The DOI matches its Crossref record; 13 of the 13 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
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Functional architecture of basal ganglia circuits: neural substrates of parallel processing
Alexander, G. E., & Crutcher, M. D. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90107-l · Cited by 4,131
Exploring the brain network: A review on resting-state fMRI functional connectivity
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2010.03.008 · Cited by 3,326
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561
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
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2011) · Nature reviews. Neuroscience · doi:10.1038/nrn2961 · Cited by 1,798
Pfurtscheller, G., Stancák, A., & Neuper, C. (1996) · International Journal of Psychophysiology · doi:10.1016/s0167-8760(96)00066-9 · Cited by 1,205
Large-scale cortical correlation structure of spontaneous oscillatory activity
Hipp, J. F., Hawellek, D. J., Corbetta, M., Siegel, M., & Engel, A. K. (2012) · Nature Neuroscience · doi:10.1038/nn.3101 · Cited by 1,113
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
The restless brain: How intrinsic activity organizes brain function
Raichle, M. E. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0172 · Cited by 433
Florin, E., & Baillet, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.054 · Cited by 184
Cited by
Neural Entrainment and Attentional Selection in the Listening Brain
Obleser, J., & Kayser, C. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.08.004 · Cited by 432
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics
Demirtaş, M., Burt, J. B., Helmer, M., Ji, J. L., Adkinson, B. D., Glasser, M. F., Van Essen, D. C., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2019) · Neuron · doi:10.1016/j.neuron.2019.01.017 · Cited by 359
Keitel, A., Gross, J., & Kayser, C. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2004473 · Cited by 312
Self-citationFrauscher, B., von Ellenrieder, N., Zelmann, R., Doležalová, I., Minotti, L., Olivier, A., Hall, J., Hoffmann, D., Nguyen, D. K., Kahane, P., et al. (2018) · Brain · doi:10.1093/brain/awy035 · Cited by 283
Proactive Sensing of Periodic and Aperiodic Auditory Patterns
Rimmele, J. M., Morillon, B., Poeppel, D., & Arnal, L. H. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.003 · Cited by 267
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
- 13
- FWCI
- 9.60
- Type
- journal article
- Language
- en
- Open access
- gold
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