Evidence map›Paper›PMID 29480872›Full record

ArticleMedicine2018

Generate the scale-free brain music from BOLD signals.

Jing Lu, Sijia Guo, Mingming Chen, Weixia Wang, Hua Yang, Daqing Guo, Dezhong Yao

Abstract read
In one paragraph

Article in Medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jing LuThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China Department of Composition, Sichuan Conservatory of Music, Chengdu, Sichuan, China.
Sijia Guo
Mingming Chen
Weixia Wang
Hua Yang
Daqing Guo
Dezhong Yao

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Magnetic Resonance ImagingModels, NeurologicalMusicArousalAuditory PerceptionBrainBrain MappingCerebrovascular CirculationDiffusion Tensor ImagingEpilepsyHumansJudgmentOxygenOxygen

Identifiers

PMID29480872
PMCPMC5943892

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.