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Journal article

Non-parametric Bayesian graph models reveal community structure in resting state fMRI

In Neuroimage 2014, Volume 100, pp. 301-315
From

Department of Applied Mathematics and Computer Science, Technical University of Denmark1

Danish Research Centre for Magnetic Resonance2

University of Copenhagen3

Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark4

Modeling of resting state functional magnetic resonance imaging (rs-fMRI) data using network models is of increasing interest. It is often desirable to group nodes into clusters to interpret the communication patterns between nodes. In this study we consider three different nonparametric Bayesian models for node clustering in complex networks.

In particular, we test their ability to predict unseen data and their ability to reproduce clustering across datasets. The three generative models considered are the Infinite Relational Model (IRM), Bayesian Community Detection (BCD), and the Infinite Diagonal Model (IDM). The models define probabilities of generating links within and between clusters and the difference between the models lies in the restrictions they impose upon the between-cluster link probabilities.

IRM is the most flexible model with no restrictions on the probabilities of links between clusters. BCD restricts the between-cluster link probabilities to be strictly lower than within-cluster link probabilities to conform to the community structure typically seen in social networks. IDM only models a single between-cluster link probability, which can be interpreted as a background noise probability.

These probabilistic models are compared against three other approaches for node clustering, namely Infomap, Louvain modularity, and hierarchical clustering. Using 3 different datasets comprising healthy volunteers' rs-fMRI we found that the BCD model was in general the most predictive and reproducible model.

This suggests that rs-fMRI data exhibits community structure and furthermore points to the significance of modeling heterogeneous between-cluster link probabilities.

Language: English
Year: 2014
Pages: 301-315
ISSN: 10959572 and 10538119
Types: Journal article
DOI: 10.1016/j.neuroimage.2014.05.083
ORCIDs: 0000-0001-8606-7641 , Schmidt, Mikkel Nørgaard , Mørup, Morten and Hansen, Lars Kai

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