ArticleBrain and behavior2023
Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs.
Article in Brain and behavior, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- When Can Brain Connectivity Track the Working Mind? A Large-Scale Benchmark of Dynamic Functional Connectivity Across Cognitive Paradigms.bioRxiv : the preprint server for biology · 2026Article
- MRecover: A Conditional Generative Model for Recovering Motion- Corrupted MR images Using AI Generated Contrast.Research square · 2026Article
- A rest-task fMRI study of spatial working memory in HIV-infected individuals across cognitive states.BMC medical imaging · 2026Article
- Hippocampal connectivity predicting recognition and categorization abilities.Frontiers in cognition · 2026Article
- Hippocampal-Cortical Networks Predict Conceptual versus Perceptually Guided Narrative Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Balancing Data Quality and Bias: Investigating Functional Connectivity Exclusions in the Adolescent Brain Cognitive Development℠ (ABCD Study) Across Quality Control Pathways.Human brain mapping · 2025Article
- Patient-specific geometrical distortion corrections of MRI images improve dosimetric planning accuracy of vestibular schwannoma treated with gamma knife stereotactic radiosurgery.Journal of applied clinical medical physics · 2023Article
- Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs.Brain and behavior · 2023Article
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Abstract
introductionResting-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.
methodHere, 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.
resultWe 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.
conclusionTogether, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.
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