Neurosurgical intervention is the best available treatment for selected patients with drug resistant epilepsy. For these patients, surgical planning requires biomarkers that delineate the epileptogenic zone, the brain area that is indispensable for the generation of seizures. Interictal spikes recorded with electrophysiological techniques are considered key biomarkers of epilepsy. Yet, they lack specificity mostly because they propagate across brain areas forming networks. Understanding the relationship between interictal spike propagation and functional connections among the involved brain areas may help develop novel biomarkers that can delineate the epileptogenic zone with high precision. Here, we reveal the relationship between spike propagation and effective connectivity among onset and areas of spread and assess the prognostic value of resecting these areas. We analyzed intracranial electroencephalography data from 43 children with drug resistant epilepsy who underwent invasive monitoring for neurosurgical planning. Using electric source imaging, we mapped spike propagation in the source domain and identified three zones: onset, early-spread, and late-spread. For each zone, we calculated the overlap and distance from surgical resection. We then estimated a virtual sensor for each zone and, the direction of information flow among them via Granger Causality. Finally, we compared the prognostic value of resecting these zones, the clinically-defined seizure onset zone, and the spike-onset on intracranial electroencephalography channels, by estimating their overlap with resection. We observed a spike propagation in source space for 37 patients with a median duration of 95 ms (IQR: 34-206), a spatial displacement of 14 cm (7.5-22), and a velocity of 0.5 m/s (0.3-0.8). In patients with good surgical outcome (25 patients, Engel I), the onset had higher overlap with resection [96% (40-100%)] than early-spread [86% (34-100%), P = 0.01] and late-spread [59% (12-100%), P = 0.002], and it was also closer to resection than late-spread [5 vs. 9 mm, P = 0.007]. We found an information flow from onset to early-spread in 66% of patients with good outcomes, and from early-spread to onset in 50% of patients with poor outcome. Finally, resection of spike-onset, but not area of spike-spread or the seizure onset zone, predicted outcome with positive predictive value of 79% and negative predictive value of 56% (P = 0.04). Spatiotemporal mapping of spike propagation reveals information flow from onset to areas of spread in epilepsy brain. Surgical resection of the spike-onset disrupts the epileptogenic network and may render patients with drug resistant epilepsy seizure-free without having to wait for a seizure to occur during intracranial monitoring.

/pdf/spike-propagation-mapping-reveals-effective-connectivity-and-30zys806.pdf

Spike propagation mapping reveals effective connectivity and predicts surgical outcome in epilepsy.

EEG has been the primary diagnostic tool in clinical epilepsy for nearly a century. Its review is performed using qualitative clinical methods that have changed little over time. However, the intersection of higher resolution digital EEG and analytical tools developed in the last decade invites a re-exploration of relevant methodology. In addition to the established spatial and temporal markers of spikes and high frequency oscillations, novel markers involving advanced post-processing and active probing of the interictal EEG are gaining ground. This review provides an overview of the EEG-based passive and active markers of cortical excitability in epilepsy and of the techniques developed to facilitate their identification. Several different emerging tools are discussed in the context of specific EEG applications and the barriers we must overcome to translate these tools into clinical practice.

Passive and active markers of cortical excitability in epilepsy.

Interictal high-frequency oscillations (HFO) detected in electroencephalography recordings have been proposed as biomarkers of epileptogenesis, seizure propensity, disease severity, and treatment response. Automatic HFO detectors typically analyze the data offline using complex time-consuming algorithms, which limits their clinical application. Neuromorphic circuits offer the possibility of building compact and low-power processing systems that can analyze data on-line and in real time. In this review, we describe a fully automated detection pipeline for HFO that uses, for the first time, spiking neural networks and neuromorphic technology. We demonstrated that our HFO detection pipeline can be applied to recordings from different modalities (intracranial electroencephalography, electrocorticography, and scalp electroencephalography) and validated its operation in a custom-designed neuromorphic processor. Our HFO detection approach resulted in high accuracy and specificity in the prediction of seizure outcome in patients implanted with intracranial electroencephalography and electrocorticography, and in the prediction of epilepsy severity in patients recorded with scalp electroencephalography. Our research provides a further step toward the real-time detection of HFO using compact and low-power neuromorphic devices. The real-time detection of HFO in the operation room may improve the seizure outcome of epilepsy surgery, while the use of our neuromorphic processor for non-invasive therapy monitoring might allow for more effective medication strategies to achieve seizure control. Therefore, this work has the potential to improve the quality of life in patients with epilepsy by improving epilepsy diagnostics and treatment.

https://www.frontiersin.org/articles/10.3389/fnins.2022.861480/pdf

Automatic Detection of High-Frequency Oscillations With Neuromorphic Spiking Neural Networks

Abstract The cortex generates high-frequency oscillations (HFO) nested in slow waves during sleep, and these signals are especially elevated in the seizure onset zone. Thus, HFO occurrence rate and Modulation Index (MI), which quantifies the strength of coupling between HFO amplitude and slow-wave phase, represent promising epilepsy biomarkers. However, their diagnostic utility may be suboptimal because the endogenous developmental distributions are unknown. To improve age-appropriate localization of the epileptogenic zone, we hence constructed normative brain atlases demonstrating the developmental changes in MI and HFO rate. Our study investigated extraoperative, intracranial EEG data from 114 patients with focal epilepsy (ages 1.0 to 41.5 years) who achieved International League Against Epilepsy class I outcomes following resective surgery. We analyzed 20-minute slow-wave sleep epochs at 8,251 nonepileptic electrode sites (those outside the seizure onset zone, interictal spike zone, or MRI-visible lesions). Each electrode was transposed onto a standard brain template, and we then calculated its MI and HFO occurrence rate using four different detector toolboxes. Linear and nonlinear regression models determined the developmental slope of MI and HFO rate at each cortical mesh point. Mixed model analysis established the significance of MI and HFO rate developmental changes in each region of interest, while accounting for the independent effects of patient and epilepsy profiles. Finally, we created a dynamic tractography movie visualizing white matter pathways connecting cortical regions showing developmental co-growth in MI. We found that the occipital lobe exhibited enhanced MI compared to other lobes in both children and adults. Increased age, square root of age, and log base 10 of age were independently associated with elevated MI exclusively in the occipital lobe. The cortical regions showing developmental co-growth in MI were connected via the vertical occipital fasciculi and posterior callosal fibers. In contrast, we did not observe any significant association between age measures and HFO rate in the occipital lobe, but rather noted an inverse relationship between age and HFO rate in the temporal, frontal, and parietal lobes. Our study suggests that phase-amplitude coupling between physiologic HFO and delta waves, as rated by MI, is strengthened during development, in the occipital lobe particularly during toddlerhood and preschool. Given that occipital delta-nested HFO are believed to support visual memory consolidation, our observations imply that process may be significantly strengthened during early childhood. The data is publicly available to provide investigators with a crucial reference for MI and HFO-based presurgical evaluation of the epileptogenic zone. 

/pdf/developmental-atlas-of-phase-amplitude-coupling-between-1jp1tzgi.pdf

Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves

Abstract In drug-resistant focal epilepsy, interictal high-frequency oscillations (HFOs) recorded from intracranial EEG (iEEG) may provide clinical information for delineating epileptogenic brain tissue. The iEEG electrode contacts that contain HFO are hypothesized to delineate the epileptogenic zone; their resection should then lead to postsurgical seizure freedom. We test whether our prospective definition of clinically relevant HFO is in agreement with postsurgical seizure outcome. The algorithm is fully automated and is equally applied to all data sets. The aim is to assess the reliability of the proposed detector and analysis approach. We use an automated data-independent prospective definition of clinically relevant HFO that has been validated in data from two independent epilepsy centres. In this study, we combine retrospectively collected data sets from nine independent epilepsy centres. The analysis is blinded to clinical outcome. We use iEEG recordings during NREM sleep with a minimum of 12 epochs of 5 min of NREM sleep. We automatically detect HFO in the ripple (80–250 Hz) and in the fast ripple (250–500 Hz) band. There is no manual rejection of events in this fully automated algorithm. The type of HFO that we consider clinically relevant is defined as the simultaneous occurrence of a fast ripple and a ripple. We calculate the temporal consistency of each patient’s HFO rates over several data epochs within and between nights. Patients with temporal consistency <50% are excluded from further analysis. We determine whether all electrode contacts with high HFO rate are included in the resection volume and whether seizure freedom (ILAE 1) was achieved at ≥2 years follow-up. Applying a previously validated algorithm to a large cohort from several independent epilepsy centres may advance the clinical relevance and the generalizability of HFO analysis as essential next step for use of HFO in clinical practice.

https://academic.oup.com/braincomms/article-pdf/4/3/fcac151/44253859/fcac151.pdf

Lucas Imbach

Papers

Protocol for multicentre comparison of interictal high-frequency oscillations as a predictor of seizure freedom