Parameterizing neural power spectra

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.

The relationship between the temporal structure of magnetoencephalography recorded brain activity and capacity to form discrete auditory representations

“…Although less common, broadband relationships to behaviour, neural activity and experience are increasingly being recognised (Manning et al., ; Miller et al., ; Palva et al., ; Wen & Liu, ) and have even been applied where a priori band‐specific hypotheses failed to capture the data (Muthukumaraswamy et al., ). Narrow and broadband functionality are not mutually exclusive (Haller et al., ) and the observations made here support the proposal for a general low‐level function of the field potential activity in the alignment of activity, constraining and allowing representations, which may apply across a broad range of frequencies but is also likely to have frequency‐specific expressions.…”

Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What’s signal irregularity got to do with it?

“…peaks (Haller et al, 2018;Kosciessa et al, 2019) with the latter themselves being temporal 761 averages of potentially non-stationary spectral events (Kosciessa et al, 2019). Notably, 762…”

Distinct Narrow and Broadband Gamma Responses in Human Visual Cortex

“…For these reasons, caution must be taken when simply quantifying amplitude or power changes within this NBG range as synonymous with shifts in an oscillatory signal, as recently argued by Haller et al (35). To deal with these issues of spectral overlap, and genuine changes in NBG oscillations, we employed a further fitting procedure to automatically identify oscillatory peaks above the broadband aperiodic component in the power spectrum (35), similar to prior studies of NBG (22). NBG oscillations, while prominent for the visual gratings, were not present (or not detectable) for natural images, as it was not possible to model the responses as containing one or more oscillations in the 20-150 Hz range (see SI).…”