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 connectivityBrain 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 matricesData 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 information