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 connectivityAssessing 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 decomposition
2023
A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data
Rokham H, Falakshahi H, Calhoun V. A Deep Learning Approach for Psychosis Spectrum Label Noise Detection from Multimodal Neuroimaging Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38082903, DOI: 10.1109/embc40787.2023.10339949.Peer-Reviewed Original ResearchConceptsStructural MRI dataResting-state functional MRI dataFunctional MRI dataFunctional magnetic resonance imaging dataMRI dataMagnetic resonance imaging dataSchizophrenia patientsFunctional connectivity featuresBrain imaging modalitiesMental disordersNeuroimaging dataNeuroimaging techniquesBorderline subjectsHealthy control groupSchizophrenia datasetSchizophreniaConnectivity featuresBrainPsychosisMoodNosologyControl groupDisordersLabel noiseSubjects