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Quantifying uncertainty in brain network measures using Bayesian connectomics

The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While...

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Autores principales: Janssen, Ronald J., Hinne, Max, Heskes, Tom, van Gerven, Marcel A. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4189434/
https://www.ncbi.nlm.nih.gov/pubmed/25339896
http://dx.doi.org/10.3389/fncom.2014.00126
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author Janssen, Ronald J.
Hinne, Max
Heskes, Tom
van Gerven, Marcel A. J.
author_facet Janssen, Ronald J.
Hinne, Max
Heskes, Tom
van Gerven, Marcel A. J.
author_sort Janssen, Ronald J.
collection PubMed
description The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics.
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spelling pubmed-41894342014-10-22 Quantifying uncertainty in brain network measures using Bayesian connectomics Janssen, Ronald J. Hinne, Max Heskes, Tom van Gerven, Marcel A. J. Front Comput Neurosci Neuroscience The wiring diagram of the human brain can be described in terms of graph measures that characterize structural regularities. These measures require an estimate of whole-brain structural connectivity for which one may resort to deterministic or thresholded probabilistic streamlining procedures. While these procedures have provided important insights about the characteristics of human brain networks, they ultimately rely on unwarranted assumptions such as those of noise-free data or the use of an arbitrary threshold. Therefore, resulting structural connectivity estimates as well as derived graph measures fail to fully take into account the inherent uncertainty in the structural estimate. In this paper, we illustrate an easy way of obtaining posterior distributions over graph metrics using Bayesian inference. It is shown that this posterior distribution can be used to quantify uncertainty about graph-theoretical measures at the single subject level, thereby providing a more nuanced view of the graph-theoretical properties of human brain connectivity. We refer to this model-based approach to connectivity analysis as Bayesian connectomics. Frontiers Media S.A. 2014-10-08 /pmc/articles/PMC4189434/ /pubmed/25339896 http://dx.doi.org/10.3389/fncom.2014.00126 Text en Copyright © 2014 Janssen, Hinne, Heskes and van Gerven. http://creativecommons.org/licenses/by/4.0/ 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
Janssen, Ronald J.
Hinne, Max
Heskes, Tom
van Gerven, Marcel A. J.
Quantifying uncertainty in brain network measures using Bayesian connectomics
title Quantifying uncertainty in brain network measures using Bayesian connectomics
title_full Quantifying uncertainty in brain network measures using Bayesian connectomics
title_fullStr Quantifying uncertainty in brain network measures using Bayesian connectomics
title_full_unstemmed Quantifying uncertainty in brain network measures using Bayesian connectomics
title_short Quantifying uncertainty in brain network measures using Bayesian connectomics
title_sort quantifying uncertainty in brain network measures using bayesian connectomics
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4189434/
https://www.ncbi.nlm.nih.gov/pubmed/25339896
http://dx.doi.org/10.3389/fncom.2014.00126
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