PhD Scientific Days 2022

Mental Health Sciences II. (Poster discussion will take place in the Aula during the Coffee Break)

Sleep EEG spectral exponents and slow wave activity in ADHD

Text of the abstract

Introductions: Attention deficit hyperactivity disorder (ADHD) is relatively frequent neurodevelopmental disorder, affecting 3—5% of school-aged children. ADHD is often associated with sleep problems, which affects daytime cognitive functions and development. However, few studies have unravelled these peculiarities whereas the analytical methods of sleep EEG were limited to a few methods with circumscribed focuses.

Aims: Here we aim to reveal the differences in adolescents’ sleep EEG patterns analysed by FFT routine and power spectrum parametrization obtained for selected EEG derivations.

Methods: Polysomnographic sleep was monitored in 22 adolescents with ADHD, 31 typically developing controls and 13 adolescents with subclinical symptoms of ADHD (overall: N=66 children, age range 14–18 years) by using a home sleep recording with the DREEM headband. We analysed the first 4 artefact-free NREM sleep periods (cycles) of each participant, and the sleep stages were scored by the modified criteria of Rechtschaffen and Kales. Analysis was performed by FFT routine (4 s, Hanning taper) and power spectrum obtained for selected EEG derivations. The log-log power was fitted with a linear, and a peak detection was applied in the 9–18 Hz range. Statistical analysis was based on general linear models.

Results: Slow wave activity, spectral slopes and offsets (intercepts) and spectral peak parameters showed a significant cycle effect in each group, however there were no significant differences among groups. The significant cycle effect (decreasing SWA and spectral slopes) indicate and expected decrease in sleep depth during the night in all groups.

Conclusions: Results suggest significant cycle effect in the frontopolar region, whereas the formerly reported differences in slow wave activity of ADHD children should be carefully reinvestigated in further studies.

Funding: Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-EGA-25 funding scheme