Data mining neocortical high-frequency oscillations in epilepsy and controls

Abstract

Transient high-frequency (100-500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100-250 Hz) and fast ripple (250-500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100-500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.

Electrophysiological Biomarkers of Epilepsy

“…Furthermore, scalp and intracranial EEG recordings contain physiological and epileptiform sharp transients and often artifacts (electrode noise, eye-, and muscle-related activity) that contain high frequency power, and digital filtering of these events could be incorrectly interpreted as HFOs [72,73]. Studies have implemented supervised and unsupervised computer-automated algorithms, some that incorporate aspects other than spectral power to improve specificity [63,[74][75][76][77] and one for specific types of HFOs [78]. While all detection strategies thus far have demonstrated strengths and weaknesses, a feasible and accurate approach will likely include a combination of computer-automated methods to process large data sets and manual inspection of a randomly selected sample of HFOs by experienced investigators.…”

Accumulated source imaging of brain activity with both low and high-frequency neuromagnetic signals

“…First, the size of high sampling rate data can be over 12 terabytes (TB) (Blanco et al, 2011). The size of high sampling rate data can cause a substantial amount of data, posing a challenge for data transfer, storage, archiving, sharing and analysis (Van Essen et al, 2012; Worrell et al, 2012; Zafeiriou and Vargiami, 2012; Zijlmans et al, 2012b).…”

Identification of seizure onset zone and preictal state based on characteristics of high frequency oscillations

“…Two of the classes were consistent with ripple and fast ripple oscillations, and a third consisted of mixed-frequency events. Blanco et al (2011) present an analysis of these classified groups of events with respect to seizure onset zone channels and other regions. The same dataset and methodology was used (Pearce et al, 2013) to investigate temporal changes of different types of HFO, their rate and proportions during interictal, preictal, ictal and postictal periods.…”