The neurocognitive correlates of brain entropy estimated by resting state fMRI
Open accessAbstract
Resting state brain activity consumes most of brain energy, likely creating and maintaining a reserve of general brain functionality. The latent reserve if it exists may be reflected by the profound long-range fluctuations of resting brain activity. The long-range temporal coherence (LRTC) can be characterized by resting state fMRI (rsfMRI)-based brain entropy (BEN) mapping. While BEN mapping results have shown sensitivity to neuromodulations or disease conditions, the underlying neuromechanisms especially the associations of BEN or LRTC to neurocognition still remain unclear. To address this standing question and to test a novel hypothesis that resting BEN reflects a latent functional reserve through the link to general functionality, we mapped resting BEN of 862 young adults and comprehensively examined its associations to neurocognitions using data from the Human Connectome Project (HCP). Our results unanimously highlighted two brain circuits: the default mode network (DMN) and executive control network (ECN) through their negative associations of BEN to general functionality, which is independent of age and sex. While BEN in DMN/ECN increases with age, it decreases with education years. These results demonstrated the neurocognitive correlates of resting BEN in DMN/ECN and suggest resting BEN in DMN/ECN as a potential proxy of the latent functional reserve that facilitates general brain functionality and may be enhanced by education.
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Zhou, F., Zhuang, Y., Gong, H., Zhan, J., Grossman, M., & Wang, Z. (2016) · PLoS ONE · doi:10.1371/journal.pone.0146080 · Cited by 76 · see citing works
Self-citationResting-state brain entropy in schizophrenia
Xue, S., Yu, Q., Guo, Y., Song, D., & Wang, Z. (2018) · Comprehensive Psychiatry · doi:10.1016/j.comppsych.2018.11.015 · Cited by 72 · see citing works
Self-citationShen, X., Cox, S. R., Adams, M. J., Howard, D. M., Lawrie, S. M., Ritchie, S. J., Bastin, M. E., Deary, I. J., McIntosh, A. M., & Whalley, H. C. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.06.007 · Cited by 71 · see citing works
Temporal coherence structure rapidly shapes neuronal interactions
Lu, K., Xu, Y., Yin, P., Oxenham, A. J., Fritz, J. B., & Shamma, S. (2017) · Nature Communications · doi:10.1038/ncomms13900 · Cited by 69 · see citing works
Resting connectivity predicts task activation in pre-surgical populations
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Sex Differences in Visual Motion Processing
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The Increase of the Functional Entropy of the Human Brain with Age
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Spike-field activity in parietal area LIP during coordinated reach and saccade movements
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Resting-state brain information flow predicts cognitive flexibility in humans
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Hyper-resting brain entropy within chronic smokers and its moderation by Sex
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Flagged first, then by citations
Research works printed but not linked: 2
- Raichle ME, 2006. Neuroscience. The brain’s dark energy. Science 314, 1249– 1250. 17124311
- Wang Z, 2012b. Stable and Self-Organized Entropy in the Resting Brain. The Third Biennial Conference on Resting State Brain Connectivity, Magdeburg, Germany, p. 208.
Cited by
Sorted by citations
What does it mean for consciousness to be multidimensional? A narrative review
Páleník, J. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1430262 · Cited by 4 · see citing works
“Evidence on the exact nature of the relationship between intelligence and complexity measures is currently mixed (Saxe et al., 2018; Dreszer et al., 2020; Wang, 2021; Thiele et al., 2023).”
Background
- Authors
- Wang, Z.
- Affiliations
- University of Maryland, Baltimore (US)
- Corresponding author
- Wang, Ze
- ORCID
- Wang, Ze
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 232
- Pages
- 117893
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC8138544
- OpenAlex
- W3023013569
- Cited by
- 77
- References
- 117
- Citations by year
- 2026: 15 · 2025: 18 · 2024: 25 · 2023: 13 · 2022: 5 · 2020: 1
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- Institute for Clinical and Translational Research, University of Wisconsin, Madison
- National Institutes of Health
- McDonnell Center for Systems Neuroscience
- National Institute on Aging (R01 AG060054)
- Washington University in St. Louis
- NIH Blueprint for Neuroscience Research
- National Center for Advancing Translational Sciences (1U54MH091657, 1UL1TR003098)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- brain entropy, resting-state fMRI, default mode network, frontoparietal network, neurocognitive function, age differences, neuromodulation
- MeSH terms
- Mental Status and Dementia Tests, Adult, Brain, Cognition, Female, Humans, Magnetic Resonance Imaging, Male, Psychomotor Performance, Rest, Cohort Studies, Entropy, Young Adult
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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.
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