2024
Absence of BOLD adaptation in chronic fatigue syndrome revealed by task functional MRI.
Schönberg L, Mohamed A, Yu Q, Kwiatek R, Del Fante P, Calhoun V, Shan Z. Absence of BOLD adaptation in chronic fatigue syndrome revealed by task functional MRI. Cerebrovascular And Brain Metabolism Reviews 2024, 271678x241270528. PMID: 39113421, DOI: 10.1177/0271678x241270528.Peer-Reviewed Original ResearchBlood oxygen level-dependentFunctional MRIME/CFS participantsHealthy controlsTask-based functional MRISymbol Digit Modalities TestFatigue syndromeRight postcentral gyrusTask functional MRIFMRI signal changesOxygen level-dependentMyalgic encephalomyelitis/chronic fatigue syndromeLeft primary motor cortexChronic fatigue syndromeTask blocksCognitive tasksPostcentral gyrusCognitive cortexBOLD adaptationNeurophysiological processesNeurophysiological mechanismsPrimary motor cortexLevel-dependentModal testingFatigue severityAssociation between the oral microbiome and brain resting state connectivity in schizophrenia
Lin D, Fu Z, Liu J, Perrone-Bizzozero N, Hutchison K, Bustillo J, Du Y, Pearlson G, Calhoun V. Association between the oral microbiome and brain resting state connectivity in schizophrenia. Schizophrenia Research 2024, 270: 392-402. PMID: 38986386, DOI: 10.1016/j.schres.2024.06.045.Peer-Reviewed Original ResearchOral microbiomeMicrobial speciesArea under curveResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingMicrobial 16S rRNA sequencingBrain circuit dysfunctionHealthy controlsBrain functional connectivity alterationsFunctional connectivity alterationsFunctional neuroimaging techniquesHypothalamic-pituitary-adrenal axisBrain functional connectivityFunctional network connectivityBrain functional activityBrain functional network connectivityHealthy control subjectsNeurotransmitter signaling pathwaysBeta diversityMicrobiome communitiesOral microbiome dysbiosisRRNA sequencingCircuit dysfunctionConnectivity alterationsSchizophreniaMulti-modal deep learning from imaging genomic data for schizophrenia classification
Kanyal A, Mazumder B, Calhoun V, Preda A, Turner J, Ford J, Ye D. Multi-modal deep learning from imaging genomic data for schizophrenia classification. Frontiers In Psychiatry 2024, 15: 1384842. PMID: 39006822, PMCID: PMC11239396, DOI: 10.3389/fpsyt.2024.1384842.Peer-Reviewed Original ResearchSingle nucleotide polymorphismsGenomic dataGenetic markersGenomic markersBrains of individualsNucleotide polymorphismsEtiology of SZFunctional magnetic resonance imagingStructural magnetic resonance imagingMorphological featuresLayerwise relevance propagationHereditary aspectsHealthy controlsMarkersA Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia
Blair D, Miller R, Calhoun V. A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia. Entropy 2024, 26: 545. PMID: 39056908, PMCID: PMC11275472, DOI: 10.3390/e26070545.Peer-Reviewed Original ResearchSubjective trajectoriesBrain connectivity measuresPatient’s brain functionCognitive performancePsychiatric diseasesCourse of developmentBrain functionInformation theoryCortical hierarchyInformation processingConnectivity measuresSchizophreniaHealthy controlsDynamical systems theoryFunctional imagingTransit alterationsTransitionConnectivity statesPerspective of dynamical systemsStateTheoryEntropy generationDynamical systemsDynamicsNeuroimagingA confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity
Hassanzadeh R, Abrol A, Pearlson G, Turner J, Calhoun V. A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer’s disease using resting-state functional network connectivity. PLOS ONE 2024, 19: e0293053. PMID: 38768123, PMCID: PMC11104643, DOI: 10.1371/journal.pone.0293053.Peer-Reviewed Original ResearchConceptsResting-state functional network connectivityFunctional network connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingAlzheimer's diseaseClassification of schizophreniaNetwork pairsPatients to healthy controlsSchizophrenia patientsNeurobiological mechanismsSZ patientsSubcortical networksCerebellum networkSchizophreniaRs-fMRIDisorder developmentMotor networkCompare patient groupsSubcortical domainSZ disorderHealthy controlsMagnetic resonance imagingDisordersNetwork connectivityFunctional abnormalitiesCross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia
Zhao C, Jiang R, Bustillo J, Kochunov P, Turner J, Liang C, Fu Z, Zhang D, Qi S, Calhoun V. Cross‐cohort replicable resting‐state functional connectivity in predicting symptoms and cognition of schizophrenia. Human Brain Mapping 2024, 45: e26694. PMID: 38727014, PMCID: PMC11083889, DOI: 10.1002/hbm.26694.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingNegative symptomsFunctional connectivityCognitive impairmentPrediction of negative symptomsResting-state functional connectivityAssociated with reduced cognitive functionDebilitating mental illnessHealthy controlsPredicting functional connectivityEarly adulthood onsetPositive symptomsNeural underpinningsSchizophreniaCognitive functionSensorimotor networkPredicting symptomsMental illnessConnectivity patternsClinical interventionsMagnetic resonance imagingAdulthood onsetSymptomsImpairmentResonance imagingThe overlap across psychotic disorders: A functional network connectivity analysis
Dini H, Bruni L, Ramsøy T, Calhoun V, Sendi M. The overlap across psychotic disorders: A functional network connectivity analysis. International Journal Of Psychophysiology 2024, 201: 112354. PMID: 38670348, PMCID: PMC11163820, DOI: 10.1016/j.ijpsycho.2024.112354.Peer-Reviewed Original ResearchConceptsFunctional network connectivitySchizoaffective disorderPsychotic disordersHealthy controlsBipolar-Schizophrenia NetworkFunctional network connectivity analysisStatic functional network connectivityResting-state fMRINetwork connectivity analysisPatterns of activityPsychiatric disordersDisorder groupSchizophreniaConnectivity analysisHC groupBipolarConnectivity patternsDisordersPatient groupSymptom scoresGroup of patientsPANSSSchizoaffectiveFMRINetwork connectivityDistribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls
Maksymchuk N, Miller R, Calhoun V. Distribution of Connectivity Strengths Across Functional Regions has Higher Entropy in Schizophrenia Patients than in Controls. 2024, 00: 37-40. DOI: 10.1109/ssiai59505.2024.10508663.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingGroup independent component analysisSchizophrenia patientsCognitive controlResting-state functional magnetic resonance imagingIntrinsic connectivity networksHealthy controlsGender-matched healthy controlsSZ patientsNeuropsychiatric disordersBrain areasBrain networksSchizophreniaDisrupted integrityBrain domainsConnection strengthIndependent component analysisConnectivity networksMagnetic resonance imagingSomatomotorDistribution of connection strengthsResonance imagingCross-sectional dataPatientsDiagnostic testsMarkov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia
Miller R, Vergara V, Calhoun V. Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia. 2024, 00: 13-16. DOI: 10.1109/ssiai59505.2024.10508684.Peer-Reviewed Original ResearchReconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression
Xu M, Li X, Teng T, Huang Y, Liu M, Long Y, Lv F, Zhi D, Li X, Feng A, Yu S, Calhoun V, Zhou X, Sui J. Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Network Open 2024, 7: e241933. PMID: 38470418, PMCID: PMC10933730, DOI: 10.1001/jamanetworkopen.2024.1933.Peer-Reviewed Original ResearchConceptsAdolescent major depressive disorderMajor depressive disorderSC-FC couplingIncreased SC-FC couplingSC-FCMode networkSuicide attemptsFirst-episode major depressive disorderVisual networkMDD subgroupsNonsuicidal self-injurious behaviorRates of self-injuryHealthy controlsResting-state functional MRI dataMagnetic resonance imagingCross-sectional studySelf-injurious behaviorOutpatient psychiatry clinicFunctional MRI dataMajor life eventsFirst Affiliated Hospital of Chongqing Medical UniversityDepressive disorderNeurobiological mechanismsChildhood traumaSelf-injuryMore reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method
Xing Y, van Erp T, Pearlson G, Kochunov P, Calhoun V, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. IScience 2024, 27: 109319. PMID: 38482500, PMCID: PMC10933544, DOI: 10.1016/j.isci.2024.109319.Peer-Reviewed Original ResearchDiagnosis of mental disordersMental disordersDiagnostic labelsIntegration of neuroimagingSchizophrenia patientsNeuroimaging measuresNeuroimaging perspectiveFMRI dataStable abnormalitiesNeuroimagingDisordersHealthy controlsIndependent subjectsSchizophreniaFMRIDimensional predictionsSubjectsAccurate diagnosisClassification accuracyConnectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study
Georgiadis F, Larivière S, Glahn D, Hong L, Kochunov P, Mowry B, Loughland C, Pantelis C, Henskens F, Green M, Cairns M, Michie P, Rasser P, Catts S, Tooney P, Scott R, Schall U, Carr V, Quidé Y, Krug A, Stein F, Nenadić I, Brosch K, Kircher T, Gur R, Gur R, Satterthwaite T, Karuk A, Pomarol- Clotet E, Radua J, Fuentes-Claramonte P, Salvador R, Spalletta G, Voineskos A, Sim K, Crespo-Facorro B, Tordesillas Gutiérrez D, Ehrlich S, Crossley N, Grotegerd D, Repple J, Lencer R, Dannlowski U, Calhoun V, Rootes-Murdy K, Demro C, Ramsay I, Sponheim S, Schmidt A, Borgwardt S, Tomyshev A, Lebedeva I, Höschl C, Spaniel F, Preda A, Nguyen D, Uhlmann A, Stein D, Howells F, Temmingh H, Diaz Zuluaga A, López Jaramillo C, Iasevoli F, Ji E, Homan S, Omlor W, Homan P, Kaiser S, Seifritz E, Misic B, Valk S, Thompson P, van Erp T, Turner J, Bernhardt B, Kirschner M. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Molecular Psychiatry 2024, 1-13. PMID: 38336840, DOI: 10.1038/s41380-024-02442-7.Peer-Reviewed Original ResearchConnectivity profilesCortical alterationsCourse of schizophreniaBrain morphological alterationsAssociation of schizophreniaBrain network architectureAnatomical MRI scansTransdiagnostic comparisonsHuman Connectome ProjectDepressive disorderAffective disordersPathophysiological continuityPatient-specific symptomsSchizophreniaFrontal regionsDisease-related alterationsENIGMA studyCortical thinningNormative dataConnectome architectureIndividual symptomsConnectome ProjectDisease stageAlteration patternsHealthy controls
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 informationDynamicsPhaseIdentifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering
Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner J, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun V. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophrenia Research 2023, 264: 130-139. PMID: 38128344, DOI: 10.1016/j.schres.2023.12.013.Peer-Reviewed Original ResearchPsychosis subtypesSchizoaffective disorderBipolar disorderClinical phenotypeFirst-degree relativesTemporal-occipital cortexAmygdala-hippocampusClinical symptomsNeuroimaging featuresBipolar-Schizophrenia NetworkBrain alterationsHealthy controlsIntermediate Phenotypes (B-SNIP) consortiumOccipital cortexDecreased connectivitySubtypesStructural covarianceFractional amplitudeSubtype IILow-frequency fluctuationsNeurobiological heterogeneityGreater predispositionPsychosis spectrumGroup differencesDiagnostic classificationDynamic 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 dimensionsLongitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls
Verdijk J, van de Mortel L, Doesschate F, Pottkämper J, Stuiver S, Bruin W, Abbott C, Argyelan M, Ousdal O, Bartsch H, Narr K, Tendolkar I, Calhoun V, Lukemire J, Guo Y, Oltedal L, van Wingen G, van Waarde J. Longitudinal resting-state network connectivity changes in electroconvulsive therapy patients compared to healthy controls. Brain Stimulation 2023, 17: 140-147. PMID: 38101469, PMCID: PMC11145948, DOI: 10.1016/j.brs.2023.12.005.Peer-Reviewed Original ResearchDefault mode networkElectroconvulsive therapyHealthy controlsECT patientsResting-state networksTreatment effectivenessSalience networkElectroconvulsive therapy patientsWhole-brain voxel-wise analysisMajor depressive episodeCanonical resting-state networksRight frontoparietal networkVoxel-wise changesHigh treatment effectivenessVoxel-wise analysisNetwork connectivity changesTest-retest variabilityMulticenter studyDepressive episodeDepressed patientsTherapy patientsMagnetic resonance imaging dataDMN connectivityPatientsConnectivity changesPairing 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 measuresExtraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia
Han Y, Lin Q, Kuang L, Hao Y, Li W, Gong X, Calhoun V. Extraction of One Time Point Dynamic Group Features via Tucker Decomposition of Multi-subject FMRI Data: Application to Schizophrenia. Communications In Computer And Information Science 2023, 1963: 518-527. DOI: 10.1007/978-981-99-8138-0_41.Peer-Reviewed Original ResearchAmplitude of low frequency fluctuationsFMRI dataLow frequency fluctuationsSchizophrenia groupHealthy controlsMulti-subject fMRI dataInferior parietal lobuleProperties of brain functionParietal lobuleFMRI-dataMental disordersSchizophreniaBrain functionActivity differencesFrequency fluctuationsTwo-sample t-testSliding-window techniqueTucker decompositionExtracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data
Wiseman N, Iraji A, Haacke E, Calhoun V, Kou Z. Extracting functional connectivity brain networks at the resting state from pulsed arterial spin labeling data. Meta-Radiology 2023, 1: 100023. PMID: 38298860, PMCID: PMC10830167, DOI: 10.1016/j.metrad.2023.100023.Peer-Reviewed Original ResearchDefault mode networkMild traumatic brain injuryBrain networksRsfMRI dataFunctional connectivity brain networksResting state networksArterial spin labelingMild traumatic brain injury patientsResting state functional magnetic resonance imagingFunctional magnetic resonance imagingHealthy controlsFunctional connectivity informationBlood oxygen levelTraumatic brain injuryBOLD signalMode networkFunctional connectivitySpin labelingBlood flow changesMagnetic resonance imagingNeuronal activityResponse to activationPulsed ASLBOLDRetrospective studyMulti-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
Yan W, Yu L, Liu D, Sui J, Calhoun V, Lin Z. Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG. Frontiers In Psychiatry 2023, 14: 1202049. PMID: 37441141, PMCID: PMC10333510, DOI: 10.3389/fpsyt.2023.1202049.Peer-Reviewed Original ResearchConvolutional recurrent neural networkRecurrent neural networkResting-state EEGNeural networkPsychiatric disordersDeep learning classification modelLow-dimensional subspaceTwo-class classificationDesigning individualized treatmentLearning classification modelsEEG backgroundClassification modelHealthy controlsDepressive disorderSpatiotemporal informationClinical observationsDisease severityAccurate classificationIndividualized treatmentBiomarkersDisorder classificationDisorder identificationDisordersClassificationNeuroimaging biomarkers