While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.

Epilepsy affects over 50 million people worldwide. Drug-resistant epilepsy (DRE) accounts for up to a third of these cases, and neuro-inflammation is thought to play a role in such cases. Despite being a long-debated issue in the field of DRE, the mechanisms underlying neuroinflammation have yet to be fully elucidated. The pro-inflammatory microenvironment within the brain tissue of people with DRE has been probed using single-cell multimodal transcriptomics. Evidence suggests that inflammatory cells and pro-inflammatory cytokines in the nervous system can lead to extensive biochemical changes, such as connexin hemichannel excitability and disruption of neurotransmitter homeostasis. The presence of inflammation may give rise to neuronal network abnormalities that suppress endogenous antiepileptic systems. We focus on the role of neuroinflammation and brain network anomalies in DRE from multiple perspectives to identify critical points for clinical application. We hope to provide an insightful overview to advance the quest for better DRE treatments.

Background: Temporal lobe epilepsy (TLE) is among the most common types of epilepsy and often leads to cognitive, emotional, and psychiatric issues due to the frequent seizures. A notable pathological change related to TLE is hippocampal sclerosis (HS), which is characterized by neuronal loss, gliosis, and an increased neuron fibre density. The mechanisms underlying TLE-HS development remain unclear, but the reactive transcriptomic changes in glial cells and neurons of the hippocampus post-epileptogenesis may provide insights. Methods: To induce TLE, 200 nl of kainic acid (KA) was stereotactically injected into the hippocampal CA1 region of mice, followed by a 7-day postinjection period. Single-cell RNA sequencing (ScRNA-seq), single-nucleus RNA sequencing (SnRNA-seq), and Xenium-based spatial transcriptomics analyses were employed to evaluate the changes in mRNA expression in glial cells and neurons. Results: From the ScRNA-seq and SnRNA-seq data, 31,390 glial cells and 48,221 neuronal nuclei were identified. Analysis of the differentially expressed genes (DEGs) revealed significant transcriptomic alterations in the hippocampal cells of mice with TLE, affecting hundreds to thousands of mRNAs and their signalling pathways. Enrichment analysis indicated notable activation of stress and inflammatory pathways in the TLE hippocampus, while pathways related to axonal development and neural support were suppressed. Xenium analysis demonstrated the expression of all 247 genes across mouse brain sections, revealing the spatial distributions of their expression in 27 cell types. Integrated analysis of the DEGs identified via the three sequencing techniques revealed that Spp1, Trem2, and Cd68 were upregulated in all glial cell types and in the Xenium data; Penk, Sorcs3, and Plekha2 were upregulated in all neuronal cell types and in the Xenium data; and Tle4 and Sipa1l3 were downregulated in all glial cell types and in the Xenium data. Conclusion: In this study, a high-resolution single-cell transcriptomic atlas of the hippocampus in mice with TLE was established, revealing potential intrinsic mechanisms driving TLE-associated inflammatory activation and altered cell interactions. These findings provide valuable insights for further exploration of HS development and epileptogenesis.

Objective: Stereo‐electroencephalography‐guided three‐dimensional radiofrequency thermocoagulation (SEEG‐3D RFTC) is a minimally invasive treatment for mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE‐HS). This study aimed to investigate the long‐term prognosis after SEEG‐3D RFTC treatment in patients with MTLE‐HS.Methods: This single‐center retrospective study included 28 patients with MTLE‐HS treated with SEEG‐3D RFTC from January 2016 to May 2018. Postoperative curative effects were evaluated using the Engel classification, and the patients were followed up for 5 years.Results: The proportions of patients categorized as Engel I between 1 and 5 years after surgery were 72.41% (12 months after surgery), 67.86% (18 months after surgery), 62.07% (24 months after surgery), 50.00% (36 months after surgery), 42.86% (48 months after surgery), and 42.86% (60 months after surgery), respectively. Regarding long‐term efficacy, based on the Engel classification, SEEG‐3D RFTC showed room for improvement.Significance: This was the first study to evaluate the efficacy of SEEG‐3D RFTC for MTLE‐HS with long‐term follow‐up. SEEG‐3D RFTC is a promising alternative for patients with MTLE‐HS.Plain Language Summary: This study explored the potential of stereoelectroencephalography‐guided three‐dimensional radiofrequency thermocoagulation, a minimally invasive approach, for treating medial temporal lobe epilepsy with hippocampal sclerosis. Involving 28 patients, the research tracked the treatment’s success over five years using the Engel classification. Initial results were promising, with 72.41% of patients achieving the most favorable outcome (Engel I) at one year. While there was a gradual decrease in this proportion over time, 42.86% of patients maintained this positive outcome at five years, highlighting the treatment's potential for long‐term efficacy.

Successful surgery on drug-resistant epilepsy patients (DRE) needs precise localization of the seizure onset zone (SOZ). Previous studies analyzing this issue still face limitations, such as inadequate analysis of features, low sensitivity and limited generality. Our study proposed an innovative and effective SOZ localization method based on multiple epileptogenic biomarkers (spike and HFOs), and analysis of single-contact (MEBM-SC) to address the above problems. We extracted contacts epileptic features from signal distributions and signal energy based on machine learning and end-to-end deep learning. Among them, a normalized pathological ripple rate was designed to reduce the disturbance of physiological ripple and enhance the performance of SOZ localization. Then, a feature selection algorithm based on Shapley value and hypothetical testing (ShapHT+) was used to limit interference from irrelevant features. Moreover, an attention mechanism and a focal loss algorithm were used on the classifier to learn significant features and overcome the unbalance of SOZ/nSOZ contacts. Finally, we provided an SOZ prediction and visualization on magnetic resonance imaging (MRI). Ten patients with DRE were selected to verify our method. The experiment performed cross-validation and revealed that MEBM-SC obtains higher sensitivity. Additionally, the spike has better sensitivity while HFOs have better specificity, and the combination of these biomarkers can achieve the best performance. The study confirmed that MEBM-SC can increase the sensitivity and accuracy of SOZ localization and help clinicians to perform a precise and reliable preoperative evaluation based on interictal SEEG.