title: High-speed real-time resting-state FMRI using multi-slab echo-volumar imaging.
abstract: 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). 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. 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. 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. In patients with motor impairment, resting-state fMRI provided focal localization of sensorimotor cortex compared with more diffuse activation in task-based fMRI. 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. Mapping cardiac pulsation in cortical gray matter may carry important functional information that distinguishes healthy from diseased tissue vasculature. 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. 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.
authors.affiliation: Department of Neurology, School of Medicine, The University of New Mexico , Albuquerque, NM , USA ; Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, NM , USA ; Department of Physics and Astronomy, The University of New Mexico , Albuquerque, NM , USA.
authors.forename: Stefan
authors.initials: S
authors.lastname: Posse
authors.forename: Elena
authors.initials: E
authors.lastname: Ackley
authors.forename: Radu
authors.initials: R
authors.lastname: Mutihac
authors.forename: Tongsheng
authors.initials: T
authors.lastname: Zhang
authors.forename: Ruslan
authors.initials: R
authors.lastname: Hummatov
authors.forename: Massoud
authors.initials: M
authors.lastname: Akhtari
authors.forename: Muhammad
authors.initials: M
authors.lastname: Chohan
authors.forename: Bruce
authors.initials: B
authors.lastname: Fisch
authors.forename: Howard
authors.initials: H
authors.lastname: Yonas
keywords: arteriovenous malformation; brain tumor; cerebrovascular pulsatility; epilepsy; independent component analysis (ICA); multi-slab echo-volumar imaging; real-time resting state fMRI; seed-based functional connectivity