# A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex

## TL;DR

This paper introduces a Bayesian non-parametric mixture model for cortical connectivity-based parcellation, aiming to improve the identification of functionally distinct brain regions without predefining their number.

## Contribution

It proposes a novel, data-driven Bayesian model that adaptively determines the number of cortical regions based on connectivity data.

## Key findings

- Effective in identifying distinct cortical regions
- Flexible model that adapts to data complexity
- Potential for improved brain mapping accuracy

## Abstract

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.