Detection of high-frequency oscillations by hybrid depth electrodes in standard clinical intracranial EEG recordings

High-frequency oscillations (HFOs) have been proposed as a novel marker for epileptogenic tissue, spurring tremendous research interest into the characterization of these transient events. A wealth of continuously recorded intracranial electroencephalographic (iEEG) data is currently available from patients undergoing invasive monitoring for the surgical treatment of epilepsy. In contrast to data recorded on research-customized recording systems, data from clinical acquisition systems remain an underutilized resource for HFO detection in most centers. The effective and reliable use of this clinically obtained data would be an important advance in the ongoing study of HFOs and their relationship to ictogenesis. The diagnostic utility of HFOs ultimately will be limited by the ability of clinicians to detect these brief, sporadic, and low amplitude events in an electrically noisy clinical environment. Indeed, one of the most significant factors limiting the use of such clinical recordings for research purposes is their low signal to noise ratio, especially in the higher frequency bands. In order to investigate the presence of HFOs in clinical data, we first obtained continuous intracranial recordings in a typical clinical environment using a commercially available, commonly utilized data acquisition system and "off the shelf" hybrid macro-/micro-depth electrodes. These data were then inspected for the presence of HFOs using semi-automated methods and expert manual review. With targeted removal of noise frequency content, HFOs were detected on both macro- and micro-contacts, and preferentially localized to seizure onset zones. HFOs detected by the offline, semi-automated method were also validated in the clinical viewer, demonstrating that (1) this clinical system allows for the visualization of HFOs and (2) with effective signal processing, clinical recordings can yield valuable information for offline analysis. © 2014 Kondylis, Wozny, Lipski, Popescu, DeStefino, Esmaeili, Raghu, Bagic and Richardson

Preictal variability of high‐frequency oscillation rates in refractory epilepsy

ObjectiveHigh‐frequency oscillations (HFOs) have shown promising utility in the spatial localization of the seizure onset zone for patients with focal refractory epilepsy. Comparatively few studies have addressed potential temporal variations in HFOs, or their role in the preictal period. Here, we introduce a novel evaluation of the instantaneous HFO rate through interictal and peri‐ictal epochs to assess their usefulness in identifying imminent seizure onset.MethodsUtilizing an automated HFO detector, we analyzed intracranial electroencephalographic data from 30 patients with refractory epilepsy undergoing long‐term presurgical evaluation. We evaluated HFO rates both as a 30‐minute average and as a continuous function of time and used nonparametric statistical methods to compare individual and population‐level differences in rate during peri‐ictal and interictal periods.ResultsMean HFO rate was significantly higher for all epochs in seizure onset zone channels versus other channels. Across the 30 patients of our cohort, we found no statistically significant differences in mean HFO rate during preictal and interictal epochs. For continuous HFO rates in seizure onset zone channels, however, we found significant population‐wide increases in preictal trends relative to interictal periods. Using a data‐driven analysis, we identified a subset of 11 patients in whom either preictal HFO rates or their continuous trends were significantly increased relative to those of interictal baseline and the rest of the population.SignificanceThese results corroborate existing findings that HFO rates within epileptic tissue are higher during interictal periods. We show this finding is also present in preictal, ictal, and postictal data, and identify a novel biomarker of preictal state: an upward trend in HFO rate leading into seizures in some patients. Overall, our findings provide preliminary evidence that HFOs can function as a temporal biomarker of seizure onset.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163961/1/epi16680.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163961/2/epi16680_am.pd

Detection of Pathological HFO Using Supervised Machine Learning and iEEG Data

Epilepsy is the second most common neurological disorder and it affects approxi mately 50 million people worldwide. One of the main characteristics of this disorder is the presence of recurrent seizures which tend to be controlled through medication. Nonetheless, 20% of the patients with this disorder are resistant to drug treatment meaning that they need to go through alternative procedures

Experimental treatment options in absence epilepsy

Contains fulltext : 182124.pdf (preprint version ) (Open Access)Background: The benign character of absence epilepsy compared to other genetic generalized epilepsy syndromes has often hampered the search for new treatment options. Absence epilepsy is most often treated with ethosuximide or valproic acid. However, both drugs are not always well tolerated or fail, and seizure freedom for a larger proportion of patients remains to be achieved. The availability of genuine animal models of epilepsy does allow to search for new treatment options not only for absence epilepsy perse but also for other genetic - previously called idiopathic - forms of epilepsy. The recent discovery of a highly excitable cortical zone in these models is considered as a new therapeutic target area. Methods: Here, we provide an overview regarding the search for new therapeutical options as has been investigated in the genetic rodent models (mainly WAG/Rij and GAERS) including drugs and whether antiepileptogenesis can be achieved, various types of electrical and optogenetical invasive stimulations, different types of non-invasive stimulation and finally whether absence seizures can be predicted and prevented. Results: Many factors determine either the cortical and or thalamic excitability or the interaction between cortex and thalamus and offer new possibilities for new anti-absence drugs, among others metabotropic glutamatergic positive and negative allosteric modulators. The inhibition of epileptogenesis by various drugs with its widespread consequences seems feasible, although its mechanisms remain obscure and seems different from the anti-absence action. Surgical intervention on the cortical zone initiating seizures, either with radiosurgery using synchrotron-generated microbeams, or ablation techniques might reduce spike-and-wave discharges in the rodent models. High frequency electrical subcortical or cortical stimulation might be a good way to abort ongoing spike-and-wave discharges. In addition, possibilities for prevention with real-time EEG analyses in combination with electrical stimulation could also be a way to fully control these seizures. Conclusion: Although it is obvious that some of these treatment possibilities will not be used for absence epilepsy and/or need to be further developed, all can be considered as proof of principle and provide clear directives for further developments

Temporal Characteristics of High-Frequency Oscillations as a Biomarker of Human Epilepsy

Epilepsy is a debilitating neurological disorder characterized by recurrent spontaneous seizures. While seizures themselves adversely affect physiological function for short time periods relative to normal brain states, their cumulative impact can significantly decrease patient quality of life in myriad ways. For many, anti-epileptic drugs are effective first-line therapies. One third of all patients do not respond to chemical intervention, however, and require invasive resective surgery to remove epileptic tissue. While this is still the most effective last-line treatment, many patients with ‘refractory’ epilepsy still experience seizures afterward, while some are not even surgical candidates. Thus, a significant portion of patients lack further recourse to manage their seizures – which additionally impacts their quality of life. High-frequency oscillations (HFOs) are a recently discovered electrical biomarker with significant clinical potential in refractory human epilepsy. As a spatial biomarker, HFOs occur more frequently in epileptic tissue, and surgical removal of areas with high HFO rates can result in improved outcomes. There is also limited preliminary evidence that HFOs change prior to seizures, though it is currently unknown if HFOs function as temporal biomarkers of epilepsy and imminent seizure onset. No such temporal biomarker has ever been identified, though if it were to exist, it could be exploited in online seizure prediction algorithms. If these algorithms were clinically implemented in implantable neuromodulatory devices, improvements to quality of life for refractory epilepsy patients might be possible. Thus, the overall aim of this work is to investigate HFOs as potential temporal biomarkers of seizures and epilepsy, and further to determine whether their time-varying properties can be exploited in seizure prediction. In the first study we explore population-level evidence for the existence of this temporal effect in a large clinical cohort with refractory epilepsy. Using sophisticated automated HFO detection and big-data processing techniques, a continuous measure of HFO rates was developed to explore gradual changes in HFO rates prior to seizures, which were analyzed in aggregate to assess their stereotypical response. These methods resulted in the identification of a subset of patients in whom HFOs from epileptic tissue gradually increased before seizures. In the second study, we use machine learning techniques to investigate temporal changes in HFO rates within individuals, and to assess their potential usefulness in patient-specific seizure prediction. Here, we identified a subset of patients whose predictive models sufficiently differentiated the preictal (before seizure) state better than random chance. In the third study, we extend our prediction framework to include the signal properties of HFOs. We explore their ability to improve the identification of preictal periods, and additionally translate their predictive models into a proof-of-concept seizure warning system. For some patients, positive results from this demonstration show that seizure prediction using HFOs could be possible. These studies overall provide convincing evidence that HFOs can change in measurable ways prior to seizure start. While this effect was not significant in some individuals, for many it enabled seizures to be predicted above random chance. Due to data limitations in overall recording duration and number of seizures captured, these findings require further validation with much larger high-density intracranial EEG datasets. Still, they provide a preliminary framework for the eventual use of HFOs in patient-specific seizure prediction with the potential to improve the lives of those with refractory epilepsy.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168079/1/jaredmsc_1.pd

The organization of functional neurocognitive networks in focal epilepsy correlates with domain-specific cognitive performance

Understanding and diagnosing cognitive impairment in epilepsy remains a prominent challenge. New etiological models suggest that cognitive difficulties might not be directly linked to seizure activity, but are rather a manifestation of a broader brain pathology. Consequently, treating seizures is not sufficient to alleviate cognitive symptoms, highlighting the need for novel diagnostic tools. Here, we investigated whether the organization of three intrinsic, resting-state functional connectivity networks was correlated with domain-specific cognitive test performance. Using individualized EEG source reconstruction and graph theory, we examined the association between network small worldness and cognitive test performance in 23 patients with focal epilepsy and 17 healthy controls, who underwent a series of standardized pencil-and-paper and digital cognitive tests. We observed that the specific networks robustly correlated with test performance in distinct cognitive domains. Specifically, correlations were evident between the default mode network and memory in patients, the central-executive network and executive functioning in controls, and the salience network and social cognition in both groups. Interestingly, the correlations were evident in both groups, but in different domains, suggesting an alteration in these functional neurocognitive networks in focal epilepsy. The present findings highlight the potential clinical relevance of functional brain network dysfunction in cognitive impairment.Peer reviewe