Cognitive effort drives workspace configuration of human brain functional networks
Open accessAbstract
Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers ( N = 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the β-band (16–32 Hz) and γ-band (32–63 Hz) networks, compared with both lower (α-band; 8–16 Hz) and higher (high γ-band; 63–125 Hz) frequency intervals. Workspace configuration of β-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting <2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.
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In healthy young adults, higher global efficiency in structural and functional networks is associated with better IQ, attention, and working memory.
“Greater global efficiency in structural and functional networks has been linked to higher IQ, attentional capacity, and working memory in healthy young adults (Cohen and D’Esposito, 2016; Kitzbichler et al., 2011; Li et al., 2009; Stanley et al., 2015; van den Heuvel et al., 2009)”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Kitzbichler et al., 2011
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- Authors
- Kitzbichler, M. G., Henson, R. N. A., Smith, M. L., Nathan, P. J., & Bullmore, E. T.
- Affiliations
- University of Cambridge (GB)
- MRC Cognition and Brain Sciences Unit (GB)
- GlaxoSmithKline (United Kingdom) (GB)
- ORCID
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2011-06-01
- Volume
- 31
- Issue
- 22
- Pages
- 8259-8270
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6622866
- OpenAlex
- W1999174202
- Cited by
- 427
- References
- 67
- Citations by year
- 2026: 19 · 2025: 17 · 2024: 16 · 2023: 32 · 2022: 25 · 2021: 36 · 2020: 21 · 2019: 51 · 2018: 29 · 2017: 33 · 2016: 42 · 2015: 38 · 2014: 25 · 2013: 26 · 2012: 14
- FWCI
- 11.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Neuroscience (publishedVersion, cc-by-nc-sa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- BIROn (Birkbeck, University of London) (submittedVersion)
- Funders
- Wellcome Trust
- Medical Research Council
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- global neuronal workspace theory, cognitive effort, functional brain networks, network topology, network modularity, beta oscillations, gamma oscillations, response latency, network efficiency
- MeSH terms
- Adult, Cognition, Female, Humans, Memory, Short-Term, Nerve Net, Psychological Theory, Psychomotor Performance, Magnetoencephalography, Brain Waves
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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