Journal
NATURE NEUROSCIENCE
Volume 23, Issue 12, Pages 1655-U288Publisher
NATURE RESEARCH
DOI: 10.1038/s41593-020-00744-x
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Funding
- NIGMS NIH HHS [R01 GM134363] Funding Source: Medline
- NIMH NIH HHS [R01 MH121448, R01 MH117763, F32 MH075317, P50 MH109429] Funding Source: Medline
- NINDS NIH HHS [R37 NS021135, R01 NS021135, U19 NS107609] Funding Source: Medline
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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.
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