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An fMRI dataset for whole-body somatotopic mapping in humans

The somatotopic representation of the body is a well-established organizational principle in the human brain. Classic invasive direct electrical stimulation for somatotopic mapping cannot be used to map the whole-body topographical representation of healthy individuals. Functional magnetic resonance...

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Autores principales: Ma, Sai, Huang, Taicheng, Qu, Yukun, Chen, Xiayu, Zhang, Yajie, Zhen, Zonglei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399117/
https://www.ncbi.nlm.nih.gov/pubmed/35999222
http://dx.doi.org/10.1038/s41597-022-01644-4
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author Ma, Sai
Huang, Taicheng
Qu, Yukun
Chen, Xiayu
Zhang, Yajie
Zhen, Zonglei
author_facet Ma, Sai
Huang, Taicheng
Qu, Yukun
Chen, Xiayu
Zhang, Yajie
Zhen, Zonglei
author_sort Ma, Sai
collection PubMed
description The somatotopic representation of the body is a well-established organizational principle in the human brain. Classic invasive direct electrical stimulation for somatotopic mapping cannot be used to map the whole-body topographical representation of healthy individuals. Functional magnetic resonance imaging (fMRI) has become an indispensable tool for the noninvasive investigation of somatotopic organization of the human brain using voluntary movement tasks. Unfortunately, body movements during fMRI scanning often cause large head motion artifacts. Consequently, there remains a lack of publicly accessible fMRI datasets for whole-body somatotopic mapping. Here, we present public high-resolution fMRI data to map the somatotopic organization based on motor movements in a large cohort of healthy adults (N = 62). In contrast to previous studies that were mostly designed to distinguish few body representations, most body parts are considered, including toe, ankle, leg, finger, wrist, forearm, upper arm, jaw, lip, tongue, and eyes. Moreover, the fMRI data are denoised by combining spatial independent component analysis with manual identification to clean artifacts from head motion associated with body movements.
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spelling pubmed-93991172022-08-25 An fMRI dataset for whole-body somatotopic mapping in humans Ma, Sai Huang, Taicheng Qu, Yukun Chen, Xiayu Zhang, Yajie Zhen, Zonglei Sci Data Data Descriptor The somatotopic representation of the body is a well-established organizational principle in the human brain. Classic invasive direct electrical stimulation for somatotopic mapping cannot be used to map the whole-body topographical representation of healthy individuals. Functional magnetic resonance imaging (fMRI) has become an indispensable tool for the noninvasive investigation of somatotopic organization of the human brain using voluntary movement tasks. Unfortunately, body movements during fMRI scanning often cause large head motion artifacts. Consequently, there remains a lack of publicly accessible fMRI datasets for whole-body somatotopic mapping. Here, we present public high-resolution fMRI data to map the somatotopic organization based on motor movements in a large cohort of healthy adults (N = 62). In contrast to previous studies that were mostly designed to distinguish few body representations, most body parts are considered, including toe, ankle, leg, finger, wrist, forearm, upper arm, jaw, lip, tongue, and eyes. Moreover, the fMRI data are denoised by combining spatial independent component analysis with manual identification to clean artifacts from head motion associated with body movements. Nature Publishing Group UK 2022-08-23 /pmc/articles/PMC9399117/ /pubmed/35999222 http://dx.doi.org/10.1038/s41597-022-01644-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Data Descriptor
Ma, Sai
Huang, Taicheng
Qu, Yukun
Chen, Xiayu
Zhang, Yajie
Zhen, Zonglei
An fMRI dataset for whole-body somatotopic mapping in humans
title An fMRI dataset for whole-body somatotopic mapping in humans
title_full An fMRI dataset for whole-body somatotopic mapping in humans
title_fullStr An fMRI dataset for whole-body somatotopic mapping in humans
title_full_unstemmed An fMRI dataset for whole-body somatotopic mapping in humans
title_short An fMRI dataset for whole-body somatotopic mapping in humans
title_sort fmri dataset for whole-body somatotopic mapping in humans
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399117/
https://www.ncbi.nlm.nih.gov/pubmed/35999222
http://dx.doi.org/10.1038/s41597-022-01644-4
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