Interindividual differences in gray and white matter properties are associated with early complex motor skill acquisition

Abstract

Brain circuits mediate but also constrain experience‐induced plasticity and corresponding behavioral changes. Here we tested whether interindividual behavioral differences in learning a challenging new motor skill correlate with variations in brain anatomy. Young, healthy participants were scanned using structural magnetic resonance imaging (T1‐weighted MPRAGE, n = 75 and/or diffusion‐weighted MRI, n = 59) and practiced a complex whole‐body balancing task on a seesaw‐like platform. Using conjunction tests based on the nonparametric combination (NPC) methodology, we found that gray matter volume (GMV) in the right orbitrofrontal cortex was positively related to the subjects' initial level of proficiency and their ability to improve performance during practice. Similarly, we obtained a strong trend toward a positive correlation between baseline fractional anisotropy (FA) in commissural prefrontal fiber pathways and later motor learning. FA results were influenced more strongly by radial than axial diffusivity. However, we did not find unique anatomical correlates of initial performance and learning to rate. Our findings reveal structural predispositions for successful motor skill performance and acquisition in frontal brain structures and underlying frontal white matter tracts. Together with previous results, these findings support the view that structural constraints imposed by the brain determine subsequent behavioral success and underline the importance of structural brain network constitution before learning starts.

Colocalized White Matter Plasticity and Increased Cerebral Blood Flow Mediate the Beneficial Effect of Cardiovascular Exercise on Long-Term Motor Learning

“…Specifically, we identified microstructural changes in frontotemporal white matter tracts and increased prefrontal CBF as putative mechanisms underlying exercise-induced improvement of motor learning. Therefore, we provide evidence that prefrontal regions of the brain, which are known to be implicated in DBT learning (Taubert et al, 2010;Lehmann et al, 2019), were directly influenced by the intervention.…”

“…Additionally, a separate proton density image (m0) was collected at the start of the acquisition to scale the perfusion signal differences by the equilibrium brain tissue signal (calibration). Because we expected exercise-induced mediation effects in brain regions that were previously found to be related with DBT performance (Taubert et al, 2010;Lehmann et al, 2019), we ensured that the imaging slab covers the prefrontal and motor regions of the brain, at the expense of occipital regions and the cerebellum (Alfini et al, 2019).…”

“…Note that statistical analyses were conducted on residualized change images (MacKinnon et al, 2013;Voss et al, 2013a); i.e., the baseline measurement of each modality was regressed out from the pre/postpercentage change image of the same modality using FSL's fsl_glm. This procedure allows to consider (1) a potential relationship between initial brain structure/function and brain's responsiveness to treatment, as well as (2) a potential effect of baseline brain structure/function on future motor learning success (Sampaio-Baptista et al, 2014;Lehmann et al, 2019).…”

“…However, the potential behavioral implications of exercise-induced neuroplasticity have often not been assessed at all or, if so, without statistically testing for mediation (Stillman et al, 2016). This is surprising, because structural and functional properties of the brain are associated with subsequent cognitive and motor performance and learning (Deary et al, 2006;Cole et al, 2012;Sampaio-Baptista et al, 2014;Lehmann et al, 2019). Furthermore, several studies have reported that long-term cognitive and motor training may elicit behaviorally-relevant structural and functional changes of the brain (Draganski et al, 2004;Taubert et al, 2010Taubert et al, , 2011Engvig et al, 2012).…”

Lenticular nucleus volume predicts performance in real-time strategy game - cross-sectional and training approach using voxel-based morphometry

“…Given the above described examples, the debate about the predictive neural markers of learning has thus far been inconclusive and still is one of the most challenging areas in cognitive neuroscience. Currently we can observe a growing interest in how individual differences in the structure of the human brain can influence the ability to learn and master complex skills (34,35). Particularly, since the brain's gray matter characteristics are one of the most adequate biological structures that could determine cognitive abilities, it is essential to look at it as a variable predicting training efficacy.…”