Evidence map›Paper›PMID 27261162›Full record

ArticleNeuroImage2016

Detection of functional brain network reconfiguration during task-driven cognitive states.

Qawi K Telesford, Mary-Ellen Lynall, Jean Vettel, Michael B Miller, Scott T Grafton, Danielle S Bassett

Abstract read
In one paragraph

Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed, 1 pooled it
–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

84 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  4. Elevation in network dynamics amplifies amyloid-dependent tau pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  15. Cortisol awakening response prompts dynamic reconfiguration of brain networks in emotional and executive functioning.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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24 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.

Qawi K TelesfordDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Army Research Laboratory, Aberdeen Proving Ground, MD 21001, USA.
Mary-Ellen LynallDepartment of Psychiatry, University of Cambridge, Cambridge, UK; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Jean VettelArmy Research Laboratory, Aberdeen Proving Ground, MD 21001, USA; Department Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Michael B MillerDepartment Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Scott T GraftonDepartment Psychological and Brain Science, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
Danielle S BassettDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: dsb@seas.upenn.edu.

Funding

Evolution of the Linked Architecture of Network Control and Executive Function in AdolescenceR21MH106799 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, SATTERTHWAITE, THEODORE · 2016 to 2017
$515k
CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic NetworksR01HD086888 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH · 2015 to 2018
$506k
NICHD NIH HHS R01 HD086888NIMH NIH HHS R21 MH106799
6 · The paper itself

Abstract

Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches.

Indexed as

AdultAttentionBrainConnectomeFemaleHumansMagnetic Resonance ImagingMalePattern Recognition, VisualPsychomotor PerformanceRecognition, PsychologyTime Factors

Identifiers

PMID27261162
PMCPMC5133201

What OpenQuestion holds

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Registered trials

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.