Linking Multivariate Patterns of Plasticity in the M1 and S1 to Sensorimotor Training
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:
1613
Submission Type:
Abstract Submission
Authors:
Martin Matke1, Nisha Prabhu2, Norman Aye2, Nico Lehmann2, Marco Taubert2
Institutions:
1Institute of Cognitive Neurology and Dementia Research, Magdeburg, Saxony-Anhalt, 2Otto-von-Guericke University Magdeburg, Magdeburg, Saxony-Anhalt
First Author:
Co-Author(s):
Introduction:
Targeted training interventions can improve cognitive abilities in older adults (Erickson et al., 2011) and induce brain structural changes (Lövdén et al., 2013). Wenger et al. (2017) proposed a framework describing grey matter expansion followed by renormalisation in targeted regions.
Methods:
We conducted a study to examine the expansion-renormalisation process during motor-skill acquisition in 60 healthy adults aged 60–74. Participants were randomly assigned to one of two groups and completed a 5-week balance board training (1 hour/week), previously shown to induce plasticity effects (Taubert et al., 2016; Lehmann et al., 2021). Both groups underwent identical testing, MRI sessions, and time on the board.
In the optimised group (n=40), task difficulty was individually adapted based on performance. In the suboptimal group (n=20), difficulty was altered the same number of times but in a predetermined, non-adaptive manner.
Group differences in demographics (sex, age), physiological markers (weight, height, BMI), and behavioural markers (pre-performance, adaptability) were tested using two-sided tests. Performance improvements were assessed with a Linear Mixed Effects (LME) model, incorporating time, group-by-time interaction, and random intercepts/slopes.
Participants underwent up to six MRI sessions (345 total) using MP-RAGE, MPM, and DWI sequences. Preprocessing of MP-RAGE data employed the CAT12 longitudinal pipeline (Gaser et al., 2022), including 8 mm FWHM smoothing, masking to task-related areas, and downsampling to 3 mm resolution. The analysis shown here focuses on superior and medial parts of the M1 and S1.
The imaging data were analysed using the ALASCA toolbox (Jarmund et al., 2022), which combines LME for longitudinal analysis and PCA for multivariate modelling. The was used to recover patterns that change similarly within groups but differently across groups over time.
We defined an LME model that included time, group-by-time interaction, and random intercepts for each voxel. Using the ALASCA toolbox, we recovered a group-by-time interaction pattern in our masked area. Individual session data were then mapped onto this recovered pattern, and second-order polynomial fitting was used to parametrise the time course of changes at an individual level. Finally, we performed a linear correlation between these plasticity parameters and the random slope parameters from the behavioural LME to characterise individual learning.
In the optimised group (n=40), task difficulty was individually adapted based on performance. In the suboptimal group (n=20), difficulty was altered the same number of times but in a predetermined, non-adaptive manner.
Group differences in demographics (sex, age), physiological markers (weight, height, BMI), and behavioural markers (pre-performance, adaptability) were tested using two-sided tests. Performance improvements were assessed with a Linear Mixed Effects (LME) model, incorporating time, group-by-time interaction, and random intercepts/slopes.
Participants underwent up to six MRI sessions (345 total) using MP-RAGE, MPM, and DWI sequences. Preprocessing of MP-RAGE data employed the CAT12 longitudinal pipeline (Gaser et al., 2022), including 8 mm FWHM smoothing, masking to task-related areas, and downsampling to 3 mm resolution. The analysis shown here focuses on superior and medial parts of the M1 and S1.
The imaging data were analysed using the ALASCA toolbox (Jarmund et al., 2022), which combines LME for longitudinal analysis and PCA for multivariate modelling. The was used to recover patterns that change similarly within groups but differently across groups over time.
We defined an LME model that included time, group-by-time interaction, and random intercepts for each voxel. Using the ALASCA toolbox, we recovered a group-by-time interaction pattern in our masked area. Individual session data were then mapped onto this recovered pattern, and second-order polynomial fitting was used to parametrise the time course of changes at an individual level. Finally, we performed a linear correlation between these plasticity parameters and the random slope parameters from the behavioural LME to characterise individual learning.
Results:
Demographic and physiological characteristics were comparable across groups, with no baseline differences. The behavioural LME revealed significant performance gains (p < 0.001), with a group-by-time interaction (p = 0.023) indicating a 30% greater improvement in the optimised group.
The recovered pattern using ALASCA loaded primarily on the most medial and superior parts of the masked region, consistent with our expectations. However, the time course of this pattern revealed an unexpected contraction and renormalisation in the optimised group compared to the suboptimal group, which served as a control (Fig 1).
As described in the Methods, individual sessions were projected onto the pattern, and second-order polynomials were fitted to these projections to parametrise individual plasticity changes. The correlation between the linear and quadratic components of the polynomial fits and the random slope parameters representing individual learning is shown in Figure 2. The correlations in the optimised group hint at a potential link between the recovered plasticity pattern and performance improvements in the trained task.
The recovered pattern using ALASCA loaded primarily on the most medial and superior parts of the masked region, consistent with our expectations. However, the time course of this pattern revealed an unexpected contraction and renormalisation in the optimised group compared to the suboptimal group, which served as a control (Fig 1).
As described in the Methods, individual sessions were projected onto the pattern, and second-order polynomials were fitted to these projections to parametrise individual plasticity changes. The correlation between the linear and quadratic components of the polynomial fits and the random slope parameters representing individual learning is shown in Figure 2. The correlations in the optimised group hint at a potential link between the recovered plasticity pattern and performance improvements in the trained task.
·Figure 1: Recovered pattern (bottom) and associated time course in optimised group referenced to the suboptimal group (top left) from the ALASCA analysis and variance explained of the other PCs.
·Figure 2: Correlation of random slopes from behavioral LME (y) and individual characterisation of plasticity from 2nd order polynomial fitting of sessions mapped onto the recovered pattern (x).
Conclusions:
Based on our analyses, we conclude that our training optimisation approach effectively enhances performance. The multivariate analysis indicates a potential pattern linking task learning to plasticity changes in the medial and superior parts of S1 and M1. Future analyses of microstructural data may help to interpret the observed changes further (Edwards et al., 2018).
Learning and Memory:
Neural Plasticity and Recovery of Function 2
Skill Learning
Modeling and Analysis Methods:
Multivariate Approaches 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Perception, Attention and Motor Behavior:
Perception: Tactile/Somatosensory
Keywords:
Learning
Modeling
Motor
Multivariate
Plasticity
Somatosensory
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
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.
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.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Erickson, K. I. (2011). Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 3017–3022. https://doi.org/10.1073/pnas.1015950108
Gaser, C., Dahnke, R., Thompson, P. M., Kurth, F., & Luders, E. (2022). CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data. Cold Spring Harbor Laboratory. https://doi.org/10.1101/2022.06.11.495736
Jarmund, A. H., Madssen, T. S., & Giskeødegård, G. F. (2022). ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods. In Frontiers in Molecular Biosciences (Vol. 9). Frontiers Media SA. https://doi.org/10.3389/fmolb.2022.962431
Lehmann, N. (2021). Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter. Neuroscience, 457, 165–185. https://doi.org/10.1016/j.neuroscience.2021.01.005
Lövdén, M. (2013). Structural brain plasticity in adult learning and development. Neuroscience and biobehavioral reviews, 37(9 Pt B), 2296–2310. https://doi.org/10.1016/j.neubiorev.2013.02.014
Taubert, M. (2016). Rapid and specific gray matter changes in M1 induced by balance training. NeuroImage, 133, 399–407. https://doi.org/10.1016/j.neuroimage.2016.03.017
Wenger, E. (2017). Expansion and Renormalisation of Human Brain Structure During Skill Acquisition. Trends in cognitive sciences, 21(12), 930–939. https://doi.org/10.1016/j.tics.2017.09.008