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Interictal high-frequency oscillations (HFOs) as predictors of high frequency and conventional seizure onset zones.

ABSTRACT: We investigated the relationship between the interictal high-frequency oscillations (HFOs) and the seizure onset zones (SOZs) defined by the ictal HFOs or conventional frequency activity (CFA), and evaluated the usefulness of the interictal HFOs as spatial markers of the SOZs. We analysed seizures showing discrete HFOs at onset on intracranial EEGs acquired at ?1000-Hz sampling rate in a training cohort of 10 patients with temporal and extratemporal epilepsy. We classified each ictal channel as: HFO+ (HFOs at onset with subsequent evolution), HFO- (HFOs at onset without evolution), CFA (1.6-70-Hz activity at onset with evolution), or non-ictal. We defined the SOZs as: hSOZ (HFO+ channels only), hfo+&-SOZ (HFO+ and HFO- channels), and cSOZ (CFA channels). Using automated methods, we detected the interictal HFOs and extracted five features: density, connectivity, peak frequency, log power, and amplitude. We created logistic regression models using these features, and tested their performance in a separate replication cohort of three patients. The models containing the five interictal HFO features reliably differentiated the channels located inside the SOZ from those outside in the training cohort (p<0.001), reaching the highest accuracy for the classification of hSOZ. Log power and connectivity had the highest odds ratios, both being higher for the channels inside the SOZ compared with those outside the SOZ. In the replication cohort of novel patients, the same models differentiated the HFO+ from HFO- channels, and predicted the extents of the hSOZ and hfo+&-SOZ (F1 measure >0.5) but not the cSOZ. Our study shows that the interictal HFOs are useful in defining the spatial extent of the SOZ, and predicting whether or not a given channel in a novel patient would be involved in the seizure. The findings support the existence of an abnormal network of tightly-linked ictal and interictal HFOs in patients with intractable epilepsy.

SUBMITTER: Modur P

PROVIDER: S-EPMC5674993 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Interictal high-frequency oscillations (HFOs) as predictors of high frequency and conventional seizure onset zones.

Modur Pradeep P Miocinovic Svjetlana S

Epileptic disorders : international epilepsy journal with videotape 20151201 4

We investigated the relationship between the interictal high-frequency oscillations (HFOs) and the seizure onset zones (SOZs) defined by the ictal HFOs or conventional frequency activity (CFA), and evaluated the usefulness of the interictal HFOs as spatial markers of the SOZs. We analysed seizures showing discrete HFOs at onset on intracranial EEGs acquired at ≥1000-Hz sampling rate in a training cohort of 10 patients with temporal and extratemporal epilepsy. We classified each ictal channel as: ...[more]

PMID: 26620382

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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.

Dataset Information

Interictal high-frequency oscillations (HFOs) as predictors of high frequency and conventional seizure onset zones.

Publications

Interictal high-frequency oscillations (HFOs) as predictors of high frequency and conventional seizure onset zones.

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