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      Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

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          Abstract

          The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain’s global signal (GS) amplitude (henceforth referred to as “fluctuation”) increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

          Abstract

          The brain exhibits substantial diurnal variation in physiology and function. A large-scale fMRI study reveals that the brain’s global signal amplitude, typically elevated during drowsy states, unexpectedly reduces steadily as the day progresses.

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          An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.

          Several recent reports in large, independent samples have demonstrated the influence of motion artifact on resting-state functional connectivity MRI (rsfc-MRI). Standard rsfc-MRI preprocessing typically includes regression of confounding signals and band-pass filtering. However, substantial heterogeneity exists in how these techniques are implemented across studies, and no prior study has examined the effect of differing approaches for the control of motion-induced artifacts. To better understand how in-scanner head motion affects rsfc-MRI data, we describe the spatial, temporal, and spectral characteristics of motion artifacts in a sample of 348 adolescents. Analyses utilize a novel approach for describing head motion on a voxelwise basis. Next, we systematically evaluate the efficacy of a range of confound regression and filtering techniques for the control of motion-induced artifacts. Results reveal that the effectiveness of preprocessing procedures on the control of motion is heterogeneous, and that improved preprocessing provides a substantial benefit beyond typical procedures. These results demonstrate that the effect of motion on rsfc-MRI can be substantially attenuated through improved preprocessing procedures, but not completely removed. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Extensive and divergent circadian gene expression in liver and heart.

            Many mammalian peripheral tissues have circadian clocks; endogenous oscillators that generate transcriptional rhythms thought to be important for the daily timing of physiological processes. The extent of circadian gene regulation in peripheral tissues is unclear, and to what degree circadian regulation in different tissues involves common or specialized pathways is unknown. Here we report a comparative analysis of circadian gene expression in vivo in mouse liver and heart using oligonucleotide arrays representing 12,488 genes. We find that peripheral circadian gene regulation is extensive (> or = 8-10% of the genes expressed in each tissue), that the distributions of circadian phases in the two tissues are markedly different, and that very few genes show circadian regulation in both tissues. This specificity of circadian regulation cannot be accounted for by tissue-specific gene expression. Despite this divergence, the clock-regulated genes in liver and heart participate in overlapping, extremely diverse processes. A core set of 37 genes with similar circadian regulation in both tissues includes candidates for new clock genes and output genes, and it contains genes responsive to circulating factors with circadian or diurnal rhythms.
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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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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: SoftwareRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Biol
                PLoS Biol
                plos
                plosbiol
                PLoS Biology
                Public Library of Science (San Francisco, CA USA )
                1544-9173
                1545-7885
                18 February 2020
                February 2020
                18 February 2020
                : 18
                : 2
                : e3000602
                Affiliations
                [1 ] Department of Electrical and Computer Engineering, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
                [2 ] Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
                [3 ] Clinical Imaging and Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
                [4 ] Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London, United Kingdom
                [5 ] Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
                [6 ] Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States of America
                [7 ] NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
                University of Cambridge, UNITED KINGDOM
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9133-3561
                http://orcid.org/0000-0001-7842-0329
                http://orcid.org/0000-0002-6395-8801
                http://orcid.org/0000-0002-6087-0548
                http://orcid.org/0000-0002-0119-3276
                Article
                PBIOLOGY-D-19-01472
                10.1371/journal.pbio.3000602
                7028250
                32069275
                0239358d-6069-4e91-82c8-9b9adb846145
                © 2020 Orban et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 May 2019
                : 15 January 2020
                Page count
                Figures: 6, Tables: 0, Pages: 30
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001352, National University of Singapore;
                Award ID: DPRT/944/09/14
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001352, National University of Singapore;
                Award ID: R185000271720
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001349, National Medical Research Council;
                Award ID: CBRG/0088/2015
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001352, National University of Singapore;
                Award ID: Young Investigator Award
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001381, National Research Foundation Singapore;
                Award ID: Fellowship (Class of 2017)
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001459, Ministry of Education - Singapore;
                Award ID: MOE2014-T2-2-016
                Award Recipient :
                We are currently supported by Singapore Ministry of Education Tier 2 (MOE2014-T2-2-016), National University of Singapore (NUS) Strategic Research (DPRT/944/09/14), NUS School of Medicine Aspiration Fund (R185000271720), Singapore National Medical Research Council (NMRC/STaR/015/2013; CBRG/0088/2015), NUS Young Investigator Award, and the Singapore National Research Foundation (NRF) Fellowship (Class of 2017). Our research also utilized resources provided by the Center for Functional Neuroimaging Technologies, NIH P41EB015896 and instruments supported by NIH 1S10RR023401, NIH 1S10RR019307, and NIH 1S10RR023043 from the Athinoula A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. Our computational work was partially performed on resources of the National Supercomputing Centre, Singapore ( https://www.nscc.sg). Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                Data are from the Human Connectome Project S1200, which is publicly available at https://www.humanconnectome.org/. Data points underlying Figs 1 and 3 and S1, S3, S4, S8, S9, and S10 Figs are presented in S1 Data, and data points underlying Fig 2 and S2 Fig are presented in S2 Data. The list of participants who passed our visual quality screening of their pulse and respiratory data can be found at the GitHub repository maintained by the Computational Brain Imaging Group at https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Orban2020_tod.

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