The Dance and Music Training Enhanced the structure-function Coupling in the Attention Networks
Poster No:
2090
Submission Type:
Abstract Submission
Authors:
Gujing Li1,2,3, Hui He1,2,3, Kexin Gao1,2,3, Bao Lu1,2,3, Lupeng Yue4, Cheng Luo1,2,3, Dezhong Yao1,2,3
Institutions:
1School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China, 2MOE Key Lab for Neuroinformation, Center for Information in Medicine, Chengdu, Sichuan, China, 3The Clinical Hospital of Chengdu Brain Science Institute, Chengdu, Sichuan, China, 4Education Center for Students Cultural Qualities, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
First Author:
Gujing Li
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Co-Author(s):
Hui He
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Kexin Gao
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Bao Lu
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Lupeng Yue
Education Center for Students Cultural Qualities
University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Education Center for Students Cultural Qualities
University of Electronic Science and Technology of China, Chengdu, Sichuan, China
Cheng Luo
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Dezhong Yao
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
School of life Science and technology, University of Electronic Science and Technology of China|MOE Key Lab for Neuroinformation, Center for Information in Medicine|The Clinical Hospital of Chengdu Brain Science Institute
Chengdu, Sichuan, China|Chengdu, Sichuan, China|Chengdu, Sichuan, China
Introduction:
Dance and music trainings are well known for their sensorimotor skills which recruit massive attention processes. Numerous neuroimaging studies have proven that dance and music training results in structural and functional adaptations within the attention networks (Schlaug 2015; Elst et al. 2023; Li et al. 2015; Li et al. 2021; Li et al. 2019). However, it is still blurred about the influence of these trainings on the relationship between brain structure and function within this network. Thus, we utilized graph signal processing (Preti and Van De Ville 2019) to measure the regional structure-function coupling induced by prolonged dance and music training.
Methods:
Proficient dancers, musicians, and matched controls were recruited in this study. Then, 510s resting-state functional data (TR=2s, TE=30ms), T1-weighted anatomical images, and diffusion tensor images (DTI, diffusion direction=30, b=1000s/mm2) were collected on a 3T MRI scanner.
Firstly, the number of streamlines connecting two regions (estimated by probabilistic streamline tractography on DTI data) divided by the region volumes (estimated on T1 data) was measured as the structure connectome. It was then used to construct graph Laplacian operator to characterize the brain as a graph. Secondly, regionally averaged BOLD signals were extracted. Thirdly, the eigendecomposition of Laplacian operator provided the harmonic components to build graph Fourier transform of functional signal. Low-frequency components represent signals that vary smoothly across the graph, whereas high-frequency components denote signals that vary highly across the graph. It means that when the frequency is higher, the functional signal is less coupled with structure. Finally, the cut-off frequency was defined as the frequency that split average energy spectral density (across time) into two parts with equal energy. Coupled and decoupled components of the functional signal were distracted by graph signal filtering. The logarithm of the ratio between the L2 norms of the two components across time was determined as structural-decoupling index to quantify the structure-function coupling.
Firstly, the number of streamlines connecting two regions (estimated by probabilistic streamline tractography on DTI data) divided by the region volumes (estimated on T1 data) was measured as the structure connectome. It was then used to construct graph Laplacian operator to characterize the brain as a graph. Secondly, regionally averaged BOLD signals were extracted. Thirdly, the eigendecomposition of Laplacian operator provided the harmonic components to build graph Fourier transform of functional signal. Low-frequency components represent signals that vary smoothly across the graph, whereas high-frequency components denote signals that vary highly across the graph. It means that when the frequency is higher, the functional signal is less coupled with structure. Finally, the cut-off frequency was defined as the frequency that split average energy spectral density (across time) into two parts with equal energy. Coupled and decoupled components of the functional signal were distracted by graph signal filtering. The logarithm of the ratio between the L2 norms of the two components across time was determined as structural-decoupling index to quantify the structure-function coupling.
Results:
The statistical results indicated both dance and music groups significantly decreased the decoupling strength of the subcortical attention network, such as the right ventromedial putamen. Distinctly, only the dance group showed the increased coupling strength of the right inferior frontal gyrus opercular area in cortical attention network, which is associated with training intensity. Besides, the coupling FC between the right middle frontal gyrus ventral area and the left middle frontal gyrus area 46 increased only in the dance group. Furthermore, the dance group also indicated increased coupling FC between the left inferior parietal lobule caudal area and the left superior parietal lobule intraparietal area.
Conclusions:
This study deepened the understanding of the regional plasticity effect of dance and music training. The enhanced structure-function coupling degree in cortical and subcortical attention network might be the neuron correlates of the skilled whole-body movement of dancer and the delicate movement of musicians. Prolonged dance training might have more positive impacts on the structure-function coupling in the attention networks.
Higher Cognitive Functions:
Higher Cognitive Functions Other 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 1
Keywords:
MRI
Other - Structure-function coupling; Dance training; Music training; Attention network
1|2Indicates the priority used for review
Provide references using author date format
Li, G. J. (2021), 'Dance Training Affects the Neural Mechanism of Positive Empathy: A fMRI Study', International Journal of Psychophysiology, 168: S173-S74.
Li, G. J. (2015), 'Identifying enhanced cortico-basal ganglia loops associated with prolonged dance training', Scientific Reports, 5.
Li, G. J. (2019), 'Increased Insular Connectivity and Enhanced Empathic Ability Associated with Dance/Music Training', Neural Plasticity, 2019.
Preti, M. G. (2019), 'Decoupling of brain function from structure reveals regional behavioral specialization in humans', Nature Communications, 10.
Schlaug, G. (2015), 'Musicians and music making as a model for the study of brain plasticity', Music, Neurology, and Neuroscience: Evolution, the Musical Brain, Medical Conditions, and Therapies, 217: 37-55.