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One of the most important goals in epilepsy research is to identify better biomarkers, both of acute changes and of disease progression. Using neural engineering techniques, a major goal of the lab is to characterize new biomarkers that indicate the timing and location of seizures, especially those seen in EEG signals. The most promising biomarker is currently High Frequency Oscillations (HFOs). HFOs have a clear relationship with epileptic networks, but it is still unclear how to implement them in a clinically-meaningful and safe fashion. The primary goal of this project is to develop and translate tools that will allow clinicians to utilize HFOs and improve clinical care.
Project 1: HFO detection
The most important limitation of HFO research is identifying them. Most clinical studies have relied upon manual review, which takes many hours and is not a viable clinical solution. Automated algorithms can be useful, but are very prone to identifying artifacts.
We developed an algorithm that removes artifacts and greatly improves precision. This algorithm runs in real time and has already identified over 5 million HFOs on 25 patients.
Project 2: Seizure onset zone detection
After identifying HFOs, we developed an automated, prospective method of identifying regions of brain tissue with anomalously high HFO rates. This algorithm correctly identifies electrodes within the seizure onset zone in many patients, but importantly also avoids making predictions in patients for whom the rate is unreliable.
Project 4: HFO features as an epilepsy biomarker
In addition to rate, HFOs have many other features that may be useful as an epilepsy biomarker. We have analyzed massive datasets of HFOs to identify relationships with seizure onset zone and time until the next seizure. This analysis is now moving forward with the algorithms above, which allow much more robust analysis.
In addition, we are analyzing HFO features to help distinguish between HFOs that are produced by normal brain rhythms versus epileptic tissue. This work involves using and developing cutting edge Big Data tools.
A unique aspect of our HFO work is the ability to analyze many hours-the entire hospital recording-for HFOs. This has provided us unique insights into the temporal and spatial characteristics of HFOs. Using this tool we found that the location of maximum HFOs can vary over time, much like different areas of the brain become active with seizures over time. We also found that HFOs change prior to seizure onset.
Project 5: High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery
High frequency oscillations are a promising biomarker of outcome in intractable epilepsy. Prior high frequency oscillation work focused on counting high frequency oscillations on individual channels, and it is still unclear how to translate those results into clinical care. We show that high frequency oscillations arise as network discharges that have valuable properties as predictive biomarkers. Here, we develop a tool to predict patient outcome before surgical resection is performed, based on only prospective information. In addition to determining high frequency oscillation rate on every channel, we performed a correlational analysis to evaluate the functional connectivity of high frequency oscillations in 28 patients with intracranial electrodes. We found that high frequency oscillations were often not solitary events on a single channel, but part of a local network discharge. Eigenvector and outcloseness centrality were used to rank channel importance within the connectivity network, then used to compare patient outcome by comparison with the seizure onset zone or a proportion within the proposed resected channels (critical resection percentage). Combining the knowledge of each patient's seizure onset zone resection plan along with our computed high frequency oscillation network centralities and high frequency oscillation rate, we develop a Naïve Bayes model that predicts outcome (positive predictive value: 100%) better than predicting based upon fully resecting the seizure onset zone (positive predictive value: 71%). Surgical margins had a large effect on outcomes: non-palliative patients in whom most of the seizure onset zone was resected ('definitive surgery', ≥ 80% resected) had predictable outcomes, whereas palliative surgeries (<80% resected) were not predictable. These results suggest that the addition of network properties of high frequency oscillations is more accurate in predicting patient outcome than seizure onset zone alone in patients with most of the seizure onset zone removed and offer great promise for informing clinical decisions in surgery for refractory epilepsy.
Project 6: Variability in the location of high frequency oscillations during prolonged intracranial EEG recordings
The rate of interictal high frequency oscillations (HFOs) is a promising biomarker of the seizure onset zone, though little is known about its consistency over hours to days. Here we test whether the highest HFO-rate channels are consistent across different 10-min segments of EEG during sleep. An automated HFO detector and blind source separation are applied to nearly 3000 total hours of data from 121 subjects, including 12 control subjects without epilepsy. Although interictal HFOs are significantly correlated with the seizure onset zone, the precise localization is consistent in only 22% of patients. The remaining patients either have one intermittent source (16%), different sources varying over time (45%), or insufficient HFOs (17%). Multiple HFO networks are found in patients with both one and multiple seizure foci. These results indicate that robust HFO interpretation requires prolonged analysis in context with other clinical data, rather than isolated review of short data segments.
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