Cerebrospinal fluid levels of the endocannabinoid anandamide are reduced in patients with untreated newly diagnosed temporal lobe epilepsy.

PURPOSE: The endocannabinoid system is involved in excitatory/inhibitory balance mechanisms within the central nervous system (CNS). Growing evidence shows that its perturbation leads to development of epileptic seizures in experimental models, thus indicating that endocannabinoids play an intrinsic protective role in suppressing pathologic neuronal excitability. Experimental data also demonstrate that the endocannabinoid anandamide (AEA) can antagonize epileptic discharges in hippocampal tissue. The objective of our study was to measure endocannabinoids levels in the cerebrospinal fluid (CSF) of drug-naive patients affected by temporal lobe epilepsy (TLE). METHODS: We measured the levels of both AEA and the other endocannabinoid, 2-arachidonoylglycerol (2-AG), in the CSF of drug-naive patients with TLE. RESULTS: A significant reduction of AEA was found in the CSF of patients with compared with healthy controls (epileptic patients = 2.55 +/- 1.78 pmol/ml; healthy controls = 11.65 +/- 7.53 pmol/ml; n = 9 for both groups, p < 0.01). 2-AG levels, however, were not affected (epileptic patients = 209.5 +/- 146.56; healthy controls = 159.6 +/- 110.2) (n = 6 for both groups, p = 0.48). DISCUSSION: Our findings seem to be consistent with experimental evidence demonstrating a significant prevention of epileptic seizures induced by endocannabinoids in models of epilepsy. Furthermore, they support the hypothesis that AEA may be involved in its pathogenesis, suggesting a hypothetical primary impairment of the endocannabinoid system in untreated TLE. The actual role of this in vivo dysregulation still remains unclear.

Comparison of different cortical connectivity estimators for high-resolution EEG recordings.

The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.