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      Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

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          Abstract

          Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuroelectrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes, such as in center frequency or bandwidth, and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features and find that multiple disparate features influence ratio measures. For example, the commonly applied θ/β ratio is most reflective of differences in aperiodic activity, and not oscillatory θ or β power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach.

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          Parameterizing neural power spectra into periodic and aperiodic components

          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.
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            EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis

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              MNE software for processing MEG and EEG data.

              Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions. MNE, whose name stems from its capability to compute cortically-constrained minimum-norm current estimates from M/EEG data, is a software package that provides comprehensive analysis tools and workflows including preprocessing, source estimation, time-frequency analysis, statistical analysis, and several methods to estimate functional connectivity between distributed brain regions. The present paper gives detailed information about the MNE package and describes typical use cases while also warning about potential caveats in analysis. The MNE package is a collaborative effort of multiple institutes striving to implement and share best methods and to facilitate distribution of analysis pipelines to advance reproducibility of research. Full documentation is available at http://martinos.org/mne. © 2013.
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                Author and article information

                Journal
                eNeuro
                eNeuro
                eneuro
                eneuro
                eNeuro
                eNeuro
                Society for Neuroscience
                2373-2822
                25 September 2020
                17 December 2020
                Nov-Dec 2020
                : 7
                : 6
                : ENEURO.0192-20.2020
                Affiliations
                [1 ]Department of Cognitive Science, University of California, San Diego
                [2 ]Halıcıoğlu Data Science Institute, University of California, San Diego
                [3 ]Neurosciences Graduate Program, University of California, San Diego
                [4 ]Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA 92093
                Author notes

                The authors declare no competing financial interests.

                Author contributions: T.D. and B.V. designed research; T.D. and J.D. performed research; T.D. and J.D. analyzed data; T.D., J.D., and B.V. wrote the paper.

                B.V. is supported by the Whitehall Foundation Grant 2017-12-73, the National Science Foundation Grant BCS-1736028, the National Institutes of Health National Institute of General Medical Sciences Grant R01GM134363 01, and a Halıcıoğlu Data Science Institute Fellowship. J.D. is supported by the University of California San Diego Triton Research and Experiential Learning Scholarship (TRELS).

                Correspondence should be addressed to Thomas Donoghue at tdonoghue.research@ 123456gmail.com .
                Author information
                https://orcid.org/0000-0001-5911-0472
                https://orcid.org/0000-0003-1640-2525
                Article
                eN-NWR-0192-20
                10.1523/ENEURO.0192-20.2020
                7768281
                32978216
                2de89666-97c8-4fcd-b46e-d2266420d626
                Copyright © 2020 Donoghue et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 12 May 2020
                : 1 September 2020
                : 8 September 2020
                Page count
                Figures: 8, Tables: 2, Equations: 8, References: 61, Pages: 14, Words: 00
                Funding
                Funded by: http://doi.org/10.13039/100001391Whitehall Foundation (Whitehall Foundation, Inc.)
                Award ID: 2017-12-73
                Funded by: http://doi.org/10.13039/100000169NSF | SBE | Division of Behavioral and Cognitive Sciences (BCS)
                Award ID: BCS-1736028
                Funded by: http://doi.org/10.13039/100000057HHS | NIH | National Institute of General Medical Sciences (NIGMS)
                Award ID: R01GM134363-01
                Funded by: Halicoglu Data Science Research Fellowship
                Funded by: UC San Diego Triton Research and Experiential Learning Scholarship
                Categories
                1
                Research Article: New Research
                Cognition and Behavior
                Custom metadata
                November/December 2020

                aperiodic neural activity,frequency band ratios,neural oscillations,spectral analyses,θ/β ratio

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