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International Journal of Neural Systems, Vol. 25, No. 5 (2015) 1550022 (12 pages) c World Scientific Publishing Company DOI: 10.1142/S0129065715500227
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the Epileptogenic Zone Elisa Bellistri∗ , Ivana Sartori† , Veronica Pelliccia† , Stefano Francione† , Francesco Cardinale† , Marco de Curtis∗ and Vadym Gnatkovsky∗,‡ ∗ Unit of Epileptology and Experimental Neurophysiology Fondazione IRCCS, Istituto Neurologico Carlo Besta 20133 Milano, Italy †
Claudio Munari Epilepsy Surgery Center, Ospedale Niguarda Ca’ Granda, Milano, Italy ‡ [email protected] Accepted 24 April 2015 Published Online 28 May 2015
Epilepsy is a disease characterized by aberrant connections between brain areas. The altered activity patterns generated by epileptic networks can be analyzed with intracerebral electrodes during presurgical stereo-electroencephalographic (EEG) monitoring in patients candidate to epilepsy surgery. The responses to high frequency stimulation (HFS) at 50Hz performed for diagnostic purposes during SEEG were analyzed with a new algorithm, to evaluate signal parameters that are masked to visual inspection and to define the boundaries of the epileptogenic network. The analysis was focused on 60–80 Hz activity that represented the largest frequency component evoked by HFS. The distribution of HFS-evoked fast activity across all (up to 162) recording contacts allowed to define different clusters of contacts that retrospectively correlated to the epileptogenic zone identified by the clinicians on the basis of traditional visual analysis. The study demonstrates that computer-assisted analysis of HFS-evoked activities may contribute to the definition of the epileptogenic network on intracranial recordings performed in a pre-surgical setting. Keywords: Focal epilepsy; epilepsy surgery; intracerebral recordings; stereo-EEG; high-frequency stimulation.
1. Introduction One third of the patients that suffer from pharmacoresistant focal epilepsy are potential candidates for therapeutic surgery. The goal of epilepsy surgery is to remove, disconnect or inactivate the epileptogenic cortex to obtain seizure freedom.1 A correct presurgical identification of the epileptogenic zone (EZ) has an impact on postsurgical outcome.2 In 20–50% of the patients candidate to surgery, invasive recordings by either subdural grids/strips or intracerebral stereo-electroencephalographic (SEEG) electrodes is necessary to identify the boundaries of the EZ. In spite of the accurate EZ identification that the clinical expertise produces, a non-negligible rate of
failure in the outcome after epilepsy surgery (mainly in extra-temporal cortical areas) indicates that the definition of the EZ could be improved.3 In recent years, an increased number of studies has taken advantages of computer-assisted analysis (CAA) of intracranial EEG signals to-identify biomarkers that support human EZ detection.3–8 Pre-surgical intracranial SEEG monitoring is performed for several days, to acquire the necessary information to define the zone of initiation and propagation of seizures. During SEEG, stimulation sessions are routinely performed to map symptomatogenic areas and to identify eloquent zones that have to be preserved during the surgery. Electrical
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stimulation of the cortex can elicit after discharges, subclinical EEG seizures, habitual or non habitual auras and seizures.9 Typically, two different stimulation protocols are used: single pulse stimulation and high frequency stimulation (HFS) at 50 Hz. The analysis of SEEG during intracerebral stimulation might provide information about the dynamic of signal propagation in the epileptogenic networks.10–12 Previous works analyzed responses evoked by singlepulse stimulation protocols to improve the EZ definition.12,13 and to identify intracranial markers of EZ in adults.13,14 and children.15 Singlepulse and train stimulations were used in humans to evoke epileptiform transients resembling EEG spikes13 and to-study human cortical connectivity in both epileptic,16–19 and physiological networks.20,21 The aim of the present report is to characterize the dynamic of the responses evoked by trains of HFS at 50 Hz, in order to characterize the EZ and the mechanisms of propagation of epileptiform activities across physiological and pathological networks in pre-surgical setting. The high throughput of information collected during intracranial recording is further analyzed both in the time and in the frequency domain and the parameters obtained from this analysis are subsequently organized using graph theory tools and clustering methods. In addition, the proposed method (i) contributes to evaluate if and to what extent different brain areas are connected, (ii) offers a framework to characterize the organization of functional networks and (iii) allows to examine different properties of the signal in a large dataset. 2.
Material and Methods
2.1. SEEG recording and definition of the epileptogenic area HFS is part of a standard diagnostic SEEG protocol for patients candidate to epilepsy surgery. The SEEG diagnostic procedure was approved on 12 December 2013 by the local Ethics Committee (protocol no. ID 939 by the Comitato Etico Scientifico Milano Area C). Patients were implanted with depth electrodes for pre-surgical evaluation, according to the stereotactic method.22–24 A total of 12 to 16 intracerebral multichannel electrodes (Dixi Medical, France and ALCIS, France; 5–18 contacts each; length, 2 mm, diameter, 0.8 mm; 1.5 mm
apart. According to physical parameters, resulting in a total charge density of 20 µC/cm2 /phase, thus significantly lower than the maximum safe value of 60 µC/cm2 /phase) were implanted, for a total number of 105–162 recording sites per patient. SEEG recordings with 0.016–300 Hz band-pass filter were performed using Neurofax EEG-1100 system (Nihon Kohden, Tokyo, Japan) at 1 kHz sampling rate and 16-bit resolution. Intracerebral recording sites were identified on 3D MR reconstructions of the patient brain, according to the standard method described in Ref. 25. Recording contacts located either in the EZ26 or in the surrounding early-propagation zone (EPZ; defined as the area of early propagation of the ictal discharge) or in the normal tissue that surrounds these areas (not-epileptogenic tissue; NET) were identified for each patient by expert clinical neurophysiologists. The standard clinical protocol utilized in pre-surgical settings requires the independent revision of SEEG data by two expert neurophysiologists, to avoid subjective bias in the selection. Electrodes included in the EZ were defined by the time onset of fast activity included in the first second of the seizure. Electrodes in which fast activity was observed after 1 s were identified as EPZ. All five retrospectively evaluated patients were surgically treated by EZ-EPZ excision and EZ/EPZs were confirmed by the postsurgical outcome according to the Engel scale.27 Intracerebral HFS trains were delivered as part of the routine clinical assessment to locate both the epileptogenic and eloquent regions. HFS was delivered in only one session between 9 am and 5 pm; each HFS session lasted less than 1 h. Bipolar 1 ms pulses of variable intensity (from 0.3 to 3 mA; current density between 0.06 and 0.6 mA/mm2 ) were applied for 1–6 s at 50 Hz to pairs of contiguous contacts on the same electrode shaft.23,25,28 Intensity and duration of the stimulation are chosen according to the brain region connectivity and the patient response. Inter-HFS interval was 1–5 min. HFS was performed in 15–40% of the leads available on implanted electrodes (see Table 1). Since the dataset is derived from a retrospective evaluation, HF stimulation was not performed on the total couples of contacts, but was delivered at sites suspected by clinicians to be within or around the EZ: each patients had a variable number of contacts stimulated within and outside EZ and EPZ.
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
Table 1. Data of the SEEG electrodes utilized for recording and HF stimulation in the five patients included in the present study. In the column SEEG contacts, the numbers within brackets exclude contacts positioned in the white matter (WM) verified by MR reconstruction of the electrode tracks. The EZ and EPZ contacts were identified by traditional visual analysis by the clinical neurophysiologist. Age at surgery
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P1 P2 P3 P4 P5
SEEG contacts (w/o WM)
5 38 18 30 18
128 159 180 157 138
(80) (92) (94) (114) (102)
HFS contacts (%) 26 39 38 43 52
EZ contacts (%)
(32.5) (42) (40) (38) (50)
8 7 4 18 26
(10) (4) (4) (16) (25)
EPZ contacts (%)
ENGEL scal
21 (25) 22 (24) 4 (4) 4 (3.5) 3 (3)
1A 1A 1A 1B 1A
HFS session durations varied depending on patient’s clinical condition and on the number and type of seizures elicited. The proportion of recorded and stimulated contacts situated in EZ, EPZ and in the WM is summarized in Table 1 for the five patients analyzed. 2.2. Electrophysiological data analysis SEEG data obtained during HFS were analyzed using software (http://www.elpho.it/) developed in LabView (National Instruments, Austin, TX). The protocol utilized for the analysis is shown in Fig. 1. Stimulation artifacts were removed from 20-s trace epochs that include HFS. The artifact-removing algorithm detects the positive peak of each stimulus of the train (>3 times the standard deviation of the background signal), a segment of 10 ms symmetric around stimulus peak was passed to LabView spline interpolation function and 6 ms were substituted with trace interpolated by 2 ms on each side (Fig. 2(b), middle traces) (see Ref. 29 for a comparison between different interpolation methods). The signal was then filtered between 2 and 400 Hz (lower traces in Fig. 2(b) and the power spectrum was calculated in 1-s bins without overlap using the multi-taper function in MatLab (The MathWorks Inc., Natick, MA). Frequency resolution was 0.98 Hz. Power spectrum density (PSD) of 60–80 Hz activities was calculated on artifact-subtracted, pass-band filtered signals, as illustrated in the lower part of Fig. 2(b). 2.3. PSD cluster analysis Specific 60–80 Hz PSD changes during HFS (Fig. 2(c); see also Sec. 3) were calculated and were plotted
Fig. 1. Outline of the process to define EZ and EPZ after HFS protocol with the computer-assisted method described in the study.
on two-dimensional scatter graphs. PSD measurements were calculated for equivalent epochs before and during HFS (Fig. 3(c)). Contacts parameters
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Fig. 2. (Color online) SEEG signal processing. (a) Positions of the two contacts (A10 and B1) on MR reconstruction of patient 1 brain. (b) Representative examples of the responses evoked by HFS (50Hz) simultaneously recorded on these two electrode contacts. In the upper traces, the raw signals are shown in 20-s epochs that included the period of HFS. In the middle traces, the HFS artifact was subtracted. In the lower traces, subtracted signals were band-pass filtered at 2–400 Hz. In the insets, two examples of artifact removal, magnified in a 100ms window. Spectrograms of the filtered traces are illustrated for 80–400 Hz (upper spectrogram) and 50–80 Hz (lower spectrograms) frequency ranges. No frequency increase was found in the range 100–400 Hz in both cases; Harmonics of 50Hz stimulation artifacts were observed in A10. In the example on the left (contact A10), the intensity of the frequencies during the train did not change compared to the background activity (before the train). An increase of the frequencies between 60 and 80 Hz was evident in the correspondence of the HFS window in the traces and in the frequency plot (lower panel) in the contact B1 (right traces). (c) Integral frequencies of average PSDs across 60–80 Hz during the stimulation period, in a representative subset of contacts of electrodes A (including contact A10) and B (including contact B1). Integral values of the two contacts showed as examples in B are marked in red bars. 1550022-4
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
Fig. 3. Method to calculate PSD integral evoked by 50 Hz HFS. (a) MR reconstruction of Patient 1 brain with the position of the implanted SEEG electrodes. HFS was delivered in the contact pair G’1–2. (b) Signals recorded in all contacts considering a window of 20 s around the HFS period (gray shading). The gray histogram on the right side represents the integral of average PSD across 60–80 Hz frequencies evoked by HFS recorded in all contacts, as detailed in Fig. 2(c). The recordings obtained in a subset of contacts on one electrode is outlined by the dotted line and is illustrated with expanded time base in (c) Leads 1–3 positioned in the EZ clearly show high frequency activity. PSD integrals in the pre-stimulation window (a) and during HFS stimulation (b) are shown. PSD values obtained by subtracting (b)–(a) are illustrated on the right histogram in panel C. PSD integral in (b) and the difference (b)–(a) constitute the two variables plotted utilized in the scatter diagrams of Fig. 4.
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in a two-dimensional space were grouped into different clusters, automatically selected by a k-mean cluster algorithm. Cluster analysis was implemented as described in Ref. 30. Given a set of observations (x1, x2, . . . , xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k sets
(k ≤ n)S = {S1, S2, Sk} so as to minimize the withincluster sum of squares (WCSS): argmin
k
xj − µ2 ,
(1)
i=1 xj ∈Si
where µi is the mean of points in Si .
(a)
(b)
(c)
(d)
Fig. 4. (Color online) (a) PSD integral (b in Fig. 3(c)) and variation of PSD integral (b − a in Fig. 3(c)) in response to HFS in one single contact pair calculated for each contact are represented in a two-dimensional graph. K-mean cluster algorithm identifies two different clusters. (b) Individual scatter plots obtained in all recording contacts following HFS in contact pairs in Patient 1 are superimposed in a single diagram; values are normalized to compare clusters obtained at different sessions. The large majority of contacts from NET cluster in the left side of the graph, whereas contacts in the EZ and EPZ group in the right part. EZ, EPZ and NET contacts identified by the clinical neurophysiologist are colored in red, orange and white/gray, respectively. The contacts with enhanced PSD integral response form a virtual cluster (blue line) identified by the algorithm. (c) Contacts in the cluster outlined in B are represented in a connection diagram with a circular layout. Contacts that lay outside the circle respond only to a single position of HFS during the entire protocol. (d) Histogram of the inbound degree of the contacts selected and represented in (c). Contacts outside the graph circle with an inbound degree of 1. Note that all these contacts are NET. EZ and EPZ tend to have high values of inbound degree. 1550022-6
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
Table 2. Comparison between the number of contacts included in EZ, EPZ and NET identified by the computer-assisted analysis (CAA) and by the clinical evaluation (CE). P1
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Total no. contacts EZ contacts EPZ contacts NET contacts
P2
P3
P4
P5
CAA
CE
CAA
CE
CAA
CE
CAA
CE
CAA
CE
37 8 16 86
29 8 21 99
34 6 9 112
29 7 22 131
16 4 1 155
8 4 4 166
34 15 3 119
22 18 4 135
31 22 1 101
29 26 3 109
The number of identified clusters varied from 2 to 4. A variable number of clusters allowed to test different group formations, to get the best fitting around the group of contacts that visually separated from the background and to exclude outlier produced by artifact responses. Once the number of clusters was defined, the algorithm procedure was implemented for each stimulation pairs and the list of contacts included in the selected cluster was appended in a text file. The data list obtained during the iteration of the procedure for each stimulation contact pairs constitute a group of contacts where same contacts can be replicated (Figs. 4(a) and 4(b)). The overall group of contacts was represented in a scatter plot similar to previous one. To allow comparison between different stimulations, values were normalized on both axes, respectively to the highest PSD and ∆PSD obtained for each stimulation pair. The rate of response occurrences of each contact to all HFS was evaluated for each patient and represented in a circular layout using Cytoscape 3.2.0 (http://www.cytoscape.org)31 as illustrated in Fig. 4(c). 2.4. Statistical analysis The matching between the algorithm selection and the clinical classification of the contacts was evaluated by the indexes of Accuracy, Sensitivity and Specificity, obtaining a quantification of the algorithm performance. We label as True Positive (TP) the number of contacts selected by the algorithm that matched the EZ and EPZ clinical classification; as True Negative (TN) the group of contacts not selected by the algorithm and classified as NET; as False Positive (FP) the contacts selected by the algorithm and classified as NET and finally as False Negative (FN) the contacts not selected but classified in the EZ or EPZ groups. Moreover,
P and N are respectively the totality of EZ + EPZ (the former) and NET (the latter) contacts according to clinical classification. Accuracy is defined as (TP + TN)/P + N . Sensitivity is defined as TP/P . Specificity is defined as TN/N . The quantification of the amount of contacts in each group, according to algorithm selection and clinical classification, is summarized in Table 2. 3.
Results
Signals from all SEEG recorded contacts were processed following the protocol outlined in Fig. 1. The frequency content in each SEEG trace was plotted using a color code in a time–frequency graph (lower traces in Fig. 2(b). Power spectral analysis demonstrated that in all patients considered, the dominant frequencies during HFS were included between 50 and 80 Hz; frequencies higher than 80 Hz were not consistently represented during HFS (upper colored time–frequency graphs in Fig. 2(b). To exclude 50 Hz artifacts due to the stimuli trains, data from contacts that developed a fast activity component during the HFS were visually inspected in the 60–80 Hz frequency band (lower spectograms in Fig. 2(b). The integral of the average 60–80 Hz PSD for each channel was evaluated with a sliding temporal window of 5 s. PSD analysis allowed to highlight a subset of contacts in which HFS induced fast activity at 60–80 Hz during the period of stimulation (i.e. contacts B1B3 in Fig. 2(c)). The analysis was repeated for each SEEG channel (Figs. 3(a) and 3(b)). As illustrated in Fig. 3(c), a second parameter was calculated for each channel as the difference between the average PSD during the HFS epoch (b) and the average PSD in an equal temporal window calculated on baseline traces immediately before the beginning of the HFS train (a). The subtracted value
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(b − a in Fig. 3(c)) represented the PSD changes specifically associated with HFS. The PSDs during HFS (values b) and the subtracted PSDs (b − a) obtained in all SEEG contacts during HFS in a single pair of contacts were plotted in a two-dimensional scatter graph (Fig. 4(a)). Contacts parameters in a two-dimensional space were grouped into different clusters, automatically selected by a k-mean cluster algorithm (dotted lines Ref. 30). A semi-automatic, operator-supervised procedure was used to confirm or discard each cluster defined by the algorithm. The identification of clusters clearly separated from the rest of contacts was supervised by the operator, on the basis of the retrospective inclusion of contacts defined by the clinical neurophysiologist as inside or outside the EZ and the EPZ. This procedure was repeated for each HF stimulated contact. Figure 4(b) illustrates the result of the evaluation of PSD analysis in one patient following HFS in all stimulation sites and recordings from all the contacts. Contacts labeled in each cluster were assigned to either the EZ or the EPZ according to the clinical classification derived from the retrospective evaluation based on the post-surgical seizure outcome (Sec. 2). The next step was to evaluate the correlation between the contacts selected and clustered by the computer assisted analysis and the contacts identified during the standard visual procedure by the clinical neurophysiologist. Contacts extracted by the algorithm were labeled as EZ, EPZ and NET according to the clinical selection. The algorithm does not provide any classification, but generates a list of contacts that are subsequently identified as EZ, EPZ and NET by comparison with the clinical classification; such a procedure is used to test the algorithm performance for the detection of contacts clinically classified as epileptogenic. These results are represented for one patient in the cumulative cluster in Fig. 4(b). During HFS protocol both epileptogenic and normal contacts were stimulated (Table 1). An a priori network involves all connections between a stimulated contact and all other channels. The subset of connections highlighted by the algorithm were represented in a circular layout graph developed in Cytoscape 3.2.0 on these selected group of contacts that generated fast activity. Connections that did not generate 60–80 Hz in response to HFS were excluded by the graph. Contacts that responded only
once during all the stimulations (inbound degree = 1) were clearly found in the outside portion of the circular graph. Contacts in the circle demonstrated different degrees of connectivity. The distribution of the different number of connections of each responding contact were sorted by inbound degree values, as illustrated in the histogram in Figure 4(d). A threshold of two inbound degrees, that identifies contacts responding to at least two stimulation sites, was applied to remove the less likely connected contacts (treated as well as outliers). Contacts with an inbound degree of 1 were discarded from the selected contacts list utilized to compare clinicallyand computer-defined data, illustrated in Fig. 5. Finally, the list of contacts with >2 inbound degree values was analyzed and compared with medical attribution of contacts to EZ and EPZ defined by clinical means. The percentage of matching between the computer-assisted method and the traditional clinical method based on visual inspection observed in five patients is shown in Fig. 5. On average, the matching proportion was 90% (+/−8%) for the EZ, 50% (+/−23%) for EPZ and 10.7% (+/−3%) for selected NET contacts.
Fig. 5. (Color online) Computer-defined contacts in five patients. The diagram shows the percentage of matching between contacts selected by semi-automatic algorithm and the EZ, EPZ and NET contacts labeled by the clinical neurophysiologist. The percentages of contacts extracted by the algorithm clinically identified as EZ, EPZ and NET are illustrated as red, orange and gray bars, respectively. The ability of the algorithm to recognize epileptogenic contacts is confirmed by the high number of EZ and a low number of NET. The variable percentage of EPZ contacts in the five patients is discussed in the text.
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
In spite of the different number of contact stimulated (summarized in Table 1), the matching between the computer-based classification and the clinical classification was relatively stable, with a high matching proportion for the EZs (red columns), and a slightly lower matching in the case of EPZs (orange columns). Some contacts identified by the algorithm were labeled as NET by clinicians. These represents the false positive (gray bars in Fig. 5). A high coincidence between contacts identified as NET by the traditional and contacts discarded by computer methods was also found. Performance rate of both EZ and EPZ selection (percentage of matching) was highly significant (p < 0.001) in comparison to the rate of false positive (percentage of matching between selected contacts and NET contacts). Coincidence values between contacts evaluated on the five considered patients are detailed in Table 2.
4. Discussion Our study demonstrates that in patients with drugresistant focal epilepsy HFS performed during SEEG evokes fast activity (60–80 Hz) responses in contacts recorded in the brain area responsible for seizure generation/propagation. The proposed method demonstrates that PSD analysis during HFS protocols can be used to identify epileptogenic regions in humans. In comparison with the static values extracted from the analysis of spontaneous epileptiform events (i.e. ictal and interictal discharges), PSD of HFSevoked fast activity responses explores the dynamic nature of the epileptogenic area and the intrinsic organization of the physiological and pathological networks of an epileptic brain. This approach contributes to evaluate the results obtained by the clinical-based visual examination of the EZ. During presurgical SEEG monitoring mainly two states of the epileptic brain are evaluated: spontaneous events (seizures and interictal activities) and responses to either single-pulse or high-frequency electrical stimulation. The majority of works available in the literature focused on the computer assisted analysis of spontaneous activity and singlepulse evoked responses.7–8,11–21,32 To our knowledge, there are no published reports that study the effect of HFS protocols on the brain neuronal network during SEEG.
HFS is used during clinical SEEG pre-surgical monitoring to achieve two main goals: (1) to identify eloquent regions that have to be preserved by surgery and (2) to evoke seizure-like symptoms to localize the seizure-genic (epileptogenic) cortical areas. While extensive studies focus on clinical features evoked by HFS (summarized in a review by Selimbeyoglu and Parvizi.33 ) the neurophysiological response to HFS are usually ignored. One of the reasons could be that the rapid changes in the ongoing SEEG signals during HFS are masked by the stimulation artifact and are difficult to detect. Since HFS are performed mainly to induce symptoms that mimic seizure features, we need to consider a possible bias in our analysis. The choice of the stimulated contacts depends on the areas explored by SEEG electrodes and varies from a patient to patient (Table 1, see Sec. 3). HFS of pairs of contacts in mesial temporal structures is usually avoided because it can precipitate seizures and stimulation of electrodes positioned in the WM is often omitted because it is considered not to contribute to the localization of eloquent and/or symptomato-genic cortical areas. Therefore, not all contacts utilized for SEEG exploration were utilized in the study here described (see Table 1). When EZ and peri-EZ cortical contacts are included in HFS protocols, results show a quite stable response at 60–80 Hz in the epileptogenic area that includes EZ and EPZ. HFS of the EZ/EPZ usually generate fast activities, while HFS in NET only sporadically induced high frequencies patterns. The different responses observed in EZ/EPZ and NET are probably due to the over-excitability of the epileptogenic region.34 that is prone to generate fast activity in the beta/gamma range at seizure onset35–37 The EZ activation during HFS is possibly sufficient to entrain responses at 60–80 Hz. The difference in response to HFS between the EZ/EPZ and the NET generates a clear bimodal PSD distribution that is expressed by the cluster separation illustrated in the Results. We assumed that the mere increase in gamma frequency PSD describes only in part the propagation dynamic of activity evoked by HFS in brain networks, and thus analyzed two other parameters: the PSD changes in comparison to background activity just before HFS and the degree of connections between stimuli and regions that showed PSD increases. The comparison between PSD integral during the HFS period and the variation of PSD
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integral calculated in time window of the same duration just before the stimulation (see Methods) was utilized to exclude conditions in which HFS-induced responses were generated by an enduring state of hyperexcitability: these data demonstrate the specificity of HFS-induced changes. The evaluation of inbound degree with graph analysis was used to quantify and separate subpopulations of contacts that occasionally responded to one or two neighboring HFS from other contacts that responded to stimulations at different cortical contact pairs and showed a high connectivity pattern. High values of inbound degree are mostly associated with EZ contacts, whereas low values correlated with NET contacts. The use of graph representation in our study are consistent to previous graph theory findings applied to epilepsy EEG and magneto-EEG analysis that demonstrated an increase of connectivity degree in EZ contacts. These findings are consistent with the concept that EZ networks tend to organize with a pattern elsewhere defined as small world configuration.38–41 Even if it was carried out on a limited number of patients, the retrospective comparison between the algorithm-generated results and the CE generated an excellent matching of mean accuracy rate (87%) and mean sensitivity rate (94.5%). The lower specificity rate (mean = 53.7%) suggested to explore more in detail the algorithm output and to refine the contacts selection. The observation that low inbound degree values are typical of NET contacts, allowed to define a threshold of inbound degree assumed to describe NET. The exclusion of these presumed NET contacts decreases the number of contacts classified as epileptogenic by the algorithm, improving the specificity of the method. Post-comparisons between the two classifications allowed to identify contacts that represented outliers in the clustering evaluation (gray contacts in Fig. 4(b)). To reduce FP rate, contacts located next to the stimulated pair were removed from final selection submitted to analysis, since the frequency increase observed in very close-by contacts is assumed to be generated by passive electrical propagation of high intensity current. The histogram representation of contact matching in Fig. 5 shows that the specificity rate is very high considering EZ clinical- and algorithm-classified contacts, and decreases for EPZ contacts. It is worth to point out that our algorithm is not designed to
distinguish between EZ and EPZ, but simply identifies neurobiological properties that are assumed to be specific of the epileptogenic network. The results are then compared with the identification of contacts as EZ, EPZ and NET by the clinical neurophysiologists. The differences in classification are most relevant in EPZ, a region not primarily involved in seizure initiation and, as such, a potential boundary area between EZ and NET. The clinical classification of EPZ contacts is highly dependent on the time value and the velocity of propagation selected to define seizure propagation areas; small changes of these values will alter the number of contacts included or excluded from the EPZ. To this end, a contact classification based on the pattern of activity generated by HFS is less arbitrary and could, in principle, be utilized to better characterize this region and the confines of the surgical excision. 5.
Conclusions
The presented method is here described and retrospectively applied to a small number of patients. These preliminary results suggest that the dynamic of the responses to HFS could be useful to define the epileptogenic networks and their functional properties. Further prospective validation of the method on a larger group of patients is needed to estimate the potential clinical impact on the identification of the EZ. In addition, this study suggests that HFS protocols should be improved to extract valuable information relevant for EZ identification and ultimately to guide the surgical procedure. Disclosure Statement The authors have nothing to disclose. The authors declare that this is an original unpublished work and the manuscript or any variation of it has not been submitted to another publication previously. Acknowledgment Grant of the Ministry of Health (RG 151-2012 and Ricerca Corrente 2013–2014). References
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1. F. Rosenow and H. L¨ uders, Presurgical evaluation of epilepsy, Brain 124(pt 9) (2001) 1683–1700.
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
2. P. Kahane, E. Landre, L. Minotti, S. Francione and P. Ryvlin, The Bancaud and Talairach view on the epileptogenic zone: A working hypothesis, Epileptic Disord. 8(Suppl 2) (2006) S16–S26. 3. F. Bartolomei , P. Chauvel and F. Wendling, Epileptogenicity of brain structures in human temporal lobe epilepsy: A quantified study from intracerebral EEG, Brain 131(Pt 7) (2008) 1818–1830. 4. R. G. Andrzejak, F. Mormann, G. Widman, T. Kreuz, C. E. Elger and K. Lehnertz, Improved spatial characterization of the epileptic brain by focusing on nonlinearity, Epilepsy Res. 69(1) (2006) 30–44. 5. B. Cr´epon, V. Navarro, D. Hasboun, S. Clemenceau, J. Martinerie, M. Baulac, C. Adam and M. Le Van Quyen, Mapping interictal oscillations greater than 200 hz recorded with intracranial macroelectrodes in human epilepsy, Brain 133(pt 1) (2010) 33–45. 6. O. David, T. Blauwblomme, A. Job, S. Chabard`es, D. Hoffmann, L. Minotti and P. Kahane, Imaging the seizure onset zone with stereo-electroencephalography, Brain 134(pt 10) (2011) 2898–2911. 7. V. Gnatkovsky, S. Francione, F. Cardinale, R. Mai, L. Tassi, G. Lo Russo and M. de Curtis, Identification of reproducible ictal patterns based on quantified frequency analysis of intracranial EEG signals, Epilepsia 52(3) (2011) 477–488. 8. V. Gnatkovsky, M. de Curtis, C. Pastori, F. Cardinale, G. Lo Russo, R. Mai, L. Nobili, I. Sartori, L. Tassi and S. Francione, Biomarkers of epileptogenic zone defined by quantified stereo-EEG analysis, Epilepsia 55(2) (2014) 296–305. 9. R. Schulz, Zone of electrical stimulation induced seizures in subdural electrodes, in ed. H. L¨ uders, Textbook of Epilepsy Surgery (Informa Health-care; London, 2008), pp. 706–708. 10. R. Wennberg, F. Arruda, L.F. Quesney and A. Olivier, Preeminence of extrahippocampal structures in the generation of mesial temporal seizures: Evidence from human depth electrode recordings, Epilepsia 43(7) (2002) 716–726. 11. M. Le Van Quyen, C. Adam, J. Martinerie, M. Baulac, S. Cl´emenceau and F. Varela, Spatiotemporal characterizations of non-linear changes in intracranial activities prior to human temporal lobe seizures, Eur. J. Neurosci. 12(6) (2000) 2124–2134. 12. D. Boido, D. Kapetis, V. Gnatkovsky, C. Pastori, B. Galbardi, I. Sartori, L. Tassi, F. Cardinale, S. Francione and M. de Curtis, Stimulus-evoked potentials contribute to map the epileptogenic zone during stereo-EEG presurgical monitoring, Hum. Brain Mapp. 35(9) (2014) 4267–4281. 13. A. Valent´ın, M. Anderson, G. Alarc´ on, J. J. Seoane, R. Selway, C. D. Binnie and C.E. Polkey, Responses to single pulse electrical stimulation identify epileptogenesis in the human brain in vivo, Brain. 125(Pt 8) (2002) 1709–1718.
14. A. Valent´ın, G. Alarc´ on, J. J. Garc´ıa-Seoane, M. E. Lacruz, S. D. Nayak, M. Honavar, R. P. Selway, C. D. Binnie and C. E. Polkey, Single-pulse electrical stimulation identifies epileptogenic frontal cortex in the human brain, Neurology 65(3) (2005) 426– 435. 15. D. Flanagan, A. Valent´ın, J. Garc´ıa Seoane Jorge, G. Alarc´ on and S. G. Boyd, Single-pulse electrical stimulation helps to identify epileptogenic cortex in children, Epilepsia 50(7) (2009) 1793–1803. 16. O. David, A. Wo´zniak, L. Minotti and P. Kahane, Preictal short-term plasticity induced by intracerebral 1 hz stimulation, Neuroimage 39(4) (2008) 1633–1646. 17. R. Matsumoto, D. R. Nair, E. LaPresto et al., Functional connectivity in the human language system: A cortico-cortical evoked potential study, Brain 127(10) (2004) 2316–2330. 18. M. A. van’t Klooster, M. Zijlmans, F. S. Leijten, CH, Ferrier, M. J. van Putten and G. J. Huiskamp, Timefrequency analysis of single pulse electrical stimulation to assist delineation of epileptogenic cortex, Brain 134(10) (2011) 2855–2866. 19. S. Kalitzin, D. Velis, P. Suffczynski, J. Parra and F. L. da Silva, Electrical brain-stimulation paradigm for estimating the seizure onset site and the time to ictal transition in temporal lobe epilepsy, Clin. Neurophysiol. 116(3) (2005) 718–728. 20. H. Catenoix, M. Magnin, F. Maugui`ere and P. Ryvlin, Evoked potential study of hippocampal efferent projections in the human brain, Clin. Neurophysiol. 122(12) (2011) 2488–2497. 21. O. David, A. Job, De Palma Luca, D. Hoffmann, L. Minotti and P. Kahane, Probabilistic functional tractography of the human cortex, Neuroimage 80 (2013) 307–317. 22. M. Cossu, F. Cardinale, L. Castana et al., Stereoelectroencephalography in the presurgical evaluation of focal epilepsy: A retrospective analysis of 215 procedures, Neurosurgery 57(4) (2005) 706–718. 23. C. Munari, P. Kahane, L. Tassi, S. Francione, D. Hoffmann, G. Lo Russo and A. L. Benabid, Intracerebral low frequency electrical stimulation: A new tool for the definition of the epileptogenic area? Acta Neurochir Suppl (Wien) 58(1993) 181–185. 24. J. Talairach, J. Bancaud, G. Szikla, A. Bonis, S. Geier and C. Vedrenne, New approach to the neurosurgery of epilepsy. Stereotaxic methodology and therapeutic results. 1. Introduction and history, Neurochirurgie 20(Suppl 1) (1974) 1–240. 25. F. Cardinale, M. Cossu, L. Castana, G. Casaceli, M. P. Schiariti, A. Miserocchi, D. Fuschillo, A. Moscato, C. Caborni, G. Arnulfo and G. Lo Russo, Stereoelectroencephalography: Surgical methodology, safety, and stereotactic application accuracy in 500 procedures, Neurosurgery 72(3) (2013) 353– 366.
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26. H. O. L¨ uders, J. J. Engel and C. Munari, General principles, in Surgical Treatment of the Epilepsies (Raven Press, New York, 1993), pp. 137–153. 27. J. Engel, Intracerebral recordings: Organization of the human epileptogenic region, J. Clin. Neurophysiol. 10(1) (1993) 90–98. 28. S. S. Nathan, R. P Lesser, B. Gordon and N. V. Thakor, Electrical stimulation of the human cerebral cortex. theoretical approach, Adv. Neurol. 63 (1993) 61–85. 29. R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina and D. A. Boas, A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy, Front. Neurosci. 11(6) (2012) 147. 30. K. Teknomo, K-means clustering tutorial (2004) Available at http://people.revoledu.com/kardi/tutorial/kMean. 31. P. Shannon, A. Markiel, O. B. Ozier, N. S. et al., Cytoscape: A software environment for integrated models of biomolecular interaction networks, Genome Res. 13 (2003) 2498–504. 32. F. Trombin, V. Gnatkovsky and M. de Curtis, Changes in action potential features during focal seizure discharges in the entorhinal cortex of the in vitro isolated guinea pig brain, J. Neurophysiol. 106(3) (2011) 1411–1423. 33. A. Selimbeyoglu and J. Parvizi, Electrical stimulation of the human brain: Perceptual and behavioral phenomena reported in the old and new literature, Front. Hum. Neurosci. 4 (2010) 46. 34. J. Dyhrfjeld-Johnsen, V. Santhakumar, R. J. Morgan, R. Huerta, L. Tsimring and I. Soltesz,
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Topological determinants of epileptogenesis in largescale structural and functional models of the dentate gyrus derived from experimental data, J. Neurophysiol. 97(2) (2007) 1566–1587. R. S. Fisher, W. R. Webber, R. P. Lesser, S. Arroyo and S. Uematsu, High-frequency EEG activity at the start of seizures, J. Clin. Neurophysiol. 9(3) (1992) 441–448. M. de Curtis and V. Gnatkovsky, Reevaluating the mechanisms of focal ictogenesis: The role of lowvoltage fast activity, Epilepsia 50(12) (2009) 2514– 2525. J. Gotman, V. Levtova and A. Olivier, Frequency of the electroencephalographic discharge in seizures of focal and widespread onset in intracerebral recordings, Epilepsia 36(7) (1995) 697–703. M. Guye, F. Bartolomei and J. Ranjeva, Imaging structural and functional connectivity: Towards a unified definition of human brain organization?Curr. Opin. Neurol. 21(4) (2008) 393–403. M. Guye, G. Bettus, F. Bartolomei and P. J. Cozzone, Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks, MAGMA 23(5–6) (2010) 409– 421. E. Bullmore and O. Sporns, Complex brain networks: Graph theoretical analysis of structural and functional systems, Nat. Rev. Neurosci. 10(3) (2009) 186–198. C. J. Stam, Functional connectivity patterns of human magnetoencephalographic recordings: A ‘small-world’ network? Neurosci. Lett. 355(1–2) (2004) 25–28.
International Journal of Neural Systems, Vol. 25, No. 5 (2015) 1550022 (12 pages) c World Scientific Publishing Company DOI: 10.1142/S0129065715500227
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the Epileptogenic Zone Elisa Bellistri∗ , Ivana Sartori† , Veronica Pelliccia† , Stefano Francione† , Francesco Cardinale† , Marco de Curtis∗ and Vadym Gnatkovsky∗,‡ ∗ Unit of Epileptology and Experimental Neurophysiology Fondazione IRCCS, Istituto Neurologico Carlo Besta 20133 Milano, Italy †
Claudio Munari Epilepsy Surgery Center, Ospedale Niguarda Ca’ Granda, Milano, Italy ‡ [email protected] Accepted 24 April 2015 Published Online 28 May 2015
Epilepsy is a disease characterized by aberrant connections between brain areas. The altered activity patterns generated by epileptic networks can be analyzed with intracerebral electrodes during presurgical stereo-electroencephalographic (EEG) monitoring in patients candidate to epilepsy surgery. The responses to high frequency stimulation (HFS) at 50Hz performed for diagnostic purposes during SEEG were analyzed with a new algorithm, to evaluate signal parameters that are masked to visual inspection and to define the boundaries of the epileptogenic network. The analysis was focused on 60–80 Hz activity that represented the largest frequency component evoked by HFS. The distribution of HFS-evoked fast activity across all (up to 162) recording contacts allowed to define different clusters of contacts that retrospectively correlated to the epileptogenic zone identified by the clinicians on the basis of traditional visual analysis. The study demonstrates that computer-assisted analysis of HFS-evoked activities may contribute to the definition of the epileptogenic network on intracranial recordings performed in a pre-surgical setting. Keywords: Focal epilepsy; epilepsy surgery; intracerebral recordings; stereo-EEG; high-frequency stimulation.
1. Introduction One third of the patients that suffer from pharmacoresistant focal epilepsy are potential candidates for therapeutic surgery. The goal of epilepsy surgery is to remove, disconnect or inactivate the epileptogenic cortex to obtain seizure freedom.1 A correct presurgical identification of the epileptogenic zone (EZ) has an impact on postsurgical outcome.2 In 20–50% of the patients candidate to surgery, invasive recordings by either subdural grids/strips or intracerebral stereo-electroencephalographic (SEEG) electrodes is necessary to identify the boundaries of the EZ. In spite of the accurate EZ identification that the clinical expertise produces, a non-negligible rate of
failure in the outcome after epilepsy surgery (mainly in extra-temporal cortical areas) indicates that the definition of the EZ could be improved.3 In recent years, an increased number of studies has taken advantages of computer-assisted analysis (CAA) of intracranial EEG signals to-identify biomarkers that support human EZ detection.3–8 Pre-surgical intracranial SEEG monitoring is performed for several days, to acquire the necessary information to define the zone of initiation and propagation of seizures. During SEEG, stimulation sessions are routinely performed to map symptomatogenic areas and to identify eloquent zones that have to be preserved during the surgery. Electrical
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stimulation of the cortex can elicit after discharges, subclinical EEG seizures, habitual or non habitual auras and seizures.9 Typically, two different stimulation protocols are used: single pulse stimulation and high frequency stimulation (HFS) at 50 Hz. The analysis of SEEG during intracerebral stimulation might provide information about the dynamic of signal propagation in the epileptogenic networks.10–12 Previous works analyzed responses evoked by singlepulse stimulation protocols to improve the EZ definition.12,13 and to identify intracranial markers of EZ in adults.13,14 and children.15 Singlepulse and train stimulations were used in humans to evoke epileptiform transients resembling EEG spikes13 and to-study human cortical connectivity in both epileptic,16–19 and physiological networks.20,21 The aim of the present report is to characterize the dynamic of the responses evoked by trains of HFS at 50 Hz, in order to characterize the EZ and the mechanisms of propagation of epileptiform activities across physiological and pathological networks in pre-surgical setting. The high throughput of information collected during intracranial recording is further analyzed both in the time and in the frequency domain and the parameters obtained from this analysis are subsequently organized using graph theory tools and clustering methods. In addition, the proposed method (i) contributes to evaluate if and to what extent different brain areas are connected, (ii) offers a framework to characterize the organization of functional networks and (iii) allows to examine different properties of the signal in a large dataset. 2.
Material and Methods
2.1. SEEG recording and definition of the epileptogenic area HFS is part of a standard diagnostic SEEG protocol for patients candidate to epilepsy surgery. The SEEG diagnostic procedure was approved on 12 December 2013 by the local Ethics Committee (protocol no. ID 939 by the Comitato Etico Scientifico Milano Area C). Patients were implanted with depth electrodes for pre-surgical evaluation, according to the stereotactic method.22–24 A total of 12 to 16 intracerebral multichannel electrodes (Dixi Medical, France and ALCIS, France; 5–18 contacts each; length, 2 mm, diameter, 0.8 mm; 1.5 mm
apart. According to physical parameters, resulting in a total charge density of 20 µC/cm2 /phase, thus significantly lower than the maximum safe value of 60 µC/cm2 /phase) were implanted, for a total number of 105–162 recording sites per patient. SEEG recordings with 0.016–300 Hz band-pass filter were performed using Neurofax EEG-1100 system (Nihon Kohden, Tokyo, Japan) at 1 kHz sampling rate and 16-bit resolution. Intracerebral recording sites were identified on 3D MR reconstructions of the patient brain, according to the standard method described in Ref. 25. Recording contacts located either in the EZ26 or in the surrounding early-propagation zone (EPZ; defined as the area of early propagation of the ictal discharge) or in the normal tissue that surrounds these areas (not-epileptogenic tissue; NET) were identified for each patient by expert clinical neurophysiologists. The standard clinical protocol utilized in pre-surgical settings requires the independent revision of SEEG data by two expert neurophysiologists, to avoid subjective bias in the selection. Electrodes included in the EZ were defined by the time onset of fast activity included in the first second of the seizure. Electrodes in which fast activity was observed after 1 s were identified as EPZ. All five retrospectively evaluated patients were surgically treated by EZ-EPZ excision and EZ/EPZs were confirmed by the postsurgical outcome according to the Engel scale.27 Intracerebral HFS trains were delivered as part of the routine clinical assessment to locate both the epileptogenic and eloquent regions. HFS was delivered in only one session between 9 am and 5 pm; each HFS session lasted less than 1 h. Bipolar 1 ms pulses of variable intensity (from 0.3 to 3 mA; current density between 0.06 and 0.6 mA/mm2 ) were applied for 1–6 s at 50 Hz to pairs of contiguous contacts on the same electrode shaft.23,25,28 Intensity and duration of the stimulation are chosen according to the brain region connectivity and the patient response. Inter-HFS interval was 1–5 min. HFS was performed in 15–40% of the leads available on implanted electrodes (see Table 1). Since the dataset is derived from a retrospective evaluation, HF stimulation was not performed on the total couples of contacts, but was delivered at sites suspected by clinicians to be within or around the EZ: each patients had a variable number of contacts stimulated within and outside EZ and EPZ.
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Table 1. Data of the SEEG electrodes utilized for recording and HF stimulation in the five patients included in the present study. In the column SEEG contacts, the numbers within brackets exclude contacts positioned in the white matter (WM) verified by MR reconstruction of the electrode tracks. The EZ and EPZ contacts were identified by traditional visual analysis by the clinical neurophysiologist. Age at surgery
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P1 P2 P3 P4 P5
SEEG contacts (w/o WM)
5 38 18 30 18
128 159 180 157 138
(80) (92) (94) (114) (102)
HFS contacts (%) 26 39 38 43 52
EZ contacts (%)
(32.5) (42) (40) (38) (50)
8 7 4 18 26
(10) (4) (4) (16) (25)
EPZ contacts (%)
ENGEL scal
21 (25) 22 (24) 4 (4) 4 (3.5) 3 (3)
1A 1A 1A 1B 1A
HFS session durations varied depending on patient’s clinical condition and on the number and type of seizures elicited. The proportion of recorded and stimulated contacts situated in EZ, EPZ and in the WM is summarized in Table 1 for the five patients analyzed. 2.2. Electrophysiological data analysis SEEG data obtained during HFS were analyzed using software (http://www.elpho.it/) developed in LabView (National Instruments, Austin, TX). The protocol utilized for the analysis is shown in Fig. 1. Stimulation artifacts were removed from 20-s trace epochs that include HFS. The artifact-removing algorithm detects the positive peak of each stimulus of the train (>3 times the standard deviation of the background signal), a segment of 10 ms symmetric around stimulus peak was passed to LabView spline interpolation function and 6 ms were substituted with trace interpolated by 2 ms on each side (Fig. 2(b), middle traces) (see Ref. 29 for a comparison between different interpolation methods). The signal was then filtered between 2 and 400 Hz (lower traces in Fig. 2(b) and the power spectrum was calculated in 1-s bins without overlap using the multi-taper function in MatLab (The MathWorks Inc., Natick, MA). Frequency resolution was 0.98 Hz. Power spectrum density (PSD) of 60–80 Hz activities was calculated on artifact-subtracted, pass-band filtered signals, as illustrated in the lower part of Fig. 2(b). 2.3. PSD cluster analysis Specific 60–80 Hz PSD changes during HFS (Fig. 2(c); see also Sec. 3) were calculated and were plotted
Fig. 1. Outline of the process to define EZ and EPZ after HFS protocol with the computer-assisted method described in the study.
on two-dimensional scatter graphs. PSD measurements were calculated for equivalent epochs before and during HFS (Fig. 3(c)). Contacts parameters
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Fig. 2. (Color online) SEEG signal processing. (a) Positions of the two contacts (A10 and B1) on MR reconstruction of patient 1 brain. (b) Representative examples of the responses evoked by HFS (50Hz) simultaneously recorded on these two electrode contacts. In the upper traces, the raw signals are shown in 20-s epochs that included the period of HFS. In the middle traces, the HFS artifact was subtracted. In the lower traces, subtracted signals were band-pass filtered at 2–400 Hz. In the insets, two examples of artifact removal, magnified in a 100ms window. Spectrograms of the filtered traces are illustrated for 80–400 Hz (upper spectrogram) and 50–80 Hz (lower spectrograms) frequency ranges. No frequency increase was found in the range 100–400 Hz in both cases; Harmonics of 50Hz stimulation artifacts were observed in A10. In the example on the left (contact A10), the intensity of the frequencies during the train did not change compared to the background activity (before the train). An increase of the frequencies between 60 and 80 Hz was evident in the correspondence of the HFS window in the traces and in the frequency plot (lower panel) in the contact B1 (right traces). (c) Integral frequencies of average PSDs across 60–80 Hz during the stimulation period, in a representative subset of contacts of electrodes A (including contact A10) and B (including contact B1). Integral values of the two contacts showed as examples in B are marked in red bars. 1550022-4
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Fig. 3. Method to calculate PSD integral evoked by 50 Hz HFS. (a) MR reconstruction of Patient 1 brain with the position of the implanted SEEG electrodes. HFS was delivered in the contact pair G’1–2. (b) Signals recorded in all contacts considering a window of 20 s around the HFS period (gray shading). The gray histogram on the right side represents the integral of average PSD across 60–80 Hz frequencies evoked by HFS recorded in all contacts, as detailed in Fig. 2(c). The recordings obtained in a subset of contacts on one electrode is outlined by the dotted line and is illustrated with expanded time base in (c) Leads 1–3 positioned in the EZ clearly show high frequency activity. PSD integrals in the pre-stimulation window (a) and during HFS stimulation (b) are shown. PSD values obtained by subtracting (b)–(a) are illustrated on the right histogram in panel C. PSD integral in (b) and the difference (b)–(a) constitute the two variables plotted utilized in the scatter diagrams of Fig. 4.
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in a two-dimensional space were grouped into different clusters, automatically selected by a k-mean cluster algorithm. Cluster analysis was implemented as described in Ref. 30. Given a set of observations (x1, x2, . . . , xn), where each observation is a d-dimensional real vector, k-means clustering aims to partition the n observations into k sets
(k ≤ n)S = {S1, S2, Sk} so as to minimize the withincluster sum of squares (WCSS): argmin
k
xj − µ2 ,
(1)
i=1 xj ∈Si
where µi is the mean of points in Si .
(a)
(b)
(c)
(d)
Fig. 4. (Color online) (a) PSD integral (b in Fig. 3(c)) and variation of PSD integral (b − a in Fig. 3(c)) in response to HFS in one single contact pair calculated for each contact are represented in a two-dimensional graph. K-mean cluster algorithm identifies two different clusters. (b) Individual scatter plots obtained in all recording contacts following HFS in contact pairs in Patient 1 are superimposed in a single diagram; values are normalized to compare clusters obtained at different sessions. The large majority of contacts from NET cluster in the left side of the graph, whereas contacts in the EZ and EPZ group in the right part. EZ, EPZ and NET contacts identified by the clinical neurophysiologist are colored in red, orange and white/gray, respectively. The contacts with enhanced PSD integral response form a virtual cluster (blue line) identified by the algorithm. (c) Contacts in the cluster outlined in B are represented in a connection diagram with a circular layout. Contacts that lay outside the circle respond only to a single position of HFS during the entire protocol. (d) Histogram of the inbound degree of the contacts selected and represented in (c). Contacts outside the graph circle with an inbound degree of 1. Note that all these contacts are NET. EZ and EPZ tend to have high values of inbound degree. 1550022-6
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Table 2. Comparison between the number of contacts included in EZ, EPZ and NET identified by the computer-assisted analysis (CAA) and by the clinical evaluation (CE). P1
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Total no. contacts EZ contacts EPZ contacts NET contacts
P2
P3
P4
P5
CAA
CE
CAA
CE
CAA
CE
CAA
CE
CAA
CE
37 8 16 86
29 8 21 99
34 6 9 112
29 7 22 131
16 4 1 155
8 4 4 166
34 15 3 119
22 18 4 135
31 22 1 101
29 26 3 109
The number of identified clusters varied from 2 to 4. A variable number of clusters allowed to test different group formations, to get the best fitting around the group of contacts that visually separated from the background and to exclude outlier produced by artifact responses. Once the number of clusters was defined, the algorithm procedure was implemented for each stimulation pairs and the list of contacts included in the selected cluster was appended in a text file. The data list obtained during the iteration of the procedure for each stimulation contact pairs constitute a group of contacts where same contacts can be replicated (Figs. 4(a) and 4(b)). The overall group of contacts was represented in a scatter plot similar to previous one. To allow comparison between different stimulations, values were normalized on both axes, respectively to the highest PSD and ∆PSD obtained for each stimulation pair. The rate of response occurrences of each contact to all HFS was evaluated for each patient and represented in a circular layout using Cytoscape 3.2.0 (http://www.cytoscape.org)31 as illustrated in Fig. 4(c). 2.4. Statistical analysis The matching between the algorithm selection and the clinical classification of the contacts was evaluated by the indexes of Accuracy, Sensitivity and Specificity, obtaining a quantification of the algorithm performance. We label as True Positive (TP) the number of contacts selected by the algorithm that matched the EZ and EPZ clinical classification; as True Negative (TN) the group of contacts not selected by the algorithm and classified as NET; as False Positive (FP) the contacts selected by the algorithm and classified as NET and finally as False Negative (FN) the contacts not selected but classified in the EZ or EPZ groups. Moreover,
P and N are respectively the totality of EZ + EPZ (the former) and NET (the latter) contacts according to clinical classification. Accuracy is defined as (TP + TN)/P + N . Sensitivity is defined as TP/P . Specificity is defined as TN/N . The quantification of the amount of contacts in each group, according to algorithm selection and clinical classification, is summarized in Table 2. 3.
Results
Signals from all SEEG recorded contacts were processed following the protocol outlined in Fig. 1. The frequency content in each SEEG trace was plotted using a color code in a time–frequency graph (lower traces in Fig. 2(b). Power spectral analysis demonstrated that in all patients considered, the dominant frequencies during HFS were included between 50 and 80 Hz; frequencies higher than 80 Hz were not consistently represented during HFS (upper colored time–frequency graphs in Fig. 2(b). To exclude 50 Hz artifacts due to the stimuli trains, data from contacts that developed a fast activity component during the HFS were visually inspected in the 60–80 Hz frequency band (lower spectograms in Fig. 2(b). The integral of the average 60–80 Hz PSD for each channel was evaluated with a sliding temporal window of 5 s. PSD analysis allowed to highlight a subset of contacts in which HFS induced fast activity at 60–80 Hz during the period of stimulation (i.e. contacts B1B3 in Fig. 2(c)). The analysis was repeated for each SEEG channel (Figs. 3(a) and 3(b)). As illustrated in Fig. 3(c), a second parameter was calculated for each channel as the difference between the average PSD during the HFS epoch (b) and the average PSD in an equal temporal window calculated on baseline traces immediately before the beginning of the HFS train (a). The subtracted value
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(b − a in Fig. 3(c)) represented the PSD changes specifically associated with HFS. The PSDs during HFS (values b) and the subtracted PSDs (b − a) obtained in all SEEG contacts during HFS in a single pair of contacts were plotted in a two-dimensional scatter graph (Fig. 4(a)). Contacts parameters in a two-dimensional space were grouped into different clusters, automatically selected by a k-mean cluster algorithm (dotted lines Ref. 30). A semi-automatic, operator-supervised procedure was used to confirm or discard each cluster defined by the algorithm. The identification of clusters clearly separated from the rest of contacts was supervised by the operator, on the basis of the retrospective inclusion of contacts defined by the clinical neurophysiologist as inside or outside the EZ and the EPZ. This procedure was repeated for each HF stimulated contact. Figure 4(b) illustrates the result of the evaluation of PSD analysis in one patient following HFS in all stimulation sites and recordings from all the contacts. Contacts labeled in each cluster were assigned to either the EZ or the EPZ according to the clinical classification derived from the retrospective evaluation based on the post-surgical seizure outcome (Sec. 2). The next step was to evaluate the correlation between the contacts selected and clustered by the computer assisted analysis and the contacts identified during the standard visual procedure by the clinical neurophysiologist. Contacts extracted by the algorithm were labeled as EZ, EPZ and NET according to the clinical selection. The algorithm does not provide any classification, but generates a list of contacts that are subsequently identified as EZ, EPZ and NET by comparison with the clinical classification; such a procedure is used to test the algorithm performance for the detection of contacts clinically classified as epileptogenic. These results are represented for one patient in the cumulative cluster in Fig. 4(b). During HFS protocol both epileptogenic and normal contacts were stimulated (Table 1). An a priori network involves all connections between a stimulated contact and all other channels. The subset of connections highlighted by the algorithm were represented in a circular layout graph developed in Cytoscape 3.2.0 on these selected group of contacts that generated fast activity. Connections that did not generate 60–80 Hz in response to HFS were excluded by the graph. Contacts that responded only
once during all the stimulations (inbound degree = 1) were clearly found in the outside portion of the circular graph. Contacts in the circle demonstrated different degrees of connectivity. The distribution of the different number of connections of each responding contact were sorted by inbound degree values, as illustrated in the histogram in Figure 4(d). A threshold of two inbound degrees, that identifies contacts responding to at least two stimulation sites, was applied to remove the less likely connected contacts (treated as well as outliers). Contacts with an inbound degree of 1 were discarded from the selected contacts list utilized to compare clinicallyand computer-defined data, illustrated in Fig. 5. Finally, the list of contacts with >2 inbound degree values was analyzed and compared with medical attribution of contacts to EZ and EPZ defined by clinical means. The percentage of matching between the computer-assisted method and the traditional clinical method based on visual inspection observed in five patients is shown in Fig. 5. On average, the matching proportion was 90% (+/−8%) for the EZ, 50% (+/−23%) for EPZ and 10.7% (+/−3%) for selected NET contacts.
Fig. 5. (Color online) Computer-defined contacts in five patients. The diagram shows the percentage of matching between contacts selected by semi-automatic algorithm and the EZ, EPZ and NET contacts labeled by the clinical neurophysiologist. The percentages of contacts extracted by the algorithm clinically identified as EZ, EPZ and NET are illustrated as red, orange and gray bars, respectively. The ability of the algorithm to recognize epileptogenic contacts is confirmed by the high number of EZ and a low number of NET. The variable percentage of EPZ contacts in the five patients is discussed in the text.
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Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the EZ
In spite of the different number of contact stimulated (summarized in Table 1), the matching between the computer-based classification and the clinical classification was relatively stable, with a high matching proportion for the EZs (red columns), and a slightly lower matching in the case of EPZs (orange columns). Some contacts identified by the algorithm were labeled as NET by clinicians. These represents the false positive (gray bars in Fig. 5). A high coincidence between contacts identified as NET by the traditional and contacts discarded by computer methods was also found. Performance rate of both EZ and EPZ selection (percentage of matching) was highly significant (p < 0.001) in comparison to the rate of false positive (percentage of matching between selected contacts and NET contacts). Coincidence values between contacts evaluated on the five considered patients are detailed in Table 2.
4. Discussion Our study demonstrates that in patients with drugresistant focal epilepsy HFS performed during SEEG evokes fast activity (60–80 Hz) responses in contacts recorded in the brain area responsible for seizure generation/propagation. The proposed method demonstrates that PSD analysis during HFS protocols can be used to identify epileptogenic regions in humans. In comparison with the static values extracted from the analysis of spontaneous epileptiform events (i.e. ictal and interictal discharges), PSD of HFSevoked fast activity responses explores the dynamic nature of the epileptogenic area and the intrinsic organization of the physiological and pathological networks of an epileptic brain. This approach contributes to evaluate the results obtained by the clinical-based visual examination of the EZ. During presurgical SEEG monitoring mainly two states of the epileptic brain are evaluated: spontaneous events (seizures and interictal activities) and responses to either single-pulse or high-frequency electrical stimulation. The majority of works available in the literature focused on the computer assisted analysis of spontaneous activity and singlepulse evoked responses.7–8,11–21,32 To our knowledge, there are no published reports that study the effect of HFS protocols on the brain neuronal network during SEEG.
HFS is used during clinical SEEG pre-surgical monitoring to achieve two main goals: (1) to identify eloquent regions that have to be preserved by surgery and (2) to evoke seizure-like symptoms to localize the seizure-genic (epileptogenic) cortical areas. While extensive studies focus on clinical features evoked by HFS (summarized in a review by Selimbeyoglu and Parvizi.33 ) the neurophysiological response to HFS are usually ignored. One of the reasons could be that the rapid changes in the ongoing SEEG signals during HFS are masked by the stimulation artifact and are difficult to detect. Since HFS are performed mainly to induce symptoms that mimic seizure features, we need to consider a possible bias in our analysis. The choice of the stimulated contacts depends on the areas explored by SEEG electrodes and varies from a patient to patient (Table 1, see Sec. 3). HFS of pairs of contacts in mesial temporal structures is usually avoided because it can precipitate seizures and stimulation of electrodes positioned in the WM is often omitted because it is considered not to contribute to the localization of eloquent and/or symptomato-genic cortical areas. Therefore, not all contacts utilized for SEEG exploration were utilized in the study here described (see Table 1). When EZ and peri-EZ cortical contacts are included in HFS protocols, results show a quite stable response at 60–80 Hz in the epileptogenic area that includes EZ and EPZ. HFS of the EZ/EPZ usually generate fast activities, while HFS in NET only sporadically induced high frequencies patterns. The different responses observed in EZ/EPZ and NET are probably due to the over-excitability of the epileptogenic region.34 that is prone to generate fast activity in the beta/gamma range at seizure onset35–37 The EZ activation during HFS is possibly sufficient to entrain responses at 60–80 Hz. The difference in response to HFS between the EZ/EPZ and the NET generates a clear bimodal PSD distribution that is expressed by the cluster separation illustrated in the Results. We assumed that the mere increase in gamma frequency PSD describes only in part the propagation dynamic of activity evoked by HFS in brain networks, and thus analyzed two other parameters: the PSD changes in comparison to background activity just before HFS and the degree of connections between stimuli and regions that showed PSD increases. The comparison between PSD integral during the HFS period and the variation of PSD
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integral calculated in time window of the same duration just before the stimulation (see Methods) was utilized to exclude conditions in which HFS-induced responses were generated by an enduring state of hyperexcitability: these data demonstrate the specificity of HFS-induced changes. The evaluation of inbound degree with graph analysis was used to quantify and separate subpopulations of contacts that occasionally responded to one or two neighboring HFS from other contacts that responded to stimulations at different cortical contact pairs and showed a high connectivity pattern. High values of inbound degree are mostly associated with EZ contacts, whereas low values correlated with NET contacts. The use of graph representation in our study are consistent to previous graph theory findings applied to epilepsy EEG and magneto-EEG analysis that demonstrated an increase of connectivity degree in EZ contacts. These findings are consistent with the concept that EZ networks tend to organize with a pattern elsewhere defined as small world configuration.38–41 Even if it was carried out on a limited number of patients, the retrospective comparison between the algorithm-generated results and the CE generated an excellent matching of mean accuracy rate (87%) and mean sensitivity rate (94.5%). The lower specificity rate (mean = 53.7%) suggested to explore more in detail the algorithm output and to refine the contacts selection. The observation that low inbound degree values are typical of NET contacts, allowed to define a threshold of inbound degree assumed to describe NET. The exclusion of these presumed NET contacts decreases the number of contacts classified as epileptogenic by the algorithm, improving the specificity of the method. Post-comparisons between the two classifications allowed to identify contacts that represented outliers in the clustering evaluation (gray contacts in Fig. 4(b)). To reduce FP rate, contacts located next to the stimulated pair were removed from final selection submitted to analysis, since the frequency increase observed in very close-by contacts is assumed to be generated by passive electrical propagation of high intensity current. The histogram representation of contact matching in Fig. 5 shows that the specificity rate is very high considering EZ clinical- and algorithm-classified contacts, and decreases for EPZ contacts. It is worth to point out that our algorithm is not designed to
distinguish between EZ and EPZ, but simply identifies neurobiological properties that are assumed to be specific of the epileptogenic network. The results are then compared with the identification of contacts as EZ, EPZ and NET by the clinical neurophysiologists. The differences in classification are most relevant in EPZ, a region not primarily involved in seizure initiation and, as such, a potential boundary area between EZ and NET. The clinical classification of EPZ contacts is highly dependent on the time value and the velocity of propagation selected to define seizure propagation areas; small changes of these values will alter the number of contacts included or excluded from the EPZ. To this end, a contact classification based on the pattern of activity generated by HFS is less arbitrary and could, in principle, be utilized to better characterize this region and the confines of the surgical excision. 5.
Conclusions
The presented method is here described and retrospectively applied to a small number of patients. These preliminary results suggest that the dynamic of the responses to HFS could be useful to define the epileptogenic networks and their functional properties. Further prospective validation of the method on a larger group of patients is needed to estimate the potential clinical impact on the identification of the EZ. In addition, this study suggests that HFS protocols should be improved to extract valuable information relevant for EZ identification and ultimately to guide the surgical procedure. Disclosure Statement The authors have nothing to disclose. The authors declare that this is an original unpublished work and the manuscript or any variation of it has not been submitted to another publication previously. Acknowledgment Grant of the Ministry of Health (RG 151-2012 and Ricerca Corrente 2013–2014). References
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