2024
A 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 connectivityAn Explainable Unified Framework of Spatio-Temporal Coupling Learning with Application to Dynamic Brain Functional Connectivity Analysis
Gao B, Yu A, Qiao C, Calhoun V, Stephen J, Wilson T, Wang Y. An Explainable Unified Framework of Spatio-Temporal Coupling Learning with Application to Dynamic Brain Functional Connectivity Analysis. IEEE Transactions On Medical Imaging 2024, PP: 1-1. PMID: 39320999, DOI: 10.1109/tmi.2024.3467384.Peer-Reviewed Original ResearchSpatio-temporal informationDeep learning networkInter-node connectivitySpatio-temporal correlationMachine learning modelsNode representationsPoor explainabilityCoupling learningLearning frameworkDeep learningLearning networkLearning modelsExplainabilityTime series dataExperimental resultsCoupling associationFramework constructionLearningDynamic functional connectivityFrameworkBrain functional connectivity analysisBrain dynamic functional connectivityInformationConnectionNetworkCommon and unique brain aging patterns between females and males quantified by large‐scale deep learning
Du Y, Yuan Z, Sui J, Calhoun V. Common and unique brain aging patterns between females and males quantified by large‐scale deep learning. Human Brain Mapping 2024, 45: e70005. PMID: 39225381, PMCID: PMC11369911, DOI: 10.1002/hbm.70005.Peer-Reviewed Original ResearchConceptsBrain functional changesFunctional connectivityCognitive controlBrain agingBrain functionPatterns of brain agingResting-state brain functional connectivityBrain functional interactionsBrain functional connectivityHuman brain functionBrain aging patternsGender commonalitiesAge-related changesDeep learningHealthy participantsNormal agingNegative connectionFunctional changesBrainPositive connectionDeep learning modelsFunctional domainsAge effectsFunctional interactionsCross-validation schemeIdentifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Ellis C, Sancho M, Miller R, Calhoun V. Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures. Communications In Computer And Information Science 2024, 2156: 102-124. DOI: 10.1007/978-3-031-63803-9_6.Peer-Reviewed Original ResearchDeep learning modelsExplainability methodsExplainability analysisConvolutional neural network architectureLearning modelsRaw electroencephalogramNeural network architectureDeep learning architectureMajor depressive disorderLearning architectureNetwork architectureDeep learningModel architectureMultichannel electroencephalogramTraining approachArchitectureBiomarkers of depressionFrequency bandElectroencephalogramResearch contextDepressive disorderElectroencephalogram biomarkerAccuracyRight hemisphereExplainabilityVoxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning
Itkyal V, Abrol A, LaGrow T, Calhoun V. Voxelwise Intensity Projection for the Spatial Representation of Resting State Functional MRI Networks and Multimodal Deep Learning. 2024, 00: 1-4. DOI: 10.1109/isbi56570.2024.10635831.Peer-Reviewed Original ResearchMulti-channel convolutional neural networkConvolutional neural networkMultimodal deep learningSpatial information extractionAmplitude of low-frequency fluctuationSaliency visualizationInformation extractionClassification performanceDeep learningAlzheimer's Disease Neuroimaging Initiative datasetFusion resultsNeural networkAUC scoreAD classificationTemporal dependenciesFMRI networksTraditional metricsTest accuracyInitiative datasetNetworkLow-frequency fluctuationsInterpretable 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 abilitiesEmbeddingNetworkGraph
2023
Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*
Ellis C, Sattiraju A, Miller R, Calhoun V. Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data*. 2023, 00: 2466-2473. DOI: 10.1109/bibm58861.2023.10385424.Peer-Reviewed Original ResearchDeep learning methodsLearning methodsTransfer learningEEG datasetManually engineered featuresTransfer learning approachDeep learning modelsDeep learning performanceMachine learning methodsClassification datasetsLearned representationsElectroencephalography classifierDeep learningEEG classificationResting-state electroencephalographyDiagnosis of major depressive disorderRaw electroencephalographyLearning approachLearning modelsMajor depressive disorder diagnosisMajor depressive disorderLearning performanceClassifierDatasetEngineering featuresDeep 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 mechanismAn Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders
Du Y, Wu F, Niu J, Calhoun V. An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders. 2023, 00: 1-4. DOI: 10.1109/isbi53787.2023.10230805.Peer-Reviewed Original ResearchFashion-MNIST dataDeep clustering methodsFunctional magnetic resonance imagingMNIST dataAutism spectrum disorderClustering methodPsychiatric disordersSemi-supervised clusteringPsychiatric disorder symptomsUnlabeled samplesClustering performanceDeep clusteringLabeled samplesDeep learningClustering techniqueDisorder symptomsSpectrum disorderNeuroimaging dataUseful informationSchizophreniaTraditional methodsMagnetic resonance imagingDisordersResonance imagingHigh confidence level