Mapping the Musician's Brain Through Multimodal MRI
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1133
Submission Type:
Abstract Submission
Authors:
Amir Mano1, Yaniv Assaf2
Institutions:
1Tel Aviv University, Tel Aviv, Israel, 2Tel Aviv University, Tel Aviv, Outside the U.S. & Canada
First Author:
Co-Author:
Introduction:
Neuroplasticity, the brain's ability to adapt throughout life, plays a critical role in skill learning. MRI studies have shown neural network changes across various modalities, including structural and functional imaging, each offering unique insights into learning processes and outcomes. Playing a musical instrument, a complex task involving higher cognition, multisensory integration, and motor control, serves as an ideal model for studying skill learning (Olszewska et al., 2021; Schlaug, 2015).
Musicians often begin training in childhood and practice daily for years, with early lessons shaping brain development (Hyde et al., 2009). Comparing musicians to non-musicians can reveal changes linked to life-long training and provide insights into long-term plasticity. To explore these effects, we employed a multimodal MRI approach.
Musicians often begin training in childhood and practice daily for years, with early lessons shaping brain development (Hyde et al., 2009). Comparing musicians to non-musicians can reveal changes linked to life-long training and provide insights into long-term plasticity. To explore these effects, we employed a multimodal MRI approach.
Methods:
-Participants-
The study includes musicians (M) with many years spent practicing music, and non-musicians (NM) drawn from the SNBB (Strauss Neuroplasticity Brain Bank) database. All subjects were MRI scanned with the same protocol. The analyses are based on 100 subjects per group.
-MRI data analysis-
DWI images were corrected for movement and distortions using FSL tools (Andersson & Sotiropoulos, 2016), and analyzed with ExploreDTI (Leemans et al., n.d.) for MD, RD, AD maps, atlas registration, parcellation, and connectivity matrices. The Brainnetome atlas (Fan et al., 2016) was used for parcellation. T1-weighted images (MPRAGE) were processed with CAT12 (Gaser et al., 2024) to extract gray matter volumes (VGM), using the same atlas.
-Statistical Analyses-
Statistical analyses in 'python' 3.10 and MATLAB R2023a included ROI-based calculations for 5 main metrics (MD, RD, AD, VGM, and betweenness centrality). Betweenness centrality was computed using Brain Connectivity Toolbox (bctpy; Rubinov & Sporns, 2010). Outlier voxels (3+ scaled from the median) and non-gray matter voxels were excluded.
To explore M/NM differences, t-tests identified areas showing group differences. Logistic regression classification models were built based on the data after splitting into training (80%) and test sets (20%). F-tests were used as feature selection, creating 3 models per metric. AUC-ROC was calculated for each model, alongside feature importance to visualize the results over the brain. PCA-based models using components explaining 80% variance were also tested.
The study includes musicians (M) with many years spent practicing music, and non-musicians (NM) drawn from the SNBB (Strauss Neuroplasticity Brain Bank) database. All subjects were MRI scanned with the same protocol. The analyses are based on 100 subjects per group.
-MRI data analysis-
DWI images were corrected for movement and distortions using FSL tools (Andersson & Sotiropoulos, 2016), and analyzed with ExploreDTI (Leemans et al., n.d.) for MD, RD, AD maps, atlas registration, parcellation, and connectivity matrices. The Brainnetome atlas (Fan et al., 2016) was used for parcellation. T1-weighted images (MPRAGE) were processed with CAT12 (Gaser et al., 2024) to extract gray matter volumes (VGM), using the same atlas.
-Statistical Analyses-
Statistical analyses in 'python' 3.10 and MATLAB R2023a included ROI-based calculations for 5 main metrics (MD, RD, AD, VGM, and betweenness centrality). Betweenness centrality was computed using Brain Connectivity Toolbox (bctpy; Rubinov & Sporns, 2010). Outlier voxels (3+ scaled from the median) and non-gray matter voxels were excluded.
To explore M/NM differences, t-tests identified areas showing group differences. Logistic regression classification models were built based on the data after splitting into training (80%) and test sets (20%). F-tests were used as feature selection, creating 3 models per metric. AUC-ROC was calculated for each model, alongside feature importance to visualize the results over the brain. PCA-based models using components explaining 80% variance were also tested.
Results:
To explore M/NM differences, we examined variations in brain features across multiple modalities. We focused on 5 metrics – MD, AD, RD, gray matter volume (VGM), and betweenness centrality – averaged over 274 ROIs.
T-tests identified the most prominent M/NM differences (p<0.01 after FDR) in higher cognitive (e.g. R-DLPFC, MD), motor (e.g. R-BA4-upper limb, VGM), and language (e.g. L-Broca's, MD) areas – Fig. 1
Logistic regression models, developed using the most distinct regions per metric, assessed whether each metric could predict group membership. A model based on VGM (with 50% features) achieved the highest AUC-ROC score (65%), outperforming models combining all metrics (56%). Feature importance highlighted the DLPFC and M1 as key contributors to this model – Fig. 2
T-tests identified the most prominent M/NM differences (p<0.01 after FDR) in higher cognitive (e.g. R-DLPFC, MD), motor (e.g. R-BA4-upper limb, VGM), and language (e.g. L-Broca's, MD) areas – Fig. 1
Logistic regression models, developed using the most distinct regions per metric, assessed whether each metric could predict group membership. A model based on VGM (with 50% features) achieved the highest AUC-ROC score (65%), outperforming models combining all metrics (56%). Feature importance highlighted the DLPFC and M1 as key contributors to this model – Fig. 2
·t-test comparison of mean MD values between the M/NM groups
·M/NM classification model performance
Conclusions:
Our findings reveal significant neuroplastic differences between musicians and non-musicians across brain regions and modalities, particularly in areas linked to higher cognition, motor functions, and language. These differences reflect the diverse demands of musical expertise.
The multimodal approach revealed that each modality highlights distinct aspects of these differences, with GM volume showing the strongest predictive power for group classification.
While this study focused on lifelong musicianship, future research could explore whether these patterns generalize across different levels of musical engagement. Our findings provide a foundation for understanding how such features develop during learning, providing insights into music learning and broader skill acquisition.
The multimodal approach revealed that each modality highlights distinct aspects of these differences, with GM volume showing the strongest predictive power for group classification.
While this study focused on lifelong musicianship, future research could explore whether these patterns generalize across different levels of musical engagement. Our findings provide a foundation for understanding how such features develop during learning, providing insights into music learning and broader skill acquisition.
Higher Cognitive Functions:
Music 2
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
Classification and Predictive Modeling 1
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Cognition
Computational Neuroscience
Cortex
Learning
MRI
Plasticity
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Music; Musicians
1|2Indicates the priority used for review
Abstract Information
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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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For human MRI, what field strength scanner do you use?
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Provide references using APA citation style.
2. Fan, L., Li, H., Zhuo, J., Zhang, Y., Wang, J., Chen, L., Yang, Z., Chu, C., Xie, S., Laird, A. R., Fox, P. T., Eickhoff, S. B., Yu, C., & Jiang, T. (2016). The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cerebral Cortex, 26(8), 3508–3526. https://doi.org/10.1093/cercor/bhw157
3. Gaser, C., Dahnke, R., Thompson, P. M., Kurth, F., Luders, E., & the Alzheimer’s Disease Neuroimaging Initiative. (2024). CAT: A computational anatomy toolbox for the analysis of structural MRI data. GigaScience, 13, giae049. https://doi.org/10.1093/gigascience/giae049
4. Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C., & Schlaug, G. (2009). Musical Training Shapes Structural Brain Development. The Journal of Neuroscience, 29(10), 3019–3025. https://doi.org/10.1523/JNEUROSCI.5118-08.2009
5. Leemans, A., Jeurissen, B., Sijbers, J., & Jones, D. K. (n.d.). ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data.
6. 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, 630829. https://doi.org/10.3389/fnins.2021.630829
7. Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
8. Schlaug, G. (2015). Musicians and music making as a model for the study of brain plasticity. In Progress in Brain Research (Vol. 217, pp. 37–55). Elsevier. https://doi.org/10.1016/bs.pbr.2014.11.020