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      Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

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          Abstract

          Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies.

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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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            Functional connectivity in single and multislice echoplanar imaging using resting-state fluctuations.

            A previous report of correlations in low-frequency resting-state fluctuations between right and left hemisphere motor cortices in rapidly sampled single-slice echoplanar data is confirmed using a whole-body echoplanar MRI scanner at 1.5 T. These correlations are extended to lower sampling rate multislice echoplanar acquisitions and other right/left hemisphere-symmetric functional cortices. The specificity of the correlations in the lower sampling-rate acquisitions is lower due to cardiac and respiratory-cycle effects which are aliased into the pass-band of the low-pass filter. Data are combined for three normal right-handed male subjects. Correlations to left hemisphere motor cortex, visual cortex, and amygdala are measured in long resting-state scans.
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              Resting-state fMRI confounds and cleanup.

              The goal of resting-state functional magnetic resonance imaging (fMRI) is to investigate the brain's functional connections by using the temporal similarity between blood oxygenation level dependent (BOLD) signals in different regions of the brain "at rest" as an indicator of synchronous neural activity. Since this measure relies on the temporal correlation of fMRI signal changes between different parts of the brain, any non-neural activity-related process that affects the signals will influence the measure of functional connectivity, yielding spurious results. To understand the sources of these resting-state fMRI confounds, this article describes the origins of the BOLD signal in terms of MR physics and cerebral physiology. Potential confounds arising from motion, cardiac and respiratory cycles, arterial CO₂ concentration, blood pressure/cerebral autoregulation, and vasomotion are discussed. Two classes of techniques to remove confounds from resting-state BOLD time series are reviewed: 1) those utilising external recordings of physiology and 2) data-based cleanup methods that only use the resting-state fMRI data itself. Further methods that remove noise from functional connectivity measures at a group level are also discussed. For successful interpretation of resting-state fMRI comparisons and results, noise cleanup is an often over-looked but essential step in the analysis pipeline. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                02 November 2018
                2018
                : 12
                : 788
                Affiliations
                [1] 1High-Field Magnetic Resonance Department, Max Planck Institute for Biological Cybernetics , Tuebingen, Germany
                [2] 2Graduate Training Centre of Neuroscience, International Max Planck Research School, University of Tuebingen , Tuebingen, Germany
                [3] 3Department of Biomedical Engineering, New Jersey Institute of Technology , Newark, NJ, United States
                [4] 4Department for Biomedical Magnetic Resonance, University of Tuebingen , Tuebingen, Germany
                [5] 5Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School , Charlestown, MA, United States
                Author notes

                Edited by: Federico Giove, Centro Fermi – Museo Storico della Fisica e Centro Studi e Ricerche Enrico Fermi, Italy

                Reviewed by: Cornelius Faber, Universitätsklinikum Münster, Germany; Pasquina Marzola, Università degli Studi di Verona, Italy

                *Correspondence: Xin Yu, xin.yu@ 123456tuebingen.mpg.de

                This article was submitted to Brain Imaging Methods, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2018.00788
                6230988
                30455623
                5e8df53f-821f-4484-9c4e-950474a364d1
                Copyright © 2018 Pais-Roldán, Biswal, Scheffler and Yu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 August 2018
                : 12 October 2018
                Page count
                Figures: 5, Tables: 2, Equations: 1, References: 98, Pages: 14, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                rat fmri,physiological noise,epi,ventilation rate,repetition time,resting state networks
                Neurosciences
                rat fmri, physiological noise, epi, ventilation rate, repetition time, resting state networks

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