Brain Wave Abnormalities Tied to Symptom Severity, Age at Seizure Onset
Stronger brain wave abnormalities are significantly associated with more severe cognitive, motor, and communication symptoms and earlier seizure onset in children and adolescents with Angelman syndrome (AS), a study shows.
These findings support brain activity abnormalities as a core Angelman feature, and they support the use of electroencephalography (EEG) as a biomarker in clinical trials of potential Angelman therapies. EEG is a non-invasive method used to measure electrical activity in the brain that provides readouts in the form of waves (i.e., brainwaves).
The study, “Electrophysiological Abnormalities in Angelman Syndrome Correlate With Symptom Severity,” was published in the journal Biological Psychiatry Global Open Science.
Angelman syndrome is a complex neurological condition caused by a lack of working UBE3A protein in nerve cells due to genetic defects.
People with Angelman have highly abnormal EEG results, with excessive spontaneous delta waves as the most characteristic and robust disease feature across all ages and types of disease-causing genetic deficits.
Electrical activity in the brain can be measured as brainwaves (oscillations) with different frequencies. Brainwaves are produced by electrical pulses from nerve cells (neurons) communicating with each other. They are divided into different bandwidths, specifically infra-low, delta, theta, alpha, beta, and gamma, that change according to what an individual is doing and feeling.
Delta waves are large, low-frequency waves typically found during deep sleep.
While these abnormalities may contribute to the neurological deficits associated with Angelman, whether EEG abnormalities are associated with disease symptoms and their significance in the mechanisms underlying Angelman remain unclear.
“Solving this open question is highly important to gain a better understanding of AS [Angelman syndrome] and to understand the utility of EEG-derived metrics as biomarkers for clinical trials in this population,” the researchers wrote.
To address this, scientists at Roche and at children’s hospitals in the U.S. analyzed demographic, clinical, and EEG data from 45 pediatric patients (30 boys and 15 girls) who participated in the AS Natural History Study (NCT00296764).
In all patients, the disease was caused by a deletion of a large region of DNA containing at least the UBE3A gene — which provides the instructions to produce UBE3A. This type of genetic abnormality accounts for about 70% of Angelman cases, and usually causes a more severe form of the disease.
Children’s mean age was 59 months (nearly 5 years), 42 (93.3%) of them had epilepsy, and 16 (35.6%) had more than one and up to five visits, at least one year apart, during the study.
Motor, cognitive, and language skills were assessed with the Bayley Scales of Infant and Toddler Development (BSID), adaptive behavior and motor function through the caregiver-reported Vineland Adaptive Behavior Scales (VABS), and Angelman-specific symptoms using the AS Clinical Severity Scale. EEG was done when patients were awake.
The researchers evaluated potential links between EEG abnormalities, particularly those involving delta waves, and these validated clinical measures, as well as with age of seizure onset.
After adjusting for age, results showed that stronger delta wave abnormalities were significantly associated with earlier epilepsy onset and lower scores in several clinical measures and domains, reflecting worse global development, cognitive function, communication abilities, motor skills, and adaptive behaviors.
“This is consistent with the global and severe impairment in AS characterized by a high degree of correlation between symptom domains,” the researchers wrote.
Whether changes in symptoms across different visits corresponded to changes in delta waves was then assessed. Data from the 16 children with multiple visits showed that greater delta wave abnormalities were associated with poorer performance in all but one clinical score over time.
These links reached statistical significance for the BSID mean as an indirect measure of global development, BSID’s cognitive score, and VABS’s daily living skills-personal, and social-coping skills.
Further analyses using machine learning showed that EEG signals at other frequencies (other wave types) also significantly associated with clinical severity. Machine learning is a type of artificial intelligence that uses algorithms to analyze data, learn from its analyses, and then make a prediction about something.
Including all wave types in the analysis resulted in stronger associations (by about 45%) between EEG abnormalities and symptom severity, reaching statistical significance in some additional scores relative to the delta wave-exclusive analysis.
These findings highlight that “the abnormal delta-band EEG in AS is related cross-sectionally and longitudinally to severity as measured with several different clinical scales as well as the age of epilepsy onset,” the researchers wrote.
As such, the data “provide strong evidence that excess low-frequency neuronal oscillations reflect a core aspect of AS [mechanisms],” they added.
The work also “strengthens the rationale for using EEG as a biomarker in the development of treatments for AS,” as it could serve as a short-term indirect measure of symptom severity, “offering a more immediate and objective assessment of whether a treatment is likely to be beneficial,” the team wrote.
Future studies are needed to confirm whether these associations are also true for patients with other disease-causing genetic abnormalities, as well as to “disentangle the contributions of different EEG features and link them to specific [disease features],” the researchers concluded.