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Small-World Networks and Functional Connectivity in Alzheimer's Disease
Open accessAbstract
We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.
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Citations
Cites
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459 · cited 1×
“…in the computation of the SL is to convert the time series x i and y i recorded from X and Y as a series of state space vectors using the method of time delay embedding (Takens 1981): X i = ð x i ; x i + L ; x i + 2 3 L ; x i + 3 3 L ; ... ; where L is the time lag and m the embedding dimension.”
MethodFunctional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308627101 · Cited by 3,684
Tononi, G., Sporns, O., & Edelman, G. M. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.11.5033 · Cited by 1,626
Beyond Phrenology: What Can Neuroimaging Tell Us About Distributed Circuitry?
Friston, K. (2002) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.25.112701.142846 · Cited by 465
Cited by
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Complex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
The economy of brain network organization
Bullmore, E., & Sporns, O. (2012) · Nature Reviews Neuroscience · doi:10.1038/nrn3214 · Cited by 3,284
Network-based statistic: identifying differences in brain networks
Zalesky, A., Fornito, A., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.041 · Cited by 2,750
Large-scale brain networks in cognition: emerging methods and principles
Bressler, S. L., & Menon, V. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.04.004 · Cited by 2,348
Neural synchrony in brain disorders: Relevance for cognitive dysfunctions and pathophysiology
Uhlhaas, P. J., & Singer, W. (2006) · Neuron · doi:10.1016/j.neuron.2006.09.020 · Cited by 2,194
Whole-brain anatomical networks: does the choice of nodes matter?
Zalesky, A., Fornito, A., Harding, I. H., Cocchi, L., Yücel, M., Pantelis, C., & Bullmore, E. T. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.027 · Cited by 1,169
Comparing Brain Networks of Different Size and Connectivity Density Using Graph Theory
van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013701 · Cited by 1,161
Self-citationNetwork Analysis of Intrinsic Functional Brain Connectivity in Alzheimer's Disease
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000100 · Cited by 1,148
Gong, G., He, Y., Concha, L., Lebel, C., Gross, D. W., Evans, A. C., & Beaulieu, C. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn102 · Cited by 1,112
van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Human Brain Mapping · doi:10.1002/hbm.20737 · Cited by 987
He, Y., Chen, Z., & Evans, A. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.0141-08.2008 · Cited by 965
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814
Mapping Human Whole-Brain Structural Networks with Diffusion MRI
Hagmann, P., Kurant, M., Gigandet, X., Thiran, P., Wedeen, V. J., Meuli, R., & Thiran, J.-P. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000597 · Cited by 788
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Self-citationCan structure predict function in the human brain?
Honey, C. J., Thivierge, J.-P., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.071 · Cited by 629
Graph-based network analysis of resting-state functional MRI
Wang, J. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00016 · Cited by 614
Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A. R. B., Maris, E., Barkhof, F., Scheltens, P., & Stam, C. J. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013788 · Cited by 608
Self-citationBrain Anatomical Network and Intelligence
Li, Y., Liu, Y., Li, J., Qin, W., Li, K., Yu, C., & Jiang, T. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000395 · Cited by 597
Functional brain connectivity using fMRI in aging and Alzheimer’s disease
Dennis, E. L., & Thompson, P. M. (2014) · Neuropsychology Review · doi:10.1007/s11065-014-9249-6 · Cited by 583
Chen, Z. J., He, Y., Rosa-Neto, P., Germann, J., & Evans, A. C. (2008) · Cerebral Cortex · doi:10.1093/cercor/bhn003 · Cited by 484
Intrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.038 · Cited by 471
Self-citationPolanía, R., Nitsche, M. A., & Paulus, W. (2011) · Human Brain Mapping · doi:10.1002/hbm.21104 · Cited by 434
Disrupted Functional Brain Connectome in Individuals at Risk for Alzheimer's Disease
Wang, J., Zuo, X., Dai, Z., Xia, M., Zhao, Z., Zhao, X., Jia, J., Han, Y., & He, Y. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.03.026 · Cited by 432
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429
Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex
Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.04.014 · Cited by 374
van Diessen, E., Numan, T., van Dellen, E., van der Kooi, A. W., Boersma, M., Hofman, D., van Lutterveld, R., van Dijk, B. W., van Straaten, E. C. W., Hillebrand, A., et al. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.11.018 · Cited by 417
Self-citationAlzheimer's disease: connecting findings from graph theoretical studies of brain networks
Tijms, B. M., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.02.020 · Cited by 416
Self-citationA tutorial in connectome analysis: Topological and spatial features of brain networks
Kaiser, M. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.05.025 · Cited by 371
Network neuroscience theory of human intelligence
Barbey, A. K. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.10.001 · Cited by 355
Using network science to evaluate exercise-associated brain changes in older adults.
Burdette (2010) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2010.00023 · Cited by 320
Barttfeld, P., Wicker, B., Cukier, S., Navarta, S., Lew, S., & Sigman, M. (2011) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2010.11.024 · Cited by 311
Gaiteri, C., Ding, Y., French, B., Tseng, G. C., & Sibille, E. (2014) · Genes, Brain and Behavior · doi:10.1111/gbb.12106 · Cited by 289
Neuroimaging advances regarding subjective cognitive decline in preclinical Alzheimer's disease
Wang, X., Huang, W., Su, L., Xing, Y., Jessen, F., Sun, Y., Shu, N., & Han, Y. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00395-3 · Cited by 265
Using graph theoretical analysis of multi channel EEG to evaluate the neural efficiency hypothesis
Micheloyannis, S., Pachou, E., Stam, C. J., Vourkas, M., Erimaki, S., & Tsirka, V. (2006) · Neuroscience Letters · doi:10.1016/j.neulet.2006.04.006 · Cited by 277
Self-citationBartolomei, F., Bosma, I., Klein, M., Baayen, J. C., Reijneveld, J. C., Postma, T. J., Heimans, J. J., van Dijk, B. W., de Munck, J. C., de Jongh, A., et al. (2006) · Clinical Neurophysiology · doi:10.1016/j.clinph.2006.05.018 · Cited by 276
Self-citationDevelopment of a large-scale functional brain network during human non-rapid eye movement sleep
Spoormaker, V. I., Schröter, M. S., Gleiser, P. M., Andrade, K. C., Dresler, M., Wehrle, R., Sämann, P. G., & Czisch, M. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.2015-10.2010 · Cited by 273
Breathing as a fundamental rhythm of brain function.
Heck, D. H., McAfee, S. S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W. J., Wheless, J. W., Papanicolaou, A. C., Ruszinkó, M., Sokolov, Y., et al. (2017) · Frontiers in Neural Circuits · doi:10.3389/fncir.2016.00115 · Cited by 272
Human non-olfactory cognition phase-locked with inhalation
Perl, O., Ravia, A., Rubinson, M., Eisen, A., Soroka, T., Mor, N., Secundo, L., & Sobel, N. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0556-z · Cited by 220
Simulation of robustness against lesions of cortical networks
Kaiser, M., Martin, R., Andras, P., & Young, M. P. (2007) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2007.05574.x · Cited by 244
Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory
Khazaee, A., Ebrahimzadeh, A., & Babajani-Feremi, A. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2015.02.060 · Cited by 234
Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study
Zhao, X., Liu, Y., Wang, X., Liu, B., Xi, Q., Guo, Q., Jiang, H., Jiang, T., & Wang, P. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033540 · Cited by 215
Traumatic brain injury impairs small-world topology
Pandit, A. S., Expert, P., Lambiotte, R., Bonnelle, V., Leech, R., Turkheimer, F. E., & Sharp, D. J. (2013) · Neurology · doi:10.1212/wnl.0b013e3182929f38 · Cited by 176
Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression
Lord, A., Horn, D., Breakspear, M., & Walter, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041282 · Cited by 174
Self-citationDaianu, M., Jahanshad, N., Nir, T. M., Toga, A. W., Jack, C. R., Weiner, M. W., & Thompson, F. T. A. D. (2013) · Brain Connectivity · doi:10.1089/brain.2012.0137 · Cited by 173
Dai, Z., & He, Y. (2014) · Neuroscience Bulletin · doi:10.1007/s12264-013-1421-0 · Cited by 173
The brain matures with stronger functional connectivity and decreased randomness of its network
Smit, D. J. A., Boersma, M., Schnack, H. G., Micheloyannis, S., Boomsma, D. I., Hulshoff Pol, H. E., Stam, C. J., & de Geus, E. J. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036896 · Cited by 143
Self-citationDisturbed resting state eeg synchronization in bipolar disorder: A graph-theoretic analysis
Kim, D.-J., Bolbecker, A. R., Howell, J., Rass, O., Sporns, O., Hetrick, W. P., Breier, A., & O'Donnell, B. F. (2013) · NeuroImage Clinical · doi:10.1016/j.nicl.2013.03.007 · Cited by 141
Bosma, I., Reijneveld, J. C., Klein, M., Douw, L., van Dijk, B. W., Heimans, J. J., & Stam, C. J. (2009) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-3-9 · Cited by 139
“The authors showed that these AD patients had a longer path length with relative sparing of local clustering [22].”
BackgroundSelf-citationJin, C., Gao, C., Chen, C., Ma, S., Netra, R., Wang, Y., Zhang, M., & Li, D. (2011) · Neuroscience Letters · doi:10.1016/j.neulet.2011.08.017 · Cited by 136
Abnormal small-world architecture of top-down control networks in obsessive-compulsive disorder
Zhang, T. (2011) · Journal of Psychiatry & Neuroscience · doi:10.1503/jpn.100006 · Cited by 134
Single-Subject Grey Matter Graphs in Alzheimer's Disease
Tijms, B. M., Möller, C., Vrenken, H., Wink, A. M., de Haan, W., van der Flier, W. M., Stam, C. J., Scheltens, P., & Barkhof, F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058921 · Cited by 132
Self-citationHan, K., Mac Donald, C. L., Johnson, A. M., Barnes, Y., Wierzechowski, L., Zonies, D., Oh, J., Flaherty, S., Fang, R., Raichle, M. E., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.017 · Cited by 123
Sun, Y., Yin, Q., Fang, R., Yan, X., Wang, Y., Bezerianos, A., Tang, H., Miao, F., & Sun, J. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096505 · Cited by 122
Ginestet, C. E., & Simmons, A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.030 · Cited by 112
Heringa, S. M., on behalf of the Utrecht Vascular Cognitive Impairment (VCI) Study Group, Reijmer, Y. D., Leemans, A., Koek, H. L., Kappelle, L. J., & Biessels, G. J. (2013) · Journal of Alzheimer's Disease · doi:10.3233/jad-130542 · Cited by 100
Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., & Shen, D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22411 · Cited by 89
Vecchio, F., Miraglia, F., Iberite, F., Lacidogna, G., Guglielmi, V., Marra, C., Pasqualetti, P., Tiziano, F. D., & Rossini, P. M. (2018) · Annals of Neurology · doi:10.1002/ana.25289 · Cited by 89
Functional properties of resting state networks in healthy full-term newborns
De Asis-Cruz, J., Bouyssi-Kobar, M., Evangelou, I., Vezina, G., & Limperopoulos, C. (2015) · Scientific Reports · doi:10.1038/srep17755 · Cited by 85
Hart, M. G., Ypma, R. J. F., Romero-Garcia, R., Price, S. J., & Suckling, J. (2016) · Journal of Neurosurgery · doi:10.3171/2015.4.jns142683 · Cited by 74
Development of cortical anatomical properties from early childhood to early adulthood
Nie, J., Li, G., & Shen, D. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.021 · Cited by 70
Chen, Y., Chen, K., Zhang, J., Li, X., Shu, N., Wang, J., Zhang, Z., & Reiman, E. M. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2014.302 · Cited by 67
Sedeño, L., Piguet, O., Abrevaya, S., Desmaras, H., García‐Cordero, I., Baez, S., Alethia de la Fuente, L., Reyes, P., Tu, S., Moguilner, S., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23627 · Cited by 55
Affective learning enhances activity and functional connectivity in early visual cortex
Damaraju, E., Huang, Y.-M., Barrett, L. F., & Pessoa, L. (2009) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2009.04.023 · Cited by 53
McCarthy, P., Benuskova, L., & Franz, E. A. (2014) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2014.00301 · Cited by 48
Liang, X., Connelly, A., & Calamante, F. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.013 · Cited by 48
Cluster-based statistics for brain connectivity in correlation with behavioral measures.
Han, C. E., Yoo, S. W., Seo, S. W., Na, D. L., & Seong, J.-K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072332 · Cited by 49
Aydın, S., & Onbaşı, L. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09931-5 · Cited by 46
Zhou, Y., & Lui, Y. W. (2013) · ISRN Geriatrics · doi:10.1155/2013/542080 · Cited by 44
Wada, A., Tsuruta, K., Irie, R., Kamagata, K., Maekawa, T., Fujita, S., Koshino, S., Kumamaru, K., Suzuki, M., Nakanishi, A., et al. (2019) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2018-0091 · Cited by 37
Kesler, S. R., Acton, P., Rao, V., & Ray, W. J. (2018) · Network Neuroscience · doi:10.1162/netn_a_00048 · Cited by 34
“, 2016) and electroencephalography (EEG)-based networks (Stam et al., 2007).”
Yu, K., Wang, J., Deng, B., & Wei, X. (2013) · Cognitive Neurodynamics · doi:10.1007/s11571-012-9233-x · Cited by 17
Metadata
- Authors
- Stam, C., Jones, B., Nolte, G., Breakspear, M., & Scheltens, P.
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-02-01
- Volume
- 17
- Issue
- 1
- Pages
- 92-99
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2118323338
- Cited by
- 1,275
- References
- 5
- FWCI
- 18.43
- Type
- journal article
- Language
- en
- Open access
- bronze
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