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Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging

Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact o...

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Autores principales: Geerligs, Linda, Tsvetanov, Kamen A., Cam‐CAN, Henson, Richard N.
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/PMC5518296/
https://www.ncbi.nlm.nih.gov/pubmed/28544076
http://dx.doi.org/10.1002/hbm.23653
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author Geerligs, Linda
Tsvetanov, Kamen A.
Cam‐CAN,
Henson, Richard N.
author_facet Geerligs, Linda
Tsvetanov, Kamen A.
Cam‐CAN,
Henson, Richard N.
author_sort Geerligs, Linda
collection PubMed
description Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (http://www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc.
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spelling pubmed-55182962017-08-03 Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging Geerligs, Linda Tsvetanov, Kamen A. Cam‐CAN, Henson, Richard N. Hum Brain Mapp Research Articles Many studies report individual differences in functional connectivity, such as those related to age. However, estimates of connectivity from fMRI are confounded by other factors, such as vascular health, head motion and changes in the location of functional regions. Here, we investigate the impact of these confounds, and pre‐processing strategies that can mitigate them, using data from the Cambridge Centre for Ageing & Neuroscience (http://www.cam-can.com). This dataset contained two sessions of resting‐state fMRI from 214 adults aged 18–88. Functional connectivity between all regions was strongly related to vascular health, most likely reflecting respiratory and cardiac signals. These variations in mean connectivity limit the validity of between‐participant comparisons of connectivity estimates, and were best mitigated by regression of mean connectivity over participants. We also showed that high‐pass filtering, instead of band‐pass filtering, produced stronger and more reliable age‐effects. Head motion was correlated with gray‐matter volume in selected brain regions, and with various cognitive measures, suggesting that it has a biological (trait) component, and warning against regressing out motion over participants. Finally, we showed that the location of functional regions was more variable in older adults, which was alleviated by smoothing the data, or using a multivariate measure of connectivity. These results demonstrate that analysis choices have a dramatic impact on connectivity differences between individuals, ultimately affecting the associations found between connectivity and cognition. It is important that fMRI connectivity studies address these issues, and we suggest a number of ways to optimize analysis choices. Hum Brain Mapp 38:4125–4156, 2017. © 2017 Wiley Periodicals, Inc. John Wiley and Sons Inc. 2017-05-23 /pmc/articles/PMC5518296/ /pubmed/28544076 http://dx.doi.org/10.1002/hbm.23653 Text en © 2017 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
Geerligs, Linda
Tsvetanov, Kamen A.
Cam‐CAN,
Henson, Richard N.
Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title_full Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title_fullStr Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title_full_unstemmed Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title_short Challenges in measuring individual differences in functional connectivity using fMRI: The case of healthy aging
title_sort challenges in measuring individual differences in functional connectivity using fmri: the case of healthy aging
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5518296/
https://www.ncbi.nlm.nih.gov/pubmed/28544076
http://dx.doi.org/10.1002/hbm.23653
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