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Brain parcellation based on information theory.

Ester Bonmati¹, Anton Bardera¹, Imma Boada¹

¹Institute of Informatics and Applications, University of Girona, Campus Montilivi, 17003 Girona, Spain.

Computer Methods and Programs in Biomedicine

|September 27, 2017

Summary

This summary is machine-generated.

This study introduces a robust whole-brain parcellation method for computational neuroimaging. The approach preserves global network structure and offers consistent results across subjects for studying the human connectome.

Keywords:

Brain parcellation Hierarchical clustering Human brain connectome Markov process Mutual information

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Area of Science:

Computational neuroimaging
Network neuroscience
Brain connectivity analysis

Background:

Brain parcellation is crucial for network analysis in neuroimaging.
Existing hierarchical clustering methods have limitations in scale and similarity measures.
A robust whole-brain parcellation preserving global network structure is needed.

Purpose of the Study:

To present a novel, robust whole-brain hierarchical parcellation method.
To preserve the global structure of brain networks across different granularities.
To enable comprehensive study of the human connectome.

Main Methods:

Modeling brain regions as a random walk on the connectome.
Deriving a Markov process to quantify network structure.
Employing an agglomerative information bottleneck method with mutual information for clustering.

Main Results:

The proposed parcellation method was tested on synthetic and real human connectomes (structural and functional).
Results demonstrate preservation of key network properties.
The method shows consistency across subjects and outperforms k-means in preserving global structure.

Conclusions:

This work offers a new framework for analyzing the human connectome.
The method supports the study of brain networks using both functional and anatomical connectivity.
It enables analysis at multiple levels of granularity.