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Thalamus and posterior temporal lobe show greater inter-network connectivity at rest and across sensory paradigms in schizophrenia.

, , , , , , , , and . NeuroImage, (2014)

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Complex-valued analysis and visualization of fMRI data for event-related and block-design paradigms., , and . MLSP, page 1-6. IEEE, (2012)Higher dimensional fMRI connectivity dynamics show reduced dynamism in schizophrenia patients., , , and . PRNI, page 1-4. IEEE, (2014)An ICA Framework for Integrating fMRI, ERP and Genetic Data.. ISBI, page 824-824. IEEE, (2009)Complex ICA for fMRI analysis: performance of several approaches., and . ICASSP (2), page 717-720. IEEE, (2003)Capturing group variability using IVA: A simulation study and graph-theoretical analysis., , , and . ICASSP, page 3128-3132. IEEE, (2013)Phase correction and denoising for ICA of complex FMRI data., , , , and . ICASSP, page 497-500. IEEE, (2010)Wavelet-based denoising and independent component analysis for improving multi-group inference in fMRI data., , , , , and . ISBI, page 456-459. IEEE, (2011)Extraction of Time-Varying Spatiotemporal Networks Using Parameter-Tuned Constrained IVA., , , and . IEEE Trans. Med. Imaging, 38 (7): 1715-1725 (2019)Dynamic Functional Network Connectivity in Schizophrenia with Magnetoencephalography and Functional Magnetic Resonance Imaging: Do Different Timescales Tell a Different Story?, , and . Brain Connectivity, 9 (3): 251-262 (2019)Volumetric BOLD fMRI simulation: from neurovascular coupling to multivoxel imaging., and . BMC Medical Imaging, (2012)