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Classification of temporal ICA components for separating global noise from fMRI data: Reply to Power., , , , , , , and . NeuroImage, (2019)An Empirical Comparison of Meta- and Mega-Analysis With Data From the ENIGMA Obsessive-Compulsive Disorder Working Group., , , , , , , , , and 65 other author(s). Front. Neuroinform., (2019)Symmetric abnormalities in sulcal patterning in schizophrenia., , , , , , and . NeuroImage, 43 (3): 440-446 (2008)Mapping the human brain's cortical-subcortical functional network organization., , , , , and . NeuroImage, (2019)Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data., , , , , , , and . NeuroImage, (2018)Comparing surface-based and volume-based analyses of functional neuroimaging data in patients with schizophrenia., , , , , , and . NeuroImage, 41 (3): 835-848 (2008)When less is more: TPJ and default network deactivation during encoding predicts working memory performance., , , and . NeuroImage, 49 (3): 2638-2648 (2010)Automated landmark identification for human cortical surface-based registration., , , , , , and . NeuroImage, 59 (3): 2539-2547 (2012)Ciftify: A framework for surface-based analysis of legacy MR acquisitions., , , , , , , , , and . NeuroImage, (2019)