Electrophysiological signatures of brain aging in autism spectrum disorder

Dickinson, Abigail; Jeste, Shafali; Milne, Betsy

doi:10.1016/j.cortex.2021.09.022

Cited by 8 publications

(10 citation statements)

References 97 publications

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“…Considering prior literature indicating that the effects of age and cognitive ability on PAF are likely nonlinear (Dickinson et al, 2022; Rempe et al, 2023) and that these relations differ based on the diagnostic group (Dickinson et al, 2018; Dickinson et al, 2022; Edgar et al, 2015; Edgar et al, 2019), a generalized additive model (GAM; Hastie & Tibshirani, 1990) was fit using the mcgv package in R (Wood, 2011) to assess the joint effect of age, cognitive ability, and diagnostic status on PAF. In contrast to linear regression which estimates constant regression coefficients (β coefficients) for effects of interest, GAMs allow for more flexible modeling and detection of nonlinear effects by estimating smooth basis functions for predictors interest.…”

Section: Methodsmentioning

confidence: 99%

“…DAS-II scores were available for 43 (95.6%) ASD participants and 34 (100%) NT participants. To account for the well-documented discrepancies between verbal and non-verbal cognitive performance among autistic children (Coolican et al, 2008;Lincoln et al, 1998) and to best align with prior PAF studies (Dickinson et al, 2018;Dickinson et al, 2022;Edgar et al, 2019), analyses were focused on NVIQ. Note: Significance values correspond to results of independent samples t tests or chi-squared tests to assess between-group differences on key demographic measures.…”

Section: Methodsmentioning

confidence: 99%

“…Because we were interested only in the alpha peak, we constrained our model to fit a single Gaussian, in the range of 7-13 Hz, while fitting a power law distribution from 6 to 50 Hz. This approach allows for the estimation of the peak frequency that is not contaminated by high-amplitude low-frequency background activity; similar approaches have been successful in the literature (Dickinson et al, 2022;Donoghue et al, 2020;Levin et al, 2020). The peak fitting and data extraction process is depicted in Figure 1.…”

Section: Eeg Protocolsmentioning

confidence: 97%

“…Distinct developmental trajectories of PAF are also seen in autistic adults. In a large sample of adults ages 18–70, Dickinson et al (2022) found a significant diagnosis‐by‐age interaction, whereby age‐related decreases in PAF were more pronounced in autistic adults compared to NT controls, suggesting that age‐related oscillatory slowing may be more accelerated in ASD. Taken together, extant findings suggest that differences in PAF seen in ASD may in fact depend upon age and cognitive ability, underscoring the utility of considering developmental trajectories of PAF to estimate neural maturation in ASD.…”

Section: Introductionmentioning

confidence: 99%

“…Prior work investigating relations among age, cognitive ability, and PAF have utilized analytic methods assuming linear relations among variables (e.g., comparing differences in the linear relation between age and PAF in autistic and non‐autistic children) despite evidence that these relations are likely nonlinear (Dickinson et al, 2022; Freschl et al, 2022; Rempe et al, 2023). Statistical methods assuming linear, straight‐line relationships between two variables suggest a constant increase in the dependent variable (PAF) across the specified range of the independent variable (e.g., age or cognitive ability).…”

Section: Introductionmentioning

confidence: 99%

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Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children

Finn,

Han,

Naples

et al. 2023

Autism Research

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Electroencephalographic peak alpha frequency (PAF) is a marker of neural maturation that increases with age throughout childhood. Distinct maturation of PAF is observed in children with autism spectrum disorder such that PAF does not increase with age and is instead positively associated with cognitive ability. The current study clarifies and extends previous findings by characterizing the effects of age and cognitive ability on PAF between diagnostic groups in a sample of children and adolescents with and without autism spectrum disorder. Resting EEG data and behavioral measures were collected from 45 autistic children and 34 neurotypical controls aged 8 to 18 years. Utilizing generalized additive models to account for nonlinear relations, we examined differences in the joint effect of age and nonverbal IQ by diagnosis as well as bivariate relations between age, nonverbal IQ, and PAF across diagnostic groups. Age was positively associated with PAF among neurotypical children but not among autistic children. In contrast, nonverbal IQ but not age was positively associated with PAF among autistic children. Models accounting for nonlinear relations revealed different developmental trajectories as a function of age and cognitive ability based on diagnostic status. Results align with prior evidence indicating that typical age‐related increases in PAF are absent in autistic children and that PAF instead increases with cognitive ability in these children. Findings suggest the potential of PAF to index distinct trajectories of neural maturation in autistic children.

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