The BOLD response to interictal epileptiform discharges

We studied single-event and average BOLD responses to EEG interictal epileptic discharges (IEDs) in four patients with focal epilepsy, using continuous EEG-fMRI during 80-min sessions. The detection of activated areas was performed by comparing the BOLD signal at each voxel to a model of the expected signal. Since little is known about the BOLD response to IEDs, we modeled it with the response to brief auditory events (G. H., NeuroImage 9, 416-429). For each activated area, we then obtained the time course of the BOLD signal for the complete session and computed the actual average hemodynamic response function (HRF) to IEDs. In two of four patients, we observed clear BOLD responses to single IEDs. The average response was composed of a positive lobe peaking between 6 and 7 s in all patients and a negative undershoot in three patients. There were important variations in amplitude and shape between average HRFs across patients. The average HRF presented a wider positive lobe than the Glover model in three patients and a longer undershoot in two. There was a remarkable similarity in the shape of the HRF across areas for patients presenting multiple activation sites. There was no clear correlation between the amplitude of individual BOLD responses and the amplitude of the corresponding EEG spike. The possibility of a longer HRF could be used to improve statistical detection of activation in simultaneous EEG-fMRI. The variability in average HRFs across patients could reflect in part different pathophysiological mechanisms.     

Using voxel-specific hemodynamic response function in EEG-fMRI data analysis: An estimation and detection model

Research groups who study epileptic spikes with simultaneous EEG-fMRI have used mostly the general linear model (GLM). A shortcoming of the GLM is that the specification of a simple hemodynamic response function (HRF) may lead to biased results. Other methods, which predict the hemodynamic response from the measured data, have been termed "recognition models". The merit of recognition models lies in the power of estimating the region-specific or voxel-specific HRF. We propose an approach that merges these two models in a general framework: estimate the HRF on the training data sets, and applying the estimated HRF on the other part of the data sets. The merit of this framework is that it can utilize the advantages of both models. A comparison of performance is made between the GLM with three fixed HRFs and the new model with voxel-specific HRFs. The main results are as follows: (1) in 18 of the 21 patients, the new model has a higher adjusted coefficient of multiple determination than the GLM with fixed HRF; (2) in some subjects, with the new model, we found areas of activation that had not been detected with the three fixed HRFs at our threshold of significance. The results suggest that the new model can do better than the fixed HRF GLM for the analysis of epileptic activity with EEG-fMRI.     

EEG-fMRI of epileptic spikes: concordance with EEG source localization and intracranial EEG

Simultaneous EEG and fMRI recordings permit the non-invasive investigation of the generators of spontaneous brain activity such as epileptic spikes. Despite a growing interest in this technique, the precise relationship between its results and the actual regions of activated cortex is not clear. In this study, we have quantified for the first time the concordance between EEG-fMRI results and stereotaxic EEG (SEEG) recordings in 5 patients with partial epilepsy. We also compared fMRI and SEEG with other non-invasive maps based on scalp EEG alone. We found that SEEG measures largely validated the results of EEG and fMRI. Indeed, when there is an intracranial electrode in the vicinity of an EEG or fMRI peak (in the range 20-40 mm), then it usually includes one active contact. This was the case for both increases ('activations') and decreases ('deactivations') of the fMRI signal: in our patients, fMRI signal decrease could be as important in understanding the complete picture of activity as increase of fMRI signal. The concordance between EEG and fMRI was not as good as the concordance between either of these non-invasive techniques and SEEG. This shows that the two techniques can show different regions of activity: they are complementary for the localization of the areas involved in the generation of epileptic spikes. Moreover, we found that the sign of the fMRI response correlated with the low frequency content of the SEEG epileptic transients, this latter being a reflection of the slow waves. Thus, we observed a higher proportion of energy in the low frequencies for the SEEG recorded in regions with fMRI signal increase compared to the regions with fMRI signal decrease. This could reflect an increase of metabolism linked to the presence of slow waves, which suggests that fMRI is a new source of information on the mechanisms of spike generation.     

Variability of the hemodynamic response as a function of age and frequency of epileptic discharge in children with epilepsy

EEG-fMRI is a non-invasive tool to investigate epileptogenic networks in patients with epilepsy. Different patterns of BOLD responses have been observed in children as compared to adults. A high intra- and intersubject variability of the hemodynamic response function (HRF) to epileptic discharges has been observed in adults. The actual HRF to epileptic discharges in children and its dependence on age are unknown. We analyzed 64 EEG-fMRI event types in 37 children (3 months to 18 years), 92% showing a significant BOLD response. HRFs were calculated for each BOLD cluster using a Fourier basis set. After excluding HRFs with a low signal-to-noise ratio, 126 positive and 98 negative HRFs were analyzed. We evaluated age-dependent changes as well as the effect of increasing numbers of spikes. Peak time, amplitude and signal-to-noise ratio of the HRF and the t-statistic score of the cluster were used as dependent variables. We observed significantly longer peak times of the HRF in the youngest children (0 to 2 years), suggesting that the use of multiple HRFs might be important in this group. A different coupling between neuronal activity and metabolism or blood flow in young children may cause this phenomenon. Even if the t-value increased with frequent spikes, the amplitude of the HRF decreased significantly with spike frequency. This reflects a violation of the assumptions of the General Linear Model and therefore the use of alternative analysis techniques may be more appropriate with high spiking rates, a common situation in children.     

BOLD changes occur prior to epileptic spikes seen on scalp EEG

This study examined BOLD changes prior to interictal discharges in the EEG of patients with epilepsy. From a database of 143 EEG-fMRI studies, we selected the 16 data sets that showed both strong fMRI activation in the original analysis and only a single spike type in the EEG. Scans were then analyzed using seven model HRFs, peaking 3 or 1 s before the event, or 1, 3, 5, 7, or 9 s after it. An HRF was calculated using a deconvolution method for all activations seen in each analysis. The results showed that seven data sets had HRFs that peaked 1 s after the event or earlier, indicating a BOLD change starting prior to the spike seen on the scalp EEG. This is surprising since the BOLD change is expected to result from the spike. For most of the data sets with early peaking HRFs, the maximum activation in all of the statistical maps was when the model HRF peaked 1 s after the event, suggesting that the early activation was at least as important as any later activation. We suggest that this early activity is the result of neuronal changes occurring several seconds prior to a surface EEG event, but that these changes are not visible on the scalp. This is the first report of a BOLD response occurring several seconds prior to an interictal event seen on the scalp and could have important implications for our understanding of the generation of epileptic discharges.     

Resting functional MRI with temporal clustering analysis for localization of epileptic activity without EEG

We report on the methods and initial findings of a novel noninvasive technique, resting functional magnetic resonance imaging (fMRI) with temporal clustering analysis (TCA), for localizing interictal epileptic activity. Nine subjects were studied including six temporal lobe epilepsy (TLE) patients with confirmed localization indicated by successful seizure control after resection. The remaining three subjects had standard presurgical evaluations with inconsistent results or suspected extratemporal lobe foci. Peaks of activity, presumably epileptic, were detected in all nine subjects, using the resting functional MRI with temporal clustering analysis. In all six patients who underwent resective surgery, the fMRI with temporal clustering analysis accurately determined the epileptogenic hippocampal hemisphere (P = 0.005). In the three subjects without confirmed localization, the technique determined regions of activity consistent with those determined by the presurgical assessments. Though more studies are required to validate this technique, the results demonstrate the potential of the resting fMRI with temporal clustering technique to detect and localize epileptic activity without the need for simultaneous electroencephalography (EEG). The greatest potential benefit of this technique will be in the evaluation of patients with suspected extratemporal lobe epilepsy and patients whose standard assessments are discordant.     

Event-related fMRI with simultaneous and continuous EEG: description of the method and initial case report

We report on the initial imaging findings with a new technique for the simultaneous and continuous acquisition of functional MRI data and EEG recording. Thirty-seven stereotyped interictal epileptiform discharges (spikes) were identified on EEG recorded continuously during the fMRI acquisition on a patient with epilepsy. Localization of the BOLD activation associated with the EEG events was consistent with previous findings and EEG source modeling. The time course of activation was comparable with the physiological hemodynamic response function (HRF). The new methodology could lead to novel and important applications in many areas of neuroscience.     

Using voxel-specific hemodynamic response function in EEG-fMRI data analysis

Most existing analytical techniques for EEG-fMRI data need specific assumptions about the hemodynamic response function (HRF). These assumptions may not be appropriate when the HRF varies from subject to subject or from region to region. In this article, we introduce a deconvolution method for EEG-fMRI activation detection, which can be implemented with voxel-specific HRFs. A comparison of performance is made between three fixed HRFs and the deconvolution method under the framework of the general linear model. The main results are as follows: (1) the volume of detected regions from the deconvolved HRFs is larger. (2) In some subjects, the deconvolution technique can find areas of activation that have not been detected with the three fixed HRFs at our threshold of significance. (3) Deconvolution obtained higher adjusted coefficients of multiple determination compared to those obtained with the three fixed HRFs. The results suggest that the fixed HRF methods may not be the most appropriate for the analysis of epileptic activity with EEG-fMRI, and the deconvolution method may be a better choice.     

Integrating EEG and fMRI in epilepsy

Integrating electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) studies enables to non-invasively investigate human brain function and to find the direct correlation of these two important measures of brain activity. Presurgical evaluation of patients with epilepsy is one of the areas where EEG and fMRI integration has considerable clinical relevance for localizing the brain regions generating interictal epileptiform activity. The conventional analysis of EEG-fMRI data is based on the visual identification of the interictal epileptiform discharges (IEDs) on scalp EEG. The convolution of these EEG events, represented as stick functions, with a model of the fMRI response, i.e. the hemodynamic response function, provides the regressor for general linear model (GLM) analysis of fMRI data. However, the conventional analysis is not automatic and suffers of some subjectivity in IEDs classification. Here, we present an easy-to-use and automatic approach for combined EEG-fMRI analysis able to improve IEDs identification based on Independent Component Analysis and wavelet analysis. EEG signal due to IED is reconstructed and its wavelet power is used as a regressor in GLM. The method was validated on simulated data and then applied on real data set consisting of 2 normal subjects and 5 patients with partial epilepsy. In all continuous EEG-fMRI recording sessions a good quality EEG was obtained allowing the detection of spontaneous IEDs and the analysis of the related BOLD activation. The main clinical finding in EEG-fMRI studies of patients with partial epilepsy is that focal interictal slow-wave activity was invariably associated with increased focal BOLD responses in a spatially related brain area. Our study extends current knowledge on epileptic foci localization and confirms previous reports suggesting that BOLD activation associated with slow activity might have a role in localizing the epileptogenic region even in the absence of clear interictal spikes.     

Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses

Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.     

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.     

fMRI temporal clustering analysis in patients with frequent interictal epileptiform discharges: comparison with EEG-driven analysis

Temporal clustering analysis (TCA) is an exploratory data-driven technique that has been proposed for the analysis of resting fMRI to localise epileptiform activity without need for simultaneous EEG. Conventionally, fMRI of epileptic activity has been limited to those patients with subtle clinical events or frequent interictal epileptiform EEG discharges, requiring simultaneous EEG recording, from which a linear model is derived to make valid statistical inferences from the fMRI data. We sought to evaluate TCA by comparing the results with those of EEG correlated fMRI in eight selected cases. Cases were selected with clear epileptogenic localisation or lateralisation on the basis of concordant EEG and structural MRI findings, in addition to concordant activations seen on EEG-derived fMRI analyses. In three, areas of activation were seen with TCA but none corresponding to the electro-clinical localisation or activations obtained with EEG driven analysis. Temporal clusters were closely coincident with times of maximal head motion. We feel this is a serious confound to this approach and recommend that interpretation of TCA that does not address motion and physiological noise be treated with caution. New techniques to localise epileptogenic activity with fMRI alone require validation with an appropriate independent measure. In the investigation of interictal epileptiform activity, this is best done with simultaneous EEG recording.     

Independent component analysis reveals dynamic ictal BOLD responses in EEG-fMRI data from focal epilepsy patients

IntroductionSeizures occur rarely during EEG-fMRI acquisitions of epilepsy patients, but can potentially offer a better estimation of the epileptogenic zone than interictal activity. Independent component analysis (ICA) is a data-driven method that imposes minimal constraints on the hemodynamic response function (HRF). In particular, the investigation of HRFs with clear peaks, but varying latency, may be used to differentiate the ictal focus from propagated activity.MethodsICA was applied on ictal EEG-fMRI data from 15 patients. Components related to seizures were identified by fitting an HRF to the component time courses at the time of the ictal EEG events. HRFs with a clear peak were used to derive maps of significant BOLD responses and their associated peak delay. The results were then compared with those obtained from a general linear model (GLM) method. Concordance with the presumed epileptogenic focus was also assessed.ResultsThe ICA maps were significantly correlated with the GLM maps for each patient (Spearman's test, p < 0.05). The ictal BOLD responses identified by ICA always included the presumed epileptogenic zone, but were also more widespread, accounting for 20.3% of the brain volume on average. The method provided a classification of the components as a function of peak delay. BOLD response clusters associated with early HRF peaks were concordant with the suspected epileptogenic focus, while subsequent HRF peaks may correspond to ictal propagation.ConclusionICA applied to EEG-fMRI can detect areas of significant BOLD response to ictal events without having to predefine an HRF. By estimating the HRF peak time in each identified region, the method could also potentially provide a dynamic analysis of ictal BOLD responses, distinguishing onset from propagated activity.     

BOLD correlates of continuously fluctuating epileptic activity isolated by independent component analysis

Combined EEG/fMRI recordings offer a promising opportunity to detect brain areas with altered BOLD signal during interictal epileptic discharges (IEDs). These areas are likely to represent the irritative zone, which is itself a reflection of the epileptogenic zone. This paper reports on the imaging findings using independent component analysis (ICA) to continuously quantify epileptiform activity in simultaneously acquired EEG and fMRI. Using ICA derived factors coding for the epileptic activity takes into account that epileptic activity is continuously fluctuating with each spike differing in amplitude, duration and maybe topography, including subthreshold epileptic activity besides clear IEDs and may thus increase the sensitivity and statistical power of combined EEG/fMRI in epilepsy. Twenty patients with different types of focal and generalized epilepsy syndromes were investigated. ICA separated epileptiform activity from normal physiological brain activity and artifacts. In 16/20 patients, BOLD correlates of epileptic activity matched the EEG sources, the clinical semiology, and, if present, the structural lesions. In clinically equivocal cases, the BOLD correlates aided to attribute proper diagnosis of the underlying epilepsy syndrome. Furthermore, in one patient with temporal lobe epilepsy, BOLD correlates of rhythmic delta activity could be employed to delineate the affected hippocampus. Compared to BOLD correlates of manually identified IEDs, the sensitivity was improved from 50% (10/20) to 80%. The ICA EEG/fMRI approach is a safe, non-invasive and easily applicable technique, which can be used to identify regions with altered hemodynamic effects related to IEDs as well as intermittent rhythmic discharges in different types of epilepsy.     

BOLD signal increase preceeds EEG spike activity--a dynamic penicillin induced focal epilepsy in deep anesthesia

In 40-60% of cases with interictal activity in EEG, fMRI cannot locate any focus or foci with simultaneous EEG/fMRI. In experimental focal epilepsy, a priori knowledge exists of the location of the epileptogenic area. This study aimed to develop and to test an experimental focal epilepsy model, which includes dynamic induction of epileptic activity, simultaneous EEG/fMRI, and deep anesthesia. Reported results are from seven pigs (23 +/- 2 kg) studied under isoflurane anesthesia (1.2-1.6 MAC, burst-suppression EEG) and muscle relaxant. Hypo- and hypercapnia were tested in one pig. Penicillin (6000 IU) was injected via a plastic catheter (inserted into the somatosensory cortex) during fMRI (GRE-EPI, TE = 40 ms, 300 ms/two slices, acquisition delay 1700 ms) in 1.5 T (N = 6). Epileptic spikes between acquisition artifacts were reviewed and EEG total power calculated. Cross-correlation between voxel time series and three model functions resembling induced spike activity were tested. Activation map averages were calculated. Development of penicillin induced focal epileptic activity was associated with linear increase and saturation up to approximately 10-20%, in BOLD activation map average. Its initial linear increase reached 2.5-10% at the appearance of the first distinguished spike in ipsilateral EEG in all six animals. Correlated voxels were located mainly in the vicinity of the penicillin injection site and midline, but few in the thalamus. In conclusion, development of focal epileptic activity can be detected as a BOLD signal change, even preceding the spike activity in scalp EEG. This experimental model contains potential for development and testing different localization methods and revealing the characteristic time sequence of epileptic activity with fMRI during deep anesthesia.     

联合应用SPECT、EEG、MRI定位癫痫致痫灶53例分析

目的探讨单光子发射计算机断层(SPECT)脑血流灌注显像联合动态脑电图和(或)视频脑电图、磁共振成像(MRI)检查在定位癫痫致痫灶中的意义.方法对53例癫痫患者于发作间期进行99mTc-ECD局部脑血流灌注显像,并与同期动态脑电图和(或)视频脑电图、MRI检查、术中皮层脑电图检查结果进行对比分析.结果 SPECT脑血流灌注显像诊断癫痫的阳性率为88.68%,与脑电图(EEG)检查的阳性率(86.79%)一致,但二者均明显高于MRI检查阳性率(33.96%),且STECT联合EEG检查,阳性率可提升至98.11%(52/53),明显高于SPECT或EEG单独检查的阳性率;在定位致痫灶过程中,SPECT脑血流显像与EEG在致痫灶的定位上具有良好的吻合性,其符合率(包括定位一致及基本一致者)为60.37%,明显高于SPECT与MRI检查的符合率(35.85%)及EEG与MRI的符合率(32.07%);经术中皮层脑电图检查证实SPECT定位致痫灶的准确率达83.33%,若与EEG、MRI联合分析则其定位准确率提升至95.8%(23/24).结论 SPECT脑血流灌注显像不仅能灵敏地检出癫痫灶,且能较准确地定位诊断癫痫灶,与EEG和MRI的联合应用可大大提高其诊断的灵敏度和定位的准确性.     

Independent component analysis of interictal fMRI in focal epilepsy: comparison with general linear model-based EEG-correlated fMRI

The general linear model (GLM) has been used to analyze simultaneous EEG-fMRI to reveal BOLD changes linked to interictal epileptic discharges (IED) identified on scalp EEG. This approach is ineffective when IED are not evident in the EEG. Data-driven fMRI analysis techniques that do not require an EEG derived model may offer a solution in these circumstances. We compared the findings of independent components analysis (ICA) and EEG-based GLM analyses of fMRI data from eight patients with focal epilepsy. Spatial ICA was used to extract independent components (IC) which were automatically classified as either BOLD-related, motion artefacts, EPI-susceptibility artefacts, large blood vessels, noise at high spatial or temporal frequency. The classifier reduced the number of candidate IC by 78%, with an average of 16 BOLD-related IC. Concordance between the ICA and GLM-derived results was assessed based on spatio-temporal criteria. In each patient, one of the IC satisfied the criteria to correspond to IED-based GLM result. The remaining IC were consistent with BOLD patterns of spontaneous brain activity and may include epileptic activity that was not evident on the scalp EEG. In conclusion, ICA of fMRI is capable of revealing areas of epileptic activity in patients with focal epilepsy and may be useful for the analysis of EEG-fMRI data in which abnormalities are not apparent on scalp EEG.     

PET／CT与脑电图和MRI对难治性癫痫的对比研究

目的探讨正电子发射断层扫描/计算机断层成像（PET/CT）、脑电图（EEG）和磁共振成像（MRI）在难治性癫痫病灶定位方面的诊断价值。方法 2005年12月至2009年12月就诊于内蒙古医学院附属医院的50例难治性癫痫患者,男32例,女18例,平均年龄（20±18）岁,病程平均（7±11）年,均行EEG和MRI检查及18F-FDG PET/CT（发作期PET/CT显像8例,发作间期显像42例）,并对全部病例癫痫病灶的检出率及一致性进行比较。结果 PET/CT显像异常率92%（46例）,其中低代谢灶39例,高代谢灶5例,低代谢灶和高代谢灶共存2例。EEG异常率74%（37例）,MRI异常率36%（18例）。在病灶定位诊断上PET/CT与EEG一致率为72%,PET/CT与MRI的一致率为36%,MRI与EEG的一致率为32%。结论 PET/CT显像对难治性癫痫病灶的探测敏感性高于EEG和MRI,在病灶诊断上PET/CT与EEG一致性高于MRI与EEG。     

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.     

局部脑血流SPECT显像与其他影像对癫痫定位诊断比较的研究

目的:研究癫痫发作期与发作间期同侧对照99mTc-ECD局部脑血流(rCBF)单光子发射计算机体层摄影术(SPECT)显像对癫痫灶的定位诊断价值。方法:对54例癫痫患者行发作间期显像,其中10例行发作期显像,并与皮层脑电图(EEG)、计算机断层扫描或磁共振显像(CT/M RI)、深部皮层脑电图(EcoG)结果进行对比分析。结果:癫痫发作期与发作间期rCBF SPECT显像对癫痫灶具有较高的检出率和定位率,优于EEG、CT/M RI,低于EcoG,但rCBF在发作期与发作间期的比较差异无显著性(P>0.05)。结论:发作间期rCBF SPECT显像可以成为癫痫灶方便而有效的定位方法,若加上发作期rCBF显像则定位更佳。