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Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder.

, , , , and . EMBC, page 1477-1480. IEEE, (2022)

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Supervised nonlinear dimension reduction of functional magnetic resonance imaging data using Sliced Inverse Regression., , , , , and . EMBC, page 2641-2644. IEEE, (2015)A least across-segment variance (LASV) method for the correction of EEG-fMRI desynchronization., , , , , and . NER, page 5-8. IEEE, (2017)Prediction of Progression to Alzheimer's disease with Deep InfoMax., , , , , , and . BHI, page 1-5. IEEE, (2019)Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent Brain., , , , and . BHI, page 1-5. IEEE, (2023)Phase and amplitude, two sides of functional connectivity., , , and . EMBC, page 1-4. IEEE, (2023)A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction., , , , , , , and . BIBE, page 41-44. IEEE, (2022)Whole MILC: Generalizing Learned Dynamics Across Tasks, Datasets, and Populations., , , , , , and . MICCAI (7), volume 12267 of Lecture Notes in Computer Science, page 407-417. Springer, (2020)Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series., , , and . IJCNN, page 1-10. IEEE, (2022)An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease., , , , , , , , and . CoRR, (2024)Joint source separation of simultaneous EEG-fMRI recording in two experimental conditions using common spatial patterns., , , , and . EMBC, page 2633-2636. IEEE, (2015)