Predisposition and Neural Modifications Following Musical Skill Acquisition
Poster No:
781
Submission Type:
Abstract Submission
Authors:
Tamar Ben David1, Shachar Gal2, Romi Kaplan1, Asaf Madar1, Niv Tik1, Ido Tavor1
Institutions:
1Tel Aviv University, Tel Aviv, Israel, 2Bar Ilan University, Ramat Gan, Israel
First Author:
Co-Author(s):
Introduction:
Our ability to acquire a new skill depends on the brain's pre-learning state and its capacity to modify throughout the learning process. Predisposition refers to the innate characteristics or pre-existing neural states that set the stage for learning, while neuroplasticity reflects the brain's ability to adapt and reorganize in response to training or experience. These factors determine how effectively a new skill is learned and the underlying neural processes that enable this transformation (Herholz et al., 2016; Olszewska et al., 2021; Zatorre, 2013). The interplay between these factors may explain individual variability in learning outcomes within the same protocol as well as the ability to generalize learning to related domains. In this study, we aim to identify the neural patterns associated with these two factors, investigating both shared and distinct contributions to the state of predisposition and the skill-learning process.
Methods:
Eighty musically naïve young adults participated in a 4-lesson piano training, learning to play Beethoven's Für Elise using in-house software in an individualized yet standardized approach. The learning process was recorded, generating accuracy, rhythm, and tempo matrices to assess performance. Using functional magnetic resonance imaging (fMRI), we examined neural responses to learned and unlearned melodies and instruments. Brain-based set models (Sripada et al., 2020) were employed for classification and regression, with 10-fold cross-validation and permutation testing for significance. The regression model predicted behavioral performance based on pre-training neural activity during passive listening to various auditory stimuli. To capture neuroplasticity, we created classification models to distinguish between neural responses pre- and post-learning across various auditory conditions. Finally, contribution maps for both models were generated to find the most influential brain regions, and the top quartile of the two maps were overlapped to identify regions of convergence and divergence. A voxel-wise GLM was conducted to identify changes following training in learned and unlearned contexts for reproducibility.
Results:
A significant increase in activity was found in skill-related areas, including the bilateral premotor cortex, supplementary motor areas, middle temporal gyrus, and intraparietal sulcus (P<0.05 FDR corrected; Figure 1). The biggest changes were detected in response to Für Elise playing the piano. Moreover, the changes following learning were greater in the melody than in the instrument context. We could significantly classify activation maps into pre- or post-learning in the learned context (area under the curve AUC=0.82 P<0.05). Furthermore, pre-learning scans were predictive of the accuracy scores throughout the learning process (correlation between prediction and actual score: r=0.22, P<0.01). Importantly, we identified both unique and overlapping regions that contributed to successful predictions and classifications, highlighting their distinct and shared roles in the skill acquisition process (figure 2).
Conclusions:
We demonstrated neuroplasticity following musical training, reflected in increased activity in skill- related areas during passive listening. Interestingly, similar changes were observed when the melody was played on a different instrument but not when another melody was played on the learned instrument. This suggests that the acquired skill generalizes to a new context while also emphasizing the greater impact of specific auditory features on plasticity. Additionally, we demonstrated predisposition when successfully predicting performance scores out of the brain pre- learning. Finally, Identifying overlapping regions in the contribution maps supporting predisposition and neuroplasticity, alongside distinct regions unique to each factor, offers insights into the neural mechanisms underlying learning predisposition and adaptability.
Higher Cognitive Functions:
Music 1
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Classification and Predictive Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Acquisition
fMRI CONTRAST MECHANISMS
Learning
Machine Learning
Plasticity
Other - Music
1|2Indicates the priority used for review
Abstract Information
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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.
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Provide references using APA citation style.
Olszewska, A. M., Gaca, M., Herman, A. M., Jednoróg, K., & Marchewka, A. (2021). How Musical Training Shapes the Adult Brain: Predispositions and Neuroplasticity. Frontiers in Neuroscience, 15, 204. https://doi.org/10.3389/FNINS.2021.630829/BIBTEX
Sripada, C., Angstadt, M., Rutherford, S., Taxali, A., & Shedden, K. (2020). Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain. Human Brain Mapping, 41(12), 3186–3197. https://doi.org/10.1002/HBM.25007
Zatorre, R. J. (2013). Predispositions and plasticity in music and speech learning: Neural correlates and implications. Science, 342(6158), 585–589. https://doi.org/10.1126/SCIENCE.1238414/SUPPL_FILE/585.MP3