An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
Neurons and Cognition
2016-11-21 v1 Neural and Evolutionary Computing
Abstract
In the present work we demonstrate the use of a parcellation free connectivity model based on Poisson point processes. This model produces for each subject a continuous bivariate intensity function that represents for every possible pair of points the relative rate at which we observe tracts terminating at those points. We fit this model to explore degree sequence equivalents for spatial continuum graphs, and to investigate the local differences between estimated intensity functions for two different tractography methods. This is a companion paper to Moyer et al. (2016), where the model was originally defined.
Cite
@article{arxiv.1611.06197,
title = {An Empirical Study of Continuous Connectivity Degree Sequence Equivalents},
author = {Daniel Moyer and Boris A. Gutman and Joshua Faskowitz and Neda Jahanshad and Paul M. Thompson},
journal= {arXiv preprint arXiv:1611.06197},
year = {2016}
}
Comments
Presented at The MICCAI-BACON 16 Workshop (https://arxiv.org/abs/1611.03363)