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Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder., , , , , , , , , и 4 other автор(ы). Brain Connect., 11 (10): 838-849 (2021)A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia., , , , , и . PRNI, стр. 1-4. IEEE, (2014)Dynamic coherence analysis of resting fMRI data to jointly capture state-based phase, frequency, and time-domain information., , , и . NeuroImage, (2015)Mapping and interpreting the dynamic connectivity of the brain., , , и . 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., , , , , , , , , и 1 other автор(ы). NeuroImage, (2015)Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity., , , , , , и . NeuroImage, (2016)Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse., , , , и . CoRR, (2024)Multiframe Evolving Dynamic Functional Network Connectivity Motifs (Evodfncs) from Continuity-Preserving Planar Embedding., , и . EMBC, стр. 3066-3069. IEEE, (2021)An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity., , , и . EMBC, стр. 4449-4452. IEEE, (2022)Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients., и . PRNI, стр. 1-4. IEEE, (2018)