Separating scale‐free and oscillatory components of neural activity in schizophrenia

Abstract

Introduction Alterations in narrow‐band spectral power of electroencephalography (EEG) recordings are commonly reported in patients with schizophrenia (SZ). It is well established however that electrophysiological signals comprise a broadband scale‐free (or fractal) component generated by mechanisms different from those producing oscillatory neural activity. Despite this known feature, it has not yet been investigated if spectral abnormalities found in SZ could be attributed to scale‐free or oscillatory brain function. Methods In this study, we analyzed resting‐state EEG recordings of 14 SZ patients and 14 healthy controls. Scale‐free and oscillatory components of the power spectral density (PSD) were separated, and band‐limited power (BLP) of the original (mixed) PSD, as well as its fractal and oscillatory components, was estimated in five frequency bands. The scaling property of the fractal component was characterized by its spectral exponent in two distinct frequency ranges (1–13 and 13–30 Hz). Results Analysis of the mixed PSD revealed a decrease of BLP in the delta band in SZ over the central regions; however, this difference could be attributed almost exclusively to a shift of power toward higher frequencies in the fractal component. Broadband neural activity expressed a true bimodal nature in all except frontal regions. Furthermore, both low‐ and high‐range spectral exponents exhibited a characteristic topology over the cortex in both groups. Conclusion Our results imply strong functional significance of scale‐free neural activity in SZ and suggest that abnormalities in PSD may emerge from alterations of the fractal and not only the oscillatory components of neural activity.

Deficient auditory gamma-BOLD coupling in schizophrenia is related to sensory gating deficits

“…However, results of resting-state studies are less consistent, with some studies also reporting increased gamma power during rest (Grent-'t-Jong et al, 2018); (see White and Siegel (2016) for review). In contrast with gamma band studies, there is limited prior work examining the aperiodic slope in SZ with a preliminary report of reduced spectral exponent during a working memory task (Peterson et al, 2018) and no apparent difference in another study (Racz et al, 2021). Given that we are unable to identify group differences in either of our putative measures of E/I balance in the context of a simultaneous EEG-fMRI study, we suggest that features of the scanner environment, and scanner sound in particular (as we discuss below), may be driving neural excitability.…”

“…Therefore, E/I imbalance in SZ may impair coordination across or between brain networks as revealed by well studied BOLD resting-state abnormalities (Allen et al, 2019; Manoliu et al, 2014; Menon, 2011; Palaniyappan et al, 2013; Pettersson-Yeo et al, 2011; Whitfield-Gabrieli and Ford, 2012). While preliminary evidence suggests aperiodic changes are present in SZ (Peterson et al, 2018), but see (Racz et al, 2021), the neuroanatomy associated with aperiodic EEG in SZ remains unknown. Our goals are twofold: first, to test for group differences in aperiodic spectral slope in SZ and second, to examine the relationship between resting-state EEG markers of E/I balance with regional BOLD activity in SZ.…”

Multiple-Resampling Cross-Spectral Analysis: An Unbiased Tool for Estimating Fractal Connectivity With an Application to Neurophysiological Signals

“…In case of neurophysiological signals, it is common that oscillatory components do not only appear at a single frequency but instead rather appear as a ‘bump’ spreading across a narrow frequency range, such as the alpha peak that is typically localized in the 9–12 Hz ( Buzsaki et al, 2012 ). In this case using a too small range of h values (such as h ε[1.05;1.5] in 0.05 increments) would only ‘smear’ the peak into two smaller bumps below and over the central frequency of the narrow-band component ( Racz et al, 2021 ). This is a direct result of the central principle behind IRASA and MRCSA: although the alpha peak gets relocated with each down- and upsampling, since the resampling factors are not large enough the outside parts of the relocated peaks overlap and thus result in remnant oscillatory peaks ( Racz et al, 2021 ).…”

“…In this case using a too small range of h values (such as h ε[1.05;1.5] in 0.05 increments) would only ‘smear’ the peak into two smaller bumps below and over the central frequency of the narrow-band component ( Racz et al, 2021 ). This is a direct result of the central principle behind IRASA and MRCSA: although the alpha peak gets relocated with each down- and upsampling, since the resampling factors are not large enough the outside parts of the relocated peaks overlap and thus result in remnant oscillatory peaks ( Racz et al, 2021 ). Note that in case of overlap at the largest h value this effect cannot be compensated by increasing the number of rescaling factors within the given range if the overlap, as the number of outliers at the affected frequencies will never drop below 50%.…”

“…Even though multiple-resampling by non-integer factors does not affect the location of the breakpoint in the fractal spectrum, it does have a ‘blurring’ effect ( Wen and Liu, 2016 ). Notably, this blurring effect also depends on the range of resampling factors: a wider range of h might provide a better elimination of narrow-band peaks, however, it will also result in more substantial smoothing of sharp breakpoints ( Racz et al, 2021 ).…”