A joint study from Children’s National and UCLA findound that peak alpha frequency (PAF), a simple EEG signal, may help track brain growth in Angelman syndrome.
Angelman syndrome (AS) affects how a child’s brain develops and communicates. Families often face limited or absent speech, seizures, sleep problems and global delays. As several treatments move through late-stage trials, teams need clear ways to see if a therapy is changing the brain. A joint study from Children’s National Hospital and the University of California, Los Angeles (UCLA) points to a simple signal from an electroencephalogram (EEG), a test that records brain activity through small sensors on the scalp.
The signal is called peak alpha frequency (PAF). Alpha is a natural brain rhythm. In most children, the speed of this rhythm rises through childhood as networks mature. The study shows that this pattern is different in Angelman syndrome. Many children with AS have no clear alpha peak. When a peak is present, it is slower than in peers and does not speed up with age. That pattern suggests stalled or altered maturation of brain circuits.
Researchers looked at hundreds of EEGs from children with AS and from age-matched peers. They measured alpha activity in two independent ways to make sure the results were not due to a single analysis choice. Both approaches told the same story. In children who are typically developing, more than nine out of ten EEGs showed a distinct alpha peak, and that peak tended to get faster with age. In children with AS, fewer than half of EEGs showed a peak. When a peak appeared, it sat at a lower frequency and stayed flat across ages.
The team also confirmed a second feature of Angelman EEGs that others have reported: more slow-wave (delta) activity and less overall alpha power than peers. Taken together, these signals point to broad differences in how circuits work in Angelman syndrome.
What could this mean for care and for trials? EEG is familiar, safe, and widely available. If a therapy begins to change how circuits function, that shift could show up in PAF before day-to-day behavior changes are obvious. Adding PAF to trial measures could give clinicians and families an earlier read on brain effects. It can also sit alongside delta power to give a fuller picture – delta reflects broad disruption, while PAF tracks the speed of a key rhythm tied to growth.
There are limits to keep in mind. The EEGs came from several sites and systems, and some recordings were longer than others. The team accounted for these factors in their models, but future work with unified setups will help. A major next step is to follow the same children over time to see whether PAF changes within a child and how those changes relate to speech, movement, seizures, or sleep. The study also hints that genetic subtypes may differ, which could sharpen how trials are designed.
This project was a true collaboration between Children’s National Hospital (PI and corresponding author: Michael S. Sidorov, PhD, co-first author: Sapphire Bowen-Kauth) and UCLA (co-first author: Abigail H. Dickinson, PhD), with contributions from colleagues across both centers. Funding came from the Foundation for Angelman Syndrome Therapeutics (FAST).
For families, the takeaway is hopeful. A routine test can capture a signal linked to brain growth. In Angelman syndrome, that signal is often missing or slow, but it can be measured and tracked. As trials continue, PAF may help teams spot early signs that a therapy is reaching the brain’s core rhythms, information that matters for decisions in clinics and for progress in research.
Read the full study, “Atypical alpha oscillatory EEG dynamics in children with Angelman syndrome,” in Neuroimage: Clinical.