High frequency oscillation network dynamics predict outcome in non-palliative epilepsy surgery

Author:

Lin Jack1,Smith Garnett C2,Gliske Stephen V3ORCID,Zochowski Michal14,Shedden Kerby5,Stacey William C678ORCID

Affiliation:

1. Neuroscience Graduate Program, University of Michigan , Ann Arbor, MI 48109 , USA

2. Department of Pediatrics, University of Michigan , Ann Arbor, MI 48109 , USA

3. Department of Neurosurgery, University of Nebraska Medical Center , Omaha, NE 68198 , USA

4. Department of Physics and Biophysics, University of Michigan , Ann Arbor, MI 48109 , USA

5. Department of Statistics and Biostatistics, University of Michigan , Ann Arbor, MI 48109 , USA

6. Department of Neurology, University of Michigan , Ann Arbor, MI 48109 , USA

7. Department of Biomedical Engineering, BioInterfaces Institute, University of Michigan , Ann Arbor, MI 48109 , USA

8. Division of Neurology, Ann Arbor VA Health System , Ann Arbor, MI 48109 , USA

Abstract

Abstract 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.

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

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