Shared genetic basis between genetic generalized epilepsy and background electroencephalographic oscillations

The importance of discriminative power rather than significance when evaluating potential clinical biomarkers in epilepsy research

OBJECTIVE

The quest for epilepsy biomarkers is on the rise. Variables with statistically significant group-level differences are often misinterpreted as biomarkers with sufficient discriminative power. This study aimed to demonstrate the relationship between significant group-level differences and a variable's power to discriminate between individuals.

METHODS

We simulated normal-distributed datasets from hypothetical populations with varying sample sizes (25-800), effect sizes (Cohen's d: .25-2.50), and variability (standard deviation: 10-35) to assess the impact of these parameters on significance and discriminative power. The simulation data were illustrated by assessing the discriminative power of a potential real-case biomarker-the EEG beta band power-to diagnose generalized epilepsy, using data from 66 children with generalized epilepsy and 385 controls. Additionally, we evaluated recently reported epilepsy biomarkers by comparing their effect sizes to our simulation-derived effect size criterion.

RESULTS

Group size affects significance but not discriminative power. Discriminative power is much more related to variability and effect size. Our real data example supported these simulation results by demonstrating that group-level significance does not translate, one to one, into discriminative power. Although we found a significant difference in the beta band power between children with and without epilepsy, the discriminative power was poor due to a small effect size. A Cohen's d of at least 1.25 is required to reach good discriminative power in univariable prediction modeling. Slightly over 60% of the biomarkers in our literature search met this criterion.

SIGNIFICANCE

Rather than statistical significance of group-level differences, effect size should be used as an indicator of a variable's biomarker potential. The minimal required effects size for individual biomarkers-a Cohen's d of 1.25-is large. This calls for multivariable approaches, in which combining multiple variables with smaller effect sizes could increase the overall effect size and discriminative power.

Drug resistance in idiopathic generalized epilepsies: Evidence and concepts

Although approximately 10%-15% of patients with idiopathic generalized epilepsy (IGE)/genetic generalized epilepsy remain drug-resistant, there is no consensus or established concept regarding the underlying mechanisms and prevalence. This review summarizes the recent data and the current hypotheses on mechanisms that may contribute to drug-resistant IGE. A literature search was conducted in PubMed and Embase for studies on mechanisms of drug resistance published since 1980. The literature shows neither consensus on the definition nor a widely accepted model to explain drug resistance in IGE or one of its subsyndromes. Large-scale genetic studies have failed to identify distinct genetic causes or affected genes involved in pharmacokinetics. We found clinical and experimental evidence in support of four hypotheses: (1) "network hypothesis"-the degree of drug resistance in IGE reflects the severity of cortical network alterations, (2) "minor focal lesion in a predisposed brain hypothesis"-minor cortical lesions are important for drug resistance, (3) "interneuron hypothesis"-impaired functioning of γ-aminobutyric acidergic interneurons contributes to drug resistance, and (4) "changes in drug kinetics"-genetically impaired kinetics of antiseizure medication (ASM) reduce the effectiveness of available ASMs. In summary, the exact definition and cause of drug resistance in IGE is unknown. However, published evidence suggests four different mechanisms that may warrant further investigation.

Applicability of polygenic risk scores in endometriosis clinical presentation

Background

Risk prediction is an essential part of preventative medicine and in recent years genomic information has become an interesting factor in risk models. Polygenic risk scores (PRS) combine the effect of many genetic variations into a single score which has been shown to have predictive value for many diseases. This study aimed to investigate the association between PRS for endometriosis and the clinical presentation of the disease.

Methods

Women with endometriosis (N = 172) were identified at the Department of Gynecology. All participants answered questionnaires regarding sociodemographic factors, lifestyle habits and medical history, registered bowel symptoms on the Visual Analog Scale for Irritable Bowel Syndrome and passed blood samples. DNA was extracted and samples were genotyped, and a PRS was calculated based on previous genome-wide association studies of endometriosis. Inflammatory proteins and TSH receptor antibodies (TRAb) in serum were analyzed.

Results

Inverse associations were identified between PRS and spread of endometriosis, involvement of the gastrointestinal tract and hormone treatment. However, significance was lost when calculated as p for trend and the specificity and sensitivity were low. There were no correlations between PRS and TRAb or inflammatory proteins.

Conclusion

The findings indicate that specific PRS should be developed to predict clinical presentations in patient with endometriosis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-022-01788-w.

Combined electrophysiological and morphological phenotypes in patients with genetic generalized epilepsy and their healthy siblings

OBJECTIVE

Genetic generalized epilepsy is characterized by aberrant neuronal dynamics and subtle structural alterations. We evaluated whether a combination of magnetic and electrical neuronal signals and cortical thickness would provide complementary information about network pathology in GGE. We also investigated if these imaging phenotypes were present in healthy siblings of the patients to test for genetic influence.

METHODS

In this cross-sectional study, we analyzed five minutes of resting-state data acquired using electroencephalography (EEG) and magnetoencephalography (MEG) in patients, their siblings, and controls, matched for age and sex. We computed source-reconstructed power and connectivity in six frequency bands (1-40 Hz) and cortical thickness (derived from magnetic resonance imaging (MRI)). Group differences were assessed using permutation analysis of linear models for each modality separately and jointly for all modalities using a non-parametric combination.

RESULTS

Patients with GGE (n = 23) had higher power than controls (n = 35) in all frequencies, with a more posterior focus in MEG than EEG. Connectivity was also increased, particularly in frontotemporal and central regions in theta (strongest in EEG) and low beta frequencies (strongest in MEG), which was eminent in the joint EEG/MEG analysis. EEG showed weaker connectivity differences in higher frequencies, possibly related to drug effects. The inclusion of cortical thickness reinforced group differences in connectivity and power. Siblings (n = 18) had functional and structural patterns intermediate between those of patients and controls.

SIGNIFICANCE

EEG detected increased connectivity and power in GGE similar to MEG, but with different spectral sensitivity, highlighting the importance of theta and beta oscillations. Cortical thickness reductions in GGE corresponded to functional imaging patterns. Our multimodal approach extends the understanding of the resting-state in GGE and points to genetic underpinnings of the imaging markers studied, providing new insights into the causes and consequences of epilepsy.