Developmental Trajectories of Resting EEG Power: An Endophenotype of Autism Spectrum Disorder

Abstract

Current research suggests that autism spectrum disorder (ASD) is characterized by asynchronous neural oscillations. However, it is unclear whether changes in neural oscillations represent an index of the disorder or are shared more broadly among both affected and unaffected family members. Additionally, it remains unclear how early these differences emerge in development and whether they remain constant or change over time. In this study we examined developmental trajectories in spectral power in infants at high- or low-risk for ASD. Spectral power was extracted from resting EEG recorded over frontal regions of the scalp when infants were 6, 9, 12, 18 and 24 months of age. We used multilevel modeling to assess change over time between risk groups in the delta, theta, low alpha, high alpha, beta, and gamma frequency bands. The results indicated that across all bands, spectral power was lower in high-risk infants as compared to low-risk infants at 6-months of age. Furthermore high-risk infants showed different trajectories of change in spectral power in the subsequent developmental window indicating that not only are the patterns of change different, but that group differences are dynamic within the first two years of life. These findings remained the same after removing data from a subset of participants who displayed ASD related behaviors at 24 or 36 months. These differences in the nature of the trajectories of EEG power represent important endophenotypes of ASD.

Placebo‐controlled crossover assessment of mecasermin for the treatment of Rett syndrome

“…Significant increases in frontal delta power with decreases in beta and gamma power suggest an increased contribution of slow‐wave activity following drug administration. These findings are consistent with reports of underlying cortical dysfunction related to various neurological disorders and encephalopathies, regardless of the underlying etiology 25, 33, 34. As these changes were seen in both main and secondary analyses, they represent relative strong findings.…”

“…Data were collected using a 128‐channel Hydrocel Geodesic Sensor Net System and a Net Amps 300 amplifier via Net Station software (all equipment and software from Electrical Geodesics, Inc., Eugene, OR), sampled at 1000 Hz, filtered, amplified, and referenced to the vertex (electrode Cz). In‐house Matlab‐based software was used to calculate frontal power spectral density binned into bandwidths previously described 25. Alpha asymmetry scores were calculated by subtracting the average natural log alpha power (6–13 Hz) on the left side from the natural log alpha power on the right side,26, 27, 28, 29 with positive values corresponding to higher power in the right hemisphere but greater activation in the left hemisphere.…”

Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders

“…Elsabbagh et al [55] found higher baseline and lower eventrelated gamma power in high-risk infants during an eye-gaze processing paradigm, whereas Tierney et al reported lower gamma power at age 6 months and a flattened slope of gamma change over the first 2 years of life in high-risk infants [56]. These findings suggest a delayed maturation of cortical connections or local temporal binding, particularly in response to social stimuli.…”

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

“…As will be discussed below (see Section 5), inconsistent results across EEG/MEG connectivity studies may reflect such developmental factors in GBA (Tierney et al, 2012) and also differences in the patterns of connectivity as reflected by different brain oscillation frequencies Von Stein, Chiang, & König, 2000).…”