Dataset Information

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

ABSTRACT: High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-frequency oscillations occur in a repetitive fashion with a similar waveform morphology that specifically indicates seizure onset zones. We investigated the waveform patterns of automatically detected high-frequency oscillations in 13 epilepsy patients and five control subjects, with an average of 73 subdural and intracerebral electrodes recorded per patient. The repetitive oscillatory waveforms were identified by using a pipeline of unsupervised machine learning techniques and were then correlated with independently clinician-defined seizure onset zones. Consistently in all patients, the stereotypical high-frequency oscillations with the highest degree of waveform similarity were localized within the seizure onset zones only, whereas the channels generating high-frequency oscillations embedded in random waveforms were found in the functional regions independent from the epileptogenic locations. The repetitive waveform pattern was more evident in fast ripples compared to ripples, suggesting a potential association between waveform repetition and the underlying pathological network. Our findings provided a new tool for the interpretation of pathological high-frequency oscillations that can be efficiently applied to distinguish seizure onset zones from functionally important sites, which is a critical step towards the translation of these signature events into valid clinical biomarkers.awx374media15721572971001.

SUBMITTER: Liu S

PROVIDER: S-EPMC6715109 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

Liu Su S Gurses Candan C Sha Zhiyi Z Quach Michael M MM Sencer Altay A Bebek Nerses N Curry Daniel J DJ Prabhu Sujit S Tummala Sudhakar S Henry Thomas R TR Ince Nuri F NF

Brain : a journal of neurology 20180301 3

High-frequency oscillations in local field potentials recorded with intracranial EEG are putative biomarkers of seizure onset zones in epileptic brain. However, localized 80-500 Hz oscillations can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, physiological high-frequency oscillations are indistinguishable from pathological ones, which limit their application in epilepsy presurgical planning. We hypothesized that pathological high-fre ...[more]

PMID: 29394328

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Project description:Successful surgical treatment of drug-resistant epilepsy traditionally relies on the identification of seizure onset zones (SOZs). Connectome-based analyses of electrographic data from stereo electroencephalography (SEEG) may empower improved detection of SOZs. Specifically, connectome-based analyses based on the interictal suppression hypothesis posit that when the patient is not having a seizure, SOZs are inhibited by non-SOZs through high inward connectivity and low outward connectivity. However, it is not clear whether there are other motifs that can better identify potential SOZs. Thus, we sought to use unsupervised machine learning to identify network motifs that elucidate SOZs and investigate if there is another motif that outperforms the ISH. Resting-state SEEG data from 81 patients with drug-resistant epilepsy undergoing a pre-surgical evaluation at Vanderbilt University Medical Center were collected. Directed connectivity matrices were computed using the alpha band (8-13 Hz). Principal component analysis (PCA) was performed on each patient's connectivity matrix. Each patient's components were analysed qualitatively to identify common patterns across patients. A quantitative definition was then used to identify the component that most closely matched the observed pattern in each patient. A motif characteristic of the interictal suppression hypothesis (high-inward and low-outward connectivity) was present in all individuals and found to be the most robust motif for identification of SOZs in 64/81 (79%) patients. This principal component demonstrated significant differences in SOZs compared to non-SOZs. While other motifs for identifying SOZs were present in other patients, they differed for each patient, suggesting that seizure networks are patient specific, but the ISH is present in nearly all networks. We discovered that a potentially suppressive motif based on the interictal suppression hypothesis was present in all patients, and it was the most robust motif for SOZs in 79% of patients. Each patient had additional motifs that further characterized SOZs, but these motifs were not common across all patients. This work has the potential to augment clinical identification of SOZs to improve epilepsy treatment.

Project description:OBJECTIVE:An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. APPROACH:Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. MAIN RESULTS:Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90?min and 2?h are sufficient to localize SOZs with accuracies that may prove clinically relevant. SIGNIFICANCE:The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.

Project description:PurposeTo investigate the characteristics of intracranial ictal high-frequency oscillations (HFOs).MethodsAmong neocortical epilepsy patients who underwent intracranial monitoring and surgery, we studied patients with well-defined, unifocal seizure onsets characterized by discrete HFOs (≥70 Hz). Patients with multifocal or bilateral independent seizure onsets, electroencephalography (EEG) acquired at <1,000 Hz sampling rate, and nonresective surgery were excluded. Based on a prospectively defined protocol, we defined the seizure-onset zone (SOZ) presurgically to include only those channels with HFOs that showed subsequent sustained evolution (HFOs+ channels) but not the channels that lacked evolution (HFOs- channels). We then resected the SOZ as defined above, 1 cm of the surrounding cortex, and immediate spread area, modified by the presence of eloquent cortex in the vicinity. For purposes of this study, we also defined the SOZ based on the conventional frequency activity (CFA, <70 Hz) at seizure onset, although that information was not considered for preoperative determination of the surgical boundary. We investigated the temporal and spatial characteristics of the ictal HFOs post hoc by visual and spectral methods, and also compared them to the seizure onset defined by the CFA.Key findingsOf 14 consecutive neocortical epilepsy patients, six patients met the inclusion criteria. Magnetic resonance imaging (MRI) was normal or showed heterotopia. All had subdural electrodes, with additional intracerebral depth electrodes in some. Electrode coverage was extensive (median 94 channels), including limited contralateral coverage. Seizure onsets were lobar or multilobar. Resections were performed per protocol, except in two patients where complete resection of the SOZ could not be done due to overlap with speech area. Histology was abnormal in all patients. Postoperative outcome was class I/II (n = 5, 83%) or class III over a mean follow-up of 27 months. Post hoc analysis of 15 representative seizures showed that the ictal HFOs were widespread at seizure onset but evolved subsequently with different characteristics. In contrast to HFOs-, the HFOs+ were significantly higher in peak frequency (97.1 vs. 89.1 Hz, p = 0.001), more robust (nearly twofold higher peak power, p < 0.0001), and spatially restricted [mean 12.2 vs. 22.4 channels; odds ratio (OR) 0.51, 95% confidence interval (CI) 0.42-0.62; p < 0.0001]. The seizure onset defined by HFOs+ was earlier (by an average of 0.41 s), and occurred in a significantly different and smaller distribution (OR 0.27, 95% CI 0.21-0.34, p < 0.0001), than the seizure onset defined by the CFA. As intended, the HFOs+ channels were 10 times more likely to have been resected than the HFOs- channels (OR 9.7, 95% CI 5-17, p < 0.0001).SignificanceOur study demonstrates the widespread occurrence of ictal HFOs at seizure onset, outlines a practical method to localize the SOZ based on their restricted pattern of evolution, and highlights the differences between the SOZs defined by HFOs and CFA. We show that smaller resections, restricted mainly to the HFOs channels with evolution, can lead to favorable seizure outcome. Our findings support the notion of widespread epileptic networks underlying neocortical epilepsy.

Dataset Information

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

Publications

Stereotyped high-frequency oscillations discriminate seizure onset zones and critical functional cortex in focal epilepsy.

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