Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs
Abstract 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 patt...
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| Format: | Article |
| Language: | English |
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Wiley
2023-06-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.3015 |
| _version_ | 1797802418202738688 |
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| author | Lea E. Frank Dagmar Zeithamova |
| author_facet | Lea E. Frank Dagmar Zeithamova |
| author_sort | Lea E. Frank |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-13T05:05:24Z |
| format | Article |
| id | doaj.art-fafc2678840c4b9cbd71cb786fed8677 |
| institution | Directory Open Access Journal |
| issn | 2162-3279 |
| language | English |
| last_indexed | 2024-03-13T05:05:24Z |
| publishDate | 2023-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj.art-fafc2678840c4b9cbd71cb786fed86772023-06-16T18:11:55ZengWileyBrain and Behavior2162-32792023-06-01136n/an/a10.1002/brb3.3015Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designsLea E. Frank0Dagmar Zeithamova1Department of Psychology University of Oregon Eugene Oregon USADepartment of Psychology University of Oregon Eugene Oregon USAAbstract 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.https://doi.org/10.1002/brb3.3015background connectivityconnectivity fingerprintindividual differencesresting‐state functional connectivitytask‐based functional connectivity |
| spellingShingle | Lea E. Frank Dagmar Zeithamova Evaluating methods for measuring background connectivity in slow event‐related functional magnetic resonance imaging designs Brain and Behavior background connectivity connectivity fingerprint individual differences resting‐state functional connectivity task‐based functional connectivity |
| 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 | background connectivity connectivity fingerprint individual differences resting‐state functional connectivity task‐based functional connectivity |
| url | https://doi.org/10.1002/brb3.3015 |
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