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. PMID: 39764901, PMCID: PMC11762193, 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
A spatially constrained independent component analysis jointly informed by structural and functional network connectivity
Fouladivanda M, Iraji A, Wu L, van Erp T, Belger A, Hawamdeh F, Pearlson G, Calhoun V. A spatially constrained independent component analysis jointly informed by structural and functional network connectivity. Network Neuroscience 2024, 8: 1212-1242. PMID: 39735500, PMCID: PMC11674407, DOI: 10.1162/netn_a_00398.Peer-Reviewed Original ResearchIntrinsic connectivity networksFunctional brain connectivityBrain connectivityStructural connectivityFunctional connectivityIndependent component analysisResting-state functional MRIAnalysis of group differencesBrain functional organizationFunctional network connectivityStructural-functional connectivityNeuroimaging studiesFunctional MRIWhole-brain tractographyGroup differencesRs-fMRIBrain disordersFunctional couplingSchizophreniaStatistical analysis of group differencesSubject levelFunctional organizationConnectivity networksBrainDiffusion-weighted MRIA multimodal Neuroimaging-Based risk score for mild cognitive impairment
Zendehrouh E, Sendi M, Abrol A, Batta I, Hassanzadeh R, Calhoun V. A multimodal Neuroimaging-Based risk score for mild cognitive impairment. NeuroImage Clinical 2024, 45: 103719. PMID: 39637673, PMCID: PMC11664180, DOI: 10.1016/j.nicl.2024.103719.Peer-Reviewed Original ResearchMild cognitive impairment riskMild cognitive impairmentMild cognitive impairment groupRisk of mild cognitive impairmentRisk scoreUK Biobank participantsFunctional network connectivityCognitive impairmentPrecursor to ADSignificant cognitive declineBiobank participantsUK BiobankMild cognitive impairment individualsGenetic risk factorsAlzheimer's diseaseFunctional MRIHigh-risk groupStructural MRIAD riskRisk factorsCognitive declineFeatures of CNGray matterDifferentiate CNParticipantsA multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityMultimodal predictive modeling: Scalable imaging informed approaches to predict future brain health
Ajith M, Spence J, Chapman S, Calhoun V. Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health. Journal Of Neuroscience Methods 2024, 414: 110322. PMID: 39608579, PMCID: PMC11687617, DOI: 10.1016/j.jneumeth.2024.110322.Peer-Reviewed Original ResearchStatic functional network connectivityHealth constructsNeuroimaging dataBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingSupport vector regressionFunctional network connectivityRandom forestCognitive performanceAssessment-onlyRs-fMRINeural patternsBehavioral outcomesBehavioral dataDiverse data sourcesNeural connectionsPsychological stateTraining stageMagnetic resonance imagingLongitudinal changesNetwork connectivityBrainPerformance evaluationVector regressionFunctional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance
Feng A, Zhi D, Feng Y, Jiang R, Fu Z, Xu M, Zhao M, Yu S, Stevens M, Sun L, Calhoun V, Sui J. Functional imaging derived ADHD biotypes based on deep clustering: a study on personalized medication therapy guidance. EClinicalMedicine 2024, 77: 102876. PMID: 39763511, PMCID: PMC11701483, DOI: 10.1016/j.eclinm.2024.102876.Peer-Reviewed Original ResearchAttention deficit hyperactivity disorderAttention deficit hyperactivity disorder patientsFunctional network connectivityAttention Deficit Hyperactivity Disorder AdolescentTreatment of attention deficit hyperactivity disorderPeking University Sixth HospitalBackground Attention deficit hyperactivity disorderDeficit hyperactivity disorderTreated with methylphenidateADHD subtypesHyperactivity/impulsivity symptomsAdolescent brainMedical treatment effectsHyperactivity disorderCognitive performanceABCD studyProblem scoresNeuroimaging markersNeurodevelopmental disordersMedical treatmentChildhood onsetChina Postdoctoral Science FoundationNational Natural Science Foundation of ChinaNatural Science Foundation of ChinaPrimary medicationFrequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study
Faghiri A, Yang K, Faria A, Ishizuka K, Sawa A, Adali T, Calhoun V. Frequency modulation increases the specificity of time-resolved connectivity: A resting-state fMRI study. Network Neuroscience 2024, 8: 734-761. PMID: 39355435, PMCID: PMC11349031, DOI: 10.1162/netn_a_00372.Peer-Reviewed Original ResearchSliding window Pearson correlationTime-resolved networksSingle sideband modulationTime-resolved connectivityResting-state fMRI studiesSideband modulationFunctional magnetic resonance imagingFunctional network connectivityResting-state functional magnetic resonance imagingActivity time seriesTypical controlsFrequency modulationLow-frequency informationStateEpisode of psychosisNetwork connectivityHuman brainSub-corticalSuperior performanceFMRI studyCortical regionsAssociation 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 alterationsSchizophreniaNeural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment
Soleimani N, Calhoun V. Neural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039582, DOI: 10.1109/embc53108.2024.10781623.Peer-Reviewed Original ResearchConceptsFunctional network connectivityAutism spectrum disorderBipolar disorderPsychiatric disordersDepressive disorderAdolescent brainNeural underpinningsPolygenic risk scoresPsychiatric riskSpectrum disorderDifferential contributionsDisordersMDDHigh-risk scoreSchizophreniaHealthy controlsRisk scoreScoresIndividualsPsychiatricAutismNetwork connectivityNeuroimagingRisk score assessmentElevated risk scoresCopula linked parallel ICA jointly estimates linked structural and functional MRI brain networks
Agcaoglu O, Alacam D, Adalı T, Calhoun V, Silva R, Plis S, Bostami B. Copula linked parallel ICA jointly estimates linked structural and functional MRI brain networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40040121, DOI: 10.1109/embc53108.2024.10781658.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingStructural MRIAmplitude of low frequency fluctuationsBrain imaging methodsStructural MRI dataFunctional network connectivityLow frequency fluctuationsEstimated independent sourcesBrain networksRegional homogeneityFMRI networksTemporal informationMagnetic resonance imagingFrequency fluctuationsAlzheimer's studiesBrainResonance imagingFusion approachUnmixing matrixNetwork connectivityReal-dataSensorimotorNetworkCerebellumCross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers
Hassanzadeh R, Abrol A, Hassanzadeh H, Calhoun V. Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039975, DOI: 10.1109/embc53108.2024.10781737.Peer-Reviewed Original ResearchConceptsFunctional network connectivityGenerative adversarial networkStructural similarity index measureT1-weighted structural magnetic resonance imaging dataAdversarial networkStructural magnetic resonance imaging dataIncreased functional connectivityMagnetic resonance imaging dataSimilarity index measureCross-modal transformerCross-modal translationPatterns of atrophyAlzheimer's diseaseFunctional connectivityReduced connectivityMotor-visualTemporal regionsWeak supervisionAlzheimer's disease biomarkersControl networkCycle-GANCross-modalAlzheimer patientsContext of Alzheimer's diseaseGeneration approachA deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer’s in asymptomatic individuals
Wei Y, Abrol A, Lah J, Qiu D, Calhoun V. A deep spatio-temporal attention model of dynamic functional network connectivity shows sensitivity to Alzheimer’s in asymptomatic individuals. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039841, DOI: 10.1109/embc53108.2024.10781740.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityFunctional magnetic resonance imagingSpatio-temporal attention modelNetwork connectivityMild cognitive impairmentDeep learning advancesFunctional network connectivityMachine learning methodsSelf-attentionAttention modelAt-risk subjectsLearning methodsLearning advancesAlzheimer's diseaseNetwork dependenceIdentifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age
Bajracharya P, Faghiri A, Fu Z, Calhoun V, Shultz S, Iraji A. Identifying Canonical multi-scale Intrinsic Connectivity Networks in Infant resting-state fMRI and their Association with Age. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039283, DOI: 10.1109/embc53108.2024.10782404.Peer-Reviewed Original ResearchConceptsIntrinsic connectivity networksStatic functional network connectivitySubject-specific intrinsic connectivity networksResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional brain organizationResting-state fMRIFunctional network connectivityConnectivity networksCognitive domainsCognitive processesBrain organizationSub-corticalRsfMRI dataIndependent component analysisMagnetic resonance imagingNeuromarkersDistinct patternsMotor controlNeurodevelopmental disabilitiesResonance imagingEarly identificationSensory perceptionAssociated with ageFMRIBeyond Artifacts: Rethinking Motion-Related Signals in Resting-State fMRI Analysis
Kumar S, Kinsey S, Jensen K, Bajracharya P, Calhoun V, Iraji A. Beyond Artifacts: Rethinking Motion-Related Signals in Resting-State fMRI Analysis. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40040138, DOI: 10.1109/embc53108.2024.10782518.Peer-Reviewed Original ResearchConceptsFunctional network connectivityBOLD time seriesImpact of head motionHead motion dataLarge-scale brain networksIntrinsic brain functional connectivityResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional brain connectivityResting-state fMRI analysisRsfMRI dataBOLD fMRIHead motionBrain functional connectivityHealthy controlsBOLD signalBrain connectivityBrain networksMotion dataFMRI analysisFunctional connectivityClinical populationsMotion-related signalsClinical implicationsBOLDMultiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses
Behzadfar N, Iraji A, Calhoun V. Multiband Group Independent Component Analysis: Unveiling Frequency-Dependent Dynamics of Functional Connectivity in Group-Level fMRI Analyses. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-5. PMID: 40040173, DOI: 10.1109/embc53108.2024.10782601.Peer-Reviewed Original ResearchConceptsIndependent component analysisTask-related componentsDynamics of functional connectivitySubband informationGroup independent component analysisMultisubject fMRI dataFunctional network connectivityFMRI dataNetwork connectivityBandpass filterSampling rateSubbandBack-reconstructionApplication of bandpass filtersSpatially independent mapsFrequency rangeFunctional connectivityFMRI analysisUncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features
Seraji M, Ellis C, Sendi M, Miller R, Calhoun V. Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039134, DOI: 10.1109/embc53108.2024.10782953.Peer-Reviewed Original ResearchConceptsDynamic functional network connectivityDFNC dataDynamic functional network connectivity stateResting state functional magnetic resonance imagingFunctional network connectivityFunctional magnetic resonance imagingHealthy controlsEffect of schizophreniaCingulate cortexNetwork connectivity featuresNeuropsychiatric disordersSchizophreniaAnticorrelationDynamicsEstimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Li H, Calhoun V. Estimation of complete mutual information exploiting nonlinear magnitude-phase dependence: Application to spatial FNC for complex-valued fMRI data. Journal Of Neuroscience Methods 2024, 409: 110207. PMID: 38944128, DOI: 10.1016/j.jneumeth.2024.110207.Peer-Reviewed Original ResearchConceptsComplex-valued fMRI dataMutual informationJoint entropyNetwork connectivityComplex-valued signalsFunctional network connectivityMagnitude-phase dependenceDensity estimation methodMI estimationHistogram-basedKernel density estimation methodFMRI dataEstimation accuracyProbability density functionJoint probability density functionSimulated signalsChain rulePhase dependenceEstimation methodHigh-orderDensity functionControl networkInaccurate estimationNonlinear dependenceDependenceA Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data
Ajith M, M. Aycock D, B. Tone E, Liu J, B. Misiura M, Ellis R, M. Plis S, Z. King T, M. Dotson V, Calhoun V. A Trifecta of Deep Learning Models: Assessing Brain Health by Integrating Assessment and Neuroimaging Data. Aperture Neuro 2024, 4 DOI: 10.52294/001c.118576.Peer-Reviewed Original ResearchStatic functional network connectivityBrain health indexBrain healthResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingPsychological assessment measuresAssessment dataFunctional network connectivityMental health disordersBrain systemsEvaluating brain healthNeuroimaging dataRs-fMRINeural patternsPhysical well-beingCognitive declineAssessment measuresHealth disordersVariational autoencoderNeuroimagingHealthy brainBrainMagnetic resonance imagingTesting phaseWell-beingGray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity
Bi Y, Abrol A, Jia S, Sui J, Calhoun V. Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity. NeuroImage 2024, 297: 120674. PMID: 38851549, DOI: 10.1016/j.neuroimage.2024.120674.Peer-Reviewed Original ResearchFunctional network connectivityMedial prefrontal cortexBrain structuresFunctional network connectivity matricesPrefrontal cortexStructural MRINetwork connectivityGray matterSelf-attention mechanismGenerative adversarial networkDeep learning architectureBrain disordersDorsolateral prefrontal cortexResearch of schizophreniaNeural signal processingIdentified functional connectivityCross-domain analysisAttention mapsStructural biomarkersAdversarial networkLearning architectureDL-PFCICA algorithmSchizophrenia patientsHigh-dimensional fMRI dataDouble Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses
Soleimani N, Pearlson G, Iraji A, Calhoun V. Double Functionally Independent Primitives Provide Disorder Specific Fingerprints of Mental Illnesses. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635116.Peer-Reviewed Original ResearchAutism spectrum disorderMental illnessBipolar disorderMental disordersManifestations of mental illnessAssociated with mental illnessFunctional network connectivityFunctional network connectivity patternsNetwork connectivity patternsDisorder-specificDepressive disorderNeural underpinningsSpectrum disorderPsychological disordersNeuroimaging techniquesConnectivity patternsDisordersSchizophreniaHealthy controlsIllnessBrainFunctional changesMDDAutismNetwork connectivity
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