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Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.

, , , , , , , , , , , , , and . Brain Connect., 11 (10): 838-849 (2021)

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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)Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series., , , and . IJCNN, page 1-10. IEEE, (2022)Phase and amplitude, two sides of functional connectivity., , , and . EMBC, page 1-4. IEEE, (2023)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)Multimodal Fusion of Functional and Structural Data to Recognize Longitudinal Change Patterns in the Adolescent Brain., , , , and . BHI, page 1-5. IEEE, (2023)A deep generative multimodal imaging genomics framework for Alzheimer's disease prediction., , , , , , , and . BIBE, page 41-44. IEEE, (2022)Fusing multimodal neuroimaging data with a variational autoencoder., , , , , and . EMBC, page 3630-3633. IEEE, (2021)Learnt dynamics generalizes across tasks, datasets, and populations., , , , , and . CoRR, (2019)