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

  title={A Restaurant Process Mixture Model for Connectivity Based Parcellation of the Cortex},
  author={Daniel Moyer and Boris Gutman and Neda Jahanshad and Paul M. Thompson},
One of the primary objectives of human brain mapping is the division of the cortical surface into functionally distinct regions, i.e. parcellation. [] Key Method Towards this end, we present a parcellation method based on a Bayesian non-parametric mixture model of cortical connectivity.
Product Space Decompositions for Continuous Representations of Brain Connectivity
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Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex
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Connectivity‐based parcellation: Critique and implications
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A Continuous Model of Cortical Connectivity
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The parcellation method is specifically designed to respect spatial layout and identify locally-connected clusters, corresponding to plausible coherent units such as strings of adjacent DNA base pairs, subregions of the brain, animal communities, or geographic ecosystems.
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A probabilistic model for connectivity-based parcellation of the human brain is introduced that allows quantification of the uncertainty in cluster assignments and shows that, while most clusters are clearly delineated, some regions are more difficult to assign.