Ictal propagation of high frequency activity is recapitulated in interictal recordings: effective connectivity of epileptogenic networks recorded with intracranial EEG.

Seizures are increasingly understood to arise from epileptogenic networks across which ictal activity is propagated and sustained. In patients undergoing invasive monitoring for epilepsy surgery, high frequency oscillations have been observed within the seizure onset zone during both ictal and interictal intervals. We hypothesized that the patterns by which high frequency activity is propagated would help elucidate epileptogenic networks and thereby identify network nodes relevant for surgical planning. Intracranial EEG recordings were analyzed with a multivariate autoregressive modeling technique (short-time direct directed transfer function--SdDTF), based on the concept of Granger causality, to estimate the directionality and intensity of propagation of high frequency activity (70-175 Hz) during ictal and interictal recordings. These analyses revealed prominent divergence and convergence of high frequency activity propagation at sites identified by epileptologists as part of the ictal onset zone. In contrast, relatively little propagation of this activity was observed among the other analyzed sites. This pattern was observed in both subdural and depth electrode recordings of patients with focal ictal onset, but not in patients with a widely distributed ictal onset. In patients with focal ictal onsets, the patterns of propagation recorded during pre-ictal (up to 5 min immediately preceding ictal onset) and interictal (more than 24h before and after seizures) intervals were very similar to those recorded during seizures. The ability to characterize epileptogenic networks from interictal recordings could have important clinical implications for epilepsy surgery planning by reducing the need for prolonged invasive monitoring to record spontaneous seizures.

Studies of properties of "Pain Networks" as predictors of targets of stimulation for treatment of pain.

Two decades of functional imaging studies have demonstrated pain-related activations of primary somatic sensory cortex (S1), parasylvian cortical structures (PS), and medial frontal cortical structures (MF), which are often described as modules in a "pain network." The directionality and temporal dynamics of interactions between and within the cortical and thalamic modules are uncertain. We now describe our studies of these interactions based upon recordings of local field potentials (LFPs) carried out in an epilepsy monitoring unit over the one week period between the implantation and removal of cortical electrodes during the surgical treatment of epilepsy. These recordings have unprecedented clarity and resolution for the study of LFPs related to the experimental pain induced by cutaneous application of a Thulium YAG laser. We also used attention and distraction as behavioral probes to study the psychophysics and neuroscience of the cortical "pain network." In these studies, electrical activation of cortex was measured by event-related desynchronization (ERD), over SI, PS, and MF modules, and was more widespread and intense while attending to painful stimuli than while being distracted from them. This difference was particularly prominent over PS. In addition, greater perceived intensity of painful stimuli was associated with more widespread and intense ERD. Connectivity of these modules was then examined for dynamic causal interactions within and between modules by using the Granger causality (GRC). Prior to the laser stimuli, a task involving attention to the painful stimulus consistently increased the number of event-related causality (ERC) pairs both within the SI cortex, and from SI upon PS (SI > PS). After the laser stimulus, attention to a painful stimulus increased the number of ERC pairs from SI > PS, and SI > MF, and within the SI module. LFP at some electrode sites (critical sites) exerted ERC influences upon signals at multiple widespread electrodes, both in other cortical modules and within the module where the critical site was located. In summary, critical sites and SI modules may bind the cortical modules together into a "pain network," and disruption of that network by stimulation might be used to treat pain. These results in humans may be uniquely useful to design and optimize anatomically based pain therapies, such as stimulation of the S1 or critical sites through transcutaneous magnetic fields or implanted electrodes.

Gabor atom density as a measure of seizure complexity.

The Gabor atom density (GAD) is a measure of complexity of a signal. It is based on the time-frequency decomposition obtained by the matching pursuit (MP) algorithm. The GAD/MP method was applied to EEG data recorded from intracranial electrodes in patients with intractable complex partial seizures. GAD shows that epileptic seizures, which are reflections of increased neuronal synchrony, are also periods of increased and changing signal complexity. The GAD/MP method is well suited to analyzing these signals from seizures characterized by rapid dynamical changes. The period of organized rhythmic activity exhibits lower complexity than that seen during other phases of the seizure.

Software system for data management and distributed processing of multichannel biomedical signals.

The presented software is designed for efficient utilization of cluster of PC computers for signal analysis of multichannel physiological data. The system consists of three main components: 1) a library of input and output procedures, 2) a database storing additional information about location in a storage system, 3) a user interface for selecting data for analysis, choosing programs for analysis, and distributing computing and output data on cluster nodes. The system allows for processing multichannel time series data in multiple binary formats. The description of data format, channels and time of recording are included in separate text files. Definition and selection of multiple channel montages is possible. Epochs for analysis can be selected both manually and automatically. Implementation of a new signal processing procedures is possible with a minimal programming overhead for the input/output processing and user interface. The number of nodes in cluster used for computations and amount of storage can be changed with no major modification to software. Current implementations include the time-frequency analysis of multiday, multichannel recordings of intracranial EEG of epileptic patients as well as evoked response analyses of repeated cognitive tasks.

Is waking electroencephalographic activity a predictor of daytime sleepiness in sleep-related breathing disorders?

No data are available in the literature assessing the potential use of waking electroencephalographic (EEG) activity in the detection of excessive daytime sleepiness (EDS) in patients with sleep-related breathing disorders (SRBD). The aim of this study was to evaluate whether waking EEG spectral power reflects the level of EDS in SRBD patients. The study was performed in 48 patients in whom quantitative EEG analysis, including the alpha attenuation coefficient (AAC), was performed. Sleepiness was assessed by the Epworth Sleepiness Scale, the Stanford Sleepiness Scale, the Visual Analogue Scale and the maintenance of wakefulness test. Although AAC and EEG spectral power tended to vary throughout the day, none of these variations correlated with EDS measures. Waking EEG measures were not different between snorers and apnoeic patients. Compared to nonsleepy patients, sleepy patients had greater theta and slow alpha powers, but the differences did not reach statistical significance. The EEG slowing was independent of hypoxaemia, severity of SRBD, or degree of sleep disruption. The authors conclude that waking electroencephalographic measures are not sensitive enough to predict variation in alertness or to differentiate sleepy from nonsleepy sleep-related breathing disorders patients. The degree of electroencephalographic slowing was related neither to sleep disruption nor to severity of sleep-related breathing disorders.

EEG spectral activity during paradoxical sleep: further evidence for cognitive processing.

Paradoxical sleep (PS), in which periods with (phasic) and without (tonic) rapid eye movements are intermingled, is hypothesized to be related to cognitive processing and dreaming. Based on polysomnographic data from 12 healthy subjects, this study focuses on the spectral differentiation between phasic and tonic periods. Phasic PS periods exhibited decreased theta and alpha power in the posterior brain areas suggesting the interference of visual processing related to dream imagery. Phasic PS periods were also characterized by a shift from beta to gamma activity in frontal, central and occipital areas reflecting specific phasic related activation. Together, these findings bring new evidence for the existence of visual imagery and cognitive processing during phasic PS.

Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response.

OBJECTIVES: One major subject of discussion in sleep studies is whether bursts of K-complexes (K-bursts) and delta waves (D-bursts), expressions of a subcortical arousal, truly reflect an arousal response during sleep. To address this question we studied the changes in heart rate (HR) during spontaneous arousals in healthy subjects. METHODS: Twenty-seven healthy adults were examined. Arousals were graded in 4 levels, including the standard definition of a microarousal (MA), phases of transitory activation (PAT), D-bursts and K-bursts. HR was analyzed for 10 beats before and 20 beats during arousal. EEG spectral analysis was performed for all types of arousals, including in the analysis the 20 s period preceding the actual event. RESULTS: Each type of arousal was associated with HR changes consisting of a tachycardia followed by a bradycardia. Changes were more pronounced during MA and PAT. Detailed analysis of the HR response showed that HR always increased before MA and PAT onset, associated with a rise in delta, theta and fast EEG activities, and suggesting a cerebral activation. CONCLUSIONS: Our data suggest that such subcortical arousals represent a real arousal response inducing cardiac activation similar to that found during MA and PAT. During MA and PAT, a rise in HR appears before the onset of the actual arousal associated with an increase in EEG slow and fast activity. The link between EEG and HR variation during MA and PAT and the fluctuations in HR during subcortical arousal suggest a continuous spectrum in the arousal mechanisms, starting at the brainstem level and progressing to cortical areas.

High frequency waking EEG: reflection of a slow ultradian rhythm in daytime arousal.

The ultradian dynamics of the human waking EEG was studied using a short visual fixation task repeated every 10 min throughout the daytime. The EEG spectra obtained from the tasks were assessed for time effect and ultradian periodicity. Fronto-central EEG high frequency powers (22.5-44.5 Hz) decreased at the time of the midafternoon vigilance dip (14.00-17.00 h) along with slight concomitant increases in parietal alpha (7.5-13.5 Hz) and delta (1-3 Hz) powers. A slow ultradian rhythm with a 3-4 h periodicity strongly modulated EEG power in all frequency bands between 1 and 44.5 Hz. The high frequency waking EEG may well reflect the activity of a brain arousal process underlying maintenance of the waking state probably throughout the 24 h cycle.

Cortisol secretion is related to electroencephalographic alertness in human subjects during daytime wakefulness.

To determine whether human hypothalamo-pituitary-adrenal axis activity is related to the alertness level during wakefulness, 10 healthy young men were studied under resting conditions in the daytime (0900-1800 h) after an 8-h nighttime sleep (2300-0700 h). A serial 70-sec gaze fixation task was required every 10 min throughout the daytime experimental session. The corresponding waking electroencephalographic (EEG) segments were submitted to quantitative spectral analysis, from which EEG beta activity (absolute power density in the 13-35 Hz frequency band), an index of central alertness, was computed. Blood was collected continuously through an indwelling venous catheter and sampled at 10-min intervals. Plasma cortisol concentrations were measured by RIA, and the corresponding secretory rates were determined by a deconvolution procedure. Analysis of individual profiles demonstrated a declining tendency for EEG beta activity and cortisol secretory rate, with an overall temporal relationship indicated by positive and significant cross-correlation coefficients between the two variables in all subjects (average r=0.565, P < 0.001). Changes in cortisol secretion lagged behind fluctuations in EEG beta activity, with an average delay of 10 min for all the subjects. On the average, 4.6+/-0.4 cortisol secretory pulses and 4.9+/-0.5 peaks in EEG beta activity were identified by a detection algorithm. A significant, although not systematic, association between the episodes in the two variables was found: 44% of the peaks in EEG beta activity (relative amplitude, near 125%; P < 0.001) occurred during an ascending phase of cortisol secretion, cortisol secretory rates increasing by 40% (P < 0.01) 10-min after peaks in EEG beta activity. However, no significant change in EEG beta activity was observed during the period from 50 min before to 50 min after pulses in cortisol secretion. In conclusion, the present study describes a temporal coupling between cortisol release and central alertness, as reflected in the waking EEG beta activity. These findings suggest the existence of connections between the mechanisms involved in the control of hypothalamo-pituitary-adrenal activity and the activation processes of the brain, which undergoes varying degrees of alertness throughout daytime wakefulness.

Pulsatile cortisol secretion and EEG delta waves are controlled by two independent but synchronized generators.

We have previously described a temporal relationship between plasma cortisol pulses and slow-wave sleep and, more recently, an inverse significant cross-correlation between cortisol secretory rates and delta wave activity of the sleep electroencephalogram (EEG). The aim of this study was to observe ACTH, cortisol, and sleep delta wave activity variations throughout 24 h to get a better insight into their initiating mechanisms. Two groups of 10 subjects participated in a 24-h study, one group with a night sleep (2300-0700) and the other with a day sleep (0700-1500). Cortisol secretory rates were calculated by a deconvolution procedure from plasma levels measured at 10-min intervals. Delta wave activity was computed during sleep by spectral analysis of the sleep EEG. When delta waves and cortisol were present at the same time at the end of the night sleep as well as during the daytime sleep, they were negatively correlated, cortisol changes preceding variations in delta wave activity by approximately 10 min. Increases in delta wave activity occurred in the absence of cortisol pulses, as observed at the beginning of the night. Cortisol pulses occurred without any concomitant variations of sleep delta wave activity, as observed during wakefulness and intrasleep awakenings. In no case did delta wave activity increase together with an increase in cortisol secretory rates. In conclusion, cortisol secretion and delta wave activity have independent generators. They can oscillate independently from each other, but when they are present at the same time, they are oscillating in phase opposition.