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Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain.
PLoS One 2010; 5(4):e10232Plos

Abstract

Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.

Authors+Show Affiliations

Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. lohmann@cbs.mpg.deNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20436911

Citation

Lohmann, Gabriele, et al. "Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain." PloS One, vol. 5, no. 4, 2010, pp. e10232.
Lohmann G, Margulies DS, Horstmann A, et al. Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PLoS ONE. 2010;5(4):e10232.
Lohmann, G., Margulies, D. S., Horstmann, A., Pleger, B., Lepsien, J., Goldhahn, D., ... Turner, R. (2010). Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. PloS One, 5(4), pp. e10232. doi:10.1371/journal.pone.0010232.
Lohmann G, et al. Eigenvector Centrality Mapping for Analyzing Connectivity Patterns in fMRI Data of the Human Brain. PLoS ONE. 2010 Apr 27;5(4):e10232. PubMed PMID: 20436911.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Eigenvector centrality mapping for analyzing connectivity patterns in fMRI data of the human brain. AU - Lohmann,Gabriele, AU - Margulies,Daniel S, AU - Horstmann,Annette, AU - Pleger,Burkhard, AU - Lepsien,Joeran, AU - Goldhahn,Dirk, AU - Schloegl,Haiko, AU - Stumvoll,Michael, AU - Villringer,Arno, AU - Turner,Robert, Y1 - 2010/04/27/ PY - 2009/11/06/received PY - 2010/03/22/accepted PY - 2010/5/4/entrez PY - 2010/5/4/pubmed PY - 2011/6/17/medline SP - e10232 EP - e10232 JF - PloS one JO - PLoS ONE VL - 5 IS - 4 N2 - Functional magnetic resonance data acquired in a task-absent condition ("resting state") require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular "betweenness centrality" - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/20436911/Eigenvector_centrality_mapping_for_analyzing_connectivity_patterns_in_fMRI_data_of_the_human_brain_ L2 - http://dx.plos.org/10.1371/journal.pone.0010232 DB - PRIME DP - Unbound Medicine ER -