Chamberland, M., Raven, E. P, Genc, S., Duffy, K., Descoteaux, M., Parker, G. D, Tax, C. M & Jones, DK. (2019). Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. NeuroImage,200 89-100. Netherlands: Elsevier BV. Retrieved from https://doi.org/10.1016/j.neuroimage.2019.06.020
Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.
Mary MacKillop Institute for Health Research
Open Access Journal Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.