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 informationGray 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 dataCross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs
Bi Y, Abrol A, Sui J, Calhoun V. Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs. 2024, 00: 1756-1760. DOI: 10.1109/icassp48485.2024.10446450.Peer-Reviewed Original ResearchFunctional network connectivityStructural magnetic resonance imagingCross-modality synthesisFunctional network connectivity matricesGenerative adversarial networkFunctional connectivity networksAdversarial networkSubcortical brain regionsMedical imagesNetwork connectivityFusion of MRIConnectivity networksA 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 patterns