2025
Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis
Premi E, Cantoni V, Benussi A, Iraji A, Calhoun V, Corbo D, Gasparotti R, Tinazzi M, Borroni B, Magoni M. Impaired spatial dynamic functional network connectivity and neurophysiological correlates in functional hemiparesis. NeuroImage Clinical 2025, 45: 103731. DOI: 10.1016/j.nicl.2025.103731.Peer-Reviewed Original ResearchDynamic functional network connectivitySomatomotor networkSalience networkFunctional network connectivityGABAergic neurotransmissionResting-state functional MRI scansResting-state fMRI dataFunctional MRI scansDynamic brain statesBrain network dynamicsStatic functional connectivityDynamic brain networksBrain networksGlutamatergic transmissionNeurophysiological correlatesFunctional connectivityTranscranial magnetic stimulation protocolFMRI dataGABAergic inhibitionMagnetic stimulation protocolBrain statesNeurotransmissionHealthy controlsDMNNetwork connectivity
2024
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Ellis C, Miller R, Calhoun V. Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia. Frontiers In Psychiatry 2024, 15: 1165424. PMID: 38495909, PMCID: PMC10941842, DOI: 10.3389/fpsyt.2024.1165424.Peer-Reviewed Original ResearchHard clusteringNetwork dynamicsDynamic functional network connectivityFuzzy clustering frameworkExtract several featuresFuzzy clusteringC-meansExplainability approachesExplainability metricsData spaceClustering frameworkK-meansDynamic functional network connectivity stateNetwork connectivityModerate anticorrelationImage dataNetworkState dynamicsAnalysis frameworkConnectivity dynamicsFunctional network connectivityAnticorrelationCentroidFunctional magnetic resonance imaging dataFramework
2023
Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia
Li W, Lin Q, Zhao B, Kuang L, Zhang C, Han Y, Calhoun V. Dynamic functional network connectivity based on spatial source phase maps of complex-valued fMRI data: Application to schizophrenia. Journal Of Neuroscience Methods 2023, 403: 110049. PMID: 38151187, DOI: 10.1016/j.jneumeth.2023.110049.Peer-Reviewed Original ResearchConceptsSchizophrenia patientsFMRI dataFunctional network connectivityHealthy controlsDynamic functional network connectivityPsychotic diagnosesMental disordersSchizophreniaComplex-valued fMRI dataPotential imaging biomarkersDetect functional alterationsFMRIState transitionsNetwork connectivityPhase informationFunctional alterationsComplex valuesBrain informationMutual informationDynamicsPhaseDynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study
van der Horn H, de Koning M, Visser K, Kok M, Spikman J, Scheenen M, Renken R, Calhoun V, Vergara V, Cabral J, Mayer A, van der Naalt J. Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study. PLOS ONE 2023, 18: e0295984. PMID: 38100479, PMCID: PMC10723684, DOI: 10.1371/journal.pone.0295984.Peer-Reviewed Original ResearchConceptsMild traumatic brain injuryDynamic functional network connectivityTraumatic brain injurySymptom severityEmotional instabilityAssociated with lower symptom severityMild traumatic brain injury patientsHead Injury Symptom ChecklistHealthy controlsPatients relative to HCMaladaptive personality characteristicsDefault mode networkTract-based spatial statisticsLower symptom severityBrain injuryPosterior corona radiataFunctional network connectivityPost-injuryMonths post-injuryLow extraversionHigh neuroticismSuperordinate factorDiffusion MRINeural underpinningsPersonality dimensionsPairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics
Ellis C, Miller R, Calhoun V. Pairing explainable deep learning classification with clustering to uncover effects of schizophrenia upon whole brain functional network connectivity dynamics. Neuroimage Reports 2023, 3: 100186. DOI: 10.1016/j.ynirp.2023.100186.Peer-Reviewed Original ResearchEffect of schizophreniaDynamic functional network connectivityBrain network dynamicsNeuropsychiatric disordersBrain activityFunctional magnetic resonance imagingInteractions of brain regionsFunctional network connectivityNetwork dynamicsBrain regionsSchizophreniaClustering algorithmEffect of SZHealthy controlsLearning classificationBrainMagnetic resonance imagingDeep learning modelsDeep learning classificationDisordersNetwork interactionsMachine learning classificationResonance imagingClustersNovel measuresNeuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *
Ellis C, Miller R, Calhoun V. Neuropsychiatric Disorder Subtyping Via Clustered Deep Learning Classifier Explanations *. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083012, DOI: 10.1109/embc40787.2023.10340837.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityResting-state functional magnetic resonanceFunctional magnetic resonanceNeuropsychiatric disordersFunctional network connectivityCharacterization of schizophreniaCognitive controlDeep learning classifierContext of schizophreniaAuditory networkBrain activityBrain networksVisual networkSubcortical networksCerebellar networkResting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank
Sendi M, Zendehrouh E, Miller R, Salat D, Calhoun V. Resting‐state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065832.Peer-Reviewed Original ResearchFunctional network connectivityDynamic FNCDynamic functional network connectivityCognitive scoresFluid intelligenceCognitive declineAge-related cognitive declineGroup independent component analysisResting-state functional MRIBrain functional changesResting-state fMRIBrain functional network connectivityReaction timeParticipants of UK BiobankRT taskFunctional MRIRs-fMRIPairing taskCognitionIndependent component analysisUK BiobankBrainNetwork connectivityHealthy adultsData-driven componentsPrediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis
Sendi M, Dini H, Zendehrouh E, Salat D, Calhoun V. Prediction of sleep quality scores using dynamic functional network connectivity of young adults: A reproducibility analysis. Alzheimer's & Dementia 2023, 19 DOI: 10.1002/alz.065778.Peer-Reviewed Original ResearchDynamic functional network connectivityHuman Connectome ProjectFunctional network connectivityHuman Connectome Project datasetDistinct statesResting-state fMRIRs-fMRIConnectome ProjectResting-state functional magnetic resonance imagingState vectorPittsburgh Sleep Quality IndexYoung adultsFunctional magnetic resonance imagingNight sleep timeRs-fMRI sessionsInterpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment
Gao Y, Lewis N, Calhoun V, Miller R. Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment. Computers In Biology And Medicine 2023, 161: 107005. PMID: 37211004, PMCID: PMC10365638, DOI: 10.1016/j.compbiomed.2023.107005.Peer-Reviewed Original ResearchConceptsMild cognitive impairmentDynamic functional network connectivityCognitive impairmentResting-state functional magnetic resonance imagingDementia interventionsFunctional magnetic resonance imagingAlzheimer's diseaseCognitive healthPatient deteriorationFunctional network connectivityCognitive abilitiesShort-term memoryRs-fMRIBrain connectivityResolved measurementsDynamic brain connectivityMagnetic resonance imagingIdentifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance
Ellis C, Miller R, Calhoun V. Identifying Neuropsychiatric Disorder Subtypes and Subtype-Dependent Variation in Diagnostic Deep Learning Classifier Performance. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230384.Peer-Reviewed Original ResearchClinical decision support systemsDynamic functional network connectivityDeep learning classifier’s performanceDisorder subtypesDeep learning classifierDecision support systemClassifier performanceLearning classifiersNetwork connectivityClassifierFunctional network connectivitySupport systemSchizophrenia subtypesStudy disordersPerformanceDisordersSchizophreniaSubtypesNeuropsychiatricSystemNeuroimagingSubtype-dependentCapability