Jan Pukropski

Deep learning and ultra-high-field 7 Tesla (7T) MRI offer potential to improve detection of focal cortical dysplasia (FCD). However, existing models are predominantly trained on 3T MRI, and sufficiently large, annotated 7T datasets are not yet available. We evaluated a 3T-trained FCD detection model on paired 3T and 7T MRI acquired prospectively during presurgical assessment in 2023-2024. The cohort included 115 patients with epilepsy and 10 healthy controls, all undergoing T1-weighted and fluid-attenuated inversion recovery imaging at both field strengths. A publicly available 3D U-Net generated candidate lesions (>0.1 mL), which were compared with manual annotations and independently reviewed by two neuroradiologists and two epileptologists. Based on clinical MRI reports, patients were classified as MRI-suspected FCD (n=11), MRI-negative (n=64), or other lesion (n=40). In MRI-suspected FCD, sensitivity was 45% (5/11) at 7T versus 55% (6/11) at 3T. Among MRI-negative patients, diagnostic yield was 2% (1/64) at 7T versus 11% (7/64) at 3T. Across MRI-negative and other-lesion groups, the model generated eight false-positive candidate lesions at 7T versus 24 at 3T. Cross-field prediction concordance was 71% (5/7 locations) in MRI-suspected FCD, but only 26% (8/31) combined MRI-negative and other-lesion subgroups. Thus, a 3T-trained model can identify FCD on 7T MRI without retraining, but 7T MRI did not improve diagnostic yield. Domain adaptation and improved discrimination between FCD and non-FCD findings are needed before clinical deployment at 7T.

Objective: This study aims to report human performance in the detection of Focal Cortical Dysplasias (FCDs) using an openly available dataset. Additionally, it defines a subset of this data as a “difficult” test set to establish a public baseline benchmark against which new methods for automated FCD detection can be evaluated.Methods: The performance of 28 human readers with varying levels of expertise in detecting FCDs was originally analyzed using 146 subjects (not all of which are openly available), we analyzed the openly available subset of 85 cases. Performance was measured based on the overlap between predicted regions of interest (ROIs) and ground‐truth lesion masks, using the Dice‐Soerensen coefficient (DSC). The benchmark test set was chosen to consist of 15 subjects most predictive for human performance and 13 subjects identified by at most 3 of the 28 readers.Results: Expert readers achieved an average detection rate of 68%, compared to 45% for non‐experts and 27% for laypersons. Neuroradiologists detected the highest percentage of lesions (64%), while psychiatrists detected the least (34%). Neurosurgeons had the highest ROI sensitivity (0.70), and psychiatrists had the highest ROI precision (0.78). The benchmark test set revealed an expert detection rate of 49%.Significance: Reporting human performance in FCD detection provides a critical baseline for assessing the effectiveness of automated detection methods in a clinically relevant context. The defined benchmark test set serves as a useful indicator for evaluating advancements in computer‐aided FCD detection approaches.Plain Language Summary: Focal cortical dysplasias (FCDs) are malformations of cortical development and one of the most common causes of drug‐resistant focal epilepsy. Once found, FCDs can be neurosurgically resected, which leads to seizure freedom in many cases. However, FCDs are difficult to detect in the visual assessment of magnetic resonance imaging. A myriad of algorithms for automated FCD detection have been developed, but their true clinical value remains unclear since there is no benchmark dataset for evaluation and comparison to human performance. Here, we use human FCD detection performance to define a benchmark dataset with which new methods for automated detection can be evaluated.

The detection of transient peri‐ictal magnetic resonance imaging (MRI) abnormalities has been variable after epileptic seizures. The most common reason for this variability is that abnormalities may disappear if the interval between seizure and scan acquisition is prolonged using conventional high‐field systems. Here, we deployed a portable ultra‐low‐field MRI system in the presurgical evaluation at the bedside of individuals with epilepsy. We hypothesized that this novel technology enables rapid postictal scans and reliably shows focal peri‐ictal MRI abnormalities in the seizure onset zone. A .064‐T Swoop Portable MR Imaging System was used. Postictally, an axial diffusion‐weighted sequence was acquired. The interictal MRI consisted of the diffusion‐weighted and three‐dimensional T1‐weighted sequences. Postictal–interictal difference maps of diffusion‐weighted volumes were calculated. Three individuals were included. Two individuals with focal aware seizures scanned 29 s and 19 min after the seizure, respectively, showed focal restrictions in diffusivity in the seizure onset zone, and a third individual scanned 5 h 45 min after a focal to bilateral tonic–clonic seizure showed global restrictions of diffusivity. Portable ultra‐low‐field MRI opens a new line of inquiry with the aim to establish postictal seizure imaging as part of the presurgical evaluation of people with epilepsy.

Transcutaneous auricular vagus nerve stimulation (taVNS) is a novel non-invasive treatment option for different diseases and symptoms, such as epilepsy or depression. Its mechanism of action, however, is still not fully understood. We investigated short-term taVNS-induced changes of local and global properties of EEG-derived, evolving functional brain networks from eighteen subjects who underwent two 1 h stimulation phases (morning and afternoon) during continuous EEG-recording. In the majority of subjects, taVNS induced measurable modifications of network properties. Network alterations induced by stimulation in the afternoon were clearly more pronounced than those induced by stimulation in the morning. Alterations mostly affected the networks’ topology and stability properties. On the local network scale, no clear-cut spatial stimulation-related patterns could be discerned. Our findings indicate that the possible impact of diurnal influences on taVNS-induced network modifications would need to be considered for future research and clinical studies of this non-pharmaceutical intervention approach.