Multiparametric Characterization of Focal Cortical Dysplasia Using 3D MR Fingerprinting.

OBJECTIVES: To develop a multiparametric machine-learning (ML) framework using high-resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD). MATERIALS: We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age- and gender-matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole-brain 3 Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z-score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual-level classification. Fivefold cross-validation was performed and performance metrics were reported using receiver-operating-characteristic-curve analyses. RESULTS: FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF-ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross-validation trials for the magnetic resonance imaging negative group and MAP negative group. Type II versus non-type-II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7/12) of the IIb cases. INTERPRETATION: The MRF-ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024.

Neuropathology and epilepsy surgery - 2024 update.

Neuropathology-based studies in neurosurgically resected brain tissue obtained from carefully examined patients with focal epilepsies remain a treasure box for excellent insights into human neuroscience, including avenues to better understand the neurobiology of human brain organization and neuronal hyperexcitability at the cellular level including glio-neuronal interaction. It also allows to translate results from animal models in order to develop personalized treatment strategies in the near future. A nice example of this is the discovery of a new disease entity in 2017, termed mild malformation of cortical development with oligodendroglial hyperplasia in epilepsy or MOGHE, in the frontal lobe of young children with intractable seizures. In 2021, a brain somatic missense mutation of the galactose transporter SLC35A2 leading to altered glycosylation of lipoproteins in the Golgi apparatus was detected in 50 % of MOGHE samples. In 2023, the first clinical trial evaluated galactose supplementation in patients with histopathologically confirmed MOGHE carrying brain somatic SLC35A2 mutations that were not seizure free after surgery. The promising results of this pilot trial are an example of personalized medicine in the arena of epileptology. Besides this, neuropathological studies of epilepsy samples have revealed many other fascinating results for the main disease categories in focal epilepsies, such as the first deep-learning based classifier for Focal Cortical Dysplasia, or the genomic landscape of cortical malformations showing new candidate genes such as PTPN11, which is associated with ganglioglioma and adverse clinical outcome. This update will also ask why common pathogenic variants accumulate in certain brain regions, e.g., MTOR in the frontal lobe, and BRAF in the temporal lobe. Finally, I will highlight the ongoing discussion addressing commonalities between temporal lobe epilepsy and Alzheimer's disease, the impact of adult neurogenesis and gliogenesis for the initiation and progression of temporal lobe seizures in the human brain as well as the immunopathogenesis of glutamic acid decarboxylase antibody associated temporal lobe epilepsy as a meaningful disease entity. This review will update the reader on some of these fascinating publications from 2022 and 2023 which were selected carefully, yet subjectively, by the author.

Combining magnetic resonance fingerprinting with voxel-based morphometric analysis to reduce false positives for focal cortical dysplasia detection.

OBJECTIVE: We aim to improve focal cortical dysplasia (FCD) detection by combining high-resolution, three-dimensional (3D) magnetic resonance fingerprinting (MRF) with voxel-based morphometric magnetic resonance imaging (MRI) analysis. METHODS: We included 37 patients with pharmacoresistant focal epilepsy and FCD (10 IIa, 15 IIb, 10 mild Malformation of Cortical Development [mMCD], and 2 mMCD with oligodendroglial hyperplasia and epilepsy [MOGHE]). Fifty-nine healthy controls (HCs) were also included. 3D lesion labels were manually created. Whole-brain MRF scans were obtained with 1 mm3 isotropic resolution, from which quantitative T1 and T2 maps were reconstructed. Voxel-based MRI postprocessing, implemented with the morphometric analysis program (MAP18), was performed for FCD detection using clinical T1w images, outputting clusters with voxel-wise lesion probabilities. Average MRF T1 and T2 were calculated in each cluster from MAP18 output for gray matter (GM) and white matter (WM) separately. Normalized MRF T1 and T2 were calculated by z-scores using HCs. Clusters that overlapped with the lesion labels were considered true positives (TPs); clusters with no overlap were considered false positives (FPs). Two-sample t-tests were performed to compare MRF measures between TP/FP clusters. A neural network model was trained using MRF values and cluster volume to distinguish TP/FP clusters. Ten-fold cross-validation was used to evaluate model performance at the cluster level. Leave-one-patient-out cross-validation was used to evaluate performance at the patient level. RESULTS: MRF metrics were significantly higher in TP than FP clusters, including GM T1, normalized WM T1, and normalized WM T2. The neural network model with normalized MRF measures and cluster volume as input achieved mean area under the curve (AUC) of .83, sensitivity of 82.1%, and specificity of 71.7%. This model showed superior performance over direct thresholding of MAP18 FCD probability map at both the cluster and patient levels, eliminating ≥75% FP clusters in 30% of patients and ≥50% of FP clusters in 91% of patients. SIGNIFICANCE: This pilot study suggests the efficacy of MRF for reducing FPs in FCD detection, due to its quantitative values reflecting in vivo pathological changes. © 2024 International League Against Epilepsy.

Brain expression profiles of two SCN1A antisense RNAs in children and adolescents with epilepsy.

Objective: Heterozygous mutations within the voltage-gated sodium channel α subunit (SCN1A) are responsible for the majority of cases of Dravet syndrome (DS), a severe developmental and epileptic encephalopathy. Development of novel therapeutic approaches is mandatory in order to directly target the molecular consequences of the genetic defect. The aim of the present study was to investigate whether cis-acting long non-coding RNAs (lncRNAs) of SCN1A are expressed in brain specimens of children and adolescent with epilepsy as these molecules comprise possible targets for precision-based therapy approaches. Methods: We investigated SCN1A mRNA expression and expression of two SCN1A related antisense RNAs in brain tissues in different age groups of pediatric non-Dravet patients who underwent surgery for drug resistant epilepsy. The effect of different antisense oligonucleotides (ASOs) directed against SCN1A specific antisense RNAs on SCN1A expression was tested. Results: The SCN1A related antisense RNAs SCN1A-dsAS (downstream antisense, RefSeq identifier: NR_110598) and SCN1A-usAS (upstream AS, SCN1A-AS, RefSeq identifier: NR_110260) were widely expressed in the brain of pediatric patients. Expression patterns revealed a negative correlation of SCN1A-dsAS and a positive correlation of lncRNA SCN1A-usAS with SCN1A mRNA expression. Transfection of SK-N-AS cells with an ASO targeted against SCN1A-dsAS was associated with a significant enhancement of SCN1A mRNA expression and reduction in SCN1A-dsAS transcripts. Conclusion: These findings support the role of SCN1A-dsAS in the suppression of SCN1A mRNA generation. Considering the haploinsufficiency in genetic SCN1A related DS, SCN1A-dsAS is an interesting target candidate for the development of ASOs (AntagoNATs) based precision medicine therapeutic approaches aiming to enhance SCN1A expression in DS.