NeurologyNeurosciencePeer-reviewed journal article
Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG
Zhuying CheniD, David Bruce GraydeniD, Anthony Neville BurkittiD, Udaya K. SeneviratneiD, Wendyl Jude D'SouzaiD, Chris R. FrenchiD, Philippa J. KarolyiD, Katrina L. DelliD, Kent W. Leyde, Mark James CookiD, Matias I. MaturanaiD
Abstract
ObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings. MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 28
- FWCI
- 2.74
- Field percentile
- top 9%
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