Ge Tan

Objective: Epilepsy is associated with increased risk of excess mortality compared to the general population. Identifying causes of death is critical for guiding prevention strategies. Earlier studies in resource‐limited settings suggested that accidental deaths were the main causes, but whether the mortality pattern has changed over time remains unclear. This study aimed to update the knowledge on mortality and causes of death of people with epilepsy in rural China. Methods: In this nationwide cohort study, we analyzed data from the Epilepsy Prevention and Management Project in rural China between January 1, 2018, and December 31, 2020. Mortality outcomes included all‐cause mortality, causes of death, and standardized mortality ratios (SMRs). Kaplan–Meier, multivariable Cox, and restricted cubic spline analyses were used to assess mortality risk and associated factors. Results: Among 17 515 participants followed for 25 922 person‐years, 381 deaths were recorded, yielding an all‐cause mortality rate of 14.7 per 1000 person‐years. The leading causes of death were circulatory system diseases (47.8%), including cerebrovascular disease (28.9%), and heart disease (18.9%). The all‐cause SMR was 3.51 (95% confidence interval = 3.33–3.70). The highest cause‐specific excess mortality was observed for brain malignancies (SMR = 20.30). Male sex, older age at epilepsy onset, longer disease duration, and higher baseline seizure frequency were independently associated with mortality. Significance: People with epilepsy in rural China continue to experience substantial and potentially preventable excess mortality. As mortality patterns shift, prevention strategies should extend beyond seizure control and injury prevention to include vascular risk management and timely neuroimaging for suspected structural lesions. Prospective studies are needed to determine whether these strategies improve survival.

Objective: This study was undertaken to examine the associations between rapid eye movement (REM) sleep proportion, depressive and anxiety symptoms, and quality of life (QOL) in newly diagnosed epilepsy (NDE). Methods: A total of 104 treatment‐naive people with NDE and 50 age−/gender‐matched healthy controls were enrolled. Assessments included polysomnography and the Chinese version of the Neurological Disorders Depression Inventory for Epilepsy (C‐NDDI‐E), Generalized Anxiety Disorder‐7, and Quality of Life in Epilepsy Inventory‐31 (QOLIE‐31). Eighty‐eight patients completed 12‐month QOL follow‐up. Cross‐sectional analyses included Spearman correlation and mediation (PROCESS Model 4, 5000 bootstraps). Longitudinal analyses used logistic and linear regression with sequentially added covariates, and least absolute shrinkage and selection operator for variable selection. Results: Baseline REM sleep correlated negatively with C‐NDDI‐E ( r _s = −.512, p < .001) and positively with QOLIE‐31 ( r _s = .394, p < .001). C‐NDDI‐E correlated negatively with QOLIE‐31 ( r _s = −.461, p < .001). Depressive symptoms partially mediated the REM–QOL association (indirect effect = .253, 52.5% of total effect). Anxiety did not mediate REM–QOL but partially mediated REM–depression. Longitudinally, the REM–12‐month QOL association was significant after adjusting for age and gender (odds ratio [OR] = 1.113, p = .005), but not after additionally adjusting for baseline C‐NDDI‐E (OR = .968, p = .542). No independent linear association remained ( B = .087, 95% confidence interval = –.119 to .292, p = .405). Significance: Reduced REM sleep is associated with increased depressive severity in NDE. Depressive symptoms mediate both cross‐sectional and prospective REM–QOL associations, with little independent effect of REM sleep on QOL. Anxiety does not directly mediate REM–QOL but partially mediates REM–depression.

This study intents to detect graphical network features associated with seizure relapse following antiseizure medication (ASM) withdrawal. Twenty-four patients remaining seizure-free (SF-group) and 22 experiencing seizure relapse (SR-group) following ASM withdrawal as well as 46 matched healthy participants (Control) were included. Individualized morphological similarity network was constructed using T1-weighted images, and graphic metrics were compared between groups. Relative to the Control, the SF-group exhibited lower local efficiency, while the SR-group displayed lower global and local efficiency and longer characteristic path length. Both patient groups displayed reduced centrality in certain subcortical and cortical nodes than the Control, with a more pronounced reduction in the SR-group. Additionally, the SR-group exhibited lower centrality of the right pallidum than the SF-group. Decreased subcortical-cortical connectivity was found in both patient groups than the Control, with a more extensive decrease in the SR-group. Furthermore, an edge connecting the right pallidum and left middle temporal gyrus exhibited decreased connectivity in the SR-group than in the SF-group. A weaker small-worldization network upon medication withdrawal, potentially underpinned by node decentralization and subcortical-cortical decoupling, may elevate the risk of seizure relapse.

Objective To characterize the features of thalamocortical functional connectivity during seizure recurrence at the time of antiseizure medication (ASM) withdrawal. Methods Patients with chronic epilepsy who attempted to discontinue medications were prospectively registered and followed up; 19 patients remained seizure-free (SF-group), 18 patients had seizure relapses (SR-group) after ASM withdrawal, and 28 healthy controls were recruited. Resting-state functional magnetic resonance imaging was performed before ASM withdrawal. Thalamus subdivisions were set as seeds to calculate voxelwise functional connectivity. Partial correlation analysis between functional connectivity and clinical variables was performed. A support vector machine was used to assess the predictive ability of the specific functional connectivity for seizure relapse. Results The within-group comparison indicated that the SR-group had more extensive functional connectivity than the SF-group; the left inferior pulvinar, left medial pulvinar, and right anterior pulvinar showed a significantly stronger functional connection with the precuneus in the SR-group than in the SF-group (Gaussian random field correction, voxel-level p < .001 and cluster-level p < .05). In the SR-group, a positive correlation was found between the left inferior pulvinar–precuneus connectivity and the active period (r = .46, p = .05), seizure-free period (r = .67, p = .002), and disease duration (r = .53, p = .02), and between the left medial pulvinar–precuneus connectivity and the seizure-free period (r = .58, p = .01). The combination of these thalamocortical connections showed a high predictive ability, with an area under the curve of .92 and accuracy of .90 (p = .01). Significance This study determined distinct features of thalamocortical functional connectivity at the time of ASM withdrawal in patients with and without seizure relapse, showing a potential for predicting seizure outcomes following ASM withdrawal.

Purpose We aimed to analyze the characteristics of brain function and microstructure linked to epilepsy relapse after drug withdrawal in patients with focal epilepsy. Methods Resting-state functional magnetic resonance imaging and high-resolution T1-weighted images were acquired within 1 month prior to drug withdrawal from 15 patients who did not have epilepsy relapse (PER − group) and 16 patients who subsequently had epilepsy relapse (PER + group). Additionally, 23 healthy participants undergoing the same scanning protocol were included as controls. Fractional amplitude of low-frequency fluctuation (fALFF) and gray matter density (GMD) were compared among groups. Subgroup and correlation analyses were also performed. Results There were no significant differences in fALFF between patient groups, but the PER + group showed lower GMD in the bilateral calcarine, left precuneus, and right superior temporal gyrus than the PER − group (Gaussian random field correction, voxel-level P < 0.001 and cluster-level P < 0.05). Both increased seizure number and polytherapy were associated with lower GMD; also, patients using other antiseizure medications showed lower GMD than those using only levetiracetam (Gaussian random field correction, voxel-level P < 0.001, and cluster-level P < 0.05). The active period and disease duration showed both positive and negative correlations with GMD, while the seizure-free period mainly showed positive correlations with GMD (uncorrected, P < 0.001). Conclusion Gray matter microstructure, but not local functional activity, showed distinct characteristics between patients with and without epilepsy relapse and may serve as a potential biomarker for predicting seizure recurrence upon drug withdrawal.