2024
Brain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 39708510, PMCID: PMC11877132, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchConceptsGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesArchitectureGPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction
Sun F, Liang C, Adali T, Zhang D, Jiang R, Calhoun V, Qi S. GPR-SCSANet: Unequal-Length Time Series Normalization with Split-Channel Residual Convolution and Self-Attention for Brain Age Prediction. 2024, 00: 5097-5103. DOI: 10.1109/bibm62325.2024.10822453.Peer-Reviewed Original ResearchSelf-attention mechanismResidual convolutionGaussian process regressionFunctional magnetic resonance imagingReal-world scenariosAge prediction taskSelf-attentionPrediction taskBrain age estimationAge predictionInherent informationBrain age predictionFMRI time coursesLength of time seriesProcess regressionVariables conflictBrain functional alterationsConvolutionPrediction accuracyUnequal-lengthTraditional methodsMotion artifactsDownstream applicationsTime series normalizationPrediction modelAnxiety symptoms are differentially associated with facial expression processing in boys and girls
Doucet G, Kruse J, Keefe A, Rice D, Coutant A, Pulliam H, Smith O, Calhoun V, Stephen J, Wang Y, White S, Picci G, Taylor B, Wilson T. Anxiety symptoms are differentially associated with facial expression processing in boys and girls. Social Cognitive And Affective Neuroscience 2024, 19: nsae085. PMID: 39587034, PMCID: PMC11631531, DOI: 10.1093/scan/nsae085.Peer-Reviewed Original ResearchFacial expression processingAssociated with psychiatric disordersExpression processingFacial expressionsFunctional magnetic resonance imagingFace processing taskMedial temporal cortexTypically-developing youthLevels of anxietyEmotional facesNeutral contrastAnxiety symptomsPosterior networkPsychiatric disordersFacial emotionsBrain responsesTemporal cortexNeural mechanismsHigher anxietyFMRI dataAnxietySocial informationAnxiety levelsBehavioral changesMagnetic resonance imagingMultimodal 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 regressionBrain networks and intelligence: A graph neural network based approach to resting state fMRI data
Thapaliya B, Akbas E, Chen J, Sapkota R, Ray B, Suresh P, Calhoun V, Liu J. Brain networks and intelligence: A graph neural network based approach to resting state fMRI data. Medical Image Analysis 2024, 101: 103433. PMID: 37986729, PMCID: PMC10659448, DOI: 10.1016/j.media.2024.103433.Peer-Reviewed Original ResearchGraph neural networksNeural networkGraph isomorphism networkGraph convolutional layersGraph convolutional networkMachine learning modelsMean square errorNetwork connectivity matrixCognitive processesConvolutional layersConvolutional networkPrediction taskModel architectureGraph architectureAdolescent Brain Cognitive Development datasetResting-state functional magnetic resonance imagingFunctional magnetic resonance imagingLearning modelsMiddle frontal gyrusPredicting individual differencesResting state fMRI dataPredictive intelligenceIntelligenceNetworkFunctional network connectivity matricesAssessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis
Belyaeva I, Wang Y, Wilson T, Calhoun V, Stephen J, Adali T. Assessing Pediatric Cognitive Development via Multisensory Brain Imaging Analysis. 2015 23rd European Signal Processing Conference (EUSIPCO) 2024, 1362-1366. DOI: 10.23919/eusipco63174.2024.10714926.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional magnetic resonance imaging dataMultisensory integrationSensory stimuliEffect of multisensory integrationMultisensory integration effectsMultiple sensory stimuliBrain imaging modalitiesCognitive developmentBrain image analysisBrain developmental patternsSensory modalitiesBrain componentsLearning paradigmMagnetoencephalographyMagnetic resonance imagingBrainDevelopmental patternsStimuliMultiple sensesCanonical polyadic tensor decompositionMultimodal data fusion frameworkAdolescentsMultitask learning paradigmPolyadic tensor decompositionReproducibility and Replicability in Neuroimaging: Constrained IVA as an Effective Assessment Tool
Laport F, Dapena A, Vu T, Yang H, Calhoun V, Adali T. Reproducibility and Replicability in Neuroimaging: Constrained IVA as an Effective Assessment Tool. 2015 23rd European Signal Processing Conference (EUSIPCO) 2024, 802-806. DOI: 10.23919/eusipco63174.2024.10715160.Peer-Reviewed Original ResearchBlind source separationMatrix decomposition techniqueLinear blind source separationMulti-subject functional magnetic resonance imagingIndependent vector analysisPermutation ambiguityBSS techniquesDecomposition techniqueModel order selectionSource separationData-driven approachFunctional magnetic resonance imagingModel matchingModel orderComputational reproducibilityOrder selectionFMRI datasetsSuboptimal resultsMatchingDatasetFrequency 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 regionsData augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model
Yang Y, Ma S, Cao S, Jia S, Bi Y, Calhoun V. Data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model. Proceedings Of SPIE--the International Society For Optical Engineering 2024, 13252: 1325214-1325214-7. DOI: 10.1117/12.3044654.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFunctional network connectivity matricesIndependent component analysisVision Transformer (ViTAdvanced artificial intelligence techniquesTraditional U-NetArtificial intelligence techniquesFunctional magnetic resonance imaging dataGroup independent component analysisNetwork connectivity matrixDenoising functionData augmentationImage generationIntelligence techniquesU-NetSmall datasetsDiagnosed schizophreniaSchizophrenia diagnosisGeneration taskNeuroimaging dataSchizophreniaComputational burdenConnectivity matrixMagnetic resonance imagingRelevant information4D 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 networksDynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence
Treves I, Marusak H, Decker A, Kucyi A, Hubbard N, Bauer C, Leonard J, Grotzinger H, Giebler M, Torres Y, Imhof A, Romeo R, Calhoun V, Gabrieli J. Dynamic Functional Connectivity Correlates of Trait Mindfulness in Early Adolescence. Biological Psychiatry Global Open Science 2024, 4: 100367. PMID: 39286525, PMCID: PMC11402920, DOI: 10.1016/j.bpsgos.2024.100367.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingTrait mindfulnessFunctional connectivity analysisDynamic functional connectivity analysisBrain statesConnectivity analysisSelf-reported trait mindfulnessResting-state fMRI scansHigher trait mindfulnessPresent-moment experienceFunctional connectivity correlatesDynamic brain statesStatic functional connectivityState-of-mindTest-retest reliabilityAdolescent anxietyFMRI scanningNeural basisPsychiatric disordersDepressive symptomsNeural mechanismsLower anxietyFunctional connectivityEarly adolescenceConnectivity correlationsA new transfer entropy method for measuring directed connectivity from complex-valued fMRI data
Li W, Lin Q, Zhang C, Han Y, Calhoun V. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Frontiers In Neuroscience 2024, 18: 1423014. PMID: 39050665, PMCID: PMC11266018, DOI: 10.3389/fnins.2024.1423014.Peer-Reviewed Original ResearchFunctional magnetic resonance imagingFMRI dataBrain regionsAnatomical Automatic LabelingTransfer entropyFunctional magnetic resonance imaging dataConnectivity of brain regionsFrontal-parietal regionsConsistent with previous findingsSignificant group differencesRight frontal-parietal regionPartial transfer entropyPredicting mental disordersMental disordersParietal regionsGroup differencesMagnitude effectExperimental fMRI dataDirectional connectivityComplex-valued fMRI dataSchizophreniaMagnetic resonance imagingComplex-valued approachEntropyMagnitude dataAssociation 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 alterationsSchizophreniaCopula 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-dataSensorimotorNetworkCerebellumA 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 dependenceScepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks
Kazemivash B, Suresh P, Liu J, Ye D, Calhoun V. Scepter: Weakly Supervised Framework for Spatiotemporal Dense Prediction of 4D Dynamic Brain Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039527, DOI: 10.1109/embc53108.2024.10781876.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingDynamic brain networksDense predictionBrain networksDynamic patterns of neural activityPatterns of neural activityBrain dynamicsSpatiotemporal brain dynamicsConsistent with previous findingsWeakly supervised frameworkComputer visionWeak supervisionModel architectureNetwork issuesSupervised frameworkFMRI dataBrain parcellation methodBrain functionNeural activityNeuroscience researchComplexity of brain functionNeural interactionsDeep-stackingExperimental resultsNetworkIdentifying 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 implicationsBOLDParallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks
Khalilullah K, Agcaoglu O, Duda M, Calhoun V. Parallel Multilink Joint ICA for Multimodal Fusion of Gray Matter and Multiple Resting fMRI Networks. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2024, 00: 1-4. PMID: 40039683, DOI: 10.1109/embc53108.2024.10782528.Peer-Reviewed Original ResearchConceptsFunctional magnetic resonance imagingJoint independent component analysisAssociated with Alzheimer's diseaseFalse discovery rateMultimodal fusion approachGray matterAssess group differencesHealthy controlsMultimodal fusionIndependent component analysisFusion approachSensorimotor domainBrain regionsSMRI dataGroup differencesParacentral lobuleBrain functionAD pathologyConnectivity patternsDiscovery rateJoint ICAJoint relationshipAlzheimer's diseaseActivity patternsMagnetic resonance imagingUncovering 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 disordersSchizophreniaAnticorrelationDynamics
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