The development of theta and alpha neural oscillations from ages 3 to 24 years

Cellier, Dillan; Riddle, Justin; Petersen, Isaac T.; Hwang, Kai

doi:10.1016/j.dcn.2021.100969

Cited by 145 publications

(205 citation statements)

References 131 publications

(205 reference statements)

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“…These findings were present both when using the signal averaged across the entire scalp, and when using electrode clusters covering anterior, central, and posterior regions separately (Figure 2B and 2C), thus suggesting that systematic variations with age are a relatively widespread phenomenon. Importantly, our findings also replicate recent observations of flattening of the aperiodic exponent with age in infancy , as well as in cohorts with ages ranging from childhood into adulthood (Cellier et al, 2021;Donoghue et al, 2020a;He et al, 2019;Tröndle et al, 2020). The present results, in conjunction with previous findings, are therefore supportive of quantitative neurodevelopmental changes in the aperiodic component of the EEG signal.…”

Section: Age Predicts Aperiodic Properties Of the Eeg Signalsupporting

confidence: 92%

“…A further study containing EEG recordings from both children and young adults (age range 5-21 years), the majority (~ 88%) of whom had a psychiatric diagnosis, also reported a flattening of the aperiodic exponent, and reduction in offset with increasing age (Tröndle et al, 2020). Similarly, Cellier et al (2021) recently reported a similar trend in a sample containing both children and adults (age range 3-24 years).…”

Section: Introductionmentioning

confidence: 74%

“…Our present findings also corroborate recent reports of age-related changes in aperiodic-adjusted alpha centre frequency. Specifically, Cellier et al (2021) showed that peak frequency within the 4-12 Hz range increased with age in a cohort which included both children and adults (3-24 years of age); while He et al (2019) reported a positive association between age and alpha centre frequency in a small sample 14 (N = 24) of children using MEG recordings. The same trend was also reported by Carter-Leno et al (2021) in a longitudinal sample of young children from 1 to 3 years of age.…”

Section: Age Predicts Alpha Centre Frequency But Not Powermentioning

confidence: 97%

“…Using a model driven approach, the FOOOF algorithm is able to extract both periodic and aperiodic components within the overall power spectra (Donoghue et al, 2020b). For the present study, we extracted the aperiodic exponent and offset across a broad frequency range between 1 and 40 Hz, similar to prior studies (Cellier et al, 2021;Molina et al, 2020;Ostlund et al, 2021a), and as recommended in the FOOOF documentation in order to allow for reliable estimation of the aperiodic component of the data. Fitting was performed using the 'fixed' aperiodic mode due to the absence of a clear 'knee' in the power spectrum when the output was visually inspected in log-log space (i.e., the signal was approximately linear across the specified frequency range).…”

Section: Parameterisation Of the Spectral Datamentioning

confidence: 99%

“…A common trend observed in studies examining maturational changes in resting-state brain rhythms in children and adolescents is a reduction in power with increasing age within lower frequency ranges (i.e., delta and theta bands; Clarke et al, 2001;Gasser et al, 1988;Gómez et al, 2013;John et al, 1980) which is also often also accompanied by a concomitant increase in power within faster rhythms, particularly the alpha and beta bands (Benninger et al, 1984;Gasser et al, 1988;Gómez et al, 2013;Marshall et al, 2002;Saby & Marshall, 2012). In addition to changes in spectral power, the peak frequency of the dominant posterior alpha rhythm also increases with age until around late childhood, or early adulthood (Cellier et al, 2021;Chiang et al, 2011;Eeg-Olofsson et al, 1971;Marshall et al, 2002;Miskovic et al, 2015;Stroganova et al, 1999). These shifts in oscillatory dynamics likely reflect multiple structural and functional neurodevelopmental processes, including differentiation and specialisation of cortical regions/networks, synaptic and axonal pruning, and alterations in excitatory and inhibitory (E/I) circuits (De Bellis et al, 2001;Feinberg & Campbell, 2010;Lujan et al, 2005;Uhlhaas et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Periodic and Aperiodic Neural Activity Displays Age-Dependent Changes Across Early-to-Middle Childhood

Hill

Clark

Bigelow

et al. 2021

Preprint

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The neurodevelopmental period spanning early-to-middle childhood represents a time of significant growth and reorganisation throughout the cortex. Such changes are critical for the emergence and maturation of a range of social and cognitive processes. Here, we utilised both eyes open and eyes closed resting-state electroencephalography (EEG) to examine maturational changes in both oscillatory (i.e., periodic) and non-oscillatory (aperiodic, ‘1/f-like’) activity in a large cohort of participants ranging from 4-to-12 years of age (N=139, average age=9.41 years, SD=1.95). The EEG signal was parameterised into aperiodic and periodic components, and linear regression models were used to evaluate if chronological age could predict aperiodic exponent and offset, as well as well as peak frequency and power within the alpha and beta ranges. Exponent and offset were found to both decrease with age, while aperiodic-adjusted alpha peak frequency increased with age; however, there was no association between age and peak frequency for the beta band. Age was also unrelated to aperiodic-adjusted spectral power within either the alpha or beta bands, despite both frequency ranges being correlated with the aperiodic signal. Overall, these results highlight the capacity for both periodic and aperiodic features of the EEG to elucidate age-related functional changes within the developing brain.

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Section: Age Predicts Aperiodic Properties Of the Eeg Signalsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 74%

Section: Age Predicts Alpha Centre Frequency But Not Powermentioning

confidence: 97%

Section: Parameterisation Of the Spectral Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Periodic and Aperiodic Neural Activity Displays Age-Dependent Changes Across Early-to-Middle Childhood

Hill

Clark

Bigelow

et al. 2021

Preprint

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The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children

Plueckebaum,

Meyer,

Beck

et al. 2023

Autism Research

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Autism is a neurodevelopmental condition that has been related to an overall imbalance between the brain's excitatory (E) and inhibitory (I) systems. Such an EI imbalance can lead to structural and functional cortical deviances and thus alter information processing in the brain, ultimately giving rise to autism traits. However, the developmental trajectory of EI imbalances across childhood and adolescence has not been investigated yet. Therefore, its relationship to autism traits is not well understood. In the present study, we determined a functional measure of the EI balance (f‐EIB) from resting‐state electrophysiological recordings for a final sample of 92 autistic children from 6 to 17 years of age and 100 allistic (i.e., non‐autistic) children matched by age, sex, and nonverbal‐IQ. We related the developmental trajectory of f‐EIB to behavioral assessments of autism traits as well as language ability. Our results revealed differential EI trajectories for autistic compared to allistic children. Importantly, the developmental trajectory of f‐EIB values related to individual language ability. In particular, elevated excitability in late childhood and early adolescence was linked to decreased listening comprehension. Our findings provide evidence against a general EI imbalance in autistic children when correcting for non‐verbal IQ. Instead, we show that the developmental trajectory of EI balance shares variance with autism trait development at a specific age range. This is consistent with the proposal that the late development of inhibitory brain activity is a key substrate of autism traits.

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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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The development of theta and alpha neural oscillations from ages 3 to 24 years

Abstract: Graphical abstract

Cited by 145 publications

References 131 publications

Periodic and Aperiodic Neural Activity Displays Age-Dependent Changes Across Early-to-Middle Childhood

Periodic and Aperiodic Neural Activity Displays Age-Dependent Changes Across Early-to-Middle Childhood

The developmental trajectory of functional excitation‐inhibition balance relates to language abilities in autistic and allistic children

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

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