Microstructural correlations of white matter tracts in the human brain

Abstract

The purpose of this study is to investigate whether specific patterns of correlation exist in diffusion tensor imaging (DTI) parameters across different white matter tracts in the normal human brain, and whether the relative strengths of these putative microstructural correlations might reflect phylogenetic and functional similarities between tracts. We performed quantitative DTI fiber tracking on 44 healthy adult volunteers to obtain tract-based measures of mean diffusivity (MD), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) from four homologous pairs of neocortical association pathways (arcuate fasciculi, inferior fronto-occipital fasciculi, inferior longitudinal fasciculi, and uncinate fasciculi bilaterally), a homologous pair of limbic association pathways (left and right dorsal cingulum bundles), and a homologous pair of cortical-subcortical projection pathways (left and right corticospinal tracts). From the resulting inter-tract correlation matrices, we show that there are statistically significant correlations of DTI parameters between tracts, and that there are statistically significant variations among these inter-tract correlations. Furthermore, we observe that many, but by no means all, of the strongest correlations are between homologous tracts in the left and right hemispheres. Even among homologous pairs of tracts, there are wide variations in the degree of coupling. Finally, we generate a data-driven hierarchical clustering of the fiber pathways based on pairwise FA correlations to demonstrate that the neocortical association pathways tend to group separately from the limbic pathways at trend-level statistical significance, and that the projection pathways of the left and right corticospinal tracts comprise the most distant outgroup with high confidence (p<0.01). Hence, specific patterns of microstructural correlation exist between tracts and may reflect phylogenetic and functional similarities between tracts. The study of these microstructural relationships between white matter pathways might aid research on the genetic basis and on the behavioral effects of axonal connectivity, as well as provide a revealing new perspective with which to investigate neurological and psychiatric disorders.

Copyright 2010 Elsevier Inc. All rights reserved.

Figures

Figure 1

Mid-sagittal projections of 3D DTI fiber tracking of the (A) dorsal cingulate bundle; (B) arcuate fasciculus; (C) inferior fronto-occipital fasciculus; (D) inferior longitudinal fasciculus; (E) uncinate fasciculus; and (F) corticospinal tract. The fiber tracts are overlaid on directionally-encoded color fractional anisotropy images of a healthy volunteer.

Figure 2

Scatterplots of FA values show wide variation of inter-tract correlations between homologous pairs (A, B) and non-homologous pairs (C, D), as measured by the Spearman rank correlation coefficient ρ. The left and right IFO (A) are the most strongly correlated tracts among the 12 tracts studied. However, the FA correlation between the right UF and left IFO (C) exceeds that between a homologous pair, the left and right AF (B). The correlation between a projection tract, the left CST, and an association tract such as the right CB, is very weak (D).

Figure 3

Hierarchical clustering of FA correlational distances displayed as a dendrogram. The distance measure is 1 - ρ, where ρ is the Spearman rank correlation coefficient. The statistical confidence level of each linkage in the dendrogram is given as a percentage above the edge representing the linkage. For example, there is a 92% confidence level that the left and right UF form a cluster. The edge number, reflecting the order in which the linkages were formed during agglomeration of the groups, is given in italics below each edge. For example, the left and right IFO were the first tracts to be linked into a cluster by the hierarchical clustering algorithm.

Figure 4

Hierarchical clustering of MD correlational distances displayed as a dendrogram. All conventions are as in Figure 2.

Figure 5

Hierarchical clustering of AD correlational distances displayed as a dendrogram. All conventions are as in Figure 2.

Figure 6

Hierarchical clustering of RD correlational distances displayed as a dendrogram. All conventions are as in Figure 2.