Title: Modeling conduction delays in the corpus callosum using MRI-measured g-rat
Author: Shai Berman, Shir Filo and Aviv A. Mezer
Published in NeuroImage 195 on July 2019
Distant brain regions communicate via electrical signals that travel along the long range white matter connections known as axons. The conduction properties of these signals are affected by the structural features of the axon. An important feature is the axonal g-ratio, the ratio between the inner and outer diameters of the myelin sheath, insulating sleeves surrounding the axon. The effect of g-ratio variance on conduction properties has been quantitatively evaluated using single-axon models. It has recently become possible to estimate a g-ratio weighted measurement in vivo using quantitative MRI. Nevertheless, it is still unclear whether the variance in the g-ratio in the healthy human brain leads to significant differences in conduction velocity. In this work we tested whether the MRI measurement of g-ratio can be used to predict conduction delays in the corpus callosum.
We present a comprehensive framework in which the structural properties of fibers (i.e. length and g-ratio, measured using MRI), are incorporated in a biophysical model of axon conduction, to model conduction delays of long-range white matter fibers. We applied this framework to the corpus callosum fiber that connect the two hemispheres of the human brain. We found conduction delay estimates that are compatible with the existing literature.
Conduction delays have been suggested to increase with age. Therefore, we investigated whether there are differences in the g-ratio and the fiber length between young and old adults, and whether this leads to a difference in conduction speed and delays. We found very small differences between the predicted delays of the two groups in the motor fibers of the corpus callosum. We also found that the motor fibers of the corpus callosum have the fastest conduction estimates.
Our study provides a framework for predicting conduction latencies in vivo. The framework could have major implications for future studies of white matter diseases and large range network computations. Our results highlight the need for improving additional in vivo measurements of white matter microstructure.