Elicit: Biomarkers in Lennox-Gastaut Syndrome Seizures (public)
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.
Study Design:
- Identify the type of study design used: observational study (e.g., cross-sectional, cohort, case-control), experimental study, or not clearly specified.
Biomarkers Investigated:
- List ALL biomarkers examined in the study related to Lennox-Gastaut syndrome.
Participant Demographics:
- 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.
Biomarker Findings:
- Report statistical significance of findings, Note any correlations with clinical features, Include specific numerical values (mean, standard deviation, p-values).
Clinical Implications:
- Extract the authors’ conclusions about the potential diagnostic or prognostic value of identified biomarkers.
Results
Characteristics of Included Studies
| Study | Study Design | Population Size | Biomarker Types Studied | Primary Outcomes | Full text retrieved |
|---|---|---|---|---|---|
| Romero Milà et al., 2025 | Observational cohort study | 15 | EEG functional connectivity | Evolution from infantile epileptic spasms syndrome (IESS) to Lennox-Gastaut syndrome (LGS), treatment response | No |
| Kobayashi et al., 2009 | Observational (cross-sectional) | 20 | EEG gamma rhythms | Pathophysiology of tonic seizures | No |
| Bare et al., 1998 | Observational (cross-sectional) | 38 | EEG (video monitoring) | Identification of atypical absence seizures | No |
| Hödl et al., 2021 | Observational (cross-sectional) | 7 | Heart rate variability (HRV) | Autonomic imbalance, vagus nerve stimulation (VNS) response | No |
| Deering et al., 2024 | Retrospective observational cohort | 33 | EEG slow spike-wave (SSW), clinical markers | Seizure-free periods, SSW as early biomarker | No |
| Dalic et al., 2022a | Randomized controlled trial | 17 | EEG generalized paroxysmal fast activity (GPFA), electrographic seizures | Deep brain stimulation (DBS) response, seizure frequency | No |
| Cho et al., 2025 | Observational (cohort) | 14 | Stereoelectroencephalography (SEEG) indices | Epileptogenic zone network (EZN) mapping, surgical outcome | No |
| Zhou et al., 2025 | Observational (retrospective) | Not reported in abstract | EEG GPFA, mutual information (MI) | Steroid response, network topology | No |
| Bullinger et al., 2025 | Experimental (clinical trial) | Not reported in abstract | Thalamic EEG (ictal/interictal) | Ictal/interictal patterns, closed-loop stimulation | No |
| Yagi, 2015 | Observational (retrospective cohort) | Not reported | EEG (polysomnography) | Seizure types, sleep effects, pathophysiology | No |
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:
- 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:
- 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
Primary Biomarker Categories
EEG-Based Biomarkers
| Study | Biomarker Pattern | Detection Method | Clinical Validation Status | Frequency of Use |
|---|---|---|---|---|
| Romero Milà et al., 2025 | Functional connectivity | Cross-correlation EEG | Correlates with LGS evolution, treatment response | Moderate |
| Kobayashi et al., 2009 | Gamma rhythms | Spectral EEG | Pathophysiology of tonic seizures | Moderate |
| Bare et al., 1998 | Atypical absence, SSW | Video EEG | Diagnostic accuracy | High |
| Deering et al., 2024 | SSW | EEG | Early biomarker, predictive | High |
| Dalic et al., 2022a | GPFA, electrographic seizures | Sleep EEG | DBS response prediction | High |
| Cho et al., 2025 | EI, CEI, spikes, HFOs | SEEG | EZN mapping, surgical outcome | Moderate |
| Zhou et al., 2025 | GPFA, MI | Scalp EEG | Steroid response prediction | Moderate |
| Bullinger et al., 2025 | Ictal/interictal patterns | Thalamic EEG | Closed-loop stimulation | Moderate |
| Yagi, 2015 | SSW, fast rhythms | Polysomnography | Seizure type, sleep effects | High |
| Dalic et al., 2022b | GPFA | EEG | DBS response | High |
Neurophysiological Biomarkers
| Study | Biomarker | Detection Method | Clinical Validation Status | Frequency of Use |
|---|---|---|---|---|
| Hödl et al., 2021 | Heart rate variability (high-frequency power) | Video-EEG, HRV analysis | Autonomic imbalance, SUDEP risk | Low |
| Bullinger et al., 2025 | Thalamic EEG patterns | Intracranial EEG | Closed-loop stimulation | Moderate |
| Badawy et al., 2012 | Cortical excitability | Transcranial magnetic stimulation (TMS) | Pathophysiology | Low |
| Warren et al., 2024a | Thalamocortical activity | Intracranial EEG | Network mapping | Moderate |
| Warren et al., 2024b | Thalamocortical activity | Intracranial EEG | Network mapping | Moderate |
Molecular and Other Biomarkers
| Study | Biomarker | Detection Method | Clinical Validation Status | Frequency of Use |
|---|---|---|---|---|
| Cherkezzade et al., 2024 | GFAP, HMGB1, CHI3L1, sCD163, TREM2 | Serum ELISA | Neuroinflammation, prognosis | Low |
| Kumar et al., 2015 | P-glycoprotein | PBMC assay | Drug resistance | Low |
| Espinosa Zacarías et al., 2002 | CSF immunoglobulin G (IgG) | CSF analysis | IVIG response | Low |
| Widdess-Walsh et al., 2013 | De novo mutations, chromosome analysis | Blood, exome sequencing | Genetic etiology | Low |
| Ennaim et al., 2013 | Brain atrophy (voxel-based morphometry) | MRI | Network mapping | Moderate |
| Balfroid et al., 2023 | FDG-PET hypometabolism | PET | Network mapping, cognitive status | Moderate |
| Alanazi et al., 2022 | Centromedian nucleus activity, MRI | Imaging, EEG | DBS targeting | Moderate |
| Micheu et al., 2012 | CT, MRI, US | Imaging | Early diagnosis | Low |
| Pedersen et al., 2015 | fMRI network properties | fMRI | Network mapping | Low |
Clinical Applications and Validation
Diagnostic Applications
- EEG-based biomarkers: SSW and GPFA are reported as central to the diagnosis of Lennox-Gastaut syndrome with high sensitivity and specificity, though based on smaller sample sizes.
- Video-EEG monitoring: Essential for distinguishing seizure types, especially atypical absence seizures.
- Imaging biomarkers: MRI and PET as adjuncts for network mapping and structural etiologies.
Treatment Monitoring
- Changes in GPFA and EEG functional connectivity related to treatment response, particularly neuromodulation and steroid therapy.
- Automated EEG analysis offers rapid assessments of treatment efficacy.
Prognostic Value
- Longer SSW discharges and disorganized EEG backgrounds correlated with poorer outcomes.
- Serum glial markers and P-glycoprotein may predict disease severity and drug resistance, but more evidence is needed.
Summary
- EEG-based biomarkers: SSW and GPFA are the most commonly reported and validated biomarkers for seizures associated with Lennox-Gastaut syndrome. These biomarkers are foundational for diagnosis and monitoring.
- Imaging and molecular biomarkers are emerging as significant adjuncts, but more validation is needed for clinical use.
- Trends indicate a movement toward quantitative, automated, and network-based approaches, although evidence is limited by small sample sizes and incomplete reporting.