Bonmati, E;
Bardera, A;
Boada, I;
(2017)
Brain parcellation based on information theory.
Computer Methods and Programs in Biomedicine
, 151
pp. 203-212.
10.1016/j.cmpb.2017.07.012.
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Abstract
BACKGROUND AND OBJECTIVE: In computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network. METHODS: Brain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure. RESULTS: The method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects. CONCLUSION: This work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels.
| Type: | Article |
|---|---|
| Title: | Brain parcellation based on information theory |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.cmpb.2017.07.012 |
| Publisher version: | https://doi.org/10.1016/j.cmpb.2017.07.012 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Brain parcellation, Hierarchical clustering, Mutual information, Human brain connectome, Markov process |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10046678 |
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