Properties of beta oscillations in Dup15q syndrome
(Summary by JoAnne Mosel, Joe Elassal, edited Vidya Saravanapandian)
By Vidya Saravanapandian, Joel Frohlich, Joerg F. Hipp, Carly Hyde, Aaron W. Scheffler, Peyman Golshani, Edwin H. Cook, Lawrence T. Reiter, Damla Senturk and Shafali S. Jeste.
A previous study, “Mechanisms underlying the EEG biomarker in Dup15q syndrome” confirmed the presence of beta waves on EEGs of children with dup15q and determined “GABAA receptor” genes play a role in these unique beta brain waves. This signature EEG pattern could help scientists develop animal models to better understand dup15q and to aid in clinical trials of drugs that target these GABA receptors and potentially help treat symptoms including seizures in our kids.
This study represents a follow-up to the study mentioned above and looked at a larger number of children with dup15q to further delve into understanding the potential for using this EEG signature (excessive beta oscillations) as a biomarker in Dup15q.
We can use as an endpoint this unique beta wave signature as a measure to reflect effective drug target engagement. For example, a drug that targets GABA receptors could potentially reduce the amount of beta waves on EEG before it shows changes in a child’s behavior. To be able to determine how useful these beta wave signatures might be in the setting in a clinical trial, researchers wanted to evaluate its association with certain clinical findings.
There were several questions the researchers aimed to answer with this study. Does the beta wave signature change as a child gets older? Does it change based on the different duplication types they have (Isodicentric vs. Interstitial)? Does it change (or get worse) if a child has epilepsy? Does the beta wave signature change over time? If it does change with a child’s age it may not be a good marker that can be used clinical trials as it would be hard to evaluate whether the change in beta waves seen after a drug-treatment was due to a successful drug-target engagement or simply because the child got older. Finally, does the beta wave signature change depending on whether we record a child in a research lab verses a clinic setting (i.e., based on different recording set-ups)?
To answer these questions, the authors examined two EEG measures of beta waves: 1) “beta power”, which represents the amount of EEG activity within the beta wave frequency (beta waves have a frequency of 12 to 30 hertz, as they are oscillations that repeat for 12 to 30 cycles per second) and 2) “beta peak frequency (BPF)”, which represents the frequency at which the beta wave activity was the highest.
The authors investigated these measures in four parallel studies.
In study 1, neither beta power nor BPF differed depending on what type of duplication a dup15q child had or what age they were. Likewise, beta power did not differ between those with or without epilepsy but BPF did – those with epilepsy had significantly lower frequency than those without.
In study 2, neither measures differed based on the level of cognition or adaptive skills. The exception was lower peak frequency seen in the EEGs of those kids with lower adaptive skills.
Study 3 looked at the stability of these changes over time. Here, the researchers demonstrated from multiple EEGs in each child that both the beta power and BPF findings were consistent over time.
Finally, study 4 confirmed that what the researchers saw in the research EEGs were similar to “low-density” EEGs that are more typical of what is measured in clinical practice. Low-density means that fewer electrodes are placed on the patient during the procedure.
In conclusion, signature beta oscillations have been further proven through this research to be a robust and reproducible biomarker that can serve as a measure of drug target engagement for our Dup15 children.
IMPACT for families: The small sample size is a limitation of the study which is a limitation found in many studies of rare genetic conditions. The more we participate in studies, particularly those that involve analyzing EEG data from our children, the larger the sampling population they will have to work with and the better the data quality will be.
