Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2,254

The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.

Parameterizing neural power spectra into periodic and aperiodic components

Open access
Donoghue, Thomas, Haller, Matar, Peterson, Erik J., Varma, Paroma, Sebastian, Priyadarshini, Gao, Richard, Noto, Torben, Lara, Antonio H., Wallis, Joni D., Knight, Robert T., Shestyuk, Avgusta, Voytek, Bradley·2020·Nature Neuroscience
DOI 10.1038/s41593-020-00744-x

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis. A method for parameterizing electrophysiological neural power spectra into periodic and aperiodic components is introduced, addressing limitations of common approaches. The method is validated in simulation and demonstrated on real data applications.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41593-020-00744-x

Cites

12 of 12 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 12 references

Research works printed but not linked: 61

  1. Voytek, B. et al. Oscillatory dynamics coordinating human frontal networks in support of goal maintenance. Nat. Neurosci. 18, 1318–1324 (2015). · DOI · Paper page
  2. Kopell, N. J., Gritton, H. J., Whittington, M. A. & Kramer, M. A. Beyond the connectome: the dynome. Neuron 83, 1319–1328 (2014). · DOI · Paper page
  3. Voytek, B. & Knight, R. T. Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease. Biol. Psychiatry 77, 1089–1097 (2015). · DOI · Paper page
  4. He, B. J. Scale-free brain activity: past, present, and future. Trends Cogn. Sci. 18, 480–487 (2014). · DOI · Paper page
  5. Jasper, H. & Penfield, W. Electrocorticograms in man: effect of voluntary movement upon the electrical activity of the precentral gyrus. Arch. Psychiatr. Nervenkr. 183, 163–174 (1949). · DOI · Paper page
  6. Crone, N. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. Brain 121, 2271–2299 (1998). · DOI · Paper page
  7. Haegens, S., Cousijn, H., Wallis, G., Harrison, P. J. & Nobre, A. C. Inter- and intra-individual variability in alpha peak frequency. NeuroImage 92, 46–55 (2014). · DOI · Paper page
  8. Mierau, A., Klimesch, W. & Lefebvre, J. State-dependent alpha peak frequency shifts: experimental evidence, potential mechanisms and functional implications. Neuroscience 360, 146–154 (2017). · DOI · Paper page
  9. Manning, J. R., Jacobs, J., Fried, I. & Kahana, M. J. Broadband shifts in local field potential power spectra are correlated with single-neuron spiking in humans. J. Neurosci. 29, 13613–13620 (2009). · DOI · Paper page
  10. Miller, K. J. et al. Human motor cortical activity is selectively phase-entrained on underlying rhythms. PLoS Comput. Biol. 8, e1002655 (2012). · DOI · Paper page
  11. Winawer, J. et al. Asynchronous broadband signals are the principal source of the BOLD response in human visual cortex. Curr. Biol. 23, 1145–1153 (2013). · DOI · Paper page
  12. Freeman, W. J. & Zhai, J. Simulated power spectral density (PSD) of background electrocorticogram (ECoG). Cogn. Neurodyn. 3, 97–103 (2009). · DOI · Paper page
  13. Podvalny, E. et al. A unifying principle underlying the extracellular field potential spectral responses in the human cortex. J. Neurophysiol. 114, 505–519 (2015). · DOI · Paper page
  14. Voytek, B. et al. Age-related changes in 1/f neural electrophysiological noise. J. Neurosci. 35, 13257–13265 (2015). · DOI · Paper page
  15. Gao, R., Peterson, E. J. & Voytek, B. Inferring synaptic excitation/inhibition balance from field potentials. NeuroImage 158, 70–78 (2017). · DOI · Paper page
  16. Dustman, R. E., Shearer, D. E. & Emmerson, R. Y. EEG and event-related potentials in normal aging. Prog. Neurobiol. 41, 369–401 (1993). · DOI
  17. Cole, S. & Voytek, B. Cycle-by-cycle analysis of neural oscillations. J. Neurophysiol. 122, 849–861 (2019). · DOI · Paper page
  18. Lansbergen, M. M., Arns, M., van Dongen-Boomsma, M., Spronk, D. & Buitelaar, J. K. The increase in theta/beta ratio on resting-state EEG in boys with attention-deficit/hyperactivity disorder is mediated by slow alpha peak frequency. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 47–52 (2011). · DOI · Paper page
  19. Bullock, T. H., Mcclune, M. C. & Enright, J. T. Are the electroencephalograms mainly rhythmic? Assessment of periodicity in wide-band time series. Neuroscience 121, 233–252 (2003). · DOI
  20. Miller, K. J., Sorensen, L. B., Ojemann, J. G. & den Nijs, M. Power-law scaling in the brain surface electric potential. PLoS Comput. Biol. 5, e1000609 (2009). · DOI · Paper page
  21. Groppe, D. M. et al. Dominant frequencies of resting human brain activity as measured by the electrocorticogram. NeuroImage 79, 223–233 (2013). · DOI · Paper page
  22. He, W. et al. Co-increasing neuronal noise and beta power in the developing brain. Preprint at bioRxiv https://doi.org/10.1101/839258 (2019). · DOI
  23. Robertson, M. M. et al. EEG power spectral slope differs by ADHD status and stimulant medication exposure in early childhood. J. Neurophysiol. 122, 2427–2437 (2019). · DOI · Paper page
  24. Molina, J. L. et al. Memantine effects on electroencephalographic measures of putative excitatory/inhibitory balance in schizophrenia. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 5, 562–568 (2020).
  25. Becker, R., Van de Ville, D. & Kleinschmidt, A. Alpha oscillations reduce temporal long-range dependence in spontaneous human brain activity. J. Neurosci. 38, 755–764 (2018). · DOI
  26. Gao, R., van den Brink, R. L., Pfeffer, T. & Voytek, B. Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture. Preprint at bioRxiv https://doi.org/10.1101/2020.05.25.115378 (2020). · doi:10.1101/2020.05.25.115378
  27. Tran, T. T., Hoffner, N. C., LaHue, S. C., Tseng, L. & Voytek, B. Alpha phase dynamics predict age-related visual working memory decline. NeuroImage 143, 196–203 (2016). · DOI
  28. Niso, G. et al. OMEGA: the Open MEG Archive. NeuroImage 124, 1182–1187 (2016). · DOI
  29. Lim, S. & Goldman, M. S. Balanced cortical microcircuitry for maintaining information in working memory. Nat. Neurosci. 16, 1306–1314 (2013). · DOI · Paper page
  30. Demirtaş, M. et al. Hierarchical heterogeneity across human cortex shapes large-scale neural dynamics. Neuron 101, 1181–1194.e13 (2019). · DOI · Paper page
  31. Frauscher, B. et al. Atlas of the normal intracranial electroencephalogram: neurophysiological awake activity in different cortical areas. Brain 141, 1130–1144 (2018). · DOI · Paper page
  32. Mukamel, R. et al. Coupling between neuronal firing, field potentials, and fMRI in human auditory cortex. Science 309, 951–954 (2005). · DOI · Paper page
  33. Bartoli, E. et al. Functionally distinct gamma range activity revealed by stimulus tuning in human visual cortex. Curr. Biol. 29, 3345–3358.e7 (2019). · DOI
  34. Muthukumaraswamy, S. D., Singh, K. D., Swettenham, J. B. & Jones, D. K. Visual gamma oscillations and evoked responses: variability, repeatability and structural MRI correlates. NeuroImage 49, 3349–3357 (2010). · DOI · Paper page
  35. van der Meij, R., Kahana, M. & Maris, E. Phase-amplitude coupling in human electrocorticography is spatially distributed and phase diverse. J. Neurosci. 32, 111–123 (2012). · DOI
  36. Muthukumaraswamy, S. D. & Liley, D. T. J. 1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes. NeuroImage 179, 582–595 (2018). · DOI
  37. Tran, T. T., Rolle, C. E., Gazzaley, A. & Voytek, B. Linked sources of neural noise contribute to age-related cognitive decline. J. Cogn. Neurosci. 32, 1813–1822 (2020). · DOI
  38. Reimann, M. W. et al. A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79, 375–390 (2013). · DOI · Paper page
  39. Pascual-Marqui, R. D., Valdés-Sosa, P. A. & Alvarez-Amador, A. A parametric model for multichannel EEG spectra. Int. J. Neurosci. 40, 89–99 (1988). · DOI
  40. Hughes, A. M., Whitten, T. A., Caplan, J. B. & Dickson, C. T. BOSC: a better oscillation detection method, extracts both sustained and transient rhythms from rat hippocampal recordings. Hippocampus 22, 1417–1428 (2012). · DOI
  41. Wen, H. & Liu, Z. Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain Topogr. 29, 13–26 (2016). · DOI · Paper page
  42. Cole, S. R. & Voytek, B. Brain oscillations and the importance of waveform shape. Trends Cogn. Sci. 21, 137–149 (2017). · DOI · Paper page
  43. Caplan, J. B., Bottomley, M., Kang, P. & Dixon, R. A. Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging. NeuroImage 112, 341–352 (2015). · DOI
  44. Welch, P. The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoustics 15, 70–73 (1967). · DOI · Paper page
  45. Cole, S., Donoghue, T., Gao, R. & Voytek, B. NeuroDSP: a package for neural digital signal processing. J. Open Source Softw. 4, 1272 (2019). · DOI
  46. Vogel, E. K. & Machizawa, M. G. Neural activity predicts individual differences in visual working memory capacity. Nature 428, 748–751 (2004). · DOI · Paper page
  47. Lara, A. H. & Wallis, J. D. Executive control processes underlying multi-item working memory. Nat. Neurosci. 17, 876–883 (2014). · DOI
  48. Haegens, S., Nacher, V., Luna, R., Romo, R. & Jensen, O. Alpha-oscillations in the monkey sensorimotor network influence discrimination performance by rhythmical inhibition of neuronal spiking. Proc. Natl Acad. Sci. USA 108, 19377–19382 (2011). · DOI · Paper page
  49. Gramfort, A. et al. MNE software for processing MEG and EEG data. NeuroImage 86, 446–460 (2014). · DOI · Paper page
  50. Voytek, B. & Knight, R. T. Prefrontal cortex and basal ganglia contributions to visual working memory. Proc. Natl Acad. Sci. USA 107, 18167–18172 (2010). · DOI · Paper page
  51. Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F. & Gramfort, A. Autoreject: automated artifact rejection for MEG and EEG data. NeuroImage 159, 417–429 (2017). · DOI · Paper page
  52. Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Rev. 29, 169–195 (1999). · DOI · Paper page
  53. Van Essen, D. C. et al. The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013). · DOI · Paper page
  54. Van Essen, D. C. et al. The Human Connectome Project: a data acquisition perspective. NeuroImage 62, 2222–2231 (2012). · DOI · Paper page
  55. Gross, J. et al. Good practice for conducting and reporting MEG research. NeuroImage 65, 349–363 (2013). · DOI · Paper page
  56. Tadel, F., Baillet, S., Mosher, J. C., Pantazis, D. & Leahy, R. M. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput. Intell. Neurosci. 2011, 1–13 (2011). · DOI · Paper page
  57. Nolte, G. & Curio, G. The effect of artifact rejection by signal-space projection on source localization accuracy in MEG measurements. IEEE Trans. Biomed. Eng. 46, 400–408 (1999). · DOI
  58. Huang, M. X., Mosher, J. C. & Leahy, R. M. A sensor-weighted overlapping-sphere head model and exhaustive head model comparison for MEG. Phys. Med. Biol. 44, 423–440 (1999). · DOI · Paper page
  59. Baillet, S. et al. Evaluation of inverse methods and head models for EEG source localization using a human skull phantom. Phys. Med. Biol. 46, 77–96 (2001). · DOI · Paper page
  60. Fonov, V., Evans, A., McKinstry, R., Almli, C. & Collins, D. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102 (2009). · DOI · Paper page
  61. Izhikevich, L., Gao, R., Peterson, E. & Voytek, B. Measuring the average power of neural oscillations. Preprint at bioRxiv https://doi.org/10.1101/441626 (2018). · DOI

Cited by

See all 2,254

Metadata

Authors
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., Noto, T., Lara, A. H., Wallis, J. D., Knight, R. T., Shestyuk, A., & Voytek, B.
Venue
Nature Neuroscience
Publisher
Springer Science and Business Media LLC
Published
2020-11-23
Volume
23
Issue
12
Pages
1655-1665
ISSN
  • 1097-6256 (ISSN-L)
  • 1546-1726
DOI
10.1038/s41593-020-00744-x
OpenAlex
W3110151578
Cited by
2,254
References
12
FWCI
93.39
Type
journal article
Language
en
Open access
green
Licence
Funders
  • National Science Foundation (DGE1106400, BCS-1736028)
  • UC San Diego Frontiers of Innovation Scholars Program
  • UC San Diego Kavli Innovative Research Grant; Frontiers for Innovation Scholars Program fellowship; Katzin Prize
  • U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (R01-MH121448, R01-MH117763, Conte Center Grant P50MH109429, U19 Brain Initiative Grant U19NS107609, F32MH75317)
  • Alfred P. Sloan Foundation (FG-2015-66057)
  • Whitehall Foundation (2017-12-73)
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM134363-01)
  • U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (R37NS21135)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Parameterizing neural power spectra into periodic and aperiodic components. https://app.cochat.ai/papers/10.1038/s41593-020-00744-x
The paper
Donoghue, T., Haller, M., Peterson, E. J., Varma, P., Sebastian, P., Gao, R., Noto, T., Lara, A. H., Wallis, J. D., Knight, R. T., Shestyuk, A., & Voytek, B. (2020). Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience. https://doi.org/10.1038/s41593-020-00744-x

Data sources

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.