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

TY - JOUR

T1 - Non-parametric Bayesian graph models reveal community structure in resting state fMRI

AU - Andersen, Kasper Winther

AU - Madsen, Kristoffer H

AU - Siebner, Hartwig Roman

AU - Schmidt, Mikkel N

AU - Mørup, Morten

AU - Hansen, Lars Kai

N1 - Copyright © 2014 Elsevier Inc. All rights reserved.

PY - 2014/10/15

Y1 - 2014/10/15

N2 - 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.

AB - 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.

KW - Adult

KW - Connectome

KW - Female

KW - Humans

KW - Magnetic Resonance Imaging

KW - Male

KW - Models, Statistical

KW - Nerve Net

KW - Neural Networks (Computer)

U2 - 10.1016/j.neuroimage.2014.05.083

DO - 10.1016/j.neuroimage.2014.05.083

M3 - Journal article

C2 - 24914522

SN - 1053-8119

VL - 100

SP - 301

EP - 315

JO - NeuroImage

JF - NeuroImage

ER -