4.6 Article

Physiological and pathological high frequency oscillations in focal epilepsy

Journal

ANNALS OF CLINICAL AND TRANSLATIONAL NEUROLOGY
Volume 5, Issue 9, Pages 1062-1076

Publisher

WILEY
DOI: 10.1002/acn3.618

Keywords

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Funding

  1. National Institutes of Health [R01-NS063039, R01-NS078136]
  2. Mayo Clinic Discovery Translation Grant from the National Program of Sustainability II (MEYS CR) [LQ1605]
  3. Ministry of Youth and Sports of the Czech Republic [LH15047]
  4. MEYS CR [LO1212]

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ObjectiveThis study investigates high-frequency oscillations (HFOs; 65-600Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFOs (pathHFO) from physiological HFOs (physHFO). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. MethodsWe recorded HFOs using intracranial EEG (iEEG) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative physHFOs were induced by cognitive or motor tasks. HFOs were identified using validated detectors. A support vector machine (SVM) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. ResultsThere was significant overlap in the amplitude, frequency, and duration distributions for spontaneous physHFO, task induced physHFO, and pathHFO, but the amplitudes of the pathHFO were higher (P<0.0001). High gamma pathHFO had the strongest association with seizure onset zone (SOZ), and were elevated on SOZ electrodes in 70% of epilepsy patients (P<0.0001). Failure to resect tissue generating high gamma pathHFO was associated with poor outcomes (P<0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. InterpretationA broader range of interictal pathHFO (65-600Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFOs into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

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