EEG rhythmic and arrhythmic spectral components and functional connectivity at resting state may predict the development of synucleinopathies in idiopathic REM sleep behavior disorder.

STUDY OBJECTIVES: Idiopathic/isolated REM-sleep behavior disorder (iRBD) often precedes the onset of synucleinopathies. Here, we investigated whether baseline resting-state EEG advanced spectral power and functional connectivity differ between iRBD patients who converted towards a synucleinopathy at follow-up and those who did not. METHODS: Eighty-one participants with iRBD (66.89±6.91 years) underwent a baseline resting-state EEG recording, a neuropsychological assessment and a neurological examination. We estimated EEG power spectral density using standard analyses and derived spectral estimates of rhythmic and arrhythmic components. Global and pairwise EEG functional connectivity analyses were computed using the weighted phase-lag index (wPLI). Pixel-based permutation tests were used to compare groups. RESULTS: After a mean follow-up of 5.01±2.76 years, 34 patients were diagnosed with a synucleinopathy (67.81±7.34 years) and 47 remained disease-free (65.53±7.09 years). Among patients who converted, 22 were diagnosed with Parkinson's disease and 12 with dementia with Lewy bodies. As compared to patients who did not convert, patients who converted exhibited at baseline higher relative theta standard power, steeper slopes of the arrhythmic component and higher theta rhythmic power mostly in occipital regions. Furthermore, patients who converted showed higher beta global wPLI but lower alpha wPLI between left temporal and occipital regions. CONCLUSION: Analyses of resting-state EEG rhythmic and arrhythmic components and functional connectivity suggest an imbalanced excitatory-to-inhibitory activity within large-scale networks, which is associated with later development of a synucleinopathy in iRBD patients.

Deconvolution of hemodynamic responses along the cortical surface using personalized functional near infrared spectroscopy.

In functional near infrared spectroscopy (fNIRS), deconvolution analysis of oxy and deoxy-hemoglobin concentration changes allows estimating specific hemodynamic response functions (HRF) elicited by neuronal activity, taking advantage of the fNIRS excellent temporal resolution. Diffuse optical tomography (DOT) is also becoming the new standard reconstruction procedure as it is more accurate than the modified Beer Lambert law approach at the sensor level. The objective of this study was to assess the relevance of HRF deconvolution after DOT constrained along the cortical surface. We used local personalized fNIRS montages which consists in optimizing the position of fNIRS optodes to ensure maximal sensitivity to subject specific target brain regions. We carefully evaluated the accuracy of deconvolution when applied after DOT, using realistic simulations involving several HRF models at different signal to noise ratio (SNR) levels and on real data related to motor and visual tasks in healthy subjects and from spontaneous pathological activity in one patient with epilepsy. We demonstrated that DOT followed by deconvolution was able to accurately recover a large variability of HRFs over a large range of SNRs. We found good performances of deconvolution analysis for SNR levels usually encountered in our applications and we were able to reconstruct accurately the temporal dynamics of HRFs in real conditions.

Potential antiviral agents of Rosmarinus officinalis extract against herpes viruses 1 and 2.

Herpes simplex viruses 1 and 2 (HSV-1 and HSV-2) belong to the herpesviridae family and cause neurological disorders by infecting the nervous system. The present study aimed to investigate the effects of Rosmarinus officinalis L. (rosemary) extract against HSV-1 and HSV-2 in vitro. The antioxidant activity of this extract was investigated by superoxide anion and 2,2-diphenyl-1-picrylhydrazyl (DPPH) free-radical assays. Rosemary extract was evaluated by an HSV-1 antiviral assay, in which viral replication in Vero cells was determined and quantified using a cytopathic effect assay. The present study showed that rosemary extract at 30 µg/ml caused 55% inhibition of HSV-1 plaques, whereas 40 µg/ml rosemary extract caused 65% inhibition of HSV-2 plaques. The extracts completely inhibited HSV-1 and HSV-2 plaque formation at 50 µg/ml. Scavenging activity of the superoxide anion radical was observed at 65.74 mg/ml, whereas 50% scavenging activity of the DPPH radical was observed at 67.34 mg/ml. These data suggest that rosemary extract may be suitable as a topical prophylactic or therapeutic agent for herpes viral infections. However, further research is required to elucidate the plant's active constituents, which may be useful in drug development.

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.

Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm2). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods.Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies.

Optimal positioning of optodes on the scalp for personalized functional near-infrared spectroscopy investigations.

BACKGROUND: Application of functional Near InfraRed Spectroscopy (fNIRS) in neurology is still limited as a good optical coupling and optimized optode coverage of specific brain regions remains challenging, notably for prolonged monitoring. METHODS: We propose to evaluate a new procedure allowing accurate investigation of specific brain regions. The procedure consists in: (i) A priori maximization of spatial sensitivity of fNIRS measurements targeting specific brain regions, while reducing the number of applied optodes in order to decrease installation time and improve subject comfort. (ii) Utilization of a 3D neuronavigation device and usage of collodion to glue optodes on the scalp, ensuring good optical contact for prolonged investigations. (iii) Local reconstruction of the hemodynamic activity along the cortical surface using inverse modelling. RESULTS: Using realistic simulations, we demonstrated that maps derived from optimal montage acquisitions showed, after reconstruction, spatial resolution only slightly lower to that of ultra high density montages while significantly reducing the number of optodes. The optimal montages provided overall good quantitative accuracy especially at the peak of the spatially reconstructed map. We also evaluated real motor responses in two healthy subjects and obtained reproducible motor responses over different sessions. COMPARISON WITH EXISTING METHODS: We are among the first to propose a mathematical optimization strategy, allowing high sensitivity measurements. CONCLUSIONS: Our results support that using personalized optimal montages should allow to conduct accurate fNIRS studies in clinical settings and realistic lifestyle conditions.

Comparison of the spatial resolution of source imaging techniques in high-density EEG and MEG.

BACKGROUND: The present study aims at evaluating and comparing electrical and magnetic distributed source imaging methods applied to high-density Electroencephalography (hdEEG) and Magnetoencephalography (MEG) data. We used resolution matrices to characterize spatial resolution properties of Minimum Norm Estimate (MNE), dynamic Statistical Parametric Mapping (dSPM), standardized Low-Resolution Electromagnetic Tomography (sLORETA) and coherent Maximum Entropy on the Mean (cMEM, an entropy-based technique). The resolution matrix provides information of the Point Spread Functions (PSF) and of the Crosstalk functions (CT), this latter being also called source leakage, as it reflects the influence of a source on its neighbors. METHODS: The spatial resolution of the inverse operators was first evaluated theoretically and then with real data acquired using electrical median nerve stimulation on five healthy participants. We evaluated the Dipole Localization Error (DLE) and the Spatial Dispersion (SD) of each PSF and CT map. RESULTS: cMEM showed the smallest spatial spread (SD) for both PSF and CT maps, whereas localization errors (DLE) were similar for all methods. Whereas cMEM SD values were lower in MEG compared to hdEEG, the other methods slightly favored hdEEG over MEG. In real data, cMEM provided similar localization error and significantly less spatial spread than other methods for both MEG and hdEEG. Whereas both MEG and hdEEG provided very accurate localizations, all the source imaging methods actually performed better in MEG compared to hdEEG according to all evaluation metrics, probably due to the higher signal-to-noise ratio of the data in MEG. CONCLUSION: Our overall results show that all investigated methods provide similar localization errors, suggesting very accurate localization for both MEG and hdEEG when similar number of sensors are considered for both modalities. Intrinsic properties of source imaging methods as well as their behavior for well-controlled tasks, suggest an overall better performance of cMEM in regards to spatial resolution and spatial leakage for both hdEEG and MEG. This indicates that cMEM would be a good candidate for studying source localization of focal and extended generators as well as functional connectivity studies.

Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data.

Electric Source Imaging (ESI) and Magnetic Source Imaging (MSI) of EEG and MEG signals are widely used to determine the origin of interictal epileptic discharges during the pre-surgical evaluation of patients with epilepsy. Epileptic discharges are detectable on EEG/MEG scalp recordings only when associated with a spatially extended cortical generator of several square centimeters, therefore it is essential to assess the ability of source localization methods to recover such spatial extent. In this study we evaluated two source localization methods that have been developed for localizing spatially extended sources using EEG/MEG data: coherent Maximum Entropy on the Mean (cMEM) and 4th order Extended Source Multiple Signal Classification (4-ExSo-MUSIC). In order to propose a fair comparison of the performances of the two methods in MEG versus EEG, this study considered realistic simulations of simultaneous EEG/MEG acquisitions taking into account an equivalent number of channels in EEG (257 electrodes) and MEG (275 sensors), involving a biophysical computational neural mass model of neuronal discharges and realistically shaped head models. cMEM and 4-ExSo-MUSIC were evaluated for their sensitivity to localize complex patterns of epileptic discharges which includes (a) different locations and spatial extents of multiple synchronous sources, and (b) propagation patterns exhibited by epileptic discharges. Performance of the source localization methods was assessed using a detection accuracy index (Area Under receiver operating characteristic Curve, AUC) and a Spatial Dispersion (SD) metric. Finally, we also presented two examples illustrating the performance of cMEM and 4-ExSo-MUSIC on clinical data recorded using high resolution EEG and MEG. When simulating single sources at different locations, both 4-ExSo-MUSIC and cMEM exhibited excellent performance (median AUC significantly larger than 0.8 for EEG and MEG), whereas, only for EEG, 4-ExSo-MUSIC showed significantly larger AUC values than cMEM. On the other hand, cMEM showed significantly lower SD values than 4-ExSo-MUSIC for both EEG and MEG. When assessing the impact of the source spatial extent, both methods provided consistent and reliable detection accuracy for a wide range of source spatial extents (source sizes ranging from 3 to 20cm2 for MEG and 3 to 30cm2 for EEG). For both EEG and MEG, 4-ExSo-MUSIC localized single source of large signal-to-noise ratio better than cMEM. In the presence of two synchronous sources, cMEM was able to distinguish well the two sources (their location and spatial extent), while 4-ExSo-MUSIC only retrieved one of them. cMEM was able to detect the spatio-temporal propagation patterns of two synchronous activities while 4-ExSo-MUSIC favored the strongest source activity. Overall, in the context of localizing sources of epileptic discharges from EEG and MEG data, 4-ExSo-MUSIC and cMEM were found accurately sensitive to the location and spatial extent of the sources, with some complementarities. Therefore, they are both eligible for application on clinical data.

Age-related changes in sleep spindles characteristics during daytime recovery following a 25-hour sleep deprivation.

OBJECTIVES: The mechanisms underlying sleep spindles (~11-15 Hz; >0.5 s) help to protect sleep. With age, it becomes increasingly difficult to maintain sleep at a challenging time (e.g., daytime), even after sleep loss. This study compared spindle characteristics during daytime recovery and nocturnal sleep in young and middle-aged adults. In addition, we explored whether spindles characteristics in baseline nocturnal sleep were associated with the ability to maintain sleep during daytime recovery periods in both age groups. METHODS: Twenty-nine young (15 women and 14 men; 27.3 y ± 5.0) and 31 middle-aged (19 women and 13 men; 51.6 y ± 5.1) healthy subjects participated in a baseline nocturnal sleep and a daytime recovery sleep after 25 hours of sleep deprivation. Spindles were detected on artifact-free Non-rapid eye movement (NREM) sleep epochs. Spindle density (nb/min), amplitude (µV), frequency (Hz), and duration (s) were analyzed on parasagittal (linked-ears) derivations. RESULTS: In young subjects, spindle frequency increased during daytime recovery sleep as compared to baseline nocturnal sleep in all derivations, whereas middle-aged subjects showed spindle frequency enhancement only in the prefrontal derivation. No other significant interaction between age group and sleep condition was observed. Spindle density for all derivations and centro-occipital spindle amplitude decreased whereas prefrontal spindle amplitude increased from baseline to daytime recovery sleep in both age groups. Finally, no significant correlation was found between spindle characteristics during baseline nocturnal sleep and the marked reduction in sleep efficiency during daytime recovery sleep in both young and middle-aged subjects. CONCLUSION: These results suggest that the interaction between homeostatic and circadian pressure modulates spindle frequency differently in aging. Spindle characteristics do not seem to be linked with the ability to maintain daytime recovery sleep.

Wavelet-based localization of oscillatory sources from magnetoencephalography data.

Transient brain oscillatory activities recorded with Eelectroencephalography (EEG) or magnetoencephalography (MEG) are characteristic features in physiological and pathological processes. This study is aimed at describing, evaluating, and illustrating with clinical data a new method for localizing the sources of oscillatory cortical activity recorded by MEG. The method combines time-frequency representation and an entropic regularization technique in a common framework, assuming that brain activity is sparse in time and space. Spatial sparsity relies on the assumption that brain activity is organized among cortical parcels. Sparsity in time is achieved by transposing the inverse problem in the wavelet representation, for both data and sources. We propose an estimator of the wavelet coefficients of the sources based on the maximum entropy on the mean (MEM) principle. The full dynamics of the sources is obtained from the inverse wavelet transform, and principal component analysis of the reconstructed time courses is applied to extract oscillatory components. This methodology is evaluated using realistic simulations of single-trial signals, combining fast and sudden discharges (spike) along with bursts of oscillating activity. The method is finally illustrated with a clinical application using MEG data acquired on a patient with a right orbitofrontal epilepsy.

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

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

Recording and analysis techniques for high-frequency oscillations.

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

Detection of epileptic activity in fMRI without recording the EEG.

EEG-fMRI localizes epileptic foci by detecting cerebral hemodynamic changes that are correlated to epileptic events visible in EEG. However, scalp EEG is insensitive to activity restricted to deep structures and recording the EEG in the scanner is complex and results in major artifacts that are difficult to remove. This study presents a new framework for identifying the BOLD manifestations of epileptic discharges without having to record the EEG. The first stage is based on the detection of epileptic events for each voxel by sparse representation in the wavelet domain. The second stage is to gather voxels according to proximity in time and space of detected activities. This technique was evaluated on data generated by superposing artificial responses at different locations and responses amplitude in the brain for 6 control subject runs. The method was able to detect effectively and consistently for responses amplitude of at least 1% above baseline. 46 runs from 15 patients with focal epilepsy were investigated. The results demonstrate that the method detected at least one concordant event in 37/41 runs. The maps of activation obtained from our method were more similar to those obtained by EEG-fMRI than to those obtained by the other method used in this context, 2D-Temporal Cluster Analysis. For 5 runs without event read on scalp EEG, 3 runs showed an activation concordant with the patient's diagnostic. It may therefore be possible, at least when spikes are infrequent, to detect their BOLD manifestations without having to record the EEG.

Detection of hemodynamic responses to epileptic activity using simultaneous Electro-EncephaloGraphy (EEG)/Near Infra Red Spectroscopy (NIRS) acquisitions.

Simultaneous recordings of Electro-EncephaloGraphy (EEG) with Near InfraRed Spectroscopy (NIRS) allow measuring hemodynamic changes (changes in the concentration of oxy- and deoxyhemoglobin) at the time of epileptic discharges detected on scalp EEG. Two NIRS detection methods based on the General Linear Model (GLM) respectively in the time domain and in the time-frequency domain are investigated in this study using realistic simulations of spontaneous interictal epileptic activity. We evaluated the sensitivity at different Signal to Noise Ratios (SNR), the effect of either a large or a small number of discharges and the impact of model misspecification (e.g. omission or false detection of epileptic discharges). We also explored the effect on the estimation of key parameters, which set the model order. Simulations showed that both methods become inaccurate in lower SNR conditions, leading to many false positive detections. However, the time-frequency estimator showed better performance than the time-domain one. Key parameters for each algorithm were identified and results suggest to model confounds in the GLM differently for oxy- and deoxyhemoglobin. We also demonstrated that an inaccurate marking of epileptic events has a small impact on the detection statistics whereas an inaccurate specification of the hemodynamic response function delay decreases drastically the detection abilities. Finally, we illustrated the two methods on clinical EEG/NIRS data of one patient with focal epilepsy, showing an increase of regional Cerebral Blood Volume (rCBV) spatially concordant with the presumed epileptogenic focus.

Wavelet-based estimation of the hemodynamic responses in diffuse optical imaging.

Diffuse optical imaging uses light to provide a surrogate measure of neuronal activation through the hemodynamic responses. The relative low absorption of near-infrared light enables measurements of hemoglobin changes at depths reaching the first centimeter of the cortex. The rapid rate of acquisition and the access to both oxy and deoxy-hemoglobin leads to new challenges when trying to uncouple physiology from the signal of interest. In particular, recent work provided evidence of the presence of a 1/f noise structure in optical signals and showed that a general linear model based on wavelets can be used to decorrelate the structured noise and provide a superior estimator of response amplitude when compared with conventional techniques. In this work the wavelet techniques are extended to recover the full temporal shape of the hemodynamic responses. A comparison with other models is provided as well as a case study on finger-tapping data.

Complex wavelets applied to diffuse optical spectroscopy for brain activity detection.

The analysis of diffuse optical imaging (DOI) data has seen significant developments over the last few years. When compared to fMRI, signals originating from optical imaging are tainted by more physiology and the separation of activation from this background can be difficult in some cases. In this work, we show that the use of time-frequency techniques based on wavelets distinguish different physiological sources from the evoked response to a given stimulus. In particular, we show that analytical complex wavelets identify synchronies in the signal at different scales. These synchronies are then used to extract activation information from the DOI data in order to estimate the evoked hemodynamic response or to define a new type of contrast between two conditions. This work presents both simulations and applications with real data (visual stimulation and motor tasks experiments).

Concordance between distributed EEG source localization and simultaneous EEG-fMRI studies of epileptic spikes.

In order to analyze where epileptic spikes are generated, we assessed the level of concordance between EEG source localization using distributed source models and simultaneous EEG-fMRI which measures the hemodynamic correlates of EEG activity. Data to be compared were first estimated on the same cortical surface and two comparison strategies were used: (1) MEM-concordance: a comparison between EEG sources localized with the Maximum Entropy on the Mean (MEM) method and fMRI clusters showing a significant hemodynamic response. Minimal geodesic distances between local extrema and overlap measurements between spatial extents of EEG sources and fMRI clusters were used to quantify MEM-concordance. (2) fMRI-relevance: estimation of the fMRI-relevance index alpha quantifying if sources located in an fMRI cluster could explain some scalp EEG data, when this fMRI cluster was used to constrain the EEG inverse problem. Combining MEM-concordance and fMRI-relevance (alpha) indexes, each fMRI cluster showing a significant hemodynamic response (p<0.05 corrected) was classified according to its concordance with EEG data. Nine patients with focal epilepsy who underwent EEG-fMRI examination followed by EEG recording outside the scanner were selected for this study. Among the 62 fMRI clusters analyzed (7 patients), 15 (24%) found in 6 patients were highly concordant with EEG according to both MEM-concordance and fMRI-relevance. EEG concordance was found for 5 clusters (8%) according to alpha only, suggesting sources missed by the MEM. No concordance with EEG was found for 30 clusters (48%) and for 10 clusters (16%) alpha was significantly negative, suggesting EEG-fMRI discordance. We proposed two complementary strategies to assess and classify EEG-fMRI concordance. We showed that for most patients, part of the hemodynamic response to spikes was highly concordant with EEG sources, whereas other fMRI clusters in response to the same spikes were found distant or discordant with EEG sources.

Activation detection in diffuse optical imaging by means of the general linear model.

Due to its non-invasive nature and low cost, diffuse optical imaging (DOI) is becoming a commonly used technique to assess functional activation in the brain. When imaging with DOI, two major issues arise in the data analysis: (i) the separation of noise of physiological origin and the recovery of the functional response; (ii) the tomographic image reconstruction problem. This paper focuses on the first issue. Although the general linear model (GLM) has been extensively used in functional magnetic resonance imaging (fMRI), DOI has mostly relied on filtering and averaging of raw data to recover brain functional activation. This is mainly due to the high temporal resolution of DOI which implies a new design of the drift basis modelling physiology. In this paper, we provide (i) a filtering method based on cosine functions that is more adapted than standard averaging techniques for DOI specifically; (ii) a new mode-locking technique to recover small signals and locate them temporally with high precision (shift method). Results on real data show the capability of the shift method to retrieve HbR and HbO(2) peak locations.

Evaluation of EEG localization methods using realistic simulations of interictal spikes.

Performing an accurate localization of sources of interictal spikes from EEG scalp measurements is of particular interest during the presurgical investigation of epilepsy. The purpose of this paper is to study the ability of six distributed source localization methods to recover extended sources of activated cortex. Due to the frequent lack of a gold standard to evaluate source localization methods, our evaluation was performed in a controlled environment using realistic simulations of EEG interictal spikes, involving several anatomical locations with several spatial extents. Simulated data were corrupted by physiological EEG noise. Simulations involving pairs of sources with the same amplitude were also studied. In addition to standard validation criteria (e.g., geodesic distance or mean square error), we proposed an original criterion dedicated to assess detection accuracy, based on receiver operating characteristic (ROC) analysis. Six source localization methods were evaluated: the minimum norm, the minimum norm weighted by multivariate source prelocalization (MSP), cortical LORETA with or without additional minimum norm regularization, and two derivations of the maximum entropy on the mean (MEM) approach. Results showed that LORETA-based and MEM-based methods were able to accurately recover sources of different spatial extents, with the exception of sources in temporo-mesial and fronto-mesial regions. Several spurious sources were generated by those methods, however, whereas methods using the MSP always located very accurately the maximum of activity but not its spatial extent. These findings suggest that one should always take into account the results from different localization methods when analyzing real interictal spikes.