People with epilepsy have greatly increased probability of premature mortality due to sudden unexpected death in epilepsy (SUDEP). Identifying which patients are most at risk of SUDEP is hindered by a complex genetic etiology, incomplete understanding of the underlying pathophysiology and lack of prognostic biomarkers. Here we evaluated heterozygous Scn2a gene deletion (Scn2a+/-) as a protective genetic modifier in the Kcna1 knockout mouse (Kcna1-/-) model of SUDEP, while searching for biomarkers of SUDEP risk embedded in electroencephalography (EEG) and electrocardiography (ECG) recordings. The human epilepsy gene Kcna1 encodes voltage-gated Kv1.1 potassium channels that act to dampen neuronal excitability whereas Scn2a encodes voltage-gated Nav1.2 sodium channels important for action potential initiation and conduction. SUDEP-prone Kcna1-/- mice with partial genetic ablation of Nav1.2 channels (i.e. Scn2a+/-; Kcna1-/-) exhibited a two-fold increase in survival. Classical analysis of EEG and ECG recordings separately showed significantly decreased seizure durations in Scn2a+/-; Kcna1-/- mice compared with Kcna1-/- mice, without substantial modification of cardiac abnormalities. Novel analysis of the EEG and ECG together revealed a significant reduction in EEG-ECG association in Kcna1-/- mice compared with wild types, which was partially restored in Scn2a+/-; Kcna1-/- mice. The degree of EEG-ECG association was also proportional to the survival rate of mice across genotypes. These results show that Scn2a gene deletion acts as protective genetic modifier of SUDEP and suggest measures of brain-heart association as potential indices of SUDEP susceptibility.

No abstract

Fragile X Syndrome (FXS) is a monogenetic form of intellectual disability and autism in which well-established knockout (KO) animal models point to neuronal hyperexcitability and abnormal gamma-frequency physiology as a basis for key disorder features. Translating these findings into patients may identify tractable treatment targets. Using source modeling of resting-state electroencephalography data, we report findings in FXS, including 1) increases in localized gamma activity, 2) pervasive changes of theta/alpha activity, indicative of disrupted thalamocortical modulation coupled with elevated gamma power, 3) stepwise moderation of low and high-frequency abnormalities based on female sex, and 4) relationship of this physiology to intellectual disability and neuropsychiatric symptoms. Our observations extend findings in Fmr1−/− KO mice to patients with FXS and raise a key role for disrupted thalamocortical modulation in local hyperexcitability. This systems-level mechanism has received limited preclinical attention but has implications for understanding fundamental disease mechanisms.

No abstract

Abstract:Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time-frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures' onset. These trends pointed to a slow decrease of the epileptic brain's global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.