renedo D, Sheth K, Falcone G, Acosta J. Genal: a Python toolkit for genetic risk scoring and Mendelian randomization. Bioinformatics Advances 2024, 5: vbae207. PMID: 39776894, PMCID: PMC11706532, DOI: 10.1093/bioadv/vbae207.Peer-Reviewed Original ResearchPolygenic risk scoresMendelian randomizationMR analysisGenome-wide association studiesPRS computationRisk scoreGenetic association dataGenetic risk scoreGenetic epidemiological studiesMR-PRESSOAssociation studiesGenetic epidemiologyMultiple R packagesMR methodsAssociation dataR packageEpidemiological studiesComputational experimentsGenalePython toolkitPython packageScoresPLINKComputation timeEpidemiologyMachine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT)
Dierksen F, Sommer J, Tran A, Lin H, Haider S, Maier I, Aneja S, Sanelli P, Malhotra A, Qureshi A, Claassen J, Park S, Murthy S, Falcone G, Sheth K, Payabvash S. Machine Learning Models for 3-Month Outcome Prediction Using Radiomics of Intracerebral Hemorrhage and Perihematomal Edema from Admission Head Computed Tomography (CT). Diagnostics 2024, 14: 2827. PMID: 39767188, PMCID: PMC11674633, DOI: 10.3390/diagnostics14242827.Peer-Reviewed Original ResearchIntegrated discrimination indexNet reclassification indexPerihematomal edemaHead computed tomographyIntracerebral hemorrhageComputed tomographyClinical variablesClinical predictors of poor outcomeOutcome predictionAcute supratentorial intracerebral hemorrhageAdmission head computed tomographyRadiomic featuresNon-contrast head computed tomographyPredictors of poor outcomeModified Rankin Scale scoreIntracerebral hemorrhage scoreSupratentorial intracerebral hemorrhageIntracerebral hemorrhage patientsClinical risk factorsRankin Scale scoreReceiver operating characteristic areaOperating characteristics areaSecondary brain injuryHematoma evacuationPatient selection
Clinical Trials
Current Trials
Biomarker and Edema Attenuation in IntraCerebral Hemorrhage (BEACH)
HIC ID2000031665RoleSub InvestigatorPrimary Completion Date12/01/2025Recruiting ParticipantsGenderBothAge18+ yearsRegulating Blood Pressure During Recovery from Intracerebral Hemorrhage and Ischemic Stroke (REDUCE)
HIC ID2000029811RoleSub InvestigatorPrimary Completion Date01/31/2025Recruiting ParticipantsGenderBothAge18+ yearsBrain Oxygen Optimization in Severe TBI, Phase 3 (BOOST3)
HIC ID2000024956RoleSub InvestigatorPrimary Completion Date07/01/2023Recruiting ParticipantsGenderBothAge14+ yearsAnticoagulation for Stroke Prevention and Recovery After ICH (ASPIRE)
HIC ID2000026409RolePrincipal InvestigatorPrimary Completion Date04/30/2028Recruiting ParticipantsGenderBothAge18+ yearsTransforming Acute Stroke Detection through Real Time Neurological Monitoring
HIC ID1605017863RoleSub InvestigatorPrimary Completion Date07/01/2017Recruiting ParticipantsGenderBothAge18+ years
Academic Achievements & Community Involvement
Clinical Care
Overview
Guido Falcone, MD, ScD, MPH, is a critical care neurologist who treats patients with severe brain injuries from trauma, strokes, hemorrhages, and seizures, among other conditions. “I usually meet patients with these injuries immediately after they come to the hospital,” Dr. Falcone says. He also sees patients suffering from symptoms caused by neuromuscular diseases or complications from brain surgery.
“One important characteristic of our specialty is that many important decisions need to be made in those initial few minutes to hours,” Dr. Falcone says. “We also need to factor in the patient’s wishes, but often they are unconscious and cannot communicate.”
In those cases, Dr. Falcone relies on the patient’s family for guidance. “This can cause a tremendous amount of stress as they carry the huge responsibility of representing their loved ones,” he says.
Dr. Falcone keeps this additional stress in mind when he’s talking with the patient’s family about a diagnosis and what to expect next. “It’s very important for us to be honest and explain to them what we know and don’t know so that this uncertainty can be taken into consideration when we’re making a clinical decision,” he says. Dr. Falcone says he and his colleagues in the Neuroscience Intensive Care Unit (Neuro ICU) frequently update families on the status of a patient’s condition and progress.
“Something I came to realize after a few years in the field is that we help patients and families all the time. Sometimes, we help them get better,” Dr. Falcone says. “But another important part of our job is to give the very best end-of-life care, with the same approach we use when curing a disease or saving lives, if that is necessary.”
In his research, Dr. Falcone specializes in population genetics and genomic medicine, two related fields that involve analyzing large amounts of data and searching for different variants of genes that might influence human disease. He works with a team that uses information from across disciplines, such as neuroimaging data, for example, to conduct studies. “We want to use data to understand not just what causes disease, but also who is at high risk of developing it,” Dr. Falcone says. “Genes are such a powerful tool in patient care because our genetic information is constant from birth.”
Clinical Specialties
News & Links
News
- July 05, 2023
Cyprien Rivier, MD, MSc Wins ESOC Young Research Investigator Award in Stroke
- February 08, 2023
Santiago Clocchiatti-Tuozzo, MD Honored with Bernard J. Tyson Career Development Award and Stroke Underrepresented Racial and Ethnic Groups Travel Grant
- February 07, 2023
Daniela Renedo, MD Wins American Heart Association Stroke Basic Science Award
- January 31, 2023
Yale Study Links Genetics and Blood Pressure Control in Stroke Survivors
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New Haven, CT 06511
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203.785.6288Business Office Fax
203.737.4419Patient Care Locations
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