Epileptogenic Tubers Are Associated with Increased Kurtosis of Susceptibility Values: A Combined Quantitative Susceptibility Mapping and Stereoelectroencephalography Pilot Study

BACKGROUND AND PURPOSE

Prior studies have found an association between calcification and the epileptogenicity of tubers in tuberous sclerosis complex. Quantitative susceptibility mapping is a novel tool sensitive to magnetic susceptibility alterations due to tissue calcification. We assessed the utility of quantitative susceptibility mapping in identifying putative epileptogenic tubers in tuberous sclerosis complex using stereoelectroencephalography data as ground truth.

MATERIALS AND METHODS

We studied patients with tuberous sclerosis complex undergoing stereoelectroencephalography at a single center who had multiecho gradient-echo sequences available. Quantitative susceptibility mapping and R2* values were extracted for all tubers on the basis of manually drawn 3D ROIs using T1- and T2-FLAIR sequences. Characteristics of quantitative susceptibility mapping and R2* distributions from implanted tubers were compared using binary logistic generalized estimating equation models designed to identify ictal (involved in seizure onset) and interictal (persistent interictal epileptiform activity) tubers. These models were then applied to the unimplanted tubers to identify potential ictal and interictal tubers that were not sampled by stereoelectroencephalography.

RESULTS

A total of 146 tubers were identified in 10 patients, 76 of which were sampled using stereoelectroencephalography. Increased kurtosis of the tuber quantitative susceptibility mapping values was associated with epileptogenicity (P = .04 for the ictal group and P = .005 for the interictal group) by the generalized estimating equation model. Both groups had poor sensitivity (35.0% and 44.1%, respectively) but high specificity (94.6% and 78.6%, respectively).

CONCLUSIONS

Our finding of increased kurtosis of quantitative susceptibility mapping values (heavy-tailed distribution) was highly specific, suggesting that it may be a useful biomarker to identify putative epileptogenic tubers in tuberous sclerosis complex. This finding motivates the investigation of underlying tuber mineralization and other properties driving kurtosis changes in quantitative susceptibility mapping values.

Evaluation of DISORDER: Retrospective Image Motion Correction for Volumetric Brain MRI in a Pediatric Setting

BACKGROUND AND PURPOSE

Head motion causes image degradation in brain MR imaging examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MR imaging acquisitions.

MATERIALS AND METHODS

We prospectively acquired brain MR imaging at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted TSE, and FLAIR in 32 unsedated children, including 7 with epilepsy (age range, 2-18 years). We implemented a novel motion correction technique through a modification of k-space data acquisition: Distributed and Incoherent Sample Orders for Reconstruction Deblurring by using Encoding Redundancy (DISORDER). For each participant and technique, we obtained 3 reconstructions as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: gradient entropy (GE) and MPRAGE white matter (WM) homogeneity. As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability.

RESULTS

Both image quality metrics improved after motion correction for all modalities, and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z = -3.164 for MPRAGE; z = -2.066 for TSE; z = -2.645 for FLAIR; all P < .05).

CONCLUSIONS

Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least 1 sequence was improved by this approach, indicating the benefit of this technique in unsedated children for both clinical and research environments.

A study of the electro-haemodynamic coupling using simultaneously acquired intracranial EEG and fMRI data in humans

In current fMRI studies designed to map BOLD changes related to interictal epileptiform discharges (IED), which are recorded on simultaneous EEG, the information contained in the morphology and field extent of the EEG events is exclusively used for their classification. Usually, a BOLD predictor based on IED onset times alone is constructed, effectively treating all events as identical. We used intracranial EEG (icEEG)-fMRI data simultaneously recorded in humans to investigate the effect of including any of the features: amplitude, width (duration), slope of the rising phase, energy (area under the curve), or spatial field extent (number of contacts over which the sharp wave was observed) of the fast wave of the IED (the sharp wave), into the BOLD model, to better understand the neurophysiological origin of sharp wave-related BOLD changes, in the immediate vicinity of the recording contacts. Among the features considered, the width was the only one found to explain a significant amount of additional variance, suggesting that the amplitude of the BOLD signal depends more on the duration of the underlying field potential (reflected in the sharp wave width) than on the degree of neuronal activity synchrony (reflected in the sharp wave amplitude), and, consequently, that including inter-event variations of the sharp wave width in the BOLD signal model may increase the sensitivity of forthcoming EEG-fMRI studies of epileptic activity.

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First study of EEG morphology using simultaneous intracranial EEG-fMRI in humans

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The duration of sharp waves is significantly correlated with the BOLD signal amplitude.

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BOLD amplitude reflects more field potential duration than neuronal synchrony.

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Sharp wave duration should be included in BOLD models of epileptic discharges.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.

Simultaneous intracranial EEG-fMRI in humans: protocol considerations and data quality

We have recently performed simultaneous intracranial EEG and fMRI recordings (icEEG-fMRI) in patients with epilepsy. In this technical note, we examine limited thermometric data for potential electrode heating during our protocol and found that heating was ≤0.1 °C in-vitro at least 10 fold less than in-vivo limits. We quantify EEG quality, which can be degraded by MRI scanner-induced artefacts, and fMRI image (gradient echo echo-planar imaging: GE-EPI) signal quality around the electrodes, which can be degraded by electrode interactions with B1 (radiofrequency) and B0 (static) magnetic fields. We recorded EEG outside and within the MRI scanner with and without scanning. EEG quality was largely preserved during scanning and in particular heartbeat-related artefacts were small compared to epileptic events. To assess the GE-EPI signal reduction around the electrodes, we compared image signal intensity along paths into the brain normal to its surface originating from the individual platinum-iridium electrode contacts. GE-EPI images were obtained at 1.5 T with an echo time (TE) of 40 ms and repetition time (TR) of 3000 ms and a slice thickness of 2.5 mm. We found that GE-EPI signal intensity reduction was confined to a 10 mm radius and that it was reduced on average by less than 50% at 5mm from the electrode contacts. The GE-EPI image signal reduction also varied with electrode orientation relative to the MRI scanner axes; in particular, cortical grid contacts with a normal along the scanner's main magnetic field (B(0)) axis have higher artefact levels relative to those with a normal perpendicular to the z-axis. This suggests that the artefacts were predominantly susceptibility-related rather than due to B1 interactions. This information can be used to guide interpretation of results of icEEG-fMRI experiments proximal to the electrodes, and to optimise artefact reduction strategies.

Improving the sensitivity of EEG-fMRI studies of epileptic activity by modelling eye blinks, swallowing and other video-EEG detected physiological confounds

RATIONALE

To improve the sensitivity and specificity of simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) it is prudent to devise modelling strategies explaining the residual variance. The purpose of this study is to investigate the potential value of including additional regressors for physiological activities, derived from video-EEG, in the modelling of haemodynamic patterns linked to interictal epileptiform discharges (IEDs) using simultaneously recorded video-EEG-fMRI.

METHODS

Ten patients with IED (focal epilepsy: 6, idiopathic generalized epilepsy (IGE):4) were studied. BOLD-sensitive fMRI images were acquired on a 3T MRI scanner. 64-channel EEG was recorded using MR-compatible system. A custom made, dual-video-camera system synchronised with EEG was used to record video simultaneously. IEDs and physiological activities were identified and labelled on video-EEG using Brain Analyzer2. fMRI time-series data were pre-processed and analysed using SPM5 software. Two general linear models (GLM) were created; GLM1: IEDs were convolved with the canonical haemodynamic response function and its derivatives. Realignment parameters and pulse regressors were included in the design matrix as confounds, GLM2: GLM1 and additional regressors identified on video-EEG including: eye blinks, hand or foot movement, chewing and swallowing were also included in the design matrix. SPM [F] maps (p<0.05, corrected for family wise error and p<0.001, uncorrected) were generated for both models. We compared the resulting blood oxygen level dependent (BOLD) maps for cluster size, statistical significance and degree of concordance with the irritative zone.

RESULTS

BOLD changes relating to physiological activities were generally seen in expected brain areas. In patients with focal epilepsy, the extent and Z-score of the IED-related global maximum BOLD clusters increased in 4/6 patients and additional IED-related BOLD clusters were observed in 3/6 patients for GLM2. Also, the degree of concordance of IED-related maps with irritative zone improved for one patient for GLM2 and was unchanged for the other cases. In patients with IGE, the size and statistical significance for global maximum and other BOLD clusters increased in 2/4 patients. We conclude that the inclusion of additional regressors, derived from video based information, in the design matrix explains a greater amount of variance and can reveal additional IED-related BOLD clusters which may be part of the epileptic networks.

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.