The Role of Neural Plasticity in Vocal Skill Acquisition and Learning Among Novice Singers

Abstract

This study presents a comprehensive framework for the analysis of neuroimaging data to uncover patterns of neural activation and connectivity changes before and after training among novice singers. The proposed framework encompasses various stages, including Data Preparation, Descriptive Analysis, Skill Improvement Analysis, Neural Changes Analysis, Correlation Analysis, Group Comparison, and Multipolynomial Lasso Regression Analysis. Four fundamental metrics, namely Percent Signal Change (PSC), Functional Connectivity (FC), Amplitude of Low-Frequency Fluctuations (ALFF), and Graph Theory Metrics, are employed within this framework to elucidate neuroplasticity alterations. PSC quantifies relative shifts in neural activation, FC assesses synchronized activity between brain regions, ALFF gauges regional spontaneous neural activity, and Graph Theory Metrics, including Degree Centrality, unveil the centrality and connectivity of specific brain regions within networks. By applying this comprehensive framework and the specified metrics and equations, this research endeavors to provide a robust understanding of the neural mechanisms underpinning vocal skill acquisition and their correlation with subjective skill improvement. This study offers valuable insights into the plasticity of the human brain in response to vocal training among novice singers.