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      Sympathetic activity contributes to the fMRI signal

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          Abstract

          The interpretation of functional magnetic resonance imaging (fMRI) studies of brain activity is often hampered by the presence of brain-wide signal variations that may arise from a variety of neuronal and non-neuronal sources. Recent work suggests a contribution from the sympathetic vascular innervation, which may affect the fMRI signal through its putative and poorly understood role in cerebral blood flow (CBF) regulation. By analyzing fMRI and (electro-) physiological signals concurrently acquired during sleep, we found that widespread fMRI signal changes often co-occur with electroencephalography (EEG) K-complexes, signatures of sub-cortical arousal, and episodic drops in finger skin vascular tone; phenomena that have been associated with intermittent sympathetic activity. These findings support the notion that the extrinsic sympathetic innervation of the cerebral vasculature contributes to CBF regulation and the fMRI signal. Accounting for this mechanism could help separate systemic from local signal contributions and improve interpretation of fMRI studies.

          Abstract

          Özbay et al. show the contribution of fluctuations in sympathetic activation on global fMRI signals in human brain during sleep. Such an inference is based on simultaneously acquiring and correlating EEG K-complexes and episodic drops in finger skin signatures with BOLD-fMRI changes during sleep.

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          Most cited references65

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          Automatically Parcellating the Human Cerebral Cortex

          B Fischl (2004)
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            Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI.

            Subtle changes in a subject's breathing rate or depth, which occur naturally during rest at low frequencies (<0.1 Hz), have been shown to be significantly correlated with fMRI signal changes throughout gray matter and near large vessels. The goal of this study was to investigate the impact of these low-frequency respiration variations on both task activation fMRI studies and resting-state functional connectivity analysis. Unlike MR signal changes correlated with the breathing motion ( approximately 0.3 Hz), BOLD signal changes correlated with across-breath variations in respiratory volume ( approximately 0.03 Hz) appear localized to blood vessels and regions with high blood volume, such as gray matter, similar to changes seen in response to a breath-hold challenge. In addition, the respiration-variation-induced signal changes were found to coincide with many of the areas identified as part of the 'default mode' network, a set of brain regions hypothesized to be more active at rest. Regions could therefore be classified as being part of a resting network based on their similar respiration-induced changes rather than their synchronized neuronal activity. Monitoring and removing these respiration variations led to a significant improvement in the identification of task-related activation and deactivation and only slight differences in regions correlated with the posterior cingulate at rest. Regressing out global signal changes or cueing the subject to breathe at a constant rate and depth resulted in an improved spatial overlap between deactivations and resting-state correlations among areas that showed deactivation.
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              Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep.

              The mining of huge databases of resting-state brain activity recordings represents state of the art in the assessment of endogenous neuronal activity-and may be a promising tool in the search for functional biomarkers. However, the resting state is an uncontrolled condition and its heterogeneity is neither sufficiently understood nor accounted for. We test the hypothesis that subjects exhibit unstable wakefulness, i.e., drift into sleep during typical resting-state experiments. Analyzing 1,147 resting-state functional magnetic resonance data sets, we revealed a reliable loss of wakefulness in a third of subjects within 3 min and demonstrated the dynamic nature of the resting state, with fundamental changes in the associated functional neuroanatomy. Implications include the necessity of wakefulness monitoring and modeling, taking measures to maintain a state of wakefulness, acknowledging the possibility of sleep and exploring its consequences, and especially the critical assessment of possible false-positive or false-negative results. Copyright © 2014 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Jeff.Duyn@nih.gov
                Journal
                Commun Biol
                Commun Biol
                Communications Biology
                Nature Publishing Group UK (London )
                2399-3642
                18 November 2019
                18 November 2019
                2019
                : 2
                : 421
                Affiliations
                [1 ]ISNI 0000 0001 2297 5165, GRID grid.94365.3d, Advanced MRI Section, LFMI, NINDS, , National Institutes of Health, ; Bethesda, MD USA
                [2 ]ISNI 0000 0001 2264 7217, GRID grid.152326.1, Vanderbilt University, ; Nashville, TN USA
                [3 ]ISNI 0000 0001 0668 7243, GRID grid.266093.8, University of California, ; Irvine, CA USA
                Author information
                http://orcid.org/0000-0003-0274-3599
                http://orcid.org/0000-0001-8155-8185
                Article
                659
                10.1038/s42003-019-0659-0
                6861267
                31754651
                5b538258-469d-4d75-b1e8-5264d55a322a
                © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 May 2019
                : 21 October 2019
                Funding
                Funded by: Intramural program of the National Institute of Neurological Disorders and Stroke
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                neuro-vascular interactions,neurophysiology,non-rem sleep

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