Statistical parametric network analysis of functional connectivity dynamics during a working memory task
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.
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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).”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [14]
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)”
From Dynamic reconfiguration of frontal brain networks during executive cognition in humans (2015) · cites this paper as [14]
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- 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 ›
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