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Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses

The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to cha...

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Autores principales: Stephen, Emily P., Lepage, Kyle Q., Eden, Uri T., Brunner, Peter, Schalk, Gerwin, Brumberg, Jonathan S., Guenther, Frank H., Kramer, Mark A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958753/
https://www.ncbi.nlm.nih.gov/pubmed/24678295
http://dx.doi.org/10.3389/fncom.2014.00031
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author Stephen, Emily P.
Lepage, Kyle Q.
Eden, Uri T.
Brunner, Peter
Schalk, Gerwin
Brumberg, Jonathan S.
Guenther, Frank H.
Kramer, Mark A.
author_facet Stephen, Emily P.
Lepage, Kyle Q.
Eden, Uri T.
Brunner, Peter
Schalk, Gerwin
Brumberg, Jonathan S.
Guenther, Frank H.
Kramer, Mark A.
author_sort Stephen, Emily P.
collection PubMed
description The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.
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spelling pubmed-39587532014-03-27 Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses Stephen, Emily P. Lepage, Kyle Q. Eden, Uri T. Brunner, Peter Schalk, Gerwin Brumberg, Jonathan S. Guenther, Frank H. Kramer, Mark A. Front Comput Neurosci Neuroscience The brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience. Frontiers Media S.A. 2014-03-19 /pmc/articles/PMC3958753/ /pubmed/24678295 http://dx.doi.org/10.3389/fncom.2014.00031 Text en Copyright © 2014 Stephen, Lepage, Eden, Brunner, Schalk, Brumberg, Guenther and Kramer. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Stephen, Emily P.
Lepage, Kyle Q.
Eden, Uri T.
Brunner, Peter
Schalk, Gerwin
Brumberg, Jonathan S.
Guenther, Frank H.
Kramer, Mark A.
Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title_full Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title_fullStr Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title_full_unstemmed Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title_short Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
title_sort assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3958753/
https://www.ncbi.nlm.nih.gov/pubmed/24678295
http://dx.doi.org/10.3389/fncom.2014.00031
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