• Cortical dysplasia;
  • EEG ;
  • Intracranial electrodes;
  • Epileptogenesis;
  • Epilepsy surgery;
  • High frequency oscillations


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References


The study analyzes the occurrence of high frequency oscillations in different types of focal cortical dysplasia in 22 patients with refractory epilepsy. High frequency oscillations are biomarkers for epileptic tissue, but it is unknown whether they can reflect increasingly dysplastic tissue changes as well as epileptic disease activity.


High frequency oscillations (80–450 Hz) were visually marked by two independent reviewers in all channels of intracranial implanted grid, strips, and depth electrodes in patients with focal cortical dysplasia and refractory epilepsy. Rates of high frequency oscillations in patients with pathologically confirmed focal cortical dysplasia of Palmini type 1a and b were compared with those in type 2a and b.

Key Findings

Patients with focal cortical dysplasia type 2 had significantly more seizures than those with type 1 (p < 0.001). Rates of high frequency oscillations were significantly higher in patients with focal cortical dysplasia type 2 versus type 1 (p < 0.001). In addition, it could be confirmed that rates of high frequency oscillations were significantly higher in presumed epileptogenic areas than outside (p < 0.001).


Activity of high frequency oscillations mirrors the higher epileptogenicity of focal cortical dysplasia type 2 lesions compared to type 1 lesions. Therefore, rates of high frequency oscillations can reflect disease activity of a lesion. This has implications for the use of high frequency oscillations as biomarkers for epileptogenic areas, because a detailed analysis of their rates may be necessary to use high frequency oscillations as a predictive tool in epilepsy surgery.

In 30–40% of patients with focal epilepsy, epileptic seizures cannot be prevented successfully by pharmacotherapy (Schuele & Lüders, 2008). Epilepsy surgery represents an important treatment option for those patients. Intracranial electroencephalography (iEEG) investigations are indicated for patients in whom noninvasive methods fail to identify a single focal seizure generator that can be surgically removed. For the identification of the epileptogenic zone by means of iEEG, reliable markers are crucial. Current markers such as the seizure-onset zone (SOZ) and interictal spikes are used with limited success.

Focal cortical dysplasia (FCD) is a frequent cause of pharmacoresistant focal epilepsy (Tassi et al., 2002). According to Palmini et al. (2004), FCD is classified into two different histopathologic subtypes (FCD type 1 and FCD type 2). The mild form of FCD type 1 with isolated abnormalities such as dyslamination (FCD type 1a) or immature neurons (FCD type 1b) is considered to be less epileptogenic than FCD type 2, which is defined by the presence of dysplastic neurons (FCD type 2a) and balloon cells (FCD type 2b). FCD type 2 manifests itself at an earlier age and with a higher frequency of seizures (Lawson et al., 2005; Lerner et al., 2009; Palmini et al., 2010).

High frequency oscillations (HFOs), i.e., ripples (80–200 Hz), and fast ripples (200–500 Hz), are new markers of epileptogenicity. They can be recorded with macroelectrodes during clinical iEEG investigation. HFOs are more specific in indicating the SOZ than spikes (Jacobs et al., 2008). In addition, they have been linked to the SOZ independent of the underlying lesion (Jacobs et al., 2009), and removal of HFO-generating tissue correlated with a good postsurgical outcome (Jacobs et al., 2010a; Wu et al., 2010). There is evidence suggesting that HFOs not only identify the SOZ but reflect seizure propensity: In rats, the rate of HFOs correlates with the rate of seizures (Bragin et al., 2004). Furthermore, rates of HFOs increase similar to seizures when the dosage of anticonvulsive drugs is reduced in patients with epilepsy (Zijlmans et al., 2009). Because FCD occurs with various grades of epileptogenicity, this might also be reflected in the rate of HFOs. We hypothesize that HFOs occur more frequently in the severe FCD type 2 than in the mild FCD type 1, and therefore reflect not only epileptogenicity but also different grades of epileptic activity.


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References

Patient selection

A total of 58 patients underwent intracranial electrode implantation in the epilepsy unit of the Freiburg University Medical Center. The decision to use iEEG was based exclusively on clinical reasons. The data were analyzed retrospectively. The inclusion criteria were met by patients with confirmed FCD and at least one EEG segment without artifacts recorded with a sampling rate of 1,024 Hz. Patients with frequent seizures that prevented the selection of an EEG segment with an interictal interval of at least 2 h were excluded. This study was approved by the local ethics committee, and all patients provided written informed consent to the procedure.

Recording methods

A combination of grid, strip, and depth electrodes was implanted. All electrodes were manufactured from steel by AD-TECH Medical Instrument Corporation (Racine, WI, U.S.A.). Grid electrodes had a surface area of 4 mm (2.3 mm exposed) and were located at a distance of 1 cm from each other. The length of depth electrodes was 39 mm and the surface area of its contacts was 1 mm.

iEEG studies were undertaken using the IT med-EEG-System (Natus Europe GmbH, Munich, Germany). The iEEG was low-pass filtered at 450 Hz and sampled at 1,024 Hz. The input impedance of the amplifier is approximately 10 MΩ. The recordings were performed using a referential montage with an epidural indifferent reference electrode placed in the frontal vertex. Analyses were performed with bipolar montages for an average duration of 9 days.

Channel selection and marking of interictal events

Interictal samples of 3 min of slow-wave sleep were analyzed, as HFOs and spikes occur more frequently during slow-wave sleep (Staba et al., 2004; Bagshaw et al., 2009). The EEG analysis was conducted using the Harmonie monitoring system (Stellate, Montreal, QC, Canada).

To ensure stable rates, 3 min of iEEG were selected with high delta and low electromyography (EMG) power from the beginning of monitoring when antiepileptic drug withdrawal had not yet taken place. Spikes and HFOs were visually marked as described previously (Jacobs et al., 2010a,b). HFOs were marked in the first minute of EEG of each patient by two reviewers (K.K. and J.J.) separately, and the concordance between marked HFOs was assessed using Cohen's kappa coefficient for each contact. Both observers jointly reviewed the events in contacts with κ < 0.5 (Zelmann et al., 2009) and established a consensus. Based on this consensus the remaining 2 min of EEG were marked by one of the reviewers. In addition, a radial basic function (RBF) automatic detector was applied to validate rates of visually marked HFOs using Cohen's kappa coefficient (Dümpelmann et al., 2012). The combination of visual and automatic detection was preferred to exclusive automatic detection to ensure the highest possible objectivity in the identification of events, and through visual analysis the detection of polyspikes, which are frequent in FCD and will always be detected as HFO activity by automatic detection.

Classification of contacts and postsurgical outcome

The SOZ was defined by a board certified clinical neurophysiologist as the area showing the first ictal activity during iEEG recording. First ictal activity was defined as rhythmic changes clearly distinguished from background activity that result in clinical seizure manifestation including fast wave bursts with fast and early propagation (<1 s; Gotman et al., 1993; Asano et al., 2009). If seizures were originating from more than one area independently, all contacts within these areas were regarded as SOZ. For clinical review the EEG is usually displayed at a time scale of 10 s/page and without high pass filters. Therefore, the very short and small HFOs are not visible with the traditional display and HFOs that may have been present at seizure onset were therefore not taken into account for the determination of the SOZ.

The type of FCD was determined according to Palmini's classification (Palmini & Lüders, 2002) by an experienced neuropathologist after resection of the dysplastic tissue. All contacts of all patients with FCD type 1 were analyzed and compared with all contacts of patients with FCD type 2. No attempt was made to distinguish patients with FCD type 1a and b and FCD type 2a and b, as patient numbers were too low for this additional comparison.

The localization of the electrode contacts with respect to the resection area was determined by comparing presurgical three-dimensional (3D) reconstructions of magnetic resonance imaging (MRI), including the implanted electrodes with the surgical cavity in the postsurgical MRI. Electrodes that were placed at the border of the resected area were excluded from the statistical analysis.

The postsurgical outcome was classified according to Engel et al. (1993) by a board-certified clinical epileptologist at 3, 6, and 12 months after surgical resection and on a year-by-year basis thereafter. For statistical analysis, patients were divided into two groups depending on whether they were completely seizure free since surgery (good outcome = Engel class 1a) or presented with remaining seizures (bad outcome = Engel class 1b–4c).

Statistical analysis

Rates of ripples, fast ripples, and spikes as well as the co-occurrence of spikes and HFOs were calculated for each contact by using a MATLAB (The MathWorks Inc., Natick, MA, U.S.A.) program.

The following comparisons between rates of HFOs were performed:

  1. SOZ versus non-SOZ.
  2. FCD type 1 versus type 2.
  3. SOZ/FCD type 1 versus SOZ/FCD type 2 versus non-SOZ/FCD type 1 versus non-SOZ/FCD type 2.
  4. Removal of HFOs generating tissue in patients with seizure-free outcome (Engel class 1a) versus patients with remaining seizures (Engel class 1b–4c).

Rates of HFOs were compared using analysis of variance (ANOVA). Statistical results were corrected for multiple comparisons and were analyzed with the post hoc Tukey's HSD (Honestly Significant Differences) test. The level of significance was set at p = 0.05. In addition, the percentage of spikes co-occurring with HFOs was calculated.

Furthermore, the relationship between seizures and HFO rates was analyzed to evaluate the influence of seizure frequencies on the overall results. Seizure frequencies were calculated for each patient by taking the total number of seizures divided by the recording days. Seizure frequencies between both FCD groups were compared during the time of iEEG recordings using a Mann-Whitney test. Statistics were calculated for the total number of seizures (clinical and electrographic) as well as for clinical seizures only.

A spearman correlation between each patient's ripple and fast ripples rates and the patient's seizure frequency during the recording period was calculated. This was done for all channels, and the subset of SOZ and non-SOZ channels, respectively.


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References

Patient inclusion

Twenty-two of 58 patients were included. Thirty-six were excluded for various reasons: 33 did not have FCD, 2 had frequent seizures that prevented selecting a seizure-free EEG-segment of 2 h, and 1 patient in the majority of EEG channels had permanent artifacts.

Nine patients had a mild FCD (type 1a and b) and 13 had a severe FCD (type 2a and b). There were no relevant differences between the two groups regarding age at implantation, gender, number of channels, or dosage of antiepileptic medication. In patients with FCD type 2, the rate of seizures in daily life was significantly higher (p = 0.01) and the mean first-time manifestation earlier (7th year) than in patients with FCD type 1 (14th year). No significant difference was found for the seizure frequency during the recording period between patients with FCD type 1 and type 2. There was no significant correlation between seizure frequency and ripple or fast ripple rates.

In 16 patients, postsurgical outcome could be evaluated >12 months after resection; 6 patients were excluded from comparisons concerning postsurgical outcome because they did not present for clinical follow-up visits in the epilepsy unit of the Freiburg University Medical Center after the first postsurgical year. Eleven patients had a good postsurgical outcome (Engel class 1a) and five patients a bad postsurgical outcome >12 months (Engel class 1b–4). Clinical details are given in Table 1.

Table 1. Clinical data of patients
Patient no.FCD typeAge/genderType of seizureMRIMedicationSurgeryOutcome
  1. A, amygdala; ant., anterior; CBZ, carbamazepine; CG, cingular gyrus; CLB, clobazam; CPS, complex partial seizure; DD, differential diagnosis; F, frontal; FCD, focal cortical dysplasia; Fe, female; GTCS, generalized tonic–clonic seizure; H, hippocampus; HS, hippocampal sclerosis; I, insula; ITG, inferior temporal gyrus; L, left; LEV, levetiracetam; LTG, lamotrigine; M, male; M1, primary motor cortex; MFG, middle frontal gyrus; MST, multiple subpial transection; MTG, middle temporal gyrus; OXC, oxcarbazepine; P, parietal; PB, phenobarbital; PCS, postcentral sulcus; PGB, pregabalin; R, right; sAHC, selective amygdalohippocampectomy; SFG, superior frontal gyrus; SPS, simple partial seizure; T, temporal; TPM, Topiramate; O, occipital; ZNS, zonisamide.

  2. Pediatric patients are highlighted in blue. The postsurgical outcome is classified according to Engel et al. (1993) >12 months after resection.

11a25/MCPS, GTCSFCD L FLTG, LEVL F resectionNone
21a47/MSPS, GTCSFCD R OOXC, LEVR O lesionectomyNone
31a57/MCPS, GTCSLesion L F HS LLTG, PBL P O lesionectomy2b
41a21/MSPS, CPSNormalLTG, PGBT lesionectomy1a
51b15/FeSPS, CPSLesion R MFGCLB, LTGR F lesionectomyNone
61b18/MGTCSFCD SFGOXC, ZNSR F resectionNone
71b39/MSPS, CPS, GTCSFCD R TZNS, LEVR MTG, R ITG resection4b
81b27/MSPSFCD L ILEV, OXC, ZNSSFG, L-CG, MST L-M1 resectionNone
91b52/MCPS, GTCSHS ROXC, LTGR T, A resection1a
102a17/MSPS, CPS, GTCSFCD R TOXC, LEVR T resection1a
112a8/MCPSFCD R TPGB, ZNSR T O resection1a
122a11/MSPS, CPSFCD R P, atrophy L TOXC, LEV, ZNSR-P lesionectomy1a
132a16/FeSPS, CPS, GTCSFCD R FLTGR F lesionectomy3a
142a42/MSPS, CPS, GTCSFCD R T; lenticulostriatal infarction LNoneR T lesionectomy1a
152a33/MSPSFCD R TLEV, OXCR T pole, A resection1a
162a17/MSPS, CPS, GTCSAtrophy L HOXCL-sAHC, ant. T resection1a
172a21/MSPS, CPSFCD R MTGCBZ, LEV, TPMR T resection1b
182a40/MSPS, GTCSHS L FCD LLEVL T resection1a
192b40/MSPS, CPSNormalLTG; CBZL F lesionectomyNone
202b38/FeSPS, GTCSTransmantle dysplasia L PCSNoneL P lesionectomy1b
212b21/FeSPS, CPS, GTCSFCD R FLEV, OXCR F, MST R M1 lesionectomy1a
222b18/MSPS, CPS, GTCSFCD L TOXC, LEVL T lesionectomy1a

In total 1,228 channels were analyzed: 519 in patients with FCD type 1 and 709 in patients with FCD type 2; 293 were in an SOZ area and 935 were outside. In all patients, the ripples, fast ripples, and spikes could be identified. The linear and rank correlation between visual and automatic detected HFO counts over the channels were significant for all recordings. (Refer to published data for more details;Dümpelmann et al., 2012.) In the following, rates of HFO are presented as mean rate ± standard error (se).

Comparison of SOZ and non-SOZ channels

Rates of ripples in the SOZ were significantly higher than rates in non-SOZ channels (Fig. 1A, SOZ: 38.8 ± 2.1/min [95% confidence interval (95% CI) 34.7–42.9] vs. non-SOZ: 22.7 ± 1.0/min [95% CI 20.7–24.7], F = 46.5, p < 0.001). Fast ripples showed a similar distribution: they were also significantly more frequent in the SOZ (Fig. 1B, 10.2 ± 0.7/min [95% CI 8.8–11.6]) than in non-SOZ areas (4.1 ± 0.4/min [95% CI 3.3–4.9], F = 55.2, p < 0.001).


Figure 1. Rates of HFOs from all patients. On the left side: rates of ripples; on the right side: rates of fast ripples. (A, B) The rate of HFOs for the comparison between channels inside and outside the SOZ (*p < 0.001). (C, D) The comparison between rates of HFOs in patients with FCD type 1 and type 2 (*p < 0.001). (E, F) Demonstrate that independently of the FCD type, HFOs are always significantly more frequent in the SOZ than outside.

Download figure to PowerPoint

Comparison of FCD type 1 and FCD type 2 channels

The rate of ripples was significantly higher in channels of FCD type 2 than in channels of FCD type 1 (Fig. 1C, FCD type 2: 37.6 ± 1.3/min [95% CI 35.1–40.1] vs. FCD type 1: 23.9 ± 1.9/min [95% CI 20.2–27.6], F = 33.7, p < 0.001). Similarly, the rate of fast ripples was significantly higher in channels of FCD type 2 (Fig. 1D, 8.7 ± 0.5/min [95% CI 7.72–9.68]) than of FCD type 1 (5.7 ± 0.7/min [95% CI 4.3–7.07], F = 13.5, p < 0.001). Patient examples are shown in Figures 2 and 3.


Figure 2. Example of patient 3 with FCD type 1a. Low rates of fast ripples are observed generally with very few channels of high rates of HFOs around the SOZ area. The height of the columns represents the rate of HFOs per channel.

Download figure to PowerPoint


Figure 3. Examples of patients 12 and 13 with FCD type 2a. High rates of fast ripples are observed in these patients in comparison to patients with FCD Type 1. The height of the columns represents the rate of HFOs per channel. Electrode positions are placed on the 3D reconstructed MRI according to the presurgical and postsurgical MRI as well as the surgical information.

Download figure to PowerPoint

Comparison of SOZ and non-SOZ in FCD type 1 and 2 channels

In addition to the effects reported earlier, interaction effects were investigated with ANOVA (Fig. 1E,F). There was no significant interaction effect between the SOZ factor and the FCD type factor (ripples: F = 0.5, p = 0.476; fast ripples: F = 0.09, p = 0.76).

The highest rates of ripples were found in channels within the SOZ in FCD type 2 (46.5 ± 2.3/min [95% CI 42.0–51.0]). They were significantly higher than rates in the SOZ in FCD type 1 (31.1 ± 3.6/min [95% CI 24.0–38.2], p = 0.002). Furthermore, rates were significantly higher than in non-SOZ channels in FCD type 2 (28.7 ± 1.4/min [95% CI 25.9–31.44], p < 0.001). Rates of ripples in FCD type 1 were significantly higher in the SOZ channels than outside (16.7 ± 1.5/min [95% CI 13.8–19.6], p < 0.001). When comparing channels outside the SOZ, rates of ripples were significantly higher in FCD type 2 than in type 1 (p < 0.001).

Rates of fast ripple were also highest in SOZ channels in FCD type 2 (11.8 ± 0.8/min [95% CI 10.2–13.4]). These rates were not significantly different from rates in SOZ channels in FCD type 1 (8.6 ± 1.2/min [95% CI 6.25–11.0], p = 0.123) but did show a difference with rates of fast ripples outside the SOZ in type 2 (5.5 ± 0.5/min [95% CI 4.5–6.5], p < 0.001). Rates of fast ripples in FCD type 1 were significantly higher inside the SOZ than outside (2.8 ± 0.5/min [95% CI 1.8–3.8], p < 0.001). In non-SOZ channels, fast ripples were significantly more frequent in FCD type 2 than in type 1 (p < 0.001).

Correlation between surgical seizure outcome and removal of HFO-generating tissue

There was no significant difference in the amount of ripples generated over removed areas between patients with a good and bad postsurgical outcome. In patients with seizure-free outcome the amount of fast ripples over removed brain areas was significantly higher than over remaining brain areas (p = 0.016). Patient examples are shown in Fig. 4.


Figure 4. Examples of the correlation between removal of fast ripples generating areas and the postsurgical seizure outcome. On the left side: patient 11 with a seizure-free outcome. All areas generating fast ripples were removed (red channels), only few areas covered with electrodes and without fast ripple activity (blue channels) remained. On the right side: patient 13 with a poor surgical outcome. Due to functional reasons, areas under the contacts in blue with high rates of fast ripples were not removed. These areas were not part of the SOZ, which was defined as lying beneath the frontal contacts (red channels with high rates of fast ripples). In this case the high rate of fast ripples over the remaining brain areas could have suggested a poor outcome prior to surgery.

Download figure to PowerPoint


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References

Our main finding is that HFOs reflect different grades of epileptic disease activity in FCD. As hypothesized, patients with FCD type 2 had more severe epilepsy than those with type 1. Respectively, rates of HFOs were higher in the severe FCD type 2 than in the mild FCD type 1. In addition, we could confirm in this large group of patients with FCD findings of former studies (Urrestarazu et al., 2007; Jacobs et al., 2008, 2009) in which HFOs are closely related to the site of seizure onset and the removal of HFO-generating brain tissue correlates with the postsurgical outcome (Jacobs et al., 2010a; Wu et al., 2010; Akiyama et al., 2011).

Methodologic considerations

In our study, we used macroelectrodes, the standard EEG equipment for clinical purposes, to record HFOs. With use of macroelectrodes, the signal-to-noise-ratio between pathologic HFOs and the EEG background activity might be reduced. It is thus a subject of discussion whether recordings with macroelectrodes may underestimate the number of HFOs (Worrell et al., 2008; Châtillon et al., 2011). Independent of this, several studies have successfully investigated rates of HFOs using macrocontacts. In the present study the same electrode type was used for all patients. It is therefore unlikely that the contact size systematically biased the results.

HFOs were recorded using a sampling rate of 1,024 Hz, which is low compared to previous studies (Urrestarazu et al., 2007; Jacobs et al., 2008, 2009; Zelmann et al., 2009; Zijlmans et al., 2009; Jacobs et al., 2010a). This might result in limited recording of HFOs, particularly of fast ripples. However, there is evidence that HFOs of frequencies below 300 Hz sampled at <1,024 Hz are specific for areas of seizure onset (Ochi et al., 2007; Worrell et al., 2008; Crépon et al., 2010).

HFOs are detected more often during slow wave sleep (Staba et al., 2004; Bagshaw et al., 2009). In addition, artifacts caused by motor cortex activity are less frequent during sleep. As it remains unclear whether HFO activity might change in the periictal period, we tried to exclude a possible influence by using segments at least 2 h apart from any seizure. EEG segments were also chosen at the beginning of monitoring when the potential increase in seizure frequency due to drug withdrawal had not yet taken place. In our patient group, seizures during daily life were more common in patients with FCD type 2 than type 1. Therefore, we analyzed whether the seizure frequency during the recording period was significantly different for both groups and whether seizure frequency correlated with HFO rates in each patient. No interaction could be seen, which further supports our hypothesis that pathology and baseline disease activity is reflected by the difference in HFO rates between patients with FCD types 1 and 2.

HFOs were marked in an EEG segment of 3 min duration that is sufficient to ensuring stable rates (Zelmann et al., 2009). The range of frequencies set for the identification and distinction of ripples and fast ripples implies 200 Hz as lower limit for fast ripples according to Staba et al. (2002).

Because FCD is associated with frequent polyspikes, excluding a potential influence exerted by spikes on our analysis was a major concern that could be addressed only by visual marking. We therefore analyzed HFOs occurring without spikes separately and demonstrated that they led to the described results independently of spikes. To ensure stable rates, the concordance between HFOs that were visually marked by two reviewers was assessed using Cohen's kappa coefficient for each contact. After visual analysis was performed, a radial basis function neural network automatically detected HFOs in a copy of the same iEEG segment to additionally reduce reviewer bias. Our comparison between automatic detections and visual analysis provided evidence that both methods would have resulted in similar differences of HFO rates between types of FCDs (Dümpelmann et al., 2012). In principle, both automatic and visual assessments have advantages and disadvantages. For the present study, visual analysis was the preferred choice of method because automatic detection does not allow correction for channels with polyspikes, which may result in false identification of HFOs (Bénar et al., 2010; Crépon et al., 2010). Conversely, future clinical studies will rely heavily on automatic detection, as prospective studies using visual identification will be impossible owing to time limitations.

It has to be noticed that in our study the SOZ was defined before surgery by a clinical epileptologist, whereas analysis of HFOs was completed retrospectively after surgery.

Because the new International League Against Epilepsy (ILAE) classification of cortical malformations (Blümcke et al., 2011) had not yet been introduced during the time of our study, FCDs were categorized according to Palmini et al. (2004) into types 1a and b and 2a and b. However, because none of the FCDs included fulfilled the criteria of the new category FCD type 3, using the new classification would not have led to different results.

HFOs and epileptogenicity

HFOs have been investigated extensively as new markers of epileptogenicity over recent years. They are more specific indicators of the SOZ than spikes (Jacobs et al., 2008; Crépon et al., 2010) and might also identify epileptic areas outside the SOZ (Jacobs et al., 2010b). The strongest evidence for the validity of HFOs as markers of epileptogenic areas have been several independent observations that connect the surgical removal of HFO generating tissue with the postsurgical outcome (Ochi et al., 2007; Jacobs et al., 2010a; Wu et al., 2010; Akiyama et al., 2011; Nariai et al., 2011). The present results again confirm these observations and the potential value of HFOs in the presurgical evaluation. Because HFOs can be spontaneously generated by the nonepileptogenic eloquent cortex during slow-wave sleep (Blanco et al., 2011; Nagasawa et al., 2012), the specificity of HFOs as a biomarker of epileptogenicity remains to be determined. Clear differentiation between physiologic and pathologic HFOs is especially difficult in sensorimotor cortices (Fukuda et al., 2008), as in the case of patients 12 and 13.

Although fast ripples were nonetheless closely linked to epileptic areas in both studies of microelectrodes and macroelectrodes (Bragin et al., 2002; Staba et al., 2007; Engel et al., 2009), ripples were found to be pathologic only in the most recent studies (Jacobs et al., 2008; Worrell et al., 2008; Jacobs et al., 2010a). The results from the studies discussed earlier suggest that physiologic as well as pathologic ripples may exist (Engel et al., 2009; Wang et al., 2012) and differentiation by frequency band is not sufficient to distinguish between both. In the present study, ripples occurred over more widespread areas than fast ripples. Therefore, ripples correlated with the postsurgical outcome to a lesser extent than fast ripples. However, the majority of the described significant differences were observed for both ripples and fast ripples.

In addition, several studies suggest that rates of HFOs may be dependent on the epileptic disease activity of the underlying tissue and vary largely between patients (Jacobs et al., 2008; Akiyama et al., 2011). Moreover, rates of HFOs increase when antiepileptic drugs are reduced (Zijlmans et al., 2009). From a clinical perspective it is important to understand the variability of rates of HFOs so that they may eventually become a useful predictive marker of postoperative outcome. FCD is a frequent finding in patients with refractory epilepsy, and depending on the pathology of FCD the extent of dysplastic tissue and cortical disorganization may vary largely. For this reason we have chosen a homogenous group of patients with different types of FCD to evaluate whether patients with more severe dysplastic changes and thus higher epileptogenic disease activity generate more HFOs than those with less severe lesions.

Epileptogenicity in FCD

There is evidence that the different types of FCD classified according to Palmini show different grades of epileptogenicity (Kloss et al., 2002; Boonyapisit et al., 2003; Kral et al., 2003; Lawson et al., 2005; Widdess-Walsh et al., 2005; Lerner et al., 2009; Palmini et al., 2010). FCD type 2 occurs at an earlier stage during ontogenesis than type 1 (Cepeda et al., 2006), explaining the more undifferentiated and dysmorphic cells. Histologic abnormalities in FCD type 2 resulting in a loss of normal cell physiology are more clearly visible in MRI than in FCD type 1: FCD type 1 reveals only subtle signal changes being thereby frequently overseen, whereas FCD type 2 presents with increased cortical thickness and pronounced blurring of the gray and white matter junction (Colombo et al., 2009; Krsek et al., 2009; Lerner et al., 2009). In the present patient group, the diagnosis of FCD type 1 could be made from MRI findings in only half of the patients, whereas 85% of patients with FCD type 2 had a visible MRI lesion. The first clinical manifestation of epilepsy occurs at an earlier age in FCD type 2 and with a higher frequency of seizures (Lawson et al., 2005; Lerner et al., 2009; Palmini et al., 2010). Our study confirms these findings due to the observation of earlier mean first-time manifestation as well as higher rates of seizures in FCD type 2. In iEEG, higher rates of repetitive spiking patterns were found in patients with FCD type 2a than in type 1 (Boonyapisit et al., 2003).

FCD and HFOs

It was not the aim of this study to differentiate between lesional and nonlesional areas and their overlap with the SOZ. Even though we could confirm previous findings that HFOs are more linked to the SOZ than the lesion (Jacobs et al., 2009), the main aim was to investigate whether rates of HFOs in general were higher in patients with FCD type 2 compared to type 1. Studies analyzing HFOs in patients with FCD are rare and inconsistent (Jacobs et al., 2009; Brázdil et al., 2010). In a previous study, one patient with FCD with high rates of HFOs stood out of a heterogeneous group of patients owing to high rates of fast ripples outside the SOZ (Urrestarazu et al., 2007). Jacobs et al. (2009) found low rates of HFOs for four patients with FCD, but could see a significantly higher rate of fast ripples inside than outside the SOZ, whereas ripples did not show a significant difference. In contrast, Brázdil et al. (2010) saw ripples to occur more frequently in the SOZ, whereas fast ripples were less reliable markers in four patients with FCD. The differences of our results are most likely because of the larger number of patients and a greater variety of different types of FCD in our study.

To our knowledge no study looked at HFOs in different types of FCD. A link between the degree of underlying tissue change and the rates of HFOs has been shown only in patients with mesial temporal sclerosis (Staba et al., 2007; Ogren et al., 2009). The present results for the first time describe a correlation between the histologic findings and HFOs in neocortical epilepsy. Moreover, the results suggest that HFOs can reflect the epileptic disease activity of the underlying lesion and that the latter is higher in FCD type 2. It can be concluded that HFOs do not solely represent the localization of epileptogenic areas but distinguish between areas with lower and higher epileptogenicity. For clinical epileptology this important finding highlights that for the clinical use of HFOs it is not only important to look at the localization of HFOs but also at their rate. The use of HFOs as a prognostic biomarker is promising, but thorough prospective investigations in various types of epilepsies are needed prior to their widespread clinical use.


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References

Julia Jacobs was funded partially by the Morris Coole Award of the International League against Epilepsy.


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References

None of the authors have any conflict of interest to disclose. We confirm that we have read the Journal's position on issues involved in ethical publication and affirm that this report is consistent with those guidelines.


  1. Top of page
  2. Summary
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgments
  7. Disclosure
  8. References
  • Akiyama T, McCoy B, Go CY, Ochi A, Elliott IM, Akiyama M, Donner EJ, Weiss SK, Snead OC, Rutka JT, Drake JM, Otsubo H. (2011) Focal resection of fast ripples on extraoperative intracranial EEG improves seizure outcome in pediatric epilepsy. Epilepsia 52:18021811.
  • Asano E, Juhász C, Shah A, Sood S, Chugani HT. (2009) Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery. Brain 132:10381047.
  • Bagshaw AP, Jacobs J, LeVan P, Dubeau F, Gotman J. (2009) Effect of sleep stage on interictal high frequency oscillations recorded from depth macroelectrodes in patients with focal epilepsy. Epilepsia 50:617628.
  • Bénar CG, Chauvière L, Bartolomei F, Wendling F. (2010) Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on “false” ripples. Clin Neurophysiol 121:301310.
  • Blanco JA, Stead M, Krieger A, Stacey W, Maus D, Marsh E, Viventi J, Lee KH, Marsh R, Litt B, Worrell GA. (2011) Data mining neocortical high-frequency oscillations in epilepsy and controls. Brain 134:29482959.
  • Blümcke I, Thom M, Aronica E, Armstrong DD, Vinters HV, Palmini A, Jacques TS, Avanzini G, Barkovich AJ, Battaglia G, Becker A, Cepeda C, Cendes F, Colombo N, Crino P, Cross JH, Delalande O, Dubeau F, Duncan J, Guerrini R, Kahane P, Mathern G, Najm I, Özkara C, Raybaud C, Represa A, Roper SN, Salamon N, Schulze-Bonhage A, Tassi L, Vezzani A, Spreafico R. (2011) The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission. Epilepsia 52:158174.
  • Boonyapisit K, Najm I, Klem G, Ying Z, Burrier C, LaPresto E, Nair D, Bingaman W, Prayson R, Lüders H. (2003) Epileptogenicity of focal malformations due to abnormal cortical development: direct electrocorticographic-histopathologic correlations. Epilepsia 44:6976.
  • Bragin A, Wilson CL, Staba RJ, Reddick M, Fried I, Engel J Jr. (2002) Interictal high frequency oscillations (80–500 Hz) in the human epileptic brain: entorhinal cortex. Ann Neurol 52:407415.
  • Bragin A, Wilson CL, Almajano J, Mody I, Engel J Jr. (2004) High frequency oscillations after status epilepticus: epileptogenesis and seizure genesis. Epilepsia 45:10171023.
  • Brázdil M, Halámek J, Jurák P, Daniel P, Kuba R, Chrastina J, Novák Z, Rektor I. (2010) Interictal high-frequency oscillations indicate seizure onset zone in patients with focal cortical dysplasia. Epilepsy Res 90:2832.
  • Cepeda C, André VM, Levine MS, Salamon N, Miyata H, Vinters HV, Mathern GW. (2006) Epileptogenesis in pediatric cortical dysplasia: the dysmature cerebral developmental hypothesis. Epilepsy Behav 9:219235.
  • Châtillon CE, Zelmann R, Bortel A, Avoli M, Gotman J. (2011) Contact size does not affect high frequency oscillation detection in intracerebral EEG recordings in a rat epilepsy model. Clin Neurophysiol 122:17011705.
  • Colombo N, Salamon N, Raybaud C, Ízkara Ã, Barkovich AJ. (2009) Imaging of malformations of cortical development. Epileptic Disord 11:194205.
  • Crépon B, Navarro V, Hasboun D, Clemenceau S, Martinerie J, Baulac M, Adam C, Le Van Quyen M. (2010) Mapping interictal oscillations greater than 200 Hz recorded with intracranial macroelectrodes in human epilepsy. Brain 133:3345.
  • Dümpelmann M, Jacobs J, Kerber K, Schulze-Bonhage A. (2012) Automatic 80–250 Hz “ripple” high frequency oscillation detection in invasive subdural grid and strip recordings in epilepsy by a radial basis function neural network. Clin Neurophysiol 123:17211731.
  • Engel J Jr, Van Ness PC, Rasmussen TB, Ojemann LM. (1993) Outcome with respect to epileptic seizures. In Engel J Jr (Ed) Surgical treatment of the epilepsies. Raven Press, New York, pp. 609621.
  • Engel J Jr, Bragin A, Staba R, Mody I. (2009) High frequency oscillations: what is normal and what is not? Epilepsia 50:598604.
  • Fukuda M, Nishida M, Juhász C, Muzik O, Sood S, Chugani HT, Asano E. (2008) Short-latency median-nerve somatosensory-evoked potentials and induced gamma-oscillations in humans. Brain 131:17931805.
  • Gotman J, Levtova V, Farine B. (1993) Graphic representation of the EEG during epileptic seizures. Electroencephalogr Clin Neurophysiol 87:206214.
  • Jacobs J, LeVan P, Chander R, Hall J, Dubeau F, Gotman J. (2008) Interictal high frequency oscillations (80–500 Hz) are an indicator of seizure onset areas independent of spikes in the human epileptic brain. Epilepsia 49:18931907.
  • Jacobs J, LeVan P, Châtillon CE, Olivier A, Dubeau F, Gotman J. (2009) High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type. Brain 132:10221037.
  • Jacobs J, Zijlmans M, Zelmann R, Châtillon C, Hall J, Olivier A, Dubeau F, Gotman J. (2010a) High frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery. Ann Neurol 67:209220.
  • Jacobs J, Zijlmans M, Zelmann R, Olivier A, Hall J, Gotman J, Dubeau F. (2010b) Value of electrical stimulation and high frequency oscillations (80–500 Hz) in identifying epileptogenic areas during intracranial EEG recordings. Epilepsia 51:573582.
  • Kloss S, Pieper T, Pannek H, Holthausen H, Tuxhorn I. (2002) Epilepsy surgery in children with focal cortical dysplasia (FCD): results of long-term seizure outcome. Neuropediatrics 33:2126.
  • Kral T, Clusmann H, Blümcke I, Fimmers R, Ostertun B, Kurthen M, Schramm J. (2003) Outcome of epilepsy surgery in focal cortical dysplasia. J Neurol Neurosurg Psychiatry 74:183.
  • Krsek P, Pieper T, Karlmeier A, Hildebrandt M, Kolodziejczyk D, Winkler P, Pauli E, Blümcke I, Holthausen H. (2009) Different presurgical characteristics and seizure outcomes in children with focal cortical dysplasia type I or II. Epilepsia 50:125137.
  • Lawson JA, Birchansky S, Pacheco E, Jayakar P, Resnick TJ, Dean P, Duchowny MS. (2005) Distinct clinicopathologic subtypes of cortical dysplasia of Taylor. Neurology 64:5561.
  • Lerner JT, Salamon N, Hauptman JS, Velasco TR, Hemb M, Wu JY, Sankar R, Donald Shields W, Engel J Jr, Fried I. (2009) Assessment and surgical outcomes for mild type I and severe type II cortical dysplasia: a critical review and the UCLA experience. Epilepsia 50:13101335.
  • Nagasawa T, Juhász C, Rothermel R, Hoechstetter K, Sood S, Asano E. (2012) Spontaneous and visually driven high-frequency oscillations in the occipital cortex: intracranial recording in epileptic patients. Hum Brain Mapp 33:569583.
  • Nariai H, Nagasawa T, Juhász C, Sood S, Chugani HT, Asano E. (2011) Statistical mapping of ictal high-frequency oscillations in epileptic spasms. Epilepsia 52:6374.
  • Ochi A, Otsubo H, Donner EJ, Elliott I, Iwata R, Funaki T, Akizuki Y, Akiyama T, Imai K, Rutka JT. (2007) Dynamic changes of ictal high frequency oscillations in neocortical epilepsy: using multiple band frequency analysis. Epilepsia 48:286296.
  • Ogren JA, Wilson CL, Bragin A, Lin JJ, Salamon N, Dutton RA, Lüders E, Fields TA, Fried I, Toga AW. (2009) Three dimensional surface maps link local atrophy and fast ripples in human epileptic hippocampus. Ann Neurol 66:783791.
  • Palmini A, Lüders HO. (2002) Classification issues in malformations caused by abnormalities of cortical development. Neurosurg Clin N Am 13:116.
  • Palmini A, Najm I, Avanzini G, Babb T, Guerrini R, Foldvary-Schaefer N, Jackson G, Lüders HO, Prayson R, Spreafico R. (2004) Terminology and classification of the cortical dysplasias. Neurology 62:S2S8.
  • Palmini A, Jacques TS, Avanzini A, Barkovich J. (2010) The clinicopathologic spectrum of focal cortical dysplasias: a consensus classification proposed by an ad hoc Task Force of the ILAE Diagnostic Methods Commission. Epilepsia 1:17.
  • Schuele SU, Lüders HO. (2008) Intractable epilepsy: management and therapeutic alternatives. Lancet Neurol 7:514524.
  • Staba RJ, Wilson CL, Bragin A, Fried I, Engel J Jr. (2002) Quantitative analysis of high-frequency oscillations (80–500 Hz) recorded in human epileptic hippocampus and entorhinal cortex. J Neurophysiol 88:17431752.
  • Staba RJ, Wilson CL, Bragin A, Jhung D, Fried I, Engel J Jr. (2004) High frequency oscillations recorded in human medial temporal lobe during sleep. Ann Neurol 56:108115.
  • Staba RJ, Frighetto L, Behnke EJ, Mathern GW, Fields T, Bragin A, Ogren J, Fried I, Wilson CL, Engel J Jr. (2007) Increased fast ripple to ripple ratios correlate with reduced hippocampal volumes and neuron loss in temporal lobe epilepsy patients. Epilepsia 48:21302138.
  • Tassi L, Colombo N, Garbelli R, Tassi L, Colombo N, Garbelli R, Francione S, Lo Russo G, Mai R, Cardinale F, Cossu M, Ferrario A, Galli C. (2002) Focal cortical dysplasia: neuropathological subtypes, EEG, neuroimaging and surgical outcome. Brain 125:17191732.
  • Urrestarazu E, Chander R, Dubeau F, Gotman J. (2007) Interictal high-frequency oscillations (100–500 Hz) in the intracerebral EEG of epileptic patients. Brain 130:23542366.
  • Wang S, Wang IZ, Bulaci JC, Mosher JC, Gonzalez-Martinez J, Alexopoulos AV, Najm IM, So NK. (2012) Ripple classification helps to localize the seizure-onset zone in neocortical epilepsy. Epilepsia 54:370376.
  • Widdess-Walsh P, Kellinghaus C, Jeha L, Kotagal P, Prayson R, Bingaman W, Najm IM. (2005) Electro-clinical and imaging characteristics of focal cortical dysplasia: correlation with pathological subtypes. Epilepsy Res 67:2533.
  • Worrell GA, Gardner AB, Stead SM, Hu S, Goerss S, Cascino GJ, Meyer FB, Marsh R, Litt B. (2008) High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings. Brain 131:928937.
  • Wu JY, Sankar R, Lerner JT, Matsumoto JH, Vinters HV, Mathern GW. (2010) Removing interictal fast ripples on electrocorticography linked with seizure freedom in children. Neurology 75:16861694.
  • Zelmann R, Zijlmans M, Jacobs J, Châtillon CE, Gotman J. (2009) Improving the identification of high frequency oscillations. Clin Neurophysiol 120:14571464.
  • Zijlmans M, Jacobs J, Zelmann R, Dubeau F, Gotman J. (2009) High-frequency oscillations mirror disease activity in patients with epilepsy. Neurology 72:979986.