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      Sustained attention operates via dissociable neural mechanisms across different eccentric locations

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          Abstract

          In primates, foveal and peripheral vision have distinct neural architectures and functions. However, it has been debated if selective attention operates via the same or different neural mechanisms across eccentricities. We tested these alternative accounts by examining the effects of selective attention on the steady-state visually evoked potential (SSVEP) and the fronto-parietal signal measured via EEG from human subjects performing a sustained visuospatial attention task. With a negligible level of eye movements, both SSVEP and SND exhibited the heterogeneous patterns of attentional modulations across eccentricities. Specifically, the attentional modulations of these signals peaked at the parafoveal locations and such modulations wore off as visual stimuli appeared closer to the fovea or further away towards the periphery. However, with a relatively higher level of eye movements, the heterogeneous patterns of attentional modulations of these neural signals were less robust. These data demonstrate that the top-down influence of covert visuospatial attention on early sensory processing in human cortex depends on eccentricity and the level of saccadic responses. Taken together, the results suggest that sustained visuospatial attention operates differently across different eccentric locations, providing new understanding of how attention augments sensory representations regardless of where the attended stimulus appears.

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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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            The Psychophysics Toolbox

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              The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

              During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.
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                Author and article information

                Contributors
                itthipuripat.sirawaj@gmail.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 May 2024
                16 May 2024
                2024
                : 14
                : 11188
                Affiliations
                [1 ]Neuroscience Center for Research and Innovation (NX), Learning Institute, King Mongkut’s University of Technology Thonburi (KMUTT), ( https://ror.org/0057ax056) Bangkok, 10140 Thailand
                [2 ]Big Data Experience Center (BX), King Mongkut’s University of Technology Thonburi (KMUTT), ( https://ror.org/0057ax056) Bangkok, 10600 Thailand
                [3 ]Department of Computer Engineering, King Mongkut’s University of Technology Thonburi (KMUTT), ( https://ror.org/0057ax056) Bangkok, 10140 Thailand
                [4 ]Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, ( https://ror.org/01znkr924) Nakhon Pathom, 73170 Thailand
                [5 ]GRID grid.425537.2, ISNI 0000 0001 2191 4408, National Nanotechnology Center, , National Science and Technology Development Agency, ; Pathum Thani, 12120 Thailand
                [6 ]Department of Experimental and Applied Psychology, Vrije Universiteit Amsterdam, ( https://ror.org/008xxew50) Amsterdam, The Netherlands
                [7 ]Department of Psychology, Vanderbilt University, ( https://ror.org/02vm5rt34) Nashville, TN 37235 USA
                [8 ]Cognitive Clinical and Computational Neuroscience Center of Excellence, Department of Internal Medicine, Faculty of Medicine, Chulalongkorn University, ( https://ror.org/028wp3y58) Bangkok, 10330 Thailand
                [9 ]Chula Neuroscience Center, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, 10330 Thailand
                [10 ]Department of Psychological and Brain Sciences, University of California Santa Barbara, ( https://ror.org/02t274463) Santa Barbara, CA 93106 USA
                Article
                61171
                10.1038/s41598-024-61171-7
                11099062
                38755251
                44cb4edc-0687-4242-a5b0-d762ff200b99
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 5 November 2023
                : 2 May 2024
                Funding
                Funded by: KMUTT Partnering initiative
                Award ID: fiscal year 2021
                Funded by: The National Research Council of Thailand
                Award ID: fiscal year 2021-2024
                Funded by: Program Management Unit for Human Resources and Institutional Development, Research and Innovation
                Award ID: fiscal year 2023, Projects 188576 and 188579
                Funded by: Research and Innovation for Sustainability Center, Magnolia Quality Development Corporation Limited
                Funded by: FundRef http://dx.doi.org/10.13039/100000053, National Eye Institute;
                Award ID: NEI R01-EY019882
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: NIMH R01-MH110378
                Award Recipient :
                Funded by: The Thailand Science Research and Innovation (TSRI) Basic Research Fund
                Award ID: fiscal year 2023 under project number FRB660073/0164, fiscal year 2022 under project number FRB650048/0164, fiscal year 2021 under project number FRB640008 and fiscal year 2020 under project number 62W1501
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100007684, Asahi Glass Foundation;
                Funded by: startup fund from King Mongkut’s University of Technology Thonburi (KMUTT)
                Funded by: National Science and Technology Development Agency (NSTDA)
                Funded by: KMUTT’s Frontier Research Unit Grant for Neuroscience Center for Research and Innovation
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                attention,eccentricity,foveal vision,peripheral vision,eeg,ssvep,visual cortex,frontoparietal cortex,neuroscience,psychology

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