Summary How motor skills are stored in the nervous system represents a fundamental question in neuroscience. Although musical motor skills are associated with a variety of adaptations [,  and ], it remains unclear how these changes are linked to the known superior motor performance of expert musicians. Here we establish a direct and specific relationship between the functional organization of the corticomuscular system and skilled musical performance. Principal component analysis was used to identify joint correlation patterns in finger movements evoked by transcranial magnetic stimulation over the primary motor cortex while subjects were at rest. Linear combinations of a selected subset of these patterns were used to reconstruct active instrumental playing or grasping movements. Reconstruction quality of instrumental playing was superior in skilled musicians compared to musically untrained subjects, displayed taxonomic specificity for the trained movement repertoire, and correlated with the cumulated long-term training exposure, but not with the recent past training history. In violinists, the reconstruction quality of grasping movements correlated negatively with the long-term training history of violin playing. Our results indicate that experience-dependent motor skills are specifically encoded in the functional organization of the primary motor cortex and its efferent system and are consistent with a model of skill coding by a modular neuronal architecture .
The authors use PCA on TMS induced postures to show that motor cortex represents building blocks of movements which adapt to everyday requirements. To be precise, the authors recorded finger movements which were induced by TMS over primary motor cortex and extracted for each of the different stimulations the posture which had the largest deviation from rest. From the resulting set of postures they computed the first 4 principal components (PCs) and looked how well a linear combination of the PCs could reconstruct postures recorded during normal behaviour of the subjects. This is made more interesting by comparing groups of subjects with different motor experience. They use highly trained violinists and pianists and a group of non-musicians and then compare the different combinations of who is used for determining PCs and what is trying to be reconstructed (violin playing, piano playing, or grasping where grasping can be that of violinists or non-musicians). Basis of comparison is a correlation (R) between the series of joint angle vectors as defined in Shadmehr1994 which can be interpreted as something like the average correlation between data points of the two sequences measured across joint angles (cf. normalised inner product matrix in GPLVM). Don’t ask me why they take exactly this measure, but probably it doesn’t matter. The main finding is that the PCs from violinists are significantly better in reconstructing violin playing than either the piano PCs, or the non-musician PCs. This table is missing in the text (but the data is there, showing mean R and its standard deviation):
R violinists pianists non-musicians
violin 0.69+0.09 0.63+0.11 0.64+0.09
piano 0.70+0.06 0.74+0.06 0.70+0.07
grasp 0.76+0.09 0.76+0.09 0.76+0.10
what is not discussed in the paper is that pianists’ PCs are worse in reconstructing violin playing than PCs of non-musicians. An interesting finding is that the years of intensive training of violinists correlates significantly with the reconstruction quality for violin playing of violinist PCs while it is anticorrelated with the reconstruction quality for grasping indicating that the postures activated in primary motor cortex become more adapted to frequently executed tasks. However, it has to be noted that this correlation analysis is based on only 9 data points.
In the beginning of the paper they show an analysis of the recorded behaviour which simply is supposed to ensure that violin playing, piano playing and grasping movements are sufficiently different which we may believe, although piano playing and grasping apparently are somewhat similar.