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      Neuroplastic Reorganization Induced by Sensory Augmentation for Self-Localization During Locomotion

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          Abstract

          Sensory skills can be augmented through training and technological support. This process is underpinned by neural plasticity in the brain. We previously demonstrated that auditory-based sensory augmentation can be used to assist self-localization during locomotion. However, the neural mechanisms underlying this phenomenon remain unclear. Here, by using functional magnetic resonance imaging, we aimed to identify the neuroplastic reorganization induced by sensory augmentation training for self-localization during locomotion. We compared activation in response to auditory cues for self-localization before, the day after, and 1 month after 8 days of sensory augmentation training in a simulated driving environment. Self-localization accuracy improved after sensory augmentation training, compared with the control (normal driving) condition; importantly, sensory augmentation training resulted in auditory responses not only in temporal auditory areas but also in higher-order somatosensory areas extending to the supramarginal gyrus and the parietal operculum. This sensory reorganization had disappeared by 1 month after the end of the training. These results suggest that the use of auditory cues for self-localization during locomotion relies on multimodality in higher-order somatosensory areas, despite substantial evidence that information for self-localization during driving is estimated from visual cues on the proximal part of the road. Our findings imply that the involvement of higher-order somatosensory, rather than visual, areas is crucial for acquiring augmented sensory skills for self-localization during locomotion.

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          Most cited references38

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            Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions.

            Classic cognitive theory conceptualizes executive functions as involving multiple specific domains, including initiation, inhibition, working memory, flexibility, planning, and vigilance. Lesion and neuroimaging experiments over the past two decades have suggested that both common and unique processes contribute to executive functions during higher cognition. It has been suggested that a superordinate fronto-cingulo-parietal network supporting cognitive control may also underlie a range of distinct executive functions. To test this hypothesis in the largest sample to date, we used quantitative meta-analytic methods to analyze 193 functional neuroimaging studies of 2,832 healthy individuals, ages 18-60, in which performance on executive function measures was contrasted with an active control condition. A common pattern of activation was observed in the prefrontal, dorsal anterior cingulate, and parietal cortices across executive function domains, supporting the idea that executive functions are supported by a superordinate cognitive control network. However, domain-specific analyses showed some variation in the recruitment of anterior prefrontal cortex, anterior and midcingulate regions, and unique subcortical regions such as the basal ganglia and cerebellum. These results are consistent with the existence of a superordinate cognitive control network in the brain, involving dorsolateral prefrontal, anterior cingulate, and parietal cortices, that supports a broad range of executive functions.
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              Generalized autocalibrating partially parallel acquisitions (GRAPPA).

              In this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD-AUTO-SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k-space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique. Copyright 2002 Wiley-Liss, Inc.
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                Author and article information

                Contributors
                Journal
                Front Neurogenom
                Front Neurogenom
                Front. Neuroergon.
                Frontiers in Neuroergonomics
                Frontiers Media S.A.
                2673-6195
                13 August 2021
                2021
                : 2
                : 691993
                Affiliations
                [1] 1Human Science Laboratory, Toyota Central R&D Laboratories, Inc. , Tokyo, Japan
                [2] 2TOYOTA Collaboration Center, RIKEN Center for Brain Science , Wako, Japan
                [3] 3Support Unit for Functional Magnetic Resonance Imaging, RIKEN Center for Brain Science , Wako, Japan
                [4] 4Graduate School of Informatics, Kyoto University , Kyoto, Japan
                Author notes

                Edited by: Lewis L. Chuang, Ludwig Maximilian University of Munich, Germany

                Reviewed by: Emanuelle Reynaud, Université de Lyon, France; Giacinto Barresi, Italian Institute of Technology (IIT), Italy

                *Correspondence: Hiroyuki Sakai sakai@ 123456mosk.tytlabs.co.jp

                This article was submitted to Cognitive Neuroergonomics, a section of the journal Frontiers in Neuroergonomics

                Article
                10.3389/fnrgo.2021.691993
                10790880
                38235242
                b3198e90-4501-4110-bdc2-472575d79566
                Copyright © 2021 Sakai, Ueda, Ueno and Kumada.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 07 April 2021
                : 21 July 2021
                Page count
                Figures: 4, Tables: 1, Equations: 0, References: 38, Pages: 10, Words: 6991
                Categories
                Neuroergonomics
                Original Research

                augmentation,plasticity,driving,fmri,locomotion
                augmentation, plasticity, driving, fmri, locomotion

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