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Proc Natl Acad Sci U S A. 2016 Apr 19;113(16):4518-23. doi: 10.1073/pnas.1520613113. Epub 2016 Apr 5.

Tracking brain arousal fluctuations with fMRI.

Author information

1
Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892; catie.chang@nih.gov.
2
Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD 20892;
3
Ernst Strüngmann Institute for Neuroscience in Cooperation with Max Planck Society, 60528 Frankfurt am Main, Germany;
4
Advanced Magnetic Resonance Imaging Section, Laboratory of Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892;
5
Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, and National Eye Institute, National Institutes of Health, Bethesda, MD 20892;
6
Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892.

Abstract

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.

KEYWORDS:

arousal; electrophysiology; resting-state fMRI; spontaneous fluctuations

PMID:
27051064
PMCID:
PMC4843437
DOI:
10.1073/pnas.1520613113
[Indexed for MEDLINE]
Free PMC Article

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