Evidence map›Paper›PMID 30597260›Full record

ArticleNeuroImage2019

Task activations produce spurious but systematic inflation of task functional connectivity estimates.

Michael W Cole, Takuya Ito, Douglas Schultz, Ravi Mill, Richard Chen, Carrisa Cocuzza

Abstract read
In one paragraph

Article in NeuroImage, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers.

0numbers the graph read from it
0cells of the map it votes in
145citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

145 citing papers in PubMed.

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  6. Frontoparietal Hub Connectivity Integrates Information from Multiple Sources.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
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85 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Michael W ColeCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA. Electronic address: michael.cole@rutgers.edu.
Takuya ItoCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA.
Douglas SchultzCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.
Ravi MillCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA.
Richard ChenCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA.
Carrisa CocuzzaCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA; Behavioral and Neural Sciences PhD Program, Rutgers University, Newark, NJ, 07102, USA.

Funding

Mapping the Human Connectome: Structure, Function, and HeritabilityU54MH091657 · NIMH · WASHINGTON UNIVERSITY · PI UGURBIL, KAMIL, VAN ESSEN, DAVID C · 2010 to 2014
$34.7M
Brain Network Mechanisms of Aging-Related Cognitive DeclineR01AG055556 · NIA · RUTGERS THE STATE UNIV OF NJ NEWARK · PI COLE, MICHAEL WILLIAM · 2017 to 2022
$2.3M
Brain Network Mechanisms of Instructed LearningR01MH109520 · NIMH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI COLE, MICHAEL WILLIAM · 2016 to 2020
$2.0M
NIA NIH HHS R01 AG055556NIMH NIH HHS R01 MH109520NIMH NIH HHS U54 MH091657
6 · The paper itself

Abstract

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) - statistical association between brain activity time series during task performance - moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of task-evoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference "likely active or interacting during the task", rather than the intended inference "likely interacting during the task". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.

Indexed as

Models, TheoreticalNeural Networks, ComputerBrainConnectomeFunctional NeuroimagingHumansMagnetic Resonance ImagingMental ProcessesComputational modelingfMRIFunctional connectivityMethod validationNetworks

Identifiers

PMID30597260
PMCPMC6422749

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None linked

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.