Changes in community structure of resting state functional connectivity in unipolar depression.

Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenou...

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Autores principales:	Anton Lord, Dorothea Horn, Michael Breakspear, Martin Walter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca2f3e21e2404415ae426188ab7a12d5
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spelling	oai:doaj.org-article:ca2f3e21e2404415ae426188ab7a12d52021-11-18T07:08:17ZChanges in community structure of resting state functional connectivity in unipolar depression.1932-620310.1371/journal.pone.0041282https://doaj.org/article/ca2f3e21e2404415ae426188ab7a12d52012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22916105/?tool=EBIhttps://doaj.org/toc/1932-6203Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.Anton LordDorothea HornMichael BreakspearMartin WalterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 8, p e41282 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anton Lord Dorothea Horn Michael Breakspear Martin Walter Changes in community structure of resting state functional connectivity in unipolar depression.
description	Major depression is a prevalent disorder that imposes a significant burden on society, yet objective laboratory-style tests to assist in diagnosis are lacking. We employed network-based analyses of "resting state" functional neuroimaging data to ascertain group differences in the endogenous cortical activity between healthy and depressed subjects.We additionally sought to use machine learning techniques to explore the ability of these network-based measures of resting state activity to provide diagnostic information for depression. Resting state fMRI data were acquired from twenty two depressed outpatients and twenty two healthy subjects matched for age and gender. These data were anatomically parcellated and functional connectivity matrices were then derived using the linear correlations between the BOLD signal fluctuations of all pairs of cortical and subcortical regions.We characterised the hierarchical organization of these matrices using network-based matrics, with an emphasis on their mid-scale "modularity" arrangement. Whilst whole brain measures of organization did not differ between groups, a significant rearrangement of their community structure was observed. Furthermore we were able to classify individuals with a high level of accuracy using a support vector machine, primarily through the use of a modularity-based metric known as the participation index.In conclusion, the application of machine learning techniques to features of resting state fMRI network activity shows promising potential to assist in the diagnosis of major depression, now suggesting the need for validation in independent data sets.
format	article
author	Anton Lord Dorothea Horn Michael Breakspear Martin Walter
author_facet	Anton Lord Dorothea Horn Michael Breakspear Martin Walter
author_sort	Anton Lord
title	Changes in community structure of resting state functional connectivity in unipolar depression.
title_short	Changes in community structure of resting state functional connectivity in unipolar depression.
title_full	Changes in community structure of resting state functional connectivity in unipolar depression.
title_fullStr	Changes in community structure of resting state functional connectivity in unipolar depression.
title_full_unstemmed	Changes in community structure of resting state functional connectivity in unipolar depression.
title_sort	changes in community structure of resting state functional connectivity in unipolar depression.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/ca2f3e21e2404415ae426188ab7a12d5
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Changes in community structure of resting state functional connectivity in unipolar depression.

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