The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors
Open accessAbstract
The ongoing activity of the brain at rest, i.e., under no stimulation and in absence of any task, is astonishingly highly structured into spatiotemporal patterns. These spatiotemporal patterns, called resting state networks, display low-frequency characteristics (<0.1 Hz) observed typically in the BOLD-fMRI signal of human subjects. We aim here to understand the origins of resting state activity through modeling via a global spiking attractor network of the brain. This approach offers a realistic mechanistic model at the level of each single brain area based on spiking neurons and realistic AMPA, NMDA, and GABA synapses. Integrating the biologically realistic diffusion tensor imaging/diffusion spectrum imaging-based neuroanatomical connectivity into the brain model, the resultant emerging resting state functional connectivity of the brain network fits quantitatively best the experimentally observed functional connectivity in humans when the brain network operates at the edge of instability. Under these conditions, the slow fluctuating (<0.1 Hz) resting state networks emerge as structured noise fluctuations around a stable low firing activity equilibrium state in the presence of latent “ghost” multistable attractors. The multistable attractor landscape defines a functionally meaningful dynamic repertoire of the brain network that is inherently present in the neuroanatomical connectivity.
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Cited by
Carhart-Harris, R. L., Leech, R., Hellyer, P. J., Shanahan, M., Feilding, A., Tagliazucchi, E., Chialvo, D. R., & Nutt, D. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00020 · Cited by 1,252
Dynamic models of large-scale brain activity
Breakspear, M. (2017) · Nature Neuroscience · doi:10.1038/nn.4497 · Cited by 1,239
Time-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753
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
Tognoli, E., & Kelso, J. A. S. (2014) · Neuron · doi:10.1016/j.neuron.2013.12.022 · Cited by 718
Metadata
- Authors
- Deco, G., & Jirsa, V. K.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2012-03-07
- Volume
- 32
- Issue
- 10
- Pages
- 3366-3375
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- OpenAlex
- W2027063265
- Cited by
- 699
- References
- 47
- FWCI
- 21.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Deco, G., & Jirsa, V. K. (2012). Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors. The Journal of Neuroscience. https://doi.org/10.1523/jneurosci.2523-11.2012
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
