BACKGROUND: Anteromesial temporal resection (AMTR) is well established as effective in patients with intractable mesial temporal epilepsy. However, little electroclinical information is available relevant to poor surgical outcome after AMTR. We examined the postoperative electroclinical features based on postoperative MRI and video-EEG monitoring (VEM) in patients with poor surgical outcome. METHODS: We reviewed clinical features and postoperative VEM results in 20 patients with failure in AMTR. According to the postoperative electroclinical features, we classified them into mesial temporal (MT), bitemporal (BT), extramesial temporal (XMT), combined (C), and unclassified groups. The postoperative VEM results were compared among the groups. Surgical outcome was assessed in five patients who underwent reoperation. RESULTS: Patients comprised 6 MT, 2 BT, 6 XMT, 1 C, and 6 unclassified. Aura and automatism were more frequent in MT (50.0%, 83.3%) than in XMT (16.7%, 33.3%). Theta to delta rhythm, during the ictal onset and build-up period, was more frequent in MT (83.3%, 66.7%) than in XMT (33.3%, 33.3%). The ictal onset and build-up pattern of ictal EEG were most frequently localized to the frontotemporal region in MT (66.7%, 100.0%), while there was no predominantly localized region in XMT. The surgical outcome after reoperation was better in MT group than in XMT and C groups. CONCLUSIONS: Postoperative MRI and VEM are useful to assess the postoperative electroclinical features in failed AMTR. Reoperation of the residual mesiotemporal structures after confirming epileptogenic foci may have good surgical outcome.
Automatism ; Delta Rhythm ; Electroencephalography ; Epilepsy ; Humans ; Reoperation ; Seizures

BACKGROUND AND PURPOSE: The purpose of this study was to characterize abnormal cortical activity during sleep in restless legs syndrome (RLS) patients and to determine the effects of treatment with a dopamine agonist. Based on whole-brain electroencephalograms, we attempted to verify alterations in the functional network as well as the spectral power of neural activities during sleep in RLS patients and to determine whether the changes are reversed by treatment with pramipexole. METHODS: Twelve drug-naïve RLS patients participated in the study. Overnight polysomnography was performed before and after treatment: the first recording was made immediately prior to administering the first dose of pramipexole, and the second recording was made 12–16 weeks after commencing pramipexole administration. Sixteen age-matched healthy participants served as a control group. The spectral power and interregional phase synchrony were analyzed in 30-s epochs. The functional characteristics of the cortical network were quantified using graph-theory measures. RESULTS: The delta-band power was significantly increased and the small-world network characteristics in the delta band were disrupted in RLS patients compared to the healthy controls. These abnormalities were successfully treated by dopaminergic medication. The delta-band power was significantly correlated with the RLS severity score in the RLS patients prior to treatment. CONCLUSIONS: Our findings suggest that the spectral and functional network characteristics of neural activities during sleep become abnormal in RLS patients, and these abnormalities can be successfully treated by a dopamine agonist.
Delta Rhythm* ; Dopamine Agonists* ; Dopamine* ; Electroencephalography ; Healthy Volunteers ; Humans ; Polysomnography ; Restless Legs Syndrome*

Traditional sleep scoring system describes the sleep EEG characterized by features in time domain as well as frequency domain. Power Spectral Density (PSD) is one of the well-used methods to observe the occurrence of specified rhythms. However, the parameter model based PSD estimation is used with the assumption that the model order is determined as low as possible through prior knowledge. This paper briefs the development of Autoregressive Model Order (ARMO) criterion, and provides the distribution of ARMOs for specified sleep EEG, which shows that ARMOs concentrate on several well separated regions that are indicative of the microstructure and transition states. This study suggests the promising perspective of ARMO as a special EEG feature for weighing complexity, randomness and rhythm components.
Delta Rhythm ; Electroencephalography ; Humans ; Models, Neurological ; Regression Analysis ; Signal Processing, Computer-Assisted ; Sleep Stages ; physiology

To investigate the non-stationary time-frequency features in rat Electroencephalogram (EEG) under different vigilance states, the methods of multi-resolution wavelet transform (WT) and statistical histogram analysis were used. EEGs of the freely moving rats were recorded with implanted electrodes under the vigilance states of waking, slow wave sleep (SWS) and rapid eye movement sleep (REM). The EEGs were firstly decomposed into four frequency components of delta, theta, alpha and beta by using multi-resolution wavelet transform. Then, the parameters of mean value, standard deviation, skewness and kurtosis of the logarithm power histograms and the power percentage histograms of each of the frequency components were calculated. The results showed that the distributions of the logarithm power histograms were not quite different from the normal distribution. However, most of the power percentage histograms were significantly different from the normal distribution. The results of one-way ANOVA indicated that there were significant differences in the parameter values of the histograms both among different states and among different frequency components. Moreover, Skewness and kurtosis of the logarithm power histograms of some characteristic waves in EEG, such as delta wave during SWS and theta wave during waking and REM, obtained high values. Thus, the histogram parameters of EEG WT components might become as quantitative measures to describe the dynamic time-frequency features of EEG.
Animals ; Delta Rhythm ; Electrodes, Implanted ; Electroencephalography ; Fourier Analysis ; Image Enhancement ; methods ; Rats ; Signal Processing, Computer-Assisted ; Sleep ; physiology ; Sleep Stages ; physiology ; Sleep, REM ; physiology