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      The Sensory and Motor Components of the Cortical Hierarchy Are Coupled to the Rhythm of the Stomach during Rest

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          Abstract

          Bodily rhythms appear as novel scaffolding mechanisms orchestrating the spatiotemporal organization of spontaneous brain activity. Here, we follow-up on the discovery of the gastric resting-state network ( Rebollo et al., 2018), composed of brain regions in which the fMRI signal is phase-synchronized to the slow (0.05 Hz) electrical rhythm of the stomach. Using a larger sample size ( n = 63 human participants, both genders), we further characterize the anatomy and effect sizes of gastric-brain coupling across resting-state networks, a fine grained cortical parcellation, as well as along the main gradients of cortical organization. Most (67%) of the gastric network is included in the somato-motor-auditory (38%) and visual (29%) resting state networks (RSNs). Gastric brain coupling also occurs in the granular insula and, to a lesser extent, in the piriform cortex. Thus, all sensory and motor cortices corresponding to both exteroceptive and interoceptive modalities are coupled to the gastric rhythm during rest. Conversely, little gastric-brain coupling occurs in cognitive networks and transmodal regions. These results suggest not only that gastric rhythm and sensory-motor processes are likely to interact, but also that gastric-brain coupling might be a mechanism of sensory and motor integration that mostly bypasses cognition, complementing the classical hierarchical organization of the human brain.

          SIGNIFICANCE STATEMENT While there is growing interest for brain-body communication in general and brain-viscera communication in particular, little is known about how the brain interacts with the gastric rhythm, the slow electrical rhythm continuously produced in the stomach. Here, we show in human participants at rest that the gastric network, composed of brain regions synchronized with delays to the gastric rhythm, includes all motor and sensory (vision, audition, touch and interoception, olfaction) regions, but only few of the transmodal regions associated with higher-level cognition. Such results prompt for a reconsideration of the classical view of cortical organization, where the different sensory modalities are considered as relatively independent modules.

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          EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

          We have developed a toolbox and graphic user interface, EEGLAB, running under the crossplatform MATLAB environment (The Mathworks, Inc.) for processing collections of single-trial and/or averaged EEG data of any number of channels. Available functions include EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), independent component analysis (ICA) and time/frequency decompositions including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling. EEGLAB functions are organized into three layers. Top-layer functions allow users to interact with the data through the graphic interface without needing to use MATLAB syntax. Menu options allow users to tune the behavior of EEGLAB to available memory. Middle-layer functions allow users to customize data processing using command history and interactive 'pop' functions. Experienced MATLAB users can use EEGLAB data structures and stand-alone signal processing functions to write custom and/or batch analysis scripts. Extensive function help and tutorial information are included. A 'plug-in' facility allows easy incorporation of new EEG modules into the main menu. EEGLAB is freely available (http://www.sccn.ucsd.edu/eeglab/) under the GNU public license for noncommercial use and open source development, together with sample data, user tutorial and extensive documentation.
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            An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

            In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
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              The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

              Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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                Author and article information

                Journal
                J Neurosci
                J Neurosci
                jneuro
                J. Neurosci
                The Journal of Neuroscience
                Society for Neuroscience
                0270-6474
                1529-2401
                16 March 2022
                : 42
                : 11
                : 2205-2220
                Affiliations
                [1] 1Laboratoire de Neurosciences Cognitives et Computationnelles, Institut National de la Santé et de la Recherche Médicale, Ecole Normale Supérieure, Paris Sciences et Lettres University, Paris 75005, France
                [2] 2German Institute of Human Nutrition Potsdam-Rehbrücke, Nuthetal 14558, Germany
                Author notes
                Correspondence should be addressed to Ignacio Rebollo at ignarebo@ 123456gmail.com

                Author contributions: I.R. and C.T.-B. designed research; I.R. performed research; I.R. analyzed data; I.R. wrote the first draft of the paper; I.R. and C.T.-B. edited the paper; I.R. and C.T.-B. wrote the paper.

                Author information
                https://orcid.org/0000-0002-4119-9955
                https://orcid.org/0000-0001-8480-5831
                Article
                JN-RM-1285-21
                10.1523/JNEUROSCI.1285-21.2021
                8936619
                35074866
                2169b9c9-2f67-4fc7-be86-73787df1ffc7
                Copyright © 2022 Rebollo and Tallon-Baudry

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 22 June 2021
                : 30 November 2021
                : 17 December 2021
                Funding
                Funded by: EC | European Research Council (ERC), doi 10.13039/501100000781;
                Award ID: 670325
                Funded by: Agence Nationale de la Recherche (ANR), doi 10.13039/501100001665;
                Award ID: ANR-17-EURE-0017
                Categories
                Research Articles
                Systems/Circuits
                Custom metadata
                true
                cellular

                autonomic,cortical gradients,electrogastrogram,gastric,phase synchronization,resting state networks

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