Chewing-gum flavor affects measures of global complexity of multichannel EEG

Global complexity of spontaneous brain electric activity was studied before and after chewing gum without flavor and with 2 different flavors. One-minute, 19-channel, eyes-closed electroencephalograms (EEG) were recorded from 20 healthy males before and after using 3 types of chewing gum: regular gum containing sugar and aromatic additives, gum containing 200 mg theanine (a constituent of Japanese green tea), and gum base (no sugar, no aromatic additives); each was chewed for 5 min in randomized sequence. Brain electric activity was assessed through Global Omega (Ω)-Complexity and Global Dimensional Complexity (GDC), quantitative measures of complexity of the trajectory of EEG map series in state space; their differences from pre-chewing data were compared across gum-chewing conditions. Friedman Anova (p < 0.043) showed that effects on Ω-Complexity differed significantly between conditions and differences were maximal between gum base and theanine gum. No differences were found using GDC. Global Omega-Complexity appears to be a sensitive measure for subtle, central effects of chewing gum with and without flavor.

High frequency repetitive transcranial magnetic stimulation (rTMS) of the left dorsolateral cortex: EEG topography during waking and subsequent sleep.

Repetitive transcranial magnetic stimulation (rTMS) is a novel research tool in neurology and psychiatry. It is currently being evaluated as a conceivable alternative to electroconvulsive therapy for the treatment of mood disorders. Eight healthy young (age range 21-25 years) right-handed men without sleep complaints participated in the study. Two sessions at a 1-week interval, each consisting of an adaptation night (sham stimulation) and an experimental night (rTMS in the left dorsolateral prefrontal cortex or sham stimulation; crossover design), were scheduled. In each subject, 40 trains of 2-s duration of rTMS (inter-train interval 28 s) were applied at a frequency of 20 Hz (i.e. 1600 pulses per session) and at an intensity of 90% of the motor threshold. Stimulations were scheduled 80 min before lights off. The waking EEG was recorded for 10-min intervals approximately 30 min prior to and after the 20-min stimulations, and polysomnographic recordings were obtained during the subsequent sleep episode (23.00-07.00 h). The power spectra of two referential derivations, as well as of bipolar derivations along the antero-posterior axis over the left and right hemispheres, were analyzed. rTMS induced a small reduction of sleep stage 1 (in min and percentage of total sleep time) over the whole night and a small enhancement of sleep stage 4 during the first non-REM sleep episode. Other sleep variables were not affected. rTMS of the left dorsolateral cortex did not alter the topography of EEG power spectra in waking following stimulation, in the all-night sleep EEG, or during the first non-REM sleep episode. Our results indicate that a single session of rTMS using parameters like those used in depression treatment protocols has no detectable side effects with respect to sleep in young healthy males.

Intracranial EEG reveals a time- and frequency-specific role for the right inferior frontal gyrus and primary motor cortex in stopping initiated responses.

Inappropriate response tendencies may be stopped via a specific fronto/basal ganglia/primary motor cortical network. We sought to characterize the functional role of two regions in this putative stopping network, the right inferior frontal gyrus (IFG) and the primary motor cortex (M1), using electocorticography from subdural electrodes in four patients while they performed a stop-signal task. On each trial, a motor response was initiated, and on a minority of trials a stop signal instructed the patient to try to stop the response. For each patient, there was a greater right IFG response in the beta frequency band ( approximately 16 Hz) for successful versus unsuccessful stop trials. This finding adds to evidence for a functional network for stopping because changes in beta frequency activity have also been observed in the basal ganglia in association with behavioral stopping. In addition, the right IFG response occurred 100-250 ms after the stop signal, a time range consistent with a putative inhibitory control process rather than with stop-signal processing or feedback regarding success. A downstream target of inhibitory control is M1. In each patient, there was alpha/beta band desynchronization in M1 for stop trials. However, the degree of desynchronization in M1 was less for successfully than unsuccessfully stopped trials. This reduced desynchronization on successful stop trials could relate to increased GABA inhibition in M1. Together with other findings, the results suggest that behavioral stopping is implemented via synchronized activity in the beta frequency band in a right IFG/basal ganglia network, with downstream effects on M1.

Can quantitative EEG measures predict clinical outcome in subjects at Clinical High Risk for psychosis? A prospective multicenter study

BACKGROUND Prediction studies in subjects at Clinical High Risk (CHR) for psychosis are hampered by a high proportion of uncertain outcomes. We therefore investigated whether quantitative EEG (QEEG) parameters can contribute to an improved identification of CHR subjects with a later conversion to psychosis. METHODS This investigation was a project within the European Prediction of Psychosis Study (EPOS), a prospective multicenter, naturalistic field study with an 18-month follow-up period. QEEG spectral power and alpha peak frequencies (APF) were determined in 113 CHR subjects. The primary outcome measure was conversion to psychosis. RESULTS Cox regression yielded a model including frontal theta (HR=1.82; [95% CI 1.00-3.32]) and delta (HR=2.60; [95% CI 1.30-5.20]) power, and occipital-parietal APF (HR=.52; [95% CI .35-.80]) as predictors of conversion to psychosis. The resulting equation enabled the development of a prognostic index with three risk classes (hazard rate 0.057 to 0.81). CONCLUSIONS Power in theta and delta ranges and APF contribute to the short-term prediction of psychosis and enable a further stratification of risk in CHR samples. Combined with (other) clinical ratings, EEG parameters may therefore be a useful tool for individualized risk estimation and, consequently, targeted prevention.

Regional differences in trait-like characteristics of the waking EEG in early adolescence

BACKGROUND The human waking EEG spectrum shows high heritability and stability and, despite maturational cortical changes, high test-retest reliability in children and teens. These phenomena have also been shown to be region specific. We examined the stability of the morphology of the wake EEG spectrum in children aged 11 to 13 years recorded over weekly intervals and assessed whether the waking EEG spectrum in children may also be trait-like. Three minutes of eyes open and three minutes of eyes closed waking EEG was recorded in 22 healthy children once a week for three consecutive weeks. Eyes open and closed EEG power density spectra were calculated for two central (C3LM and C4LM) and two occipital (O1LM and O2LM) derivations. A hierarchical cluster analysis was performed to determine whether the morphology of the waking EEG spectrum between 1 and 20 Hz is trait-like. We also examined the stability of the alpha peak using an ANOVA. RESULTS The morphology of the EEG spectrum recorded from central derivations was highly stable and unique to an individual (correctly classified in 85% of participants), while the EEG recorded from occipital derivations, while stable, was much less unique across individuals (correctly classified in 42% of participants). Furthermore, our analysis revealed an increase in alpha peak height concurrent with a decline in the frequency of the alpha peak across weeks for occipital derivations. No changes in either measure were observed in the central derivations. CONCLUSIONS Our results indicate that across weekly recordings, power spectra at central derivations exhibit more "trait-like" characteristics than occipital derivations. These results may be relevant for future studies searching for links between phenotypes, such as psychiatric diagnoses, and the underlying genes (i.e., endophenotypes) by suggesting that such studies should make use of more anterior rather than posterior EEG derivations.

Adolescence and parental history of alcoholism: insights from the sleep EEG

BACKGROUND Disrupted sleep is a common complaint of individuals with alcohol use disorder and in abstinent alcoholics. Furthermore, among recovering alcoholics, poor sleep predicts relapse to drinking. Whether disrupted sleep in these populations results from prolonged alcohol use or precedes the onset of drinking is not known. The aim of this study was to examine the sleep electroencephalogram (EEG) in alcohol-naïve, parental history positive (PH+), and negative (PH-) boys and girls. METHODS All-night sleep EEG recordings in 2 longitudinal cohorts (child and teen) followed at 1.5 to 3 year intervals were analyzed. The child and teen participants were 9/10 and 15/16 years old at the initial assessment, respectively. Parental history status was classified by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria applied to structured interviews (DIS-IV) resulting in 14 PH- and 10 PH+ children and 14 PH- and 10 PH+ teens. Sleep data were visually scored in 30-second epochs using standard criteria. Power spectra were calculated for EEG derivations C3/A2, C4/A1, O2/A1, O1/A2 for nonrapid eye movement (NREM) and rapid eye movement (REM) sleep. RESULTS We found no difference between PH+ and PH- individuals in either cohort for any visually scored sleep stage variable. Spectral power declined in both cohorts across assessments for NREM and REM sleep in all derivations and across frequencies independent of parental history status. With regard to parental history, NREM sleep EEG power was lower for the delta band in PH+ teens at both assessments for the central derivations. Furthermore, power in the sigma band for the right occipital derivation in both NREM and REM sleep was lower in PH+ children only at the initial assessment. CONCLUSIONS We found no gross signs of sleep disruption as a function of parental history. Modest differences in spectral EEG power between PH+ and PH- children and teens indicate that a marker of parental alcohol history may be detectable in teens at risk for problem drinking.

Standing waves as an explanation for generic stationary correlation patterns in noninvasive EEG of focal onset seizures.

Cerebral electrical activity is highly nonstationary because the brain reacts to ever changing external stimuli and continuously monitors internal control circuits. However, a large amount of energy is spent to maintain remarkably stationary activity patterns and functional inter-relations between different brain regions. Here we examine linear EEG correlations in the peri-ictal transition of focal onset seizures, which are typically understood to be manifestations of dramatically changing inter-relations. Contrary to expectations we find stable correlation patterns with a high similarity across different patients and different frequency bands. This skeleton of spatial correlations may be interpreted as a signature of standing waves of electrical brain activity constituting a dynamical ground state. Such a state could promote the formation of spatiotemporal neuronal assemblies and may be important for the integration of information stemming from different local circuits of the functional brain network.

Ordinal patterns in epileptic brains: Analysis of intracranial EEG and simultaneous EEG-fMRI

Epileptic seizures are associated with high behavioral stereotypy of the patients. In the EEG of epilepsy patients characteristic signal patterns can be found during and between seizures. Here we use ordinal patterns to analyze EEGs of epilepsy patients and quantify the degree of signal determinism. Besides relative signal redundancy and the fraction of forbidden patterns we introduce the fraction of under-represented patterns as a new measure. Using the logistic map, parameter scans are performed to explore the sensitivity of the measures to signal determinism. Thereafter, application is made to two types of EEGs recorded in two epilepsy patients. Intracranial EEG shows pronounced determinism peaks during seizures. Finally, we demonstrate that ordinal patterns may be useful for improving analysis of non-invasive simultaneous EEG-fMRI.

Is excessive EEG beta activity associated with delinquent behavior in adult subjects with ADHD?

Objective: The attention deficit/hyperactivity disorder (ADHD) shows an increased prevalence in arrested offenders compared to the normal population. ADHD and delinquency seem to share some neurophysiological abnormalities. In recent studies, a subgroup of subjects with ADHD as well as delinquents displayed excessive EEG activity in the beta band compared to controls, which has been associated with antisocial behavior and aggression in ADHD children. The goal of the present study was to investigate whether delinquent behavior in ADHD is related to excessive beta activity. Methods: We compared the resting state EEGs (eyes closed and eyes open) of 13 non-delinquent and 13 delinquent subjects with ADHD and 13 controls regarding power spectra and topography of the EEG activity. Results: Offenders with ADHD showed more beta power mainly at frontal, central and parietal brain regions than non-delinquent subjects with ADHD. Conclusions: Excessive beta power may represent a risk-factor for delinquent behavior in adults with ADHD. Significance: The awareness of such risk-factors may be helpful in the assessment of the risk for delinquent behavior in a psychiatric context and may provide a neurobiological background for therapeutic interventions.

Improved Localization of Implanted Subdural Electrode Contacts on MRI Using an Elastic Image Fusion Algorithm in Invasive EEG Recording

BACKGROUND: Accurate projection of implanted subdural electrode contacts in presurgical evaluation of pharmacoresistant epilepsy cases by invasive EEG is highly relevant. Linear fusion of CT and MRI images may display the contacts in the wrong position due to brain shift effects. OBJECTIVE: A retrospective study in five patients with pharmacoresistant epilepsy was performed to evaluate whether an elastic image fusion algorithm can provide a more accurate projection of the electrode contacts on the pre-implantation MRI as compared to linear fusion. METHODS: An automated elastic image fusion algorithm (AEF), a guided elastic image fusion algorithm (GEF), and a standard linear fusion algorithm (LF) were used on preoperative MRI and post-implantation CT scans. Vertical correction of virtual contact positions, total virtual contact shift, corrections of midline shift and brain shifts due to pneumencephalus were measured. RESULTS: Both AEF and GEF worked well with all 5 cases. An average midline shift of 1.7mm (SD 1.25) was corrected to 0.4mm (SD 0.8) after AEF and to 0.0mm (SD 0) after GEF. Median virtual distances between contacts and cortical surface were corrected by a significant amount, from 2.3mm after LF to 0.0mm after AEF and GEF (p<.001). Mean total relative corrections of 3.1 mm (SD 1.85) after AEF and 3.0mm (SD 1.77) after GEF were achieved. The tested version of GEF did not achieve a satisfying virtual correction of pneumencephalus. CONCLUSION: The technique provided a clear improvement in fusion of pre- and post-implantation scans, although the accuracy is difficult to evaluate.

Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches

In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.