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

Voxel-based and surface-based cortical morphometric MRI applications for identifying the epileptogenic zone: A narrative review

Approximately 40% of patients with drug-resistant epilepsy referred for surgical evaluation have no epileptogenic lesion on MRI (MRI-negative). MRI-negative epilepsy is associated with poorer seizure freedom prognosis and has therefore motivated the development of structural post-processing methods to "convert" MRI-negative to MRI-positive cases. In this article, we review the principles, advances, and challenges of voxel- and surface-based cortical morphometric MRI techniques in detecting the epileptogenic zone. The ground truth for the presumed epileptogenic zone in imaging studies can be classified into lesion-based (MRI lesion mask or histopathology) or epileptogenicity-based ground truth (anatomical-electroclinical correlations or resections that lead to seizure freedom). Voxel-based techniques are reported to have a 13%-97% concordance rate, while surface-based techniques have 67%-92% compared to lesion-based ground truths. Epileptogenicity-based ground truth may be more relevant in the case of MRI-negative cases; however, the sensitivity and concordance rate (voxel-based technique 7.1%-66.7%, and surface-based technique 62%) are limited by the reliance on scalp EEG and qualitative analysis of seizure-onset pattern. The use of stereo-EEG and quantitative EEG analysis may fill this gap to evaluate the correlation between cortical morphometry results and electrophysiological epileptogenic biomarkers of the epileptogenic zone and help improve the yield of structural post-processing tools. PLAIN LANGUAGE SUMMARY: Locating the epileptogenic zone (the brain area that is responsible for seizure generation) is important to diagnose and plan epilepsy treatments. An abnormal brain imaging (MRI) result can help clinical decision-making; however, around 40% of patients have normal MRI results (MRI-negative). We are reviewing the potential of two advanced MRI methods (voxel- and surface-based cortical morphometry) to localize the epileptogenic zone in the presence or absence of visible MRI abnormalities. We also describe the current challenge of applying the above methods in daily clinical practice and propose using advanced brain recording analysis to aid this translation process.

Development and prospective clinical validation of a convolutional neural network for automated detection and segmentation of focal cortical dysplasias

PURPOSE

Focal cortical dysplasias (FCDs) are a leading cause of drug-resistant epilepsy. Early detection and resection of FCDs have favorable prognostic implications for postoperative seizure freedom. Despite advancements in imaging methods, FCD detection remains challenging. House et al. (2021) introduced a convolutional neural network (CNN) for automated FCD detection and segmentation, achieving a sensitivity of 77.8%. However, its clinical applicability was limited due to a low specificity of 5.5%. The objective of this study was to improve the CNN's performance through data-driven training and algorithm optimization, followed by a prospective validation on daily-routine MRIs.

MATERIAL AND METHODS

A dataset of 300 3 T MRIs from daily clinical practice, including 3D T1 and FLAIR sequences, was prospectively compiled. The MRIs were visually evaluated by two neuroradiologists and underwent morphometric assessment by two epileptologists. The dataset included 30 FCD cases (11 female, mean age: 28.1 ± 10.1 years) and a control group of 150 normal cases (97 female, mean age: 32.8 ± 14.9 years), along with 120 non-FCD pathological cases (64 female, mean age: 38.4 ± 18.4 years). The dataset was divided into three subsets, each analyzed by the CNN. Subsequently, the CNN underwent a two-phase-training process, incorporating subset MRIs and expert-labeled FCD maps. This training employed both classical and continual learning techniques. The CNN's performance was validated by comparing the baseline model with the trained models at two training levels.

RESULTS

In prospective validation, the best model trained using continual learning achieved a sensitivity of 90.0%, specificity of 70.0%, and accuracy of 72.0%, with an average of 0.41 false positive clusters detected per MRI. For FCD segmentation, an average Dice coefficient of 0.56 was attained. The model's performance improved in each training phase while maintaining a high level of sensitivity. Continual learning outperformed classical learning in this regard.

CONCLUSIONS

Our study presents a promising CNN for FCD detection and segmentation, exhibiting both high sensitivity and specificity. Furthermore, the model demonstrates continuous improvement with the inclusion of more clinical MRI data. We consider our CNN a valuable tool for automated, examiner-independent FCD detection in daily clinical practice, potentially addressing the underutilization of epilepsy surgery in drug-resistant focal epilepsy and thereby improving patient outcomes.

Automated detection of focal cortical dysplasia based on magnetic resonance imaging and positron emission tomography

PURPOSE

Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN).

METHODS

MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN.

RESULTS

On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65.

SIGNIFICANCE

Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.

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).

Multimodal imaging-based diagnostic approach for MRI-negative posterior cortex epilepsy

Background

Posterior cortex epilepsy (PCE) primarily comprises seizures originating from the occipital, parietal, and/or posterior edge of the temporal lobe. Electroclinical dissociation and subtle imaging representation render the diagnosis of PCE challenging. Improved methods for accurately identifying patients with PCE are necessary.

Objectives

To develop a novel voxel-based image postprocessing method for better visual identification of the neuroimaging abnormalities associated with PCE.

Design

Multicenter, retrospective study.

Methods

Clinical and imaging features of 165 patients with PCE were retrospectively reviewed and collected from five epilepsy centers. A total of 37 patients (32.4% female, 20.2 ± 8.9 years old) with magnetic resonance imaging (MRI)-negative PCE were finally included for analysis. Image postprocessing features were calculated over a neighborhood for each voxel in the multimodality data. The postprocessed maps comprised structural deformation, hyperintense signal, and hypometabolism. Five raters from three different centers were blinded to the clinical diagnosis and determined the neuroimaging abnormalities in the postprocessed maps.

Results

The average accuracy of correct identification was 55.7% (range from 43.2 to 62.2%) and correct lateralization was 74.1% (range from 64.9 to 81.1%). The Cronbach's alpha was 0.766 for the correct identification and 0.683 for the correct lateralization with similar results of the interclass correlation coefficient, thus indicating reliable agreement between the raters.

Conclusion

The image postprocessing method developed in this study can potentially improve the visual detection of MRI-negative PCE. The technique could lead to an increase in the number of patients with PCE who could benefit from the surgery.

Advances in Image Processing for Epileptogenic Zone Detection with MRI

Focal epilepsy is a common and severe neurologic disorder. Neuroimaging aims to identify the epileptogenic zone (EZ), preferably as a macroscopic structural lesion. For approximately a third of patients with chronic drug-resistant focal epilepsy, the EZ cannot be precisely identified using standard 3.0-T MRI. This may be due to either the EZ being undetectable at imaging or the seizure activity being caused by a physiologic abnormality rather than a structural lesion. Computational image processing has recently been shown to aid radiologic assessments and increase the success rate of uncovering suspicious regions by enhancing their visual conspicuity. While structural image analysis is at the forefront of EZ detection, physiologic image analysis has also been shown to provide valuable information about EZ location. This narrative review summarizes and explains the current state-of-the-art computational approaches for image analysis and presents their potential for EZ detection. Current limitations of the methods and possible future directions to augment EZ detection are discussed.

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.

Black Line Sign in Focal Cortical Dysplasia IIB: A 7T MRI and Electroclinicopathologic Study

BACKGROUND AND OBJECTIVES

We aim to provide detailed imaging-electroclinicopathologic characterization of the black line sign, a novel MRI marker for focal cortical dysplasia (FCD) IIB.

METHODS

7T T2*-weighted gradient-echo (T2*w-GRE) images were retrospectively reviewed in a consecutive cohort of patients with medically intractable epilepsy with pathology-proven FCD II, for the occurrence of the black line sign. We examined the overlap between the black line region and the seizure-onset zone (SOZ) defined by intracranial EEG (ICEEG) and additionally assessed whether complete inclusion of the black line region in the surgical resection was associated with postoperative seizure freedom. The histopathologic specimen was aligned with the MRI to investigate the pathologic underpinning of the black line sign. Region-of-interest-based quantitative MRI (qMRI) analysis on the 7T T1 map was performed in the black line region, entire lesional gray matter (GM), and contralateral/ipsilateral normal gray and white matter (WM).

RESULTS

We included 20 patients with FCD II (14 IIB and 6 IIA). The black line sign was identified in 12/14 (85.7%) of FCD IIB and 0/6 of FCD IIA on 7T T2*w-GRE. The black line region was highly concordant with the ICEEG-defined SOZ (5/7 complete and 2/7 partial overlap). Seizure freedom was seen in 8/8 patients whose black line region was completely included in the surgical resection; in the 2 patients whose resection did not completely include the black line region, both had recurring seizures. Inclusion of the black line region in the surgical resection was significantly associated with seizure freedom (p = 0.02). QMRI analyses showed that the T1 mean value of the black line region was significantly different from the WM (p < 0.001), but similar to the GM. Well-matched histopathologic slices in one case revealed accumulated dysmorphic neurons and balloon cells in the black line region.

DISCUSSION

The black line sign may serve as a noninvasive marker for FCD IIB. Both MRI-pathology and qMRI analyses suggest that the black line region was an abnormal GM component within the FCD. Being highly concordant with ICEEG-defined SOZ and significantly associated with seizure freedom when included in resection, the black line sign may contribute to the planning of ICEEG/surgery of patients with medically intractable epilepsy with FCD IIB.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that in individuals with intractable focal epilepsy undergoing resection who have a 7T MRI with adequate image quality, the presence of the black line sign may suggest FCD IIB, be concordant with SOZ from ICEEG, and be associated with more seizure freedom if fully included in resection.

Focal cortical dysplasia links to sleep-related epilepsy in symptomatic focal epilepsy

OBJECTIVE

In sleep-related epilepsy (SRE), epileptic seizures predominantly occur during sleep, but the clinical characteristics of SRE remain elusive. We aimed to identify the clinical features associated with the occurrence of SRE in a large cohort of symptomatic focal epilepsy.

METHODS

We retrospectively included patients with four etiologies, including focal cortical dysplasia (FCD), low-grade tumors (LGT), temporal lobe epilepsy with hippocampal sclerosis (TLE-HS), and encephalomalacia. SRE was defined as more than 70% of seizures occurring during sleep according to the seizure diary. The correlation between SRE and other clinical variables, such as etiology of epilepsy, pharmacoresistance, seizure frequency, history of bilateral tonic-clonic seizures, and seizure localization was analyzed.

RESULTS

A total of 376 patients were included. Among them 95 (25.3%) were classified as SRE and the other 281(74.7%) as non-SRE. The incidence of SRE was 53.5% in the FCD group, which was significantly higher than the other three groups (LGT: 19.0%; TLE-HS: 9.9%; encephalomalacia: 16.7%; P < 0.001). The etiology of FCD (p < 0.001) was significantly associated with SRE (OR: 9.71, 95% CI: 3.35-28.14) as an independent risk factor. In addition, small lesion size (p = 0.009) of FCD further increased the risk of SRE (OR: 3.18, 95% CI: 1.33-7.62) in the FCD group.

SIGNIFICANCE

Our data highlight that FCD markedly increased the risk of sleep-related epilepsy independently of seizure localization. A small lesion of FCD further increased the risk of sleep-related epilepsy by 2.18 times in the FCD group.