GPR-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 modelGray 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 data
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