Microstructural asymmetry of the corticospinal tracts predicts right-left differences in circle drawing skill in right-handed adolescents.

Most humans show a strong preference to use their right hand, but strong preference for the right hand does not necessarily imply a strong right-left asymmetry in manual proficiency (i.e., dexterity). Here we tested the hypothesis that intra-individual asymmetry of manual proficiency would be reflected in microstructural differences between the right and left corticospinal tract (CST) in a cohort of 52 right-handed typically-developing adolescents (11-16 years). Participants were asked to fluently draw superimposed circles with their right dominant and left non-dominant hand. Temporal regularity of circle drawing movements was assessed for each hand using a digitizing tablet. Although all participants were right-handed, there was substantial inter-individual variation regarding the relative right-hand advantage for fluent circle drawing. All subjects underwent whole-brain diffusion tensor imaging at 3 Tesla. The right and left CST were defined as regions-of-interest and mean fractional anisotropy (FA) and diffusivity values were calculated for right and left CST. On average, mean FA values were higher in the left CST relative to right CST. The degree of right-left FA asymmetry showed a linear relationship with right-left asymmetry in fluent circle drawing after correction for age and gender. The higher the mean FA values were in the left dominant CST relative to the right non-dominant CST, the stronger was the relative right-hand advantage for regular circle drawing. These findings show that right-left differences in manual proficiency are highly variable in right-handed adolescents and that this variation is associated with a right-left microstructural asymmetry of the CST.

Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive level.

Noninvasive transcranial brain stimulation (NTBS) is widely used to elucidate the contribution of different brain regions to various cognitive functions. Here we present three modeling approaches that are informed by functional or structural brain mapping or behavior profiling and discuss how these approaches advance the scientific potential of NTBS as an interventional tool in cognitive neuroscience. (i) Leveraging the anatomical information provided by structural imaging, the electric field distribution in the brain can be modeled and simulated. Biophysical modeling approaches generate testable predictions regarding the impact of interindividual variations in cortical anatomy on the injected electric fields or the influence of the orientation of current flow on the physiological stimulation effects. (ii) Functional brain mapping of the spatiotemporal neural dynamics during cognitive tasks can be used to construct causal network models. These models can identify spatiotemporal changes in effective connectivity during distinct cognitive states and allow for examining how effective connectivity is shaped by NTBS. (iii) Modeling the NTBS effects based on neuroimaging can be complemented by behavior-based cognitive models that exploit variations in task performance. For instance, NTBS-induced changes in response speed and accuracy can be explicitly modeled in a cognitive framework accounting for the speed-accuracy trade-off. This enables to dissociate between behavioral NTBS effects that emerge in the context of rapid automatic responses or in the context of slow deliberate responses. We argue that these complementary modeling approaches facilitate the use of NTBS as a means of dissecting the causal architecture of cognitive systems of the human brain.