Improving recorded volume in mesial temporal lobe by optimizing stereotactic intracranial electrode implantation planning.

PURPOSE: Intracranial electrodes are sometimes implanted in patients with refractory epilepsy to identify epileptic foci and propagation. Maximal recording of EEG activity from regions suspected of seizure generation is paramount. However, the location of individual contacts cannot be considered with current manual planning approaches. We propose and validate a procedure for optimizing intracranial electrode implantation planning that maximizes the recording volume, while constraining trajectories to safe paths. METHODS: Retrospective data from 20 patients with epilepsy that had electrodes implanted in the mesial temporal lobes were studied. Clinical imaging data (CT/A and T1w MRI) were automatically segmented to obtain targets and structures to avoid. These data were used as input to the optimization procedure. Each electrode was modeled to assess risk, while individual contacts were modeled to estimate their recording capability. Ordered lists of trajectories per target were obtained. Global optimization generated the best set of electrodes. The procedure was integrated into a neuronavigation system. RESULTS: Trajectories planned automatically covered statistically significant larger target volumes than manual plans [Formula: see text]. Median volume coverage was [Formula: see text] for automatic plans versus [Formula: see text] for manual plans. Furthermore, automatic plans remained at statistically significant safer distance to vessels [Formula: see text] and sulci [Formula: see text]. Surgeon's scores of the optimized electrode sets indicated that 95% of the automatic trajectories would be likely considered for use in a clinical setting. CONCLUSIONS: This study suggests that automatic electrode planning for epilepsy provides safe trajectories and increases the amount of information obtained from the intracranial investigation.

High frequency oscillations in intra-operative electrocorticography before and after epilepsy surgery.

OBJECTIVE: Removal of brain tissue showing high frequency oscillations (HFOs; ripples: 80-250Hz and fast ripples: 250-500Hz) in preresection electrocorticography (preECoG) in epilepsy patients seems a predictor of good surgical outcome. We analyzed occurrence and localization of HFOs in intra-operative preECoG and postresection electrocorticography (postECoG). METHODS: HFOs were automatically detected in one-minute epochs of intra-operative ECoG sampled at 2048Hz of fourteen patients. Ripple, fast ripple, spike, ripples on a spike (RoS) and not on a spike (RnoS) rates were analyzed in pre- and postECoG for resected and nonresected electrodes. RESULTS: Ripple, spike and fast ripple rates decreased after resection. RnoS decreased less than RoS (74% vs. 83%; p=0.01). Most fast ripples in preECoG were located in resected tissue. PostECoG fast ripples occurred in one patient with poor outcome. Patients with good outcome had relatively high postECoG RnoS rates, specifically in the sensorimotor cortex. CONCLUSIONS: Our observations show that fast ripples in intra-operative ECoG, compared to ripples, may be a better biomarker for epileptogenicity. Further studies have to determine the relation between resection of epileptogenic tissue and physiological ripples generated by the sensorimotor cortex. SIGNIFICANCE: Fast ripples in intra-operative ECoG can help identify the epileptogenic zone, while ripples might also be physiological.

Scalp EEG is not a blur: it can see high frequency oscillations although their generators are small.

High frequency oscillations (HFOs) are emerging as biomarkers of epileptogenicity. They have been shown to originate from small brain regions. Surprisingly, spontaneous HFOs can be recorded from the scalp. To understand how is it possible to observe these small events on the scalp, one avenue is the analysis of the cortical correlates at the time of scalp HFOs. Using simultaneous scalp and intracranial recordings of 11 patients, we studied the spatial distribution of scalp events on the cortical surface. For typical interictal epileptiform discharges the subdural distributions were, as expected, spatially extended. On the contrary, for scalp HFOs the subdural maps corresponded to focal sources, consisting of one or a few small spatial extent activations. These topographies suggest that small cortical areas generated the HFOs seen on the scalp. Similar scalp distributions corresponded to distinct distributions on a standard 1 cm subdural grid and averaging similar scalp HFOs resulted in focal subdural maps. The assumption that a subdural grid "sees" everything that contributes to the potential of nearby scalp contacts was not valid for HFOs. The results suggest that these small extent events are spatially undersampled with standard scalp and grid inter-electrode distances. High-density scalp electrode distributions seem necessary to obtain a solid sampling of HFOs on the scalp. A better understanding of the influence of spatial sampling on the observation of high frequency brain activity on the scalp is important for their clinical use as biomarkers of epilepsy.

Influence of contact size on the detection of HFOs in human intracerebral EEG recordings.

OBJECTIVE: High frequency oscillations (HFOs) are brief electroencephalographic events associated with epileptic activity, and likely representing biological markers of the epileptogenic zone. HFOs are usually detected with intracranial EEG and detection is influenced by contact size. The size of commercially available intracerebral electrodes varies widely. This study assesses HFO detection rates from adjacent electrode contacts in human intracerebral recordings. METHODS: Intracerebral recordings were collected from 11 patients undergoing stereoelectroencephalographic investigation using hybrid depth electrodes containing adjacent large (0.8 or 5 mm(2)) and small (0.2 or 0.3 mm(2)) contacts. HFOs were marked manually during 5-min tracings in 131 pairs of adjacent large and small contacts. HFO rates per minute and mean event durations were compared between adjacent contacts. RESULTS: A minimal but statistically significant advantage in ripple detection was found in a subgroup of large contacts. Otherwise, HFO rates and mean event durations were not statistically different between groups. CONCLUSION: The size of clinical contacts within the studied range did not influence HFO detection in a clinically relevant manner. Larger contacts provide a minimal advantage for ripple detection. SIGNIFICANCE: Our findings suggest that commercially available intracerebral electrodes with contacts between 0.2 and 5 mm(2) likely possess similar HFO detection abilities.

Recording and analysis techniques for high-frequency oscillations.

In recent years, new recording technologies have advanced such that, at high temporal and spatial resolutions, high-frequency oscillations (HFO) can be recorded in human partial epilepsy. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings depends on the development of new data mining techniques to extract meaningful information relating to time, frequency and space. Here, we aim to bridge this gap by focusing on up-to-date recording techniques for measurement of HFO and new analysis tools for their quantitative assessment. In particular, we emphasize how these methods can be applied, what property might be inferred from neuronal signals, and potentially productive future directions.

A comparison between detectors of high frequency oscillations.

OBJECTIVE: High frequency oscillations (HFOs) are a biomarker of epileptogenicity. Visual marking of HFOs is highly time-consuming and inevitably subjective, making automatic detection necessary. We compare four existing detectors on the same dataset. METHODS: HFOs and baselines were identified by experienced reviewers in intracerebral EEGs from 20 patients. A new feature of our detector to deal with channels where baseline cannot be found is presented. The original and an optimal configuration are implemented. Receiver operator curves, false discovery rate, and channel ranking are used to evaluate performance. RESULTS: All detectors improve performance with the optimal configuration. Our detector had higher sensitivity, lower false positives than the others, and similar false detections. The main difference in performance was in very active channels. CONCLUSIONS: Each detector was developed for different recordings and with different aims. Our detector performed better in this dataset, but was developed on data similar to the test data. Moreover, optimizing on a particular data type improves performance in any detector. SIGNIFICANCE: Automatic HFO detection is crucial to propel their clinical use as biomarkers of epileptogenic tissue. Comparing detectors on a single dataset is important to analyze their performance and to emphasize the issues involved in validation.

Interictal scalp fast oscillations as a marker of the seizure onset zone.

OBJECTIVE: This study aims to identify if oscillations at frequencies higher than the traditional EEG can be recorded on the scalp EEG of patients with focal epilepsy and to analyze the association of these oscillations with interictal discharges and the seizure onset zone (SOZ). METHODS: The scalp EEG of 15 patients with focal epilepsy was studied. We analyzed the rates of gamma (40-80 Hz) and ripple (>80 Hz) oscillations, their co-occurrence with spikes, the number of channels with fast oscillations inside and outside the SOZ, and the specificity, sensitivity, and accuracy of gamma, ripples, and spikes to determine the SOZ. RESULTS: Gamma and ripples frequently co-occurred with spikes (77.5% and 63% of cases). For all events, the proportion of channels with events was consistently higher inside than outside the SOZ: spikes (100% vs 70%), gamma (82% vs 33%), and ripples (48% vs 11%); p < 0.0001. The mean rates (events/min) were higher inside than outside the SOZ: spikes (2.64 ± 1.70 vs 0.69 ± 0.26, p = 0.02), gamma (0.77 ± 0.71 vs 0.20 ± 0.25, p = 0.02), and ripples (0.08 ± 0.12 vs 0.04 ± 0.09, p = 0.04). The sensitivity to identify the SOZ was spikes 100%, gamma 82%, and ripples 48%; the specificity was spikes 30%, gamma 68%, and ripples 89%; and the accuracy was spikes 43%, gamma 70%, and ripples 81%. CONCLUSION: The rates and the proportion of channels with gamma and ripple fast oscillations are higher inside the SOZ, indicating that they can be used as interictal scalp EEG markers for the SOZ. These fast oscillations are less sensitive but much more specific and accurate than spikes to delineate the SOZ.

Automatic detector of high frequency oscillations for human recordings with macroelectrodes.

High Frequency Oscillations (HFOs) in the EEG are a promising biomarker of epileptogenic tissue. Given that the visual marking of HFOs is highly time-consuming and subjective, automatic detectors are necessary. In this study, we present a novel automatic detector that detects HFOs by incorporating information of previously detected baselines. The detector was trained on 72 channels and tested on 278, achieving a mean sensitivity of 96.8% with a mean false positive rate of 4.86%. This low rate is reasonable since only visually marked baseline segments were considered as the true negatives. This detector could be useful for the systematic study of HFOs and for their eventual clinical application.

High-frequency oscillations mirror disease activity in patients with epilepsy.

OBJECTIVE: High-frequency oscillations (HFOs) can be recorded in epileptic patients with clinical intracranial EEG. HFOs have been associated with seizure genesis because they occur in the seizure focus and during seizure onset. HFOs are also found interictally, partly co-occurring with epileptic spikes. We studied how HFOs are influenced by antiepileptic medication and seizure occurrence, to improve understanding of the pathophysiology and clinical meaning of HFOs. METHODS: Intracerebral depth EEG was partly sampled at 2,000 Hz in 42 patients with intractable focal epilepsy. Patients with five or more usable nights of recording were selected. A sample of slow-wave sleep from each night was analyzed, and HFOs (ripples: 80-250 Hz, fast ripples: 250-500 Hz) and spikes were identified on all artifact-free channels. The HFOs and spikes were compared before and after seizures with stable medication dose and during medication reduction with no intervening seizures. RESULTS: Twelve patients with five to eight nights were included. After seizures, there was an increase in spikes, whereas HFO rates remained the same. Medication reduction was followed by an increase in HFO rates and mean duration. CONCLUSIONS: Contrary to spikes, high-frequency oscillations (HFOs) do not increase after seizures, but do so after medication reduction, similarly to seizures. This implies that spikes and HFOs have different pathophysiologic mechanisms and that HFOs are more tightly linked to seizures than spikes. HFOs seem to play an important role in seizure genesis and can be a useful clinical marker for disease activity.