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Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates
Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural bas...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492297/ https://www.ncbi.nlm.nih.gov/pubmed/30357995 http://dx.doi.org/10.1002/hbm.24442 |
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author | Vidaurre, Diego Woolrich, Mark W. Winkler, Anderson M. Karapanagiotidis, Theodoros Smallwood, Jonathan Nichols, Thomas E. |
author_facet | Vidaurre, Diego Woolrich, Mark W. Winkler, Anderson M. Karapanagiotidis, Theodoros Smallwood, Jonathan Nichols, Thomas E. |
author_sort | Vidaurre, Diego |
collection | PubMed |
description | Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice. |
format | Online Article Text |
id | pubmed-6492297 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-64922972019-05-07 Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates Vidaurre, Diego Woolrich, Mark W. Winkler, Anderson M. Karapanagiotidis, Theodoros Smallwood, Jonathan Nichols, Thomas E. Hum Brain Mapp Research Articles Spatial or temporal aspects of neural organization are known to be important indices of how cognition is organized. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behavior. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (a) computational, for example, when using an approximate inference algorithm for which different runs can produce different results or (b) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice. John Wiley & Sons, Inc. 2018-10-25 /pmc/articles/PMC6492297/ /pubmed/30357995 http://dx.doi.org/10.1002/hbm.24442 Text en © 2018 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 Vidaurre, Diego Woolrich, Mark W. Winkler, Anderson M. Karapanagiotidis, Theodoros Smallwood, Jonathan Nichols, Thomas E. Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title | Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title_full | Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title_fullStr | Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title_full_unstemmed | Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title_short | Stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
title_sort | stable between‐subject statistical inference from unstable within‐subject functional connectivity estimates |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6492297/ https://www.ncbi.nlm.nih.gov/pubmed/30357995 http://dx.doi.org/10.1002/hbm.24442 |
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