Cargando…
Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs
INTRODUCTION: Resting‐state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large‐scale brain networks and brain–behavior relationships. Furthermore, idiosyncratic patterns of...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275534/ https://www.ncbi.nlm.nih.gov/pubmed/37062880 http://dx.doi.org/10.1002/brb3.3015 |
_version_ | 1785059891702923264 |
---|---|
author | Frank, Lea E. Zeithamova, Dagmar |
author_facet | Frank, Lea E. Zeithamova, Dagmar |
author_sort | Frank, Lea E. |
collection | PubMed |
description | INTRODUCTION: Resting‐state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large‐scale brain networks and brain–behavior relationships. Furthermore, idiosyncratic patterns of resting‐state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task‐based fMRI can be similarly leveraged for individual differences analyses. METHOD: Here, we tested the degree to which functional interactions occurring in the background of a task during slow event‐related fMRI parallel or differ from those captured during resting‐state fMRI. We compared two approaches for removing task‐evoked activity from task‐based fMRI: (1) applying a low‐pass filter to remove task‐related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task‐evoked responses. RESULT: We found that the organization of large‐scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task‐based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low‐pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low‐pass‐filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION: Together, our results highlight new avenues for the analysis of task‐based fMRI datasets and the utility of each background connectivity method. |
format | Online Article Text |
id | pubmed-10275534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102755342023-06-17 Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs Frank, Lea E. Zeithamova, Dagmar Brain Behav Method INTRODUCTION: Resting‐state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large‐scale brain networks and brain–behavior relationships. Furthermore, idiosyncratic patterns of resting‐state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task‐based fMRI can be similarly leveraged for individual differences analyses. METHOD: Here, we tested the degree to which functional interactions occurring in the background of a task during slow event‐related fMRI parallel or differ from those captured during resting‐state fMRI. We compared two approaches for removing task‐evoked activity from task‐based fMRI: (1) applying a low‐pass filter to remove task‐related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task‐evoked responses. RESULT: We found that the organization of large‐scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task‐based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low‐pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low‐pass‐filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION: Together, our results highlight new avenues for the analysis of task‐based fMRI datasets and the utility of each background connectivity method. John Wiley and Sons Inc. 2023-04-16 /pmc/articles/PMC10275534/ /pubmed/37062880 http://dx.doi.org/10.1002/brb3.3015 Text en © 2023 The Authors. Brain and Behavior published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Frank, Lea E. Zeithamova, Dagmar Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title | Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title_full | Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title_fullStr | Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title_full_unstemmed | Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title_short | Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
title_sort | evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275534/ https://www.ncbi.nlm.nih.gov/pubmed/37062880 http://dx.doi.org/10.1002/brb3.3015 |
work_keys_str_mv | AT frankleae evaluatingmethodsformeasuringbackgroundconnectivityinsloweventrelatedfunctionalmagneticresonanceimagingdesigns AT zeithamovadagmar evaluatingmethodsformeasuringbackgroundconnectivityinsloweventrelatedfunctionalmagneticresonanceimagingdesigns |