Robust intra-individual estimation of structural connectivity by Principal Component Analysis

Fiber tractography based on diffusion-weighted MRI provides a non-invasive characterization of the structural connectivity of the human brain at the macroscopic level. Quantification of structural connectivity strength is challenging and mainly reduced to streamline counting methods. These are however highly dependent on the topology of the connectome and the particular specifications for seeding and filtering, which limits their intra-subject reproducibility across repeated measurements and, in consequence, also confines their validity. Here we propose a novel method for increasing the intra-subject reproducibility of quantitative estimates of structural connectivity strength. To this end, the connectome is described by a large matrix in positional-orientational space and reduced by Principal Component Analysis to obtain the main connectivity modes. It was found that the proposed method is quite robust to structural variability of the data.

Automated summary

This study introduces a novel method to improve the intra-subject reproducibility of quantitative estimates of structural connectivity strength by reducing the dimensionality of the connectome using Principal Component Analysis. The proposed method was found to be robust to structural variability in the data.