Rationale: Obstructive sleep apnea (OSA) may impair perivascular diffusivity, a proposed glymphatic biomarker, contributing to cognitive decline. Objectives: To investigate the longitudinal impact of OSA on cognitive performance and perivascular diffusivity measured by diffusion tensor imaging along the perivascular space (DTI-ALPS) and whether changes in diffusivity mediate the relationship between OSA and cognition. Methods: In a community-based cohort, 1,110 participants underwent polysomnography, brain DTI, and cognitive assessments at baseline and 4-year follow-up. Participants were categorized by apnea–hypopnea index (AHI) at baseline and further grouped longitudinally as OSA-free, improved OSA, stable OSA, or worsened OSA. DTI-ALPS index was calculated at both time points to assess perivascular diffusivity. Multivariable models were adjusted for age, sex, education, body mass index, smoking, alcohol use, hypertension, diabetes, total sleep time, and sleep efficiency. Longitudinal models (i.e., Δ-values) also included baseline outcome values. Measurements and Main Results: Higher AHI was consistently associated with lower DTI-ALPS index. Each 1–event-per-hour increase in AHI corresponded to a 0.008 decrease in DTI-ALPS index at baseline and a 0.007 decrease over 4 years (both P < 0.001), with the worsened OSA group showing the greatest reduction. Increases in DTI-ALPS index were associated with improvements in visual memory, including immediate recall (β = 0.401), delayed recall (β = 0.351), and recognition (β = 0.145; all P < 0.001). ΔDTI-ALPS mediated the relationship between ΔAHI and changes in immediate visual recall (β = −0.015), delayed recall (β = −0.021), and recognition (β = −0.016; all P < 0.001). Conclusions: OSA severity was associated with altered perivascular diffusivity and related changes in memory, suggesting a potential pathway linking OSA to cognitive impairment via perivascular fluid dynamics.

An acute stress response is a complex process that activates the neuroendocrine and metabolic systems for homeostasis. A study on acute stress is important to understand how an organism adapts to stress for survival. However, most studies have focused on chronic stress, and there are few studies on acute stress. They have analyzed the metabolic alterations in the brain at a particular time after acute stress. This study explored the temporal variations of the brain metabolites in the hippocampus after acute restraint stress using proton magnetic resonance spectroscopy. All mice in the acute stress group were physically restrained for two hours in a 50 mL conical tube. A 9.4 T animal MRI and MRS scanner was used with point-resolved spectroscopy technique for data acquisition, which was repeated four times without interscan interval. Metabolites were quantified from the data using LCModel with a simulated basis set. Based on the change in concentration of metabolites, the data were statistically analyzed using two-way repeated-measures analysis of variance between groups and a support vector machine for all time points and Student’s t-test with FDR correction for each time point. The present study found that the differences between groups are significantly ( ) presented in alanine and glutamate. The effect of time of the two metabolites significantly exists ( ): the first, second, and third time points in alanine and the first and second time points in glutamate. A combination of stress-specific metabolites (alanine, glutamate, N-acetyl-aspartate) that best reflect the influence of acute stress was determined using a support vector machine. These findings may indicate the importance of the timing of analysis after acute stress and provide new insights into a deeper understanding of acute stress response, including the molecular mechanism of stress-related disorders and stress resilience or vulnerability.

Background/objectives: The acute stress response affects brain metabolites closely linked to the tricarboxylic acid (TCA) cycle. This response involves time-dependent changes in hormones and neurotransmitters, which contribute to resilience and the ability to adapt to acute stress while maintaining homeostasis. This physiological mechanism of metabolic dynamics, combined with time-series analysis, has prompted the development of new methods to observe the relationship between TCA cycle-related brain metabolites. This study aimed to observe the acute stress response through metabolic interactions using time-series proton magnetic resonance spectroscopy (1H-MRS) in the left hippocampus of mice. Methods: In this study, 4-week-old male C57BL/6N mice (n = 24) were divided into control (n = 12) and acute stress groups (n = 12). Acute stress was induced through a 2 h restraint protocol. Time-series 1H-MRS data were obtained on the left hippocampus of both groups using a 9.4 T 1H-MRS scanner. Time-series MRS data were quantified using LCModel, and significant metabolic interactions were identified through Spearman correlation analysis, a one-tailed sign test, and false discovery rate correction. Results: No significant metabolic correlation coefficient was observed in the control group. However, in the acute stress group, glutathione (GSH) and N-acetyl aspartate (NAA) showed a significant positive correlation over time, with a high correlation coefficient exceeding 0.5. Conclusions: Temporal measurement of GSH and NAA, combined with correlation analysis, offers a comprehensive understanding for the metabolic dynamics during acute stress. This approach emphasizes their distinct roles and interdependence in the progression of oxidative stress, mitochondrial function, and the maintenance of physiological homeostasis.

Background: Diverse factors including seizure onset age, seizure frequency, epilepsy duration, total number of antiseizure medications trialed are considered as seizures-related neurocognitive loads in children with drug-resistant focal epilepsy (DRE). However, their associations with the structural integrity of neurocognitive networks remain largely unknown.Purpose: This study investigates a novel diffusion-weighted imaging (DWI) connectome methodology that can extract seizure-associated structural abnormality biomarkers from clinical DWI tractography, use them to classify neurocognitive impairments prior to surgery, and unveil the relationship between epilepsy-related factors and neurocognitive impairments.Methods: Thirty-three DRE children (age: 11.8±3.3 years, 17 boys) and 29 age-matched healthy controls were enrolled to create seizure-affected networks whose edges connect epileptogenic regions to key brain regions of 6 neurocognitive networks. The deviations of local efficiency values were averaged across the seizure-affected brain regions and used as new imaging-based biomarkers quantifying the degrees of seizure-associated structural abnormalities accumulated on individual neurocognitive networks and classifying the neurocognitive impairments along with the epilepsy-related factors.Results: Effect sizes of the proposed biomarkers for differentiating DRE from healthy controls were consistently very large across various subgroups defined by lesion types, lobar locations of epileptogenic foci, seizure frequency categories, and seizure types (i.e., Cohen d value >1.8). Compared with the epilepsy-related factors, the proposed biomarkers demonstrated superior classification accuracy for identifying neurocognitive impairments in general, verbal, and nonverbal domains. When combined with the epilepsy-related factors, the classification performance further improved, achieving an accuracy range of 90%–98% in the independent test patients. The subsequent association analysis using the proposed biomarkers as seizure-associated structural abnormality indicators demonstrated that the inclusion of such imaging indicators significantly enhances the strength of associations between epilepsy factors and neurocognitive impairments.Conclusion: These findings offer strong potential for objectively identifying neurocognitive impairments in DRE children, supporting early, data-driven decisions for personalized interventions to mitigate long-term effects.

Background and ObjectivesCarotid plaque reflects systemic atherosclerosis and may serve as an early marker of cognitive decline, but its longitudinal association with cognitive trajectories remains...

Accurate preoperative identification of true positive white matter pathways involved in critical eloquent functions such as motor, language, and vision plays a vital role in minimizing the risk of postoperative functional deficits and improving postoperative functional outcomes in pediatric epilepsy surgery. This study proposes a novel deep learning model: "ESM-AnatTractNet" that can accurately classify true positive eloquent white matter pathways across preoperative diffusion weighted imaging tractography data of 85 drug-resistant epilepsy patients (age: 10.70 ± 4.41 years). To enhance geometric and anatomical consistency of true positive tract classification, the ESM-AnatTractNet integrated two features in a point-cloud-based framework, 1) electro-physiologically confirmed spatial coordinates using electrical stimulation mapping (ESM) and 2) anatomically-contexted labels of the end-to-end neural connection using a standard brain atlas. Its overall performance was validated by accurately classifying 14 eloquent functional areas in whole brain, objectively optimizing resection margins to preserve eloquent functions using Kalman filter, and precisely predicting postoperative language outcomes using canonical correlation. Our ESM-AnatTractNet outperformed other baseline models, achieving an accuracy of 97% in correctly classifying eloquent areas within 10mm spatial resolution of clinical subdural grid electroencephalography. The Kalman filter analysis achieved 94% accuracy in predicting no deficits when the ESM-AnatTractNet-defined preservation zones were not resected. Postoperative decrease in language-related white matter connection efficacy defined by the ESM-AnatTractNet analysis was significantly associated with worse postoperative language outcome (R=0.73, p < 0.001). Our findings demonstrate that the ESM-AnatTractNet improves non-invasive localization of true positive eloquent white matter pathways, supporting its potential to enhance current preoperative evaluation of pediatric epilepsy surgery.