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      Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability

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          Abstract

          Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy – a critical period of rapid brain development and plasticity – microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s10548-024-01043-5.

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          Most cited references65

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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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            • Record: found
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            AFNI: software for analysis and visualization of functional magnetic resonance neuroimages.

            C. R. Cox (1996)
            A package of computer programs for analysis and visualization of three-dimensional human brain functional magnetic resonance imaging (FMRI) results is described. The software can color overlay neural activation maps onto higher resolution anatomical scans. Slices in each cardinal plane can be viewed simultaneously. Manual placement of markers on anatomical landmarks allows transformation of anatomical and functional scans into stereotaxic (Talairach-Tournoux) coordinates. The techniques for automatically generating transformed functional data sets from manually labeled anatomical data sets are described. Facilities are provided for several types of statistical analyses of multiple 3D functional data sets. The programs are written in ANSI C and Motif 1.2 to run on Unix workstations.
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              • Record: found
              • Abstract: found
              • Article: not found

              Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain.

              This paper presents a new method for localizing the electric activity in the brain based on multichannel surface EEG recordings. In contrast to the models presented up to now the new method does not assume a limited number of dipolar point sources nor a distribution on a given known surface, but directly computes a current distribution throughout the full brain volume. In order to find a unique solution for the 3-dimensional distribution among the infinite set of different possible solutions, the method assumes that neighboring neurons are simultaneously and synchronously activated. The basic assumption rests on evidence from single cell recordings in the brain that demonstrates strong synchronization of adjacent neurons. In view of this physiological consideration the computational task is to select the smoothest of all possible 3-dimensional current distributions, a task that is a common procedure in generalized signal processing. The result is a true 3-dimensional tomography with the characteristic that localization is preserved with a certain amount of dispersion, i.e., it has a relatively low spatial resolution. The new method, which we call Low Resolution Electromagnetic Tomography (LORETA) is illustrated with two different sets of evoked potential data, the first showing the tomography of the P100 component to checkerboard stimulation of the left, right, upper and lower hemiretina, and the second showing the results for the auditory N100 component and the two cognitive components CNV and P300. A direct comparison of the tomography results with those obtained from fitting one and two dipoles illustrates that the new method provides physiologically meaningful results while dipolar solutions fail in many situations. In the case of the cognitive components, the method offers new hypotheses on the location of higher cognitive functions in the brain.
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                Author and article information

                Contributors
                armen.bagdasarov@duke.edu
                Journal
                Brain Topogr
                Brain Topogr
                Brain Topography
                Springer US (New York )
                0896-0267
                1573-6792
                2 March 2024
                2 March 2024
                2024
                : 37
                : 4
                : 496-513
                Affiliations
                [1 ]Department of Psychology & Neuroscience, Duke University, ( https://ror.org/00py81415) Reuben-Cooke Building, 417 Chapel Drive, Durham, NC 27708 USA
                [2 ]Department of Basic Neurosciences, University of Geneva, ( https://ror.org/01swzsf04) Campus Biotech, 9 Chemin des Mines, Geneva, 1202 Switzerland
                [3 ]GRID grid.5333.6, ISNI 0000000121839049, Center for Biomedical Imaging (CIBM) Lausanne, , EPFL AVP CP CIBM Station 6, ; Lausanne, 1015 Switzerland
                [4 ]Children’s Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI 53226 USA
                [5 ]Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, ( https://ror.org/00qqv6244) 8701 Watertown Plank Road, Milwaukee, WI 53226 USA
                Author notes

                Communicated by Thomas Koenig.

                Author information
                http://orcid.org/0000-0001-9775-7787
                http://orcid.org/0000-0003-3426-5739
                http://orcid.org/0000-0002-9334-1079
                Article
                1043
                10.1007/s10548-024-01043-5
                11199263
                38430283
                310b432e-2c67-423b-a907-1c7a9dd4bded
                © 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
                : 12 July 2023
                : 16 February 2024
                Funding
                Funded by: Charles Lafitte Foundation Program in Psychological and Neuroscience Research at Duke University
                Categories
                Original Paper
                Custom metadata
                © Springer Science+Business Media, LLC, part of Springer Nature 2024

                Neurology
                microstates,resting-state,source localization,infants,tutorial,reliability
                Neurology
                microstates, resting-state, source localization, infants, tutorial, reliability

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