Estimating trial-wise modulation of functional connectivity using event-related fMRI
- 1University of Iowa
Abstract
Understanding the neural basis of human cognition requires measuring not only localized brain activity but also how functional interactions between brain regions change in response to different cognitive demands. Event-related fMRI is an efficient design for linking trial-wise behavioral and computational variables to brain activity, but comparable methods for examining their effects on functional connectivity remain limited. Here, we introduce a regression-based beta-series modeling (BSM) framework that leverages single-trial response estimates from event-related fMRI to quantify how task-related variables modulate functional connectivity. By modeling the interaction between regional activity and task-related variables, this framework provides a statistical approach for testing connectivity modulation by both continuous and categorical variables. We evaluated this approach using comprehensive simulations across several experimental conditions and signal qualities. The regression-based BSM approach can sensitively detect ground-truth effects with well-controlled false-positive rates. Compared with generalized psychophysiological interaction, BSM achieved comparable performance across most conditions while demonstrating improved statistical power under lower signal-to-noise conditions. We further validated the approach using empirical event-related fMRI data. Distinct trial-wise cognitive variables selectively modulated functional connectivity during their corresponding trial epochs, demonstrating the temporal specificity and flexibility of the approach. Together, these findings demonstrate that regression-based BSM provides a flexible framework for examining task-modulated functional connectivity in event-related fMRI, with applications ranging from conventional experimental manipulations to continuous computational model-derived variables.
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BibTeX
@misc{hwang2026estimating,
title = {{Estimating trial-wise modulation of functional connectivity using event-related fMRI}},
author = {K. Hwang and S. E. Stokes and S. C. Leach and J. Jiang},
howpublished = {bioRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.15.738746},
url = {https://www.biorxiv.org/content/10.64898/2026.07.15.738746v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Estimating trial-wise modulation of functional connectivity using event-related fMRI
AU - K. Hwang
AU - S. E. Stokes
AU - S. C. Leach
AU - J. Jiang
PY - 2026
DA - 2026/07/15
PB - bioRxiv
DO - 10.64898/2026.07.15.738746
UR - https://www.biorxiv.org/content/10.64898/2026.07.15.738746v2
AB - Understanding the neural basis of human cognition requires measuring not only localized brain activity but also how functional interactions between brain regions change in response to different cognitive demands. Event-related fMRI is an efficient design for linking trial-wise behavioral and computational variables to brain activity, but comparable methods for examining their effects on functional connectivity remain limited. Here, we introduce a regression-based beta-series modeling (BSM) framework that leverages single-trial response estimates from event-related fMRI to quantify how task-related variables modulate functional connectivity. By modeling the interaction between regional activity and task-related variables, this framework provides a statistical approach for testing connectivity modulation by both continuous and categorical variables. We evaluated this approach using comprehensive simulations across several experimental conditions and signal qualities. The regression-based BSM approach can sensitively detect ground-truth effects with well-controlled false-positive rates. Compared with generalized psychophysiological interaction, BSM achieved comparable performance across most conditions while demonstrating improved statistical power under lower signal-to-noise conditions. We further validated the approach using empirical event-related fMRI data. Distinct trial-wise cognitive variables selectively modulated functional connectivity during their corresponding trial epochs, demonstrating the temporal specificity and flexibility of the approach. Together, these findings demonstrate that regression-based BSM provides a flexible framework for examining task-modulated functional connectivity in event-related fMRI, with applications ranging from conventional experimental manipulations to continuous computational model-derived variables.
ER -