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Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients.

, and . PRNI, page 1-4. IEEE, (2018)

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Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity., , , , , , and . NeuroImage, (2016)An Unsupervised Feature Learning Approach for Elucidating Hidden Dynamics in rs-fMRI Functional Network Connectivity., , , and . EMBC, page 4449-4452. IEEE, (2022)Multiframe Evolving Dynamic Functional Network Connectivity Motifs (Evodfncs) from Continuity-Preserving Planar Embedding., , and . EMBC, page 3066-3069. IEEE, (2021)Exploring Relationships between Functional Network Connectivity and Cognition with an Explainable Clustering Approach., , , , and . BIBE, page 293-296. IEEE, (2022)A study of spatial variation in fMRI brain networks via independent vector analysis: Application to schizophrenia., , , , , and . PRNI, page 1-4. IEEE, (2014)A Novel Explainable Fuzzy Clustering Approach for fMRI Dynamic Functional Network Connectivity Analysis., , and . EMBC, page 1-4. IEEE, (2023)Hyperlocal Spatial Flows in BOLD fMRI Expose Novel Brain-Based Correlates of Schizophrenia., , and . EMBC, page 1-4. IEEE, (2023)The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging., , , , , and . EMBC, page 5432-5435. IEEE, (2015)Dynamic Whole Brain Polarity Regimes Strongly Distinguish Controls from Schizophrenia Patients., and . PRNI, page 1-4. IEEE, (2018)Capturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity., , , , and . ISBI, page 1-4. IEEE, (2024)