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Adaptive window selection in estimating dynamic functional connectivity of resting-state fMRI.

, , , , , , and . ICICS, page 1-4. IEEE, (2013)

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A Machine Learning Model for Exploring Aberrant Functional Network Connectivity Transition in Schizophrenia., , , , , and . SSIAI, page 112-115. IEEE, (2020)Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping., , , , , , , , , and 6 other author(s). NeuroImage, (2022)Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia., , , , , , , , and . NeuroImage, 180 (Part): 619-631 (2018)A Method to Estimate Longitudinal Change Patterns in Functional Network Connectivity of the Developing Brain Relevant to Psychiatric Problems, Cognition, and Age., , , , , and . Brain Connect., 14 (2): 130-140 (2024)Joint source separation of simultaneous EEG-fMRI recording in two experimental conditions using common spatial patterns., , , , and . EMBC, page 2633-2636. IEEE, (2015)Self-Supervised Mental Disorder Classifiers via Time Reversal., , , and . CoRR, (2022)Multi network InfoMax: A pre-training method involving graph convolutional networks., , , and . CoRR, (2021)Learning Active Multimodal Subspaces in the Brain., , , and . EMBC, page 3822-3825. IEEE, (2022)Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder., , , , and . EMBC, page 1477-1480. IEEE, (2022)Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia., , , , , , , , , and 5 other author(s). BIBE, page 417-424. IEEE, (2020)