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Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity.

, , , , , , and . NeuroImage, (2016)

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The tenth annual MLSP competition: Schizophrenia classification challenge., , , , , , , and . MLSP, page 1-6. IEEE, (2014)Sensory load hierarchy-based classification of schizophrenia patients., , and . ICIP, page 467-471. IEEE, (2015)Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia., , , , , , , , and . NeuroImage, (2014)Multimodal based classification of schizophrenia patients., , , , and . EMBC, page 2629-2632. IEEE, (2015)Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity., , , , , , and . NeuroImage, (2016)The impact of data preprocessing in traumatic brain injury detection using functional magnetic resonance imaging., , , , , and . EMBC, page 5432-5435. IEEE, (2015)Shapelet Ensemble for Multi-dimensional Time Series., , and . SDM, page 307-315. SIAM, (2015)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 time-varying resting-state FMRI brain connectivity., , , , , , and . ISBI, page 251-254. IEEE, (2015)