2024
PET imaging of M4 muscarinic acetylcholine receptors in rhesus macaques using [11C]MK-6884: Quantification with kinetic modeling and receptor occupancy by CVL-231 (emraclidine), a novel positive allosteric modulator
Belov V, Guehl N, Duvvuri S, Iredale P, Moon S, Dhaynaut M, Chakilam S, MacDonagh A, Rice P, Yokell D, Renger J, Fakhri G, Normandin M. PET imaging of M4 muscarinic acetylcholine receptors in rhesus macaques using [11C]MK-6884: Quantification with kinetic modeling and receptor occupancy by CVL-231 (emraclidine), a novel positive allosteric modulator. Cerebrovascular And Brain Metabolism Reviews 2024, 44: 1329-1342. PMID: 38477292, PMCID: PMC11342722, DOI: 10.1177/0271678x241238820.Peer-Reviewed Original ResearchConceptsPositive allosteric modulatorsReceptor occupancyNon-human primatesBinding potentialPositron emission tomographyMuscarinic acetylcholine receptorsAllosteric modulatorsNon-human primate brainM4 muscarinic acetylcholine receptorStriatal hyperdopaminergiaAcetylcholine receptorsBrain regionsCaudate nucleusTotal volume of distributionDose-dependent blockReference regionVolume of distributionPositron emission tomography imagingEmission tomographyReceptor levelsFunction of dosePET scansClinical trialsBlood-basedRhesus macaques
2022
Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [18F]MK-6240 PET uptake
Fu J, Lois C, Sanchez J, Becker J, Rubinstein Z, Thibault E, Salvatore A, Sari H, Farrell M, Guehl N, Normandin M, Fakhri G, Johnson K, Price J. Kinetic evaluation and assessment of longitudinal changes in reference region and extracerebral [18F]MK-6240 PET uptake. Cerebrovascular And Brain Metabolism Reviews 2022, 43: 581-594. PMID: 36420769, PMCID: PMC10063833, DOI: 10.1177/0271678x221142139.Peer-Reviewed Original ResearchConceptsPartial volume correctionLongitudinal changesReference regionStandardized uptake valueFollow-up scansExtracerebral signalsAssessment of longitudinal changesCross-sectional effect sizesUptake valuePET uptakeDistribution volumeRegional uptakeArterial samplesExtracerebral contaminationOne-yearCognitively-normalEffect sizePonsGroup differentiationVolume correctionSubject levelSubjects
2017
Pseudoreference Regions for Glial Imaging with 11C-PBR28: Investigation in 2 Clinical Cohorts
Albrecht D, Normandin M, Shcherbinin S, Wooten D, Schwarz A, Zürcher N, Barth V, Guehl N, Akeju O, Atassi N, Veronese M, Turkheimer F, Hooker J, Loggia M. Pseudoreference Regions for Glial Imaging with 11C-PBR28: Investigation in 2 Clinical Cohorts. Journal Of Nuclear Medicine 2017, 59: 107-114. PMID: 28818984, PMCID: PMC5750517, DOI: 10.2967/jnumed.116.178335.Peer-Reviewed Original ResearchConceptsChronic low back painPseudoreference regionGroup differencesDistribution volume ratioOccipital cortexArterial input functionChronic low back pain patientsAbsence of group differencesNo significant group differencesTranslocator protein imagingLow back painRegional group differencesSignificant group differencesAmyotrophic lateral sclerosisTranslocator proteinMatched healthy controlsDetect group differencesWithin-group variabilityBack painNeuroimmune activationVoxelwise analysisPET scansHealthy controlsClinical cohortDistribution volume
2016
A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography
Zhu W, Ouyang J, Rakvongthai Y, Guehl N, Wooten D, Fakhri G, Normandin M, Fan Y. A Bayesian spatial temporal mixtures approach to kinetic parametric images in dynamic positron emission tomography. Medical Physics 2016, 43: 1222-1234. PMID: 26936707, PMCID: PMC5025019, DOI: 10.1118/1.4941010.Peer-Reviewed Original ResearchConceptsPositron emission tomographySpatial mixture modelNearby voxelsMixture modelEmission tomographyDynamic positron emission tomographyK-means methodKinetic modelKinetic parametric imagesOne-compartment kinetic modelNovel algorithmTemporal informationClassification purposesMeasurement of local perfusionLocal perfusionTime activity curvesNormal ROIsTemporal modelBayesian algorithmCardiac studiesMarkov chain Monte CarloParameter estimationNoise regionSimulation experimentsSimulated data sets