2024
Human brain state dynamics are highly reproducible and associated with neural and behavioral features
Lee K, Ji J, Fonteneau C, Berkovitch L, Rahmati M, Pan L, Repovš G, Krystal J, Murray J, Anticevic A. Human brain state dynamics are highly reproducible and associated with neural and behavioral features. PLOS Biology 2024, 22: e3002808. PMID: 39316635, PMCID: PMC11421804, DOI: 10.1371/journal.pbio.3002808.Peer-Reviewed Original ResearchConceptsCo-activation patternsResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingBehavioral featuresNeural variationsMoment-to-moment changesSingle-subject levelBrain state dynamicsEmotion regulationHealthy young adultsBehavioral phenotypesCognitive functionSubstance useNeural activityNeuroimaging markersNeural featuresYoung adultsMagnetic resonance imagingCo-activationResonance imagingCo-variationNeuroimagingIndividualsEmotionsFunctional outcomes
2022
Arousal impacts distributed hubs modulating the integration of brain functional connectivity
Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EMR, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. NeuroImage 2022, 258: 119364. PMID: 35690257, PMCID: PMC9341222, DOI: 10.1016/j.neuroimage.2022.119364.Peer-Reviewed Original Research
2019
Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis
Lee K, Khoo HM, Fourcade C, Gotman J, Grova C. Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging 2019, 58: 97-107. PMID: 30695721, DOI: 10.1016/j.mri.2019.01.019.Peer-Reviewed Original ResearchConceptsReal dataNumber of atomsSubject-specific thresholdFunctional connectivity MRI analysisState networksAutomatic removal methodSpatial priorsSet of voxelsBootstrap resamplingSparse dictionary learningStepwise regression procedureNoiseHub analysisRegression procedureInter-network communicationNew methodAtomsBand-pass filteringTemporal correlationFluctuationsPriorsSparsityDictionary learningNetworkWhole-brain signals
2015
Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis
Lee YB, Lee J, Tak S, Lee K, Na DL, Seo SW, Jeong Y, Ye JC, Initiative T. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis. NeuroImage 2015, 125: 1032-1045. PMID: 26524138, DOI: 10.1016/j.neuroimage.2015.10.081.Peer-Reviewed Original ResearchConceptsSparse graph modelIndependency assumptionEstimation problemDesign matrixSparse dictionary learningSame temporal dynamicsGraph modelResting-state functional connectivity MRI analysesVariance assumptionLocal network structureSparse combinationTheoretical analysisFunctional connectivity MRI analysisGraph theoretical analysisLocal dynamicsSPM frameworkParameter mappingSummary statisticsDynamicsGlobal brain dynamicsBrain dynamicsGroup-level inferencesLocal connectivityAforementioned individualsStatistical parameter mapping
2010
Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements
Tak S, Jang J, Lee K, Ye JC. Quantification of CMRO2 without hypercapnia using simultaneous near-infrared spectroscopy and fMRI measurements. Physics In Medicine And Biology 2010, 55: 3249-3269. PMID: 20479515, DOI: 10.1088/0031-9155/55/11/017.Peer-Reviewed Original ResearchConceptsCerebral blood flowPrimary motor cortexCerebral blood volumeFunctional MRIMotor cortexAverage cerebral blood flowBlood oxygenation level-dependent (BOLD) signalCerebral metabolic rateLevel-dependent signalNeurovascular couplingBlood flowBlood volumeBOLD changesFMRI measurementsHypercapniaCortexMain target regionMetabolic rateGroup averagesActivation mapsCMRO2Physiological componentsFinger