Which biomarkers are most commonly used for seizures associated with Lennox-Gastaut syndrome?

The most frequent biomarkers for Lennox-Gastaut syndrome seizures are electroencephalographic measurements, specifically SSW and GPFA patterns.

Abstract

Electroencephalography metrics appear most frequently for seizures in Lennox-Gastaut syndrome. Ten studies report slow spike‐wave (SSW) patterns, and seven document generalized paroxysmal fast activity (GPFA); other EEG features (such as gamma rhythms, paroxysmal fast activity, and network indices) also support diagnosis and treatment assessment. Standard, video, sleep, intracranial, and automated EEG methods serve as the primary detection techniques.

Imaging biomarkers (MRI, fMRI, PET), serum and cerebrospinal fluid markers, and measures of autonomic or cortical excitability are used less often. Studies employing these varied approaches link EEG findings to seizure type, network mapping, and treatment response, thereby establishing SSW and GPFA as the most common biomarkers in this context.

Methods

We analyzed 34 sources from an initial pool of 998, using 6 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question.

Paper search

Using your research question “Which biomarkers are most commonly used for seizures associated with Lennox-Gastaut syndrome?”, we searched across over 126 million academic papers from the Semantic Scholar corpus. We retrieved the 998 papers most relevant to the query.

Screening

We screened in sources based on their abstracts that met these criteria:

• Population - LGS Diagnosis: Does the study include patients with confirmed Lennox-Gastaut syndrome (LGS) who can be analyzed separately from patients with other epilepsy syndromes?
• Biomarker Focus: Does the study examine biological markers (molecular, cellular, imaging, or electrophysiological) in relation to seizures in LGS?
• Study Type and Measurements: Is this either a primary research study or a systematic review/meta-analysis that reports quantitative biomarker measurements?
• Temporal Association: Does the study demonstrate a clear temporal relationship between biomarker measurements and seizure activity?
• Sample Size: Does the study include 5 or more subjects?
• Study Population Type: Was the study conducted in human subjects?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

Results

Characteristics of Included Studies

Summary of Study Characteristics:

• Study design:

  • 25 studies were observational (cohort, cross-sectional, case-control, retrospective, longitudinal)
  • 4 studies were interventional (randomized controlled trial, clinical trial, DBS trial)
  • 2 studies were systematic reviews

• Biomarker types studied:

  • 26 studies: EEG-based biomarkers
  • 8 studies: Imaging-based biomarkers
  • 3 studies: Blood/serum/CSF-based biomarkers
  • 1 study: Genetic biomarkers
  • 1 study: Heart rate variability/autonomic biomarkers
  • 1 study: TMS (cortical excitability) as a biomarker

• Primary outcomes:

  • 12 studies: Treatment response
  • 12 studies: Network mapping
  • 11 studies: Seizure types

Primary Biomarker Categories

EEG-Based Biomarkers

Clinical Applications and Validation

Diagnostic Applications

• EEG-based biomarkers: Slow spike-wave (SSW) and GPFA were reported as central to diagnosis with high sensitivity and specificity.
• Video-EEG monitoring: Essential for distinguishing seizure types, especially atypical absence seizures.
• Imaging biomarkers: MRI and PET were used for network mapping and identifying structural etiologies.

Treatment Monitoring

• Changes in GPFA and EEG functional connectivity were associated with treatment response.
• Automated EEG analysis allows for objective assessment of treatment efficacy.
• Heart rate variability and thalamic electrophysiology were explored for monitoring autonomic responses.

Prognostic Value

• Longer SSW discharges were associated with poorer outcomes.
• Serum glial markers and P-glycoprotein may have prognostic value for disease severity and drug resistance.

Summary

• EEG-based biomarkers (SSW and GPFA) were the most commonly reported and validated biomarkers for seizures associated with Lennox-Gastaut syndrome.
• Imaging and molecular biomarkers are emerging as important adjuncts, but further validation is needed.
• Trends are shifting towards quantitative, automated, and network-based approaches, though evidence is limited by small sample sizes and incomplete reporting.

References

• Dalic, L. J., Warren, A. E. L., Spiegel, C., Thevathasan, W., & Roten, A. (2022). Paroxysmal fast activity is a biomarker of treatment response for Lennox–Gastaut syndrome. Epilepsia. Link
• Cherkezzade, M., Soylu, S., Tüzün, E., Yılmaz, V. et al. (2024). Association Between Serum Levels of Glial Biomarkers, Clinical Severity, and Electro-encephalography Features in Idiopathic West and Lennox-Gastaut Syndromes. Nöropsikiyatri arşivi. Link
• Nizami, F. M., Trivedi, S., Kalita, J. (2024). A systematic review of electroencephalographic findings in Lennox-Gastaut syndrome. Epilepsy Research. Link
• Kumar, A., Tripathi, D., Paliwal, V., Neyaz, Z., Agarwal, V. et al. (2015). Role of P-Glycoprotein in Refractoriness of Seizures to Antiepileptic Drugs in Lennox-Gastaut Syndrome. Journal of Child Neurology. Link
• Hu, D. K., Pinto-Orellana, M., Rana, M., Do, L., Adams, D. J. et al. (2024). Discovering EEG biomarkers of Lennox–Gastaut syndrome through unsupervised time–frequency analysis. Epilepsia. Link
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