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Effects of spatial smoothing on functional brain networks

Graph‐theoretical methods have rapidly become a standard tool in studies of the structure and function of the human brain. Whereas the structural connectome can be fairly straightforwardly mapped onto a complex network, there are more degrees of freedom in constructing networks that represent functi...

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Autores principales: Alakörkkö, Tuomas, Saarimäki, Heini, Glerean, Enrico, Saramäki, Jari, Korhonen, Onerva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5698731/
https://www.ncbi.nlm.nih.gov/pubmed/28922510
http://dx.doi.org/10.1111/ejn.13717
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author Alakörkkö, Tuomas
Saarimäki, Heini
Glerean, Enrico
Saramäki, Jari
Korhonen, Onerva
author_facet Alakörkkö, Tuomas
Saarimäki, Heini
Glerean, Enrico
Saramäki, Jari
Korhonen, Onerva
author_sort Alakörkkö, Tuomas
collection PubMed
description Graph‐theoretical methods have rapidly become a standard tool in studies of the structure and function of the human brain. Whereas the structural connectome can be fairly straightforwardly mapped onto a complex network, there are more degrees of freedom in constructing networks that represent functional connections between brain areas. For functional magnetic resonance imaging (fMRI) data, such networks are typically built by aggregating the blood‐oxygen‐level dependent signal time series of voxels into larger entities (such as Regions of Interest in some brain atlas) and determining their connection strengths from some measure of time‐series correlations. Although it is evident that the outcome must be affected by how the voxel‐level time series are treated at the preprocessing stage, there is a lack of systematic studies of the effects of preprocessing on network structure. Here, we focus on the effects of spatial smoothing, a standard preprocessing method for fMRI. We apply various levels of spatial smoothing to resting‐state fMRI data and measure the changes induced in functional networks. We show that the level of spatial smoothing clearly affects the degrees and other centrality measures of functional network nodes; these changes are non‐uniform, systematic, and depend on the geometry of the brain. The composition of the largest connected network component is also affected in a way that artificially increases the similarity of the networks of different subjects. Our conclusion is that wherever possible, spatial smoothing should be avoided when preprocessing fMRI data for network analysis.
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spelling pubmed-56987312017-11-30 Effects of spatial smoothing on functional brain networks Alakörkkö, Tuomas Saarimäki, Heini Glerean, Enrico Saramäki, Jari Korhonen, Onerva Eur J Neurosci Computational Neuroscience Graph‐theoretical methods have rapidly become a standard tool in studies of the structure and function of the human brain. Whereas the structural connectome can be fairly straightforwardly mapped onto a complex network, there are more degrees of freedom in constructing networks that represent functional connections between brain areas. For functional magnetic resonance imaging (fMRI) data, such networks are typically built by aggregating the blood‐oxygen‐level dependent signal time series of voxels into larger entities (such as Regions of Interest in some brain atlas) and determining their connection strengths from some measure of time‐series correlations. Although it is evident that the outcome must be affected by how the voxel‐level time series are treated at the preprocessing stage, there is a lack of systematic studies of the effects of preprocessing on network structure. Here, we focus on the effects of spatial smoothing, a standard preprocessing method for fMRI. We apply various levels of spatial smoothing to resting‐state fMRI data and measure the changes induced in functional networks. We show that the level of spatial smoothing clearly affects the degrees and other centrality measures of functional network nodes; these changes are non‐uniform, systematic, and depend on the geometry of the brain. The composition of the largest connected network component is also affected in a way that artificially increases the similarity of the networks of different subjects. Our conclusion is that wherever possible, spatial smoothing should be avoided when preprocessing fMRI data for network analysis. John Wiley and Sons Inc. 2017-10-20 2017-11 /pmc/articles/PMC5698731/ /pubmed/28922510 http://dx.doi.org/10.1111/ejn.13717 Text en © 2017 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution (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 Computational Neuroscience
Alakörkkö, Tuomas
Saarimäki, Heini
Glerean, Enrico
Saramäki, Jari
Korhonen, Onerva
Effects of spatial smoothing on functional brain networks
title Effects of spatial smoothing on functional brain networks
title_full Effects of spatial smoothing on functional brain networks
title_fullStr Effects of spatial smoothing on functional brain networks
title_full_unstemmed Effects of spatial smoothing on functional brain networks
title_short Effects of spatial smoothing on functional brain networks
title_sort effects of spatial smoothing on functional brain networks
topic Computational Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5698731/
https://www.ncbi.nlm.nih.gov/pubmed/28922510
http://dx.doi.org/10.1111/ejn.13717
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