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Building Models of Functional Interactions Among Brain Domains that Encode Varying Information Complexity: A Schizophrenia Case Study.

, , , , , and . Neuroinformatics, 20 (3): 777-791 (2022)

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Prediction of Progression to Alzheimer's disease with Deep InfoMax., , , , , , and . BHI, page 1-5. IEEE, (2019)Uncovering Active Structural Subspaces Associated with Changes in Indicators for Alzheimer's Disease., , and . EMBC, page 3948-3951. IEEE, (2021)Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs., , , and . ICASSP, page 1756-1760. IEEE, (2024)A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction., , , , , , , and . BIBE, page 41-44. IEEE, (2022)An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease., , , , , , , , and . CoRR, (2024)Classification As a Criterion to Select Model Order For Dynamic Functional Connectivity States in Rest-fMRI Data., , , , , and . ISBI, page 1602-1605. IEEE, (2019)Varying Information Complexity in Functional Domain Interactions in Schizophrenia., , , and . BIBE, page 1042-1047. IEEE, (2020)Deep Learning Prediction and Visualization of Gender Related Brain Changes from Longitudinal Structural MRI Data in the ABCD Study., , , and . EMBC, page 3814-3817. IEEE, (2022)Gray matters: ViT-GAN framework for identifying schizophrenia biomarkers linking structural MRI and functional network connectivity., , , , and . NeuroImage, (2024)MultiViT: Multimodal Vision Transformer for Schizophrenia Prediction using Structural MRI and Functional Network Connectivity Data., , , and . ISBI, page 1-5. IEEE, (2023)