An electroencephalogram, or EEG, is an important test for measuring electrical activity in the brain and is primarily used to diagnose and manage epilepsy. Now, a new study led by researchers at Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania has found that certain forms of epilepsy caused by genetic mutations have distinct signatures that can be observed via EEG. The findings, published recently by the journal Neurology, could help clinicians look for outcome measures and help discover more biomarkers associated with rare and complex forms of genetic epilepsy.
Most EEG data is analyzed visually, which can lead to potentially missing cases of epilepsy or subtle alterations that might indicate disease severity. However, prior studies have demonstrated the potential of quantitative biomarkers that do not require visual analysis. These have been used to identify potential cases of neurological conditions like autism, ADHD and Alzheimer’s disease. With these promising early results, researchers wanted to determine if quantitative biomarkers could be identified via EEG for genetic epilepsies, since quantifying the severity and tracking the progression of these disorders with traditional measures is often difficult.
“Biomarkers are critical in precision medicine, and our extensive research into genetic epilepsies has revealed an incredible amount of diversity in terms of diagnostic challenges and disease progression,” said senior study co-author Ingo Helbig, MD, director of Genomic Science and co-director of the Epilepsy Neurogenetics Initiative (ENGIN) at CHOP. “Not only could these initial findings help us more quickly and accurately identify cases of genetic epilepsies, but they may also eventually guide clinical trial design and aid with the management of these complex disorders.”
The researchers used retrospective data from more than1000 EEGs from patients at CHOP, including those with a variety of genetic epilepsies. A machine learning model was then used to predict diagnoses of STXBP1, SCN1A and SYNGAP1 as well as seizure frequency and motor function across a broader cohort.
Using this method, the researchers demonstrated that the model was accurately able to predict cases of STXBP1, SYNGAP1 and SCN1A against control samples and between each genetic disorder. While the models were not yet able to predict seizure frequency, they demonstrated their EEG models could predict gross motor function skills, a key measure of epilepsy and its effects, significantly better than age-based models.
“Importantly, our study was able to track changes in electrophysiological activity associated with motor development and distinct genetic epilepsies across childhood, which is crucial since age of the patient is an important factor when considering the timing of diagnosis in many of these disorders,” said lead study author Peter D. Galer, MSc, PhD, a researcher with the Center for Epilepsy and Neurodevelopmental Disorders (ENDD) at CHOP and the Center for Neuroengineering and Therapeutics (CNT) at UPenn. “We believe our method can lead to large-scale quantitative EEG analysis and biomarker discovery in support of a wide variety of genetic epilepsies.”
This study was supported by National Institutes of Health grants K23 NS121401-01A1, R01 NS127830-01A1, R01 NS131512-01, K02 NS112600 and DP1NS122038, the Burroughs Wellcome Fund, the Center for Epilepsy and Neurodevelopmental Disorders (ENDD), St. Jude's grants 1U24NS120854-02 and 1U24NS120854-03, the Hartwell Foundation Individual Biomedical Research Award, the Dravet Foundation, The Jonathan Rothberg Family Fund and the SynGAP Research Fund (SRF) through a Research Training Fellowship for Clinicians.
Galer et al, “Quantitative EEG biomarkers in the genetic epilepsies and associations with neurological outcomes.” Neurology. Online September 23, 2025. DOI: 10.1212/WNL.0000000000214148.
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An electroencephalogram, or EEG, is an important test for measuring electrical activity in the brain and is primarily used to diagnose and manage epilepsy. Now, a new study led by researchers at Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania has found that certain forms of epilepsy caused by genetic mutations have distinct signatures that can be observed via EEG. The findings, published recently by the journal Neurology, could help clinicians look for outcome measures and help discover more biomarkers associated with rare and complex forms of genetic epilepsy.
Most EEG data is analyzed visually, which can lead to potentially missing cases of epilepsy or subtle alterations that might indicate disease severity. However, prior studies have demonstrated the potential of quantitative biomarkers that do not require visual analysis. These have been used to identify potential cases of neurological conditions like autism, ADHD and Alzheimer’s disease. With these promising early results, researchers wanted to determine if quantitative biomarkers could be identified via EEG for genetic epilepsies, since quantifying the severity and tracking the progression of these disorders with traditional measures is often difficult.
“Biomarkers are critical in precision medicine, and our extensive research into genetic epilepsies has revealed an incredible amount of diversity in terms of diagnostic challenges and disease progression,” said senior study co-author Ingo Helbig, MD, director of Genomic Science and co-director of the Epilepsy Neurogenetics Initiative (ENGIN) at CHOP. “Not only could these initial findings help us more quickly and accurately identify cases of genetic epilepsies, but they may also eventually guide clinical trial design and aid with the management of these complex disorders.”
The researchers used retrospective data from more than1000 EEGs from patients at CHOP, including those with a variety of genetic epilepsies. A machine learning model was then used to predict diagnoses of STXBP1, SCN1A and SYNGAP1 as well as seizure frequency and motor function across a broader cohort.
Using this method, the researchers demonstrated that the model was accurately able to predict cases of STXBP1, SYNGAP1 and SCN1A against control samples and between each genetic disorder. While the models were not yet able to predict seizure frequency, they demonstrated their EEG models could predict gross motor function skills, a key measure of epilepsy and its effects, significantly better than age-based models.
“Importantly, our study was able to track changes in electrophysiological activity associated with motor development and distinct genetic epilepsies across childhood, which is crucial since age of the patient is an important factor when considering the timing of diagnosis in many of these disorders,” said lead study author Peter D. Galer, MSc, PhD, a researcher with the Center for Epilepsy and Neurodevelopmental Disorders (ENDD) at CHOP and the Center for Neuroengineering and Therapeutics (CNT) at UPenn. “We believe our method can lead to large-scale quantitative EEG analysis and biomarker discovery in support of a wide variety of genetic epilepsies.”
This study was supported by National Institutes of Health grants K23 NS121401-01A1, R01 NS127830-01A1, R01 NS131512-01, K02 NS112600 and DP1NS122038, the Burroughs Wellcome Fund, the Center for Epilepsy and Neurodevelopmental Disorders (ENDD), St. Jude's grants 1U24NS120854-02 and 1U24NS120854-03, the Hartwell Foundation Individual Biomedical Research Award, the Dravet Foundation, The Jonathan Rothberg Family Fund and the SynGAP Research Fund (SRF) through a Research Training Fellowship for Clinicians.
Galer et al, “Quantitative EEG biomarkers in the genetic epilepsies and associations with neurological outcomes.” Neurology. Online September 23, 2025. DOI: 10.1212/WNL.0000000000214148.
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