Voxel-based magnetic resonance image postprocessing in epilepsy

Advances in magnetic resonance imaging for the assessment of paediatric focal epilepsy: a narrative review

Background and Objective

Epilepsy affects approximately 50 million people worldwide, with 30-40% of patients not responding to medication, necessitating alternative therapies such as surgical intervention. However, the accurate localization of epileptogenic lesions, particularly in pediatric magnetic resonance imaging (MRI)-negative drug-resistant epilepsy, remains a challenge. This paper reviews advanced neuroimaging techniques aimed at improving the detection of such lesions to enhance surgical outcomes.

Methods

A comprehensive literature search was conducted using PubMed, focusing on advanced MRI sequences, focal epilepsy, and the integration of artificial intelligence (AI) in the diagnostic process.

Key Content and Findings

New MRI sequences, including magnetization prepared 2 rapid gradient echo (MP2RAGE), edge-enhancing gradient echo (EDGE), and fluid and white matter suppression (FLAWS), have demonstrated enhanced capabilities in detecting subtle epileptogenic lesions. Quantitative MRI techniques, notably magnetic resonance fingerprinting (MRF), alongside innovative post-processing methods, are emphasized for their effectiveness in delineating cortical malformations, whether used alone or in combination with ultra-high field MRI systems. Furthermore, the integration of AI in radiology is progressing, providing significant support in accurately localizing lesions, and potentially optimizing pre-surgical planning.

Conclusions

While advanced neuroimaging and AI offer significant improvements in the diagnostic process for epilepsy, some challenges remain. These include long acquisition times, the need for extensive data analysis, and a lack of large, standardized datasets for AI validation. However, the future holds promise as research continues to integrate these technologies into clinical practice. These efforts will improve the clinical applicability and effectiveness of these advanced techniques in epilepsy management, paving the way for more accurate diagnoses and better patient outcomes.

Focal cortical dysplasia lesion segmentation using multiscale transformer

OBJECTIVES

Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images.

METHODS

The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics.

RESULTS

Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods.

CONCLUSION

Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET .

CRITICAL RELEVANCE STATEMENT

This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images.

KEY POINTS

The first transformer-based model was built to explore focal cortical dysplasia lesion segmentation. Integration of global and local features enhances the segmentation performance of lesions. A valuable benchmark for model development and comparative analyses was provided.

Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Simultaneous 18F-FDG PET/MRI predicting favourable surgical outcome in refractory epilepsy patients

OBJECTIVES

To evaluate the (1) successful surgery proportion in patients with clear structural lesions on MRI and single abnormality on 18F-fluorodeoxyglucose positron emission tomography/Magnetic resonance imaging (18F-FDG PET/MRI); (2) predictive value of 18F-FDG PET/MRI for postsurgical outcome in refractory epilepsy patients.

METHODS

A retrospective study was conducted on 123 patients diagnosed with refractory epilepsy who underwent presurgical evaluation involving 18F-FDG PET/MRI and were followed for one-year post-surgery. Two neuroradiologists interpreted the PET/MRI images using visual analysis and an asymmetry index based on the standard uptake value. The Engel classification was used to assess surgical outcomes one-year post-surgery. Prognostic factors predicting post-surgical seizure outcomes were explored using univariate and binary logistic regression.

RESULTS

Definitely single lesion abnormality was observed in 35.0% (43/123) of the patients on the MRI portion of PET/MRI. The proportion increased to 74.0% (91/123) when 18 F-FDG PET portion was added. About 75% (69/91) of patients displaying a clear-cut lesion on 18 F-FDG PET/MRI were classified as Engel Class I one-year post-surgery. The proportion of Engel Class I patients was not significantly different when comparing MRI-single lesion patients with MRI-negative, PET-single lesion patients one year after surgery (81.4% vs. 70.0%, P = 0.24). Binary logistic regression analysis revealed that the detection of a clear single lesion on 18 F-FDG PET/MRI was a strong positive predictor of a favorable surgical outcome (OR 3.518, 95% CI 1.363-9.077, p = 0.009).

CONCLUSION

Single lesion detected on 18 F-FDG PET/MRI is useful to predict good surgical outcome for refractory epilepsy patients; Those patients should be considered as candidates for surgery.

Neurostimulation targeting the epileptic focus: Current understanding and perspectives for treatment

For the one third of people with epilepsy whose seizures are not controlled with medications, targeting the seizure focus with neurostimulation can be an effective therapeutic strategy. In this focused review, we summarize a discussion of targeted neurostimulation modalities during a workshop held in Frankfurt, Germany in September 2023. Topics covered include: available devices for seizure focus stimulation; alternating current (AC) and direct current (DC) stimulation to reduce focal cortical excitability; modeling approaches to simulate DC stimulation; reconciling the efficacy of focal stimulation with the network theory of epilepsy; and the emerging concept of 'neurostimulation zones,' which are defined as cortical regions where focal stimulation is most effective for reducing seizures and which may or may not directly involve the seizure onset zone. By combining experimental data, modeling results, and clinical outcome analysis, rational selection of target regions and stimulation parameters is increasingly feasible, paving the way for a broader use of neurostimulation for epilepsy in the future.

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

OBJECTIVES

Focal cortical dysplasia (FCD) represents one of the most common causes of refractory epilepsy in children. Deep learning demonstrates great power in tissue discrimination by analyzing MRI data. A prediction model was built and verified using 3D full-resolution nnU-Net for automatic lesion detection and segmentation of children with FCD II.

METHODS

High-resolution brain MRI structure data from 65 patients, confirmed with FCD II by pathology, were retrospectively studied. Experienced neuroradiologists segmented and labeled the lesions as the ground truth. Also, we used 3D full-resolution nnU-Net to segment lesions automatically, generating detection maps. The algorithm was trained using fivefold cross-validation, with data partitioned into training (N = 200) and testing (N = 15). To evaluate performance, detection maps were compared to expert manual labels. The Dice-Sørensen coefficient (DSC) and sensitivity were used to assess the algorithm performance.

RESULTS

The 3D nnU-Net showed a good performance for FCD lesion detection at the voxel level, with a sensitivity of 0.73. The best segmentation model achieved a mean DSC score of 0.57 on the testing dataset.

CONCLUSION

This pilot study confirmed that 3D full-resolution nnU-Net can automatically segment FCD lesions with reliable outcomes. This provides a novel approach to FCD lesion detection.

CRITICAL RELEVANCE STATEMENT

Our fully automatic models could process the 3D T1-MPRAGE data and segment FCD II lesions with reliable outcomes.

KEY POINTS

• Simplified image processing promotes the DL model implemented in clinical practice. • The histopathological confirmed lesion masks enhance the clinical credibility of the AI model. • The voxel-level evaluation metrics benefit lesion detection and clinical decisions.

Can Brain Volume-Driven Characteristic Features Predict the Response of Alzheimer's Patients to Repetitive Transcranial Magnetic Stimulation? A Pilot Study

This study is a post-hoc examination of baseline MRI data from a clinical trial investigating the efficacy of repetitive transcranial magnetic stimulation (rTMS) as a treatment for patients with mild-moderate Alzheimer's disease (AD). Herein, we investigated whether the analysis of baseline MRI data could predict the response of patients to rTMS treatment. Whole-brain T1-weighted MRI scans of 75 participants collected at baseline were analyzed. The analyses were run on the gray matter (GM) and white matter (WM) of the left and right dorsolateral prefrontal cortex (DLPFC), as that was the rTMS application site. The primary outcome measure was the Alzheimer's disease assessment scale-cognitive subscale (ADAS-Cog). The response to treatment was determined based on ADAS-Cog scores and secondary outcome measures. The analysis of covariance showed that responders to active treatment had a significantly lower baseline GM volume in the right DLPFC and a higher GM asymmetry index in the DLPFC region compared to those in non-responders. Logistic regression with a repeated five-fold cross-validated analysis using the MRI-driven features of the initial 75 participants provided a mean accuracy of 0.69 and an area under the receiver operating characteristic curve of 0.74 for separating responders and non-responders. The results suggest that GM volume or asymmetry in the target area of active rTMS treatment (DLPFC region in this study) may be a weak predictor of rTMS treatment efficacy. These results need more data to draw more robust conclusions.

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).

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis

Objectives

In a clinical study, diffusion kurtosis imaging (DKI) has been used to visualize and distinguish white matter (WM) structures' details. The purpose of our study is to evaluate and compare the diffusion tensor imaging (DTI) and DKI parameter values to obtain WM structure differences of healthy subjects.

Methods

Thirteen healthy volunteers (mean age, 25.2 years) were examined in this study. On a 3-T MRI system, diffusion dataset for DKI was acquired using an echo-planner imaging sequence, and T1-weghted (T1w) images were acquired. Imaging analysis was performed using Functional MRI of the brain Software Library (FSL). First, registration analysis was performed using the T1w of each subject to MNI152. Second, DTI (eg, fractional anisotropy [FA] and each diffusivity) and DKI (eg, mean kurtosis [MK], radial kurtosis [RK], and axial kurtosis [AK]) datasets were applied to above computed spline coefficients and affine matrices. Each DTI and DKI parameter value for WM areas was compared. Finally, tract-based spatial statistics (TBSS) analysis was performed using each parameter.

Results

The relationship between FA and kurtosis parameters (MK, RK, and AK) for WM areas had a strong positive correlation (FA-MK, R2 = 0.93; FA-RK, R2 = 0.89) and a strong negative correlation (FA-AK, R2 = 0.92). When comparing a TBSS connection, we found that this could be observed more clearly in MK than in RK and FA.

Conclusions

WM analysis with DKI enable us to obtain more detailed information for connectivity between nerve structures.

Advances in knowledge

Quantitative indices of neurological diseases were determined using segmenting WM regions using voxel-based morphometry processing of DKI images.

Advances in Neuroimaging and Multiple Post-Processing Techniques for Epileptogenic Zone Detection of Drug-Resistant Epilepsy

Among the approximately 20 million patients with drug-resistant epilepsy (DRE) worldwide, the vast majority can benefit from surgery to minimize seizure reduction and neurological impairment. Precise preoperative localization of epileptogenic zone (EZ) and complete resection of the lesions can influence the postoperative prognosis. However, precise localization of EZ is difficult, and the structural and functional alterations in the brain caused by DRE vary by etiology. Neuroimaging has emerged as an approach to identify the seizure-inducing structural and functional changes in the brain, and magnetic resonance imaging (MRI) and positron emission tomography (PET) have become routine noninvasive imaging tools for preoperative evaluation of DRE in many epilepsy treatment centers. Multimodal neuroimaging offers unique advantages in detecting EZ, especially in improving the detection rate of patients with negative MRI or PET findings. This approach can characterize the brain imaging characteristics of patients with DRE caused by different etiologies, serving as a bridge between clinical and pathological findings and providing a basis for individualized clinical treatment plans. In addition to the integration of multimodal imaging modalities and the development of special scanning sequences and image post-processing techniques for early and precise localization of EZ, the application of deep machine learning for extracting image features and deep learning-based artificial intelligence have gradually improved diagnostic efficiency and accuracy. These improvements can provide clinical assistance for precisely outlining the scope of EZ and indicating the relationship between EZ and functional brain areas, thereby enabling standardized and precise surgery and ensuring good prognosis. However, most existing studies have limitations imposed by factors such as their small sample sizes or hypothesis-based study designs. Therefore, we believe that the application of neuroimaging and post-processing techniques in DRE requires further development and that more efficient and accurate imaging techniques are urgently needed in clinical practice. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Quantitative analysis of the morphometric analysis program MAP in patients with truly MRI-negative focal epilepsy

OBJECTIVE

In the presurgical evaluation of epilepsy, identifying the epileptogenic zone is challenging if magnetic resonance imaging (MRI) is negative. Several studies have shown the benefit of using a morphometric analysis program (MAP) on T1-weighted MRI scans to detect subtle lesions. MAP can guide a focused re-evaluation of MRI to ultimately identify structural lesions that were previously overlooked. Data on patients where this additional review after MAP analysis did not reveal any lesions is limited. Here we evaluate the diagnostic yield of MAP in a large group of truly MRI-negative patients.

METHODS

We identified 68 patients with MRI-negative focal epilepsy and clear localization of the epileptogenic zone by intracranial EEG or postoperative seizure freedom. High resolution 3D T1 data of patients and 73 healthy controls were acquired on a 3 T scanner. Morphometric analysis was performed with MAP software, creating five z-score maps, reflecting different structural properties of the brain and a patient's deviation from the control population, and a neural network-based focal cortical dysplasia probability map. Ten brain regions were specified to quantify whether MAP findings were located in the correct region. Receiver operating characteristic (ROC) analyses were performed to identify the optimal thresholds for each map.

RESULTS

MAP-guided visual re-evaluation of the original MRI revealed overlooked lesions in three patients. The remaining 65 truly MRI-negative patients were included in the statistical analysis. At the optimal thresholds, maximum sensitivity was 84 %, with 35 % specificity. Balanced accuracy (arithmetic mean of sensitivity and specificity) of the respective maps ranged from 51 % to 60 %, creating three to six times more false positive than true positive findings.

CONCLUSION

This study confirms that MAP is useful in detecting previously overlooked subtle structural lesions. However, in truly MRI-negative patients, the additional diagnostic yield is very limited.

Preoperative Detection of Subtle Focal Cortical Dysplasia in Children by Combined Arterial Spin Labeling, Voxel-Based Morphometry, Electroencephalography-Synchronized Functional MRI, Resting-State Regional Homogeneity, and 18F-fluorodeoxyglucose Positron Emission Tomography

BACKGROUND

Focal cortical dysplasia (FCD) causes drug-resistant epilepsy in children that can be cured surgically, but the lesions are often unseen by imaging.

OBJECTIVE

To assess the efficiency of arterial spin labeling (ASL), voxel-based-morphometry (VBM), fMRI electroencephalography (EEG), resting-state regional homogeneity (ReHo), 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), and their combination in detecting pediatric FCD.

METHODS

We prospectively included 10 children for whom FCD was localized by surgical resection. They underwent 3T MR acquisition with concurrent EEG, including ASL perfusion, resting-state BOLD fMRI (allowing the processing of EEG-fMRI and ReHo), 3D T1-weighted images processed using VBM, and FDG PET-CT coregistered with MRI. Detection was assessed visually and by comparison with healthy controls (for ASL and VBM).

RESULTS

Eight children had normal MRI, and 2 had asymmetric sulci. Using MR techniques, FCD was accurately detected by ASL for 6/10, VBM for 5/10, EEG-fMRI for 5/8 (excluding 2 with uninterpretable results), and ReHo for 4/10 patients. The combination of ASL, VBM, and ReHo allowed correct FCD detection for 9/10 patients. FDG PET alone showed higher accuracy than the other techniques (7/9), and its combination with VBM allowed correct FCD detection for 8/9 patients. The detection efficiency was better for patients with asymmetric sulci (2/2 for all techniques), but advanced MR techniques and PET were useful for MR-negative patients (7/8).

CONCLUSION

A combination of multiple imaging techniques, including PET, ASL, and VBM analysis of T1-weighted images, is effective in detecting subtle FCD in children.

Multimodal combination of neuroimaging methods for localizing the epileptogenic zone in MR-negative epilepsy

The objective was to determine the optimal combination of multimodal imaging methods (IMs) for localizing the epileptogenic zone (EZ) in patients with MR-negative drug-resistant epilepsy. Data from 25 patients with MR-negative focal epilepsy (age 30 ± 10 years, 16M/9F) who underwent surgical resection of the EZ and from 110 healthy controls (age 31 ± 9 years; 56M/54F) were used to evaluate IMs based on 3T MRI, FDG-PET, HD-EEG, and SPECT. Patients with successful outcomes and/or positive histological findings were evaluated. From 38 IMs calculated per patient, 13 methods were selected by evaluating the mutual similarity of the methods and the accuracy of the EZ localization. The best results in postsurgical patients for EZ localization were found for ictal/ interictal SPECT (SISCOM), FDG-PET, arterial spin labeling (ASL), functional regional homogeneity (ReHo), gray matter volume (GMV), cortical thickness, HD electrical source imaging (ESI-HD), amplitude of low-frequency fluctuation (ALFF), diffusion tensor imaging, and kurtosis imaging. Combining IMs provides the method with the most accurate EZ identification in MR-negative epilepsy. The PET, SISCOM, and selected MRI-post-processing techniques are useful for EZ localization for surgical tailoring.

Value of ultra-high field MRI in patients with suspected focal epilepsy and negative 3 T MRI (EpiUltraStudy): protocol for a prospective, longitudinal therapeutic study

PURPOSE

Resective epilepsy surgery is a well-established, evidence-based treatment option in patients with drug-resistant focal epilepsy. A major predictive factor of good surgical outcome is visualization and delineation of a potential epileptogenic lesion by MRI. However, frequently, these lesions are subtle and may escape detection by conventional MRI (≤ 3 T).

METHODS

We present the EpiUltraStudy protocol to address the hypothesis that application of ultra-high field (UHF) MRI increases the rate of detection of structural lesions and functional brain aberrances in patients with drug-resistant focal epilepsy who are candidates for resective epilepsy surgery. Additionally, therapeutic gain will be addressed, testing whether increased lesion detection and tailored resections result in higher rates of seizure freedom 1 year after epilepsy surgery. Sixty patients enroll the study according to the following inclusion criteria: aged ≥ 12 years, diagnosed with drug-resistant focal epilepsy with a suspected epileptogenic focus, negative conventional 3 T MRI during pre-surgical work-up.

RESULTS

All patients will be evaluated by 7 T MRI; ten patients will undergo an additional 9.4 T MRI exam. Images will be evaluated independently by two neuroradiologists and a neurologist or neurosurgeon. Clinical and UHF MRI will be discussed in the multidisciplinary epilepsy surgery conference. Demographic and epilepsy characteristics, along with postoperative seizure outcome and histopathological evaluation, will be recorded.

CONCLUSION

This protocol was reviewed and approved by the local Institutional Review Board and complies with the Declaration of Helsinki and principles of Good Clinical Practice. Results will be submitted to international peer-reviewed journals and presented at international conferences.

TRIAL REGISTRATION NUMBER

www.trialregister.nl : NTR7536.

Non-parametric combination of multimodal MRI for lesion detection in focal epilepsy

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Multivariate voxel-based analysis useful for lesion detection in focal epilepsy.

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Non-parametric combination algorithm used to combine data from various MR sequences.

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Successful lesion detection demonstrated in MRI-positive and MRI-negative patients.

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Multimodal analysis detected abnormalities from diverse epileptogenic pathologies.

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Sensitivity of multivariate analysis notably higher than univariate analyses.

One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences.

Application value of molecular imaging technology in epilepsy

Epilepsy is a common neurological disease with various seizure types, complicated etiologies, and unclear mechanisms. Its diagnosis mainly relies on clinical history, but an electroencephalogram is also a crucial auxiliary examination. Recently, brain imaging technology has gained increasing attention in the diagnosis of epilepsy, and conventional magnetic resonance imaging can detect epileptic foci in some patients with epilepsy. However, the results of brain magnetic resonance imaging are normal in some patients. New molecular imaging has gradually developed in recent years and has been applied in the diagnosis of epilepsy, leading to enhanced lesion detection rates. However, the application of these technologies in epilepsy patients with negative brain magnetic resonance must be clarified. Thus, we reviewed the relevant literature and summarized the information to improve the understanding of the molecular imaging application value of epilepsy.

7T MRI with post-processing for the presurgical evaluation of pharmacoresistant focal epilepsy

Background:

We aimed to evaluate the diagnostic yield of seven-tesla (7T) magnetic resonance imaging (MRI) with post-processing of three-dimensional (3D) T1-weighted (T1W) images by the morphometric analysis program (MAP) in epilepsy surgical candidates whose 3T MRI results were inconclusive or negative.

Methods:

We recruited 35 patients with pharmacoresistant focal epilepsy. A multidisciplinary team including an experienced neuroradiologist evaluated their seizure semiology, video-electroencephalography data, 3T MRI and post-processing results, and co-registered FDG-PET. Eleven patients had suspicious lesions on 3T MRI and the other 24 patients were strictly MRI-negative. 7T MRI evaluation was then performed to aid clinical decision. Among patients with pathologically proven focal cortical dysplasia (FCD) type II, signs of FCD were retrospectively evaluated in each MRI sequence (T1W, T2W, and FLAIR), and positive rates were analyzed in each MAP feature map (junction, extension, and thickness).

Results:

7T MRI evaluation confirmed the lesion in nine of the 11 (81.8%) patients with suspicious lesions on 3T MRI. It also revealed new lesions in four of the 24 (16.7%) strictly MRI-negative patients. Histopathology showed FCD type II in 11 of the 13 (84.6%) 7T MRI-positive cases. Unexpectedly, three of the four newly identified FCD lesions were located in the posterior quadrant. Blurred gray–white boundary was the most frequently observed sign of FCD, appearing on 7T T1W image in all cases and on T2W and FLAIR images in only about half cases. The 7T junction map successfully detected FCD (10/11) in more cases than the extension (1/11) and thickness (0/11) maps. The 3D T1W images at 7T exhibited superior cerebral gray–white matter contrast, more obviously blurred gray–white boundary of FCD, and larger and brighter positive zones in post-processing than 3T T1W images.

Conclusion:

7T MRI with post-processing can enhance the detection of subtle epileptogenic lesions for MRI-negative epilepsy and may optimize surgical strategies for patients with focal epilepsy.

Voxel-based morphometry for the evaluation of patients with pharmacoresistant epilepsy with apparently normal MRI

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) is essential in the diagnosis of pharmacoresistant epilepsy (PRE), because patients with lesions detected by MRI have a better prognosis after surgery. Focal cortical dysplasia (FCD) is one of the most frequent etiologies of PRE but can be difficult to identify by MRI. Voxel-based morphometric analysis programs, like the Morphometric Analysis Program (MAP), have been developed to help improve MRI detection. Our objective was to evaluate the clinical usefulness of MAP in patients with PRE and an apparently normal MRI.

METHODS

We studied 70 patients with focal PRE and a nonlesional MRI. The 3DT1 sequence was processed with MAP, obtaining three z-score maps. Patients were classified as MAP+ if one or more z-score maps showed a suspicious area of brightness, and MAP- if the z-score maps did not show any suspicious areas. For MAP+ cases, a second-look MRI was performed with a dedicated inspection based on the MAP findings. The MAP results were correlated with the epileptogenic zone. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

RESULTS

Thirty-one percent of patients were classified as MAP+ and 69% were MAP-. Results showed a sensitivity of 0.57, specificity of 0.8, PPV of 0.91, and NPV of 0.35. In 19% of patients, an FCD was found in the second-look MRI after MAP.

CONCLUSIONS

MAP was helpful in the detection of lesions in PRE patients with a nonlesional MRI, which could have important repercussions for the clinical management and postoperative prognosis of these patients.

Automated detection and segmentation of focal cortical dysplasias (FCDs) with artificial intelligence: Presentation of a novel convolutional neural network and its prospective clinical validation

PURPOSE

Focal cortical dysplasias (FCDs) represent one of the most frequent causes of pharmaco-resistant focal epilepsies. Despite improved clinical imaging methods over the past years, FCD detection remains challenging, as FCDs vary in location, size, and shape and commonly blend into surrounding tissues without clear definable boundaries. We developed a novel convolutional neural network for FCD detection and segmentation and validated it prospectively on daily-routine MRIs.

MATERIAL AND METHODS

The neural network was trained on 201 T1 and FLAIR 3 T MRI volume sequences of 158 patients with mainly FCDs, regardless of type, and 7 focal PMG. Non-FCD/PMG MRIs, drawn from 100 normal MRIs and 50 MRIs with non-FCD/PMG pathologies, were added to the training. We applied the algorithm prospectively on 100 consecutive MRIs of patients with focal epilepsy from daily clinical practice. The results were compared with corresponding neuroradiological reports and morphometric MRI analyses evaluated by an experienced epileptologist.

RESULTS

Best training results reached a sensitivity (recall) of 70.1 % and a precision of 54.3 % for detecting FCDs. Applied on the daily-routine MRIs, 7 out of 9 FCDs were detected and segmented correctly with a sensitivity of 77.8 % and a specificity of 5.5 %. The results of conventional visual analyses were 33.3 % and 94.5 %, respectively (3/9 FCDs detected); the results of morphometric analyses with overall epileptologic evaluation were both 100 % (9/9 FCDs detected) and thus served as reference.

CONCLUSION

We developed a 3D convolutional neural network with autoencoder regularization for FCD detection and segmentation. Our algorithm employs the largest FCD training dataset to date with various types of FCDs and some focal PMG. It provided a higher sensitivity in detecting FCDs than conventional visual analyses. Despite its low specificity, the number of false positively predicted lesions per MRI was lower than with morphometric analysis. We consider our algorithm already useful for FCD pre-screening in everyday clinical practice.

Automatic Detection of Focal Cortical Dysplasia Type II in MRI: Is the Application of Surface-Based Morphometry and Machine Learning Promising?

Background and Objectives

Focal cortical dysplasia (FCD) is a type of malformations of cortical development and one of the leading causes of drug-resistant epilepsy. Postoperative results improve the diagnosis of lesions on structural MRIs. Advances in quantitative algorithms have increased the identification of FCD lesions. However, due to significant differences in size, shape, and location of the lesion in different patients and a big deal of time for the objective diagnosis of lesion as well as the dependence of individual interpretation, sensitive approaches are required to address the challenge of lesion diagnosis. In this research, a FCD computer-aided diagnostic system to improve existing methods is presented.

Methods

Magnetic resonance imaging (MRI) data were collected from 58 participants (30 with histologically confirmed FCD type II and 28 without a record of any neurological prognosis). Morphological and intensity-based features were calculated for each cortical surface and inserted into an artificial neural network. Statistical examinations evaluated classifier efficiency.

Results

Neural network evaluation metrics—sensitivity, specificity, and accuracy—were 96.7, 100, and 98.6%, respectively. Furthermore, the accuracy of the classifier for the detection of the lobe and hemisphere of the brain, where the FCD lesion is located, was 84.2 and 77.3%, respectively.

Conclusion

Analyzing surface-based features by automated machine learning can give a quantitative and objective diagnosis of FCD lesions in presurgical assessment and improve postsurgical outcomes.

Distinguishing type II focal cortical dysplasias from normal cortex: A novel normative modeling approach

OBJECTIVE

Focal cortical dysplasias (FCDs) are a common cause of apparently non-lesional drug-resistant focal epilepsy. Visual detection of subtle FCDs on MRI is clinically important and often challenging. In this study, we implement a set of 3D local image filters adapted from computer vision applications to characterize the appearance of normal cortex surrounding the gray-white junction. We create a normative model to serve as the basis for a novel multivariate constrained outlier approach to automated FCD detection.

METHODS

Standardized MPRAGE, T₂ and FLAIR MR images were obtained in 15 patients with radiologically or histologically diagnosed FCDs and 30 healthy volunteers. Multiscale 3D local image filters were computed for each MR contrast then sampled onto the gray-white junction surface. Using an iterative Gaussianization procedure, we created a normative model of cortical variability in healthy volunteers, allowing for identification of outlier regions and estimates of similarity in normal cortex and FCD lesions. We used a constrained outlier approach following local normalization to automatically detect FCD lesions based on projection onto the mean FCD feature vector.

RESULTS

FCDs as well as some normal cortical regions such as primary sensorimotor and paralimbic regions appear as outliers. Regions such as the paralimbic regions and the anterior insula have similar features to FCDs. Our constrained outlier approach allows for automated FCD detection with 80% sensitivity and 70% specificity.

SIGNIFICANCE

A normative model using multiscale local image filters can be used to describe the normal cortical variability. Although FCDs appear similar to some cortical regions such as the anterior insula and paralimbic cortices, they can be identified using a constrained outlier detection approach. Our method for detecting outliers and estimating similarity is generic and could be extended to identification of other types of lesions or atypical cortical areas.

Voxel-Based Morphometry-from Hype to Hope. A Study on Hippocampal Atrophy in Mesial Temporal Lobe Epilepsy

BACKGROUND AND PURPOSE

Automated volumetry of the hippocampus is considered useful to assist the diagnosis of hippocampal sclerosis in temporal lobe epilepsy. However, voxel-based morphometry is rarely used for individual subjects because of high rates of false-positives. We investigated whether an approach with high dimensional warping to the template and nonparametric statistics would be useful to detect hippocampal atrophy in patients with hippocampal sclerosis.

MATERIALS AND METHODS

We performed single-subject voxel-based morphometry with nonparametric statistics within the framework of Statistical Parametric Mapping to compare MRI from 26 well-characterized patients with temporal lobe epilepsy individually against a group of 110 healthy controls. The following statistical threshold was used: P < .05 corrected for multiple comparisons with family-wise error over the region of interest right and left hippocampus.

RESULTS

The sensitivity for the detection of atrophy related to hippocampal sclerosis was 0.92 (95% CI, 0.67-0.99) for the right hippocampus and 0.60 (0.31-0.83) for the left, and the specificity for volume changes was 0.98 (0.93-0.99). All clusters of decreased hippocampal volumes were correctly lateralized to the seizure focus. Hippocampal volume decrease was in accordance with neuronal cell loss on histology reports.

CONCLUSIONS

Nonparametric voxel-based morphometry is sensitive and specific for hippocampal atrophy in patients with mesial temporal lobe epilepsy and may be useful in clinical practice.

Integrative analysis of non-targeted lipidomic data and brain structural imaging identifies phosphatidylethanolamine associated with epileptogenesis

INTRODUCTION

Epilepsy is a chronic disease, while epileptogenesis is a latent period where brain will be transformed into an epileptic one. Mechanisms of epileptogenesis remain unclear.

OBJECTIVES

We aim to provide information of hippocampal lipidomic changes related with epileptogenesis in two kindling models. Combining hippocampal structural imaging indices, our study also attempts to assess biochemical alterations as a function of epileptogenesis in a non-invasive way.

METHODS

We constructed two kinds of chemical kindling models, which have long been used as models of epileptogenesis. Two kindling and one control groups were all subjected to structural imaging acquisition after successfully kindled. Voxel-based morphometry, a postprocessing method for brain imaging data, was used to segment and extract hippocampal gray matter volume for subsequent integrative analysis. LC-MS based lipidomic analysis was applied to identify distinct hippocampal lipidomic profiles between kindling and control groups. Further, we regress hippocampal structural indices on lipids to identify those associated with both epileptogenesis and brain structural changes.

RESULTS

We report distinct lipidomic profiles between kindling groups and control. A total of 638 lipids were detected in all three groups. Among them were 98 individual lipids, showing significant alterations, in particular lipid class of phosphatidylethanolamine (PE), glucosylceramide and phosphatidylcholine. Hippocampal gray matter volumes were found significant different between groups (P = 0.0223). After combining brain imaging data, we demonstrate several individual PE, namely PE(O-18:1_22:3), PE(O-18:1_22:6) and PE(18:1_18:1), are associated with both epileptogenesis and hippocampal gray matter volume.

CONCLUSION

This study suggests metabolic pathway of PE might involve in epileptogenesis. Also, for the first time, we link level of PE with structural brain imaging indices, in an attempt to potentiate the futuristic application of noninvasive brain imaging techniques to identify epileptogenesis in its latent period.

Changing concepts in presurgical assessment for epilepsy surgery

Candidates for epilepsy surgery must undergo presurgical evaluation to establish whether and how surgical treatment can stop seizures without causing neurological deficits. Various techniques, including MRI, PET, single-photon emission CT, video-EEG, magnetoencephalography and invasive EEG, aim to identify the diseased brain tissue and the involved network. Recent technical and methodological developments, encompassing both advances in existing techniques and new combinations of technologies, are enhancing the ability to define the optimal resection strategy. Multimodal interpretation and predictive computer models are expected to aid surgical planning and patient counselling, and multimodal intraoperative guidance is likely to increase surgical precision. In this Review, we discuss how the knowledge derived from these new approaches is challenging our way of thinking about surgery to stop focal seizures. In particular, we highlight the importance of looking beyond the EEG seizure onset zone and considering focal epilepsy as a brain network disease in which long-range connections need to be taken into account. We also explore how new diagnostic techniques are revealing essential information in the brain that was previously hidden from view.

Presurgical epilepsy evaluation and epilepsy surgery

With a prevalence of 0.8 to 1.2%, epilepsy represents one of the most frequent chronic neurological disorders; 30 to 40% of patients suffer from drug-resistant epilepsy (that is, seizures cannot be controlled adequately with antiepileptic drugs). Epilepsy surgery represents a valuable treatment option for 10 to 50% of these patients. Epilepsy surgery aims to control seizures by resection of the epileptogenic tissue while avoiding neuropsychological and other neurological deficits by sparing essential brain areas. The most common histopathological findings in epilepsy surgery specimens are hippocampal sclerosis in adults and focal cortical dysplasia in children. Whereas presurgical evaluations and surgeries in patients with mesial temporal sclerosis and benign tumors recently decreased in most centers, non-lesional patients, patients requiring intracranial recordings, and neocortical resections increased. Recent developments in neurophysiological techniques (high-density electroencephalography [EEG], magnetoencephalography, electrical and magnetic source imaging, EEG-functional magnetic resonance imaging [EEG-fMRI], and recording of pathological high-frequency oscillations), structural magnetic resonance imaging (MRI) (ultra-high-field imaging at 7 Tesla, novel imaging acquisition protocols, and advanced image analysis [post-processing] techniques), functional imaging (positron emission tomography and single-photon emission computed tomography co-registered to MRI), and fMRI significantly improved non-invasive presurgical evaluation and have opened the option of epilepsy surgery to patients previously not considered surgical candidates. Technical improvements of resective surgery techniques facilitate successful and safe operations in highly delicate brain areas like the perisylvian area in operculoinsular epilepsy. Novel less-invasive surgical techniques include stereotactic radiosurgery, MR-guided laser interstitial thermal therapy, and stereotactic intracerebral EEG-guided radiofrequency thermocoagulation.

Brain imaging in epilepsy

Brain imaging with MRI identifies structural cerebral pathology that may give rise to seizures. The greatest yield is from MRI at 3T using epilepsy protocols, and reported by expert neuroradiologists who possess the full clinical data. X-ray CT scanning has a role in assessing patients with seizures in the context of an acute neurological illness. Identifying a relevant structural lesion with MRI is fundamental in the consideration of epilepsy surgery; it is crucial to establish if a lesion is relevant to the epilepsy or not. If no lesion is identified, developmental MRI and image processing may identify a subtle abnormality. Positron-emission tomography (PET) and single-photon emission computed tomography (SPECT) may identify focal functional abnormalities that infer the location of an epileptic focus. Functional MRI is useful for localising eloquent cortex, and tractography delineates crucial white matter tracts, so that these may be avoided in epilepsy surgery. Reviewing data in three dimensions aids visualisation of structural relationships and helps surgical planning.

MP2RAGE multispectral voxel-based morphometry in focal epilepsy

We assessed the applicability of MP2RAGE for voxel‐based morphometry. To this end, we analyzed its brain tissue segmentation characteristics in healthy subjects and the potential for detecting focal epileptogenic lesions (previously visible and nonvisible). Automated results and expert visual interpretations were compared with conventional VBM variants (i.e., T1 and T1 + FLAIR). Thirty‐one healthy controls and 21 patients with focal epilepsy were recruited. 3D T1‐, T2‐FLAIR, and MP2RAGE images (consisting of INV1, INV2, and MP2 maps) were acquired on a 3T MRI. The effects of brain tissue segmentation and lesion detection rates were analyzed among single‐ and multispectral VBM variants. MP2‐single‐contrast gave better delineation of deep, subcortical nuclei but was prone to misclassification of dura/vessels as gray matter, even more than conventional‐T1. The addition of multispectral combinations (INV1, INV2, or FLAIR) could markedly reduce such misclassifications. MP2 + INV1 yielded generally clearer gray matter segmentation allowing better differentiation of white matter and neighboring gyri. Different models detected known lesions with a sensitivity between 60 and 100%. In non lesional cases, MP2 + INV1 was found to be best with a concordant rate of 37.5%, specificity of 51.6% and concordant to discordant ratio of 0.60. In summary, we show that multispectral MP2RAGE VBM (e.g., MP2 + INV1, MP2 + INV2) can improve brain tissue segmentation and lesion detection in epilepsy.

Utility of MRI, PET, and ictal SPECT in presurgical evaluation of non-lesional pediatric epilepsy

Children with epilepsy and normal structural MRI pose a particular challenge in localization of epileptic foci for surgical resection. Many of these patients have subtle structural lesions such as mild cortical dysplasia that can be missed by conventional MRI but may become detectable by optimized and advanced MRI acquisitions and post-processing. Specificity of objective analytic techniques such as voxel-based morphometry remains an issue. Combination of MRI with functional imaging approaches can improve the accuracy of detecting epileptogenic brain regions. Analysis of glucose positron emission tomography (PET) combined with high-resolution MRI can optimize detection of hypometabolic cortex associated with subtle cortical malformations and can also enhance presurgical evaluation in children with epileptic spasms. Additional PET tracers may detect subtle epileptogenic lesions and cortex with enhanced specificity in carefully selected subgroups with various etiologies; e.g., increased tryptophan uptake can identify epileptogenic cortical dysplasia in the interictal state. Subtraction ictal SPECT can be also useful to delineate ictal foci in those with non-localizing PET or after failed surgical resection. Presurgical delineation of language and motor cortex and the corresponding white matter tracts is increasingly reliable by functional MRI and DTI techniques; with careful preparation, these can be useful even in young and sedated children. While evidence-based pediatric guidelines are still lacking, the data accumulated in the last decade strongly indicate that multimodal imaging with combined analysis of MRI, PET, and/or ictal SPECT data can optimize the detection of subtle epileptogenic lesions and facilitate seizure-free outcome while minimizing the postsurgical functional deficit in children with normal conventional MRI.

Precision in pediatric epilepsy

Epilepsy in infants and children is one of the most common and devastating neurological disorders. In the past, we had a limited understanding of the causes of epilepsy in pediatric patients, so we treated pediatric epilepsy according to seizure type. Now with new tools and tests, we are entering the age of precision medicine in pediatric epilepsy. In this review, we use the new etiological classification system proposed by the International League Against Epilepsy to review the advances in the diagnosis of pediatric epilepsy, describe new tools to identify seizure foci for epilepsy surgery, and define treatable epilepsy syndromes.

Multimodal quantitative magnetic resonance imaging analysis with individualized postprocessing in patients with drug-resistant focal epilepsy and conventional visual inspection negative for epileptogenic lesions

OBJECTIVES

Approximately one-third of candidates for epilepsy surgery have no visible abnormalities on conventional magnetic resonance imaging. This is extremely discouraging, as these patients have a less favorable prognosis. We aimed to evaluate the utility of quantitative magnetic resonance imaging in patients with drug-resistant neocortical focal epilepsy and negative imaging.

METHODS

A prospective study including 46 patients evaluated through individualized postprocessing of five quantitative measures: cortical thickness, white and gray matter junction signal, relaxation rate, magnetization transfer ratio, and mean diffusivity. Scalp video-electroencephalography was used to suggest the epileptogenic zone. A volumetric fluid-attenuated inversion recovery sequence was performed to aid visual inspection. A critical assessment of follow-up was also conducted throughout the study.

RESULTS

In the subgroup classified as having an epileptogenic zone, individualized postprocessing detected abnormalities within the region of electroclinical origin in 9.7% to 31.0% of patients. Abnormalities outside the epileptogenic zone were more frequent, up to 51.7%. In five patients initially included with negative imaging, an epileptogenic structural abnormality was identified when a new visual magnetic resonance imaging inspection was guided by information gleaned from postprocessing. In three patients, epileptogenic lesions were detected after visual evaluation with volumetric fluid-attenuated sequence guided by video electroencephalography.

CONCLUSION

Although quantitative magnetic resonance imaging analyses may suggest hidden structural lesions, caution is warranted because of the apparent low specificity of these findings for the epileptogenic zone. Conversely, these methods can be used to prevent visible lesions from being ignored, even in referral centers. In parallel, we need to highlight the positive contribution of the volumetric fluid-attenuated sequence.

Cingulate seizures and recent treatment strategies

Cingulate epilepsy manifests with a broad range of semiologic features and seizure types. Key clinical features may elucidate ictal involvement of certain subregions of the cingulate gyrus. Ictal and interictal electroencephalogram findings in cingulate epilepsy vary and are often poorly localized, adding to the diagnostic challenge of identifying the seizure onset zone for presurgical cases, particularly in the absence of a lesion on imaging. Recent advances in multimodal imaging techniques may contribute to ictal localization and further our understanding of neural and epileptic pathways involving the cingulate gyrus. Beyond medication and surgical resection, new techniques including stereotactic laser ablation, responsive neurostimulation, and deep brain stimulation offer additional approaches for the treatment of cingulate epilepsy.

Systematic Assessment of Multispectral Voxel-Based Morphometry in Previously MRI-Negative Focal Epilepsy

BACKGROUND AND PURPOSE

Voxel-based morphometry is widely used for detecting gray matter abnormalities in epilepsy. However, its performance with changing parameters, smoothing and statistical threshold, is debatable. More important, the potential yield of combining multiple MR imaging contrasts (multispectral voxel-based morphometry) is still unclear. Our aim was to objectify smoothing and statistical cutoffs and systematically compare the performance of multispectral voxel-based morphometry with existing T1 voxel-based morphometry in patients with focal epilepsy and previously negative MRI.

MATERIALS AND METHODS

3D T1-, T2-, and T2-weighted FLAIR scans were acquired for 62 healthy volunteers and 13 patients with MR imaging negative for focal epilepsy on a Magnetom Skyra 3T scanner with an isotropic resolution of 0.9 mm³. We systematically optimized the main voxel-based morphometry parameters, smoothing level and statistical cutoff, with T1 voxel-based morphometry as a reference. As a next step, the performance of multispectral voxel-based morphometry models, T1+T2, T1+FLAIR, and T1+T2+FLAIR, was compared with that of T1 voxel-based morphometry using gray matter concentration and gray matter volume analysis.

RESULTS

We found the best performance of T1 at 12 mm and a T-threshold (statistical cutoff) of 3.7 for gray matter concentration analysis. When we incorporated these parameters, after expert visual interpretation of concordant and discordant findings, we identified T1+FLAIR as the best model with a concordant rate of 46.2% and a concordant rate/discordant rate of 1.20 compared with T1 with 30.8% and 0.67, respectively. Visual interpretation of voxel-based morphometry findings decreased concordant rates from 38.5%-46.2% to 15.4%-46.2% and discordant rates from 53.8%-84.6% to 30.8%-46.2% and increased specificity across models from 33.9%-40.3% to 46.8%-54.8%.

CONCLUSIONS

Multispectral voxel-based morphometry, especially T1+FLAIR, can yield superior results over single-channel T1 in focal epilepsy patients with a negative conventional MR imaging.

Coregistrating magnetic source and magnetic resonance imaging for epilepsy surgery in focal cortical dysplasia

Background

Epilepsy surgery for focal cortical dysplasia type II (FCD II) offers good chances for seizure freedom, but remains a challenge with respect to lesion detection, defining the epileptogenic zone and the optimal resection strategy. Integrating results from magnetic source imaging from magnetoencephalography (MEG) with magnetic resonance imaging (MRI) including MRI postprocessing may be useful for optimizing these goals.

Methods

We here present data from 21 adult FCD II patients, investigated during a 10 year period and evaluated including magnetic source imaging. 16 patients had epilepsy surgery, i.e. histopathologically verified FCD II, and a long follow up. We present our analysis of epileptogenic zones including MEG in relation to structural data according to MRI data and relate these results to surgical outcomes.

Results

FCD II in our cohort was characterized by high MEG yield and localization accuracy and MEG showed impact on surgical success-rates. MEG source localizations were detected in 95.2% of patients and were as close as 12.3 ± 8,1 mm to the MRI-lesion. After a mean follow up of >3 years, we saw >80% Engel I outcomes, with more favourable outcomes when the MEG source was completely resected (Fishers exact test 0,033).

Conclusion

We argue for a high value of conducting a combined MEG-MRI approach in the presurgical workup and the resection strategy in patients with FCD II related epilepsy.