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Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation

Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimate...

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Detalles Bibliográficos
Autores principales: Geerligs, Linda, Cam-CAN, Henson, Richard N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academic Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4922835/
https://www.ncbi.nlm.nih.gov/pubmed/27114055
http://dx.doi.org/10.1016/j.neuroimage.2016.04.047
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author Geerligs, Linda
Cam-CAN
Henson, Richard N.
author_facet Geerligs, Linda
Cam-CAN
Henson, Richard N.
author_sort Geerligs, Linda
collection PubMed
description Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data.
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spelling pubmed-49228352016-07-15 Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation Geerligs, Linda Cam-CAN Henson, Richard N. Neuroimage Article Studies of brain-wide functional connectivity or structural covariance typically use measures like the Pearson correlation coefficient, applied to data that have been averaged across voxels within regions of interest (ROIs). However, averaging across voxels may result in biased connectivity estimates when there is inhomogeneity within those ROIs, e.g., sub-regions that exhibit different patterns of functional connectivity or structural covariance. Here, we propose a new measure based on “distance correlation”; a test of multivariate dependence of high dimensional vectors, which allows for both linear and non-linear dependencies. We used simulations to show how distance correlation out-performs Pearson correlation in the face of inhomogeneous ROIs. To evaluate this new measure on real data, we use resting-state fMRI scans and T1 structural scans from 2 sessions on each of 214 participants from the Cambridge Centre for Ageing & Neuroscience (Cam-CAN) project. Pearson correlation and distance correlation showed similar average connectivity patterns, for both functional connectivity and structural covariance. Nevertheless, distance correlation was shown to be 1) more reliable across sessions, 2) more similar across participants, and 3) more robust to different sets of ROIs. Moreover, we found that the similarity between functional connectivity and structural covariance estimates was higher for distance correlation compared to Pearson correlation. We also explored the relative effects of different preprocessing options and motion artefacts on functional connectivity. Because distance correlation is easy to implement and fast to compute, it is a promising alternative to Pearson correlations for investigating ROI-based brain-wide connectivity patterns, for functional as well as structural data. Academic Press 2016-07-15 /pmc/articles/PMC4922835/ /pubmed/27114055 http://dx.doi.org/10.1016/j.neuroimage.2016.04.047 Text en © 2016 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Geerligs, Linda
Cam-CAN
Henson, Richard N.
Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title_full Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title_fullStr Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title_full_unstemmed Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title_short Functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
title_sort functional connectivity and structural covariance between regions of interest can be measured more accurately using multivariate distance correlation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4922835/
https://www.ncbi.nlm.nih.gov/pubmed/27114055
http://dx.doi.org/10.1016/j.neuroimage.2016.04.047
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