Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity
Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811923000939 |
| _version_ | 1827989367354818560 |
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| author | Weiqi Zhao Carolina Makowski Donald J. Hagler Hugh P. Garavan Wesley K. Thompson Deanna J. Greene Terry L. Jernigan Anders M. Dale |
| author_facet | Weiqi Zhao Carolina Makowski Donald J. Hagler Hugh P. Garavan Wesley K. Thompson Deanna J. Greene Terry L. Jernigan Anders M. Dale |
| author_sort | Weiqi Zhao |
| collection | DOAJ |
| description | Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns. |
| first_indexed | 2024-04-10T00:18:15Z |
| format | Article |
| id | doaj.art-e1a31013d7da4b9ba7a0d6c9e5751582 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-10T00:18:15Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-e1a31013d7da4b9ba7a0d6c9e57515822023-03-16T05:03:03ZengElsevierNeuroImage1095-95722023-04-01270119946Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivityWeiqi Zhao0Carolina Makowski1Donald J. Hagler2Hugh P. Garavan3Wesley K. Thompson4Deanna J. Greene5Terry L. Jernigan6Anders M. Dale7Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USADepartment of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USAUniversity of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USAUniversity of Vermont, Burlington, Vermont, 05405, USALaureate Institute for Brain Research, Tulsa, OK 74136, USADepartment of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USADepartment of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92161, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USADepartment of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Center for Multimodal Imaging and Genetics, University of California, San Diego School of Medicine, 9444 Medical Center Dr, La Jolla, CA 92037, USA; Department of Neuroscience, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Department of Psychiatry, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA 92037, USA; Corresponding author.Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.http://www.sciencedirect.com/science/article/pii/S1053811923000939Behavioral differencesPredictive modelingFunctional connectivityCognitive developmentBehavioral inhibition |
| spellingShingle | Weiqi Zhao Carolina Makowski Donald J. Hagler Hugh P. Garavan Wesley K. Thompson Deanna J. Greene Terry L. Jernigan Anders M. Dale Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity NeuroImage Behavioral differences Predictive modeling Functional connectivity Cognitive development Behavioral inhibition |
| title | Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity |
| title_full | Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity |
| title_fullStr | Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity |
| title_full_unstemmed | Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity |
| title_short | Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity |
| title_sort | task fmri paradigms may capture more behaviorally relevant information than resting state functional connectivity |
| topic | Behavioral differences Predictive modeling Functional connectivity Cognitive development Behavioral inhibition |
| url | http://www.sciencedirect.com/science/article/pii/S1053811923000939 |
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