Citations

Abstract Objective Anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) is an effective therapy for frontotemporal and limbic drug‐resistant focal epilepsies. The pulvinar nucleus (PUL) is an emerging neuromodulation target for seizure networks involving the posterior quadrant and temporal lobe. This study evaluates the safety and effectiveness of combined bilateral ANT plus PUL DBS for posterior quadrant and temporal lobe epilepsies. Methods We conducted a single‐center retrospective cohort study of patients receiving combined ANT/PUL DBS between 2021 and 2025. Outcomes included median seizure rate reduction (MSR), responder rate (RR; >50% seizure frequency reduction), and stimulation‐related side effects, assessed across ANT, PUL, and concurrent ANT/PUL stimulation settings. Acute surgical complications were evaluated. Statistical significance was assessed using two‐sided Wilcoxon signed‐rank tests with Holm–Bonferroni correction. Results Nine patients with combined ANT/PUL DBS were identified, and eight patients had sufficient follow‐up for inclusion (median follow‐up = 20 months). Posterior predominant structural abnormalities were present in eight of nine patients. There were no acute surgical complications. Three instances of transient stimulation‐related side effects resolved with parameter adjustments, whereas one patient required rescue antiseizure medication in addition to programming changes. Eight patients received periods of ANT stimulation, six PUL stimulation, and seven combined ANT/PUL stimulation. Regarding overall outcome, at last follow‐up, there was a significant 80.8% MSR ( p = .0078) and 87.5% RR, with two patients receiving ANT and six ANT/PUL stimulation. Regarding target‐specific MSR, concurrent ANT/PUL DBS (68.8% MSR) showed a trend toward superior performance relative to ANT (43.3%) and PUL DBS (17.5%) alone, although subgroup analysis of target‐specific performance did not reach statistical significance. Significance Combined four‐lead ANT/PUL DBS appears safe and effective for patients with drug‐resistant posterior quadrant and temporal lobe epilepsies. Dual‐network neuromodulation may provide greater flexibility to individually optimize neuromodulation for complex seizure networks.

Abstract White matter bundles play a crucial role in cognitive functions by rapidly transmitting information between brain regions. Inter-areal conduction guides the integration of information and conduction velocity is a fundamental parameter in theories and models of brain function. However, distributions of conduction velocity remain difficult to characterize in vivo in humans. In this work, we integrated diffusion magnetic resonance imaging (dMRI) tractography with intracranial electrical stimulation during clinical stereo-electroencephalography (sEEG) monitoring in 17 subjects to measure conduction velocity within four major white matter bundles. Our findings reveal that human brain conduction is characterized by high variability both within and between bundles, reflecting a predominance of slow connections alongside rare high-speed connections. Because conduction velocity in myelinated fibers follows an approximately linear relationship with axon diameter, we derive underlying axon diameter distributions and show that these estimates are comparable to previous post-mortem studies. These findings demonstrate a heavily skewed distribution of human neural conduction velocities and show that structural heterogeneity shapes the timing and integration of information in large-scale networks.

Importance Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C–related burden is critical for guiding prevention and treatment strategies. Objectives To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends. Design, Setting, and Population This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals. Exposure Population-level LDL-C concentrations. Main Outcomes and Measures Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C–attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals. Results In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C–attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings. Conclusions and Relevance Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

Vascular stents are crucial devices in the treatment of cardiovascular diseases, and their structural design and function critically affect therapeutic efficacy and patient prognosis. Conventional stents can effectively restore vascular patency by providing mechanical support to blood vessels. However, they still face significant challenges including restenosis, thrombosis, and limited adaptability to complex patient-specific lesion characteristics. To address these limitations, drug delivery offers an important strategy to modulate the pathological microenvironment, enhance long-term vascular healing, and reduce systemic side effects. Meanwhile, advances in computational simulations have provided powerful tools for optimizing stent design through structural mechanics, hemodynamics, and drug release modeling. Computational approaches enable the rational design of stent architectures with improved mechanical stability, vascular compatibility, and therapeutic regulation. Consequently, the development of vascular stents is evolving toward the synergistic integration of drug delivery, structural optimization, and intelligent design. This review summarizes the latest advances in functional vascular stents, clinical applications and computational design. This work aims to provide valuable insights for the engineering of efficient, precise, and intelligent vascular stents for cardiovascular therapies.

Objective Lennox–Gastaut syndrome (LGS) is a severe treatment‐resistant epilepsy. Although vagus nerve stimulation (VNS) and deep brain stimulation (DBS) are established therapies, comparative data and the impact of dual neuromodulation remain limited. We compared seizure outcomes across VNS, DBS, and combined DBS + VNS therapy in LGS. Methods This retrospective cohort study at Mayo Clinic (2005–2024) included LGS patients treated with VNS and/or DBS . Seizure frequency was assessed at 6, 12, and 24 months post‐stimulation. Responder was defined as 50% or more seizure reduction. Results Forty patients were included (42% female, median, 150 seizures/month). Eleven patients underwent sequential VNS followed by DBS and contributed data to 2 treatment epochs, yielding 3 treatment groups: VNS alone (n = 29), DBS alone (n = 12), and DBS + VNS (n = 10). At 24 months, median seizure reduction rate was comparable between VNS (50%; 95% confidence interval [ CI], 44–75%) and the overall DBS cohort (59%, 95% CI, 33–86%) ( p = 0.54). However, a significant difference emerged when comparing the 3 treatment arms ( p = 0.027). Specifically, DBS + VNS therapy yielded significantly greater median reduction rate (81%, 95% CI, 60–99%) than DBS alone (43%, 95% CI, 0–58%) ( p adj = 0.027) or VNS alone (50%, 95% CI, 44.4–75.0%) ( p adj = 0.048). Four patients attained sustained seizure freedom above 1 year at last follow‐up. Interpretation VNS and DBS provide comparable seizure reduction in LGS over a 2‐year follow‐up period. DBS + VNS was associated with further improvements in seizure reduction in this small, retrospective dataset. This possibility warrants further investigation. ANN NEUROL 2026

Abstract Prediction errors (PEs) drive perceptual learning by updating internal models of the sensory environment, yet it remains unclear how attention reshapes their representation across distributed thalamocortical circuits. Using intracranial stereoelectroencephalography (sEEG) from 17 patients performing a roving auditory oddball task under attended and unattended conditions, we quantified PE encoding using mutual information and co-information to capture redundant and synergistic PE representations. Attention modulated PE encoding in both the thalamus and the temporal cortex, but with distinct informational dynamics. Thalamic encoding showed a stable reduction of PE information during distraction, consistent with state-dependent thalamocortical gating. In contrast, the temporal cortex expressed two opposing learning trajectories during attended listening that converged once attention was diverted, revealing distinct cortical learning regimes rather than a uniform attentional effect. Attention further reorganized the informational content of cortical PE representations by altering the balance between redundant and synergistic information. A biologically constrained neural network showed that attention-dependent changes in inhibition and long-range connectivity reproduced these dynamics through Hebbian learning. Together, these findings suggest that attention regulates predictive learning not simply by changing the strength of PE responses, but by reshaping how distributed thalamocortical circuits represent and integrate sensory evidence over time.