Featured Publications
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 outcomesDisruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy
Lee K, Khoo HM, Lina JM, Dubeau F, Gotman J, Grova C. Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NeuroImage Clinical 2018, 20: 71-84. PMID: 30094158, PMCID: PMC6070692, DOI: 10.1016/j.nicl.2018.06.029.Peer-Reviewed Original ResearchConceptsMesial temporal lobe epilepsyTemporal lobe epilepsyLobe epilepsyState functional MRIDefault mode networkHealthy controlsBrain network hubsConnector hubsAbnormal hippocampusMotor networkHippocampal networkBrain regionsSalience networkFunctional MRIEpilepsyEpileptic subjectsMode networkLateralizationBrain networksDefault modeAbnormalitiesPathological disruptionHub reorganizationFunctional networksDisruption
2021
An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation
Cross NE, Pomares FB, Nguyen A, Perrault AA, Jegou A, Uji M, Lee K, Razavipour F, Bin Ka’b Ali O, Aydin U, Benali H, Grova C, Dang-Vu TT. An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation. PLOS Biology 2021, 19: e3001232. PMID: 34735431, PMCID: PMC8568176, DOI: 10.1371/journal.pbio.3001232.Peer-Reviewed Original ResearchConceptsSleep-deprived stateSleep deprivationCortical networksFunctional magnetic resonance imaging (fMRI) scansLarge-scale cortical networksCognitive impairmentSleep-deprived brainNight of SDHomeostatic sleep pressureExecutive tasksCognitive performanceTask performanceCognitive changesRegular nightBrain activityCognitive functionCognitive deficitsNetwork integrationInformation processingSleep pressureCortical activityBidirectional effectsConnectivity changesInterindividual differencesNonrapid eye movement sleep
2020
Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol
Horien C, Fontenelle S, Joseph K, Powell N, Nutor C, Fortes D, Butler M, Powell K, Macris D, Lee K, Greene AS, McPartland JC, Volkmar FR, Scheinost D, Chawarska K, Constable RT. Low-motion fMRI data can be obtained in pediatric participants undergoing a 60-minute scan protocol. Scientific Reports 2020, 10: 21855. PMID: 33318557, PMCID: PMC7736342, DOI: 10.1038/s41598-020-78885-z.Peer-Reviewed Original ResearchConceptsPediatric participantsMRI protocolMagnetic resonance imaging (MRI) scansFunctional magnetic resonance imaging (fMRI) scansShorter MRI protocolsScan protocolResonance imaging scansImaging scansMRI sessionsFMRI connectivity analysisFMRI dataFMRI findingsSignificant confoundScansReplication groupConnectivity analysisAutism spectrum disorderMock scanSpectrum disorderParticipantsHead motionProtocol
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
2016
SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity
Lee K, Lina JM, Gotman J, Grova C. SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. NeuroImage 2016, 134: 434-449. PMID: 27046111, DOI: 10.1016/j.neuroimage.2016.03.049.Peer-Reviewed Original Research
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