High density electric source imaging in childhood-onset epilepsy due to focal cortical dysplasia

Low-density scalp electrical source imaging of the ictal onset zone network using source coherence maps

Introduction

This study investigated low-density scalp electrical source imaging of the ictal onset zone and interictal spike ripple high-frequency oscillation networks using source coherence maps in the pediatric epilepsy surgical workup. Intracranial monitoring, the gold standard for determining epileptogenic zones, has limited spatial sampling. Source coherence analysis presents a promising new non-invasive technique.

Methods

This was a retrospective review of 12 patients who underwent focal resections. Source coherence maps were generated using standardized low-resolution electromagnetic tomography and concordance to resection margins was assessed, noting outcomes at 3 years post-surgery.

Results

Ictal source coherence maps were performed in 7/12 patients. Six of seven included the surgical resection. Five of seven cases were seizure free post-resection. Interictal spike ripple electrical source imaging and interictal spike ripple high-frequency oscillation networks using source coherence maps were performed for three cases, with two of three included in the resection and all three were seizure free.

Discussion

These findings may provide proof of principle supporting low-density scalp electrical source imaging of the ictal onset zone and spike ripple network using source coherence maps. This promising method is complementary to ictal and interictal electrical source imaging in the pediatric epilepsy surgical workup, guiding electrode placement for intracranial monitoring to identify the epileptogenic zone.

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.