Epileptic Seizure Onset Zone Research Articles

Expert Knowledge Driven Human-AI Collaboration for Medical Imaging: A Study on Epileptic Seizure Onset Zone Identification

Expert Knowledge Driven Human-AI Collaboration for Medical Imaging: A Study on Epileptic Seizure Onset Zone Identification

Brain perfusion SPECT in the presurgical evaluation of epilepsy: is additional ictal SPECT required in case of high-confidence lateralization of the seizure onset zone by interictal SPECT and vice versa?

BackgroundIctal brain perfusion SPECT provides higher sensitivity for the identification of the epileptic seizure onset zone (SOZ) than interictal SPECT. However, ictal SPECT is demanding due to the unpredictable waiting period for the next seizure to allow for ictal tracer injection. Thus, starting with an interictal scan and skipping the ictal scan if the interictal scan provides a SOZ candidate with high confidence could be an efficient approach. The current study estimated the rate of high-confidence SOZ candidates and the false lateralization rate among them for interictal and ictal SPECT.Methods177 patients (48% females, median age 38y, interquartile range 27–48y) with ictal and interictal SPECT acquired with 99mTc-HMPAO (n = 141) or -ECD (n = 36) were included retrospectively. The vast majority of the patients was suspected to have temporal lobe epilepsy. Visual interpretation of the SPECT data was performed independently by 3 readers in 3 settings: “interictal only” (interictal SPECT and statistical hypoperfusion map), “ictal only” (ictal SPECT and hyperperfusion map), and “full” setting (side-by-side interpretation of ictal and interictal SPECT including statistical maps and SISCOM analysis). The readers lateralized the SOZ (right, left, none) and characterized their confidence using a 5-score. A case was considered "lateralizing with high confidence” if all readers lateralized to the same hemisphere with at least 4 of 5 confidence points. Lateralization of the SOZ in the “full” setting was used as reference standard.ResultsThe proportion of “lateralizing with high confidence” cases was 4.5/31.6/38.4% in the “interictal only”/“ictal only”/“full” setting. One (12.5%) of the 8 cases that were “lateralizing with high confidence” in the “interictal only” setting lateralized to the wrong hemisphere. Among the 56 cases that were “lateralizing with high confidence” in the “ictal only” setting, 54 (96.4%) were also lateralizing in the “full” setting, all to the same hemisphere.ConclusionsStarting brain perfusion SPECT in the presurgical evaluation of epilepsy with an interictal scan to skip the ictal scan in case of a high-confidence interictal SOZ candidate is not a useful approach. In contrast, starting with an ictal scan to skip the interictal scan in case of a high-confidence ictal SOZ candidate can be recommended.

Comparison of Ictal SPECT with 99mTc-HMPAO versus MRI for the Epileptic Seizure Onset Zone Detection in Saudi Patients

Background: In the presurgical evaluation of patients with drug-resistant epilepsy, magnetic resonance imaging (MRI) and ictal brain perfusion SPECT with the chemical microspheres 99mTc-HMPAO are widely used for diagnosing the seizure onset zone (SOZ). For both modalities, there is theoretical controversy over favoring one over the other. This study aimed to compare the performance of 99mTc-HMPAO SPECT with MRI for SOZ identification in EEG-proved epileptic Saudi patients. Methods and Results: For this observational retrospective study, the database of the nuclear medicine departments at the Prince Sultan Military Medical City (PSMMC) and King Abdullah bin Abdulaziz University Hospital (KAAUH) were searched for male and female patients with suspected unifocal epilepsy in whom ictal brain perfusion SPECT and MRI had been performed for presurgical evaluation. A total of 14 adult epileptic patients above 18 years were included who have undergone SPECT scans using 99mTc-HMPAO and MRI between Jan 2014 and Dec 2021. 99mTc-HMPAO SPECT and MRI scans were performed simultaneously for each patient, and there was almost general agreement that 99mTc-HMPAO SPECT accurately localized and detected the SOZ in 12/14 patients (sensitivity 85.71%). It was superior to MRI, which detected and localized the SOZ in only 7/14 patients (sensitivity 50.0%). Unfortunately, the specificity of ictal brain perfusion SPECT, by using 99mTc-HMPAO in seizure localization in epileptic patients, was not detected due to the shortage of data that led to all the EEG findings being 100% positive. Conclusion: This study reported that ictal SPECT using 99mTc-HMPAO provides more valuable information about SOZ localization than MRI. However, future studies with a larger sample size are needed to assess the specificity of 99mTc-HMPAO SPECT in detecting the SOZ.

EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

Modified remora optimization based matching pursuit with density peak clustering for localization of epileptic seizure onset zones

Modified remora optimization based matching pursuit with density peak clustering for localization of epileptic seizure onset zones

Classification of the Epileptic Seizure Onset Zone Based on Partial Annotation

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.

Neural instability and delicacy while the electro-encephalography indicator of epileptic seizure (e-seizure) on set zone

More than 15 million seizure-epileptic subjects (patients) do not respond to medication globally. Surgery necessitates thorough elimination or separation of the epileptic-seizure onset zone (ESoZ), regions of epileptic brains where seizures derive (come from). Sadly, the success rates of operation differ amongst 32% and 72% and this is due to clinically and hence prognostically no authenticated or substantiated (i.e., corroborated) biological or physiological indicator of the ESoZ exists. We discuss, as well as confirm a new electro encephalography indication neural vulnerability in a retroactive study of 90 subjects (patients) by applying neural instability of the interpreted ESoZ as a metrical to expect operational (invasive) results in retrospect (retrospectively). Susceptibility expects (43/47) surgical (invasive)-failures, as well as a total likelihood precision of 76% assessed through the precision of neuroscientists at 48% (positive-results).

No Evidence to Favor 99mTc-HMPAO or 99mTc-ECD for Ictal Brain Perfusion SPECT for Identification of the Seizure Onset Zone.

Ictal brain perfusion SPECT with the tracer 99mTc-HMPAO or 99mTc-ECD is widely used for identification of the epileptic seizure onset zone (SOZ) in presurgical evaluation if standard pointers are uncertain or inconsistent. For both tracers, there are theoretical arguments to favor it over the other for this task. The aim of this study was to compare the performance of ictal brain perfusion SPECT between 99mTc-HMPAO and 99mTc-ECD in a rather large patient sample. The study retrospectively included 196 patients from clinical routine in whom ictal perfusion SPECT had been performed with stabilized 99mTc-HMPAO (n = 110) or 99mTc-ECD (n = 86). Lateralization and localization of the SOZ were obtained by the consensus of 2 independent readers based on visual inspection of the SPECT images. The 99mTc-HMPAO group and the 99mTc-ECD group were well matched with respect to age, sex, age at first seizure, duration of disease, seizure frequency, history of previous brain surgery, and findings of presurgical MRI. The proportion of lateralizing ictal SPECT did not differ significantly between 99mTc-HMPAO and 99mTc-ECD (65.5% vs 72.1%, P = 0.36). Sensitivity of ictal perfusion SPECT (independent of the tracer) for correct localization of the SOZ in 62 patients with temporal lobe epilepsy and at least worthwhile improvement (Engel scale ≤ III) 12 months after temporal epilepsy surgery was 63%. This study does not provide evidence to favor 99mTc-HMPAO or 99mTc-ECD for identification of the SOZ by ictal perfusion SPECT.

A new localization method for epileptic seizure onset zones based on time-frequency and clustering analysis

High-frequency oscillations (HFOs) are spontaneous electroencephalogram patterns that have been regarded as potential biomarkers of epileptic seizure onset zones (SOZs). Accurately detected HFOs are used to localize SOZs, which is crucial for the presurgical assessment. Since the visual marking of HFOs is time-consuming, a method is desirable to automatically detect HFOs for localizing SOZs in clinical practice. However, the existing methods cannot obtain satisfactory performance, which are not suitable for clinical application. In order to solve this problem, we present a new localization method for epileptic SOZs in this study. Firstly, a threshold method is used to detect events of interest (EoIs). Secondly, a time-frequency analysis method is adopted to acquire channels of interest (CoIs) by calculating the average power of EoIs on each channel. Then, the k-medoids clustering method is employed to detect HFOs of CoIs. Finally, the concentrations of detected HFOs are used to localize SOZs. The superiority of our localization method is demonstrated by comparing its sensitivity and specificity with some existing methods.

Multiresolution approach for artifacts removal and localization of seizure onset zone in epileptic EEG signal

Epilepsy is major challenge to medical fraternity. This research article has come up with a novel method for pre-processing of Epileptic Electroencephalogram (EEG) signals for artifact removal and localizing the epileptic seizure onset zone. The presence of physiological artifacts alters the true EEG information which can considerably affect the epileptic seizure classification accuracy. Hence, it is of prime importance to eliminate these contaminations. The analysis of epileptic EEG signal requires localization of the seizure onset zone in the epileptic EEG signal. The objective of this paper is to present an approach to eliminate physiological artifacts and to localize the epileptic region. This paper suggests a hybrid model based on Multiresolutional Analysis and Adaptive Filtering [MRAF]. Initially the EEG signal is decomposed using Discrete Wavelet Transform (DWT) which effectively helps to localize the epileptic region. Multiresolutional soft thresholding is applied to these decomposed wavelet components to remove the abrupt variations. Further, adaptive filtering is applied to remove the low frequency components which represents physiological artifacts. It is observed that the MRAF method effectively eliminate physiological artifacts and localizes the epileptic seizure zone present in the seizure EEG information. The accuracy of the proposed MRAF model is found to be 86.66% with a precision of 88.88%, since it is able to retain most of the seizure signal present in the tested datasets.

A method for detecting high-frequency oscillations using semi-supervised k-means and mean shift clustering

This paper proposes a method to detect the high-frequency oscillations (HFOs) in epileptic seizure onset zones (SOZs) localization using semi-supervised k-means and mean shift algorithm. Wavelet entropy (WE) and teager energy operator (TEO) are adopted to distinguish HFOs from normal electroencephalogram (EEG). Labeled data are used to initialize the clustering center of semi-supervised k-means algorithm, and unlabeled data are employed to obtain physiological and suspected pathological HFOs. For the suspected pathological HFOs, the mean shift algorithm is used for clustering, and the results are analyzed by the spectral center algorithm to locate SOZs. By comparing the EEG data of five patients with the results of the other three methods, it can be seen that the method proposed in this paper has good sensitivity and specificity, which is helpful for accurate localization before clinical epilepsy surgery.

A New Unsupervised Detector of High-Frequency Oscillations in Accurate Localization of Epileptic Seizure Onset Zones.

This paper presents a new unsupervised detector for automatically detecting high-frequency oscillations (HFOs) using intracranial electroencephalogram (iEEG) signals. This detector does not presuppose a specific number of clusters and has a good performance. First, the HFO candidates are detected by an initial detection method which distinguishes HFOs from background activities. Then, as significant features, fuzzy entropy, short-time energy, power ratio, and spectral centroid of the HFO candidates are investigated and constructed as a feature vector. Finally, the feature vector is used as the input of the fuzzy- -means-quantization-error-modeling-based expectation-maximization-Gaussian mixture model clustering algorithm. This algorithm has the advantages of detecting HFOs and avoiding false detection caused by artifacts. The concentrations of detected HFOs are used to localize epileptic seizure onset zones in epileptic iEEG signal analysis. A comparison shows that our detector provides better localization performance in terms of sensitivity and specificity than five existing detectors.

Assessment of the Utility of Ictal Magnetoencephalography in the Localization of the Epileptic Seizure Onset Zone

Literature on ictal magnetoencephalography (MEG) in clinical practice and the relationship to other modalities is limited because of the brevity of routine studies. To investigate the utility and reliability of ictal MEG in the localization of the epileptogenic zone. A retrospective medical record review and prospective analysis of a novel ictal rhythm analysis method was conducted at a tertiary epilepsy center with a wide base of referrals for epilepsy surgery evaluation and included consecutive cases of patients who experienced epileptic seizures during routine MEG studies from March 2008 to February 2012. A total of 377 studies screened. Data were analyzed from November 2011 to October 2015. Presurgical workup and interictal and ictal MEG data were reviewed. The localizing value of using extended-source localization of a narrow band identified visually at onset was analyzed. Of the 44 included patients, the mean (SD) age at the time of recording was 19.3 (14.9) years, and 25 (57%) were male. The mean duration of recording was 51.2 minutes. Seizures were provoked by known triggers in 3 patients and were spontaneous otherwise. Twenty-five patients (57%) had 1 seizure, 6 (14%) had 2, and 13 (30%) had 3 or more. Magnetoencephalography single equivalent current dipole analysis was possible in 29 patients (66%), of whom 8 (28%) had no clear interictal discharges. Sublobar concordance between ictal and interictal dipoles was seen in 18 of 21 patients (86%). Three patients (7%) showed clear ictal MEG patterns without electroencephalography changes. Ictal MEG dipoles correlated with the lobe of onset in 7 of 8 patients (88%) who underwent intracranial electroencephalography evaluations. Reasons for failure to identify ictal dipoles included diffuse or poor dipolar ictal patterns, no MEG changes, and movement artifact. Resection of areas containing a minimum-norm estimate of a narrow band at onset, not single equivalent current dipole, was associated with sustained seizure freedom. Ictal MEG data can provide reliable localization, including in cases that are difficult to localize by other modalities. These findings support the use of extended-source localization for seizures recorded during MEG.

S08. Electrical stimulation of SEEG and epileptic network connectivity

Introduction It has been estimated the two thirds of the human cortex lies in the sulci. Deep structures such as limbic structures, medial frontal, insular and orbital frontal regions are relatively inaccessible to subdural electrodes. Our case demonstrates the utility of Stereotactic EEG in localizing seizure onset zone and mapping the function of both cortical and subcortical structures in a patient with drug resistant focal epilepsy. Methods The patient is a 58 year old right handed woman with a history of drug resistant epilepsy since age 18. Her seizures were predominantly nocturnal and arose out of sleep. She had multiple seizures per night. She reports out of body experience, anxiety feeling and chocking sensation followed by back and forth rocking movements. Seizures usually last 30–45 s with a quick recovery from her seizures. Her ictal scalp EEG was non localizing. Her interictal FDG PET scan was normal. Her subtracted ictal-interictal SPECT scan was also non localizing. Her epilepsy protocol 3T brain MRI showed a subtle area of white–grey matter blurring on the DIR pulse sequence which involved the left anterior cingulate gyrus. The patient underwent Stereotactic EEG implantation to confirm seizure localization and for functional mapping. Her ictal intracranial EEG showed an onset of high amplitude repetitive spiking intermixed with low amplitude fast activity in the electrodes located in the left anterior cingulate followed by spread of the same pattern to electrodes in the left insular cortex within less than 500 ms from the onset. Electrical stimulation mapping was performed. Biphasic 50 Hz stimulation starting at 1 mA intensity with increments of 0.5 mA was applied. Results The patient reported the typical sensation of her aura and early symptoms of her seizures during the stimulation of left anterior cingulate contacts during stimulation with 1–3 mA which was reproducible. She also reported the usual sensation she experiences a few seconds following the onset of her seizures during the stimulation of her left insular electrodes during stimulation with 3–5 mA that was also reproducible. No after- discharge was seen during these stimulation trials. The patient subsequently underwent an open lesionectomy of the anterior cingulate MRI abnormality. Pathology showed focal cortical dysplasia type IIB with dysmorphic neurons and balloon cells. The patient has remained seizure free since surgery. No neurological deficit was seen. Conclusion Electrical stimulation mapping of SEEG can be used to study the functionality of the cortex as well as the subcortical structures. In addition to localizing eloquent cortex, electrical stimulation of SEEG can support the localization of epileptic network and seizure onset zone.

SozRank: A new approach for localizing the epileptic seizure onset zone.

Epilepsy is one of the most common neurological disorders affecting about 1% of the world population. For patients with focal seizures that cannot be treated with antiepileptic drugs, the common treatment is a surgical procedure for removal of the seizure onset zone (SOZ). In this work we introduce an algorithm for automatic localization of the seizure onset zone (SOZ) in epileptic patients based on electrocorticography (ECoG) recordings. The proposed algorithm builds upon the hypothesis that the abnormal excessive (or synchronous) neuronal activity in the brain leading to seizures starts in the SOZ and then spreads to other areas in the brain. Thus, when this abnormal activity starts, signals recorded at electrodes close to the SOZ should have a relatively large causal influence on the rest of the recorded signals. The SOZ localization is executed in two steps. First, the algorithm represents the set of electrodes using a directed graph in which nodes correspond to recording electrodes and the edges’ weights quantify the pair-wise causal influence between the recorded signals. Then, the algorithm infers the SOZ from the estimated graph using a variant of the PageRank algorithm followed by a novel post-processing phase. Inference results for 19 patients show a close match between the SOZ inferred by the proposed approach and the SOZ estimated by expert neurologists (success rate of 17 out of 19).

S196 New approach to presurgical evaluation in epilepsy surgery

Introduction and aim In order to investigate and map the precise epileptic mechanism(network) and seizure onset zone during the planning of epilepsy surgery we use more and more complex imaging and electrophysiological studies. The analysis and evaluation of the ever growing datasets needs such a complex platform which can integrate the spatial information derived from the preoperative structural and functional imaging data with the electrophysiological data coming from the implanted intracranial electrodes. Materials and methods With the development of a custom made Matlab based software we can co-register preoperative structural and functional images with the postimplantation structural images containing the implanted electrodes. We created a special interface to combine imaging data and electrophysiological datato integrate the electrophysiological maps (interictal discharges, seizure onset zone, high frequency oscillations, cortical electrical stimulation, cortico-cortical evoked potentials) into the same space where the imaging data is presented. Results The coregistration of the different structural and functional (fMRI, DTI) 3D imaging data is already possible using open source softwares. The coregistered images are automatically transformed to the patients own MRI space and to standard space as well where using the postimplantation CT images we can determine the exact position of the electrodes.Using our custom made software now we can visualize the most important electrophysiological and stimulation data on the patient’s individual anatomy using the location of the electrodes as connection between different modalities. Conclusions The presented method allows the precise and automated coregistration of different imaging modalities and electrophysiological data. The integration of the complex electrophysiological data and maps into the same space allows more precise decision mapping in complex epilepsy surgical cases and allows more precise demarcation of the epileptogenic zone. The most difficult challenge to solve in today’s epilepsy surgery is to integrate all the information collected in one patient to create the best surgical plan and to resulting in the best epileptological outcome possible. Grant KTIA NAP_13-1-2013-0001.

A four-stage localization method for epileptic seizure onset zones

This paper presents a four-stage localization method for epileptic seizure onset zones (SOZs). This method combines the advantages of both the Shannon-entropy-based complex Morlet wavelet transform (SE-CMWT) and adaptive-genetic-algorithm-based matching pursuit (AGA-MP) methods to achieve satisfactory localization performance. The Hilbert transform method is used to identify events of interest. The SE-CMWT-based power spectral density method is applied to acquire channels of interest (CoIs). By using the Morlet wavelets as the dictionary elements, the AGA-MP method combined with Morlet wavelets is developed to detect high-frequency oscillations (HFOs) on CoIs with faster speed and higher accuracy than the AGA-MP method. And the characteristics of HFOs are used to localize SOZs based on the half-maximum method. A comparison shows that our method has higher sensitivity and specificity than four existing methods.

Fast, Accurate Localization of Epileptic Seizure Onset Zones Based on Detection of High-Frequency Oscillations Using Improved Wavelet Transform and Matching Pursuit Methods.

This letter describes the improvement of two methods of detecting high-frequency oscillations (HFOs) and their use to localize epileptic seizure onset zones (SOZs). The wavelet transform (WT) method was improved by combining the complex Morlet WT with Shannon entropy to enhance the temporal-frequency resolution during HFO detection. And the matching pursuit (MP) method was improved by combining it with an adaptive genetic algorithm to improve the speed and accuracy of the calculations for HFO detection. The HFOs detected by these two methods were used to localize SOZs in five patients. A comparison shows that the improved WT method provides high specificity and quick localization and that the improved MP method provides high sensitivity.

Network dynamics of the brain and influence of the epileptic seizure onset zone.

The human brain is a dynamic networked system. Patients with partial epileptic seizures have focal regions that periodically diverge from normal brain network dynamics during seizures. We studied the evolution of brain connectivity before, during, and after seizures with graph-theoretic techniques on continuous electrocorticographic (ECoG) recordings (5.4 ± 1.7 d per patient, mean ± SD) from 12 patients with temporal, occipital, or frontal lobe partial onset seizures. Each electrode was considered a node in a graph, and edges between pairs of nodes were weighted by their coherence within a frequency band. The leading eigenvector of the connectivity matrix, which captures network structure, was tracked over time and clustered to uncover a finite set of brain network states. Across patients, we found that (i) the network connectivity is structured and defines a finite set of brain states, (ii) seizures are characterized by a consistent sequence of states, (iii) a subset of nodes is isolated from the network at seizure onset and becomes more connected with the network toward seizure termination, and (iv) the isolated nodes may identify the seizure onset zone with high specificity and sensitivity. To localize a seizure, clinicians visually inspect seizures recorded from multiple intracranial electrode contacts, a time-consuming process that may not always result in definitive localization. We show that network metrics computed from all ECoG channels capture the dynamics of the seizure onset zone as it diverges from normal overall network structure. This suggests that a state space model can be used to help localize the seizure onset zone in ECoG recordings.

Localizing epileptic seizure onsets with Granger causality

Accurate localization of the epileptic seizure onset zones (SOZs) is crucial for successful surgery, which usually depends on the information obtained from intracranial electroencephalography (IEEG) recordings. The visual criteria and univariate methods of analyzing IEEG recordings have not always produced clarity on the SOZs for resection and ultimate seizure freedom for patients. Here, to contribute to improving the localization of the SOZs and to understanding the mechanism of seizure propagation over the brain, we applied spectral interdependency methods to IEEG time series recorded from patients during seizures. We found that the high-frequency (>80 Hz) Granger causality (GC) occurs before the onset of any visible ictal activity and causal relationships involve the recording electrodes where clinically identifiable seizures later develop. These results suggest that high-frequency oscillatory network activities precede and underlie epileptic seizures, and that GC spectral measures derived from IEEG can assist in precise delineation of seizure onset times and SOZs.