Not Just Where, But How Does a Seizure Start?

The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.

Network analysis of preictal iEEG reveals changes in network structure preceding seizure onset

Seizures likely result from aberrant network activity and synchronization. Changes in brain network connectivity may underlie seizure onset. We used a novel method of rapid network model estimation from intracranial electroencephalography (iEEG) data to characterize pre-ictal changes in network structure prior to seizure onset. We analyzed iEEG data from 20 patients from the iEEG.org database. Using 10 s epochs sliding by 1 s intervals, a multiple input, single output (MISO) state space model was estimated for each output channel and time point with all other channels as inputs, generating sequential directed network graphs of channel connectivity. These networks were assessed using degree and betweenness centrality. Both degree and betweenness increased at seizure onset zone (SOZ) channels 37.0 ± 2.8 s before seizure onset. Degree rose in all channels 8.2 ± 2.2 s prior to seizure onset, with increasing connections between the SOZ and surrounding channels. Interictal networks showed low and stable connectivity. A novel MISO model-based network estimation method identified changes in brain network structure just prior to seizure onset. Increased connectivity was initially isolated within the SOZ and spread to non-SOZ channels before electrographic seizure onset. Such models could help confirm localization of SOZ regions.

[Rate and characteristics of H-type hypertension in Chinese hypertensive population and comparison with American population]

OBJECTIVE

To study the rate and characteristics of H-type hypertension in Chinese hypertensive population, and to compare them with the relevant data from the United States.

METHODS

Observational studies on the prevalence of H-type hypertension in Chinese population published before April 30, 2022 were searched in several Chinese and English databases (PubMed, Embase, Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure, Wanfang Databases, and Chinese Biome-dical Literature Database). Study selection, date extraction and quality evaluation were conducted. Random effect model was used to estimate the rate of H-type hypertension in hypertensive patients and the pooled prevalence of H-type hypertension. Stratified analysis was used to explore the distribution characteristics of H-type hypertension in China. We made meta-regression to search the source of heterogeneity. The National Health and Nutrition Examination Survey (NHANES) population from 1999 to 2006 in the United States was divided into four stages according to the time of data collection. Basic information of the participants was acquired from the database and the rate and prevalence of H-type hypertension analyzed.

RESULTS

This study was finally comprised of 33 studies, involving 78 470 patients with hypertension, among whom 59 842 patients were with H-type hypertension. The rate of H-type hypertension in hypertensive population in China was 73.1% (95%CI: 69.3%-76.9%, I²=99.4%, P < 0.001), and the prevalence of H-type hypertension in general population was 26.9% (95%CI: 21.1%-32.8%, I²=99.8%, P < 0.001). In the stratified analysis, the rate of H-type hypertension was higher among the elderly over 65 years, males, ethnic minorities, and residents in the inland, western, northern, and rural areas. During the decade from 2011 to 2020, the rate of H-type hypertension in China declined slowly (2011-2013: 79.2% vs. 2014-2016: 70.4% vs. 2017-2020: 66.6%, P < 0.001). Meta-regression showed that area was the source of heterogeneity. The rate of H-type hypertension in the United States increased over time, reaching a high value in 2003-2004 and then declining in 2005-2006. The rate of H-type hypertension in hypertensive patients and the prevalence of H-type hypertension in general population in the United States was lower than that in China.

CONCLUSION

Although the rate of H-type hypertension in Chinese hypertensive patients has a downtrend, it still far exceeds that in the United States, especially in the elderly, males, ethnic minorities, and residents in the inland, western, northern, and rural areas. Understanding the epidemiology of H-type hypertension provides scientific evidence for further prevention of cardiovascular and cerebrovascular diseases.