Irina I. Goncharova

Effects of Working Memory Load on Oscillatory Power in Human Intracranial EEG

Studies of working memory load effects on human EEG power have indicated divergent effects in different frequency bands. Although gamma power typically increases with load, the load dependency of the lower frequency theta and alpha bands is uncertain. We obtained intracranial electroencephalography measurements from 1453 electrode sites in 14 epilepsy patients performing a Sternberg task, in order to characterize the anatomical distribution of load-related changes across the frequency spectrum. Gamma power increases occurred throughout the brain, but were most common in the occipital lobe. In the theta and alpha bands, both increases and decreases were observed, but with different anatomical distributions. Increases in theta and alpha power were most prevalent in frontal midline cortex. Decreases were most commonly observed in occipital cortex, colocalized with increases in the gamma range, but were also detected in lateral frontal and parietal regions. Spatial overlap with group functional magnetic resonance imaging results was minimal except in the precentral gyrus. These findings suggest that power in any given frequency band is not a unitary phenomenon; rather, reactivity in the same frequency band varies in different brain regions, and may relate to the engagement or inhibition of a given area in a cognitive task.

Interictal spikes on intracranial recording: Behavior, physiology, and implications

Purpose:  The physiological, pathological, and clinical meaning of interictal spikes (IISs) remains controversial. We systematically analyzed the frequency, occurrence, and distribution of IISs recorded from multiple intracranial electrodes in 34 refractory epileptic patients with respect to seizures and antiepileptic drug (AED) changes.

Localization-related epilepsy exhibits significant connectivity away from the seizure-onset area.

In localization-related epilepsy, seizures are presumed to arise from a discrete cortical area. The control of seizures by epilepsy surgery can be poor, however, even when there has been complete resection of the area identified by standard clinical procedures to give rise to seizures. We used a coherence-based measure of functional connectivity to test for network effects within and outside the seizure-onset area. Connectivity was evaluated from the background intracranial electroencephalogram of six unselected patients. We show significant nonzero connectivity not only for the seizure-onset area but also several centimeters from it, for example, for the &bgr;-frequency band (P<10−5), suggesting a nonlocal character to this disorder.

The spatial and signal characteristics of physiologic high frequency oscillations

To study the incidence, spatial distribution, and signal characteristics of high frequency oscillations (HFOs) outside the epileptic network.

Spatial distribution of intracranially recorded spikes in medial and lateral temporal epilepsies.

Purpose:  Although seizures and interictal spikes are not always colocalized, there may be valuable localizing information in the spatial distribution of spikes. To test this hypothesis, we studied the spatial distribution of intracranially recorded interictal spikes in patients with medial temporal (MT) and lateral temporal (LT) neocortical seizure onset.

Background intracranial EEG spectral changes with anti-epileptic drug taper.

OBJECTIVE Previous studies have revealed a surprising decrease in spike counts and Teager energy between on- and off-AEDs states during intracranial EEG (icEEG) monitoring. Here, we expand the measures evaluated to icEEG power and frequency band power. METHODS Two icEEG epochs, on- and off-AEDs, each 1h in duration, were studied for each of 21 unselected adult patients. Spike counts, Teager energy and total power were evaluated for each electrode contact. Power was also evaluated for delta (0-4Hz), theta (4-8Hz), alpha (8-13Hz), beta (13-25Hz), gamma (25-55Hz) and high (65-128Hz) frequency bands. RESULTS A decrease in power accompanies AED taper and the previously reported decrease in spike counts and Teager energy. The decrease in power was underpinned by a spatially widespread and broadband decrease in power in delta through gamma frequency bands with maximum decrease in the lowest frequency bands. An increase in high-frequency power was observed in some patients. CONCLUSIONS There is a decrease in spike counts, Teager energy and power from on- to off-AEDs state during intracranial monitoring. The decrease in power is spatially widespread and broadband including power in the delta through gamma frequency bands. SIGNIFICANCE The decrease in cortical activity with AED taper suggests that seizure generation during intracranial monitoring may not be mediated solely by poorly regulated cortical excitation.

Intracranially recorded interictal spikes: relation to seizure onset area and effect of medication and time of day.

OBJECTIVE The relationship between seizures and interictal spikes remains undetermined. We analyzed intracranial EEG (icEEG) recordings to examine the relationship between the seizure onset area and interictal spikes. METHODS 80 unselected patients were placed into 5 temporal, 4 extratemporal, and one unlocalized groups based on the location of the seizure onset area. We studied 4-h icEEG epochs, removed from seizures, from day-time and night-time during both on- and off-medication periods. Spikes were detected automatically from electrode contacts sampling the hemisphere ipsilateral to the seizure onset area. RESULTS There was a widespread occurrence of spikes over the hemisphere ipsilateral to the seizure onset area. The spatial distributions of spike rates for the different patient groups were different (p<0.0001, chi-square test). The area with the highest spike rate coincided with the seizure onset area only in half of the patients. CONCLUSION The spatial distribution of spike rates is strongly associated with the location of the seizure onset area, suggesting the presence of a distributed spike generation network, which is related to the seizure onset area. SIGNIFICANCE The spatial distribution of spike rates, but not the area with the highest spike rate, may hold value for the localization of the seizure onset area.

Automatic detection of prominent interictal spikes in intracranial EEG: validation of an algorithm and relationsip to the seizure onset zone.

OBJECTIVE To develop an algorithm for the automatic quantitative description and detection of spikes in the intracranial EEG and quantify the relationship between prominent spikes and the seizure onset zone. METHODS An algorithm was developed for the quantification of time-frequency properties of spikes (upslope, instantaneous energy, downslope) and their statistical representation in a univariate generalized extreme value distribution. Its performance was evaluated in comparison to expert detection of spikes in intracranial EEG recordings from 10 patients. It was subsequently used in 18 patients to detect prominent spikes and quantify their spatial relationship to the seizure onset area. RESULTS The algorithm displayed an average sensitivity of 63.4% with a false detection rate of 3.2 per minute for the detection of individual spikes and an average sensitivity of 88.6% with a false detection rate of 1.4% for the detection of intracranial EEG contacts containing the most prominent spikes. Prominent spikes occurred closer to the seizure onset area than less prominent spikes but they overlapped with it only in a minority of cases (3/18). CONCLUSIONS Automatic detection and quantification of the morphology of spikes increases their utility to localize the seizure onset area. Prominent spikes tend to originate mostly from contacts located in the close vicinity of the seizure onset area rather than from within it. SIGNIFICANCE Quantitative analysis of time-frequency characteristics and spatial distribution of intracranial spikes provides complementary information that may be useful for the localization of the seizure-onset zone.

Intracranial EEG evaluation of relationship within a resting state network

OBJECTIVE We tested if a relationship between distant parts of the default mode network (DMN), a resting state network defined by fMRI studies, can be observed with intracranial EEG recorded from patients with localization-related epilepsy. METHODS Magnitude squared coherence, mutual information, cross-approximate entropy, and the coherence of the gamma power time-series were estimated, for one hour intracranial EEG recordings of background activity from 9 patients, to evaluate the relationship between two test areas which were within the DMN (anterior cingulate and orbital frontal, denoted as T1 and posterior cingulate and mesial parietal, denoted as T2), and one control area (denoted as C), which was outside the DMN. We tested if the relationship between T1 and T2 was stronger than the relationship between each of these areas and C. RESULTS A low level of relationship was observed among the 3 areas tested. The relationships among T1, T2 and C did not demonstrate support for the DMN. CONCLUSIONS This study suggests a lack of intracranial EEG support for the fMRI defined default mode network. SIGNIFICANCE The results obtained underscore the considerable difference between electrophysiological and hemodynamic measurements of brain activity and possibly suggest a lack of neuronal involvement in the DMN.

A decrease in EEG energy accompanies anti-epileptic drug taper during intracranial monitoring.

OBJECTIVE During intracranial EEG (icEEG) monitoring the likelihood of observing a seizure is increased by tapering anti-epileptic drugs (AEDs). Presumably AED taper results in an increase in cortical excitation which in turn promotes seizure emergence. We measured change in signal energy of icEEGs in response to AED taper to quantify changes in excitation which accompany the increased propensity for seizures. METHODS Twelve consecutive adult patients who completed intracranial monitoring were studied. Two icEEG epochs from before and after AED taper, each 1h in duration, during wake, matched by time-of-day and removed from seizures were selected for each patient. Teager energy, a frequency weighted measure of signal energy, was estimated for both the seizure onset region as well as all other brain areas monitored. RESULTS Considerable changes in Teager energy, evaluated at a 1-h time-resolution, occur during intracranial monitoring. The most dominant trend is a decrease to lower values than those when the patient is on AEDs. A decrease of 35% was observed for both all the brain areas monitored and the seizure onset region. CONCLUSIONS A decrease in signal energy occurs during intracranial EEG monitoring, possibly accompanying AED taper. If the decrease is due to AED taper this would suggest that AEDs prevent seizures in ways other than reduction of cortical excitation and seizure generation may be influenced by factors other than poorly regulated cortical excitation.