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Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia.

, , , , , , , , , , and . NeuroImage, (2015)

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A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia., , , , , and . PRNI, page 1-4. IEEE, (2014)Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder., , , , , , , , , and 4 other author(s). Brain Connect., 11 (10): 838-849 (2021)Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse., , , , and . CoRR, (2024)Mapping and interpreting the dynamic connectivity of the brain., , , and . NeuroImage, 180 (Part): 335-336 (2018)Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia., , , , , , , , , and 1 other author(s). NeuroImage, (2015)Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity., , , , , , and . NeuroImage, (2016)Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information., , , and . NeuroImage, (2015)Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients., and . PRNI, page 1-4. IEEE, (2018)Novel methods for elucidating modality importance in multimodal electrophysiology classifiers., , , , , , and . Frontiers Neuroinformatics, (March 2023)Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach., , , , and . BIBE, page 293-296. IEEE, (2022)