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      Transcranial focused ultrasound to V5 enhances human visual motion brain-computer interface by modulating feature-based attention

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          Abstract

          A brain-computer interface (BCI) enables users to control devices with their minds. Despite advancements, non-invasive BCIs still exhibit high error rates, prompting investigation into the potential reduction through concurrent targeted neuromodulation. Transcranial focused ultrasound (tFUS) is an emerging non-invasive neuromodulation technology with high spatiotemporal precision. This study examines whether tFUS neuromodulation can improve BCI outcomes, and explores the underlying mechanism of action using high-density electroencephalography (EEG) source imaging (ESI). As a result, V5-targeted tFUS significantly reduced the error in a BCI speller task. Source analyses revealed a significantly increase in theta and alpha activities in the tFUS condition at both V5 and downstream in the dorsal visual processing pathway. Correlation analysis indicated that the connection within the dorsal processing pathway was preserved during tFUS stimulation, while the ventral connection was weakened. These findings suggest that V5-targeted tFUS enhances feature-based attention to visual motion.

          Abstract

          Transcranial focused ultrasound (tFUS) is a non-invasive brain modulation technology with high spatiotemporal specificity. Here, authors found tFUS can significantly enhance a brain-computer interface speller by delivering low-intensity tFUS to visual cortex during the task in naïve human subjects.

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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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            Simultaneous inference in general parametric models.

            Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level. Simultaneous inference procedures have to be used which adjust for multiplicity and thus control the overall type I error rate. In this paper we describe simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters. The framework described here is quite general and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalized linear models, linear mixed effects models, the Cox model, robust linear models, etc. Several examples using a variety of different statistical models illustrate the breadth of the results. For the analyses we use the R add-on package multcomp, which provides a convenient interface to the general approach adopted here. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              Cortical surface-based analysis. I. Segmentation and surface reconstruction.

              Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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                Author and article information

                Contributors
                bhe1@andrew.cmu.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 June 2024
                11 June 2024
                2024
                : 15
                : 4382
                Affiliations
                [1 ]Department of Biomedical Engineering, Carnegie Mellon University, ( https://ror.org/05x2bcf33) Pittsburgh, PA 15237 USA
                [2 ]Department of Biomedical Engineering, Boston University, ( https://ror.org/05qwgg493) Boston, MA 02215 USA
                [3 ]GRID grid.147455.6, ISNI 0000 0001 2097 0344, Neuroscience Institute, , Carnegie Mellon University, ; Pittsburgh, PA 15237 USA
                Author information
                http://orcid.org/0000-0003-3799-4216
                http://orcid.org/0000-0002-4778-4389
                http://orcid.org/0000-0001-5560-7203
                http://orcid.org/0000-0003-2944-8602
                Article
                48576
                10.1038/s41467-024-48576-8
                11167030
                38862476
                d672ecc0-d4af-4cb3-9712-a4e249b9e8ab
                © 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
                : 20 October 2023
                : 2 May 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: R01NS124564, RF1NS131069, R01NS096761, and NS127849
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000070, U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB);
                Award ID: U18EB029354, T32EB029365
                Award ID: T32EB029365
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100008460, U.S. Department of Health & Human Services | NIH | National Center for Complementary and Integrative Health (NCCIH);
                Award ID: R01AT009263
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: DGE2140739
                Award Recipient :
                Funded by: U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (NIBIB)
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                extrastriate cortex,biomedical engineering,electroencephalography - eeg
                Uncategorized
                extrastriate cortex, biomedical engineering, electroencephalography - eeg

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