2023
Face the Future—Artificial Intelligence in Oral and Maxillofacial Surgery
Miragall M, Knoedler S, Kauke-Navarro M, Saadoun R, Grabenhorst A, Grill F, Ritschl L, Fichter A, Safi A, Knoedler L. Face the Future—Artificial Intelligence in Oral and Maxillofacial Surgery. Journal Of Clinical Medicine 2023, 12: 6843. PMID: 37959310, PMCID: PMC10649053, DOI: 10.3390/jcm12216843.Peer-Reviewed Original ResearchArtificial intelligenceDeep learning techniquesMedical imaging dataVirtual surgical simulationDifferent computational conceptsFuture artificial intelligenceLearning techniquesMachine learningImage quality improvementNeural networkComputational conceptsIntraoperative workflowSurgical simulationIntelligenceHealth technology toolsImmediate feedbackMaxillofacial surgerySolution possibilitiesImaging dataCephalometric landmarksAdvanced analysisSyntaxReference listsSurgical accuracyPreoperative planningMulti-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study
Meng X, Iraji A, Fu Z, Kochunov P, Belger A, Ford J, McEwen S, Mathalon D, Mueller B, Pearlson G, Potkin S, Preda A, Turner J, van Erp T, Sui J, Calhoun V. Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from resting data: A large N fMRI schizophrenia study. NeuroImage Clinical 2023, 38: 103434. PMID: 37209635, PMCID: PMC10209454, DOI: 10.1016/j.nicl.2023.103434.Peer-Reviewed Original ResearchConceptsIndependent component analysisData-driven approachData miningF1 scoreClassification modelReference algorithmNetwork connectivityMagnetic resonance imaging dataNetworkImaging dataPredictive resultsPatient dataFunctional magnetic resonance imaging (fMRI) dataData acquisition timeConnectivity networksFrameworkConnectivityPromising approachNew subjectMiningAnalytic approachAlgorithmDatasetAcquisition timeComponent analysisPredicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes
Sankar A, Shen X, Colic L, Goldman D, Villa L, Kim J, Pittman B, Scheinost D, Constable R, Blumberg H. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychological Medicine 2023, 53: 6656-6665. PMID: 36891769, PMCID: PMC10491744, DOI: 10.1017/s003329172300003x.Peer-Reviewed Original ResearchBipolar disorderYoung Mania Rating ScaleMania Rating ScaleFunctional connectomeBrain functional connectomeSymptom scoresHamilton DepressionMagnetic resonance imaging dataEmotion processing taskMood symptomatologyRating ScaleFunctional magnetic resonance imaging (fMRI) dataConnectomeAdultsImaging dataIndependent samplesPredictive abilitySymptomatology
2021
Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth
Zhou B, Liu C, Duncan J. Anatomy-Constrained Contrastive Learning for Synthetic Segmentation Without Ground-Truth. Lecture Notes In Computer Science 2021, 12901: 47-56. DOI: 10.1007/978-3-030-87193-2_5.Peer-Reviewed Original ResearchSegmentation networkContrastive learningManual segmentationSuperior segmentation performanceObject of interestSynthetic SegmentationManual effortSegmentation performanceTraining dataUnsupervised adaptationImaging dataSource modalitySegmentationNetworkPrevious methodsLearningLarge amountSuccessful applicationPET imaging dataImagesObjectsCodeDataNew imaging modalityComputerized technologies informing cardiac catheterization and guiding coronary intervention
Bajaj R, Parasa R, Ramasamy A, Makariou N, Foin N, Prati F, Lansky A, Mathur A, Baumbach A, Bourantas CV. Computerized technologies informing cardiac catheterization and guiding coronary intervention. American Heart Journal 2021, 240: 28-45. PMID: 34077744, DOI: 10.1016/j.ahj.2021.05.017.Peer-Reviewed Original ResearchConceptsUser-friendly softwareComputer-based technologiesComputer hardwareAccessible interfaceImage processingImaging dataGeneration of modelsComputerized technologyComputational fluid dynamics techniqueSoftwareFluid dynamics techniqueAbove advancesX-ray angiographyTechnologyHardwareSystemSilhouetteDynamics techniqueVessel geometryProcessingCapabilityDataInterfaceReliable evaluationAdvances
2020
Ultra-high field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain
Schulz E, Stankewitz A, Winkler A, Irving S, Witkovský V, Tracey I. Ultra-high field imaging reveals increased whole brain connectivity underpins cognitive strategies that attenuate pain. ELife 2020, 9: e55028. PMID: 32876049, PMCID: PMC7498261, DOI: 10.7554/elife.55028.Peer-Reviewed Original ResearchConceptsPain attenuationBrain connectivityAttenuation of painCognitive strategiesHigher functional connectivityWhole-brain connectivityTonic cold painWhite matter integrityPain processingLower painFunctional imaging dataCold painPainCingulate cortexBrain regionsFunctional connectivityHealthy participantsTrialsCognitive interventionsDiffusion tensorTrial variabilityBrain dataImaging dataParticipants
2018
Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo
James GM, Gryglewski G, Vanicek T, Berroterán-Infante N, Philippe C, Kautzky A, Nics L, Vraka C, Godbersen GM, Unterholzner J, Sigurdardottir HL, Spies M, Seiger R, Kranz GS, Hahn A, Mitterhauser M, Wadsak W, Bauer A, Hacker M, Kasper S, Lanzenberger R. Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo. Cerebral Cortex 2018, 29: 372-382. PMID: 30357321, PMCID: PMC6294402, DOI: 10.1093/cercor/bhy249.Peer-Reviewed Original ResearchConceptsPositron emission tomographyEmission tomographySerotonin neurotransmitter systemSerotonin 1ACerebral cortexEnzyme monoamine oxidase AMonoamine oxidase ANeurotransmitter systemsSerotonin systemPsychotropic drugsNeuropsychiatric disordersBrain functionHealthy participantsSerotonin transporterMolecular imaging dataMolecular targetsMolecular profileOxidase AWhole-brain coverageCortexParcellationTomographyDefined clustersClose associationImaging dataPredicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma.
Abajian A, Murali N, Savic LJ, Laage-Gaupp FM, Nezami N, Duncan JS, Schlachter T, Lin M, Geschwind JF, Chapiro J. Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma. Journal Of Visualized Experiments 2018 PMID: 30371657, PMCID: PMC6235502, DOI: 10.3791/58382.Peer-Reviewed Original ResearchConceptsIntra-arterial therapyN patientsHepatocellular carcinomaTrans-arterial therapiesIntra-arterial treatmentCohort of patientsStandard of careLikelihood of responseClinical research questionsSurgical resectionNew patientsTreatment responseUnivariate associationsPatientsTraining patientsInterventional radiologyTherapyCarcinomaTreatmentImage-guided therapyOutcomesFinal modelImaging dataResectionResponseReading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia
Plis SM, Amin MF, Chekroud A, Hjelm D, Damaraju E, Lee HJ, Bustillo JR, Cho K, Pearlson GD, Calhoun VD. Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. NeuroImage 2018, 181: 734-747. PMID: 30055372, PMCID: PMC6321628, DOI: 10.1016/j.neuroimage.2018.07.047.Peer-Reviewed Original ResearchConceptsGray matter patternsIntrinsic connectivity networksPosterior cingulate cortexHealthy controlsDynamic functional connectivityStructural MRIFunctional MRIDFNC statesGray matter densitySignificant group differencesTemporal lobeTemporal cortexCingulate cortexSame brainFunctional connectivityBrain structuresCognitive scoresStrong associationLinkage/associationMultimodal brain imaging dataImaging dataGroup differencesSignificant correlationSMRI dataCortexChanges in Cerebral Cortical Thickness Related to Weight Loss Following Bariatric Surgery
Bohon C, Garcia LC, Morton JM. Changes in Cerebral Cortical Thickness Related to Weight Loss Following Bariatric Surgery. Obesity Surgery 2018, 28: 2578-2582. PMID: 29876838, PMCID: PMC6400222, DOI: 10.1007/s11695-018-3317-6.Peer-Reviewed Original ResearchConceptsCerebral cortical thicknessCortical thicknessBariatric surgeryWeight Loss Following Bariatric SurgeryGreater excess weight lossExcess weight lossCortical gray matterMagnetic resonance imaging dataBrain changesPatientsGray matterSurgeryWeight lossPreliminary evidenceNeurocognitive measuresFurther investigationMemory recallImaging dataPrevious studiesObesityGlobal measuresLarge sampleMonthsApplication of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs
Yu Q, Du Y, Chen J, Sui J, Adalē T, Pearlson G, Calhoun VD. Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs. Proceedings Of The IEEE 2018, 106: 886-906. PMID: 30364630, PMCID: PMC6197492, DOI: 10.1109/jproc.2018.2825200.Peer-Reviewed Original ResearchRole of virtual reality in congenital heart disease
Ong CS, Krishnan A, Huang CY, Spevak P, Vricella L, Hibino N, Garcia JR, Gaur L. Role of virtual reality in congenital heart disease. Congenital Heart Disease 2018, 13: 357-361. PMID: 29399969, DOI: 10.1111/chd.12587.Peer-Reviewed Original ResearchConceptsVirtual realityReal-time viewUse of VRInteractive viewingImproved surgical planningInteractive manipulationImaging dataHigh-resolution representationInteractive capabilitiesPreoperative planning methodPlanning methodFacility of usePresurgical planningPatient-specific imaging dataComplex intraNew platformCollaborative discussionsPlanningSurgical planningUsersIllustrative viewNovel useSoftwareRealityLearning curve
2012
Ipsilateral synkinesia involves the supplementary motor area
Salardini A, Narayanan NS, Arora J, Constable T, Jabbari B. Ipsilateral synkinesia involves the supplementary motor area. Neuroscience Letters 2012, 523: 135-138. PMID: 22759337, PMCID: PMC3836003, DOI: 10.1016/j.neulet.2012.06.060.Peer-Reviewed Original ResearchConceptsSupplementary motor areaMotor areaInvoluntary coordinationFoot motor corticesHand motor taskMotor cortexMagnetic resonance imaging dataRare disorderInvoluntary movementsSynkinesiaVoluntary movementMotor tasksControl participantsFunctional magnetic resonance imaging (fMRI) dataPatientsImaging dataRhythmic tasksCentral roleCortex
2011
Biofeedback of Real-Time Functional Magnetic Resonance Imaging Data from the Supplementary Motor Area Reduces Functional Connectivity to Subcortical Regions
Hampson M, Scheinost D, Qiu M, Bhawnani J, Lacadie CM, Leckman JF, Constable RT, Papademetris X. Biofeedback of Real-Time Functional Magnetic Resonance Imaging Data from the Supplementary Motor Area Reduces Functional Connectivity to Subcortical Regions. Brain Connectivity 2011, 1: 91-98. PMID: 22432958, PMCID: PMC3621512, DOI: 10.1089/brain.2011.0002.Peer-Reviewed Original ResearchConceptsSupplementary motor areaReal-time functional magnetic resonanceFunctional connectivityTourette syndromeFunctional magnetic resonanceMotor areaSubcortical regionsBiofeedback sessionsState functional connectivityTic symptomsMagnetic resonance imaging dataHealthy subjectsTS patientsMagnetic resonanceBrain areasBiofeedbackFunctional magnetic resonance imaging (fMRI) dataFurther studiesSignificant increaseAberrant dynamicsSubjectsRecent studiesImaging dataSessionsPatients
2007
A method for functional network connectivity among spatially independent resting-state components in schizophrenia
Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially independent resting-state components in schizophrenia. NeuroImage 2007, 39: 1666-1681. PMID: 18082428, PMCID: PMC3164840, DOI: 10.1016/j.neuroimage.2007.11.001.Peer-Reviewed Original ResearchConceptsFunctional network connectivityFunctional connectivityTime courseICA time coursesHealthy controlsMagnetic resonance imaging dataHealthy individualsPatientsBrain disordersBrain regionsState fMRI dataCortical processingSchizophreniaFunctional magnetic resonance imaging (fMRI) dataSpatial independent component analysisSignificant differencesBrain networksComponent time coursesTemporal relationshipCoherent brain regionsBrainFMRI dataCourseImaging dataSeed voxels
2006
Grid enabled magnetic resonance scanners for near real-time medical image processing
Crane J, Crawford F, Nelson S. Grid enabled magnetic resonance scanners for near real-time medical image processing. Journal Of Parallel And Distributed Computing 2006, 66: 1524-1533. DOI: 10.1016/j.jpdc.2006.03.009.Peer-Reviewed Original ResearchMedical image processingHPC resourcesImage processingHigh-performance computing gridsReal-time medical image processingGraphical software toolMedical imaging dataReal-time processingComputing GridImaging data setsPrototype applicationSoftware toolsParallel reconstructionProcessing jobsAcceptable timeData setsMultiple research groupsImaging dataGridProcessingHardwareInitial resultsResourcesScannerResearch groups
1998
Interpreting functional imaging studies in terms of neurotransmitter cycling
Shulman R, Rothman D. Interpreting functional imaging studies in terms of neurotransmitter cycling. Proceedings Of The National Academy Of Sciences Of The United States Of America 1998, 95: 11993-11998. PMID: 9751778, PMCID: PMC21753, DOI: 10.1073/pnas.95.20.11993.Peer-Reviewed Original ResearchConceptsFunctional imaging experimentsNeurobiological processesFunctional magnetic resonance imagingBasis of neuroscienceSpecific mental processesFunctional imaging studiesFunctional imaging dataVivo 13C NMR measurementsCognitive psychologyCognitive tasksMental processesPsychological termsPsychological interpretationImaging experimentsNeuroscientific interpretationSensory stimulationPsychological designHuman brainFunctional imaging signalsNeurotransmitter cyclingParticular positron emission tomographyNeurotransmitter fluxBrain energy consumptionImaging studiesImaging data
1994
Evaluation of cerebral gray and white matter metabolite differences by spectroscopic imaging at 4.1T
Hetherington H, Mason G, Pan J, Ponder S, Vaughan J, Twieg D, Pohost G. Evaluation of cerebral gray and white matter metabolite differences by spectroscopic imaging at 4.1T. Magnetic Resonance In Medicine 1994, 32: 565-571. PMID: 7808257, DOI: 10.1002/mrm.1910320504.Peer-Reviewed Original Research
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