Accelerated cognitive decline in a rodent model for temporal lobe epilepsy.

OBJECTIVE: Cognitive impairment is frequently observed in patients with temporal lobe epilepsy. It is hypothesized that cumulative seizure exposure causes accelerated cognitive decline in patients with epilepsy. We investigated the influence of seizure frequency on cognitive decline in a rodent model for temporal lobe epilepsy. METHODS: Neurobehavioral assessment was performed before and after surgery, after the induction of self-sustaining limbic status epilepticus (SSLSE), and in the chronic phase in which rats experienced recurrent seizures. Furthermore, we assessed potential confounders of memory performance. RESULTS: Rats showed a deficit in spatial working memory after the induction of the SSLSE, which endured in the chronic phase. A progressive decline in recognition memory developed in SSLSE rats. Confounding factors were absent. Seizure frequency and also the severity of the status epilepticus were not correlated with the severity of cognitive deficits. SIGNIFICANCE: The effect of the seizure frequency on cognitive comorbidity in epilepsy has long been debated, possibly because of confounders such as antiepileptic medication and the heterogeneity of epileptic etiologies. In an animal model of temporal lobe epilepsy, we showed that a decrease in spatial working memory does not relate to the seizure frequency. This suggests for other mechanisms are responsible for memory decline and potentially a common pathophysiology of cognitive deterioration and the occurrence and development of epileptic seizures. Identifying this common denominator will allow development of more targeted interventions treating cognitive decline in patients with epilepsy. The treatment of interictal symptoms will increase the quality of life of many patients with epilepsy.

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC).

Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection. Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups. Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained. Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region. Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity.