2024
Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging
Qiu L, Liang C, Kochunov P, Hutchison K, Sui J, Jiang R, Zhi D, Vergara V, Yang X, Zhang D, Fu Z, Bustillo J, Qi S, Calhoun V. Associations of alcohol and tobacco use with psychotic, depressive and developmental disorders revealed via multimodal neuroimaging. Translational Psychiatry 2024, 14: 326. PMID: 39112461, PMCID: PMC11306356, DOI: 10.1038/s41398-024-03035-2.Peer-Reviewed Original ResearchConceptsFronto-limbic networkSalience networkAssociated with cognitionFronto-basal gangliaDevelopmental disordersBrain networksLimbic systemAlcohol useAssociated with alcohol useMultimodal brain networksTobacco useAssociation of alcoholPsychiatric disordersMultimodal neuroimagingDMNBrain featuresCognitionAlcohol/tobacco useDisordersAssociated with tobacco useDepressionSymptomsFunctional abnormalitiesAlcoholBrain4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia
Pusuluri K, Fu Z, Miller R, Pearlson G, Kochunov P, Van Erp T, Iraji A, Calhoun V. 4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia. Human Brain Mapping 2024, 45: e26773. PMID: 39045900, PMCID: PMC11267451, DOI: 10.1002/hbm.26773.Peer-Reviewed Original ResearchConceptsBrain networksFunctional magnetic resonance imagingAssociated with cognitive performanceDynamics of functional brain networksAssociated with cognitionFunctional brain networksVoxel-wise changesVolumetric couplingDynamical variablesCognitive performanceTypical controlsSchizophreniaCognitive impairmentNetwork pairsMagnetic resonance imagingPair of networksCognitionAtypical variabilityResonance imagingCouplingNetwork connectivityNetwork growthImpairmentBrainStatic networksThe dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity
Wiafe S, Faghiri A, Fu Z, Miller R, Preda A, Calhoun V. The dynamics of dynamic time warping in fMRI data: A method to capture inter-network stretching and shrinking via warp elasticity. Imaging Neuroscience 2024, 2: 1-23. DOI: 10.1162/imag_a_00187.Peer-Reviewed Original ResearchDynamic time warpingDynamics of brain networksBrain networksBrain network interactionsFunctional magnetic resonance imagingFunctional connectivity measuresComplexity of brain functionDiverse timescalesTime warpingBrain dynamicsVisual cortexFunctional magnetic resonance imaging dataTimescalesFunctional connectivityBrain connectivityCoupled stretchingCouplingDynamic time warping methodBrain regionsTransient couplingConnectivity measuresFunctional connectivity metricsNeuroimaging researchCluster centroidsIntricate dynamicsExplainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment
Patel B, Orlichenko A, Patel A, Qu G, Wilson T, Stephen J, Calhoun V, Wang Y. Explainable Multimodal Graph Isomorphism Network for Interpreting Sex Differences in Adolescent Neurodevelopment. Applied Sciences 2024, 14: 4144. DOI: 10.3390/app14104144.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingBlood oxygen level-dependentGraph isomorphism networkGraph neural networksBrain networksFunctional magnetic resonance imaging paradigmFunctional magnetic resonance imaging blood oxygen level-dependentSex differencesClassification accuracyExploration of sex differencesInterpreting sex differencesOxygen level-dependentState-of-the-art algorithmsAdolescent neurodevelopmentState-of-the-artNeuropsychiatric conditionsFunctional connectivityTask-related dataDeep learning modelsLevel-dependentMouth movementsFMRI datasetsFunctional networksGraph structureAdolescentsAnalysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition
Faghiri A, Iraji A, Adali T, Calhoun V. Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition. 2024, 00: 13346-13350. DOI: 10.1109/icassp48485.2024.10446864.Peer-Reviewed Original ResearchInterpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks
Qu G, Orlichenko A, Wang J, Zhang G, Xiao L, Zhang K, Wilson T, Stephen J, Calhoun V, Wang Y. Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks. IEEE Transactions On Medical Imaging 2024, 43: 1568-1578. PMID: 38109241, PMCID: PMC11090410, DOI: 10.1109/tmi.2023.3343365.Peer-Reviewed Original ResearchConceptsGraph transformation frameworkBrain imaging datasetsFunctional brain networksPhiladelphia Neurodevelopmental CohortConvolutional deep learningFeature embeddingPropagation weightsGraph embeddingHuman Connectome ProjectAttention mechanismImage datasetsDeep learningGraph transformationFunctional connectivityAnalyze functional brain networksTransformation frameworkDiffusion strategyBrain networksPositional encodingSpatial knowledgePrediction accuracyIndividual cognitive abilitiesEmbeddingNetworkGraphDistribution 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 testsA Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks
Batta I, Abrol A, Calhoun V. A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks. 2024, 00: 1-6. DOI: 10.1109/ciss59072.2024.10480204.Peer-Reviewed Original ResearchLearning frameworkBrain subsystemsSubspace learning frameworkBrain networksHigh-dimensional neuroimaging dataConvolutional neural networkLow-dimensional subspaceSupervised learning approachDeep learning frameworkStructural brain featuresPredictive performanceUnsupervised approachNeural networkAutomated frameworkDimensional subspaceAlzheimer's diseaseLearning approachBrain changesFeature importanceTraining procedureNeuroimaging dataBrain featuresSalient networkNetworkBrain disordersA Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age
Saha R, Saha D, Rahaman A, Fu Z, Liu J, Calhoun V. A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age. Brain Connectivity 2024, 14: 130-140. PMID: 38308475, PMCID: PMC10954605, DOI: 10.1089/brain.2023.0040.Peer-Reviewed Original ResearchFunctional network connectivityFunctional connectivityPsychiatric problemsFunctional network connectivity matricesNetwork connectivityMultivariate patternsWhole-brain functional networksIntrinsic functional connectivityPattern of functional changesBrain functional connectivityIntrinsic functional relationshipLongitudinal changesAdolescent brainAge-related changesBrain networksStudy developmental changesScanning sessionBrain functionAssociated with longitudinal changesCognitive scoresDevelopmental changesBrain developmentFunctional changesCognitionLongitudinal change patternsRevealing complex functional topology brain network correspondences between humans and marmosets
Li Q, Calhoun V, Iraji A. Revealing complex functional topology brain network correspondences between humans and marmosets. Neuroscience Letters 2024, 822: 137624. PMID: 38218321, DOI: 10.1016/j.neulet.2024.137624.Peer-Reviewed Original ResearchConceptsWhole-brain functional connectivityFunctional brain connectivityDorsal attention networkFunctional connectivity patternsBrain connectivityMarmoset monkey brainBrain networksTopological characteristicsMode networkFunctional connectivityCognitive functionVisual networkNon-human primatesMonkey brainAttention networkConnectivity patternsNeural connectionsBrainFunctional correspondenceConnectome
2023
Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia
Harikumar A, Solovyeva K, Misiura M, Iraji A, Plis S, Pearlson G, Turner J, Calhoun V. Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia. Current Neurology And Neuroscience Reports 2023, 23: 937-946. PMID: 37999830, PMCID: PMC11126894, DOI: 10.1007/s11910-023-01325-8.Peer-Reviewed Original ResearchConceptsNetwork dysconnectivityFunctional dysconnectivityExecutive functioningState fMRI studyAttentional deficitsFMRI studyHypothesized modelSalience networkBrain networksConnectivity findingsBehavioral symptomsNeurodevelopmental modelSymptom severityDysconnectivityHypothesized mechanismsSchizophreniaDeficitsVital modelsSummaryThis paperMotor symptomsFunctioningSymptomsFindingsPurpose of ReviewOverThought6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain Tumor
Semmel E, Calhoun V, Hillary F, Morris R, King T. 6 Graph Analysis of Resting State Functional Brain Networks and Associations with Cognitive Outcomes in Survivors on Pediatric Brain Tumor. Journal Of The International Neuropsychological Society 2023, 29: 316-317. DOI: 10.1017/s135561772300437x.Peer-Reviewed Original ResearchSurvivors of pediatric brain tumorsFunctional brain networksWorking memoryBrain networksCognitive outcomesProcessing speedLong-term neuropsychological deficitsResting state functional magnetic resonance imagingFunctional magnetic resonance imagingMeasures of attentionCore cognitive skillsLong-term cognitive outcomesStructural brain changesSmall-moderate effect sizeFunctional network propertiesSurvivors of brain tumorsBrain tumor survivorsGraph metricsResting state dataGlobal efficiencyNeuropsychological deficitsNeuropsychological testsBrain changesGroup differencesIndependence in adulthoodDeep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity
Qiao C, Gao B, Liu Y, Hu X, Hu W, Calhoun V, Wang Y. Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity. Medical Image Analysis 2023, 90: 102941. PMID: 37683445, DOI: 10.1016/j.media.2023.102941.Peer-Reviewed Original ResearchFunctional connectivityBrain functional connectivityBrain networksDynamic brain functional connectivityDeep networksFunctional brain networksInformation processing abilityBrain development studiesEmotional processingDeep learning approachFeature selection strategyMachine learning modelsProcessing abilityBrain developmentCognitive activityDeep learningAccuracy-orientedSound processingBrainDevelopmental patternsLearning approachLearning modelsMental regulationSelection strategyInformation transmission mechanismExtracting 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 studyDenoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data
Zhang C, Lin Q, Niu Y, Li W, Gong X, Cong F, Wang Y, Calhoun V. Denoising brain networks using a fixed mathematical phase change in independent component analysis of magnitude‐only fMRI data. Human Brain Mapping 2023, 44: 5712-5728. PMID: 37647216, PMCID: PMC10619417, DOI: 10.1002/hbm.26471.Peer-Reviewed Original ResearchConceptsComplex-valued dataComplex-valued fMRI dataBrain networksFMRI dataPhase informationHuman Connectome ProjectMapping frameworkMagnitude mapsExperimental fMRI dataConnectome ProjectPhase mapFMRI datasetsMagnitude dataDenoisingNetworkAmplitude thresholdComponent analysisPhase changePhaseSSP approachSpatial mappingFMRIUniversity of New MexicoThresholdA Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data
Saha D, Bohsali A, Saha R, Hajjar I, Calhoun V. A Multivariate Method for Estimating and comparing whole brain functional connectomes from fMRI and PET data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083351, DOI: 10.1109/embc40787.2023.10340631.Peer-Reviewed Original ResearchConceptsWhole-brain functional connectomePositron emission tomographyResting fMRIResting fMRI dataBrain positron emission tomographyBrain functional connectomePositron emission tomography dataResting networksMagnetic resonance imagingConnectomeFunctional connectomeBrain networksConnectome patternsFMRIFMRI dataBrain functionSubject expressionPiB-PET scansBrainEmission tomographySpatial mappingSpatial networksClinical Relevance-This studyPET scansResonance imagingNeuropsychiatric 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 networkConstrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data
Yang H, Ghayem F, Gabrielson B, Akhonda M, Calhoun V, Adali T. Constrained Independent Component Analysis Based on Entropy Bound Minimization for Subgroup Identification from Multi-subject fMRI Data. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10095816.Peer-Reviewed Original ResearchIndependent vector analysisSynthetic dataConstrained independent component analysisEntropy bound minimizationComputational complexity limitationsDemixing matrixIndependent component analysisComputational costOrthogonality requirementData identificationAlgorithmFunctional networksNetworkComponent analysisDatasetFMRI dataComputerTaskEntropyOrthogonalitySubgroup identificationVector analysisBrain networksDensity modelNew Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning
Ghayem F, Yang H, Kantar F, Kim S, Calhoun V, Adali T. New Interpretable Patterns and Discriminative Features from Brain Functional Network Connectivity using Dictionary Learning. 2023, 00: 1-5. DOI: 10.1109/icassp49357.2023.10096473.Peer-Reviewed Original ResearchDictionary learningIndependent component analysisLearned atomsDiscovery of hidden informationNetwork connectivityMulti-subject functional magnetic resonance imagingFunctional magnetic resonance imagingFunctional network connectivityDiscriminative featuresFeature vectorHidden informationEffective classificationSZ groupHealthy controlsResting-state fMRI dataExperimental resultsICA resultsDictionaryBrain functional network connectivityBrain networksMental disordersFMRI dataLearningRepresentationMental diseasesGraph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor
Semmel E, Calhoun V, Hillary F, Morris R, King T. Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor. Neuroimage Reports 2023, 3: 100178. DOI: 10.1016/j.ynirp.2023.100178.Peer-Reviewed Original ResearchSurvivors of pediatric brain tumorsFunctional brain networksWorking memoryBrain networksFunctional magnetic resonance imagingLong-term cognitive difficultiesCognitive outcomes in adulthoodCore cognitive skillsFunctional network propertiesOutcomes in adulthoodBrain tumor survivorsGraph metricsNeuroimaging researchNeuropsychological testsBrain changesCognitive difficultiesProcessing speedCognitive outcomesHub regionsSmall-worldCognitive skillsPost hoc analysisTumor survivorsPediatric brain tumor patientsSmall-world properties