Cross-cultural music processing in human brains revealed using magnetoencephalography
Poster No:
783
Submission Type:
Abstract Submission
Authors:
Qingyang Liu1,2, Zhao Xu1,2, Yangjiayi Mu1,2, Qinhui Yao1,2, Yanlin Fu1,2,3, Ziyou Wang1,2, Xinwei Liu1,2, Yuwei Jiang1,2
Institutions:
1Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, 2Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China, 3Department of Nuclear Science and Technology, Fudan University, Shanghai, China
First Author:
Qingyang Liu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Co-Author(s):
Zhao Xu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Yangjiayi Mu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Qinhui Yao
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Yanlin Fu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education|Department of Nuclear Science and Technology, Fudan University
Shanghai, China|Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education|Department of Nuclear Science and Technology, Fudan University
Shanghai, China|Shanghai, China|Shanghai, China
Ziyou Wang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Xinwei Liu
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Yuwei Jiang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University|Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education
Shanghai, China|Shanghai, China
Introduction:
Investigating the neural processing mechanisms underlying the human brain's engagement with music is a fascinating field that unravels cognitive processes and neural pathways activated when individuals listen to music. Despite a wealth of studies have investigated the neural responses to different elements of music by Western scholars, there is still a lack of research looking into the musical cultures themselves. Our research aims to bridge this gap by focusing on the neural mechanisms that distinguish the perceptual and cognitive processing of Western and Chinese music within their cultural contexts.
Methods:
We collected magnetoencephalogram (MEG) data from 20 participants in community nearby Fudan University while they listened to 4s-long clips of different Western and Chinese music. Participants had to distinguish whether each pair of music clips belonged to the same genre, and the behavioral results were also recorded. MEG data was processed following the FLUX pipeline (Ferrante, 2022) with MNE-Python 1.6.1(Gramfort, 2013; Larson, 2024). Structural T1 MRI images were used to obtain individual cortical surfaces and to compute BEM head models. The estimation of source power were obtained by applying the inverse operator to a covariance matrix (Sabbagh, 2020) with dSPM method. A normalized phase transfer entropy (dPTE) analysis (Lobier, 2014) with cross-temporal and frequency windows was employed to quantify the information flow between ROIs.
Results:
Our findings in the domain of behavioral results (Fig. 1) demonstrated that participants exhibited a clear ability to distinguish between Western and Chinese music, yet displayed some confusion when it came to differentiating between various subcategories, especially with Chinese music.
As for the neural activation of source estimation, we observed that music stimulation elicited widespread activation across various brain regions including the temporal, parietal, frontal lobes, and the medial cortices of the brain (Fig. 2a). There was no statistically significant difference in neural response when participants were passively listening to Western and Chinese music. However, when it came to actively identifying different genres of music, our findings revealed differences in neural activity (Fig. 2b), indicating that the brain engages distinct cognitive processes when categorizing music with different culture compared to passive listening. The dPTE analysis showed significant interactions between different brain regions, which illustrated neural networks involved in listening to music, and also exhibited differences within these circuits when contrasting two cultural conditions (Fig. 2c).
As for the neural activation of source estimation, we observed that music stimulation elicited widespread activation across various brain regions including the temporal, parietal, frontal lobes, and the medial cortices of the brain (Fig. 2a). There was no statistically significant difference in neural response when participants were passively listening to Western and Chinese music. However, when it came to actively identifying different genres of music, our findings revealed differences in neural activity (Fig. 2b), indicating that the brain engages distinct cognitive processes when categorizing music with different culture compared to passive listening. The dPTE analysis showed significant interactions between different brain regions, which illustrated neural networks involved in listening to music, and also exhibited differences within these circuits when contrasting two cultural conditions (Fig. 2c).
Conclusions:
Our research uncovers the neural activation patterns and circuits engaged when individuals listen to music from different cultures, highlighting both the commonalities and distinctions in brain responses to Western and Chinese music. These results provide insights into the neural mechanisms underlying the processing of music with diverse cultural origins.
Higher Cognitive Functions:
Music 1
Perception, Attention and Motor Behavior:
Perception: Auditory/ Vestibular 2
Keywords:
MEG
Perception
Other - Music
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Gramfort, A. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in neuroscience, 7, 267.
Larson, E. (2024). MNE-Python (v1.6.1). Zenodo.
Sabbagh, D. (2020). Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states. NeuroImage, 222, 116893.
Lobier, M. (2014). Phase transfer entropy: a novel phase-based measure for directed connectivity in networks coupled by oscillatory interactions. NeuroImage, 85 Pt 2, 853–872.