No significant changes in synaptic density and gray matter volume following motor learning

Introduction: In animal neurophysiology, motor learning induces long-term potentiation- and depression-like plasticity, i.e., the strengthening or weakening of connections between neurons. In humans, gray matter volume (GMV) changes, measured non-invasively by MRI, in response to motor learning are interpreted as a surrogate measure of plasticity. Here we measure synaptic density with positron emission tomography (PET) to investigate the learning-induced synaptic plasticity more directly, and MRI-based GMV to investigate structural plasticity. Methods: Twenty-two volunteers participated in a simple or complex (group factor) four-week motor training on a bimanual tracking task (BTT). Learning progress was modelled individually. [18F]SynVesT-1 PET and T1-weighted MRI were acquired at baseline (PRE) and immediately after the end of the motor training (POST), and at MID for MRI only (time factor). We restricted our analyses to six a priori chosen VOIs of the visuomotor network, extracted individually from FreeSurfer (v6.0.0) cortical surface parcellation. Average standard uptake value ratios (SUVR), GMV and average cortical thickness (aCT) in each VOI were statistically compared over time, group and time-by-group, and associated with learning progress. Results: Participants’ BTT performance improved significantly, and more for the complex than the simple training group. The VOI-based PET and GM analyses did not yield any significant results. Conclusion: We did not identify significant changes in [18F]SynVesT-1 synaptic density as a surrogate marker for plasticity after motor learning in the a priori chosen VOIs.