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Generate the scale-free brain music from BOLD signals
Many methods have been developed to translate a human electroencephalogram (EEG) into music. In addition to EEG, functional magnetic resonance imaging (fMRI) is another method used to study the brain and can reflect physiological processes. In 2012, we established a method to use simultaneously reco...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943892/ https://www.ncbi.nlm.nih.gov/pubmed/29480872 http://dx.doi.org/10.1097/MD.0000000000009628 |
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author | Lu, Jing Guo, Sijia Chen, Mingming Wang, Weixia Yang, Hua Guo, Daqing Yao, Dezhong |
author_facet | Lu, Jing Guo, Sijia Chen, Mingming Wang, Weixia Yang, Hua Guo, Daqing Yao, Dezhong |
author_sort | Lu, Jing |
collection | PubMed |
description | Many methods have been developed to translate a human electroencephalogram (EEG) into music. In addition to EEG, functional magnetic resonance imaging (fMRI) is another method used to study the brain and can reflect physiological processes. In 2012, we established a method to use simultaneously recorded fMRI and EEG signals to produce EEG-fMRI music, which represents a step toward scale-free brain music. In this study, we used a neural mass model, the Jansen–Rit model, to simulate activity in several cortical brain regions. The interactions between different brain regions were represented by the average normalized diffusion tensor imaging (DTI) structural connectivity with a coupling coefficient that modulated the coupling strength. Seventy-eight brain regions were adopted from the Automated Anatomical Labeling (AAL) template. Furthermore, we used the Balloon–Windkessel hemodynamic model to transform neural activity into a blood-oxygen-level dependent (BOLD) signal. Because the fMRI BOLD signal changes slowly, we used a sampling rate of 250 Hz to produce the temporal series for music generation. Then, the BOLD music was generated for each region using these simulated BOLD signals. Because the BOLD signal is scale free, these music pieces were also scale free, which is similar to classic music. Here, to simulate the case of an epileptic patient, we changed the parameter that determined the amplitude of the excitatory postsynaptic potential (EPSP) in the neural mass model. Finally, we obtained BOLD music for healthy and epileptic patients. The differences in levels of arousal between the 2 pieces of music may provide a potential tool for discriminating the different populations if the differences can be confirmed by more real data. |
format | Online Article Text |
id | pubmed-5943892 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-59438922018-05-15 Generate the scale-free brain music from BOLD signals Lu, Jing Guo, Sijia Chen, Mingming Wang, Weixia Yang, Hua Guo, Daqing Yao, Dezhong Medicine (Baltimore) Research Article Many methods have been developed to translate a human electroencephalogram (EEG) into music. In addition to EEG, functional magnetic resonance imaging (fMRI) is another method used to study the brain and can reflect physiological processes. In 2012, we established a method to use simultaneously recorded fMRI and EEG signals to produce EEG-fMRI music, which represents a step toward scale-free brain music. In this study, we used a neural mass model, the Jansen–Rit model, to simulate activity in several cortical brain regions. The interactions between different brain regions were represented by the average normalized diffusion tensor imaging (DTI) structural connectivity with a coupling coefficient that modulated the coupling strength. Seventy-eight brain regions were adopted from the Automated Anatomical Labeling (AAL) template. Furthermore, we used the Balloon–Windkessel hemodynamic model to transform neural activity into a blood-oxygen-level dependent (BOLD) signal. Because the fMRI BOLD signal changes slowly, we used a sampling rate of 250 Hz to produce the temporal series for music generation. Then, the BOLD music was generated for each region using these simulated BOLD signals. Because the BOLD signal is scale free, these music pieces were also scale free, which is similar to classic music. Here, to simulate the case of an epileptic patient, we changed the parameter that determined the amplitude of the excitatory postsynaptic potential (EPSP) in the neural mass model. Finally, we obtained BOLD music for healthy and epileptic patients. The differences in levels of arousal between the 2 pieces of music may provide a potential tool for discriminating the different populations if the differences can be confirmed by more real data. Wolters Kluwer Health 2018-01-12 /pmc/articles/PMC5943892/ /pubmed/29480872 http://dx.doi.org/10.1097/MD.0000000000009628 Text en Copyright © 2018 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0 |
spellingShingle | Research Article Lu, Jing Guo, Sijia Chen, Mingming Wang, Weixia Yang, Hua Guo, Daqing Yao, Dezhong Generate the scale-free brain music from BOLD signals |
title | Generate the scale-free brain music from BOLD signals |
title_full | Generate the scale-free brain music from BOLD signals |
title_fullStr | Generate the scale-free brain music from BOLD signals |
title_full_unstemmed | Generate the scale-free brain music from BOLD signals |
title_short | Generate the scale-free brain music from BOLD signals |
title_sort | generate the scale-free brain music from bold signals |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5943892/ https://www.ncbi.nlm.nih.gov/pubmed/29480872 http://dx.doi.org/10.1097/MD.0000000000009628 |
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