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Task-Evoked Negative BOLD Response in the Default Mode Network Does Not Alter Its Functional Connectivity

Overview of attention for article published in Frontiers in Computational Neuroscience, August 2018
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Title
Task-Evoked Negative BOLD Response in the Default Mode Network Does Not Alter Its Functional Connectivity
Published in
Frontiers in Computational Neuroscience, August 2018
DOI 10.3389/fncom.2018.00067
Pubmed ID
Authors

Qolamreza R. Razlighi

Abstract

While functional connectivity networks are often extracted from resting-state fMRI scans, they have been shown to be active during task performance as well. However, the effect of an in-scanner task on functional connectivity networks is not completely understood. While there is evidence that task-evoked positive BOLD response can alter functional connectivity networks, particularly in the primary sensorimotor cortices, the effect of task-evoked negative BOLD response on the functional connectivity of the Default mode network (DMN) is somewhat ambiguous. In this study, we aim to investigate whether task performance, which is associated with negative BOLD response in the DMN regions, alters the time-course of functional connectivity in the same regions obtained by independent component analysis (ICA). ICA has been used to effectively extract functional connectivity networks during task performance and resting-state. We first demonstrate that performing a simple visual-motor task alters the temporal time-course of the network extracted from the primary visual cortex. Then we show that despite detecting a robust task-evoked negative BOLD response in the DMN regions, a simple visual-motor task does not alter the functional connectivity of the DMN regions. Our findings suggest that different mechanisms may underlie the relationship between task-related activation/deactivation networks and the overlapping functional connectivity networks in the human large-scale brain networks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 23%
Student > Doctoral Student 4 13%
Student > Master 4 13%
Researcher 4 13%
Other 2 7%
Other 2 7%
Unknown 7 23%
Readers by discipline Count As %
Neuroscience 8 27%
Engineering 7 23%
Psychology 3 10%
Medicine and Dentistry 2 7%
Unknown 10 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 August 2018.
All research outputs
#18,647,094
of 23,100,534 outputs
Outputs from Frontiers in Computational Neuroscience
#1,059
of 1,358 outputs
Outputs of similar age
#256,383
of 333,688 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#30
of 31 outputs
Altmetric has tracked 23,100,534 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,358 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 13th percentile – i.e., 13% of its peers scored the same or lower than it.
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.