The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints
Open accessAbstract
The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.
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Cites
Consistent resting-state networks across healthy subjects
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Functional architecture of basal ganglia circuits: neural substrates of parallel processing
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Exploring the brain network: A review on resting-state fMRI functional connectivity
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Structural and Functional Brain Networks: From Connections to Cognition
Park, H.-J., & Friston, K. (2013) · Science · doi:10.1126/science.1238411 · Cited by 2,106
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Emerging concepts for the dynamical organization of resting-state activity in the brain
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Investigating the electrophysiological basis of resting state networks using magnetoencephalography
Brookes, M. J., Woolrich, M., Luckhoo, H., Price, D., Hale, J. R., Stephenson, M. C., Barnes, G. R., Smith, S. M., & Morris, P. G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1112685108 · Cited by 938 · cited 1×
“This approach yielded several clusters of brain areas that resemble resting-state brain networks often found in MEG [7,10] and fMRI [4,5,42] studies.”
BackgroundSpeech rhythms and multiplexed oscillatory sensory coding in the human brain
Gross, J., Hoogenboom, N., Thut, G., Schyns, P., Panzeri, S., Belin, P., & Garrod, S. (2013) · PLoS Biology · doi:10.1371/journal.pbio.1001752 · Cited by 643
Cited by
Keitel, A., Gross, J., & Kayser, C. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2004473 · Cited by 312
Self-citationCortical layers, rhythms and BOLD signals
Scheeringa, R., & Fries, P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.002 · Cited by 88
Cognition through the lens of a body–brain dynamic system
Criscuolo, A., Schwartze, M., & Kotz, S. A. (2022) · Trends in Neurosciences · doi:10.1016/j.tins.2022.06.004 · Cited by 76
Metadata
- Authors
- Keitel, A., & Gross, J.
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-06-29
- Volume
- 14
- Issue
- 6
- Pages
- e1002498
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- OpenAlex
- W2463955360
- Cited by
- 179
- References
- 12
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
