Continuous representations of brain connectivity using spatial point processes

Abstract

We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each streamline curve in a tractography as an observed event in connectome space, here the product space of the gray matter/white matter interfaces. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivity. We further present empirical results that suggest that “discrete” connectomes derived from our model have substantially higher test-retest reliability compared to standard methods. In this, the expanded form of this paper for journal publication, we also explore parcellation free analysis techniques that avoid the use of explicit partitions of the cortical surface altogether. We provide an analysis of sex effects on our proposed continuous representation, demonstrating the utility of this approach.

Test–retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering

“…Fifth, in the present study, we compared two fiber clustering and cortical-parcellation-based strategies that are widely used but relatively traditional approaches. In the past few years, there have been methods designed to improve test-retest reproducibility for constructing cortical-parcellation-based connectomes by including additional pre-and/or postprocessing steps, for example, dilating gray matter regions (Zhang, Descoteaux, Zhang, et al, 2018), constructing continuous connectome matrices (Moyer, Gutman, Faskowitz, Jahanshad, & Thompson, 2017), and filtering out implausible fiber streamlines (Smith et al, 2015). A further study could include comparison of test-retest reproducibility between the fiber clustering and cortical-parcellation-based strategies with advanced processing.…”

Quantification of Structural Brain Connectivity via a Conductance Model

“…However, the conductance values may not fit the definition of either streamlines or probability measures. As we have shown in Section 2, the proposed method can also enable the computation of voxel-wise and thus parcellationindependent connectivity (Moyer et al, 2017), which allows us to compute the connectivity between any pair of points in the white matter.…”

Connectivity-Driven Brain Parcellation via Consensus Clustering

“…From such a representation, a "discrete" connectivity graph could be computed from any particular cortical parcellation P . We follow definitions from [8] and call P =…”

“…We use construct continuous connetocmes of 400 subjects from the Human Connectome Project S900 release [13] following [8]. We use an icosahedral spehrical sampling, at a resolution of 10242 mesh vertices per hemisphere.…”