Various diffusion metrics have been proposed for characterising tissue microstructure. However, it is unclear which metric best captures individual microstructural differences. One possible approach is to explore all available metrics. However, this increases the chance of Type I error and makes interpretation difficult. Using data-reduction approaches, we identified two principal components that capture 85% of the variance in diffusion measurements. The first captures properties related to hindrance, while the second reflects tissue complexity. We demonstrate the utility of this approach by showing significant correlations with age of these new metrics, whereas little to no effects were observed with any individual metric.