MP2RAGE vs. MPRAGE surface-based morphometry in focal epilepsy

In drug-resistant focal epilepsy, detecting epileptogenic lesions using MRI poses a critical diagnostic challenge. Here, we assessed the utility of MP2RAGE-a T1-weighted sequence with self-bias correcting properties commonly utilized in ultra-high field MRI-for the detection of epileptogenic lesions using a surface-based morphometry pipeline based on FreeSurfer, and compared it to the common approach using T1w MPRAGE, both at 3T. We included data from 32 patients with focal epilepsy (5 MRI-positive, 27 MRI-negative with lobar seizure onset hypotheses) and 94 healthy controls from two epilepsy centres. Surface-based morphological measures and intensities were extracted and evaluated in univariate GLM analyses as well as multivariate unsupervised 'novelty detection' machine learning procedures. The resulting prediction maps were analyzed over a range of possible thresholds using alternative free-response receiver operating characteristic (AFROC) methodology with respect to the concordance with predefined lesion labels or hypotheses on epileptogenic zone location. We found that MP2RAGE performs at least comparable to MPRAGE and that especially analysis of MP2RAGE image intensities may provide additional diagnostic information. Secondly, we demonstrate that unsupervised novelty-detection machine learning approaches may be useful for the detection of epileptogenic lesions (maximum AFROC AUC 0.58) when there is only a limited lesional training set available. Third, we propose a statistical method of assessing lesion localization performance in MRI-negative patients with lobar hypotheses of the epileptogenic zone based on simulation of a random guessing process as null hypothesis. Based on our findings, it appears worthwhile to study similar surface-based morphometry approaches in ultra-high field MRI (≥ 7 T).

[Focal cortical dysplasia: visual assessment of MRI and MR morphometry data]

OBJECTIVE

Assessing the diagnostic significance of MR morphometry in determining the localization of focal cortical dysplasias (FCD).

MATERIAL AND METHODS

The study included 13 children after surgery for drug-resistant epilepsy caused by FCD type II and stable postoperative remission of seizures (Engel class IA, median follow-up 56 months). We analyzed the results of independent expert assessment of native MR data by three radiologists (HARNESS protocol) and MR morphometry data regarding accuracy of FCD localization. We considered 2 indicators, i.e. local cortical thickening and gray-white matter blurring.

RESULTS

FCD detection rate was higher after MR morphometry compared to visual analysis of native MR data using the HARNESS protocol. MR morphometry also makes it possible to more often identify gray-white matter blurring as a sign often missed by radiologists (p<0.05).

CONCLUSION

MR morphometry is an additional non-invasive method for assessing the localization of FCD.

Profiling PI3K-AKT-MTOR variants in focal brain malformations reveals new insights for diagnostic care

Focal malformations of cortical development including focal cortical dysplasia, hemimegalencephaly and megalencephaly, are a spectrum of neurodevelopmental disorders associated with brain overgrowth, cellular and architectural dysplasia, intractable epilepsy, autism and intellectual disability. Importantly, focal cortical dysplasia is the most common cause of focal intractable paediatric epilepsy. Gain and loss of function variants in the PI3K-AKT-MTOR pathway have been identified in this spectrum, with variable levels of mosaicism and tissue distribution. In this study, we performed deep molecular profiling of common PI3K-AKT-MTOR pathway variants in surgically resected tissues using droplet digital polymerase chain reaction (ddPCR), combined with analysis of key phenotype data. A total of 159 samples, including 124 brain tissue samples, were collected from 58 children with focal malformations of cortical development. We designed an ultra-sensitive and highly targeted molecular diagnostic panel using ddPCR for six mutational hotspots in three PI3K-AKT-MTOR pathway genes, namely PIK3CA (p.E542K, p.E545K, p.H1047R), AKT3 (p.E17K) and MTOR (p.S2215F, p.S2215Y). We quantified the level of mosaicism across all samples and correlated genotypes with key clinical, neuroimaging and histopathological data. Pathogenic variants were identified in 17 individuals, with an overall molecular solve rate of 29.31%. Variant allele fractions ranged from 0.14 to 22.67% across all mutation-positive samples. Our data show that pathogenic MTOR variants are mostly associated with focal cortical dysplasia, whereas pathogenic PIK3CA variants are more frequent in hemimegalencephaly. Further, the presence of one of these hotspot mutations correlated with earlier onset of epilepsy. However, levels of mosaicism did not correlate with the severity of the cortical malformation by neuroimaging or histopathology. Importantly, we could not identify these mutational hotspots in other types of surgically resected epileptic lesions (e.g. polymicrogyria or mesial temporal sclerosis) suggesting that PI3K-AKT-MTOR mutations are specifically causal in the focal cortical dysplasia-hemimegalencephaly spectrum. Finally, our data suggest that ultra-sensitive molecular profiling of the most common PI3K-AKT-MTOR mutations by targeted sequencing droplet digital polymerase chain reaction is an effective molecular approach for these disorders with a good diagnostic yield when paired with neuroimaging and histopathology.

Is the anatomical lesion always guilty?: A case report

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The presence of a lesion on MRI should not be considered as sufficient to identify epileptogenic zone.

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The epileptogenic zone can be independent of the anatomical lesion.

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The presurgical evaluation is a gradual and tailored process on patient’s epilepsy.

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The invasive investigations could clarify the doubts in the epilepsy surgery work-up.

During a presurgical workup, when discordant structural and electroclinical localization is identified, further evaluation with invasive EEG is often necessary. We report a 44-year-old right-handed woman without significant risk factors for epilepsy who presented at 11 years of age with focal seizures manifest as jerking of the left side of her mouth and arm with frequent evolution to bilateral tonic-clonic seizures during sleep with a weekly frequency. During video-EEG monitoring, we observed interictal left fronto-central sharp waves and some independent sharp waves in the right fronto-central region. Habitual seizures were recorded and during the post-ictal state, the patient had left arm weakness for a few minutes. The ictal discharge on EEG was characterized by a bilateral fronto-central rhythmic slow activity more prevalent over the right hemisphere. MRI of the brain revealed a left precentral structural lesion. Considering the discordant structural and electroclinical information, we performed bilateral fronto-central stereo-EEG implantation and demonstrated clear right fronto-central seizure onset. Stereo-EEG-guided radiofrequency thermocoagulation was performed in the right fronto-central leads with subsequent seizure freedom for 9 months. The patient then underwent surgery (right fronto-central cortectomy), and histology revealed focal cortical dysplasia type Ia. The post-surgical outcome was Engel Ia. This case underscores the presence of a structural lesion is not sufficient to define the epileptogenic zone if not supported by clinical and EEG evidence. In such cases, an invasive investigation is typically required.

Multicenter Validation of a Deep Learning Detection Algorithm for Focal Cortical Dysplasia

BACKGROUND AND OBJECTIVE

To test the hypothesis that a multicenter-validated computer deep learning algorithm detects MRI-negative focal cortical dysplasia (FCD).

METHODS

We used clinically acquired 3-dimensional (3D) T1-weighted and 3D fluid-attenuated inversion recovery MRI of 148 patients (median age 23 years [range 2-55 years]; 47% female) with histologically verified FCD at 9 centers to train a deep convolutional neural network (CNN) classifier. Images were initially deemed MRI-negative in 51% of patients, in whom intracranial EEG determined the focus. For risk stratification, the CNN incorporated bayesian uncertainty estimation as a measure of confidence. To evaluate performance, detection maps were compared to expert FCD manual labels. Sensitivity was tested in an independent cohort of 23 cases with FCD (13 ± 10 years). Applying the algorithm to 42 healthy controls and 89 controls with temporal lobe epilepsy disease tested specificity.

RESULTS

Overall sensitivity was 93% (137 of 148 FCD detected) using a leave-one-site-out cross-validation, with an average of 6 false positives per patient. Sensitivity in MRI-negative FCD was 85%. In 73% of patients, the FCD was among the clusters with the highest confidence; in half, it ranked the highest. Sensitivity in the independent cohort was 83% (19 of 23; average of 5 false positives per patient). Specificity was 89% in healthy and disease controls.

DISCUSSION

This first multicenter-validated deep learning detection algorithm yields the highest sensitivity to date in MRI-negative FCD. By pairing predictions with risk stratification, this classifier may assist clinicians in adjusting hypotheses relative to other tests, increasing diagnostic confidence. Moreover, generalizability across age and MRI hardware makes this approach ideal for presurgical evaluation of MRI-negative epilepsy.

CLASSIFICATION OF EVIDENCE

This study provides Class III evidence that deep learning on multimodal MRI accurately identifies FCD in patients with epilepsy initially diagnosed as MRI negative.

Localization of the Epileptogenic Zone by Multimodal Neuroimaging and High-Frequency Oscillation

Accurate localization of the epileptogenic zone (EZ) is a key factor to obtain good surgical outcome for refractory epilepsy patients. However, no technique, so far, can precisely locate the EZ, and there are barely any reports on the combined application of multiple technologies to improve the localization accuracy of the EZ. In this study, we aimed to explore the use of a multimodal method combining PET-MRI, fluid and white matter suppression (FLAWS)—a novel MRI sequence, and high-frequency oscillation (HFO) automated analysis to delineate EZ. We retrospectively collected 15 patients with refractory epilepsy who underwent surgery and used the above three methods to detect abnormal brain areas of all patients. We compared the PET-MRI, FLAWS, and HFO results with traditional methods to evaluate their diagnostic value. The sensitivities, specificities of locating the EZ, and marking extent removed versus not removed [RatioChann(ev)] of each method were compared with surgical outcome. We also tested the possibility of using different combinations to locate the EZ. The marked areas in every patient established using each method were also compared to determine the correlations among the three methods. The results showed that PET-MRI, FLAWS, and HFOs can provide more information about potential epileptic areas than traditional methods. When detecting the EZs, the sensitivities of PET-MRI, FLAWS, and HFOs were 68.75, 53.85, and 87.50%, and the specificities were 80.00, 33.33, and 100.00%. The RatioChann(ev) of HFO-marked contacts was significantly higher in patients with good outcome than those with poor outcome (p< 0.05). When intracranial electrodes covered all the abnormal areas indicated by neuroimaging with the overlapping EZs being completely removed referred to HFO analysis, patients could reach seizure-free (p < 0.01). The periphery of the lesion marked by neuroimaging may be epileptic, but not every lesion contributes to seizures. Therefore, approaches in multimodality can detect EZ more accurately, and HFO analysis may help in defining real epileptic areas that may be missed in the neuroimaging results. The implantation of intracranial electrodes guided by non-invasive PET-MRI and FLAWS findings as well as HFO analysis would be an optimized multimodal approach for locating EZ.

Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images

In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis.

Improved detection of focal cortical dysplasia in normal-appearing FLAIR images using a Bayesian classifier

PURPOSE

Focal cortical dysplasia (FCD) is a malformation of cortical development that often causes pharmacologically intractable epilepsy. However, FCD lesions are frequently characterized by minor structural abnormalities that can easily go unrecognized, making diagnosis difficult. Therefore, many epileptic patients have had pathologically confirmed FCD lesions that appeared normal in pre-surgical fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) studies. Such lesions are called "FLAIR-negative." This study aimed to improve the detection of histopathologically verified FCD in a sample of patients without visually appreciable lesions.

METHODS

The technique first extracts a series of features from a FLAIR image. Then, three naive Bayesian classifiers with probability (NBCP) are trained based on different numbers of feature maps to classify voxels as lesional or healthy voxels and assign the lesions a probability of correct classification. This method classifies the three-dimensional (3D) images of all patients using leave-one-out cross-validation (LOOCV). Finally, the 3D lesion probability map, including epileptogenic lesions, is obtained by removing false-positive voxel outliers using the morphological method. The performance of the NBCP was assessed for quantitative analysis by specificity, accuracy, recall, precision, and Dice coefficient in subject-wise, lesion-wise, and voxel-wise manners.

RESULTS

The best detection results were obtained by using four features: cortical thickness, symmetry, K-means, and modified texture energy. There were eight lesions in seven patients. The subject-wise sensitivity of the proposed method was 85.71% (6/7). Seven out of eight lesions were detected, so the lesion-wise sensitivity was 87.50% (7/8). No significant differences in effectiveness were found between automated lesion detection using four features and lesion detection using manual segmentation, as voxels were quantitatively analyzed in terms of specificity (mean ± SD = 99.64 ± 0.13), accuracy (mean ± SD = 99.62 ± 0.14), recall (mean ± SD = 73.27 ± 26.11), precision (mean ± SD = 11.93 ± 8.16), and Dice coefficient (mean ± SD = 22.82 ± 15.57).

CONCLUSION

We developed a novel automatic voxel-based method to improve the detection of FCD FLAIR-negative lesions. To the best of our knowledge, this study is the first to detect FCD lesions that appear normal in pre-surgical 3D high-resolution FLAIR images alone with a limited number of radiomics features. We optimized the algorithm and selected the best prior probability to improve the detection. For non-temporal lobe epilepsy (non-TLE) patients, lesions could be accurately located, although there were still false-positive areas.

Widespread cortical dyslamination in epilepsy patients with malformations of cortical development

PURPOSE

Recent research in epilepsy patients confirms our understanding of epilepsy as a network disorder with widespread cortical compromise. Here, we aimed to investigate the neocortical laminar architecture in patients with focal cortical dysplasia (FCD) and periventricular nodular heterotopia (PNH) using clinically feasible 3 T MRI.

METHODS

Eighteen epilepsy patients (FCD and PNH groups; n = 9 each) and age-matched healthy controls (n = 9) underwent T1 relaxation 3 T MRI, from which component probability T1 maps were utilized to extract sub-voxel composition of 6 T1 cortical layers. Seventy-eight cortical areas of the automated anatomical labeling atlas were divided into 1000 equal-volume sub-areas for better detection of cortical abnormalities, and logistic regressions were performed to compare FCD/PNH patients with healthy controls with the T1 layers composing each sub-area as regressors. Statistical significance (p < 0.05) was determined by a likelihood-ratio test with correction for false discovery rate using Benjamini-Hochberg method.

RESULTS

Widespread cortical abnormalities were observed in the patient groups. Out of 1000 sub-areas, 291 and 256 bilateral hemispheric cortical sub-areas were found to predict FCD and PNH, respectively. For each of these sub-areas, we were able to identify the T1 layer, which contributed the most to the prediction.

CONCLUSION

Our results reveal widespread cortical abnormalities in epilepsy patients with FCD and PNH, which may have a role in epileptogenesis, and likely related to recent studies showing widespread structural (e.g., cortical thinning) and diffusion abnormalities in various human epilepsy populations. Our study provides quantitative information of cortical laminar architecture in epilepsy patients that can be further targeted for study in functional and neuropathological studies.

New insights into a spectrum of developmental malformations related to mTOR dysregulations: challenges and perspectives

In recent years the role of the mammalian target of rapamycin (mTOR) pathway has emerged as crucial for normal cortical development. Therefore, it is not surprising that aberrant activation of mTOR is associated with developmental malformations and epileptogenesis. A broad spectrum of malformations of cortical development, such as focal cortical dysplasia (FCD) and tuberous sclerosis complex (TSC), have been linked to either germline or somatic mutations in mTOR pathway-related genes, commonly summarised under the umbrella term 'mTORopathies'. However, there are still a number of unanswered questions regarding the involvement of mTOR in the pathophysiology of these abnormalities. Therefore, a monogenetic disease, such as TSC, can be more easily applied as a model to study the mechanisms of epileptogenesis and identify potential new targets of therapy. Developmental neuropathology and genetics demonstrate that FCD IIb and hemimegalencephaly are the same diseases. Constitutive activation of mTOR signalling represents a shared pathogenic mechanism in a group of developmental malformations that have histopathological and clinical features in common, such as epilepsy, autism and other comorbidities. We seek to understand the effect of mTOR dysregulation in a developing cortex with the propensity to generate seizures as well as the aftermath of the surrounding environment, including the white matter.

Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning

OBJECTIVE

Focal cortical dysplasia (FCD) is a major pathology in patients undergoing surgical resection to treat pharmacoresistant epilepsy. Magnetic resonance imaging (MRI) postprocessing methods may provide essential help for detection of FCD. In this study, we utilized surface-based MRI morphometry and machine learning for automated lesion detection in a mixed cohort of patients with FCD type II from 3 different epilepsy centers.

METHODS

Sixty-one patients with pharmacoresistant epilepsy and histologically proven FCD type II were included in the study. The patients had been evaluated at 3 different epilepsy centers using 3 different MRI scanners. T1-volumetric sequence was used for postprocessing. A normal database was constructed with 120 healthy controls. We also included 35 healthy test controls and 15 disease test controls with histologically confirmed hippocampal sclerosis to assess specificity. Features were calculated and incorporated into a nonlinear neural network classifier, which was trained to identify lesional cluster. We optimized the threshold of the output probability map from the classifier by performing receiver operating characteristic (ROC) analyses. Success of detection was defined by overlap between the final cluster and the manual labeling. Performance was evaluated using k-fold cross-validation.

RESULTS

The threshold of 0.9 showed optimal sensitivity of 73.7% and specificity of 90.0%. The area under the curve for the ROC analysis was 0.75, which suggests a discriminative classifier. Sensitivity and specificity were not significantly different for patients from different centers, suggesting robustness of performance. Correct detection rate was significantly lower in patients with initially normal MRI than patients with unequivocally positive MRI. Subgroup analysis showed the size of the training group and normal control database impacted classifier performance.

SIGNIFICANCE

Automated surface-based MRI morphometry equipped with machine learning showed robust performance across cohorts from different centers and scanners. The proposed method may be a valuable tool to improve FCD detection in presurgical evaluation for patients with pharmacoresistant epilepsy.

Detection of structural abnormalities of cortical and subcortical gray matter in patients with MRI-negative refractory epilepsy using neurite orientation dispersion and density imaging

PURPOSE

NODDI (Neurite Orientation Dispersion and Density Imaging) and DTI (Diffusion tensor imaging) may be useful in identifying abnormal regions in patients with MRI-negative refractory epilepsy. The aim of this study was to determine whether NODDI and DTI maps including neurite density (ND), orientation dispersion index (ODI), mean diffusivity (MD) and fractional anisotropy (FA) can detect structural abnormalities in cortical and subcortical gray matter (GM) in these patients. The correlation between these parameters and clinical characteristics of the disease was also investigated.

METHODS

NODDI and DTI maps of 17 patients were obtained and checked visually. Region of interest (ROI) was drawn on suspected areas and contralateral regions in cortex. Contrast-to-noise ratio (CNR) was determined for each region. Furthermore volumetric data and mean values of ND, ODI, FA and MD of subcortical GM structures were calculated in both of the patients and controls. Finally, the correlations of these parameters in the subcortical with age of onset and duration of epilepsy were investigated.

RESULTS

Cortical abnormalities on ODI images were observed in eight patients qualitatively. CNR of ODI was significantly greater than FA and MD. The subcortical changes including decrease of FA and ND and increase of ODI in left nucleus accumbens and increase of the volume in right amygdala were detected in the patients.

CONCLUSIONS

The results revealed that NODDI can improve detection of microstructural changes in cortical and subcortical GM in patients with MRI negative epilepsy.

Quantitative surface analysis of combined MRI and PET enhances detection of focal cortical dysplasias

OBJECTIVE

Focal cortical dysplasias (FCDs) often cause pharmacoresistant epilepsy, and surgical resection can lead to seizure-freedom. Magnetic resonance imaging (MRI) and positron emission tomography (PET) play complementary roles in FCD identification/localization; nevertheless, many FCDs are small or subtle, and difficult to find on routine radiological inspection. We aimed to automatically detect subtle or visually-unidentifiable FCDs by building a classifier based on an optimized cortical surface sampling of combined MRI and PET features.

METHODS

Cortical surfaces of 28 patients with histopathologically-proven FCDs were extracted. Morphology and intensity-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface, and fed to a 2-step (Support Vector Machine and patch-based) classifier. Classifier performance was assessed compared to manual lesion labels.

RESULTS

Our classifier using combined feature selections from MRI and PET outperformed both quantitative MRI and multimodal visual analysis in FCD detection (93% vs 82% vs 68%). No false positives were identified in the controls, whereas 3.4% of the vertices outside FCD lesions were also classified to be lesional ("extralesional clusters"). Patients with type I or IIa FCDs displayed a higher prevalence of extralesional clusters at an intermediate distance to the FCD lesions compared to type IIb FCDs (p < 0.05). The former had a correspondingly lower chance of positive surgical outcome (71% vs 91%).

CONCLUSIONS

Machine learning with multimodal feature sampling can improve FCD detection. The spread of extralesional clusters characterize different FCD subtypes, and may represent structurally or functionally abnormal tissue on a microscopic scale, with implications for surgical outcomes.

Toward a Mechanistic Understanding of Epileptic Networks

Focal epileptic seizures have long been considered to arise from a small susceptible brain area and spread through uninvolved regions. In the past decade, the idea that focal seizures instead arise from coordinated activity across large-scale epileptic networks has become widely accepted. Understanding the network model's applicability is critical, due to its increasing influence on clinical research and surgical treatment paradigms. In this review, we examine the origins of the concept of epileptic networks as the nidus for recurring seizures. We summarize analytical and methodological elements of epileptic network studies and discuss findings from recent detailed electrophysiological investigations. Our review highlights the strengths and limitations of the epileptic network theory as a metaphor for the complex interactions that occur during seizures. We present lines of investigation that may usefully probe these interactions and thus serve to advance our understanding of the long-range effects of epileptiform activity.

[Invasive stimulation procedures and EEG diagnostics in epilepsy]

Stimulation has been performed experimentally and in small case series to treat epilepsy since the 1970s. Since the introduction of vagus nerve stimulation in 1997 and intracranial stimulation methods in 2011 into patient care, invasive stimulation has become a rapidly developing but infrequently used therapeutic option in Europe. Whereas vagus nerve stimulation is frequently used, particularly in the USA, intracranial stimulation differs in its regional availability. In order to improve the efficacy of stimulation, develop criteria for its use and assure low complication rates, a concentration on experienced centers and multicenter data acquisition and sharing are needed.Invasive electroencephalographic (EEG) monitoring with subdural electrodes and especially with stereotactically implanted depth electrodes have been used increasingly more often for presurgical evaluation in recent years. They are applied when non-invasive diagnostics show insufficient results to exactly identify the location and extent of the epileptogenic zone or cannot be adequately distinguished from eloquent cortex areas. Complications include intracranial hemorrhage, infections and increased intracranial pressure but lasting deficits or even death are rare (≤2 %). The outcome of invasive monitoring is inferior to non-invasive monitoring because of the higher degree of complexity of the cases; however, it is far superior to the seizure-free rates achieved by anticonvulsant drug treatment alone.

Contributions of EEG-fMRI to Assessing the Epileptogenicity of Focal Cortical Dysplasia

Purpose: To examine the ability of the BOLD response to EEG spikes to assess the epileptogenicity of the lesion in patients with focal cortical dysplasia (FCD).

Method: Patients with focal epilepsy and FCD who underwent 3T EEG-fMRI from 2006 to 2010 were included. Diagnosis of FCD was based on neuroradiology (MRI+), or histopathology in MRI-negative cases (MRI−). Patients underwent 120 min EEG-fMRI recording session. Spikes similar to those recorded outside the scanner were marked in the filtered EEG. The lesion (in MRI+) or the removed cortex (in MRI−) was marked on the anatomical T1 sequence, blindly to the BOLD response, after reviewing the FLAIR images. For each BOLD response we assessed the concordance with the spike field and with the lesion in MRI+ or the removed cortex in MRI−. BOLD responses were considered “concordant” if the maximal t-value was inside the marking. Follow-up after resection was used as gold-standard.

Results: Twenty patients were included (13 MRI+, 7 MRI−), but in seven the EEG was not active or there were artifacts during acquisition. In all 13 studied patients, at least one BOLD response was concordant with the spike field; in 9/13 (69%) at least one BOLD response was concordant with the lesion: in 6/7 (86%) MRI+ and in 3/6 (50%) MRI− patients.

Conclusions: Our study shows a high level of concordance between FCD and BOLD response. This data could provide useful information especially for MRI negative patients. Moreover, it shows in almost all FCD patients, a metabolic involvement of remote cortical or subcortical structures, corroborating the concept of epileptic network.

Current and Emerging Potential of Magnetoencephalography in the Detection and Localization of High-Frequency Oscillations in Epilepsy

Up to one-third of patients with epilepsy are medically intractable and need resective surgery. To be successful, epilepsy surgery requires a comprehensive preoperative evaluation to define the epileptogenic zone (EZ), the brain area that should be resected to achieve seizure freedom. Due to lack of tools and methods that measure the EZ directly, this area is defined indirectly based on concordant data from a multitude of presurgical non-invasive tests and intracranial recordings. However, the results of these tests are often insufficiently concordant or inconclusive. Thus, the presurgical evaluation of surgical candidates is frequently challenging or unsuccessful. To improve the efficacy of the surgical treatment, there is an overriding need for reliable biomarkers that can delineate the EZ. High-frequency oscillations (HFOs) have emerged over the last decade as new potential biomarkers for the delineation of the EZ. Multiple studies have shown that HFOs are spatially associated with the EZ. Despite the encouraging findings, there are still significant challenges for the translation of HFOs as epileptogenic biomarkers to the clinical practice. One of the major barriers is the difficulty to detect and localize them with non-invasive techniques, such as magnetoencephalography (MEG) or scalp electroencephalography (EEG). Although most literature has studied HFOs using invasive recordings, recent studies have reported the detection and localization of HFOs using MEG or scalp EEG. MEG seems to be particularly advantageous compared to scalp EEG due to its inherent advantages of being less affected by skull conductivity and less susceptible to contamination from muscular activity. The detection and localization of HFOs with MEG would largely expand the clinical utility of these new promising biomarkers to an earlier stage in the diagnostic process and to a wider range of patients with epilepsy. Here, we conduct a thorough critical review of the recent MEG literature that investigates HFOs in patients with epilepsy, summarizing the different methodological approaches and the main findings. Our goal is to highlight the emerging potential of MEG in the non-invasive detection and localization of HFOs for the presurgical evaluation of patients with medically refractory epilepsy (MRE).

Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy

Focal cortical dysplasia is a congenital abnormality of cortical development and the leading cause of surgically remediable drug-resistant epilepsy in children. Post-surgical outcome is improved by presurgical lesion detection on structural MRI. Automated computational techniques have improved detection of focal cortical dysplasias in adults but have not yet been effective when applied to developing brains. There is therefore a need to develop reliable and sensitive methods to address the particular challenges of a paediatric cohort. We developed a classifier using surface-based features to identify focal abnormalities of cortical development in a paediatric cohort. In addition to established measures, such as cortical thickness, grey-white matter blurring, FLAIR signal intensity, sulcal depth and curvature, our novel features included complementary metrics of surface morphology such as local cortical deformation as well as post-processing methods such as the "doughnut" method - which quantifies local variability in cortical morphometry/MRI signal intensity, and per-vertex interhemispheric asymmetry. A neural network classifier was trained using data from 22 patients with focal epilepsy (mean age = 12.1 ± 3.9, 9 females), after intra- and inter-subject normalisation using a population of 28 healthy controls (mean age = 14.6 ± 3.1, 11 females). Leave-one-out cross-validation was used to quantify classifier sensitivity using established features and the combination of established and novel features. Focal cortical dysplasias in our paediatric cohort were correctly identified with a higher sensitivity (73%) when novel features, based on our approach for detecting local cortical changes, were included, when compared to the sensitivity using only established features (59%). These methods may be applicable to aiding identification of subtle lesions in medication-resistant paediatric epilepsy as well as to the structural analysis of both healthy and abnormal cortical development.

MRI postprocessing in presurgical evaluation

PURPOSE OF REVIEW

Advanced MRI postprocessing techniques are increasingly used to complement visual analysis and elucidate structural epileptogenic lesions. This review summarizes recent developments in MRI postprocessing in the context of epilepsy presurgical evaluation, with the focus on patients with unremarkable MRI by visual analysis (i.e. 'nonlesional' MRI).

RECENT FINDINGS

Various methods of MRI postprocessing have been reported to show additional clinical values in the following areas: lesion detection on an individual level; lesion confirmation for reducing the risk of over reading the MRI; detection of sulcal/gyral morphologic changes that are particularly difficult for visual analysis; and delineation of cortical abnormalities extending beyond the visible lesion. Future directions to improve the performance of MRI postprocessing include using higher magnetic field strength for better signal-to-noise ratio and contrast-to-noise ratio adopting a multicontrast frame work and integration with other noninvasive modalities.

SUMMARY

MRI postprocessing can provide essential value to increase the yield of structural MRI and should be included as part of the presurgical evaluation of nonlesional epilepsies. MRI postprocessing allows for more accurate identification/delineation of cortical abnormalities, which should then be more confidently targeted and mapped.

Re-review of MRI with post-processing in nonlesional patients in whom epilepsy surgery has failed

Management of MRI-negative patients with intractable focal epilepsy after failed surgery is particularly challenging. In this study, we aim to investigate whether MRI post-processing could identify relevant targets for the re-evaluation of MRI-negative patients who failed the initial resective surgery. We examined a consecutive series of 56 MRI-negative patients who underwent resective surgery and had recurring seizures at 1-year follow-up. T1-weighted volumetric sequence from the pre-surgical MRI was used for voxel-based MRI post-processing which was implemented in a morphometric analysis program (MAP). MAP was positive in 15 of the 56 patients included in this study. In 5 patients, the MAP+ regions were fully resected. In 10 patients, the MAP+ regions were not or partially resected: two out of the 10 patients had a second surgery including the unresected MAP+ region, and both became seizure-free; the remaining 8 patients did not undergo further surgery, but the unresected MAP+ regions were concordant with more than one noninvasive modality in 7. In the 8 patients who had unresected MAP+ regions and intracranial-EEG before the previous surgery, the unresected MAP+ regions were concordant with ictal onset in 6. Our data suggest that scrutiny of the presurgical MRI guided by MRI post-processing may reveal relevant targets for reoperation in nonlesional epilepsies. MAP findings, when concordant with the patient's other noninvasive data, should be considered when planning invasive evaluation/reoperation for this most challenging group of patients.

Source localization of the seizure onset zone from ictal EEG/MEG data

INTRODUCTION

Surgical treatment of drug-resistant epilepsy relies on the identification of the seizure onset zone (SOZ) and often requires intracranial EEG (iEEG). We have developed a new approach for non-invasive magnetic and electric source imaging of the SOZ (MSI-SOZ and ESI-SOZ) from ictal magnetoencephalography (MEG) and EEG recordings, using wavelet-based Maximum Entropy on the Mean (wMEM) method. We compared the performance of MSI-SOZ and ESI-SOZ with interictal spike source localization (MSI-spikes and ESI-spikes) and clinical localization of the SOZ (i.e., based on iEEG or lesion topography, denoted as clinical-SOZ).

METHODS

A total of 46 MEG or EEG seizures from 13 patients were analyzed. wMEM was applied around seizure onset, centered on the frequency band showing the strongest power change. Principal component analysis applied to spatiotemporal reconstructed wMEM sources (0.4-1 s around seizure onset) identified the main spatial pattern of ictal oscillations. Qualitative sublobar concordance and quantitative measures of distance and spatial overlaps were estimated to compare MSI/ESI-SOZ with MSI/ESI-Spikes and clinical-SOZ.

RESULTS

MSI/ESI-SOZ were concordant with clinical-SOZ in 81% of seizures (MSI 90%, ESI 64%). MSI-SOZ was more accurate and identified sources closer to the clinical-SOZ (P = 0.012) and to MSI-Spikes (P = 0.040) as compared with ESI-SOZ. MSI/ESI-SOZ and MSI/ESI-Spikes did not differ in terms of concordance and distance from the clinical-SOZ.

CONCLUSIONS

wMEM allows non-invasive localization of the SOZ from ictal MEG and EEG. MSI-SOZ performs better than ESI-SOZ. MSI/ESI-SOZ can provide important additional information to MSI/ESI-Spikes during presurgical evaluation. Hum Brain Mapp 37:2528-2546, 2016. © 2016 Wiley Periodicals, Inc.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy

OBJECTIVE

To perform whole-brain morphometry in patients with frontal lobe epilepsy and evaluate the utility of group-level patterns for individualized diagnosis and prognosis.

METHODS

We compared MRI-based cortical thickness and folding complexity between 2 frontal lobe epilepsy cohorts with histologically verified focal cortical dysplasia (FCD) (13 type I; 28 type II) and 41 closely matched controls. Pattern learning algorithms evaluated the utility of group-level findings to predict histologic FCD subtype, the side of the seizure focus, and postsurgical seizure outcome in single individuals.

RESULTS

Relative to controls, FCD type I displayed multilobar cortical thinning that was most marked in ipsilateral frontal cortices. Conversely, type II showed thickening in temporal and postcentral cortices. Cortical folding also diverged, with increased complexity in prefrontal cortices in type I and decreases in type II. Group-level findings successfully guided automated FCD subtype classification (type I: 100%; type II: 96%), seizure focus lateralization (type I: 92%; type II: 86%), and outcome prediction (type I: 92%; type II: 82%).

CONCLUSION

FCD subtypes relate to diverse whole-brain structural phenotypes. While cortical thickening in type II may indicate delayed pruning, a thin cortex in type I likely results from combined effects of seizure excitotoxicity and the primary malformation. Group-level patterns have a high translational value in guiding individualized diagnostics.

Cortical feature analysis and machine learning improves detection of "MRI-negative" focal cortical dysplasia

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common lesion in adults with treatment-resistant epilepsy. Advances in MRI have revolutionized the diagnosis of FCD, resulting in higher success rates for resective epilepsy surgery. However, many patients with histologically confirmed FCD have normal presurgical MRI studies ('MRI-negative'), making presurgical diagnosis difficult. The purpose of this study was to test whether a novel MRI postprocessing method successfully detects histopathologically verified FCD in a sample of patients without visually appreciable lesions. We applied an automated quantitative morphometry approach which computed five surface-based MRI features and combined them in a machine learning model to classify lesional and nonlesional vertices. Accuracy was defined by classifying contiguous vertices as "lesional" when they fell within the surgical resection region. Our multivariate method correctly detected the lesion in 6 of 7 MRI-positive patients, which is comparable with the detection rates that have been reported in univariate vertex-based morphometry studies. More significantly, in patients that were MRI-negative, machine learning correctly identified 14 out of 24 FCD lesions (58%). This was achieved after separating abnormal thickness and thinness into distinct classifiers, as well as separating sulcal and gyral regions. Results demonstrate that MRI-negative images contain sufficient information to aid in the in vivo detection of visually elusive FCD lesions.

Voxel-based morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative epilepsies

OBJECTIVE

In the presurgical workup of magnetic resonance imaging (MRI)-negative (MRI(-) or "nonlesional") pharmacoresistant focal epilepsy (PFE) patients, discovering a previously undetected lesion can drastically change the evaluation and likely improve surgical outcome. Our study utilizes a voxel-based MRI postprocessing technique, implemented in a morphometric analysis program (MAP), to facilitate detection of subtle abnormalities in a consecutive cohort of MRI(-) surgical candidates.

METHODS

Included in this retrospective study was a consecutive cohort of 150 MRI(-) surgical patients. MAP was performed on T1-weighted MRI, with comparison to a scanner-specific normal database. Review and analysis of MAP were performed blinded to patients' clinical information. The pertinence of MAP(+) areas was confirmed by surgical outcome and pathology.

RESULTS

MAP showed a 43% positive rate, sensitivity of 0.9, and specificity of 0.67. Overall, patients with the MAP(+) region completely resected had the best seizure outcomes, followed by the MAP(-) patients, and patients who had no/partial resection of the MAP(+) region had the worst outcome (p < 0.001). Subgroup analysis revealed that visually identified subtle findings are more likely correct if also MAP(+) . False-positive rate in 52 normal controls was 2%. Surgical pathology of the resected MAP(+) areas contained mainly non-balloon-cell focal cortical dysplasia (FCD). Multiple MAP(+) regions were present in 7% of patients.

INTERPRETATION

MAP can be a practical and valuable tool to: (1) guide the search for subtle MRI abnormalities and (2) confirm visually identified questionable abnormalities in patients with PFE due to suspected FCD. A MAP(+) region, when concordant with the patient's electroclinical presentation, should provide a legitimate target for surgical exploration.

Epilepsy: new advances

Epilepsy affects 65 million people worldwide and entails a major burden in seizure-related disability, mortality, comorbidities, stigma, and costs. In the past decade, important advances have been made in the understanding of the pathophysiological mechanisms of the disease and factors affecting its prognosis. These advances have translated into new conceptual and operational definitions of epilepsy in addition to revised criteria and terminology for its diagnosis and classification. Although the number of available antiepileptic drugs has increased substantially during the past 20 years, about a third of patients remain resistant to medical treatment. Despite improved effectiveness of surgical procedures, with more than half of operated patients achieving long-term freedom from seizures, epilepsy surgery is still done in a small subset of drug-resistant patients. The lives of most people with epilepsy continue to be adversely affected by gaps in knowledge, diagnosis, treatment, advocacy, education, legislation, and research. Concerted actions to address these challenges are urgently needed.

PI3K/AKT pathway mutations cause a spectrum of brain malformations from megalencephaly to focal cortical dysplasia

Malformations of cortical development containing dysplastic neuronal and glial elements, including hemimegalencephaly and focal cortical dysplasia, are common causes of intractable paediatric epilepsy. In this study we performed multiplex targeted sequencing of 10 genes in the PI3K/AKT pathway on brain tissue from 33 children who underwent surgical resection of dysplastic cortex for the treatment of intractable epilepsy. Sequencing results were correlated with clinical, imaging, pathological and immunohistological phenotypes. We identified mosaic activating mutations in PIK3CA and AKT3 in this cohort, including cancer-associated hotspot PIK3CA mutations in dysplastic megalencephaly, hemimegalencephaly, and focal cortical dysplasia type IIa. In addition, a germline PTEN mutation was identified in a male with hemimegalencephaly but no peripheral manifestations of the PTEN hamartoma tumour syndrome. A spectrum of clinical, imaging and pathological abnormalities was found in this cohort. While patients with more severe brain imaging abnormalities and systemic manifestations were more likely to have detected mutations, routine histopathological studies did not predict mutation status. In addition, elevated levels of phosphorylated S6 ribosomal protein were identified in both neurons and astrocytes of all hemimegalencephaly and focal cortical dysplasia type II specimens, regardless of the presence or absence of detected PI3K/AKT pathway mutations. In contrast, expression patterns of the T308 and S473 phosphorylated forms of AKT and in vitro AKT kinase activities discriminated between mutation-positive dysplasia cortex, mutation-negative dysplasia cortex, and non-dysplasia epilepsy cortex. Our findings identify PI3K/AKT pathway mutations as an important cause of epileptogenic brain malformations and establish megalencephaly, hemimegalencephaly, and focal cortical dysplasia as part of a single pathogenic spectrum.

Electro-clinical-pathological correlations in focal cortical dysplasia (FCD) at young ages

The prevalence of focal cortical dysplasia (FCD) in pediatric patients with focal epilepsy is not exactly known because authors of publications in which the etiologies of epilepsies are listed, but which are not dealing specifically with epilepsy surgery issues, tend to lump together the many kinds of malformations of cortical development (MCD), of which FCDs, because of their relative frequency, are the most relevant subtypes. Out of 561 patients with MCD (children and adults) operated at centers in Europe who do feed data into the "European Epilepsy Brain Bank," 426 (76 %) had FCD.

"Mirror EPC": epilepsia partialis continua shifting sides after rolandic resection in dysplasia

BACKGROUND

Epilepsia partialis continua (EPC) is a life-threatening condition often caused by focal cortical dysplasia (FCD). Resection of the motor cortex is contemplated in the hope that the trade-off between a severe motor deficit and complete seizure control justifies the procedure.

METHODS

Report of 3 patients with EPC due to histologically confirmed FCD, who underwent resection of the motor cortex under acute electrocorticography.

RESULTS

All had re-emergence of medically intractable EPC in the other side of the body after rolandic resection. Two patients died and the third continues with refractory attacks.

CONCLUSION

In some instances, EPC due to FCD may shift sides and re-emerge in the contralateral, previously asymptomatic, hemibody. A mechanism of disinhibition by surgery of a suppressed contralateral and homologous epileptogenic zone is speculated.

Auditory aura in nocturnal frontal lobe epilepsy: a red flag to suspect an extra-frontal epileptogenic zone

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Eleven out of 165 nocturnal frontal lobe epilepsy (NFLE) patients reported an auditory aura as initial ictal symptom.

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Extra-frontal origin was documented in 55% of NFLE patients with auditory aura.

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Six patients with defined epileptogenic zone had a left temporal origin of seizures.

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Auditory aura may be a symptom suggesting an extra-frontal epileptogenic zone.

Objective

To describe the anatomo-electro-clinical findings of patients with nocturnal hypermotor seizures (NHS) preceded by auditory symptoms, to evaluate the localizing value of auditory aura.

Methods

Our database of 165 patients with nocturnal frontal lobe epilepsy (NFLE) diagnosis confirmed by videopolysomnography (VPSG) was reviewed, selecting those who reported an auditory aura as the initial ictal symptom in at least two NHS during their lifetime.

Results

Eleven patients were selected (seven males, four females). According to the anatomo-electro-clinical data, three groups were identified. Group 1 [defined epileptogenic zone (EZ)]: three subjects were studied with stereo-EEG. The EZ lay in the left superior temporal gyrus in two cases, whereas in the third case seizures arose from a dysplastic lesion located in the left temporal lobe. One of these three patients underwent left Heschl's gyrus resection, and is currently seizure-free. Group 2 (presumed EZ): three cases in which a presumed EZ was identified; in the left temporal lobe in two cases and in the left temporal lobe extending to the insula in one subject. Group 3 (uncertain EZ): five cases had anatomo-electro-clinical correlations discordant.

Conclusions

This work suggests that auditory aura may be a helpful anamnestic feature suggesting an extra-frontal seizure origin. This finding could guide secondary investigations to improve diagnostic definition and selection of candidates for surgical treatment.

Network Connectivity in Epilepsy: Resting State fMRI and EEG-fMRI Contributions

There is a growing body of evidence pointing toward large-scale networks underlying the core phenomena in epilepsy, from seizure generation to cognitive dysfunction or response to treatment. The investigation of networks in epilepsy has become a key concept to unlock a deeper understanding of the disease. Functional imaging can provide valuable information to characterize network dysfunction; in particular resting state fMRI (RS-fMRI), which is increasingly being applied to study brain networks in a number of diseases. In patients with epilepsy, network connectivity derived from RS-fMRI has found connectivity abnormalities in a number of networks; these include the epileptogenic, cognitive and sensory processing networks. However, in majority of these studies, the effect of epileptic transients in the connectivity of networks has been neglected. EEG–fMRI has frequently shown networks related to epileptic transients that in many cases are concordant with the abnormalities shown in RS studies. This points toward a relevant role of epileptic transients in the network abnormalities detected in RS-fMRI studies. In this review, we summarize the network abnormalities reported by these two techniques side by side, provide evidence of their overlapping findings, and discuss their significance in the context of the methodology of each technique. A number of clinically relevant factors that have been associated with connectivity changes are in turn associated with changes in the frequency of epileptic transients. These factors include different aspects of epilepsy ranging from treatment effects, cognitive processes, or transition between different alertness states (i.e., awake–sleep transition). For RS-fMRI to become a more effective tool to investigate clinically relevant aspects of epilepsy it is necessary to understand connectivity changes associated with epileptic transients, those associated with other clinically relevant factors and the interaction between them, which represents a gap in the current literature. We propose a framework for the investigation of network connectivity in patients with epilepsy that can integrate epileptic processes that occur across different time scales such as epileptic transients and disease duration and the implications of this approach are discussed.

Magnetic resonance imaging abnormalities in the resection region correlate with histopathological type, gliosis extent, and postoperative outcome in pediatric cortical dysplasia

OBJECT.: The authors conducted a study to correlate histopathological features, MRI findings, and postsurgical outcomes in children with cortical dysplasia (CD) by performing a novel resection site-specific evaluation.

METHODS

The study cohort comprised 43 children with intractable epilepsy and CD. The MR image review was blinded to pathology but with knowledge of the resection location. An MRI score (range 0-7) was calculated for each resection region based on the number of imaging features of CD and was classified as "lesional" or "nonlesional" according to all imaging features. Outcome was determined using the International League Against Epilepsy (ILAE) scale. The determination of pathological CD type was based on the ILAE 2011 consensus classification system, and the cortical gliosis pattern was assessed on GFAP staining.

RESULTS

There were 89 resection regions (50 ILAE Type I, 29 Type IIa, and 10 Type IIb). Eleven (25.6%) of 43 children had more than one type of CD. The authors observed MRI abnormalities in 63% of patients, characteristic enough to direct resection (lesional) in 42%. Most MRI features, MRI score ≥ 3, and lesional abnormalities were more common in patients with Type II CD. Increased cortical signal was more common in those with Type IIb (70%) rather than Type IIa (17.2%) CD (p = 0.004). A good outcome was demonstrated in 39% of children with Type I CD and 72% of those with Type II CD (61% in Type IIa and 100% in Type IIb) (p = 0.03). A lesional MRI abnormality and an MRI score greater than 3 correlated with good outcome in 78% and 90% of patients, respectively (p < 0.03). Diffuse cortical gliosis was more prevalent in Type II CD and in resection regions exhibiting MRI abnormalities. Complete surgical exclusion of the MRI abnormality was associated with a better postoperative outcome.

CONCLUSIONS

This study provides a detailed correlation of MRI findings, neuropathological features, and outcomes in children with intractable epilepsy by using a novel resection site-specific evaluation. Because 25% of the patients had multiple CD subtypes, a regional analysis approach was mandated. Those children with lesional MRI abnormalities, Type II CD, and surgical exclusion of the MRI abnormality had better outcomes. Type II CD is more detectable by MRI than other types, partly because of the greater extent of associated gliosis in Type II. Although MRI findings were correlated with the pathological CD type and outcome in this study, the majority of patients (58%) did not have MRI findings that could direct surgical therapy, underscoring the need for improved MRI techniques for detection and for the continued use of multimodal evaluation methods in patient selection.

Automated detection of cortical dysplasia type II in MRI-negative epilepsy

OBJECTIVE

To detect automatically focal cortical dysplasia (FCD) type II in patients with extratemporal epilepsy initially diagnosed as MRI-negative on routine inspection of 1.5 and 3.0T scans.

METHODS

We implemented an automated classifier relying on surface-based features of FCD morphology and intensity, taking advantage of their covariance. The method was tested on 19 patients (15 with histologically confirmed FCD) scanned at 3.0T, and cross-validated using a leave-one-out strategy. We assessed specificity in 24 healthy controls and 11 disease controls with temporal lobe epilepsy. Cross-dataset classification performance was evaluated in 20 healthy controls and 14 patients with histologically verified FCD examined at 1.5T.

RESULTS

Sensitivity was 74%, with 100% specificity (i.e., no lesions detected in healthy or disease controls). In 50% of cases, a single cluster colocalized with the FCD lesion, while in the remaining cases a median of 1 extralesional cluster was found. Applying the classifier (trained on 3.0T data) to the 1.5T dataset yielded comparable performance (sensitivity 71%, specificity 95%).

CONCLUSION

In patients initially diagnosed as MRI-negative, our fully automated multivariate approach offered a substantial gain in sensitivity over standard radiologic assessment. The proposed method showed generalizability across cohorts, scanners, and field strengths. Machine learning may assist presurgical decision-making by facilitating hypothesis formulation about the epileptogenic zone.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that automated machine learning of MRI patterns accurately identifies FCD among patients with extratemporal epilepsy initially diagnosed as MRI-negative.

Linking MRI postprocessing with magnetic source imaging in MRI-negative epilepsy

OBJECTIVE

MRI-negative (MRI-) pharmacoresistant focal epilepsy (PFE) patients are most challenging for epilepsy surgical management. This study utilizes a voxel-based MRI postprocessing technique, implemented using a morphometric analysis program (MAP), aiming to facilitate detection of subtle focal cortical dysplasia (FCD) in MRI- patients. Furthermore, the study examines the concordance between MAP-identified regions and localization from magnetic source imaging (MSI).

METHODS

Included in this retrospective study were 25 MRI- surgical patients. MAP was performed on T1-weighted MRI, with comparison to a normal database. The pertinence of MAP+ areas was confirmed by MSI, surgical outcome and pathology. Analyses of MAP and MSI were performed blindly from patients' clinical information and independently from each other.

RESULTS

The detection rate of subtle changes by MAP was 48% (12/25). Once MAP+ areas were resected, patients were more likely to be seizure-free (p=0.02). There were no false positives in the 25 age-matched normal controls. Seven patients had a concordant MSI correlate. Patients in whom a concordant area was identified by both MAP and MSI had a significantly higher chance of achieving a seizure-free outcome following complete resection of this area (p=0.008). In the 9 resected MAP+ areas, pathology revealed FCD type IA in 7 and type IIB in 2.

INTERPRETATION

MAP shows promise in identifying subtle FCD abnormalities and increasing the diagnostic yield of conventional MRI visual analysis in presurgical evaluation of PFE. Concordant MRI postprocessing and MSI analyses may lead to the noninvasive identification of a structurally and electrically abnormal subtle lesion that can be surgically targeted.

Epilepsy related to developmental tumors and malformations of cortical development

Structural abnormalities of the brain are increasingly recognized in patients with neurodevelopmental delay and intractable focal epilepsies. The access to clinically well-characterized neurosurgical material has provided a unique opportunity to better define the neuropathological, neurochemical, and molecular features of epilepsy-associated focal developmental lesions. These studies help to further understand the epileptogenic mechanisms of these lesions. Neuropathological evaluation of surgical specimens from patients with epilepsy-associated developmental lesions reveals two major pathologies: focal cortical dysplasia and low-grade developmental tumors (glioneuronal tumors). In the last few years there have been major advances in the recognition of a wide spectrum of developmental lesions associated with a intractable epilepsy, including cortical tubers in patients with tuberous sclerosis complex and hemimegalencephaly. As an increasing number of entities are identified, the development of a unified and comprehensive classification represents a great challenge and requires continuous updates. The present article reviews current knowledge of molecular pathogenesis and the pathophysiological mechanisms of epileptogenesis in this group of developmental disorders. Both emerging neuropathological and basic science evidence will be analyzed, highlighting the involvement of different, but often converging, pathogenetic and epileptogenic mechanisms, which may create the basis for new therapeutic strategies in these disorders.

Neuroimaging characteristics of MRI-negative orbitofrontal epilepsy with focus on voxel-based morphometric MRI postprocessing

PURPOSE

The orbitofrontal (OF) region is one of the least explored regions of the cerebral cortex. There are few studies on patients with electrophysiologically and surgically confirmed OF epilepsy and a negative magnetic resonance imaging (MRI) study. We aimed to examine the neuroimaging characteristics of MRI-negative OF epilepsy with the focus on a voxel-based morphometric MRI postprocessing technique.

METHODS

We included six patients with OF epilepsy, who met the following criteria: surgical resection of the OF lobe with/without adjacent cortex, seizure-free for ≥12 months, invasive video-electroencephalography (EEG) monitoring showing ictal onset from the OF area, and preoperative MRI regarded as negative. Patients were investigated in terms of their image postprocessing and functional neuroimaging characteristics, electroclinical characteristics obtained from noninvasive and invasive evaluations, and surgical pathology. MRI postprocessing on T1 -weighted high-resolution scans was implemented with a morphometric analysis program (MAP) in MATLAB.

KEY FINDINGS

Single MAP+ abnormalities were found in four patients; three were in the OF region and one in the ipsilateral mesial frontal area. These abnormalities were included in the resection. One patient had bilateral MAP+ abnormalities in the OF region, with the ipsilateral one completely removed. The MAP+ foci were concordant with invasive electrophysiologic data in the majority of MAP+ patients (four of five). The localization value of 18F-fluorodeoxyglucose-positron emission tomography (FDG-PET) and ictal single-photon emission computed tomography (SPECT) is low in this cohort. Surgical pathology included focal cortical dysplasia, remote infarct, Rosenthal fiber formation and gliosis.

SIGNIFICANCE

Our study highlights the importance of MRI postprocessing in the process of presurgical evaluation of patients with suspected orbitofrontal epilepsy and "normal" MRI. Using MAP, we were able to positively identify subtle focal abnormalities in the majority of the patients. MAP results need to be interpreted in the context of their electroclinical findings and can provide valuable targets in the process of planning invasive evaluation.

Disentangling hippocampal shape anomalies in epilepsy

Drug-resistant temporal lobe epilepsy (TLE) and epileptic syndromes related to malformations of cortical development (MCD) are associated with complex hippocampal morphology. The contribution of volume and position to the overall hippocampal shape in these conditions has not been studied. We propose a surface-based framework to localize volume changes through measurement of Jacobian determinants, and quantify fine-scale position and curvature through a medial axis model. We applied our methodology to T1-weighted 3D volumetric MRI of 88 patients with TLE and 78 patients with MCD, including focal cortical dysplasia (FCD, n = 29), heterotopia (HET, n = 40), and polymicrogyria (PMG, n = 19). Patients were compared to 46 age- and sex-matched healthy controls. Surface-based analysis of volume in TLE revealed severe ipsilateral atrophy mainly along the rostro-caudal extent of the hippocampal CA1 subfield. In MCD, patterns of volume changes included bilateral CA1 atrophy in HET and FCD, and left dentate hypertrophy in all three groups. The analysis of curvature revealed medial bending of the posterior hippocampus in TLE, whereas in MCD there was a supero-medial shift of the hippocampal body. Albeit hippocampal shape anomalies in TLE and MCD result from a combination of volume and positional changes, their nature and distribution suggest different pathogenic mechanisms.

Surgical management of cortical dysplasia in infancy and early childhood

PURPOSE

To describe operative procedures, seizure control and complications of surgery for cortical dysplasia (CD) causing intractable epilepsy in infancy and early childhood.

METHODS

Fifty-six consecutive children (less than 6years old) underwent resective epilepsy surgery for CD from December 2000 to August 2011. Age at surgery ranged from 2 to 69months (mean 23months) and the follow-up was from 1 to 11years (mean 4years 4months).

RESULTS

Half of the children underwent surgery during infancy at an age less than 10months, and the majority (80%) of these infants needed extensive surgical procedures, such as hemispherotomy and multi-lobar disconnection. Seizure free (ILAE class 1) outcome was obtained in 66% of the cases (class 1a; 55%): 85% with focal resection (n=13), 50% with lobar resection (n=18), 71% with multilobar disconnection (n=7) and 67% with hemispherotomy (n=18). Peri-ventricular and insular structures were resected in 23% of focal and 61% of lobar resections. Repeated surgery was performed in 9 children and 5 (56%) became seizure free. Histological subtypes included hemimegalencephaly (16 patients), polymicrogyria (5 patients), and FCD type I (6 patients), type IIA (19 patients), type IIB (10 patients). Polymicrogyria had the worst seizure outcome compared to other pathologies. Surgical complications included 1 post-operative hydrocephalus, 1 chronic subdural hematoma, 2 intracranial cysts, and 1 case of meningitis. No mortality or severe morbidities occurred.

CONCLUSIONS

Early surgical intervention in children with CD and intractable seizures in infancy and early childhood can yield favorable seizure outcome without mortality or severe morbidities although younger children often need extensive surgical procedures.

Neuropathology of pediatric epilepsy

The preoperative study of patients who are candidates for epilepsy surgery often classifies their epileptic foci as "lesional" or "non-lesional" based upon evidence from neuroimaging. Many lesions not detected by MRI are found by microscopic examination of the resected tissue. Advances have been made in neuropathological techniques to study resected brain tissue and to specify the types of focal cortical dysgeneses and other lesions by extending microscopic findings by applying immunocytochemical markers that identify specific types and distributions of neurons and glial cells that denote tissue architecture. There may be etiological differences between focal and extensive cortical dysplasias involving many gyri or entire lobes of cerebral cortex. Of additional importance in pediatric brain resections is that these modern techniques also denote cellular maturation and can identify abnormal cells with mixed lineage. α-B-crystallin can serve as a metabolic tissue marker of epileptic activity, regardless of the presence or absence of a "structural" lesion by MRI or by conventional histopathology. Satellitosis may contribute to epileptogenic neurons and later to death of those neurons. The classification of malformations of the brain is a process requiring continuous updates that include genetics, neuroimaging, and neuropathology as new data emerge, but should not be exclusive to one region of the brain, such as cerebral cortex or cerebellum. Standardization in neuropathological terminology enhances scientific communication. The ILAE recently published a useful consensus classification of focal cortical dysplasias that is flexible to enable future revisions and changes as new data become available.

Focal cortical dysplasia - review

Focal cortical dysplasia is a malformation of cortical development, which is the most common cause of medically refractory epilepsy in the pediatric population and the second/third most common etiology of medically intractable seizures in adults.Both genetic and acquired factors are involved in the pathogenesis of cortical dysplasia. Numerous classifications of the complex structural abnormalities of focal cortical dysplasia have been proposed - from Taylor et al. in 1971 to the last modification of Palmini classification made by Blumcke in 2011. In general, three types of cortical dysplasia are recognized.Type I focal cortical dysplasia with mild symptomatic expression and late onset, is more often seen in adults, with changes present in the temporal lobe.Clinical symptoms are more severe in type II of cortical dysplasia usually seen in children. In this type, more extensive changes occur outside the temporal lobe with predilection for the frontal lobes.New type III is one of the above dysplasias with associated another principal lesion as hippocampal sclerosis, tumor, vascular malformation or acquired pathology during early life.Brain MRI imaging shows abnormalities in the majority of type II dysplasias and in only some of type I cortical dysplasias.THE MOST COMMON FINDINGS ON MRI IMAGING INCLUDE: focal cortical thickening or thinning, areas of focal brain atrophy, blurring of the gray-white junction, increased signal on T2- and FLAIR-weighted images in the gray and subcortical white matter often tapering toward the ventricle. On the basis of the MRI findings, it is possible to differentiate between type I and type II cortical dysplasia. A complete resection of the epileptogenic zone is required for seizure-free life. MRI imaging is very helpful to identify those patients who are likely to benefit from surgical treatment in a group of patients with drug-resistant epilepsy.However, in type I cortical dysplasia, MR imaging is often normal, and also in both types the lesion seen on MRI may be smaller than the seizure-generating region seen in the EEG. The abnormalities may also involve vital for life brain parts, where curative surgery will not be an option. Therefore, other diagnostic imaging techniques such as FDG PET, MEG, DTI and intra-cranial EEG are widely used to establish the diagnosis and to decide on management.With advances in both genetics and neuroimaging, we may develop a better understanding of patients with drug-resistant epilepsy, which will help us to provide more successful pharmacological and/or surgical treatment in the future.

Voxel-based morphometric MRI post-processing in MRI-negative focal cortical dysplasia followed by simultaneously recorded MEG and stereo-EEG

We aim to report on the usefulness of a voxel-based morphometric MRI post-processing technique in detecting subtle epileptogenic structural lesions. The MRI post-processing technique was implemented in a morphometric analysis program (MAP), in a 30-year-old male with pharmacoresistant focal epilepsy and negative MRI. MAP gray-white matter junction file facilitated the identification of a suspicious structural lesion in the right frontal opercular area. The electrophysiological data by simultaneously recorded stereo-EEG and MEG confirmed the epileptogenicity of the underlying subtle structural abnormality. The patient underwent a limited right frontal opercular resection, which completely included the area detected by MAP. Surgical pathology revealed focal cortical dysplasia (FCD) type IIb. Postoperatively the patient has been seizure-free for 2 years. This study demonstrates that MAP has promise in increasing the diagnostic yield of MRI reading in challenging patients with "non-lesional" MRIs. The clinical relevance and epileptogenicity of MAP abnormalities in patients with epilepsy have not been investigated systematically; therefore it is important to confirm their pertinence by performing electrophysiological recordings. When confirmed to be epileptogenic, such MAP abnormalities may reflect an underlying subtle cortical dysplasia whose complete resection can lead to seizure-free outcome.

Development and dysgenesis of the cerebral cortex: malformations of cortical development

The cerebral cortex develops in several stages from a pseudostratified epithelium at 5 weeks to an essentially complete cortex at 47 weeks. Cortical connectivity starts with thalamocortical connections in the 3rd trimester only and continues until well after birth. Vascularity adapts to proliferation and connectivity. Malformations of cortical development are classified into disorders of specification, proliferation/apoptosis, migration, and organization. However, all processes are intermingled, as for example a dysplastic cell may migrate incompletely and not connect appropriately. However, this classification is convenient for didactic purposes as long as the complex interactions between the different processes are kept in mind.

Epileptic networks in focal cortical dysplasia revealed using electroencephalography-functional magnetic resonance imaging

OBJECTIVE

Surgical treatment of focal epilepsy in patients with focal cortical dysplasia (FCD) is most successful if all epileptogenic tissue is resected. This may not be evident on structural magnetic resonance imaging (MRI), so intracranial electroencephalography (icEEG) is needed to delineate the seizure onset zone (SOZ). EEG-functional MRI (fMRI) can reveal interictal discharge (IED)-related hemodynamic changes in the irritative zone (IZ). We assessed the value of EEG-fMRI in patients with FCD-associated focal epilepsy by examining the relationship between IED-related hemodynamic changes, icEEG findings, and postoperative outcome.

METHODS

Twenty-three patients with FCD-associated focal epilepsy undergoing presurgical evaluation including icEEG underwent simultaneous EEG-fMRI at 3T. IED-related hemodynamic changes were modeled, and results were overlaid on coregistered T1-weighted MRI scans fused with computed tomography scans showing the intracranial electrodes. IED-related hemodynamic changes were compared with the SOZ on icEEG and postoperative outcome at 1 year.

RESULTS

Twelve of 23 patients had IEDs during recording, and 11 of 12 had significant IED-related hemodynamic changes. The fMRI results were concordant with the SOZ in 5 of 11 patients, all of whom had a solitary SOZ on icEEG. Four of 5 had >50% reduction in seizure frequency following resective surgery. The remaining 6 of 11 patients had widespread or discordant regions of IED-related fMRI signal change. Five of 6 had either a poor surgical outcome (<50% reduction in seizure frequency) or widespread SOZ precluding surgery.

INTERPRETATION

Comparison of EEG-fMRI with icEEG suggests that EEG-fMRI may provide useful additional information about the SOZ in FCD. Widely distributed discordant regions of IED-related hemodynamic change appear to be associated with a widespread SOZ and poor postsurgical outcome.