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A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality
A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate t...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249601/ https://www.ncbi.nlm.nih.gov/pubmed/35789905 http://dx.doi.org/10.1016/j.dib.2022.108380 |
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author | Zhang, Bo Naya, Yuji |
author_facet | Zhang, Bo Naya, Yuji |
author_sort | Zhang, Bo |
collection | PubMed |
description | A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate the neural representation of the cognitive map defined by unique spatial relationship of three objects, as well as the neural dynamics of the cognitive map following the task demand from localizing self-location to remembering the target location relative to the self-body. The dataset, including both fMRI and MEG, was also used to investigate the neural networks involved in representing a target within and outside the visual field. The dataset included 19 and 12 university students at Peking University for fMRI and MEG experiments, respectively (fMRI: 12 women, 7 men; MEG: 4 women, 8 men). The average ages of those participants were 24.9 years (MRI: 18–30 years) and 22.5 years (MEG: 19–25 years), respectively. fMRI BOLD and T1-weighted images were acquired using a 3T Siemens Prisma scanner (Siemens, Erlangen, Germany) equipped with a 20-channel receiver head coil. MEG neuromagnetic data were acquired using a 275-channel MEG system (CTF MEG, Canada). The dataset could be further used to investigate a range of neural mechanisms involved in human spatial cognition or to develop a bioinspired deep neural network to enhance machines' abilities in spatial processing. |
format | Online Article Text |
id | pubmed-9249601 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-92496012022-07-03 A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality Zhang, Bo Naya, Yuji Data Brief Data Article A dataset consisting of whole-brain fMRI (functional magnetic resonance imaging)/MEG (magnetoencephalography) images, eye tracking files, and behavioral records from healthy adult human participants when they performed a spatial-memory paradigm in a virtual environment was collected to investigate the neural representation of the cognitive map defined by unique spatial relationship of three objects, as well as the neural dynamics of the cognitive map following the task demand from localizing self-location to remembering the target location relative to the self-body. The dataset, including both fMRI and MEG, was also used to investigate the neural networks involved in representing a target within and outside the visual field. The dataset included 19 and 12 university students at Peking University for fMRI and MEG experiments, respectively (fMRI: 12 women, 7 men; MEG: 4 women, 8 men). The average ages of those participants were 24.9 years (MRI: 18–30 years) and 22.5 years (MEG: 19–25 years), respectively. fMRI BOLD and T1-weighted images were acquired using a 3T Siemens Prisma scanner (Siemens, Erlangen, Germany) equipped with a 20-channel receiver head coil. MEG neuromagnetic data were acquired using a 275-channel MEG system (CTF MEG, Canada). The dataset could be further used to investigate a range of neural mechanisms involved in human spatial cognition or to develop a bioinspired deep neural network to enhance machines' abilities in spatial processing. Elsevier 2022-06-14 /pmc/articles/PMC9249601/ /pubmed/35789905 http://dx.doi.org/10.1016/j.dib.2022.108380 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Zhang, Bo Naya, Yuji A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title | A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title_full | A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title_fullStr | A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title_full_unstemmed | A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title_short | A dataset of human fMRI/MEG experiments with eye tracking for spatial memory research using virtual reality |
title_sort | dataset of human fmri/meg experiments with eye tracking for spatial memory research using virtual reality |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9249601/ https://www.ncbi.nlm.nih.gov/pubmed/35789905 http://dx.doi.org/10.1016/j.dib.2022.108380 |
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