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.     

LOFA: software for individualized localization of functional MRI activity

Although PET, SPECT, and fMRI studies have led to significant advances in functional mapping of the human brain, precise localization and quantification of activity in individual brains require additional procedures. Difficulties to be addressed by a localization strategy are: resolution of individual anatomic differences, differentiation of functional activity in closely juxtaposed brain regions, and management of multiple intricately shaped 3D anatomic structures. In this paper, we describe a localization tool, LOFA, which addresses these problems by forming ROIs with a user-driven interface. Using LOFA, complex 3D anatomy can be defined through open or closed loops and anatomic landmarks. Resulting partitions can be overlaid on top of each other to form multiple regions of interest (ROIs), and functional activity in these ROIs can be extracted individually, one after the other. LOFA introduces important paradigmatic advances over the other ROI analysis methods. The toolbox is interactive, fully compatible with AFNI (MCW), and requires Pv-Wave (VNI Inc.) license to run.     

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.     

Comparison of spike-triggered functional MRI BOLD activation and EEG dipole model localization.

We studied six patients with localization-related epilepsy, frequent interictal epileptiform discharges, and positive spike-triggered blood oxygen level-dependent functional MRI (BOLD-fMRI) findings. EEG source analysis solutions based on 64-channel EEG recorded in a separate session outside the scanner were obtained using dipole models and compared to the BOLD localization. The BOLD and structural images were coregistered, allowing the measurement of distances between the generator models and BOLD activation(s) and structural lesion when present. In all cases dipole models could be found that explained a sufficient amount of the data and that were anatomically concordant with the BOLD localization. In the five cases with structural abnormality visible on T1 scans, the BOLD activation overlapped or was in close proximity to the abnormality. The overall mean distance between the main moving dipole and the center of the nearest BOLD activation was 3.5 and 2.2 cm for the negative and positive peaks, respectively, including one case of a deep BOLD activation, in which the distance was 5 cm. In conclusion, the degree of agreement between the BOLD and EEG source localization indicates that the combination of these two noninvasive techniques offers the possibility of advancing the study of the generators of epileptiform electrical activity.     

Delayed verbal memory retrieval: a functional MRI study in epileptic patients with structural lesions of the left medial temporal lobe.

In a previous functional magnetic resonance imaging (fMRI) study, we suggested that in left medial temporal lobe epilepsy (LTLE) poor verbal episodic memory performances were sustained by abnormal neocortical and mesiotemporal activations. In the present study, we attempted to examine the evolution of these abnormal neocortical and mesiotemporal activations over 24 h. We thus observed the fMRI brain regions activated during the 24-h-delayed retrieval of a word list in the same sample of healthy control subjects and LTLE patients. In control subjects, a similar left occipitotemporofrontal network was activated during both immediate and 24-h-delayed retrieval conditions. In addition, the 24-h-delayed retrieval also activated a larger parietal region and the right hippocampus. This distributed neocortical and mesiotemporal network was very poorly activated during the 24-h-delayed retrieval in LTLE patients, suggesting the inability to reactivate areas that are keys to retrieving stored information.     

静息态功能MRI观察帕金森病伴疲劳患者局部一致性及功能连接

目的 观察帕金森病（PD）伴疲劳（PD-F）患者局部一致性（ReHo）及功能连接（FC）,探索其局部脑区分离功能和连接功能。方法 纳入29例PD-F患者（PD-F组）和29例PD不伴疲劳（PD-NF）患者（PD-NF组）,以35名健康对照者为对照（HC组）;采集静息态功能MRI（fMRI）,完成临床量表评估;计算ReHo,并分析其与疲劳严重程度量表（FSS）评分的相关性;将2组患者ReHo值存在差异且与FSS相关脑区设为ROI,观察全脑体素水平FC。结果 PD-F组右侧罗兰迪克岛盖ReHo值显著降低（体素P=0.001,总体错误率校正P<0.05）;PD组右侧罗兰迪克岛盖ReHo值与FSS评分呈负相关（r=-0.539,P<0.001）。PD-F组右侧罗兰迪克岛盖与双侧中扣带回的FC减弱（体素P=0.001,FWE校正P<0.05）。结论 PD-F患者右侧罗兰迪克岛盖脑区分离功能且与双侧中扣带回FC异常,并均参与PD-F病理生理机制。     

A novel temporal filtering strategy for functional MRI using UNFOLD

A major challenge for fMRI at high spatial resolution is the limited temporal resolution. The UNFOLD method increases image acquisition speed and potentially enables high acceleration factors in fMRI. Spatial aliasing artifacts due to interleaved k-space sampling are to be removed from the image time series by temporal filtering before statistical mapping in the time domain can be carried out. So far, low-pass filtering and multi-band filtering have been proposed. Particularly at high UNFOLD factors both methods are non-optimal. Low-pass filtering severely degrades temporal resolution and multi-band filtering leads to temporal autocorrelations affecting statistical modelling of activation. In this work, we present a novel temporal filtering strategy that significantly reduces temporal autocorrelations compared to multi-band filtering. Two datasets (finger-tapping and resting state) were post-processed using the proposed and the multi-band filter with varying set-ups (i.e. transition bands). When the proposed filtering strategy was used, a linear regression analysis revealed that the number of false positives was significantly decreased up to 34% whereas the number of activated voxels was not significantly affected for most filter parameters. In total, this led to an effective increase in the number of activated voxels per false positive for each filter set-up. At a significance level of 5%, the number of activated voxels was increased up to 41% by using the proposed filtering strategy.     

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.     

Spatiotemporal wavelet analysis for functional MRI

Characterizing the spatiotemporal behavior of the BOLD signal in functional Magnetic Resonance Imaging (fMRI) is a central issue in understanding brain function. While the nature of functional activation clusters is fundamentally heterogeneous, many current analysis approaches use spatially invariant models that can degrade anatomic boundaries and distort the underlying spatiotemporal signal. Furthermore, few analysis approaches use true spatiotemporal continuity in their statistical formulations. To address these issues, we present a novel spatiotemporal wavelet procedure that uses a stimulus-convolved hemodynamic signal plus correlated noise model. The wavelet fits, computed by spatially constrained maximum-likelihood estimation, provide efficient multiscale representations of heterogeneous brain structures and give well-identified, parsimonious spatial activation estimates that are modulated by the temporal fMRI dynamics. In a study of both simulated data and actual fMRI memory task experiments, our new method gave lower mean-squared error and seemed to result in more localized fMRI activation maps compared to models using standard wavelet or smoothing techniques. Our spatiotemporal wavelet framework suggests a useful tool for the analysis of fMRI studies.     

Ictal localization by source analysis of infraslow activity in DC-coupled scalp EEG recordings

New bedside long-term DC-coupled EEG techniques have demonstrated that infraslow (<0.5 Hz) activity lateralizes temporal lobe seizures (Vanhatalo, S., Holmes, M.D., Tallgren, P., Voipio, J., Kaila, K., Miller, J.W., 2003a. Very slow EEG responses indicate the laterality of temporal lobe seizures: a DC-EEG study. Neurology 60, 1098-1104). However, even high amplitude infraslow activity is difficult to localize by simple visual inspection if there is overlying faster EEG activity or slow artifact. In this study, we address this with improved DC-coupled EEG recording and analysis techniques and also extend observation to both temporal and extratemporal seizures. Recordings were performed during presurgical evaluation of medically intractable epilepsy, with 20 seizures in 11 patients analyzed. A commercial DC-coupled recording device was used, with sintered Ag/AgCl electrodes in a standard 10-10 system array, with additional anterior temporal and subtemporal electrodes. Seizures were localized with a software package by means of source montage analysis. Infraslow signals occurred with all seizures, often with amplitude orders of magnitude higher than conventional frequencies (0.5 to 70 Hz). The most reliable method to localize these signals and distinguish them from artifacts used a source montage after low-pass filtering below 0.5 Hz. Five of the eight patients who received epilepsy surgery had follow-up documenting significant seizure reduction, and infraslow signal analysis correctly localized the region of seizure onset in all five, while conventional noninvasive EEG recording and analysis localized only three of the five. Several seizures were also analyzed using principle component analysis source localization methods, with the results less consistently localizing than source montage analysis. DC-coupled EEG recordings give clinically useful information to noninvasively localize the seizure focus. The value of this method is increased by source analysis tools that reveal localized changes more clearly than direct visual inspection.     

EEG-fMRI同步联合对局灶性癫痫的定位

目的 采用脑电图功能磁共振l司步联合的方法,用于局灶性癫痫患者的术前定位.方法 11例局灶性癫痫患者,术前行脑电图功能磁共振同步联合检查,观察癫痫发作间期,痫样放电所致的腩活动情况,并结合同步EEG检查结果、术后病理及定期随访的情况对fMRI结果进行分析.结果 10 例(10/11)患者均在颅内原有病灶周围发现明显的 fMRI信号激活区,并与同步EEG检查的痫样放电脑区基本一致.术后病理证实病灶周围存在导致癫痫发作的病理性改变,术后随访证实11例患者均取得满意的手术效果.讨论局灶性癫痫间期痫样放电引起的血氧水平依赖性效应可用于癫痫灶的定位,脑电图功能磁共振同步联合的检查技术结合,可用于局灶性癫痫的术前定位.     

BOLD-contrast functional MRI signal changes related to intermittent rhythmic delta activity in EEG during voluntary hyperventilation-simultaneous EEG and fMRI study

Differences in the blood oxygen level dependent (BOLD) signal changes were studied during voluntary hyperventilation (HV) between young healthy volunteer groups, (1) with intermittent rhythmic delta activity (IRDA) (N = 4) and (2) controls (N = 4) with only diffuse arrhythmic slowing in EEG (normal response). Subjects hyperventilated (3 min) during an 8-min functional MRI in a 1.5-T scanner, with simultaneous recording of EEG (successful with N = 3 in both groups) and physiological parameters. IRDA power and average BOLD signal intensities (of selected brain regions) were calculated. Hypocapnia showed a tendency to be slightly lighter in the controls than in the IRDA group. IRDA power increased during the last minute of HV and ended 10-15 s after HV. The BOLD signal decreased in white and gray matter after the onset of HV and returned to the baseline within 2 min after HV. The BOLD signal in gray matter decreased approximately 30% more in subjects with IRDA than in controls, during the first 2 min of HV. This difference disappeared (in three subjects out of four) during IRDA in EEG. BOLD signal changes seem to depict changes, which precede IRDA. IRDA due to HV in healthy volunteers represent a model with a clearly defined EEG pattern and an observable BOLD signal change.     

强制戒断时间对海洛因成瘾者大脑局部一致性影响的静息态功能MRI

目的 探讨静息态下强制戒断时间对海洛因成瘾者大脑局部一致性（ReHo）的影响。方法 纳入20例强制戒断6个月（戒断6个月组）、16例戒断11个月（戒断11个月组）的海洛因成瘾者和30名（正常对照组）人口学数据相匹配的健康受试者,采集其静息态fMRI数据,计算并比较3组间ReHo值,利用Pearson相关分析法分析差异脑区ReHo值与戒断时间的关系。结果 3组间ReHo值有统计学差异的脑区为左侧海马旁回、尾状核、岛叶、扣带回、中央后回及右侧眶额回。戒断11个月组ReHo值低于戒断6个月组的脑区为左侧海马旁回、尾状核、岛叶、扣带回及中央后回,右侧眶额回ReHo值高于戒断6个月组及正常组。左侧海马旁回（r=0.53,P=0.000 8）、尾状核（r=0.54,P=0.000 6）、岛叶（r=0.62,P<0.000 1）、扣带回（r=0.47,P=0.003 5）及中央后回（r=0.35,P=0.034 8）的ReHo值分别与戒断时间呈负相关,右侧眶额回（r=0.66,P<0.000 1） ReHo值与戒断时间呈正相关。结论 海洛因成瘾者多数受损脑区（渴求、抑制性控制及学习记忆等）经长期戒断后功能趋于恢复正常,而动机/驱动脑区（眶额回）活动异常增高可能是戒断后复吸的原因之一。     

内侧颞叶癫痫自我参照加工受损的功能磁共振成像

目的 采用功能磁共振成像(fMRI)技术对内侧颞叶癫痫(mTLE)患者的自我参照功能受损状况进行评价。方法 12例伴有海马硬化的mTLE患者及15名健康志愿者纳入本研究。采用内向诱导与外向诱导任务,以内向诱导任务为刺激状态、外向诱导任务为控制状态,收集fMRI数据,观察mTLE患者自我参照加工相关脑区反应的改变。结果 与健康志愿者相比,mTLE患者的内侧前额叶、扣带回前部及后部等与自我参照加工相关的区域活动明显降低(P＜0.01)。结论 mTLE患者以内侧前额叶为主的自我参照加工脑区活动降低,反映其与自我参照加工相关的高级脑认知功能损伤,是mTLE患者异常影像表现的病理生理学基础。     

A method for removing imaging artifact from continuous EEG recorded during functional MRI

Combined EEG/fMRI recording has been used to localize the generators of EEG events and to identify subject state in cognitive studies and is of increasing interest. However, the large EEG artifacts induced during fMRI have precluded simultaneous EEG and fMRI recording, restricting study design. Removing this artifact is difficult, as it normally exceeds EEG significantly and contains components in the EEG frequency range. We have developed a recording system and an artifact reduction method that reduce this artifact effectively. The recording system has large dynamic range to capture both low-amplitude EEG and large imaging artifact without distortion (resolution 2 microV, range 33.3 mV), 5-kHz sampling, and low-pass filtering prior to the main gain stage. Imaging artifact is reduced by subtracting an averaged artifact waveform, followed by adaptive noise cancellation to reduce any residual artifact. This method was validated in recordings from five subjects using periodic and continuous fMRI sequences. Spectral analysis revealed differences of only 10 to 18% between EEG recorded in the scanner without fMRI and the corrected EEG. Ninety-nine percent of spike waves (median 74 microV) added to the recordings were identified in the corrected EEG compared to 12% in the uncorrected EEG. The median noise after artifact reduction was 8 microV. All these measures indicate that most of the artifact was removed, with minimal EEG distortion. Using this recording system and artifact reduction method, we have demonstrated that simultaneous EEG/fMRI studies are for the first time possible, extending the scope of EEG/fMRI studies considerably.     

颞叶致痫脑肿瘤的MRI影像分析

目的 观察和分析颞叶致痫脑肿瘤患者的临床、肿瘤病理类型和MRI表现.材料与方法 回顾性分析39例颞叶致痫肿瘤的发病年龄、病程与癫痫类型等临床资料、MRI表现、累及部位及病理类型.结果 39例颞叶肿瘤患者的癫痫发病年龄平均9.4岁,36例(92.3％)为儿童期起病;平均癫痫病程98.8个月.以复杂部分性发作居多,共29例(74.4％),其中11例伴全身性强直-震挛发作(GTCS),2例伴失神发作;GTCS和强直性发作分别为5例(12.8％)和2例(5.1％);单纯部分性发作3例(7.7％).颞叶肿瘤病理类型:节细胞胶质瘤24例(61.5％),星形细胞瘤5例(12.8％),少枝胶质细胞瘤和发育不良性神经上皮瘤各3例(7.7％),其他类型肿瘤4例(10.3％).肿瘤位于颢叶内侧和外侧分别为28例和11例;颞叶内侧肿瘤累及海马者(71.4％)显著高于颞叶外侧肿瘤(9.1％).MRI确定病灶为囊实性、实性为主和囊性为主者分别占46.2％、30.8％和23.1％; MRI增强扫描64.1％的病灶无强化.除胚胎发育不良性神经上皮瘤(DNET)位于皮层内,具有多小囊性特征外,其他各类肿瘤MRI表现缺少特异性.结论 颞叶肿瘤性癫痫好发于儿童期,多为累及颞叶内侧或外侧皮层,且为生长缓慢的小肿瘤,其中尤以节细胞胶质瘤居多.癫痫发作类型以复杂部分性发作伴有或不伴有全身强直震挛发作最为常见.     

Atlas-assisted localization analysis of functional images

OBJECTIVE: This paper introduces a method for localization analysis of functional images assisted by a brain atlas. The usefulness of the system developed, based on this method, is analyzed for human brain mapping and neuroradiology. MATERIALS AND METHODS: We use an enhanced and extended electronic Talairach-Tournoux brain atlas, containing segmented and labeled subcortical structures, Brodmann's areas, and gyri. The brain atlas serves as a tool for anatomy referencing, segmentation, labeling, registration, and providing 3D anatomical relationships. The process of localization analysis is decomposed into five steps: data loading, feature extraction, data normalization, identification and editing of loci, and getting labels and values. This analysis is supported by near real-time data-to-atlas warping based on the Talairach transformation. Metanalysis is enabled by merging the current and external lists of activation loci. RESULTS: We have designed, developed, tested, and deployed a commercial system for atlas-assisted localization analysis of functional images. This is the first system where an electronic version of the Talairach-Tournoux brain atlas is used interactively for analysis of functional images. This system runs on personal computers and provides functions for a rapid normalization of anatomical and functional volumetric data, data segmentation and labeling, readout of Talairach coordinates, and data display. It also is empowered with several unique features including: interactive warping facilitating fine tuning of the data-to-atlas fit, a backtracking mechanism to compensate for missing landmarks and enhancing the outcome of the overall process of data analysis, navigation on the triplanar formed by the data and the atlas, multiple-images-in-one display with atlas-anatomy-function blending, a fast locus-controlled generation of results, editing of loci, multiple label display, and saving and reading of loci. The system normalizes a single image in near real-time (0.7 s), so analysis of anatomical and functional datasets can be done on-the-fly regardless of the number of slices. The same task performed by the state-of-the-art non-linear registration methods may require up to several days. CONCLUSIONS: The system is a useful tool for atlas-assisted localization analysis and a helpful adjunct to function/location metanalysis in human brain mapping research. It is also a step forward in bringing the atlas and the clinical data together within a practical and powerful solution that is fast and flexible, yet low-cost and affordable.     

单光子断层扫描、脑电图、核磁共振对致痫灶定位的研究

目的探讨单光子断层扫描(SPECT)、脑电图(EEG)、核磁共振(MRI)对致痫灶定位的价值.方法对40例临床确诊的癫痫患者进行SPECT、MRI、EEG等检查,比较三种检查方法对致痫灶定位的准确性.结果 EEG(包括长程监测)特异性异常34例,具有定侧定位意义的30例,定位率75%(30/40);SPECT在发作间歇期定位率为47% (19/40) ;MRI定位率为 25%(10/40).EEG对致痫灶的定侧定位率明显高于SEPCT及MRI,组间差异有统计学意义(Q=30.1,P＜0.01).结论 EEG在致痫灶定位方面是一种敏感、经济、便捷又安全的检查方法.结合SEPCT及MRI可提高对致痫灶定位的准确性.     

Automatic localization and labeling of EEG sensors (ALLES) in MRI volume

Spatial localization of scalp EEG electrodes is a major step for dipole source localization and must be accurate, reproducible and practical. Several methods have been proposed in the last 15 years. The most widely used method is currently electromagnetic digitization. Nevertheless, this method is difficult to use in a clinical environment and has not been validated with a high number of electrodes. In this paper, we introduce a new automatic method for localizing and labeling EEG sensors using MRI. First, we design a new scalp EEG sensor. Secondly, we validate this new technique on a head phantom and then in a clinical environment with volunteers and patients. For this, we compare the reproducibility, accuracy and performance of our method with electromagnetic digitization. We demonstrate that our method provides better reproducibility with a significant difference (p<0.01). Concerning precision, both methods are equally accurate with no statistical differences. To conclude, our method offers the possibility of using MRI volume for both source localization and spatial localization of EEG sensors. Automation makes this method very reproducible and easy to handle in a routine clinical environment.     

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.