Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy

ObjectiveMesial temporal lobe epilepsy (mTLE) is a complex neurological disorder that has been recognized as a widespread global network disorder. The group-level structural covariance network (SCN) could reveal the structural connectivity disruption of the mTLE but could not reflect the heterogeneity at the individual level.MethodsThis study adopted a recently proposed individual structural covariance network (IDSCN) method to clarify the alternated structural covariance connection mode in mTLE and to associate IDSCN features with the clinical manifestations and regional brain atrophy.ResultsWe found significant IDSCN abnormalities in the ipsilesional hippocampus, ipsilesional precentral gyrus, bilateral caudate, and putamen in mTLE patients than in healthy controls. Moreover, the IDSCNs of these areas were positively correlated with the gray matter atrophy rate. Finally, we identified several connectivities with weak associations with disease duration, frequency, and surgery outcome.SignificanceOur research highlights the role of hippo-thalamic-basal-cortical circuits in the pathophysiologic process of disrupted whole-brain morphological covariance networks in mTLE, and builds a bridge between brain-wide covariance network changes and regional brain atrophy

Thyroid function and epilepsy: a two-sample Mendelian randomization study

BackgroundThyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.MethodsGenome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran’s Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.ResultsThe findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.ConclusionThis study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study

Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model

IntroductionAccurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the “OFF” and “ON” status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms.MethodsWe employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features.ResultsA total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686–0.9870; accuracy = 0.8421, 95% CI 0.6875–0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons.ConclusionThe symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the “ON” or “OFF” status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD

A bibliometric and knowledge-map analysis of the glymphatic system from 2012 to 2022

ObjectiveTo explore the development context, research hotspots and frontiers in the glymphatic system (GS) field from 2012 to 2022 by bibliometric analysis.MethodsThe Web of Science Core Collection (WoSCC) database was searched for articles published between 2012 and 2022. Microsoft Excel was used to manage the data. VOSviewer, CiteSpace, GraphPad Prism, the Web of Science, and an online analysis platform for bibliometrics (http://bibliometric.com/) were used to analyze the countries, institutions, journals, and collaboration networks among authors and the types of articles, developmental directions, references, and top keywords of published articles.ResultsA total of 412 articles were retrieved, including 39 countries/regions, 223 research institutes and 171 academic journals. The subject classifications related to the GS were Neuroscience, Clinical Neuroscience and Radiology/Nuclear Medicine/Medical Imaging. The United States has maintained its dominant and most influential position in GS research. Among research institutions and journals, the Univ Rochester and Journal of Cerebral Blood Flow and Metabolism had the highest number of academic articles, respectively. Nedergaard M had the most published article, and Iliff JJ had the most co-citations. The top two keywords with the highest frequency were “glymphatic system” and “cerebrospinal fluid.”ConclusionThis research provides valuable information for the study of the GS. The bibliometric analysis of this area will encourage potential collaborations among researchers, defining its frontiers and directions for development

Long‐term follow‐up seizure outcomes after corpus callosotomy: A systematic review with meta‐analysis

Abstract Background Corpus callosotomy (CC) is appropriate for patients with seizures of a bilateral or diffuse origin, or those with seizures of a unilateral origin with rapid spread to the contralateral cerebral hemisphere. The efficiency of CC in patients with drug‐resistant epilepsy is a long‐term concern because most articles reporting the surgical results of CC arise from small case series, and the durations of follow‐up vary. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched to identify papers published before November 8, 2021. The systematic review was completed following PRISMA guidelines. Outcomes were analyzed by meta‐analysis of the proportions. Results A total of 1644 patients with drug‐resistant epilepsy (49 retrospective or prospective case series studies) underwent CC, and the follow‐up time of all patients was at least 1 year. The rate of complete seizure freedom (SF) was 12.38% (95% confidence interval [CI], 8.17%–17.21%). Meanwhile, the rate of complete SF from drop attacks was 61.86% (95% CI, 51.87%–71.41%). The rates of complete SF after total corpus callosotomy (TCC) and anterior corpus callosotomy (ACC) were 11.41% (95% CI, 5.33%–18.91%) and 6.75% (95% CI, 2.76%–11.85%), respectively. Additionally, the rate of complete SF from drop attacks after TCC was significantly higher than that after ACC (71.52%, 95% CI, 54.22%–86.35% vs. 57.11%, 95% CI, 42.17%–71.49%). The quality of evidence for the three outcomes by GRADE assessment was low to moderate. Conclusion There was no significant difference in the rate of complete SF between TCC and ACC. TCC had a significantly higher rate of complete SF from drop attacks than did ACC. Furthermore, CC for the treatment of drug‐resistant epilepsy remains an important problem for further investigation because there are no universally accepted standardized guidelines for the extent of CC and its benefit to patients. In future research, we will focus on this issue

Comparative Efficacy of Surgical and Neuromodulatory Strategies for Drug-Resistant Epilepsy: A Systematic Review and Meta-Analysis

Objective: To evaluate the efficacy of neuromodulatory strategies for people who have drugresistant epilepsy (DRE). Methods: We searched electronic repositories, including PubMed, Web of Science, Embase, and the Cochrane Library, for randomised controlled trials, their ensuing open-label extension studies, and prospective studies focusing on surgical or neuromodulation interventions for people with DRE. We used seizure frequency reduction as the primary outcome. A single-arm meta-analysis synthesised data across all studies to assess treatment effectiveness at multiple time points. A network meta-analysis evaluated the efficacy of diverse therapies in RCTs. GRADE was applied to evaluate the overall quality of the evidence. Results: Twenty-eight studies representing 2936 individuals underwent ten treatments were included. Based on the cumulative ranking in the network meta-analysis, the top three neuromodulatory options were deep brain stimulation (DBS) with 27% probability, responsive neurostimulation (RNS) with 22.91%, and transcranial direct current stimulation with 24.31%. In the single-arm meta-analysis, in the short-to-medium term, seizure control is more effective with RNS than with vagus nerve stimulation (inVNS), which in turn is slightly more effective than DBS, though the differences are minimal. However, in the long term, inVNS appears to be less effective than DBS and RNS. Trigeminal nerve stimulation, transcranial magnetic stimulation, and transcranial alternating current stimulation did not demonstrate significant seizure frequency reduction. Conclusions: Regarding long-term efficacy, RNS and DBS outperformed inVNS. While transcranial direct current stimulation and transcutaneous auricular VNS showed promise for treating DRE, further studies are needed to confirm their long-term efficacy

Automatic Localization of Seizure Onset Zone Based on Multi-Epileptogenic Biomarkers Analysis of Single-Contact from Interictal SEEG

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