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.
Papers identified with Elicit search
n = 998
Papers screened using: Population - LGS Diagnosis, Biomarker Focus, Study Type and Measurements, Temporal Association, Sample Size, Study Population Type
n = 998
Papers screened out
n = 964
Papers included for extraction
n = 34
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.
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
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)
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
Results
Characteristics of Included Studies
| Study | Study Design | Population Size | Biomarker Types Studied | Primary Outcomes |
|---|---|---|---|---|
| Romero Milà et al., 2025 | Observational cohort study | 15 | Electroencephalography (EEG) functional connectivity | Evolution from infantile epileptic spasms syndrome (IESS) to Lennox-Gastaut syndrome (LGS), treatment response |
| Kobayashi et al., 2009 | Observational (cross-sectional) | 20 | EEG gamma rhythms | Pathophysiology of tonic seizures |
| Bare et al., 1998 | Observational (cross-sectional) | 38 | EEG (video monitoring) | Identification of atypical absence seizures |
| Hödl et al., 2021 | Observational (cross-sectional) | 7 | Heart rate variability (HRV) | Autonomic imbalance, vagus nerve stimulation (VNS) response |
| Deering et al., 2024 | Retrospective observational cohort | 33 | EEG slow spike-wave (SSW), clinical markers | Seizure-free periods, SSW as early biomarker |
| Dalic et al., 2022a | Randomized controlled trial | 17 | EEG generalized paroxysmal fast activity (GPFA), electrographic seizures | Deep brain stimulation (DBS) response, seizure frequency |
| Cho et al., 2025 | Observational (cohort) | 14 | Stereoelectroencephalography (SEEG) indices (epileptogenicity index [EI], connectivity entropy index [CEI], spikes, high-frequency oscillations [HFOs]) | Epileptogenic zone network (EZN) mapping, surgical outcome |
| Zhou et al., 2025 | Observational (retrospective) | Not reported in abstract | EEG GPFA, mutual information (MI) | Steroid response, network topology |
| Bullinger et al., 2025 | Experimental (clinical trial) | Not reported in abstract | Thalamic EEG (ictal/interictal) | Ictal/interictal patterns, closed-loop stimulation |
| Yagi, 2015 | Observational (retrospective cohort) | Not reported in abstract | EEG (polysomnography) | Seizure types, sleep effects, pathophysiology |
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 |
Key findings from included studies:
- Most frequently reported patterns: Among the included studies, slow spike-wave (SSW) was the most frequently reported biomarker pattern (10 studies). Generalized paroxysmal fast activity (GPFA) was reported in 7 studies.
- Detection methods: Standard EEG was the most common detection method (9 studies).
- Frequency of use: 11 studies were categorized as “High”, 10 as “Moderate”, and 2 as “Low” for frequency of use.
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 |
Clinical Applications and Validation
Diagnostic Applications
- EEG-based biomarkers: Slow spike-wave (SSW) and generalized paroxysmal fast activity (GPFA) were central to the diagnosis of Lennox-Gastaut syndrome with high sensitivity and specificity reported.
- Video-EEG monitoring: Essential for distinguishing seizure types, especially atypical absence seizures.
Treatment Monitoring
- EEG changes and treatment response: Changes in GPFA and EEG functional connectivity were associated with treatment response.
Prognostic Value
- EEG features and outcomes: Longer SSW discharges and disorganized EEG background were associated with poorer outcomes in the included studies.
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.
- Evidence limitations: Most studies had small sample sizes, and many relied on retrospective or cross-sectional designs.