Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Reynolds, Ashley; Vranic-Peters, Michaela Jayne; Lai, Alan; Grayden, David B.; Cook, Mark; Peterson, Andre D. H.

doi:10.1111/epi.17548

Cited by 6 publications

(3 citation statements)

References 218 publications

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“…Finally, EEG background activity did not impact HFO rates in our study, in contrast with the observation of higher scalp HFO rates with abnormal background activity in a neonatal study 28 . However, any findings regarding the impact of seizure substrates, clinical, and EEG features on HFO rates should be viewed cautiously since the limited size of available cohorts so far prohibits definitive conclusions 39 …”

Section: Discussioncontrasting

confidence: 83%

Scalp high‐frequency oscillations differentiate neonates with seizures from healthy neonates

Cserpan,

Guidi,

Alessandri

et al. 2023

Epilepsia Open

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ObjectiveWe aimed to investigate (1) whether an automated detector can capture scalp high‐frequency oscillations (HFO) in neonates and (2) whether scalp HFO rates can differentiate neonates with seizures from healthy neonates.MethodsWe considered 20 neonates with EEG‐confirmed seizures and four healthy neonates. We applied a previously validated automated HFO detector to determine scalp HFO rates in quiet sleep.ResultsEtiology in neonates with seizures included hypoxic‐ischemic encephalopathy in 11 cases, structural vascular lesions in 6, and genetic causes in 3. The HFO rates were significantly higher in neonates with seizures (0.098 ± 0.091 HFO/min) than in healthy neonates (0.038 ± 0.025 HFO/min; P = 0.02) with a Hedge's g value of 0.68 indicating a medium effect size. The HFO rate of 0.1 HFO/min/ch yielded the highest Youden index in discriminating neonates with seizures from healthy neonates. In neonates with seizures, etiology, status epilepticus, EEG background activity, and seizure patterns did not significantly impact HFO rates.SignificanceNeonatal scalp HFO can be detected automatically and differentiate neonates with seizures from healthy neonates. Our observations have significant implications for neuromonitoring in neonates. This is the first step in establishing neonatal HFO as a biomarker for neonatal seizures.

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Section: Discussioncontrasting

confidence: 83%

Scalp high‐frequency oscillations differentiate neonates with seizures from healthy neonates

Cserpan,

Guidi,

Alessandri

et al. 2023

Epilepsia Open

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“…Also, factors such as drug absorption, distribution, metabolism, and elimination, as well as target engagement and downstream signaling pathways, can influence the efficacy of ASMs [ 49 ]. Recently, evidence of genetic markers, neuroimaging findings, EEG findings, or other molecular indicators, can serve as predictors of treatment response [ 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Clinical description and evaluation of 30 pediatric patients with ultra-rare diseases: A multicenter study with real-world data from Saudi Arabia

Muthaffar,

Alazhary,

Alyazidi

et al. 2024

PLoS ONE

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Background With the advancement of next-generation sequencing, clinicians are now able to detect ultra-rare mutations that are barely encountered by the majority of physicians. Ultra-rare and rare diseases cumulatively acquire a prevalence equivalent to type 2 diabetes with 80% being genetic in origin and more prevalent among high consanguinity communities including Saudi Arabia. The challenge of these diseases is the ability to predict their prevalence and define clear phenotypic features. Methods This is a non-interventional retrospective multicenter study. We included pediatric patients with a pathogenic variant designated as ultra-rare according to the National Institute for Clinical Excellence’s criteria. Demographic, clinical, laboratory, and radiological data of all patients were collected and analyzed using multinomial regression models. Results We included 30 patients. Their mean age of diagnosis was 16.77 months (range 3–96 months) and their current age was 8.83 years (range = 2–15 years). Eleven patients were females and 19 were males. The majority were of Arab ethnicity (96.77%). Twelve patients were West-Saudis and 8 patients were South-Saudis. SCN1A mutation was reported among 19 patients. Other mutations included SZT2, ROGDI, PRF1, ATP1A3, and SHANK3. The heterozygous mutation was reported among 67.86%. Twenty-nine patients experienced seizures with GTC being the most frequently reported semiology. The mean response to ASMs was 45.50% (range 0–100%). Conclusion The results suggest that ultra-rare diseases must be viewed as a distinct category from rare diseases with potential demographic and clinical hallmarks. Additional objective and descriptive criteria to detect such cases are needed.

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“…So far, the two most reliable EEG biomarkers of ASM response are the interictal epileptiform discharges and the power spectral analysis, while the use of the Individual Alpha Frequency (IAF) peak is still debated but may represent a promising biomarker ( Reynolds et al, 2023 ). However, these linear methods mainly capture the oscillatory component of the EEG signal and do not consider non-stationarities and non-linearities present in EEG signals ( Stam, 2005 ; Klonowski, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

Porcaro,

Seppi,

Pellegrino

et al. 2024

Front. Neurosci.

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ObjectivesAn important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods.MaterialsTwenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC).MethodsEEG data were investigated from two different angles: frequency domain—spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain—FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups.ResultsThe δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ.DiscussionFD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes.ConclusionOur work suggests that FD is a promising measure to monitor the response to ASMs in FE.

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Prognostic interictal electroencephalographic biomarkers and models to assess antiseizure medication efficacy for clinical practice: A scoping review

Cited by 6 publications

References 218 publications

Scalp high‐frequency oscillations differentiate neonates with seizures from healthy neonates

Scalp high‐frequency oscillations differentiate neonates with seizures from healthy neonates

Clinical description and evaluation of 30 pediatric patients with ultra-rare diseases: A multicenter study with real-world data from Saudi Arabia

Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension

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