This work is supported by the Research Grants Program of the Epilepsy Foundation &amp; American Epilepsy Society and the Faculty Career Development Fellowship of Harvard Medical School, Office for Faculty Development.

The authors have nothing to disclose.

Converging evidence from animal and human studies has shown that HFOs are a new potential biomarker for epileptogenic tissue. Despite this evidence, HFOs have very limited use in clinical practice for the diagnosis or monitoring of epilepsy, mostly because: (i) there is no formal and global definition for HFOs; (ii) different research groups use different methodology for recording and analyzing the data; (iii) the noninvasive detection of HFOs with neuroimaging techniques is challenging; and (iv) the review process of HFOs is time-consuming and impractical, especially for multichannel EEG or MEG recordings with a high number of sensors. In an effort to provide a global standardized methodology that promotes the systematic use of HFOs in clinical practice, the methodology that is followed at Boston Children&#39;s Hospital for the noninvasive recording, detection, and localization of interictal HFOs from pediatric patients with epilepsy is presented. Representative results of HFOs detected with simultaneous scalp EEG and MEG from two children with medically refractory epilepsy are also presented.
Critical steps within the protocol
The proposed methodology includes the following critical steps: (i) the performance of high Signal-to-Noise-Ratio (SNR) EEG and MEG simultaneous recordings of interictal activity from pediatric patients with medically refractory epilepsy (steps 2.1.1 and 2.1.2); (ii) the careful preprocessing and selection of data with interictal discharges (steps 3.1 and 3.2); (iii) the visual review of the identified HFOs events with high specificity (steps 4.3.1, 4.3.2, and 4.3.3); and (iv) the reliable localization of the HFOs using an appropriate localization method (step 5.2).
The most critical step in this protocol is the visual review of the HFO events identified by the automatic detector. A rigorous review of the automatically detected HFOs is crucial to discard HFOs of noncerebral origin. However, fatigue or distraction of the human reviewer during the visual inspection of multichannel EEG and MEG data may lead to errors, reducing the specificity of the detection process.
Modifications and troubleshooting
We avoid the use of the Signal Space Projection (SSP) and Signal Space Separation (SSS) methods72,73 in order to ensure that there was no distortion of the HFO activity from their application. These methods are often used by most of the users of the particular MEG vendor to suppress external interferences and to correct for head movements72. Further studies are necessary in order to ensure that the application of these methods do not affect or distort the HFO activity or do not produce spurious effects that may resemble human HFOs. Minor modifications of the minimum threshold of the z-score of the signal envelope (step 4.1.1.3) and the threshold of activation values (step 5.2.6) may be needed to improve the sensitivity of the algorithm in the detection of HFOs and restrict the localization of the HFOs zone in a more focal area.
Limitations of the technique
The described method presents limitations that should be further addressed in future studies. First, it does not consider HFOs occurring only in the MEG or EEG signals, and it does not include the automatic detection of HFOs in the MEG signals, which implies that some actual low SNR MEG HFOs might escape visual inspection74. Furthermore, the sensitivity and specificity of the proposed method to detect the HFOs and its ability to localize them with high accuracy should be validated with simultaneous recordings of scalp EEG, MEG, and iEEG75. Our data have shown that single ECDs indicated an extended irritative zone compared to the focal HFOs zone. However, when the ECDs were averaged, then the dipole location was quite close to the HFO zone for both patients. Our data are indicative of the specificity of the 2 methods showing a possible higher specificity of the HFO zone for epileptogenicity (particularly for patient 2 for whom the HFO zone was overlapping with the SOZ) compared to the irritative zone, although secure conclusions cannot be drawn from such a small cohort of patients. More importantly, the localization of the HFO sources does not directly imply localizing the EZ that is responsible for seizures. Our findings should be validated against the outcome of the epilepsy surgery that we plan to do in a future study. Finally, to record the EEG data, a 70-channel system was used. Yet, in most centers the standard clinical EEG setting is used that records data from 19 electrodes placed according to the 10 - 20 system. More advanced pediatric EEG systems with much higher number of channels (up to 256) are currently available in the market. The use of these systems may further improve the localization accuracy of the HFOs zone detected with scalp EEG.
Significance of the technique with respect to existing/alternative methods
To our best knowledge, this is the first study that reports the noninvasive localization of interictal HFOs with simultaneous EEG and MEG, and also investigates the concordance of the localization results with those from intracranial recordings. The noninvasive recording, detection, and localization of HFOs is challenging. This is because HFOs are very weak signals generated by small brain regions on the order of cubic millimeters16,76 and furthermore hindered by noise and brain background activity. A recent study proposed that HFOs recorded non-invasively with scalp EEG represent the sum of activity of multiple spatially distributed focal and coherent sources60. So far, few studies28,29,37,38,60 managed to show that HFOs can be detected non-invasively using scalp EEG and MEG; even fewer localized this activity by solving the inverse problem37-38.
Here, evidence of interictal HFOs are presented which have been detected with simultaneous scalp EEG and MEG from two pediatric patients with epilepsy. HFOs were localized by using a previously described framework38. The representative data suggest that the noninvasive localization of interictal HFOs is feasible by using source imaging techniques performed on either scalp EEG or MEG recordings, assuming that an appropriate localization technique is used. This is in line with a previous study that used a phantom construction resembling HFOs generators, which indicates that HFOs can be noninvasively detected and accurately localized with MEG32.
The detection and labeling of interictal HFOs is traditionally performed through the visual inspection of data from human EEG experts. Although this approach is often regarded as the gold standard, it presents serious limitations since it has poor inter-reviewer reliability77,78, and is not applicable to large MEG and EEG datasets with high number of sensors. Crucial to the application of HFOs in clinical practice is the development of algorithms that detect the HFOs automatically from scalp recordings reducing the need for human input. The visual identification of scalp HFOs is in fact quite challenging due to: (i) the low SNR of HFOs on the scalp; (ii) the lower rates of HFOs in scalp recordings compared to intracranial ones, which implies the analysis of much longer recording times; and (iii) the high number of channels to analyze, particularly in high-density EEG or MEG. Several algorithms for automatic and semi-automatic detection of HFOs have been proposed in the last decade54. Earlier detectors relied on thresholds in the time domain, in order to identify events that can be distinguished from ongoing background activity49,80. Recent advances suggest also incorporating information from the frequency domain, assuming that an HFO must appear as a short-lived event with an isolated spectral peak at a distinct frequency50,56,81. Semi-automated methods seem to be the most appropriate approach for the application of HFOs in clinical practice. These methods involve 2 stages: (i) initial automatic detection of events that has high sensitivity, and (ii) visual review of events by an expert, which has high specificity. This approach provides higher specificity compared to the fully automated methods and ensures that the final reviewed events are actual HFOs of cerebral origin.
Here, a semi-automated method is presented that enables the detection of HFOs from interictal scalp EEG and MEG recordings. The proposed method extends previously described techniques for the detection of HFOs from scalp EEG60 by incorporating in the identification criteria two important features: (i) the automatic time-frequency analysis of the HFO events; and (ii) the temporal concurrence of HFOs events in both MEG and EEG recordings.
Future applications or directions after mastering this technique
The reliable localization of HFOs with non-invasive neuroimaging methods, such as scalp EEG and MEG, is critical. Mastering, improving, and validating the proposed protocol will provide physicians with a reliable, noninvasively recordable biomarker for the identification of the EZ. The development of such a biomarker has the potential to reduce the requirement for long-term monitoring and invasive intracranial recordings leading to a significant improvement in the presurgical evaluation procedure in pediatric patients. It would not only help to identify the epileptogenic tissue for surgery, but would also permit definitive differential diagnosis of epilepsy from acute symptomatic seizures, requiring an entirely different treatment approach, and from non-epileptic seizures sparing the need for long-term monitoring in some patients. Further, this might allow assessment of efficacy of therapeutic interventions without waiting for another seizure to occur.

Pediatric epilepsy is a common neurological disorder with a prevalence rate of 4 - 6 per 1,000 children1. It can have a major impact on children&#39;s development2 and may significantly affect their adult life. Long-term follow-up studies in childhood-onset epilepsy indicate that approximately 30% of patients with epilepsy become medically intractable3-6, and usually require resective epileptic surgery. In many of these patients, epilepsy surgery leads to significant reduction in seizure frequency and often to seizure freedom. To be successful, epilepsy surgery should achieve a seizure-free state with minimal or no functional deficits. This requires careful delineation of the Epileptogenic Zone (EZ)7, the &#39;area of cortex that is indispensable for the generation of epileptic seizures&#39;8. The EZ cannot be measured directly; its location is estimated based on concordant data from a multitude of tests that identify other cortical zones. Invasive intracranial electroencephalography (iEEG) serves as the gold standard for the localization of the seizure onset zone (SOZ), the region where seizures are generated and originate on ictal recordings. However iEEG is costly, reliant on cooperation of the child, carries some risk for infection and bleeding9, and may induce additional neurological damage during the implantation10. Furthermore, the recordings may lead to erroneous conclusions since large areas of the brain are left unexplored. Thus, a robust presurgical biomarker that helps in the identification of the EZ is needed to the success of surgical epilepsy treatment.
Pathological HFOs&#160;(80 - 500 Hz)11,12 have emerged over the last decade as a biomarker for the identification of the epileptogenic tissue that may improve the presurgical diagnosis and surgical outcome of patients with epilepsy13. Reports using microelectrodes combined with depth EEG&#160;electrodes showed the presence of HFOs in patients with epilepsy. HFOs were also found using standard macroelectrodes during the ictal and interictal periods. Recent studies have shown that HFOs identify the SOZ with higher sensitivity and specificity compared to the irritative zone14,15, the zone that generates the IEDs, and that the surgical removal of the HFO-generating tissue correlates with better outcomes than the removal of the SOZ or the irritative zone15. HFOs are commonly categorized as ripples (80 - 250 Hz) or fast ripples (250 - 500 Hz). Fast ripples have been more closely linked to pathological activity and to the localization of the SOZ16, but investigations of human intracranial recordings indicate that both ripples and fast ripples increase in epileptogenic regions17.
Despite these promising findings, HFOs are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: (i) the lack of a formal and global definition for HFOs; (ii) the consequent heterogeneity of the methodological approaches used for their study; and (iii) the practical difficulties to detect and localize them noninvasively from scalp recordings. The latter stems from the fact that electrodes are far away from the source of the signal, the signal might be blurred by background noise and muscle activity, and the signal might be distorted by the scalp or the fontanels and sutures in the skull, especially in infant patients. Moreover, it is difficult to distinguish between normal and abnormal HFOs18,19 since both ripples and fast ripples are present even in normal human brain tissue20. Early studies reported HFOs in scalp EEG in only a small (0.2&#160;- 3.4%) portion of patients with epilepsy21-23. However, recent studies have shown that HFOs can be detected noninvasively with scalp EEG. Ictally, HFOs have been reported at the onset of epileptic spasms in children (50 - 100 Hz24, 40 - 120 Hz25), as well as at the onset of tonic seizures in Lennox-Gastaut syndrome (50 - 100 Hz)26. Interictal HFOs (70 - 200 Hz) were first observed on scalp EEG in children with sleep-induced electrical status epilepticus27. Then, interictal HFOs (80 - 200 Hz) were identified in the scalp EEG of patients with focal epilepsy with higher rates inside the SOZ28. Interestingly, HFOs were more frequent in patients with high numbers of interictal epileptiform discharges (IEDs), and they were found to be more specific than IEDs for the SOZ29, highlighting the relation of HFOs with epileptogenicity.
MEG&#160;seems to present significant advantages compared to scalp EEG for the noninvasive detection and localization of HFOs: (i) high frequency activity in MEG is less susceptible than EEG to contamination from muscular activity30-31, (ii) MEG signals are not distorted by skull conductivity and less distorted than EEG by unfused regions of the cranial bone such as fontanel or suture, and (iii) MEG sensor arrays have higher density compared to EEG that always faces the problem of salt bridges between electrodes when the head is small, as with children. Evidence from phantom constructions that simulate HFOs generators suggested that HFOs can be detected and localized with high localization accuracy (2 - 3 mm) with MEG32. Several recent studies reported HFOs in the MEG signals recorded from patients with epilepsy in the ripple frequency band33-38. Time-frequency analysis has shown that MEG data contain high frequency components related to the EZ33-36. However, only a few studies have identified interictal HFOs as visible events standing out of the background signal in the time domain, as typically done with iEEG37-38. Van Klink et al.37 detected HFOs in the ripple band using virtual channels constructed with beamforming techniques based on spatial information obtained from IEDs. Von Ellenrieder et al.38 detected HFOs in MEG signals from the physical sensors independently of the IEDs and used the Maximum Entropy on the Mean (MEM) method to localize their sources and to investigate their correlation with the EZ. Rampp et al. (2010) also detected epileptic high gamma oscillations with MEG, which were spike-locked or spike-independent, and localized this activity with minimum-norm source analysis39. They found that characteristics of these fast oscillations (i.e., clear onset of full-band average and maximum amplitude of oscillations) were highly associated with the SOZ. HFOs were also detected with MEG during ictal activity in pediatric patients with epileptic spasms40. However, MEG presents some distinct limitations compared to scalp EEG: (i) it is insensitive to sources that have a radial orientation with respect to the center of the head, (ii) it does not allow long recordings that increase the possibility to detect and record ictal events, and (iii) its sensors cannot conform to the shape of head of each individual since the helmet and sensor array within the helmet are all fixed in shape. Thus, the ideal setup that maximizes the possibility to detect and localize the epileptogenic activity is by combining information from both scalp EEG and MEG.
In this study, we aim to illustrate the methodology we follow for the noninvasive detection of interictal HFOs by using simultaneous recordings of scalp EEG and MEG from pediatric patients with medically refractory epilepsy. We present the setup of the recordings and the pipeline of data analysis using a semi-automated method that we have developed for the detection of HFO events in simultaneous MEG and EEG data. Finally, we also present the localization of the underlying generators of scalp HFOs, obtained by solving the inverse problem, and compare it with the SOZ as this was defined by the epileptologists.

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		<tr height="21">
			<td height="21" style="height:21px;width:216px;">VectorView MEG system</td>
			<td style="width:287px;">Elekta-Neuromag, Finland</td>
			<td style="width:119px;"></td>
			<td style="width:407px;">MEG System</td>
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			<td height="21" style="height:21px;width:216px;">Magentically Shielded Room</td>
			<td style="width:287px;">Imedco, Hagendorf, Switzerland</td>
			<td style="width:119px;"></td>
			<td style="width:407px;">Three-layer MSR</td>
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			<td height="21" style="height:21px;width:216px;">EEG system</td>
			<td style="width:287px;">Elekta-Neuromag, Finland</td>
			<td style="width:119px;"></td>
			<td>70 Channel EEG system</td>
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		<tr height="21">
			<td height="21" style="height:21px;width:216px;">3D digitizer</td>
			<td style="width:287px;">Polhemus, Colchester, VT</td>
			<td style="width:119px;"></td>
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interictal high frequency oscillations detected with simultaneous magnetoencephalography and electroencephalography as biomarker of pediatric epilepsy

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency Oscillations (HFOs) have emerged as potential presurgical biomarkers for the identification of the EZ in addition to Interictal Epileptiform Discharges (IEDs) and ictal activity. Although they are promising to localize the EZ, they are not yet suited for the diagnosis or monitoring of epilepsy in clinical practice. Primary barriers remain: the lack of a formal and global definition for HFOs; the consequent heterogeneity of methodological approaches used for their study; and the practical difficulties to detect and localize them noninvasively from scalp recordings. Here, we present a methodology for the recording, detection, and localization of interictal HFOs from pediatric patients with refractory epilepsy. We report representative data of HFOs detected noninvasively from interictal scalp EEG and MEG from two children undergoing surgery.
The underlying generators of HFOs were localized by solving the inverse problem and their localization was compared to the Seizure Onset Zone (SOZ) as this was defined by the epileptologists. For both patients, Interictal Epileptogenic Discharges (IEDs) and HFOs were localized with source imaging at concordant locations. For one patient, intracranial EEG (iEEG) data were also available. For this patient, we found that the HFOs localization was concordant between noninvasive and invasive methods. The comparison of iEEG with the results from scalp recordings served to validate these findings. To our best knowledge, this is the first study that presents the source localization of scalp HFOs from simultaneous EEG and MEG recordings comparing the results with invasive recordings. These findings suggest that HFOs can be reliably detected and localized noninvasively with scalp EEG and MEG. We conclude that the noninvasive localization of interictal HFOs could significantly improve the presurgical evaluation for pediatric patients with epilepsy.

Pediatric patients with refractory epilepsy were recruited from the Epilepsy Clinic of Boston Children&#39;s Hospital (Boston, USA). Here, representative data from 2 patients are presented: a 15-year-old girl with encephalomalacia of the right middle cerebral artery (MCA) region (patient 1), and an 11-year-old boy with left parietal/superior temporal encephalomalacia (patient 2). The data were collected as part of their presurgical workup for surgery. Simultaneous MEG and EEG recordings were performed at the Athinoula A. Martinos Center for Biomedical Imaging.
For both patients, HFOs were identified in the ripple frequency band (80 - 150 Hz) occurring in both EEG and MEG and overlying IEDs. Figure 4 presents 10 s of simultaneous scalp EEG and MEG data with interictal activity (upper panels) from patient 1. The same figure also reports an extended time scale portion (2 s) of the signals, which shows the detected HFOs in the time domain (middle panels) and in the time-frequency plane (lower panel). This patient showed a rate of 8.8 HFOs/min. For clarity, the display of the time-frequency plane is restricted from 80 - 150 Hz because no prominent activity was observed for frequencies above 150 Hz. In patient 1, the automatic detection identified 248 HFOs events within 8.65 min of recordings. After visual review of the detected events, 76 events were kept that were considered to be real HFOs, obtaining a rate of 8.8 HFOs/min.
<img alt="Figure 4" src="/files/ftp_upload/54883/54883fig4.jpg" /> 
Figure 4: HFOs Overlaid on IEDs Detected from Scalp EEG and MEG. Upper panel: IEDs from patient 1 detected from scalp EEG (left) and MEG (right). 10 s of data filtered from 1 - 70 Hz. Middle panel: Extended time scale display (2 s) of the EEG and MEG data highlighted with grey background in the upper panel. Data are filtered from 80 - 150 Hz. Lower panel: Time-frequency plane of 2 representative EEG and MEG channels. <a href="http://cloudflare.jove.com/files/ftp_upload/54883/54883fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Figure 5 presents the HFOs localization results for patient 1 from both scalp EEG and MEG. Both techniques localized the HFOs at the vicinity of the lesion in a location close to the right temporo-parietal junction. MEG and EEG presented slightly different localization results: MEG activity was located more anteriorly compared to EEG and closer to the posterior boundary of the lesion. The distance between the two maxima of wMEM activity for the MEG and EEG was 15.0 mm. HFOs localization was also adjacent to the irritative zone as this was defined by MEG. The Euclidian distance between the averaged ECDs and the maximum wMEM activity was 18.7 mm for the MEG and 28.0 mm for the scalp EEG. For patient 2, we identified 8 min of data during slow-wave non-REM sleep with IED and minimal artifacts that we used for the HFO analysis.
The number of scalp HFOs was significantly lower than in patient 1 showing a rate of 0.4 HFOs/min. Figure 6 presents the source localization results of scalp HFOs for MEG and scalp EEG, the IEDs localized by MEG using ECDs, and the location of HFOs detected by iEEG. The distance between the 2 maxima of wMEM activity for the MEG and EEG was 16.4 mm. The distance between the averaged ECDs and the maximum wMEM activity was 10.9 mm for the MEG and 24.1 mm for the EEG. The HFO zone identified noninvasively by scalp EEG and MEG was at the same location where the HFOs were identified invasively with iEEG. The electrodes with the highest number of HFOs were LA51, LA52, and LA53, as shown in Figure 6, whose localization was concordant with the HFO activity localized noninvasively.
<img alt="Figure 5" src="/files/ftp_upload/54883/54883fig5.jpg" /> 
Figure 5: Localization of HFOs and IEDs. HFOs zone localized by using the wMEM with MEG and EEG from patient 1 overlaid on patient&#39;s MRI (left). The HFOs map represents the average of the normalized activation values thresholded at 60% of the maximum activity. IEDs localized by ECDs with MEG (right). The locations and orientations of the ECDs are indicated in cyan. The purple triangle indicates the location of the averaged ECDs (46 ECDs), and the red rectangle the location of the ECD from averaged IEDs (28 IEDs). For the averaging of the IEDs, we identified spikes with similar morphology. <a href="http://cloudflare.jove.com/files/ftp_upload/54883/54883fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/54883/54883fig6.jpg" /> 
Figure 6: Spatial Concordance between the HFO Zone Localized Noninvasively with Scalp EEG and MEG, the Irritative Zone, and the HFO Zone Localized Invasively with iEEG. Upper panels: Localization of HFOs from patient 2 with scalp EEG (left) and MEG (middle), and localization of IEDs with MEG (right) overlaid on patient&#39;s MRI. The HFOs map represents the average of the normalized activation values thresholded at 60% of the maximum activity. Cyan circles and bars indicate the locations and orientations of the ECDs. The purple triangle indicates the location of the averaged ECDs (30 ECDs), and the red rectangle the location of the ECD from averaged IEDs (21 IEDs). Lower panels: Localization of HFOs on iEEG (left) and 2 s&#160;of recordings from channels with the highest HFO rate. Implanted electrodes are shown on the patient&#39;s MRI. The electrode location is obtained by co-registering the post-implanted computed tomography (CT) and MR images. The electrodes with the highest rate of HFOs are highlighted on the map of the grid implanted on the patient&#39;s cortex. <a href="http://cloudflare.jove.com/files/ftp_upload/54883/54883fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The representative results presented here indicate the potential use of the described technique in the clinical setting for the noninvasive identification and localization of HFOs during the pre-surgical evaluation of pediatric patients with medically refractory epilepsy. For both patients, HFOs were localized concordant for scalp EEG and MEG (see Figure 5). Localization differences of few mm may be attributed to the superior localization ability of high-density MEG compared to scalp EEG70, or may represent different underlying generators71. The localization was also concordant with the irritative zone as this was defined by MEG (see Figure 5 - right panel). In patient 2, data from iEEG were also available. The HFOs zone localized noninvasively with scalp EEG and MEG was concordant with the HFOs zone defined invasively with iEEG (see Figure 6). The outcomes of the proposed method can guide the placement of grids during epilepsy surgery for the potential identification of the EZ.

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Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy

High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

Crucial to the success of epilepsy surgery is the availability of a robust biomarker that identifies the Epileptogenic Zone (EZ). High Frequency ...

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19.8K 조회수.  Boston Children's Hospital, Harvard Medical School. High Frequency Oscillations (HFOs) have emerged as presurgical biomarkers for the identification of the epileptogenic zone in pediatric patients with medically refractory epilepsy. A methodology for the noninvasive recording, detection, and localization of HFOs with simultaneous scalp electroencephalography (EEG) and magnetoencephalography (MEG) is presented.

비디오: Interictal High Frequency Oscillations Detected with Simultaneous Magnetoencephalography and Electroencephalography as Biomarker of Pediatric Epilepsy