The use of high frequency oscillations to guide neocortical resections in children with medically-intractable epilepsy: How do we ethically apply surgical innovations to patient care?

Synchronization of neural oscillations is thought to integrate distributed neural populations into functional cell assemblies. Epilepsy is widely regarded as a disorder of neural synchrony. Knowledge is scant, however, regarding whether ictal changes in synchrony involving epileptogenic cortex are expressed similarly across various frequency ranges. Cortical regions involved in epileptic networks also exhibit pathological high-frequency oscillations (pHFOs, >80 Hz), which are increasingly utilized as biomarkers of epileptogenic tissue. It is uncertain how pHFO amplitudes are related to epileptic network connectivity. By calculating phase-locking values among intracranial electrodes implanted in children with intractable epilepsy, we constructed ictal connectivity networks and performed graph theoretical analysis to characterize their network properties at distinct frequency bands. Ictal data from 17 children were analyzed with a hierarchical mixed-effects model adjusting for patient-level covariates. Epileptogenic cortex was defined in two ways: 1) a hypothesis-driven method using the visually defined seizure-onset zone and 2) a data-agnostic method using the high-frequency amplitude of each electrode. Epileptogenic cortex exhibited a logarithmic decrease in interregional functional connectivity at high frequencies (>30 Hz) during seizure initiation and propagation but not at termination. At slower frequencies, conversely, epileptogenic cortex expressed a relative increase in functional connectivity. Our findings suggest that pHFOs reflect epileptogenic network interactions, yielding theoretical support for their utility in the presurgical evaluation of intractable epilepsy. The view that abnormal network synchronization plays a critical role in ictogenesis and seizure dynamics is supported by the observation that functional isolation of epileptogenic cortex at high frequencies is absent at seizure termination.

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Automated Detector of High Frequency Oscillations in Epilepsy Based on Maximum Distributed Peak Points

International Journal of Neural Systems ◽

10.1142/s0129065717500290 ◽

2017 ◽

Vol 28 (01) ◽

pp. 1750029 ◽

Cited By ~ 12

Author(s):

Guo-Ping Ren ◽

Jia-Qing Yan ◽

Zhi-Xin Yu ◽

Dan Wang ◽

Xiao-Nan Li ◽

...

Keyword(s):

Sensitivity And Specificity ◽

High Frequency ◽

Intractable Epilepsy ◽

Rank Correlation ◽

Spearman’S Rank Correlation ◽

High Frequency Oscillations ◽

Spearman’S Rank Correlation Coefficient ◽

Peak Points ◽

Higher Sensitivity ◽

Novel Algorithm

High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80–200[Formula: see text]Hz), fast ripples (FRs, 200–500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman’s rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.

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Epileptogenic and ictogenic high frequency oscillations in intractable epilepsy

Neuroscience Research ◽

10.1016/j.neures.2009.09.1693 ◽

2009 ◽

Vol 65 ◽

pp. S34

Author(s):

Hiroshi Otsubo

Keyword(s):

High Frequency ◽

Intractable Epilepsy ◽

High Frequency Oscillations

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Identification of Epileptogenic and Non-epileptogenic High-Frequency Oscillations Using a Multi-Feature Convolutional Neural Network Model

Frontiers in Neurology ◽

10.3389/fneur.2021.640526 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guoping Ren ◽

Yueqian Sun ◽

Dan Wang ◽

Jiechuan Ren ◽

Jindong Dai ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

High Frequency ◽

Slow Wave Sleep ◽

Intractable Epilepsy ◽

Wavelet Spectrum ◽

Epileptogenic Zone ◽

High Frequency Oscillations ◽

Scale Transformations ◽

Better Than

Accurately identifying epileptogenic zone (EZ) using high-frequency oscillations (HFOs) is a challenge that must be mastered to transfer HFOs into clinical use. We analyzed the ability of a convolutional neural network (CNN) model to distinguish EZ and non-EZ HFOs. Nineteen medically intractable epilepsy patients with good surgical outcomes 2 years after surgery were studied. Five-minute interictal intracranial electroencephalogram epochs of slow-wave sleep were selected randomly. Then 5 s segments of ripples (80–200 Hz) and fast ripples (FRs, 200–500 Hz) were detected automatically. The EZs and non-EZs were identified using the surgery resection range. We innovatively converted all epochs into four types of images using two scales: original waveforms, filtered waveforms, wavelet spectrum images, and smoothed pseudo Wigner–Ville distribution (SPWVD) spectrum images. Two scales were fixed and fitted scales. We then used a CNN model to classify the HFOs into EZ and non-EZ categories. As a result, 7,000 epochs of ripples and 2,000 epochs of FRs were randomly selected from the EZ and non-EZ data for analysis. Our CNN model can distinguish EZ and non-EZ HFOs successfully. Except for original ripple waveforms, the results from CNN models that are trained using fixed-scale images are significantly better than those from models trained using fitted-scale images (p < 0.05). Of the four fixed-scale transformations, the CNN based on the adjusted SPWVD (ASPWVD) produced the best accuracies (80.89 ± 1.43% and 77.85 ± 1.61% for ripples and FRs, respectively, p < 0.05). The CNN using ASPWVD transformation images is an effective deep learning method that can be used to classify EZ and non-EZ HFOs.

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