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Disturbed functional brain networks and neurocognitive function in low-grade glioma patients: a graph theoretical analysis of resting-state MEG
Open accessAbstract
BACKGROUND: To understand neurophysiological mechanisms underlying cognitive dysfunction in low-grade glioma (LGG) patients by evaluating the spatial structure of 'resting-state' brain networks with graph theory. METHODS: Standardized tests measuring 6 neurocognitive domains were administered in 17 LGG patients and 17 healthy controls. Magnetoencephalography (MEG) recordings were conducted during eyes-closed 'resting state'. The phase lag index (PLI) was computed in seven frequency bands to assess functional connectivity between brain areas. Spatial patterns were characterized with graph theoretical measures such as clustering coefficient (local connectivity), path length (global integration), network small world-ness (ratio of clustering coefficient/path length) and degree correlation (the extent to which connected nodes have similar degrees). RESULTS: Compared to healthy controls, patients performed poorer on psychomotor functioning, attention, information processing, and working memory. Patients displayed higher short- and long-distance synchronization and clustering coefficient in the theta band, whereas a lower clustering coefficient and small world-ness were observed in the beta band. A lower degree correlation was found in the upper gamma band. LGG patients with higher clustering coefficient, longer path length, and lower degree correlations in delta and lower alpha band were characterized by poorer neurocognitive performance. CONCLUSION: LGG patients display higher short- and long-distance synchronization within the theta band. Network analysis revealed changes (in particularly the theta, beta, and upper gamma band) suggesting disturbed network architecture. Moreover, correlations between network characteristics and neurocognitive performance were found, Widespread changes in the strength and spatial organization of brain networks may be responsible for cognitive dysfunction in glioma patients.
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“The degree to which network features are perturbed has been correlated with neuropsychological deficits involving both local and diffuse brain regions with reduced global efficiency and small-worldness correlated with lower IQ scores.15,16,104”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [16]
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Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Studies of interference in serial verbal reactions
Stroop, J. R. (1935) · Journal of Experimental Psychology · doi:10.1037/h0054651 · Cited by 18,622
Efficient behavior of small-world networks
Latora, V., & Marchiori, M. (2001) · Physical Review Letters · doi:10.1103/physrevlett.87.198701 · Cited by 5,119
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761
Neuropsychological assessment.
Cipolotti, L., & Warrington, E. K. (1995) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.58.6.655 · Cited by 3,267
Stam, C. J., Nolte, G., & Daffertshofer, A. (2007) · Human Brain Mapping · doi:10.1002/hbm.20346 · Cited by 2,083
Self-citationGeneralized synchronization of chaos in directionally coupled chaotic systems
Rulkov, N. F., Sushchik, M. M., Tsimring, L. S., & Abarbanel, H. D. I. (1995) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.51.980 · Cited by 1,803
Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence
Humphries, M. D., & Gurney, K. (2008) · PLoS ONE · doi:10.1371/journal.pone.0002051 · Cited by 1,442
Small-World Networks and Functional Connectivity in Alzheimer's Disease
Stam, C. J., Jones, B. F., Nolte, G., Breakspear, M. J., & Scheltens, P. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhj127 · Cited by 1,295
Self-citationPerret, E. (1974) · Neuropsychologia · doi:10.1016/0028-3932(74)90047-5 · Cited by 1,007
Research works printed but not linked: 1
- Luteijn F, Ploeg van der FAE: Groningen Intelligence Test. revised manual Lisse Swets and Zeitlinger. 1983
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Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
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Self-citationFunctional network disruption in the degenerative dementias
Pievani, M., de Haan, W., Wu, T., Seeley, W. W., & Frisoni, G. B. (2011) · The Lancet Neurology · doi:10.1016/s1474-4422(11)70158-2 · Cited by 481
Functional and structural brain networks in epilepsy: What have we learned?
Diessen, E. G. A. L. V., Diederen, S., Braun, K. P. J., Jansen, F. E., & Stam, C. J. (2013) · Epilepsia · doi:10.1111/epi.12350 · Cited by 362
Self-citationGratton, C., Nomura, E. M., Pérez, F., & D’Esposito, M. (2012) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00222 · Cited by 353
Brain networks under attack: robustness properties and the impact of lesions
Aerts, H., Fias, W., Caeyenberghs, K., & Marinazzo, D. (2016) · Brain · doi:10.1093/brain/aww194 · Cited by 310
Metadata
- Authors
- Bosma, I., Reijneveld, J. C., Klein, M., Douw, L., van Dijk, B. W., Heimans, J. J., & Stam, C. J.
- Affiliations
- Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
- Amsterdam UMC Location University of Amsterdam (NL)
- Corresponding author
- Bosma, Ingeborg
- ORCID
- Venue
- Nonlinear Biomedical Physics
- Published
- 2009-08-23
- Volume
- 3
- Issue
- 1
- Pages
- 9-9
- ISSN
- 1753-4631 (ISSN-L)
- PMC
- PMC2745411
- OpenAlex
- W2162546175
- Cited by
- 139
- References
- 46
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 5 · 2023: 12 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 6 · 2018: 9 · 2017: 13 · 2016: 10 · 2015: 4 · 2014: 8 · 2013: 15 · 2012: 12
- FWCI
- 4.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nonlinear Biomedical Physics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (submittedVersion)
- Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam) (submittedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- 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 Neuroimaging Techniques and Applications
- Keywords
- low-grade glioma, resting-state brain networks, functional connectivity, graph theory, cognitive impairment, neuropsychological performance, phase lag index, beta band, gamma band, small-world networks, clustering coefficient, path length, degree correlation, working memory, attention, psychomotor performance
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