2024
Mapping the structure-function relationship along macroscale gradients in the human brain
Collins E, Chishti O, Obaid S, McGrath H, King A, Shen X, Arora J, Papademetris X, Constable R, Spencer D, Zaveri H. Mapping the structure-function relationship along macroscale gradients in the human brain. Nature Communications 2024, 15: 7063. PMID: 39152127, PMCID: PMC11329792, DOI: 10.1038/s41467-024-51395-6.Peer-Reviewed Original ResearchConceptsStructure-function correspondenceBrain regionsMacroscale gradientWhite matter connectivityHuman brain regionsStructure-function couplingNeural network propertiesAssociation cortexCognitive functionBridging neuroscienceFunctional coactivationOrganizational axisCortical thicknessHuman brainMotor cortexLanguage processingBrainCortexMotor functionNatural language processingNetwork propertiesMotorNeuroscienceNatural languageData repositoriesMultimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization
Vafaii H, Mandino F, Desrosiers-Grégoire G, O’Connor D, Markicevic M, Shen X, Ge X, Herman P, Hyder F, Papademetris X, Chakravarty M, Crair M, Constable R, Lake E, Pessoa L. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization. Nature Communications 2024, 15: 229. PMID: 38172111, PMCID: PMC10764905, DOI: 10.1038/s41467-023-44363-z.Peer-Reviewed Original Research
2023
Clustering of KOR PET images separates people with AUD into distinct responses to naltrexone
Hoye J, Key J, de Laat B, Cosgrove K, Krishnan-Sarin S, Papademetris X, Morris E. Clustering of KOR PET images separates people with AUD into distinct responses to naltrexone. Brain Imaging And Behavior 2023, 17: 367-371. PMID: 36695971, DOI: 10.1007/s11682-023-00758-6.Peer-Reviewed Original Research
2022
HALO: A software tool for real‐time head alignment in the MR scanner
Zhao Z, Galiana G, Zillo C, Camarro T, Qiu M, Papademetris X, Hampson M. HALO: A software tool for real‐time head alignment in the MR scanner. Magnetic Resonance In Medicine 2022, 89: 1506-1513. PMID: 36426774, PMCID: PMC10753491, DOI: 10.1002/mrm.29535.Peer-Reviewed Original ResearchHigh-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter
McGrath H, Zaveri H, Collins E, Jafar T, Chishti O, Obaid S, Ksendzovsky A, Wu K, Papademetris X, Spencer D. High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Scientific Reports 2022, 12: 18778. PMID: 36335146, PMCID: PMC9637135, DOI: 10.1038/s41598-022-21543-3.Peer-Reviewed Original ResearchConceptsEpilepsy surgery cohortStandardized brain regionsBrain structure-function relationshipsSurgery cohortClinical usefulnessBrain metricsAnatomical locationBrain regionsBrain localizationCortical parcellationOwn unique benefitsIntracranial electroencephalographyAnatomical designationBrain anatomyHuman brain anatomyHuman brainBrain atlasBrain landmarksBrain atlasesParcellationMagnetic resonanceNearest centimeterAnatomical featuresAnatomyCortical localization
2018
Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery
Sadda P, Imamoglu M, Dombrowski M, Papademetris X, Bahtiyar MO, Onofrey J. Deep-learned placental vessel segmentation for intraoperative video enhancement in fetoscopic surgery. International Journal Of Computer Assisted Radiology And Surgery 2018, 14: 227-235. PMID: 30484115, PMCID: PMC6438174, DOI: 10.1007/s11548-018-1886-4.Peer-Reviewed Original ResearchSegmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning
Onofrey JA, Staib LH, Papademetris X. Segmenting the Brain Surface From CT Images With Artifacts Using Locally Oriented Appearance and Dictionary Learning. IEEE Transactions On Medical Imaging 2018, 38: 596-607. PMID: 30176584, PMCID: PMC6476428, DOI: 10.1109/tmi.2018.2868045.Peer-Reviewed Original Research
2017
Using connectome-based predictive modeling to predict individual behavior from brain connectivity
Shen X, Finn ES, Scheinost D, Rosenberg MD, Chun MM, Papademetris X, Constable RT. Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols 2017, 12: 506-518. PMID: 28182017, PMCID: PMC5526681, DOI: 10.1038/nprot.2016.178.Peer-Reviewed Original Research
2015
Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients
Onofrey JA, Staib LH, Papademetris X. Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients. NeuroImage Clinical 2015, 10: 291-301. PMID: 26900569, PMCID: PMC4724039, DOI: 10.1016/j.nicl.2015.12.001.Peer-Reviewed Original Research
2011
Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms
Joshi A, Scheinost D, Okuda H, Belhachemi D, Murphy I, Staib LH, Papademetris X. Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms. Neuroinformatics 2011, 9: 69-84. PMID: 21249532, PMCID: PMC3066099, DOI: 10.1007/s12021-010-9092-8.Peer-Reviewed Original ResearchConceptsUser interface controlsUser interfaceNovel object-oriented frameworkCommand-line user interfaceGraphical user interface controlsMedical image analysisObject-oriented frameworkComplex image analysisImage analysisPlatform interoperabilitySoftware objectsReusable componentsInterface controlSource codeSuch algorithmsFramework idealMultiple platformsUnified frameworkAlgorithmRapid developmentDeploymentThorough testingPublic useFrameworkPlatform
2009
From medical image computing to computer‐aided intervention: development of a research interface for image‐guided navigation
Papademetris X, DeLorenzo C, Flossmann S, Neff M, Vives KP, Spencer DD, Staib LH, Duncan JS. From medical image computing to computer‐aided intervention: development of a research interface for image‐guided navigation. International Journal Of Medical Robotics And Computer Assisted Surgery 2009, 5: 147-157. PMID: 19301361, PMCID: PMC2796181, DOI: 10.1002/rcs.241.Peer-Reviewed Original ResearchConceptsResearch interfaceNavigation systemApplication programming interfaceDual computer systemComputer-aided interventionsSurgery navigation systemImage-guided navigation systemProgramming interfaceClient programNetwork interfacesMedical imagesImage-guided navigationResearch softwareReal timeViable solutionSoftwareImage analysis softwareTool positionVersatile linkAnalysis softwareImagesInterfaceNavigationSystemResearch techniques
2008
A Constrained Non-rigid Registration Algorithm for Use in Prostate Image-Guided Radiotherapy
Greene W, Chelikani S, Purushothaman K, Chen Z, Knisely J, Staib L, Papademetris X, Duncan J. A Constrained Non-rigid Registration Algorithm for Use in Prostate Image-Guided Radiotherapy. Lecture Notes In Computer Science 2008, 11: 780-788. PMID: 18979817, PMCID: PMC2790815, DOI: 10.1007/978-3-540-85988-8_93.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceHumansImaging, Three-DimensionalMalePattern Recognition, AutomatedProstatic NeoplasmsRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedRadiotherapy, Computer-AssistedReproducibility of ResultsSensitivity and SpecificitySubtraction TechniqueTomography, X-Ray ComputedConceptsTreatment planProstate image-guided radiotherapyOriginal treatment planRadiation dosageImage-guided radiotherapyTreatment daysRadiotherapy treatment plansCritical organsDifferent patientsPatient dataDifferent treatment daysBladderRectumProstateFemurBone motionCT imagesDosageReal patient dataPatientsRadiotherapy
2005
Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies
Scouten A, Papademetris X, Constable R. Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies. NeuroImage 2005, 30: 787-793. PMID: 16343951, DOI: 10.1016/j.neuroimage.2005.10.022.Peer-Reviewed Original ResearchConceptsSingle-subject studySpecific cortical regionsFunctional MRI studyBasic neuroscience studiesRandom effects analysisNeurosurgical interventionHemodynamic responseMRI studiesCortical activityCortical regionsFunctional MRIBrain functionFunctional anatomyNeurosurgical planningFunctional mappingSubtle activationClinical environmentGroup-level statisticsNeurophysiological underpinningsPartial volume effectsActivationNeuroscience studiesPatientsGroup statistics
2002
Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models
Papademetris* X, Sinusas AJ, Dione DP, Constable RT, Duncan JS. Estimation of 3-D Left Ventricular Deformation from Medical Images Using Biomechanical Models. IEEE Transactions On Medical Imaging 2002, 21: 786. PMID: 12374316, DOI: 10.1109/tmi.2002.801163.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsCoronary DiseaseDogsElasticityFinite Element AnalysisHeart VentriclesHumansImage EnhancementImaging, Three-DimensionalMagnetic Resonance Imaging, CineModels, CardiovascularPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityStress, MechanicalConceptsDense motion fieldRegional cardiac deformationLinear elastic modelSoft tissue deformationMotion fieldTerms of strainBiomechanical modelDeformation estimationTissue deformationFiber directionDeformationThree-dimensional image sequencesCardiac deformationHeart wallGood agreementHeart deformationGeneric methodologyMuscle fiber directionImage-derived informationImage sequencesEstimationWallSpecific directionQuantitative estimationInitial correspondence