An Empirical Study of Continuous Connectivity Degree Sequence Equivalents
This work provides an incremental analysis of connectivity models for neuroimaging researchers.
The study applied a Poisson point process model to analyze continuous connectivity in brain networks, comparing two tractography methods to identify local differences in intensity functions.
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.