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?

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.

• Study Design:

Identify the type of study design used:

• Specify whether it is an observational study (e.g., cross-sectional, cohort, case-control)

• If an experimental study, note the specific type (e.g., randomized controlled trial, interventional study)

• If multiple study designs are present, list all types

• If the design is not clearly stated, write “Not clearly specified” Look primarily in the Methods section for this information.

• Biomarkers Investigated:

List ALL biomarkers examined in the study related to Lennox-Gastaut syndrome:

• Include full names and abbreviations

• Specify the biological source of the biomarker (e.g., serum, cerebrospinal fluid)

• Note the measurement method used (e.g., ELISA, immunoassay)

• If multiple biomarkers were studied, list them in order of appearance in the text Look in Methods and Results sections for comprehensive biomarker information.

• Participant Demographics:

Extract the following participant information:

• Total number of participants with Lennox-Gastaut syndrome

• Age range or mean age

• Gender distribution (if reported)

• Inclusion/exclusion criteria

• Any specific subgroups within the LGS population Prioritize information from the Methods section, specifically participant selection details If any information is missing, note “Not reported”

• Biomarker Findings:

For each biomarker investigated:

• Report statistical significance of findings

• Note any correlations with clinical features (e.g., seizure severity, EEG characteristics)

• Include specific numerical values (mean, standard deviation, p-values)

• Highlight any comparative findings (e.g., differences from control groups) Primarily extract information from Results section If statistical details are incomplete, note “Insufficient statistical reporting”

• Clinical Implications:

Extract the authors’ conclusions about:

• Potential diagnostic or prognostic value of identified biomarkers
• Suggested clinical significance of biomarker findings
• Any recommendations for future research Look in Discussion and Conclusion sections If no clear clinical implications are stated, write “No specific clinical implications reported”

Results

Characteristics of Included Studies

Study Summary

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, interventional case series)
• 2 studies were systematic reviews
• 3 studies did not clearly specify study design

Biomarker types studied (some studies used more than one type):

• 26 studies: EEG-based biomarkers (including scalp EEG, SEEG, intracranial EEG, EEG-fMRI, and related indices)
• 8 studies: Imaging-based biomarkers (MRI, fMRI, PET, CT, US)
• 3 studies: Blood/serum/CSF-based biomarkers (serum glial markers, P-glycoprotein, CSF IgG)
• 1 study: Genetic biomarkers (exome sequencing)
• 1 study: Heart rate variability/autonomic biomarkers
• 1 study: TMS (cortical excitability) as a biomarker

Primary outcomes (some studies had multiple outcomes):

• 12 studies: Treatment response (surgical, DBS, VNS, steroid, IVIG, drug resistance)
• 12 studies: Network mapping or EEG-imaging correlation
• 11 studies: Seizure types, features, or phenotyping
• 6 studies: Biomarker validation or method development
• 5 studies: Pathophysiology or mechanism
• 2 studies: Prognosis or cluster analysis
• 2 studies: Neuroinflammation or genotype-phenotype relationships

We did not find mention of missing biomarker type or primary outcome information in the available abstracts or full texts.

Primary Biomarker Categories

EEG-Based Biomarkers

Neurophysiological Biomarkers

Molecular and Other Biomarkers

Clinical Applications and Validation

Diagnostic Applications

• EEG-based biomarkers: Slow spike-wave (SSW) and generalized paroxysmal fast activity (GPFA) were reported as central to the diagnosis of Lennox-Gastaut syndrome in several included studies, with high sensitivity and specificity reported in those studies, though most had small sample sizes or limited data.
• Video-EEG monitoring: Reported as essential for distinguishing seizure types, especially atypical absence seizures.
• Imaging biomarkers: MRI and PET were used as adjuncts for network mapping and identifying structural etiologies.

Treatment Monitoring

• EEG changes and treatment response: Changes in GPFA and EEG functional connectivity were associated with treatment response, particularly in the context of neuromodulation (deep brain stimulation, vagus nerve stimulation) and steroid therapy.
• Automated EEG analysis: Automated EEG analysis and network biomarkers were reported to allow for more rapid and objective assessment of treatment efficacy.
• Heart rate variability and thalamic electrophysiology: These were explored for monitoring autonomic and network responses to therapy, but clinical utility was limited in the included studies.

Prognostic Value

• EEG features and outcomes: Longer SSW discharges and disorganized EEG background were associated with poorer outcomes in the included studies.
• Serum glial markers and P-glycoprotein: These may have prognostic value for disease severity and drug resistance, though evidence was preliminary and based on small samples.
• Imaging biomarkers: Frontoparietal hypometabolism and brain atrophy were reported as potential predictors of cognitive outcomes and for guiding intervention planning.

Summary

• EEG-based biomarkers: Slow spike-wave (SSW) and generalized paroxysmal fast activity (GPFA) were the most commonly reported and validated biomarkers for seizures associated with Lennox-Gastaut syndrome in the included studies. These biomarkers formed the foundation for diagnosis and monitoring.
• Imaging and molecular biomarkers: These are emerging as important adjuncts, especially for network mapping and understanding disease mechanisms, but require further validation for routine clinical use.
• Trends: The field is moving toward quantitative, automated, and network-based approaches, but the evidence base is limited by small sample sizes, heterogeneity, and incomplete reporting in the included studies.
• Evidence limitations: Most studies had small sample sizes, and many relied on retrospective or cross-sectional designs. Only a few systematic reviews or larger cohorts were included.

References

1. Linda J. Dalic, Aaron E. L. Warren, Chloe Spiegel et al. (2022). Paroxysmal fast activity is a biomarker of treatment response in deep brain stimulation for Lennox–Gastaut syndrome. Epilepsia
2. Minara Cherkezzade, et al. (2024). The Association Between Serum Levels of Glial Biomarkers, Clinical Severity and Electro-encephalography Features in Idiopathic West and Lennox-Gastaut Syndromes. Nöropsikiyatri arşivi
3. Firoz M. Nizami, et al. (2024). A systematic review of electroencephalographic findings in Lennox-Gastaut syndrome. Epilepsy Research