title: Fast fMRI can detect oscillatory neural activity in humans.
abstract: Oscillatory neural dynamics play an important role in the coordination of large-scale brain networks. High-level cognitive processes depend on dynamics evolving over hundreds of milliseconds, so measuring neural activity in this frequency range is important for cognitive neuroscience. However, current noninvasive neuroimaging methods are not able to precisely localize oscillatory neural activity above 0.2 Hz. Electroencephalography and magnetoencephalography have limited spatial resolution, whereas fMRI has limited temporal resolution because it measures vascular responses rather than directly recording neural activity. We hypothesized that the recent development of fast fMRI techniques, combined with the extra sensitivity afforded by ultra-high-field systems, could enable precise localization of neural oscillations. We tested whether fMRI can detect neural oscillations using human visual cortex as a model system. We detected small oscillatory fMRI signals in response to stimuli oscillating at up to 0.75 Hz within single scan sessions, and these responses were an order of magnitude larger than predicted by canonical linear models. Simultaneous EEG-fMRI and simulations based on a biophysical model of the hemodynamic response to neuronal activity suggested that the blood oxygen level-dependent response becomes faster for rapidly varying stimuli, enabling the detection of higher frequencies than expected. Accounting for phase delays across voxels further improved detection, demonstrating that identifying vascular delays will be of increasing importance with higher-frequency activity. These results challenge the assumption that the hemodynamic response is slow, and demonstrate that fMRI has the potential to map neural oscillations directly throughout the brain.
authors.affiliation: Society of Fellows, Harvard University, Cambridge, MA 02138; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129; lauralewis@fas.harvard.edu.
authors.forename: Laura D
authors.initials: LD
authors.lastname: Lewis
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114.
authors.forename: Kawin
authors.initials: K
authors.lastname: Setsompop
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114.
authors.forename: Bruce R
authors.initials: BR
authors.lastname: Rosen
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA 02129; Department of Radiology, Harvard Medical School, Boston, MA 02114.
authors.forename: Jonathan R
authors.initials: JR
authors.lastname: Polimeni
mesh_terms: D001931:Brain Mapping; D003071:Cognition / Q000502:physiology*; D004569:Electroencephalography; D006439:Hemodynamics / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000379:methods*; D009415:Nerve Net / Q000033:anatomy & histology / Q000098:blood supply / Q000502:physiology*; D010775:Photic Stimulation; D014793:Visual Cortex / Q000033:anatomy & histology / Q000098:blood supply / Q000502:physiology*
keywords: BOLD; hemodynamics; imaging; oscillations