Evidence map›Paper›PMID 37062880›Full record

ArticleBrain and behavior2023

Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs.

Lea E Frank, Dagmar Zeithamova

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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  6. Hippocampal-Cortical Networks Predict Conceptual versus Perceptually Guided Narrative Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

2 authors.

Lea E FrankDepartment of Psychology, University of Oregon, Eugene, Oregon, USA.ORCID 0000-0002-6774-0826
Dagmar ZeithamovaDepartment of Psychology, University of Oregon, Eugene, Oregon, USA.

Funding

Neural mechanisms of specificity and generalization learningR01NS112366 · NINDS · UNIVERSITY OF OREGON · PI ZEITHAMOVA, DAGMAR · 2020 to 2024
$1.7M
NINDS NIH HHS R01 NS112366
6 · The paper itself

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.

Indexed as

ConnectomeBrainBrain MappingCognitionHumansMagnetic Resonance ImagingNerve NetRestbackground connectivityconnectivity fingerprintindividual differencesresting-state functional connectivitytask-based functional connectivity

Identifiers

PMID37062880
PMCPMC10275534

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.