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. 2022 May;64(4):e22271.
doi: 10.1002/dev.22271.

Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk

Lauren C Shuffrey  1   2 , Nicolò Pini  1   2 , Mandy Potter  3 , Priscilla Springer  4 , Maristella Lucchini  1   2 , Yael Rayport  1 , Ayesha Sania  1   2 , Morgan Firestein  1   2 , Lucy Brink  3 , Joseph R Isler  5 , Hein Odendaal  3 , William P Fifer  1   2   5
Affiliations

Aperiodic electrophysiological activity in preterm infants is linked to subsequent autism risk

Lauren C Shuffrey et al. Dev Psychobiol. 2022 May.

Abstract

Approximately 7% of preterm infants receive an autism spectrum disorder (ASD) diagnosis. Yet, there is a significant gap in the literature in identifying prospective markers of neurodevelopmental risk in preterm infants. The present study examined two electroencephalography (EEG) parameters during infancy, absolute EEG power and aperiodic activity of the power spectral density (PSD) slope, in association with subsequent autism risk and cognitive ability in a diverse cohort of children born preterm in South Africa. Participants were 71 preterm infants born between 25 and 36 weeks gestation (34.60 ± 2.34 weeks). EEG was collected during sleep between 39 and 41 weeks postmenstrual age adjusted (40.00 ± 0.42 weeks). The Bayley Scales of Infant Development and Brief Infant Toddler Social Emotional Assessment (BITSEA) were administered at approximately 3 years of age adjusted (34 ± 2.7 months). Aperiodic activity, but not the rhythmic oscillatory activity, at multiple electrode sites was associated with subsequent increased autism risk on the BITSEA at three years of age. No associations were found between the PSD slope or absolute EEG power and cognitive development. Our findings highlight the need to examine potential markers of subsequent autism risk in high-risk populations other than infants at familial risk.

Keywords: aperiodic EEG; autism risk; electroencephalography (EEG); infants; neuronal oscillations; preterm birth.

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Conflict of interest statement

Conflict of Interest: The authors have no conflicts of interest to declare.

Figures

Figure 1.
Figure 1.
Electrode Map. Topographic map depiction of a 32-channel EGI Geodesic Sensor Net with 28 active channels.
Figure 2.
Figure 2.
Power Spectral Density (PSD) Slope Example. Illustrative example of low and high frequency slope (solid black line) computation on EEG power spectral density (PSD) (solid blue line). Firstly, the PSD is fit with an estimated aperiodic component (dashed gray line). The estimated aperiodic portion of the signal is subtracted from the raw PSD. The residuals portion of the spectrum are assumed to be a mix of periodic oscillatory peaks and noise. The identified peaks (which are found above the noise threshold calculated from the standard deviation of the residuals), are fitted with a Gaussian distribution and remove via an iterative process (dotted blue line). Once these components are removed, based on the number of peaks above the noise threshold, multi-Gaussian fitting is performed on the aperiodic-adjusted signal to derive the spectral slopes.
Figure 3.
Figure 3.
The average spectrum (solid lines), standard error of the mean (shaded area), and associated spectral fit lines (dashed lines) obtained by averaging spectra in AS across participants. The separation between the low- (1–20 Hz in blue) and high- (21–40 Hz in red) frequency portions of the average spectrum is accentuated by the gray-shadowed area.
Figure 4.
Figure 4.
Association between the PSD Slope during the neonatal period during active sleep and subsequent autism risk at age 3. A steeper low-frequency (1 – 20 Hz) PSD slope in active sleep in the left frontal (Panel A), right central (Panel B), left central (Panel C), right parietal (Panel D), and left occipital (Panel E) regions are associated with increased BITSEA ASD problem scores
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