Sex-specific disease modifiers in juvenile myoclonic epilepsy

Laiou

Abela

et al. 2022

Brain Communications

Self Cite

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalised epilepsy in both ictal and interictal states. Here, we characterise differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy and assess factors influencing the heterogeneity of EEG features. We collected EEG data from 147 participants with juvenile myoclonic epilepsy through the Biology of Juvenile Myoclonic Epilepsy study. 95 control EEGs were acquired from two independent studies (Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project). We extracted frequency and functional network-based features from 10-20 s epochs of resting-state EEG, including relative power spectral density, peak alpha frequency, network topology measures and brain network ictogenicity: a computational measure of the propensity of networks to generate seizure dynamics. We tested for differences between epilepsy and control EEGs using univariate, multivariable and receiver operating curve analysis. Additionally, we explored the heterogeneity of EEG features within and between cohorts by testing for associations with potentially influential factors such as age, sex, epoch length and time, as well as testing for associations with clinical phenotypes including anti-seizure medication, and seizure characteristics in the epilepsy cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in power spectral density in delta (2-5 Hz) (p = 0.0007, hedges’ g = 0.55) and low-alpha (6-9 Hz) (p = 2.9 × 10−8, g = 0.80) frequency bands, peak alpha frequency (p = 0.000007, g = 0.66), functional network mean degree (p = 0.0006, g = 0.48) and brain network ictogenicity (p = 0.00006, g = 0.56) between epilepsy and controls. Since age (p = 0.009) and epoch length (p = 1.7 × 10−8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between epilepsy and controls remained. Receiver operating curve analysis showed low-alpha power spectral density was optimal at distinguishing epilepsy from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in epilepsy patients. To conclude, individuals with juvenile myoclonic epilepsy have increased power of neural oscillatory activity at low-alpha frequencies, and increased brain network ictogenicity compared to controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in idiopathic generalised epilepsy particularly for their use as biomarkers.

“…3B (absence seizures, lack of PPR, no morning predominance of seizures and triggered seizures) have been associated with worse seizure control in this dataset. 28 …”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Laiou

Abela

et al. 2022

Brain Communications

Self Cite

“…Shorter path length was also associated with experiencing triggered seizures, and decreased clustering coefficient was associated with not experiencing PPR, both phenotypes associated with having a worse seizure outcome in this cohort. 36 A short average path length and low clustering is representative of more random networks, whereby information can pass easily through the network from one node to functionally distinct nodes due to longer range functional connections, 41 and therefore, speculatively, may have an increased likelihood to synchronize more easily, implying an increased vulnerability to seizures. Indeed, JME networks have been shown to transition to more random network topology during spike-wave discharges, with decreased clustering in theta and beta frequency bands.…”

Section: Discussionmentioning

confidence: 99%

Heterogeneity of resting-state EEG features in juvenile myoclonic epilepsy and controls

Laiou

Abela

et al. 2022

Preprint

Self Cite

Abnormal EEG features are a hallmark of epilepsy, and abnormal frequency and network features are apparent in EEGs from people with idiopathic generalised epilepsy in both ictal and interictal states. Here, we characterise differences in the resting-state EEG of individuals with juvenile myoclonic epilepsy (JME) and assess factors influencing the heterogeneity of these EEG features. We collected EEG data from 147 participants with JME through the Biology of Juvenile Myoclonic Epilepsy (BIOJUME) study. 95 control EEGs were acquired from two independent studies (Chowdhury et al. (2014) and EU-AIMS Longitudinal European Autism Project). We extracted frequency and functional network-based features from 10-20s epochs of resting-state EEG, including relative power spectral density (PSD), peak alpha frequency, network topology measures and Brain Network Ictogenicity (BNI): a computational measure of the propensity of networks to generate seizure dynamics. The influence of covariates such as age, sex, antiseizure medication, EEG time and epoch length were investigated for each EEG feature prior to testing for differences between JME and control EEGs using univariate, multivariable and receiver operating curve (ROC) analysis. Additionally, associations of clinical phenotypes (seizure type, seizure control) with EEG features were investigated in the JME cohort. P-values were corrected for multiple comparisons. Univariate analysis showed significant differences in PSD in delta (2-5Hz) (p=0.0007, hedges' g=0.55) and low-alpha (6-9Hz) (p=2.9x10-8, g=0.80) frequency bands, peak alpha frequency (p=0.000007, g=0.66), functional network mean degree (p=0.0006, g=0.48) and BNI (p=0.00006, g=0.56) between JME and controls. Since age (p=0.009) and epoch length (p=1.7x10-8) differed between the two groups and were potential confounders, we controlled for these covariates in multivariable analysis where disparities in EEG features between JME and controls remained. ROC analysis showed low-alpha PSD was optimal at distinguishing JME from controls, with an area under the curve of 0.72. Lower average normalized clustering coefficient and shorter average normalized path length were associated with poorer seizure control in JME patients. To conclude, individuals with JME have increased power of neural oscillatory activity at low-alpha frequencies, along with increased BNI compared to controls, supporting evidence from studies in other epilepsies with considerable external validity. In addition, the impact of confounders on different frequency-based and network-based EEG features observed in this study highlights the need for careful consideration and control of these factors in future EEG research in IGE particularly for their use as biomarkers.

“…We collected clinical and genetic data from the Biology of Juvenile Myoclonic Epilepsy (BIOJUME) consortium study (n=864) 25 . We obtained informed consent from all participants and ethical approval from UK Health Research Authority: South Central Oxford C Research Ethics Committee (16/SC/0266) and all other collaborating sites.…”

Section: Methodsmentioning

confidence: 99%

SLCO5A1 and synaptic assembly genes contribute to impulsivity in juvenile myoclonic epilepsy

Panjwani

Roshandel

et al. 2022

Preprint

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Introductory ParagraphElevated impulsivity is a key component of attention-deficit hyperactivity disorder (ADHD), bipolar disorder and epilepsy1-5. We performed a genome-wide association, colocalization and pathway analysis of impulsivity in juvenile myoclonic epilepsy (JME). We identify genome-wide associated SNPs at 8q13.3 (p=7.5 × 10−9) and 10p11.21 (p=3.6 × 10−8). The 8q13.3 locus colocalizes with SLCO5A1 expression quantitative trait loci in cerebral cortex (p=9.5 × 10−3). SLCO5A1 codes for a membrane-bound organic anion transporter6 and upregulates synapse assembly/organisation genes7. Pathway analysis also demonstrates 9.3-fold enrichment for synaptic assembly genes (p=0.03) including NRXN1, NLGN1 and PTPRD. RNAi knockdown of Oatp30B, the Drosophila homolog of SLCO5A1, causes both over-reactive startling behaviour (p=8.7 × 10−3) and increased seizure-like events (p=6.8 × 10−7). Polygenic risk score for ADHD correlates with impulsivity scores (p=1.60 × 10−3), demonstrating shared genetic contributions. SLCO5A1 loss-of-function represents a novel impulsivity and seizure mechanism. Synaptic assembly genes may inform the aetiology of impulsivity in health and disease.