A study on the relationship between non-epileptic fast (40 – 200 Hz) oscillations in scalp EEG and development in children

Abstract

Objective: Physiological gamma and ripple activities may be linked to neurocognitive functions. This study investigated the relationship between development and non-epileptic, probably physiological, fast (40-200 Hz) oscillations (FOs) including gamma (40 -80 Hz) and ripple (80 -200 Hz) oscillations in scalp EEG in children with neurodevelopmental disorders.Methods: Participants were 124 children with autism spectrum disorder (ASD) and/or attention deficit/hyperactivity disorder (ADHD). Gamma and ripple oscillations were explored from 60-second-long sleep EEG data in each subject using a semiautomatic detection tool supplemented with visual confirmation and time-frequency analysis.Results: Gamma and ripple oscillations were detected in 25 (20.2%) and 22 (17.7%) children, respectively. The observation of one or more occurrence(s) of ripple oscillations, but not gamma oscillations, was significantly related to lower age at EEG recording (odds ratio, OR: 0.727 [95% confidence interval, CI: 0.568-0.929]), higher intelligence/developmental quotient (OR: 1.041, 95% CI: 1.002-1.082), and lack of a diagnosis with ADHD (OR: 0.191, 95% CI: 0.039 -0.937) according to a binominal logistic regression analysis that included diagnosis with ASD, sex, history of perinatal complications, history of febrile seizures, and use of a sedative agent for the EEG recording as the other non-significant parameters. Diagnostic group was not related to frequency or power of spectral peaks of FOs. Conclusion:The production of non-epileptic scalp ripples was confirmed to be associated with brain development and function/ dysfunction in childhood. Further investigation is necessary to interpret all of the information on higher brain functions that may be embedded in scalp FOs.

A 64‐channel scheme for autism detection via scaled conjugate gradient‐based neural network classification of electroencephalogram ripples' complexity

“…On the other hand, the spectrum of neuro‐typical case tends to have low fluctuation. The atypical power spectrum of ASD EEG ‐in ripples range of frequency‐ is consistent with literature (Oka et al, 2021).…”

“…This ripple‐based automatic classification has not been satisfactorily investigated in literature. Though, several articles mentioned the result of more chaotic spectrum in ASD in the frequencies related to ripples (Mooij et al, 2018; Oka et al, 2021; Van et al, 2015).…”

“…It is worthy to note that although the EEG high frequency oscillations in ASD have been studied by several approaches (Barik et al, 2020; Casanova et al, 2020; Mooij et al, 2018; Oka et al, 2021; Sciaraffa et al, 2020; Xiang et al, 2016), they did not consist a golden standard that can be explored for artificial intelligence techniques; in other terms, HFO are not sufficiently exploited in literature as indicators for machine learning and automatic discrimination between ASD and neuro‐typical EEG. Only few works targeted HFO as ASD automatic indicator, for example (Barik et al, 2020) that studied HFO in ASD Magnetoencephalogram (MEG) Signals.…”

Developmental outcome of electroencephalographic findings in SYNGAP1 encephalopathy

“…Furthermore, background activity is an important parameter to evaluate brain dysfunction ( Staba &Worrell, 2014 ), although its physiological mechanism is still poorly understood. During infancy, there is a progressive development of occipital alpha rhythms (8-12 Hz) while awakened and the appearance of vertex sharp transients and sleep spindles during sleep ( Oka et al, 2021 ). Conversely, poor sleep architecture has been observed in refractory epilepsy and epileptic encephalopathies ( Proost et al, 2022 ), and there is a progressive increase in slow wave activities in encephalopathies, whether the cause is metabolic, septic, toxic or structural, the extent of which parallels the severity of the brain dysfunction ( Smith, 2005 ).…”