A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex
This work addresses the challenge of brain mapping for researchers in neuroscience, though it appears incremental as it builds on existing mixture model approaches.
The authors tackled the problem of dividing the cortical surface into functionally distinct regions (parcellation) by presenting a Bayesian non-parametric mixture model based on cortical connectivity, aiming to address the unknown number and configuration of these regions as data volume increases.
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i.e. parcellation. While it is generally agreed that at macro-scale different regions of the cortex have different functions, the exact number and configuration of these regions is not known. Methods for the discovery of these regions are thus important, particularly as the volume of available information grows. Towards this end, we present a parcellation method based on a Bayesian non-parametric mixture model of cortical connectivity.