Analysis Of Intracranial EEG Research Articles

Spectrum of motor responses elicited by electrical stimulation of primary motor cortex: A polygraphic study in patients with epilepsy

ObjectiveTo study the neurophysiology of motor responses elicited by electrical stimulation of the primary motor cortex. MethodsWe studied motor responses in four patients undergoing invasive epilepsy monitoring and functional cortical mapping via electrical cortical stimulation using surface EMG electrodes. In addition, polygraphic analysis of intracranial EEG and EMG during bilateral tonic-clonic seizures, induced by cortical stimulation, was performed in two patients. Results(a) Electrical cortical stimulation: The motor responses were classified as clonic, jittery, and tonic. The clonic responses were characterized by synchronous EMG bursts of agonist and antagonistic muscles, alternating with silent periods. At stimulation frequencies of <20 Hz, EMG bursts were of ≤50 ms duration (Type I clonic). At stimulation frequencies of 20–50 Hz, EMG bursts were of >50 ms duration and had a complex morphology (Type II clonic). Increasing the current intensity at a constant frequency converted clonic responses into jittery and tonic contractions.(b) Bilateral tonic-clonic seizures: The intracranial EEG showed continuous fast spiking activity during the tonic phase along with interference pattern on surface EMG. The clonic phase was characterized by a polyspike-and-slow wave pattern. The polyspikes were time-locked with the synchronous EMG bursts of agonists and antagonists and the slow waves were time-locked with silent periods. InterpretationThese results suggest that epileptic activity involving the primary motor cortex can produce a continuum of motor responses ranging from type I clonic, type II clonic, and tonic responses to bilateral tonic-clonic seizures. This continuum is related to the frequency and intensity of the epileptiform discharges with tonic seizures representing the highest end of the spectrum.

Localizing epileptogenic network from SEEG using non-linear correlation, mutual information and graph theory analysis.

The key challenge in epilepsy surgery is precise localization and removal of the epileptogenic zone (EZ) from the brain. Localization of the epileptogenic network by visual analysis of intracranial EEG is extremely difficult. In this retrospective study, we used interictal connectivity and graph theory analysis on intracranial EEG to better delineate the epileptogenic zone. Patients who underwent surgery for drug-refractory mesial temporal and neocortical epilepsy were included. Computational measures, such as h2 nonlinear correlation and mutual information, were used to estimate the interdependency of intracranial EEGs. We observed that the Out-Degree, Out-Strength, and Betweenness centrality (graph properties) were the best predictors of EZ. From the results, we also found that graph properties with a normalized value above 0.75 were found to be a useful measure to localize the EZ with a sensitivity of 87.88 and a specificity of 87.13. Our results also validate that frequently occurring types of interictal fast discharges (IFD) with connectivity measures and graph properties can better localize the EZ. We foresee graph theory analysis of interictal intracranial EEG data can help precise localization of EZ for cortical resection as well as in minimally invasive radiofrequency ablation of epileptogenic hubs. Further, prospective validation is required for clinical use.

More Than Spikes: On the Added Value of Non-linear Intracranial EEG Analysis for Surgery Planning in Temporal Lobe Epilepsy.

Epilepsy surgery can be a very effective therapy in medication refractory patients. During patient evaluation intracranial EEG is analyzed by clinical experts to identify the brain tissue generating epileptiform events. Quantitative EEG analysis increasingly complements this approach in research settings, but not yet in clinical routine. We investigate the correspondence between epileptiform events and a specific quantitative EEG marker. We analyzed 99 preictal epochs of multichannel intracranial EEG of 40 patients with mixed etiologies. Time and channel of occurrence of epileptiform events (spikes, slow waves, sharp waves, fast oscillations) were annotated by a human expert and non-linear excess interrelations were calculated as a quantitative EEG marker. We assessed whether the visually identified preictal events predicted channels that belonged to the seizure onset zone, that were later resected or that showed strong non-linear interrelations. We also investigated whether the seizure onset zone or the resection were predicted by channels with strong non-linear interrelations. In patients with temporal lobe epilepsy (32 of 40), epileptic spikes and the seizure onset zone predicted the resected brain tissue much better in patients with favorable seizure control after surgery than in unfavorable outcomes. Beyond that, our analysis did not reveal any significant associations with epileptiform EEG events. Specifically, none of the epileptiform event types did predict non-linear interrelations. In contrast, channels with strong non-linear excess EEG interrelations predicted the resected channels better in patients with temporal lobe epilepsy and favorable outcome. Also in the small number of patients with seizure onset in the frontal and parietal lobes, no association between epileptiform events and channels with strong non-linear excess EEG interrelations was detectable. In contrast to patients with temporal seizure onset, EEG channels with strong non-linear excess interrelations did neither predict the seizure onset zone nor the resection of these patients or allow separation between patients with favorable and unfavorable seizure control. Our study indicates that non-linear excess EEG interrelations are not strictly associated with epileptiform events, which are one key concept of current clinical EEG assessment. Rather, they may provide information relevant for surgery planning in temporal lobe epilepsy. Our study suggests to incorporate quantitative EEG analysis in the workup of clinical cases. We make the EEG epochs and expert annotations publicly available in anonymized form to foster similar analyses for other quantitative EEG methods.

Intracranial EEG Biomarkers for Seizure Lateralization in Rapidly-Bisynchronous Epilepsy After Laser Corpus Callosotomy.

Objective: It has been asserted that high-frequency analysis of intracranial EEG (iEEG) data may yield information useful in localizing epileptogenic foci.Methods: We tested whether proposed biomarkers could predict lateralization based on iEEG data collected prior to corpus callosotomy (CC) in three patients with bisynchronous epilepsy, whose seizures lateralized definitively post-CC. Lateralization data derived from algorithmically-computed ictal phase-locked high gamma (PLHG), high gamma amplitude (HGA), and low-frequency (filtered) line length (LFLL), as well as interictal high-frequency oscillation (HFO) and interictal epileptiform discharge (IED) rate metrics were compared against ground-truth lateralization from post-CC ictal iEEG.Results: Pre-CC unilateral IEDs were more frequent on the more-pathologic side in all subjects. HFO rate predicted lateralization in one subject, but was sensitive to detection threshold. On pre-CC data, no ictal metric showed better predictive power than any other. All post-corpus callosotomy seizures lateralized to the pathological hemisphere using PLHG, HGA, and LFLL metrics.Conclusions: While quantitative metrics of IED rate and ictal HGA, PHLG, and LFLL all accurately lateralize based on post-CC iEEG, only IED rate consistently did so based on pre-CC data.Significance: Quantitative analysis of IEDs may be useful in lateralizing seizure pathology. More work is needed to develop reliable techniques for high-frequency iEEG analysis.

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

The analysis of biomedical signals for clinical studies and therapeutic applications can benefit from embedded devices that can process these signals locally and in real-time. An example is the analysis of intracranial EEG (iEEG) from epilepsy patients for the detection of High Frequency Oscillations (HFO), which are a biomarker for epileptogenic brain tissue. Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time. Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect HFO, thereby achieving state-of-the-art accuracy, sensitivity, and specificity. This is a first feasibility study towards identifying relevant features in iEEG in real-time using mixed-signal neuromorphic computing technologies.

Roughness-Length-Based Characteristic Analysis of Intracranial EEG and Epileptic Seizure Prediction.

To identify precursors of epileptic seizures, an EEG characteristic analysis is carried out based on a roughness-length method, where fractal dimensions and intercept values are extracted to measure the structure complexity and the amplitude roughness of EEG signals in different phases. Using the significant changes of the fractal dimension and intercept in the preictal phase with respect to those in the interictal phase, a patient-specific seizure prediction algorithm is then proposed by combining with a gradient boosting classifier. The probabilistic outputs of the trained gradient boosting classifier are further processed by threshold comparison and rule-based judgment to distinguish preictal EEG from interictal EEG and to generate seizure alerts. The prediction algorithm was evaluated on 20 patients' intracranial EEG recordings from the Freiburg EEG database, which contains the preictal periods of 65 seizures and 499[Formula: see text]h interictal EEG. Setting the seizure prediction horizon as 2[Formula: see text]min, averaged sensitivity values of 90.42% and 91.67% with averaged false prediction rates of 0.12/h and 0.10/h were achieved for seizure occurrence periods of 30 and 50[Formula: see text]min, respectively. These results demonstrate the ability of fractal dimension and intercept metrics in predicting the occurrence of seizures.

Superior Frontal Sulcus Focal Cortical Dysplasia Type II: An MRI, PET, and Quantified SEEG Study.

Purpose: The superior frontal sulcus (SFS), located in the prefrontal and premotor cortex, is considered as one of the common locations of focal cortical dysplasia (FCD). However, the characteristics of seizures arising from this area are incompletely known. The primary purpose of this study was to investigate the clinical features and the epileptic networks of seizures originating from the SFS.Methods: We included seventeen patients with type II FCD within the SFS. SFS was identified both visually and automatically. Semiological features were evaluated and grouped. Interictal 18FDG-PET imaging in all patients was compared to controls using statistical parametric mapping (SPM-PET). In those subjects with stereoelectroencephalography (SEEG), two different quantitative intracranial electroencephalography analyses were applied. Finally, the locations of the SFS-related hypometabolic regions and epileptogenic zones (EZs) were transformed into standard space for group analysis.Results: We identified two semiological groups. Group 1 (9/17) showed elementary motor signs (head version and tonic posturing), while group 2 (8/17) exhibited complex motor behavior (fear, hypermotor, and ictal pouting). Based on SPM-PET, an SFS-supplementary motor area (SMA) epileptic propagation network was found in group 1, and an SFS-middle cingulate cortex (MCC)-pregenual anterior cingulate cortex (pACC) propagation network was discovered in group 2. Intracranial EEG analysis suggested similar affected structures with high epileptogenicity. The SFS-related hypometabolic regions and EZs in these groups showed a posterior-anterior spatial relationship.Conclusions: Even though originating from the spatially restricted cortex, SFS seizures can be divided into two groups based on semiological features. The SFS-SMA and SFS-MCC-pACC epileptic propagation networks may play pivotal roles in the generation of different semiologies. The posterior-anterior spatial relationship of both hypometabolic regions and EZs provides potentially useful information for distinguishing different types of SFS seizures and surgical evaluation.

Evaluating resective surgery targets in epilepsy patients: A comparison of quantitative EEG methods

BackgroundQuantitative analysis of intracranial EEG is a promising tool to assist clinicians in the planning of resective brain surgery in patients suffering from pharmacoresistant epilepsies. Quantifying the accuracy of such tools, however, is nontrivial as a ground truth to verify predictions about hypothetical resections is missing. New methodAs one possibility to address this, we use customized hypotheses tests to examine the agreement of the methods on a common set of patients. One method uses machine learning techniques to enable the predictive modeling of EEG time series. The other estimates nonlinear interrelation between EEG channels. Both methods were independently shown to distinguish patients with excellent post-surgical outcome (Engel class I) from those without improvement (Engel class IV) when assessing the electrodes associated with the tissue that was actually resected during brain surgery. Using the AND and OR conjunction of both methods we evaluate the performance gain that can be expected when combining them. ResultsBoth methods’ assessments correlate strongly positively with the similarity between a hypothetical resection and the corresponding actual resection in class I patients. Moreover, the Spearman rank correlation between the methods’ patient rankings is significantly positive. Comparison with existing method(s)To our best knowledge, this is the first study comparing surgery target assessments from fundamentally differing techniques. ConclusionsAlthough conceptually completely independent, there is a relation between the predictions obtained from both methods. Their broad consensus supports their application in clinical practice to provide physicians additional information in the process of presurgical evaluation.

Time-frequency analysis of intracranial EEG in patients with myoclonic seizures

Myoclonic seizures are defined as generalized seizures according to the classification of seizure by the International League Against Epilepsy (ILAE). The pathogenesis of myoclonic seizures is not yet clear. There are very few studies on the focal surgical treatment of myoclonic seizures. The aim of this study is to investigate the characteristics of myoclonic seizure onset in different bands of the intracranial electroencephalogram (EEG) and their dynamic changes in temporal and spatial evolution. We studied four patients with myoclonic seizures who were under the focal resection of the epileptogenic zone. We retrospectively analyzed the semiology, electrocorticogram (ECoG) and imaging data of these patients, and conducted time-frequency analysis of broadband ECoG activity. We found that myoclonic seizures without clinical lateralizing signs could be improved by the resection of the epileptogenic zone. The ECoG power in different frequency bands increased to a peak at 0.5s before the clinical seizure onset and decreased quickly afterwards. The power of alpha activity was highest during the preictal and ictal periods. The central zone had higher power than the epileptogenic zone in all frequency bands during the preictal period, but this difference was not statistically significant. Our results suggest that myoclonic seizures in some patients might have a focal origination, with a fast bilateral propagating network in all frequency bands, especially the alpha band.

Intracranial EEG analysis in tumor-related epilepsy: Evidence of distant epileptic abnormalities

ObjectiveIn patients with tumor-related epilepsy (TRE), surgery traditionally focuses on tumor resection; but identification and removal of associated epileptogenic zone may improve seizure outcome. Here, we study spatial relationship of tumor and seizure onset and early spread zone (SOSz). We also perform quantitative analysis of interictal epileptiform activities in patients with both TRE and non-lesional epilepsy in order to better understand the electrophysiological basis of epileptogenesis. MethodsTwenty-five patients (11 with TRE and 14 with non-lesional epilepsy) underwent staged surgery using intracranial electrodes. Tumors were outlined on MRI and images were coregistered with post-implantation CT images. For each electrode, distance to the nearest tumor margin was measured. Electrodes were categorized based on distance from tumor and involvement in seizure. Quantitative EEG analysis studying frequency, amplitude, power, duration and slope of interictal spikes was performed. ResultsAt least part of the SOSz was located beyond 1.5cm from the tumor margin in 10/11 patients. Interictally, spike frequency and power were higher in the SOSz and spikes near tumor were smaller and less sharp. Interestingly, peritumoral electrodes had the highest spike frequencies and sharpest spikes, indicating greatest degree of epileptic synchrony. A complete resection of the SOSz resulted in excellent seizure outcome. ConclusionsSeizure onset and early spread often involves brain areas distant from the tumor. SignificanceUtilization of epilepsy surgery approach for TRE may provide better seizure outcome and study of the intracranial EEG may provide insight into pathophysiology of TRE.

Combined Use of Multiple Computational Intracranial EEG Analysis Techniques for the Localization of Epileptogenic Zones in Lennox–Gastaut Syndrome

Traditionally, identification of epileptogenic zones primarily relied on visual inspection of intracranial electroencephalography (iEEG) recordings by experienced epileptologists; however, removal of epileptogenic zones identified by iEEG does not always guarantee favorable surgical outcomes. To confirm visual inspection results, and assist in making decisions about surgical resection areas, computational iEEG analysis methods have recently been used for the localization of epileptogenic zones. In this study, we have proposed a new approach for the localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS), and have investigated whether the proposed approach could confirm surgical resection areas and predict seizure outcome before surgery. The proposed approach simultaneously used results of 2 iEEG analysis methods, directed transfer function (DTF) and time delay estimation, to enhance localization accuracy. This new combined method was applied to patients who became seizure-free after resective epilepsy surgery, as well as those who had unsuccessful surgery. A quantitative metric was also introduced that can measure how well the localized epileptogenic zones coincided with the surgical resection areas, with the aim of verifying whether the approach could confirm surgical resection areas determined by epileptologists. The estimated epileptogenic zones more strongly coincided with surgical resection areas in patients with successful, compared to those with unsuccessful surgical outcomes. Both qualitative and quantitative analyses showed that the combined use of 2 iEEG analyses resulted in a more accurate estimate of epileptogenic zones in LGS than the use of a single method. A combination of multiple iEEG analyses could not only enhance overall accuracy of localizing epileptogenic zones in LGS, but also has the potential to predict outcomes before resective surgery.

Biomarkers of epileptogenic zone defined by quantified stereo‐EEG analysis

In one third of patients with a diagnosis of pharmacoresistant focal epilepsy who are candidates for therapeutic surgery, cerebral areas responsible for seizure generation can be defined exclusively with invasive intracranial recordings. A correct presurgical identification of the epileptogenic zone (EZ) with intracranial electrodes has a direct impact on postsurgical outcome. We aimed at identifying biomarkers of the EZ based on computer-assisted inspection of intracranial electroencephalography (EEG). Computer-driven intracranial EEG analysis in the domains of time, frequency, and space was retrospectively applied to a population of 10 patients with focal epilepsy to detect EZ electrophysiologic markers. Next, a prospective study was performed on 14 surgery candidate patients. The stereo-EEG computer-assisted analysis of EZ boundaries performed blind from patients data was compared to that defined with the traditional visual inspection completed by neurophysiologists. In the retrospective study, the EZ was characterized by the combined detection of three biomarkers observed at seizure onset: (1) fast activity at 80-120Hz associated with (2) very slow transient polarizing shift and (3) voltage depression (flattening). Correlations between these indexes were calculated for each seizure. In the prospective study, the quantified analysis based on the three biomarkers confirmed a complete overlap between leads within the EZ identified by expert clinicians. In 2 of 14 patients the proposed biomarkers partially identified the EZ. Our findings demonstrate and validate with a prospective unbiased study the use of three neurophysiologic intracranial EEG parameters as excellent biomarkers of ictogenesis and as reliable indicators of EZ boundaries.

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.

Resection of ictal high‐frequency oscillations leads to favorable surgical outcome in pediatric epilepsy

Intracranial electroencephalography (EEG) is performed as part of an epilepsy surgery evaluation when noninvasive tests are incongruent or the putative seizure-onset zone is near eloquent cortex. Determining the seizure-onset zone using intracranial EEG has been conventionally based on identification of specific ictal patterns with visual inspection. High-frequency oscillations (HFOs, >80 Hz) have been recognized recently as highly correlated with the epileptogenic zone. However, HFOs can be difficult to detect because of their low amplitude. Therefore, the prevalence of ictal HFOs and their role in localization of epileptogenic zone on intracranial EEG are unknown. We identified 48 patients who underwent surgical treatment after the surgical evaluation with intracranial EEG, and 44 patients met criteria for this retrospective study. Results were not used in surgical decision making. Intracranial EEG recordings were collected with a sampling rate of 2,000 Hz. Recordings were first inspected visually to determine ictal onset and then analyzed further with time-frequency analysis. Forty-one (93%) of 44 patients had ictal HFOs determined with time-frequency analysis of intracranial EEG. Twenty-two (54%) of the 41 patients with ictal HFOs had complete resection of HFO regions, regardless of frequency bands. Complete resection of HFOs (n = 22) resulted in a seizure-free outcome in 18 (82%) of 22 patients, significantly higher than the seizure-free outcome with incomplete HFO resection (4/19, 21%). Our study shows that ictal HFOs are commonly found with intracranial EEG in our population largely of children with cortical dysplasia, and have localizing value. The use of ictal HFOs may add more promising information compared to interictal HFOs because of the evidence of ictal propagation and followed by clinical aspect of seizures. Complete resection of HFOs is a favorable prognostic indicator for surgical outcome.

Intracranial EEG in predicting surgical outcome in frontal lobe epilepsy

Surgery in frontal lobe epilepsy (FLE) has a worse prognosis regarding seizure freedom than anterior lobectomy in temporal lobe epilepsy. The current study aimed to assess whether intracranial interictal and ictal EEG findings in addition to clinical and scalp EEG data help to predict outcome in a series of patients who needed invasive recording for FLE surgery. Patients with FLE who had resective surgery after chronic intracranial EEG recording were included. Outcome predictors were compared in patients with seizure freedom (group 1) and those with recurrent seizures (group 2) at 19-24 months after surgery. Twenty-five patients (16 female) were included in this study. Mean age of patients at epilepsy surgery was 32.3 ± 15.6 years (range 12-70); mean duration of epilepsy was 16.9 ± 13.4 years (range 1-48). In each outcome group, magnetic resonance imaging revealed frontal lobe lesions in three patients. Fifteen patients (60%) were seizure-free (Engel class 1), 10 patients (40%) continued to have seizures (two were class II, three were class III, and five were class IV). Lack of seizure freedom was seen more often in patients with epilepsy surgery on the left frontal lobe (group 1, 13%; group 2, 70%; p = 0.009) and on the dominant (27%; 70%; p = 0.049) hemisphere as well as in patients without aura (29%; 80%; p = 0.036), whereas sex, age at surgery, duration of epilepsy, and presence of an MRI lesion in the frontal lobe or extrafrontal structures were not different between groups. Electroencephalographic characteristics associated with lack of seizure freedom included presence of interictal epileptiform discharges in scalp recordings (31%; 90%; p = 0.01). Detailed analysis of intracranial EEG revealed widespread (>2 cm) (13%; 70%; p = 0.01) in contrast to focal seizure onset as well as shorter latency to onset of seizure spread (5.8 ± 6.1 s; 1.5 ± 2.3 s; p = 0.016) and to ictal involvement of brain structures beyond the frontal lobe (23.5 ± 22.4 s; 5.8 ± 5.4 s; p = 0.025) in patients without seizure freedom. The distribution of ictal onset patterns was similar in both groups, and fast rhythmic activity in the beta to gamma range was found in 57% of seizure-free patients compared to 70% of patients with recurrent seizures. Analysis of the temporal relation between first clinical alterations and EEG seizure onset did not reveal significant differences between both groups of patients. In multivariate analysis, resection in the left hemisphere (odds ratio [OR] 12.197 95% confidence interval [95% CI] 1.33-111.832; p = 0.027) and onset of seizure spread (odds ratio [OR] 0.733, 95% CI 0.549-0.978, p = 0.035) were independent predictors of ongoing seizures. Widespread epileptogenicity as indicated by rapid onset of spread of ictal activity likely explains lack of seizure freedom following frontal resective surgery. The negative prognostic effect of surgery on the left hemisphere is less clear. Future study is needed to determine if neuronal network properties in this hemisphere point to intrinsic interhemispheric differences or if neurosurgeons are restrained by proximity to eloquent cortex.

Extent of neocortical resection and surgical outcome of epilepsy: Intracranial EEG analysis

Intracranial electroencephalography (EEG) monitoring is an important process in the presurgical evaluation for epilepsy surgery. The objective of this study was to identify the ideal resection margin in neocortical epilepsy guided by subdural electrodes. For this purpose, we investigated the relationship between the extent of resection guided by subdural electrodes and the outcome of epilepsy surgery. Intracranial EEG studies were analyzed in 177 consecutive patients who had undergone resective epilepsy surgery. We reviewed various intracranial EEG findings and resection extent. We analyzed the relationships between the surgical outcomes and intracranial EEG factors: the frequency, morphology, and distribution of ictal-onset discharges, the propagation speed, and the time lag between clinical and intracranial ictal onset. We also investigated whether the extent of resection, including the area showing ictal rhythm and various interictal abnormalities--such as frequent interictal spikes, pathologic delta waves, and paroxysmal fast activity--influenced the surgical outcome. Seventy-five patients (42%) were seizure free. A seizure-free outcome was significantly associated with a resection that included the area showing ictal spreading rhythm during the first 3 s or included all the electrodes showing pathologic delta waves or frequent interictal spikes. However, subgroup analysis revealed that the extent of resection did not affect the surgical outcome in lateral temporal lobe epilepsy. The extent of resection is closely associated with surgical outcome, especially in extratemporal lobe epilepsy. Resection that includes the area with total pathologic delta waves and frequent interictal spikes predicts a good surgical outcome.

Utility of Independent Component Analysis for Interpretation of Intracranial EEG

Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.

PO10.13 Extent of Neocortical Resection and Surgical Outcome for Epilepsy: Intracranial EEG Analysis

PO10.13 Extent of Neocortical Resection and Surgical Outcome for Epilepsy: Intracranial EEG Analysis

A simultaneous MEG and intracranial EEG study of task-related brain oscillations

In an effort to find quantitative markers of attention during reading, we performed simultaneous MEG and intracranial EEG recordings of alpha, beta and gamma band oscillatory activity in an epileptic patient during attentive vs. inattentive reading. We compared the strength and spatial organization of those oscillations between the two conditions. Time-frequency analysis of intracranial EEG revealed an attention-dependant network of gamma band energy increases and alpha/beta energy suppressions in response to word presentation. MEG data were also characterized by power increases in the gamma band and power suppressions in the alpha/beta range, mirroring the intracranial effects. The agreement we found between MEG and intracranial EEG power modulations suggests that MEG can detect attention-related modulations of cortical oscillatory processes and thus provide a reliable non-invasive measure of attention during reading in individual subjects.

側頭葉起源の複雑部分発作における発作性音声産出 頭蓋内脳波・発作同時記録による分析

側頭葉起源の複雑部分発作における発作性音声産出 頭蓋内脳波・発作同時記録による分析