Robust & Efficient White Matter Analysis using Tract Shape Modelling & Principal Components Analysis

Jonathan D. Clayden¹

Here we demonstrate how probabilistic neighbourhood tractography (PNT) and principal component analysis (PCA) may be used together to analyse properties of white matter tracts in a robust and data-efficient manner. PNT is a method for segmenting tracts in groups using a shape model, while PCA allows common factors across tracts to be identified. This approach can help reduce the multiple comparisons problems widely faced in the statistical analysis of magnetic resonance data.