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Updated: Sep 9, 2025
EEG Mu Rhythm in Typical and Atypical Development
Published on: April 9, 2014
Atypical alpha oscillatory EEG dynamics in children with Angelman syndrome
Abigail H Dickinson1, M Sapphire Bowen-Kauth2, Jeremy J Shide2
1Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, USA.
Objectives:
Biomarkers of atypical brain development are crucial for advancing clinical trials and guiding therapeutic interventions in Angelman syndrome (AS). Electroencephalography (EEG) captures well-characterized developmental changes in peak alpha frequency (PAF) that reflect underlying neural circuit maturation and may provide a sensitive metric for mapping atypical neural trajectories in AS.
Method:
We analyzed 159 EEG recordings from 95 children with AS (ages 1-15 years) and 185 age-matched typically developing (TD) controls. PAF was quantified using a well-established curve-fitting method applied to 1/f-corrected power spectra. To validate robustness, we further evaluated PAF using an alternative prominence-based peak detection approach across varying detection thresholds.
Results:
Significant disruptions in PAF were evident in children with AS. While over 90% of EEGs from TD children exhibited a clear alpha peak, fewer than 50% of EEGs from children with AS showed a detectable PAF. Furthermore, when PAF was present, its frequency was significantly lower in AS children and did not show the typical age-related increases observed in TD children. Validation analyses confirmed consistently lower rates of PAF detection in AS across varying sensitivity thresholds, demonstrating the robustness of these results.
Conclusions:
The absence and lower frequency of alpha peaks in Angelman syndrome indicate that PAF is a developmentally sensitive marker of disrupted neural maturation in this population. Further research is needed to clarify how PAF emergence and shifts relate to longitudinal developmental trajectories and specific clinical phenotypes. Nonetheless, PAF shows promise as an objective, quantitative biomarker of neural circuit dynamics that can enhance clinical-trial endpoints by indexing underlying brain function. Future analyses will examine inter-individual variability in PAF among AS participants to uncover mechanistic pathways that may inform targeted therapeutic strategies.
