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Parameterizing neural power spectra

Matar Haller, Thomas Donoghue, Erik Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Robert T. Knight, Avgusta Shestyuk, View ORCID ProfileBradley Voytek
doi: https://doi.org/10.1101/299859
Matar Haller
1Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
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Thomas Donoghue
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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Erik Peterson
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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Paroma Varma
1Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
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Priyadarshini Sebastian
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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Richard Gao
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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Torben Noto
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
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Robert T. Knight
1Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
2Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
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Avgusta Shestyuk
1Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
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Bradley Voytek
3Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.
4Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.
5Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA.
6Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA.
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  • ORCID record for Bradley Voytek
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Abstract

Electrophysiological signals across species and recording scales exhibit both periodic and aperiodic features. Periodic oscillations have been widely studied and linked to numerous physiological, cognitive, behavioral, and disease states, while the aperiodic “background” 1/f component of neural power spectra has received far less attention. Most analyses of oscillations are conducted on a priori, canonically-defined frequency bands without consideration of the underlying aperiodic structure, or verification that a periodic signal even exists in addition to the aperiodic signal. This is problematic, as recent evidence shows that the aperiodic signal is dynamic, changing with age, task demands, and cognitive state. It has also been linked to the relative excitation/inhibition of the underlying neuronal population. This means that standard analytic approaches easily conflate changes in the periodic and aperiodic signals with one another because the aperiodic parameters—along with oscillation center frequency, power, and bandwidth—are all dynamic in physiologically meaningful, but likely different, ways. In order to overcome the limitations of traditional narrowband analyses and to reduce the potentially deleterious effects of conflating these features, we introduce a novel algorithm for automatic parameterization of neural power spectral densities (PSDs) as a combination of the aperiodic signal and putative periodic oscillations. Notably, this algorithm requires no a priori specification of band limits and accounts for potentially-overlapping oscillations while minimizing the degree to which they are confounded with one another. This algorithm is amenable to large-scale data exploration and analysis, providing researchers with a tool to quickly and accurately parameterize neural power spectra.

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Posted April 11, 2018.
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Parameterizing neural power spectra
Matar Haller, Thomas Donoghue, Erik Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Robert T. Knight, Avgusta Shestyuk, Bradley Voytek
bioRxiv 299859; doi: https://doi.org/10.1101/299859
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Parameterizing neural power spectra
Matar Haller, Thomas Donoghue, Erik Peterson, Paroma Varma, Priyadarshini Sebastian, Richard Gao, Torben Noto, Robert T. Knight, Avgusta Shestyuk, Bradley Voytek
bioRxiv 299859; doi: https://doi.org/10.1101/299859

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