Indices of callosal axonal density and radius from diffusion MRI relate to upper and lower limb motor performance

Gooijers, J., De Luca, A., Zivari Adab, H., Leemans, A., Roebroeck, A., Swinnen, S. P.


NeuroImage 241


Understanding the relationship between human brain structure and functional outcome is of critical importance in systems neuroscience. Diffusion MRI (dMRI) studies show that fractional anisotropy (FA) is predictive of motor control, underscoring the importance of white matter (WM). However, as FA is a surrogate marker of WM, we aim to shed new light on the structural underpinnings of this relationship by applying a multi-compartment microstructure model providing axonal density/radius indices. Sixteen young adults (7 males / 9 females), performed a hand/foot tapping task and a Multi Limb Reaction Time task. Furthermore, diffusion (STEAM &HARDI) and fMRI (localizer hand/foot activations) data were obtained. Sphere ROIs were placed on activation clusters with highest t value to guide interhemispheric WM tractography. Axonal radius/density indices of callosal parts intersecting with tractography were calculated from STEAM, using the diffusion-time dependent AxCaliber model, and correlated with behavior. Results indicated a possible association between larger apparent axonal radii of callosal motor fibers of the hand and higher tapping scores of both hands, and faster selection-related processing (normalized reaction) times (RTs) on diagonal limb combinations. Additionally, a trend was present for faster selection-related processing (normalized reaction) times for lower limbs being related with higher axonal density of callosal foot motor fibers, and for higher FA values of callosal motor fibers in general being related with better tapping and faster selection-related processing (normalized reaction) times. Whereas FA is sensitive in demonstrating associations with motor behavior, axon radius/density (i.e., fiber geometry) measures are promising to explain the physiological source behind the observed FA changes, contributing to deeper insights into brain-behavior interactions.