Hierarchical clustering identifies hub nodes in a model of resting-state brain activity

Author

Wildie, Mark ; Shanahan, Murray

Author_Institution

Dept. of Comput., Imperial Coll. London, London, UK

fYear

2012

fDate

10-15 June 2012

Firstpage

1

Lastpage

6

Abstract

A novel clustering algorithm is presented for analyzing the temporal dynamics of synchronization in networks of coupled oscillators and applied to a model of resting-state brain activity. Connectivity in the model is based on a human-brain structural connectivity matrix derived from diffusion tensor imaging tractography. We find a strong correspondence between areas of high synchronization and highly connected “hub” nodes, anatomical regions forming the structural core of the network linking all areas of the brain. Such models have the potential to increase our understanding of the constraints placed on brain function by underlying anatomical structure.

Keywords

brain models; matrix algebra; pattern clustering; synchronisation; anatomical regions; coupled oscillators; diffusion tensor imaging tractography; hierarchical clustering; hub nodes; human-brain structural connectivity matrix; networks synchronization; resting-state brain activity model; temporal dynamics; Brain modeling; Clustering algorithms; Heuristic algorithms; Humans; Oscillators; Synchronization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2012 International Joint Conference on

Conference_Location

Brisbane, QLD

ISSN

2161-4393

Print_ISBN

978-1-4673-1488-6

Electronic_ISBN

2161-4393

Type

conf

DOI

10.1109/IJCNN.2012.6252393

Filename

6252393