Paediatric drug-resistant epilepsy (DRE) is associated with IQ deficits only partially explained by clinical seizure variables (for example, age of onset, seizure burden, seizure location). Given that intelligence depends on efficient large-scale brain networks, structural connectomics provides a complementary mechanistic framework for explaining residual IQ variance. We tested whether brain-network architecture explains additional IQ variance using global and network-averaged graph-theoretic metrics derived from streamline-count weighted diffusion MRI connectomes. Seventy-one children with DRE (50 focal epilepsy; 21 multifocal epilepsy) and 15 control participants underwent diffusion and T1-weighted MRI. For each participant, a 253 × 253 structural connectome was constructed, edge weights were defined as the number of streamlines, and graph metrics were computed using the Brain Connectivity Toolbox. Nodal metrics were averaged within Yeo's seven functional networks plus a Subcortical network. Associations with IQ were examined using correlations and general linear models (single-network and eight-predictor models). Finally, mediation analyses tested whether network metrics explained IQ differences between controls and children with DRE. Global metrics were not associated with IQ (all p > 0.05). Regionally, higher Salience-network betweenness centrality showed a nominal negative association with IQ in the multifocal subgroup (p = 0.040*, adjusted R2 = 0.142). In the eight-network GLM, higher nodal efficiency within the Default Mode Network (DMN) was positively associated with IQ (B = 122.406, p = .013), whereas higher nodal efficiency within the Subcortical Network was negatively associated with IQ (B = -51.942, p = .012). These regional associations did not survive Bonferroni correction. Exploratory mediation analyses suggested that opposing DMN and Subcortical Network effects partially accounted for the observed group difference in IQ. These findings are hypothesis-generating, as the regional associations with IQ were nominal and did not survive family-wise correction. Nevertheless, the mediation effects survived correction, highlighting opposing regional, rather than global, network alterations as candidate correlates of IQ variability in paediatric DRE.
Prithviraj Tawde, D. Velanoski, R. Piper et al.· Epilepsy & Behavior· 0 citations
Background: Hippocampal sclerosis (HS) is a common cause of drug-resistant focal epilepsy (DRFE) and amenable to neurosurgical treatment. Detection relies on MRI but can be challenging. 7 Tesla (T) ultra-high field MRI and automated MRI post-processing tools have independently been shown to improve radiological diagnosis of HS. However, combining these approaches remains underexplored. This study evaluated whether AID-HS, a tool for HS detection developed using 3T MRI, generalises to 7T MRI data. Methods: We collated a dataset of paired 3T and 7T T1-weighted MRI from four epilepsy centres, including 23 patients with HS, 39 healthy controls, and 23 individuals with focal cortical dysplasia as disease controls. Histopathology served as the gold standard for defining HS where available (n=7), otherwise radiological findings (n=16). AID-HS was applied to images acquired at both field strengths, and sensitivity and specificity for detection and lateralisation of HS were compared. Additionally, agreement of hippocampal features across 3T and 7T was evaluated. Results: We found no evidence of a difference in performance of AID-HS between 3T and 7T. Sensitivity for detection of unilateral HS was 63% (12/19) at 3T and 68% (13/19) at 7T (McNemar's exact test p=1.0). Specificity in controls was 97% (60/62) at 3T and 100% (62/62) at 7T (p=0.5). Bilateral HS was correctly flagged in 3 of 4 cases using feature-based criteria, with high specificity in controls. Quantitative hippocampal features showed moderate to good agreement across field strengths (ICC 0.70 to 0.98), with small differences observed for volume and thickness estimates. Conclusion: AID-HS provides robust detection and lateralisation of HS across multiple 7T MRI centres, highlighting its potential to enhance lesion detection. Future work is needed to investigate whether models trained on 7T data can leverage the improved image quality for further gains in HS detection performance.
C. Kronlage, M. Ripart, R. Piper et al.· medRxiv· 0 citations
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