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Comparison of continuously acquired resting state and extracted analogues from active tasks
Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4950683/ https://www.ncbi.nlm.nih.gov/pubmed/26178250 http://dx.doi.org/10.1002/hbm.22897 |
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author | Ganger, Sebastian Hahn, Andreas Küblböck, Martin Kranz, Georg S. Spies, Marie Vanicek, Thomas Seiger, René Sladky, Ronald Windischberger, Christian Kasper, Siegfried Lanzenberger, Rupert |
author_facet | Ganger, Sebastian Hahn, Andreas Küblböck, Martin Kranz, Georg S. Spies, Marie Vanicek, Thomas Seiger, René Sladky, Ronald Windischberger, Christian Kasper, Siegfried Lanzenberger, Rupert |
author_sort | Ganger, Sebastian |
collection | PubMed |
description | Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R (2)) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. |
format | Online Article Text |
id | pubmed-4950683 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-49506832016-07-29 Comparison of continuously acquired resting state and extracted analogues from active tasks Ganger, Sebastian Hahn, Andreas Küblböck, Martin Kranz, Georg S. Spies, Marie Vanicek, Thomas Seiger, René Sladky, Ronald Windischberger, Christian Kasper, Siegfried Lanzenberger, Rupert Hum Brain Mapp Research Articles Functional connectivity analysis of brain networks has become an important tool for investigation of human brain function. Although functional connectivity computations are usually based on resting‐state data, the application to task‐specific fMRI has received growing attention. Three major methods for extraction of resting‐state data from task‐related signal have been proposed (1) usage of unmanipulated task data for functional connectivity; (2) regression against task effects, subsequently using the residuals; and (3) concatenation of baseline blocks located in‐between task blocks. Despite widespread application in current research, consensus on which method best resembles resting‐state seems to be missing. We, therefore, evaluated these techniques in a sample of 26 healthy controls measured at 7 Tesla. In addition to continuous resting‐state, two different task paradigms were assessed (emotion discrimination and right finger‐tapping) and five well‐described networks were analyzed (default mode, thalamus, cuneus, sensorimotor, and auditory). Investigating the similarity to continuous resting‐state (Dice, Intraclass correlation coefficient (ICC), R (2)) showed that regression against task effects yields functional connectivity networks most alike to resting‐state. However, all methods exhibited significant differences when compared to continuous resting‐state and similarity metrics were lower than test‐retest of two resting‐state scans. Omitting global signal regression did not change these findings. Visually, the networks are highly similar, but through further investigation marked differences can be found. Therefore, our data does not support referring to resting‐state when extracting signals from task designs, although functional connectivity computed from task‐specific data may indeed yield interesting information. Hum Brain Mapp 36:4053–4063, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. John Wiley and Sons Inc. 2015-07-15 /pmc/articles/PMC4950683/ /pubmed/26178250 http://dx.doi.org/10.1002/hbm.22897 Text en © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Ganger, Sebastian Hahn, Andreas Küblböck, Martin Kranz, Georg S. Spies, Marie Vanicek, Thomas Seiger, René Sladky, Ronald Windischberger, Christian Kasper, Siegfried Lanzenberger, Rupert Comparison of continuously acquired resting state and extracted analogues from active tasks |
title | Comparison of continuously acquired resting state and extracted analogues from active tasks |
title_full | Comparison of continuously acquired resting state and extracted analogues from active tasks |
title_fullStr | Comparison of continuously acquired resting state and extracted analogues from active tasks |
title_full_unstemmed | Comparison of continuously acquired resting state and extracted analogues from active tasks |
title_short | Comparison of continuously acquired resting state and extracted analogues from active tasks |
title_sort | comparison of continuously acquired resting state and extracted analogues from active tasks |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4950683/ https://www.ncbi.nlm.nih.gov/pubmed/26178250 http://dx.doi.org/10.1002/hbm.22897 |
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