The main findings reported are: (i) in a group of neocortical patients with epilepsy, using automated detection and classification techniques without any manual data pre-selection, we found a class of high-frequency oscillations corresponding to ripple frequency oscillations whose rate of occurrence is increased in the physician-labelled seizure-onset zone in 63% of the patients and marginally or significantly increased in 50%; (ii) a class of oscillations corresponding to fast ripple high-frequency oscillations is relatively rare, and does not show significant rate increases in the neocortical seizure-onset zone compared with non-seizure-onset zone; (iii) we found no evidence that control neocortex is different from neocortex outside the seizure-onset zone in patients with epilepsy, when considering the rate at which high-frequency oscillations (of perhaps different underlying mechanisms) are generated; and (iv) while micro-electrodes on the neocortical surface do not appear to preferentially record high-frequency oscillations of higher frequency, micro-electrodes embedded in the parenchyma do.
Hz high-frequency oscillations have been rarely studied systematically in human epileptic neocortex, and high-frequency oscillation statistics from control neocortex of patients without a history of seizures are reported here for the first time, to our knowledge. We analysed more than 200
000 automatically detected high-frequency oscillation candidates in neocortical recordings from nine epilepsy and two control patients, originating in over 27
000 channel hours of intracranial EEG. Though our number of subjects was comparable with prior studies of high-frequency oscillations in human epilepsy, the overall volume of data we processed (>12 terabytes) was significantly larger. We hypothesize that analysing multi-hour continuous recordings permits more reliable estimates of global high-frequency oscillation rates across brain states than, for example, the short (e.g. 10
min) intracranial EEG segments typically examined under the constraints of human processing. Larger data sets also tend to produce more stable statistical estimates of the number of high-frequency oscillation classes (Blanco et al., 2010
The finding of an increase in neocortical ripple frequency high-frequency oscillations in the seizure-onset zone is consistent with prior studies that used human-intensive processing (Worrell et al., 2008
; Jacobs et al., 2009
). That there were two particular subjects whose seizure-onset zone increases were detected despite several non-seizure-onset zone channels with comparable ripple-like rates raises the possibility that some seizure-generating areas were missed by the clinical seizure mapping process, which currently does not take high-frequency oscillations into account. The finding that fast ripple-like events are rare and not significantly increased in the neocortical seizure-onset zone is different than previous studies investigating mesial temporal lobe epilepsy (Bragin et al., 1999
; Worrell et al., 2008
), but in reasonable accord with one study that reported that >200
Hz high-frequency oscillations were not observed in the four subjects with neocortical epilepsy that they studied (Crépon et al., 2010
). Urrestarazu et al. (2007)
also report fast ripples only rarely in neocortex. They reported fast ripple seizure-onset zone rate increases in a small number of patients, but the rate increases did not reach statistical significance. The absence of an increase in fast ripple high-frequency oscillations in neocortex reported here could be related to the fact that our epipial micro-wires are unlikely to detect neuronal unit activity. However, this seems unlikely to be the complete explanation as multiple groups have reported the ability to record fast ripple high-frequency oscillations from clinical macro-electrodes, which would not capture single unit or multi-unit neuronal activity.
The three subjects without increasing trends in Cluster 4 (‘ripple’) seizure-onset zone rates all had fewer than three macro-electrodes in the seizure-onset zone, including two subjects with only a single macro-electrode in the seizure-onset zone. A fourth subject, SZ09, anomalously had median rates of zero on both seizure-onset zone and non-seizure-onset zone channels; and though the median rate was thus not higher in seizure-onset zone channels for this subject, the mean rate was higher, by a factor of 1.7. High percentages of non-seizure-onset zone and seizure-onset zone channels without any Cluster 4 (‘ripple’) events was not surprising for this subject, given the uniquely poor quality of Subject SZ09's recordings, which is evidenced by the predominance of the colour green (Cluster 2, putative artefact) in the plots in . The average number of seizure-onset zone electrodes was 4-fold higher in the five subjects with increasing Cluster 4 (‘ripple’) trends in the seizure-onset zone. We suspect that the number of subjects in whom statistical significance was retained after correction for multiple comparisons would have been larger had the number of seizure-onset zone electrodes been greater. Unfortunately, this sampling limitation, discussed in greater detail below, is inevitable given the spatial density of current clinical electrode arrays. We anticipate that new high-density electrode array technology will be able to resolve this limitation in future work (Kim et al., 2010
; Viventi et al., 2010
We observed events of all four classes in both control patients. Moreover, event rates for control regions were not significantly different for any cluster when compared with non-seizure-onset zone regions. With only two control patients, our permutation tests had relatively low power. But they are discouraging for the prospect of finding a universally ‘normal’ rate of high-frequency oscillations that might serve as a baseline for patient-independent detection of the seizure-onset zone.
We are uncertain how to interpret the finding that micro-electrodes record high-frequency oscillations of higher frequency than macro-electrodes on depth but not surface electrodes. Given the cellular architectural differences between hippocampus, for example, and neocortex, it is plausible that this result reflects physiological differences in the micro-environments of depth and surface electrodes. An alternative explanation might be related to the fact that surface micro-electrodes are non-penetrating and rest atop the pia mater, which may act as a low-pass filter, while the depth micro-electrodes actually penetrate the parenchyma.
Our data-mining approach (as well as subsequent analyses) treats all intracranial EEG equally. On one hand, this can be viewed as a limitation: our methods do not explicitly attempt to parse whether high-frequency oscillation detections are occurring during specific states of arousal, or in conjunction with epileptiform events such as sharp waves, or within or outside of lesions, for example. That said, other authors have investigated these questions, concluding that the spatial specificity of high-frequency oscillations may be improved in non-REM sleep (Bagshaw et al., 2009
) and that high-frequency oscillations provide localizing information independent from interictal spikes (Jacobs et al., 2008
), lesions (Jacobs et al., 2009
) and seizures themselves (Worrell et al., 2008
On the other hand, this unbiased algorithmic approach leads to several ideas about high-frequency oscillations in epilepsy. First, the fact that we found a signal that increases in the seizure-onset zone without special selection of patients, channels or time-epochs for processing, suggests that the signal is strong. Second, it suggests that similar findings in prior studies of high-frequency oscillations in mesial temporal lobe, which typically use restrictive data pre-selection criteria, may actually be more generalizable, and hence potentially more practically useful in the clinical setting. Finally, that we are able to detect and classify these signals automatically, without any human intervention, adds to the promise of practical clinical utility. The results presented here support the usefulness of unbiased automated detection of high-frequency oscillations from large data sets, and the potential application to pre-surgical evaluations. The results also open a potential therapeutic opportunity with the possibility that high-frequency oscillations can be used as control signals in closed loop-implantable seizure therapy devices, which will not have the same luxury of human expert review to pre-identify optimal data for processing.
Our findings should be interpreted in light of limitations on electrode coverage. By nature, clinical intracranial EEG recordings suffer from significant spatial undersampling (Stead et al., 2010
). Electrode implants span a limited range of cortex surrounding the suspected seizure-onset zone, often leaving doubt as to whether important seizure-generating regions have been missed. For a study like ours, this means both noise in the labels of seizure-onset zone and non-seizure-onset zone, particularly where electrodes near the periphery of an array are concerned; and less than optimal sample sizes (i.e. numbers of channels in a given group), often making it difficult to detect all but very large effects statistically. Another potential sampling limitation in this study is that intra-parenchymal cortical recordings were not performed. It is possible that some classes of high-frequency oscillations, such as the fast ripples found in penetrating micro-electrode recordings in the hippocampus, are most reliably recorded from penetrating neocortex as well.
The efficacy of epilepsy surgery and implantable anti-epileptic devices is directly linked to precise identification of seizure-generating regions. Our results suggest that automated mapping of high-frequency oscillations may be of significant utility for this purpose, though it is important to underscore that the finding of a statistically significant relationship between seizure-onset zone regions and high-frequency oscillation increases does not necessarily imply good surgical outcome predictive value. It is still an open question, for instance, whether high-frequency oscillation activity on a given channel can be used to make reliable predictions about whether the tissue beneath that channel should be resected to improve the chances of favourable surgical outcome. Our findings remain to be associated with outcome and should be validated in larger prospective studies.
As clinical results for patients undergoing surgery for non-lesional neocortical epilepsy have plateaued at only modest rates, we hope that a better understanding of the spatial and temporal characteristics of high-frequency oscillations in epileptic networks will lead to improved surgical outcome.