Poster No:
1875
Submission Type:
Abstract Submission
Authors:
Yu-Hsin (Fiona) Chang1, Fredrik Ullén1, Örjan de Manzano1
Institutions:
1Max Planck Institute for Empirical Aesthetics, Frankfurt am Main, Germany
First Author:
Co-Author(s):
Fredrik Ullén
Max Planck Institute for Empirical Aesthetics
Frankfurt am Main, Germany
Örjan de Manzano
Max Planck Institute for Empirical Aesthetics
Frankfurt am Main, Germany
Introduction:
The aim of this study is to identify neuroanatomical markers of sensorimotor skill learning, using piano playing as a behavioral model. Previous studies have shown structural differences in domain relevant brain areas between musical experts and non-experts (Gaser & Schlaug, 2003), and similar within-person differences before and after training (Meyer et al., 2012; Worschech et al., 2023). For musical instrument playing, the auditory-motor regions have been identified as primary regions of interest (Zatorre, 2013). Nonetheless, there are not many studies that have investigated how baseline differences in regional brain structure (e.g. cortical thickness) predict sensorimotor skill learning across a longitudinal training intervention, particularly with regard to more specific aspects of performance, such as the accuracy and timing of movements. Here we study how baseline differences in brain structural measures are related to skill learning and performance during 6 weeks of piano practice in a sample of healthy adults.
Methods:
This study included 37 participants with less than 2 years of childhood musical training and no musical training during adulthood. Participants underwent a 6-week piano training program divided into two parts. During weeks 1-3, they followed a standardized curriculum where they learned to perform two study-specific melodies by progressively combining shorter segments into full pieces. During weeks 4-6, participants continued with a low-dose maintenance training of these two melodies and practiced familiar Swedish standard tunes at their own pace. The accumulated numbers of melodies they learned across sessions were recorded to estimate the slope of each participant's learning curve, and all responses during training were recorded to calculate the pitch and rhythm accuracies.
Neuroimaging data were acquired before the training using a 3T scanner. The MRI data included T1-weighted structural images, diffusion-weighted images, and functional MRI. In addition to imaging data, participants completed the Swedish Music Discrimination Task (SMDT) and Wiener Matrizen Test (WMT) to assess cognitive abilities. Cortical thickness was extracted from T1-weighted images using the CAT12 toolbox. Regions of interests (ROIs) were selected based on previous literatures (De Manzano & Ullén, 2018), including the rostral anterior cingulate cortex (rACC), right middle frontal gyrus (rMFG), inferior temporal gyrus (ITG), inferior frontal gyrus (IFG), Heschl's gyrus, primary motor cortex (M1), dorsal and ventral premotor areas (PMd and PMv), temporoparietal junction (TPJ), and superior temporal gyrus (STG). Correlation analyses were then conducted to examine the relationship between cortical measures and behavioral outcomes.
Results:
Spearman correlation analyses revealed positive correlations between cortical thickness in several brain regions and pitch and rhythm accuracies (p < 0.05, FDR corrected), which were calculated based on the performance across selected trials from weeks 1-3. Regions associated with pitch performance included the left ITG, left IFG pars triangularis, right TTG, M1, PMd, and right PMv. Rhythm performance was correlated with cortical thickness in the left ITG, left IFG pars orbitalis, and right M1. Additionally, negative correlations were observed between cortical thickness in several brain regions and the slope of the learning curve, though these did not remain significant after correction for multiple comparisons.
Conclusions:
Our preliminary results show that cortical thickness in specific brain regions is associated with pitch and rhythm accuracies during early stages of piano learning. These findings suggest that baseline cortical measures may predict individual differences in musical learning outcomes. Future research will continue by examining the structural differences before and after training and construct a model to further investigate the relationship between musical skill learning and brain structures.
Higher Cognitive Functions:
Music
Learning and Memory:
Skill Learning 2
Modeling and Analysis Methods:
Segmentation and Parcellation
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Anatomical MRI 1
Keywords:
Learning
MRI
Plasticity
STRUCTURAL MRI
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
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.
Yes
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.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
Diffusion MRI
Behavior
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
Free Surfer
Provide references using APA citation style.
1. De Manzano, Ö., & Ullén, F. (2018). Same Genes, Different Brains: Neuroanatomical Differences Between Monozygotic Twins Discordant for Musical Training. Cerebral Cortex, 28(1), 387-394. https://doi.org/10.1093/cercor/bhx299
2. Gaser, C., & Schlaug, G. (2003). Brain structures differ between musicians and non-musicians. Journal of Neuroscience, 23(27), 9240-9245. https://doi.org/10.1523/JNEUROSCI.23-27-09240.2003
3. Meyer, M., Elmer, S., & Jäncke, L. (2012). Musical expertise induces neuroplasticity of the planum temporale. Neurosciences and Music Iv: Learning and Memory, 1252, 116-123. https://doi.org/10.1111/j.1749-6632.2012.06450.x
4. Worschech, F., James, C. E., Junemann, K., Sinke, C., Kruger, T. H. C., Scholz, D. S., Kliegel, M., Marie, D., & Altenmuller, E. (2023). Fine motor control improves in older adults after 1 year of piano lessons: Analysis of individual development and its coupling with cognition and brain structure. Eur J Neurosci, 57(12), 2040-2061. https://doi.org/10.1111/ejn.16031
5. 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
No