The current study examined the relationship between individual differences in the ability to perform a motor task and structural brain measures collected on the final day of practice. Importantly, we compared these findings with the results of a previous functional brain imaging study in the same sample. Behavioral regression analyses found that better final synchronization performance was negatively correlated with FA bilaterally in fiber tracts underlying sensorimotor cortex, such that participants with lower FA showed better final performance. The direction of this relationship may appear counter-intuitive. However, radial diffusivity in this region was positively correlated with performance and multi-fiber tractography revealed that this region is an area of CST and SLF crossing fibers—meaning that the interpretation of FA in which bigger is better may not always apply. Functional imaging results with the same sample found positive relationships with synchronization performance in motor, parietal, and auditory cortical regions that correspond well with both identified tracts (Steele and Penhune, 2010
). These findings raise the possibility that skilled performance on this task is associated with enhanced fiber integrity in the SLF. Enhanced fiber integrity in the SLF could result in reduced FA in regions where it crosses the CST. Additional VBM analyses revealed a positive relationship between rate of improvement and GM volume in right cerebellum that were co-localized with functional decreases observed in the fMRI data (Steele and Penhune, 2010
), thus providing further evidence for the cerebellum's role in skilled motor performance.
Better final performance on the TMST was related to lower FA in the CST and SLF inferior to bilateral sensorimotor cortex, and this effect was mediated by a positive correlation with radial diffusivity (Figures , ). Our tractography result confirmed that this region contained fibers from both the CST and SLF (Makris et al., 2005
) (Figure ). Although we were unable to detect tract-specific relationships—likely as a result of the non-isotropic voxel sizes used in the current study—we speculated that greater diffusivity along the course of the SLF in this region may be responsible for the observed positive correlation of performance with radial, rather than axial, diffusivity. Though increases/greater radial diffusivity has been linked to dysmyelination in uniformly oriented fiber populations (Pierpaoli et al., 2001
; Sun et al., 2008
), the presence of crossing fibers in this region makes interpretation more difficult (Jbabdi et al., 2010
; Douaud et al., 2011
). Therefore, we have hypothesized that radial diffusivity in part reflects the fiber integrity of the SLF, where the principle diffusion direction is typically oriented anterior-posterior. The possibility that the negative correlation between FA and skilled performance could be driven by variation in the SLF is an attractive one. The SLF connects parietal and auditory cortical regions functionally implicated in performance of this task and in an fMRI study with the same participants (Penhune and Doyon, 2002
; Steele and Penhune, 2010
). In support of our hypothesis, a previous study found a positive
relationship between FA in the SLF and motor sequence learning (Tomassini et al., 2011
). This finding is in a more anterior region of the SLF (y
= −10) that would be unlikely to be influenced by crossing fibers from the CST. Also possibly consistent with our findings, a recent study showed that non-musicians
FA than musicians in bilateral CST regions similar to those observed in our results (Imfeld et al., 2009
). The authors speculate that their counter-intuitive findings are due to increased axonal permeability due to long-term sensorimotor training in musicians, but do not consider the potential effect of crossing fibers. The overlap between the regions functionally implicated in improvement on the TMST and the tractography results presented here provides further evidence for the importance of the SLF in skilled motor sequence performance.
The results of behavioral regression analyses with VBM GM values showed that individual differences in cerebellar lobules HVI and V were related to the rate of improvement of synchronization on the TMST. These regions overlap with those that showed learning-related decreases in activity in the fMRI data from the same subjects (Figure ). Co-localization of behaviorally-relevant structural differences and functional changes identified with independent analyses provides further evidence for the role of the cerebellum in motor tasks—a finding that is compatible with a proposed role for the cerebellum in processing error-related feedback (Ohyama et al., 2003
). Crucially, lobules HVI and V are structurally and functionally connected to motor cortex (Kelly and Strick, 2003
; O'Reilly et al., 2010
; Stoodley and Schmahmann, 2009
), show performance-related changes in functional activity during motor tasks (Penhune and Steele, 2012
), and form part of a network of regions responsible for the optimization of motor behavior (Ramnani, 2006
Studies identifying relationships between cerebellar GM volume and performance are rare, with only two that use non-expert populations (Tomassini et al., 2011
; Kühn et al., 2012
). Our results are in agreement with those of Kühn et al. (2012
), who found that GM volume in lobule VI was related to fine motor control, and directly support those of Tomassini et al. (2011
) who also identified a relationship between motor sequence performance and GM volume in lobule VI. Though the design of the current study did not allow us to address learning-related changes in GM volume, previous work has identified increases in cerebellar synapse number and glial cell volume as a result of practice and learning (Kleim et al., 2002
). We hypothesise that the observed performance-related individual differences in GM are in part due to differences arising from previous training and experience. Thus, greater cell or synaptic density in the cerebellum may support enhanced information processing ability (and thus a faster rate of behavioral improvement) that is related to decreasing functional activity as performance improves.
The causes of inter-individual variability in brain structure are not fully understood, but likely include pre-existing genetic contributions and contributions from learning and the environment. The design of our study was not able to disentangle these affects. FA is affected by WM properties including axon myelination, diameter, and packing density. Differences in these properties could lead to the individual differences in performance observed in our study through pre-existing differences or training-induced changes in axon conduction velocity and synaptic synchronization (Fields, 2005
), or density of innervation. Greater fiber integrity along the SLF would be consistent with the idea, proposed by Fields, that greater myelination observed in relation to performance may underlie enhancements in synchronized activity between task-relevant regions (Fields, 2005
). Similar to WM measures, individual differences in GM volume could be influenced by multiple factors such as neuronal and glial cell density, synaptic density, vascular architecture, and cortical thickness. Though the physiological basis for GM volume differences in humans has not been fully explained, previous work has established the feasibility of identifying individual differences in brain structure that are related to: timed finger tapping (Ullén et al., 2008
), performance on musical tasks (Foster and Zatorre, 2010b
), bimanual coordination (Johansen-Berg et al., 2007
), and learning of foreign language sounds (Golestani and Pallier, 2007
; Golestani et al., 2007
). This study identified regions where performance is related to brain structure but its design does not allow us to conclude whether the observed effects are due to previous experience, training, or a combination of the two. Our study comprised a brief training regime (5 days) followed by structural data acquisition on the final day. A number of studies have identified structural changes after multiple weeks of training (Draganski et al., 2004
; Boyke et al., 2008
; Scholz et al., 2009
; Taubert et al., 2010
), but others have also reported changes with short-term training (Landi et al., 2011
), TMS (May et al., 2007
), and drug intervention (Tost et al., 2010
). The current study provides a link between skilled performance and brain structure in regions known to be functionally involved with task performance. With only a single timepoint we cannot comment on how the regions that we have identified may change as a result of practice; however, given the overlap with previous fMRI results, structural changes in the SLF and cerebellar lobules HVI and V may occur with training on similar motor sequence tasks. Future studies employing longitudinal methods and longer periods of training could be used to address these questions.
We have identified individual differences in performance that are related to brain structures important for motor sequence performance. There was a negative relationship between FA and performance in a region of the CST-SLF fiber crossing that may reflect greater fiber integrity in the SLF of skilled performers—and is consistent with the idea of enhanced communication/synchronization between regions functionally important for this task. Two regions of the cerebellum (lobules HVI and V) where GM volume is important for the speed at which sequence skill is acquired were also identified. Our multimodal cross-sectional individual differences design also illustrates the importance of considering multiple structural measures (GM volume, FA, diffusivities, tractography) within the context of functional results to help provide a more global interpretation of the processes involved in skilled motor sequence performance.
Conflict of interest statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.