Cited by 112Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsComplex Network Analysis Techniques

Statistical parametric network analysis of functional connectivity dynamics during a working memory task

Ginestet, Cedric E., Simmons, Andrew·2011·NeuroImage

Abstract

Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (<0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. Using an anatomical parcellation scheme, we produced undirected weighted graphs linking 90 regions of the brain representing major cortical gyri and subcortical nuclei, in a population of healthy adults (n=43). Topological changes in these networks were investigated under different conditions of a classical working memory task - the N-back paradigm. A mass-univariate approach was adopted to construct statistical parametric networks (SPNs) that reflect significant modifications in functional connectivity between N-back conditions. Our proposed method allowed the extraction of 'lost' and 'gained' functional networks, providing concise graphical summaries of whole-brain network topological changes. Robust estimates of functional networks are obtained by pooling information about edges and vertices over subjects. Graph thresholding is therefore here supplanted by inference. The analysis proceeds by firstly considering changes in weighted cost (i.e. mean between-region correlation) over the different N-back conditions and secondly comparing small-world topological measures integrated over network cost, thereby controlling for differences in mean correlation between conditions. The results are threefold: (i) functional networks in the four conditions were all found to satisfy the small-world property and cost-integrated global and local efficiency levels were approximately preserved across the different experimental conditions; (ii) weighted cost considerably decreased as working memory load increased; and (iii) subject-specific weighted costs significantly predicted behavioral performances on the N-back task (Wald F=13.39,df(1)=1,df(2)=83,p<0.001), and therefore conferred predictive validity to functional connectivity strength, as measured by weighted cost. The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2010.11.030

Cites

88 of 88 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The empirical neuroimaging literature has identified several distinct sets of brain areas underlying working-memory performance.

    “The empirical neuroimaging literature has bolstered these conceptualizations by identifying several distinct sets of brain areas underlying working-memory performance (13–17).”
  • The identified network modules map well to biologically meaningful systems of intrinsic connectivity and specifically systems commonly engaged during working-memory performance.

    “The identified network modules map well to biologically meaningful systems of intrinsic connectivity (36) and specifically systems commonly engaged during working-memory performance (14, 38, 39)”

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Ginestet, C. E., & Simmons, A.
Affiliations
King's College London (GB)
Corresponding author
Ginestet, Cedric E.
Venue
NeuroImage
Publisher
Elsevier BV
Published
2011-03
Volume
55
Issue
2
Pages
688-704
ISSN
1053-8119 (ISSN-L)
OpenAlex
W2033689731
Cited by
112
References
88
Citations by year
2026: 1 · 2025: 5 · 2024: 3 · 2023: 3 · 2022: 2 · 2021: 7 · 2020: 5 · 2019: 2 · 2018: 5 · 2017: 6 · 2016: 12 · 2015: 10 · 2014: 12 · 2013: 20 · 2012: 16
FWCI
7.35
Type
journal article
Language
en
Open access
closed
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (submittedVersion)
Funders
UK National Institute for Health Research (NIHR) Biomedical Research Centre
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
Keywords
dynamic functional connectivity, working memory, n-back task, cognitive load, small-world networks, network efficiency, BOLD signal fluctuations, brain network topology
MeSH terms
Adult, Aged, Aged, 80 and over, Brain, Brain Mapping, Female, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Memory, Short-Term, Middle Aged, Models, Neurological, Neural Pathways

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review