Anti-correlations between the default mode network (DMN) and task-positive networks provide insights into competitive mechanisms that control resting-state fluctuations ( Fox et al., 2005 Uddin et al., 2009). Recent studies have shown that dozens of different RSNs can be measured across groups of subjects ( Abou-Elseoud et al., 2010 Allen et al., 2011). Mapping of intrinsic signal variation mostly in the low-frequency band<0.1 Hz has emerged as a powerful tool and adjunct to task-related fMRI and fiber tracking based in diffusion tensor imaging (DTI) for mapping functional connectivity within and between resting-state networks (RSNs) ( Fox et al., 2005 De Luca et al., 2006 Raichle and Snyder, 2007 Schopf et al., 2010 Li et al., 2011). In conclusion, ultra-high-real-time speed fMRI enhances the sensitivity of mapping the dynamics of resting-state connectivity and cerebro-vascular pulsatility for clinical and neuroscience research applications. This novel fMRI methodology is particularly promising for mapping eloquent cortex in patients with neurological disease, having variable degree of cooperation in task-based fMRI. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. The fast acquisition speed of MEVI enabled segregation of cardiac-related signal pulsation using ICA, which revealed distinct regional differences in pulsation amplitude and waveform, elevated signal pulsation in patients with arterio-venous malformations and a trend toward reduced pulsatility in gray matter of patients compared with healthy controls. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. We demonstrate highly sensitive mapping of eloquent cortex in the vicinity of brain tumors and arterio-venous malformations, and detection of abnormal resting-state connectivity in epilepsy. This SBCA approach is shown to minimize the effects of confounds, such as movement, and CSF and white matter signal changes, and enables real-time monitoring of RSN dynamics at time scales of tens of seconds. In the present study we characterize the sensitivity of MEVI for mapping RSN connectivity dynamics, comparing independent component analysis (ICA) and a novel seed-based connectivity analysis (SBCA) that combines sliding-window correlation analysis with meta-statistics. We recently demonstrated that ultra-high-speed real-time fMRI using multi-slab echo-volumar imaging (MEVI) significantly increases sensitivity for mapping task-related activation and resting-state networks (RSNs) compared to echo-planar imaging ( Posse et al., 2012). 7Department of Neurosurgery, School of Medicine, The University of New Mexico, Albuquerque, NM, USA.6Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA.5Division of Psychiatry and Neuroscience, Walter Reed Army Institute of Research, Silver Spring, MD, USA.4Department of Physics, University of Bucharest, Bucharest, Romania.3Department of Physics and Astronomy, The University of New Mexico, Albuquerque, NM, USA.2Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, USA.1Department of Neurology, School of Medicine, The University of New Mexico, Albuquerque, NM, USA.
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