Superficial and deep white matter diffusion abnormalities in focal epilepsies

Localizing seizure onset zone by a cortico-cortical evoked potentials-based machine learning approach in focal epilepsy

OBJECTIVE

We aimed to develop a new approach for identifying the localization of the seizure onset zone (SOZ) based on corticocortical evoked potentials (CCEPs) and to compare the connectivity patterns in patients with different clinical phenotypes.

METHODS

Fifty patients who underwent stereoelectroencephalography and CCEP procedures were included. Logistic regression was used in the model, and six CCEP metrics were input as features: root mean square of the first peak (N1RMS) and second peak (N2RMS), peak latency, onset latency, width duration, and area.

RESULTS

The area under the curve (AUC) for localizing the SOZ ranged from 0.88 to 0.93. The N1RMS values in the hippocampus sclerosis (HS) group were greater than that of the focal cortical dysplasia (FCD) IIa group (p < 0.001), independent of the distance between the recorded and stimulated sites. The sensitivity of localization was higher in the seizure-free group than in the non-seizure-free group (p = 0.036).

CONCLUSIONS

This new method can be used to predict the SOZ localization in various focal epilepsy phenotypes.

SIGNIFICANCE

This study proposed a machine-learning approach for localizing the SOZ. Moreover, we examined how clinical phenotypes impact large-scale abnormality of the epileptogenic networks.

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.

Spatial patterns of gray and white matter compromise relate to age of seizure onset in temporal lobe epilepsy

OBJECTIVE

Temporal Lobe Epilepsy (TLE) is frequently a neurodevelopmental disorder, involving subcortical volume loss, cortical atrophy, and white matter (WM) disruption. However, few studies have addressed how these pathological changes in TLE relate to one another. In this study, we investigate spatial patterns of gray and white matter degeneration in TLE and evaluate the hypothesis that the relationship among these patterns varies as a function of the age at which seizures begin.

METHODS

Eighty-two patients with TLE and 59 healthy controls were enrolled. T1-weighted images were used to obtain hippocampal volumes and cortical thickness estimates. Diffusion-weighted imaging was used to obtain fractional anisotropy (FA) and mean diffusivity (MD) of the superficial WM (SWM) and deep WM tracts. Analysis of covariance was used to examine patterns of WM and gray matter alterations in TLE relative to controls, controlling for age and sex. Sliding window correlations were then performed to examine the relationships between SWM degeneration, cortical thinning, and hippocampal atrophy across ages of seizure onset.

RESULTS

Cortical thinning in TLE followed a widespread, bilateral pattern that was pronounced in posterior centroparietal regions, whereas SWM and deep WM loss occurred mostly in ipsilateral, temporolimbic regions compared to controls. Window correlations revealed a relationship between hippocampal volume loss and whole brain SWM disruption in patients who developed epilepsy during childhood. On the other hand, in patients with adult-onset TLE, co-occurring cortical and SWM alterations were observed in the medial temporal lobe ipsilateral to the seizure focus.

SIGNIFICANCE

Our results suggest that although cortical, hippocampal and WM alterations appear spatially discordant at the group level, the relationship among these features depends on the age at which seizures begin. Whereas neurodevelopmental aspects of TLE may result in co-occurring WM and hippocampal degeneration near the epileptogenic zone, the onset of seizures in adulthood may set off a cascade of SWM microstructural loss and cortical atrophy of a neurodegenerative nature.

Multi-tensor diffusion abnormalities of gray matter in an animal model of cortical dysplasia

Focal cortical dysplasias are a type of malformations of cortical development that are a common cause of drug-resistant focal epilepsy. Surgical treatment is a viable option for some of these patients, with their outcome being highly related to complete surgical resection of lesions visible in magnetic resonance imaging (MRI). However, subtle lesions often go undetected on conventional imaging. Several methods to analyze MRI have been proposed, with the common goal of rendering subtle cortical lesions visible. However, most image-processing methods are targeted to detect the macroscopic characteristics of cortical dysplasias, which do not always correspond to the microstructural disarrangement of these cortical malformations. Quantitative analysis of diffusion-weighted MRI (dMRI) enables the inference of tissue characteristics, and novel methods provide valuable microstructural features of complex tissue, including gray matter. We investigated the ability of advanced dMRI descriptors to detect diffusion abnormalities in an animal model of cortical dysplasia. For this purpose, we induced cortical dysplasia in 18 animals that were scanned at 30 postnatal days (along with 19 control animals). We obtained multi-shell dMRI, to which we fitted single and multi-tensor representations. Quantitative dMRI parameters derived from these methods were queried using a curvilinear coordinate system to sample the cortical mantle, providing inter-subject anatomical correspondence. We found region- and layer-specific diffusion abnormalities in experimental animals. Moreover, we were able to distinguish diffusion abnormalities related to altered intra-cortical tangential fibers from those associated with radial cortical fibers. Histological examinations revealed myelo-architectural abnormalities that explain the alterations observed through dMRI. The methods for dMRI acquisition and analysis used here are available in clinical settings and our work shows their clinical relevance to detect subtle cortical dysplasias through analysis of their microstructural properties.