Cargando…
Clustering of Resting State Networks
BACKGROUND: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. METHODOLOGY/PRINCIPAL FINDINGS: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcort...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392237/ https://www.ncbi.nlm.nih.gov/pubmed/22792291 http://dx.doi.org/10.1371/journal.pone.0040370 |
_version_ | 1782237606121046016 |
---|---|
author | Lee, Megan H. Hacker, Carl D. Snyder, Abraham Z. Corbetta, Maurizio Zhang, Dongyang Leuthardt, Eric C. Shimony, Joshua S. |
author_facet | Lee, Megan H. Hacker, Carl D. Snyder, Abraham Z. Corbetta, Maurizio Zhang, Dongyang Leuthardt, Eric C. Shimony, Joshua S. |
author_sort | Lee, Megan H. |
collection | PubMed |
description | BACKGROUND: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. METHODOLOGY/PRINCIPAL FINDINGS: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization. CONCLUSIONS/SIGNIFICANCE: The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized. |
format | Online Article Text |
id | pubmed-3392237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33922372012-07-12 Clustering of Resting State Networks Lee, Megan H. Hacker, Carl D. Snyder, Abraham Z. Corbetta, Maurizio Zhang, Dongyang Leuthardt, Eric C. Shimony, Joshua S. PLoS One Research Article BACKGROUND: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. METHODOLOGY/PRINCIPAL FINDINGS: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization. CONCLUSIONS/SIGNIFICANCE: The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized. Public Library of Science 2012-07-09 /pmc/articles/PMC3392237/ /pubmed/22792291 http://dx.doi.org/10.1371/journal.pone.0040370 Text en Lee et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Lee, Megan H. Hacker, Carl D. Snyder, Abraham Z. Corbetta, Maurizio Zhang, Dongyang Leuthardt, Eric C. Shimony, Joshua S. Clustering of Resting State Networks |
title | Clustering of Resting State Networks |
title_full | Clustering of Resting State Networks |
title_fullStr | Clustering of Resting State Networks |
title_full_unstemmed | Clustering of Resting State Networks |
title_short | Clustering of Resting State Networks |
title_sort | clustering of resting state networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392237/ https://www.ncbi.nlm.nih.gov/pubmed/22792291 http://dx.doi.org/10.1371/journal.pone.0040370 |
work_keys_str_mv | AT leemeganh clusteringofrestingstatenetworks AT hackercarld clusteringofrestingstatenetworks AT snyderabrahamz clusteringofrestingstatenetworks AT corbettamaurizio clusteringofrestingstatenetworks AT zhangdongyang clusteringofrestingstatenetworks AT leuthardtericc clusteringofrestingstatenetworks AT shimonyjoshuas clusteringofrestingstatenetworks |