Lifespan music experience changes transition rates of microstate D related to executive function

1146 

Abstract Submission 

Yan Li¹, Pengyu Zou¹, Liangfeng Feng¹, Jing Lu¹, Sijia Guo¹

¹School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China

Yan Li  
School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China

Pengyu Zou  
School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China

Liangfeng Feng  
School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China

Jing Lu  
School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China

Sijia Guo  
School of Life Science and Technology, University of Electronic Science and Technology of China
Chengdu, China

Lifespan music experience benefits older adults, which was proved by many behavioral studies, such as old musicians having higher scores on a test of executive function¹ and enhanced auditory attention in musicians². The potential mechanism is still unclear but is suggested to be related to cognitive network changes, such as frontal-parietal areas³. The electroencephalogram (EEG) microstates can reveal the changes in the cognitive function network, and microstate D is related to attention and executive function^{4, 5}, which have been shown to decline during aging^{6, 7}. Microstate analysis is, therefore, helpful in understanding how music intervention can alleviate cognitive aging. This work recruited old musicians, old non-musicians, and young non-musicians to explore the relationship between microstate D and other microstates in music intervention in cognitive aging.

Sixteen old musicians (OM) were recruited from Sichuan Conservatory of Music and nineteen age-matched old non-musicians (ONM) were recruited from communities. Twenty-five young non-musicians (YNM) were recruited from universities. Five minutes of resting EEG recordings were collected using a 64-electrode channel EEG acquisition device. These electrodes were positioned to the extended 10-20 system, and the data were recorded at a sampling rate of 1000Hz. EEG recordings were preprocessed by the EEGLAB toolbox in MATLAB and then analyzed by the EEGLAB plugin for microstates. The original instantaneous maps were clustered into four microstate classes (same as previous studies, namely, microstate A, B, C and D). Finally, the transition rates between microstates were calculated and statistically analyzed. In addition, we also collected the behavioral data of the N-back task (the accuracy and reaction time of 0, 1, 2-back).

For the transition rates between microstates D and A, B, and C, all the interactions of age × music training trended significantly. For the transition rate from microstate D to A, ONM was lower than OM (p<0.01, t=-4.45) and YNM (p<0.01, t=-3.51) (Figure. 1a). For the transition rate from microstate D to B, ONM was higher than OM (p<0.05, t=3.16) and YNM (p<0.01, t=9.23), and OM was higher than YNM (p<0.01, t=5.14) (Figure. 1b). For the transition rate from microstate D to C, ONM was lower than OM (p<0.01, t=-4.76) and YNM (p<0.01, t=-4.17), and YNM was lower than OM (p<0.05, t=-2.56) (Figure.1c). P-values in the results were given after False Discovery Rate (FDR) correction. The differences of transition rates from microstates A, B, and C to microstate D between groups are similarly above results. Future more, we found that transition rate of microstate D to A in the elderly was positively correlated with the accuracy of 2-back (p=0.024, r=0.3863) (Figure.2a) and negatively correlated with the reaction time of 2-back (p=0.0268, r=-0.3796) (Figure.2b).

As reported by existing studies, the transitions between microstates are not random⁸. Some studies have found that the possibility of such transitions between microstates is related to functional networks and exhibits a tendency in patients⁹. Similar to the above studies, we found that the changes of transition rates between microstate D and other microstates reflect cognitive aging of the elderly, specifically manifested as an increase between microstate D and C as well as a decrease between microstate D and A, B. Lifespan music experience has slowed down these changing trends and contributes to the better working memory performance. Moreover, music experience makes the auditory network more robust and enables the functional separation of auditory and frontal network to be maintained during aging, which is reflected by the correlation between transition rate and accuracy, reaction time of 2-back task. In a nutshell, these results provide more information for understanding the mechanism of music intervention in cognitive aging.

Executive Function, Cognitive Control and Decision Making

Music ²

Aging ¹

Aging

Electroencephaolography (EEG)

Other - Music experience