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Abstract Background The cholinergic neurotransmitter system is crucial to cognitive function, with the basal forebrain (BF) being particularly susceptible to Alzheimer’s disease (AD) pathology. However, the interaction of white matter hyperintensities (WMH) in cholinergic pathways and BF atrophy without amyloid pathology remains poorly understood. Methods We enrolled patients who underwent neuropsychological tests, magnetic resonance imaging, and 18F-florbetaben positron emission tomography due to cognitive impairment at the teaching university hospital from 2015 to 2022. Among these, we selected patients with negative amyloid scans and additionally excluded those with Parkinson’s dementia that may be accompanied by BF atrophy. The WMH burden of cholinergic pathways was quantified by the Cholinergic Pathways Hyperintensities Scale (CHIPS) score, and categorized into tertile groups because the CHIPS score did not meet normal distribution. Segmentation of the BF on volumetric T1-weighted MRI was performed using FreeSurfer, then was normalized for total intracranial volume. Multivariable regression analysis was performed to investigate the association between BF volumes and CHIPS scores. Results A total of 187 patients were enrolled. The median CHIPS score was 12 [IQR 5.0; 24.0]. The BF volume of the highest CHIPS tertile group (mean ± SD, 3.51 ± 0.49, CHIPSt3) was significantly decreased than those of the lower CHIPS tertile groups (3.75 ± 0.53, CHIPSt2; 3.83 ± 0.53, CHIPSt1; P = 0.02). In the univariable regression analysis, factors showing significant associations with the BF volume were the CHIPSt3 group, age, female, education, diabetes mellitus, smoking, previous stroke history, periventricular WMH, and cerebral microbleeds. In multivariable regression analysis, the CHIPSt3 group (standardized beta [β std] = -0.25, P = 0.01), female (β std = 0.20, P = 0.04), and diabetes mellitus (β std = -0.22, P

BackgroundAlthough its incidence is relatively low, delayed-onset post-stroke cognitive decline (PSCD) may offer valuable insights into the “vascular contributions to cognitive impairment and dementia,” particularly concerning the roles of vascular and neurodegenerative mechanisms. We postulated that the functional segregation observed during post-stroke compensation could be disrupted by underlying amyloid pathology or cerebral small vessel disease (cSVD), leading to delayed-onset PSCD.MethodsUsing a prospective stroke registry, we identified patients who displayed normal cognitive function at baseline evaluation within a year post-stroke and received at least one subsequent assessment. Patients suspected of pre-stroke cognitive decline were excluded. Decliners [defined by a decrease of ≥3 Mini-Mental State Examination (MMSE) points annually or an absolute drop of ≥5 points between evaluations, confirmed with detailed neuropsychological tests] were compared with age- and stroke severity-matched non-decliners. Index-stroke MRI, resting-state functional MRI, and 18F-florbetaben PET were used to identify cSVD, functional network attributes, and amyloid deposits, respectively. PET data from age-, sex-, education-, and apolipoprotein E-matched stroke-free controls within a community-dwelling cohort were used to benchmark amyloid deposition.ResultsAmong 208 eligible patients, 11 decliners and 10 matched non-decliners were identified over an average follow-up of 5.7 years. No significant differences in cSVD markers were noted between the groups, except for white matter hyperintensities (WMHs), which were strongly linked with MMSE scores among decliners (rho = −0.85, p

Background and Purpose Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a deep learning algorithm to classify stroke subtype. Methods Model development was done in 2,988 patients with ischemic stroke from three centers by using U-net for infarct segmentation and EfficientNetV2 for subtype classification. Experienced neurologists (n=5) determined subtypes for external test datasets, while establishing a consensus for clinical trial datasets. Automatically segmented infarcts were fed into the model (DWI-only algorithm). Subsequently, another model was trained, with AF included as a categorical variable (DWI+AF algorithm). These models were tested: (1) internally against the opinion of the labeling experts, (2) against fresh external DWI data, and (3) against clinical trial dataset. Results In the training-and-validation datasets, the mean (±standard deviation) age was 68.0±12.5 (61.1% male). In internal testing, compared with the experts, the DWI-only and the DWI+AF algorithms respectively achieved moderate (65.3%) and near-strong (79.1%) agreement. In external testing, both algorithms again showed good agreements (59.3%–60.7% and 73.7%–74.0%, respectively). In the clinical trial dataset, compared with the expert consensus, percentage agreements and Cohen’s kappa were respectively 58.1% and 0.34 for the DWI-only vs. 72.9% and 0.57 for the DWI+AF algorithms. The corresponding values between experts were comparable (76.0% and 0.61) to the DWI+AF algorithm. Conclusion Our model trained on a large dataset of DWI (both with or without AF information) was able to classify ischemic stroke subtypes comparable to a consensus of stroke experts.

Abstract Purpose Prostate cancer (PCa) is a major urological disease that is associated with significant morbidity and mortality in men. LLGL2 is the mammalian homolog of Lgl. It acts as a tumor suppressor in breast and hepatic cancer. However, the role of LLGL2 and the underlying mechanisms in PCa have not yet been elucidated. Here, we investigate the role of LLGL2 in the regulation of epithelial-mesenchymal transition (EMT) in PCa through autophagy in vitro and in vivo. Methods PC3 cells were transfected with siLLGL2 or plasmid LLGL2 and autophagy was examined. Invasion, migration, and wound healing were assessed in PC3 cells under autophagy regulation. Tumor growth was evaluated using a shLLGL2 xenograft mouse model. Results In patients with PCa, LLGL2 levels were higher with defective autophagy and increased EMT. Our results showed that the knockdown of LLGL2 induced autophagy flux by upregulating Vps34 and ATG14L. LLGL2 knockdown inhibits EMT by upregulating E-cadherin and downregulating fibronectin and α-SMA. The pharmacological activation of autophagy by rapamycin suppressed EMT, and these effects were reversed by 3-methyladenine treatment. Interestingly, in a shLLGL2 xenograft mouse model, tumor size and EMT were decreased, which were improved by autophagy induction and worsened by autophagy inhibition. Conclusion Defective expression of LLGL2 leads to attenuation of EMT due to the upregulation of autophagy flux in PCa. Our results suggest that LLGL2 is a novel target for alleviating PCa via the regulation of autophagy.

Abstract The coronavirus disease 2019 (COVID-19) pandemic has profoundly impacted vulnerable groups, such as patients with dementia. We examined changes in mortality and loss to follow-up in patients with dementia using data from the Korean National Health Insurance Service research database. Patients with dementia who visited a medical institution with a recorded dementia-related diagnostic code, including Alzheimer’s disease, and who received anti-dementia medication between February 2018 and January 2020 were included in this study. We divided patients with dementia receiving anti-dementia medications into two cohorts: those newly diagnosed with dementia between February 2018 and January 2019 (n = 62,631) and those diagnosed between February 2019 and January 2020 (n = 54,494). Then, we conducted a one-year follow-up of their records, tracking the cohort diagnosed between February 2018 and January 2019 from February 2019 to January 2020, as well as the cohort diagnosed between February 2019 and January 2020 from February 2020 to January 2021. There was a significant increase in follow-up loss among patients newly diagnosed with dementia during the COVID-19 outbreak, from 42.04% in 2019 to 45.89% in 2020. Female sex, younger age, fewer comorbidities, diagnosis of dementia at the Department of Neurology or Psychiatry, and higher income were associated with decreased follow-up loss and mortality. This study highlights the importance of paying extra attention to patients with dementia receiving anti-dementia medications, particularly during pandemics, given their increased risk of loss to follow-up.

IntroductionAlthough white matter hyperintensity (WMH) shares similar vascular risk and pathology with small vessel occlusion (SVO) stroke, there were few studies to evaluate the impact of the burden of WMH volume on early and delayed stroke outcomes in SVO stroke.Materials and methodsUsing a multicenter registry database, we enrolled SVO stroke patients between August 2013 and November 2022. The WMH volume was estimated by automated methods using deep learning (VUNO Med-DeepBrain, Seoul, South Korea), which was a commercially available segmentation model. After propensity score matching (PSM), we evaluated the impact of WMH volume on early neurological deterioration (END) and poor functional outcomes at 3-month modified Ranking Scale (mRS), defined as mRS score >2 at 3 months, after an SVO stroke.ResultsAmong 1,718 SVO stroke cases, the prevalence of subjects with severe WMH (Fazekas score ≥ 3) was 68.9%. After PSM, END and poor functional outcomes at 3-month mRS (mRS > 2) were higher in the severe WMH group (END: 6.9 vs. 13.5%, p < 0.001; 3-month mRS > 2: 11.4 vs. 24.7%, p < 0.001). The logistic regression analysis using the PSM cohort showed that total WMH volume increased the risk of END [odd ratio [OR], 95% confidence interval [CI]; 1.01, 1.00–1.02, p = 0.048] and 3-month mRS > 2 (OR, 95% CI; 1.02, 1.01–1.03, p < 0.001). Deep WMH was associated with both END and 3-month mRS > 2, but periventricular WMH was associated with 3-month mRS > 2 only.ConclusionThis study used automated methods using a deep learning segmentation model to assess the impact of WMH burden on outcomes in SVO stroke. Our findings emphasize the significance of WMH burden in SVO stroke prognosis, encouraging tailored interventions for better patient care.

Abstract Quantification of diffusion restriction lesions in sporadic Creutzfeldt-Jakob disease (sCJD) may provide information of the disease burden. We aim to develop an automatic segmentation model for sCJD and to evaluate the volume of disease extent as a prognostic marker for overall survival. Fifty-six patients (mean age ± SD, 61.2 ± 9.9 years) were included from February 2000 to July 2020. A threshold-based segmentation was used to obtain abnormal signal intensity masks. Segmented volumes were compared with the visual grade. The Dice similarity coefficient was calculated to measure the similarity between the automatic vs. manual segmentation. Cox proportional hazards regression analysis was performed to evaluate the volume of disease extent as a prognostic marker. The automatic segmentation showed good correlation with the visual grading. The cortical lesion volumes significantly increased as the visual grade aggravated (extensive: 112.9 ± 73.2; moderate: 45.4 ± 30.4; minimal involvement: 29.6 ± 18.1 mm3) (P

Voltage source converter (VSC)-based high-voltage direct-current (HVDC) systems are being increasingly considered to support grid frequency regulation (FR) by using fast responses to reference signals. This paper proposes a control strategy for the optimal real-time FR of multi-terminal (MT) HVDC grids to reduce grid frequency variations. The novelty of the proposed strategy is that it realizes coordinated power control (CPC) of MT-HVDC system to consider the malfunctions of the HVDC converters and communication time delays between the malfunctioning and compensated HVDC converters. Accordingly, a state-space model of MT-HVDC grids is derived considering the DC-link voltage and active power controls at the rectifier and inverter, respectively, which are combined with inertia emulation (IE) and droop controllers. Based on the developed model for MT-HVDC grids, optimal secondary frequency control (SFC) is achieved using a linear quadratic Gaussian (LQG) controller. Moreover, CPC is integrated into the LQG to compensate for the inaccurate output power owing to malfunctions of the HVDC converter. Simulation case studies are performed to verify that the proposed strategy can alleviate real-time frequency fluctuations of inverter-side grids more effectively than conventional strategies.

Abstract Normal pressure hydrocephalus (NPH) patients had altered white matter tract integrities on diffusion tensor imaging (DTI). Previous studies suggested disproportionately enlarged subarachnoid space hydrocephalus (DESH) as a prognostic sign of NPH. We examined DTI indices in NPH subgroups by DESH severity and clinical symptoms. This retrospective case–control study included 33 NPH patients and 33 age-, sex-, and education-matched controls. The NPH grading scales (0–12) were used to rate neurological symptoms. Patients with NPH were categorized into two subgroups, high-DESH and low-DESH groups, by the average value of the DESH scale. DTI indices, including fractional anisotropy, were compared across 14 regions of interest (ROIs). The high-DESH group had increased axial diffusivity in the lateral side of corona radiata (1.43 ± 0.25 vs. 1.72 ± 0.25, p = 0.04), and showed decreased fractional anisotropy and increased mean, and radial diffusivity in the anterior and lateral sides of corona radiata and the periventricular white matter surrounding the anterior horn of lateral ventricle. In patients with a high NPH grading scale, fractional anisotropy in the white matter surrounding the anterior horn of the lateral ventricle was significantly reduced (0.36 ± 0.08 vs. 0.26 ± 0.06, p = 0.03). These data show that DESH may be a biomarker for DTI-detected microstructural alterations and clinical symptom severity.

Diagnosing faults is crucial for ensuring the safety and reliability of medium-voltage direct current (MVDC) systems. In this study, we propose a bidirectional long short-term memory (Bi-LSTM)-based fault diagnosis scheme for the accurate classification of faults occurring in MVDC systems. First, to ensure stability in case a fault occurs, we modeled an MVDC system that included a resistor-based fault current limiter (R-FCL) and a direct current circuit breaker (DCCB). A discrete wavelet transform (DWT) extracted the transient voltages and currents measured using DC lines and AC grids in the frequency–time domain. Based on the digital signal normalized by the DWT, using the measurement data, the Bi-LSTM algorithm was used to classify and learn the types and locations of faults, such as DC line (PTP, P-PTG, and N-PTG) and internal inverter faults. The effectiveness of the proposed fault diagnosis scheme was validated through comparative analysis within the four-terminal MVDC system, demonstrating superior accuracy and a faster diagnosis time compared to those of the existing schemes that utilize other AI algorithms, such as the CNN and LSTM. According to the test results, the proposed fault diagnosis scheme detects MVDC faults and shows a high recognition accuracy of 97.7%. Additionally, when applying the Bi-LSTM-based fault diagnosis scheme, it was confirmed that not only the training diagnosis time (TraDT) but also the average diagnosis time (AvgDT) were 0.03 ms and 0.05 ms faster than LSTM and CNN, respectively. The results validate the superior fault clarification and fast diagnosis performance of the proposed scheme over those of the other methods.

Abstract Background and objectives Post-stroke cognitive impairment (PSCI) occurs in up to 50% of patients with acute ischemic stroke (AIS). Thus, the prediction of cognitive outcomes in AIS may be useful for treatment decisions. This PSCI cohort study aimed to determine the applicability of a machine learning approach for predicting PSCI after stroke. Methods This retrospective study used a prospective PSCI cohort of patients with AIS. Demographic features, clinical characteristics, and brain imaging variables previously known to be associated with PSCI were included in the analysis. The primary outcome was PSCI at 3–6 months, defined as an adjusted z-score of less than − 2.0 standard deviation in at least one of the four cognitive domains (memory, executive/frontal, visuospatial, and language), using the Korean version of the Vascular Cognitive Impairment Harmonization Standards-Neuropsychological Protocol (VCIHS-NP). We developed four machine learning models (logistic regression, support vector machine, extreme gradient boost, and artificial neural network) and compared their accuracies for outcome variables. Results A total of 951 patients (mean age 65.7 ± 11.9; male 61.5%) with AIS were included in this study. The area under the curve for the extreme gradient boost and the artificial neural network was the highest (0.7919 and 0.7365, respectively) among the four models for predicting PSCI according to the VCIHS-NP definition. The most important features for predicting PSCI include the presence of cortical infarcts, mesial temporal lobe atrophy, initial stroke severity, stroke history, and strategic lesion infarcts. Conclusion Our findings indicate that machine-learning algorithms, particularly the extreme gradient boost and the artificial neural network models, can best predict cognitive outcomes after ischemic stroke.

Introduction: This trial, conducted according to the request of the Korean Ministry of Food and Drug Safety, investigated the potential of oxiracetam in mitigating post-stroke cognitive decline, and whether physical activity could augment its efficacy. The mechanisms behind these effects were explored using functional neuroimaging. Methods: In this multicenter, randomized, double-blind, placebo-controlled trial, we enrolled patients reporting subjective cognitive decline more than three months post-stroke and at a high risk of dementia. Participants were randomly assigned to a 36-week regimen of either 800 mg oxiracetam or a placebo, administered twice daily, in conjunction with recommended physical activity. Physical activity levels were monitored using wrist actigraphy. Cognitive function was assessed using a mini-mental state examination (MMSE) and clinical dementia rating sum of boxes (CDR-SB) at baseline and 36 weeks. Other assessments included the 60-minute protocol of Vascular Cognitive Impairment Harmonization Standards, Euro QoL, patient's global assessment, and resting-state functional MRI-derived neural network attributes. Results: Of the 500 participants (mean age 68.9 years, male 76.1%, median interval between stroke and randomization 32 months), 457 were analyzed. Both groups have equally adhered to the exercise guidelines (moderate-to-vigorous physical activity per day; 23.7±26.1 vs. 24.1±28.7 minutes; p=0.75). No significant difference was observed in changes to MMSE and CDR-SB scores between the two groups (MMSE, 0.13±2.27 vs. 0.27±2.09, p=0.49; CDR-SB -0.14±0.70 vs. -0.08±0.80, p=0.38). Although increased baseline exercise time correlated with better MMSE scores, the correlation was weak (R= 0.11, p=0.02). Verbal memory, phonemic fluency, language, NPI-Q, and geriatric depression scale also improved significantly within each group. However, only the intervention group showed significant improvements in CDR-SB and EuroQoL scores. A predetermined multivariate analysis showed a significant change in the normalized clustering coefficient for the secondary efficacy endpoint (r=0.09, SE=0.04, p=0.03). Discussion: This study failed to confirm the efficacy of oxiracetam in preventing post-stroke cognitive decline. Nonetheless, adherence to the exercise guidelines appeared to help participants maintain their cognitive functions, despite being a chronic stage post-stroke and having a high dementia risk.

Background and Rationale: Currently, there is no medication to prevent post-stroke cognitive decline. Previous studies have suggested that cilostazol may prevent cognitive decline in stroke through mechanisms such as increasing cerebral blood flow and reducing amyloid beta accumulation. We aimed to demonstrate the effectiveness of cilostazol, compared to aspirin or clopidogrel, in preventing cognitive decline in elderly patients with ischemic stroke at high risk of dementia. Study Design: We have designed a multicenter registry-based randomized controlled trial (RRCT), with an open-label blinded endpoint (PROBE) approach. We will recruit patients enrolled in the CRCS-K stroke registry who are at least three months from the onset of ischemic stroke and who meet the following conditions that suggest a high risk of developing dementia: age ≥ 75 years, age between 65 and 74 years and one of the following conditions: [diabetes mellitus, confluent white matter hyperintensities (Fazekas grade 3), medial temporal atrophy (Scheltens visual grade≥ 3), multiple cerebral microbleeds (≥ 5), multiple lacunes (> 5), APOE e4 allele]. Participants will be randomly allocated to either cilostazol (200 mg daily) or a control group receiving aspirin (100–300 mg daily) or clopidogrel (75 mg daily). Cognitive assessments will be performed at baseline, end of follow-up (up to 60 months), and whenever cognitive decline is reported by participants or caregivers. The study will be carried out across 17 centers in South Korea, with a projected enrollment of 2,362 patients. Study outcomes: Our primary outcome measure is the time from baseline to prespecified cognitive decline, defined as a decrease of 3 or more points on the MMSE and an increase of 1.5 or more points on the CDR-SOB. Secondary endpoints encompass changes in MMSE, CDR-SOB, and MoCA scores from baseline to final assessment, incident dementia, and a composite of recurrent stroke, acute myocardial infarction, and all-cause mortality. As a primary safety assessment, we will analyze the occurrence of major bleeding events. Discussion: We anticipate that the RRCT approach will enable efficient screening of a large cohort of elderly stroke patients at high risk of dementia while ensuring prolonged follow-up at a reasonable cost.

Background This study explored the risk factors, neuroimaging features, and prognostic implications of nonhypertensive white matter hyperintensity (WMH) in patients with acute ischemic stroke and transient ischemic attack. Methods and Results We included 2283 patients with hypertension and 1003 without from a pool of 10 602. Associations of moderate‐to‐severe WMH with known risk factors, functional outcome, and a composite of recurrent stroke, myocardial infarction, and all‐cause mortality were evaluated. A subset of 351 patients without hypertension and age‐ and sex‐matched pairs with hypertension and moderate‐to‐severe WMH was created for a detailed topographic examination of WMH, lacunes, and microbleeds. Approximately 35% of patients without hypertension and 65% of patients with hypertensive stroke exhibited moderate‐to‐severe WMH. WMH was associated with age, female sex, and previous stroke, irrespective of hypertension. In patients without hypertension, WMH was associated with initial systolic blood pressure and was more common in the anterior temporal region. In patients with hypertension, WMH was associated with small vessel occlusion as a stroke mechanism and was more frequent in the periventricular region near the posterior horn of the lateral ventricle. The higher prevalence of occipital microbleeds in patients without hypertension and deep subcortical lacunes in patients with hypertension were also observed. Associations of moderate‐to‐severe WMH with 3‐month functional outcome and 1‐year cumulative incidence of the composite outcome were significant (both P

Bayesian modeling of anatomical lesion patterns and clinical language outcomes in a cohort of 1401 stroke patients identifies sex-biased lesion-outcome effects after stroke.

Abstract We aimed to investigate the effects of exercise habit changes on the risk of incident dementia after ischemic stroke using the Korean National Health Insurance Services Database. This study included 223,426 patients with a new diagnosis of ischemic stroke between 2010 and 2016 who underwent two serial ambulatory health checkups. The participants were divided into four categories according to their habit change or regular exercise: persistent non-exercisers, new exercisers, exercise dropouts, and exercise maintainers. The primary outcome was new diagnosis of dementia. Multivariate Cox proportional models were used to assess the effects of changes in exercise habits on the risk of incident dementia. After a median of 4.02 years of follow-up, 22,554 (10.09%) dementia cases were observed. After adjusting for covariates, exercise dropouts, new exercisers, and exercise maintainers were significantly associated with a lower risk of incident dementia than persistent non-exercisers (adjusted hazard ratio [aHR] 0.937; 95% confidence interval [CI] 0.905–0.970, aHR 0.876; 95% CI 0.843–0.909, aHR 0.705; 95% CI 0.677–0.734, respectively). The impact of changes in exercise habit was more prominent in the 40–65 years age group. An energy expenditure ≥ 1000 metabolic equivalents of task-min/wk post-stroke, regardless of pre-stroke physical activity status, was mostly associated with a lower risk of each outcome. In this retrospective cohort study, initiating or continuing moderate-to-vigorous exercise after ischemic stroke was associated with a lower risk of dementia development. Further, pre-stroke regular physical activity also reduced the risk of incident dementia. The promotion of exercise in ambulatory stroke patients may reduce their future risk of incident dementia.

ObjectivesMore than half of patients with acute ischemic stroke develop post-stroke cognitive impairment (PSCI), a significant barrier to future neurological recovery. Thus, predicting cognitive trajectories post-AIS is crucial. Our primary objective is to determine whether brain network properties from electroencephalography (EEG) can predict post-stroke cognitive function using machine learning approach.MethodsWe enrolled consecutive stroke patients who underwent both EEG during the acute stroke phase and cognitive assessments 3 months post-stroke. We preprocessed acute stroke EEG data to eliminate low-quality epochs, then performed independent component analysis and quantified network characteristics using iSyncBrain®. Cognitive function was evaluated using the Montreal cognitive assessment (MoCA). We initially categorized participants based on the lateralization of their lesions and then developed machine learning models to predict cognitive status in the left and right hemisphere lesion groups.ResultsEighty-seven patients were included, and the accuracy of lesion laterality prediction using EEG attributes was 97.0%. In the left hemispheric lesion group, the network attributes of the theta band were significantly correlated with MoCA scores, and higher global efficiency, clustering coefficient, and lower characteristic path length were associated with higher MoCA scores. Most features related to cognitive scores were selected from the frontal lobe. The predictive powers (R-squared) were 0.76 and 0.65 for the left and right stroke groups, respectively.ConclusionEstimating EEG-based network properties in the acute phase of ischemic stroke through a machine learning model has a potential to predict cognitive outcomes after ischemic stroke.

Background and purposeTo develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg.Materials and methodsThis retrospective study included 60 subjects [30 Alzheimer’s disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference.ResultsThe single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects.ConclusionOur deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Abstract There is a paucity of research regarding the association between the risk of incident dementia and changes in smoking habits in the acute ischemic stroke population. We aimed to investigate the effects of smoking habit change on the risk of incident dementia in an ischemic stroke population using data from the Korean National Health Insurance Services Database. This nationwide population-based cohort study included 197,853 patients with ischemic stroke. The patients were divided into never smokers, former smokers, smoking quitters, sustained smokers, and new smokers, based on the 2-year change in smoking status between the two consecutive health examinations before and after the index stroke. The patients were followed up from the index date to 2018 to assess the development of dementia. Dementia was further categorized into Alzheimer’s, vascular, and other types of dementia according to the International Classification of Diseases, Tenth Revision diagnosis. Multivariable Cox proportional hazards models were used to assess the association between changes in smoking habits and the risk of dementia. After a median of 4.04 years of follow-up, 19,595 (9.9%) dementia cases were observed. Among them, 15,189 (7.7%) were diagnosed with Alzheimer’s disease dementia and 2719 (1.4%) were diagnosed with vascular dementia. After adjusting for covariates, including age, sex, alcohol intake habits, cigarette pack-year, regular physical activity, income, history of hypertension, diabetes mellitus, dyslipidemia, and chronic kidney disease, new smokers, sustained smokers, and smoking quitters were significantly associated with a higher risk of all-cause dementia than never smokers (adjusted hazard ratio [aHR] 1.395, 95% confidence interval [CI] 1.254–1.552; aHR 1.324, 95% CI 1.236–1.418; and aHR 1.170, 95% CI 1.074–1.275, respectively). Similar trends were observed for both Alzheimer’s dementia and vascular dementia, but the association between new smokers and vascular dementia was not significant. The impact of smoking habit change was more prominent in the 40–65-year-old group. New and sustained smokers had a substantially higher risk of incident dementia after ischemic stroke than never smokers. Smoking quitters also had an elevated risk of incident dementia, but the detrimental effects were lower than those in new and sustained smokers.

Abstract Background About 40–50% of patients with amnestic mild cognitive impairment (MCI) are found to have no significant Alzheimer’s pathology based on amyloid PET positivity. Notably, conversion to dementia in this population is known to occur much less often than in amyloid-positive MCI. However, the relationship between MCI and brain amyloid deposition remains largely unknown. Therefore, we investigated the influence of subthreshold levels of amyloid deposition on conversion to dementia in amnestic MCI patients with negative amyloid PET scans. Methods This study was a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain magnetic resonance imaging, and [18F]-florbetaben (FBB) positron emission tomography scan (PET). Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Regional cortical amyloid levels were calculated, and a receiver operating characteristic (ROC) curve for conversion to dementia was obtained. To increase the reliability of the results of the study, we analyzed the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset together. Results During the follow-up period, 36% (39/107) of patients converted to dementia from amnestic MCI. The dementia converter group displayed increased standardized uptake value ratio (SUVR) values of FBB on PET in the bilateral temporal, parietal, posterior cingulate, occipital, and left precuneus cortices as well as increased global SUVR. Among volume of interests, the left parietal SUVR predicted conversion to dementia with the highest accuracy in the ROC analysis (area under the curve [AUC] = 0.762, P < 0.001). The combination of precuneus, parietal cortex, and FBB composite SUVRs also showed a higher accuracy in predicting conversion to dementia than other models (AUC = 0.763). Of the results of ADNI data, the SUVR of the left precuneus SUVR showed the highest AUC (AUC = 0.596, P = 0.006). Conclusion Our findings suggest that subthreshold amyloid levels may contribute to conversion to dementia in patients with amyloid-negative amnestic MCI.

Background: We aimed to investigate the association between the ApoB/ApoA-I ratio and post-stroke cognitive impairment (PSCI) in patients with acute stroke of large artery atherosclerosis etiology. Methods: Prospective stroke registry data were used to consecutively enroll patients with acute ischemic stroke due to large artery atherosclerosis. Cognitive function assessments were conducted 3 to 6 months after stroke. PSCI was defined as a z-score of less than −2 standard deviations from age, sex, and education-adjusted means in at least one cognitive domain. The ApoB/ApoA-I ratio was calculated, and patients were categorized into five groups according to quintiles of the ratio. Logistic regression analyses were performed to assess the association between quintiles of the ApoB/ApoA-I ratio and PSCI. Results: A total of 263 patients were included, with a mean age of 65.9 ± 11.6 years. The median NIHSS score and ApoB/ApoA-I ratio upon admission were 2 (IQR, 1–5) and 0.81 (IQR, 0.76–0.88), respectively. PSCI was observed in 91 (34.6%) patients. The highest quintile (Q5) of the ApoB/ApoA-I ratio was a significant predictor of PSCI compared to the lowest quintile (Q1) (adjusted OR, 3.16; 95% CI, 1.19–8.41; p-value = 0.021) after adjusting for relevant confounders. Patients in the Q5 group exhibited significantly worse performance in the frontal domain. Conclusions: The ApoB/ApoA-I ratio in the acute stage of stroke independently predicted the development of PSCI at 3–6 months after stroke due to large artery atherosclerosis. Further, a high ApoB/ApoA-I ratio was specifically associated with frontal domain dysfunction.

The neurological symptoms of stroke have traditionally provided the foundation for functional mapping of the brain. However, there are many unresolved aspects in our understanding of cerebral activity, especially regarding high-level cognitive functions. This review provides a comprehensive look at the pathophysiology of post-stroke cognitive impairment in light of recent findings from advanced imaging techniques. Combining network neuroscience and clinical neurology, our research focuses on how changes in brain networks correlate with post-stroke cognitive prognosis. More specifically, we first discuss the general consequences of stroke lesions due to damage of canonical resting-state large-scale networks or changes in the composition of the entire brain. We also review emerging methods, such as lesion-network mapping and gradient analysis, used to study the aforementioned events caused by stroke lesions. Lastly, we examine other patient vulnerabilities, such as superimposed amyloid pathology and blood-brain barrier leakage, which potentially lead to different outcomes for the brain network compositions even in the presence of similar stroke lesions. This knowledge will allow a better understanding of the pathophysiology of post-stroke cognitive impairment and provide a theoretical basis for the development of new treatments, such as neuromodulation.

Abstract Although the obesity paradox is an important modifiable factor in cardiovascular diseases, little research has been conducted to determine how it affects post-stroke cognitive function. We aimed to investigate the association between body mass index (BMI) and domain-specific cognitive outcomes, focusing on the subdivision of each frontal domain function in post-ischemic stroke survivors. A total of 335 ischemic stroke patients were included in the study after completion of the Korean-Mini Mental Status Examination (K-MMSE) and the vascular cognitive impairment harmonization standards neuropsychological protocol at 3 months after stroke. Frontal lobe functions were analyzed using semantic/phonemic fluency, processing speed, and mental set shifting. Our study participants were categorized into four groups according to BMI quartiles. The z-scores of K-MMSE at 3 months differed significantly between the groups after adjustment for initial stroke severity (p = 0.014). Global cognitive function in stroke survivors in the Q1 (the lowest quartile) BMI group was significantly lower than those in Q2 and Q4 (the highest quartile) BMI groups (K-MMSE z-scores, Q1: − 2.10 ± 3.40 vs. Q2: 0.71 ± 1.95 and Q4: − 1.21 ± 1.65). Controlled oral word association test findings indicated that phonemic and semantic word fluency was lower in Q4 BMI group participants than in Q2 BMI group participants (p = 0.016 and p = 0.023 respectively). BMI might differentially affect cognitive domains after ischemic stroke. Although being underweight may negatively affect global cognition post-stroke, obesity could induce frontal lobe dysfunctions, specifically phonemic and semantic word fluency.

Background and ObjectiveIdentifying biomarkers for predicting progression to dementia in patients with mild cognitive impairment (MCI) is crucial. To this end, the comprehensive visual rating scale (CVRS), which is based on magnetic resonance imaging (MRI), was developed for the assessment of structural changes in the brains of patients with MCI. This study aimed to investigate the use of the CVRS score for predicting dementia in patients with MCI over a 2-year follow-up period using various machine learning (ML) algorithms.MethodsWe included 197 patients with MCI who were followed up more than once. The data used for this study were obtained from the Japanese-Alzheimer's Disease Neuroimaging Initiative study. We assessed all the patients using their CVRS scores, cortical thickness data, and clinical data to determine their progression to dementia during a follow-up period of over 2 years. ML algorithms, such as logistic regression, random forest (RF), XGBoost, and LightGBM, were applied to the combination of the dataset. Further, feature importance that contributed to the progression from MCI to dementia was analyzed to confirm the risk predictors among the various variables evaluated.ResultsOf the 197 patients, 108 (54.8%) showed progression from MCI to dementia. Tree-based classifiers, such as XGBoost, LightGBM, and RF, achieved relatively high performance. In addition, the prediction models showed better performance when clinical data and CVRS score (accuracy 0.701–0.711) were used than when clinical data and cortical thickness (accuracy 0.650–0.685) were used. The features related to CVRS helped predict progression to dementia using the tree-based models compared to logistic regression.ConclusionsTree-based ML algorithms can predict progression from MCI to dementia using baseline CVRS scores combined with clinical data.

ObjectivesThe role of three-dimensional (3D) TOF-MRA in patients with cognitive impairment is not well established. We evaluated the diagnostic yield of 3D TOF-MRA for detecting incidental extra- or intracranial artery stenosis and intracranial aneurysm in this patient group.MethodsThis retrospective study included patients with cognitive impairment undergoing our brain MRI protocol from January 2013 to February 2020. The diagnostic yield of TOF-MRA for detecting incidental vascular lesions was calculated. Patients with positive TOF-MRA results were reviewed to find whether additional treatment was performed. Logistic regression analysis was conducted to identify the clinical risk factors for positive TOF-MRA findings.ResultsIn total, 1,753 patients (mean age, 70.2 ± 10.6 years; 1,044 women) were included; 199 intracranial aneurysms were detected among 162 patients (9.2%, 162/1,753). A 3D TOF-MRA revealed significant artery stenoses (>50% stenosis) in 162 patients (9.2%, 162/1,753). The overall diagnostic yield of TOF-MRA was 16.8% (294/1,753). Among them, 92 patients (31.3%, 92/294) underwent either medical therapy, endovascular intervention, or surgery. In total, eighty-one patients with stenosis were prescribed with either antiplatelet medications or lipid-lowering agent. In total, fifteen patients (aneurysm: 11 patients, stenosis: 4 patients) were further treated with endovascular intervention or surgery. Thus, the “number needed to scan” was 19 for identifying one patient requiring treatment. Multivariate logistic regression analysis showed that being female (odds ratio [OR] 2.05) and old age (OR 1.04) were the independent risk factors for intracranial aneurysm; being male (OR 1.52), old age (OR 1.06), hypertension (OR 1.78), and ischemic heart disease history (OR 2.65) were the independent risk factors for significant artery stenosis.ConclusionsOur study demonstrated the potential benefit of 3D TOF-MRA, given that it showed high diagnostic yield for detecting vascular lesions in patients with cognitive impairment and the considerable number of these lesions required further treatment. A 3D TOF-MRA may be included in the routine MR protocol for the work-up of this patient population, especially in older patients and patients with vascular risk factors.

Abstract Blood pressure variability (BPV) is associated with higher cardiovascular morbidity risks; however, its association with cognitive decline remains unclear. We investigated whether higher BPV is associated with faster declines in cognitive function in ischemic stroke (IS) patients. Cognitive function was evaluated between April 2010 and August 2015 using the Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment in 1,240 Korean PICASSO participants. Patients for whom baseline and follow-up cognitive test results and at least five valid BP readings were available were included. A restricted maximum likelihood–based Mixed Model for Repeated Measures was used to compare changes in cognitive function over time. Among a total of 746 participants (64.6 ± 10.8 years; 35.9% female). Baseline mean-MMSE score was 24.9 ± 4.7. The median number of BP readings was 11. During a mean follow-up of 2.6 years, mean baseline and last follow-up MMSE scores were 25.4 ± 4.8 vs. 27.8 ± 4.4 (the lowest BPV group) and 23.9 ± 5.2 vs. 23.2 ± 5.9 (the highest BPV group). After adjusting for multiple variables, higher BPV was independently associated with faster cognitive decline over time. However, no significant intergroup difference in cognitive changes associated with mean systolic BP was observed. Further research is needed to elucidate how BPV might affect cognitive function.

Purpose To validate the diagnostic performance of commercially available, deep learning-based automatic white matter hyperintensity (WMH) segmentation algorithm for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia. Methods This retrospective, observational, single-institution study investigated the diagnostic performance of a deep learning-based automatic WMH volume segmentation to classify the grades of the Fazekas scale and differentiate subcortical vascular dementia. The VUNO Med-DeepBrain was used for the WMH segmentation system. The system for segmentation of WMH was designed with convolutional neural networks, in which the input image was comprised of a pre-processed axial FLAIR image, and the output was a segmented WMH mask and its volume. Patients presented with memory complaint between March 2017 and June 2018 were included and were split into training (March 2017–March 2018, n = 596) and internal validation test set (April 2018–June 2018, n = 204). Results Optimal cut-off values to categorize WMH volume as normal vs. mild/moderate/severe, normal/mild vs. moderate/severe, and normal/mild/moderate vs. severe were 3.4 mL, 9.6 mL, and 17.1 mL, respectively, and the AUC were 0.921, 0.956 and 0.960, respectively. When differentiating normal/mild vs. moderate/severe using WMH volume in the test set, sensitivity, specificity, and accuracy were 96.4%, 89.9%, and 91.7%, respectively. For distinguishing subcortical vascular dementia from others using WMH volume, sensitivity, specificity, and accuracy were 83.3%, 84.3%, and 84.3%, respectively. Conclusion Deep learning-based automatic WMH segmentation may be an accurate and promising method for classifying the grades of the Fazekas scale and differentiating subcortical vascular dementia.

Background: Post-stroke cognitive impairment (PSCI) is a common consequence of stroke. Accurate prediction of PSCI risk is challenging. The recently developed network impact score, which integrates information on infarct location and size with brain network topology, may improve PSCI risk prediction. Aims: To determine if the network impact score is an independent predictor of PSCI, and of cognitive recovery or decline. Methods: We pooled data from patients with acute ischemic stroke from 12 cohorts through the Meta VCI Map consortium. PSCI was defined as impairment in ≥ 1 cognitive domain on neuropsychological examination, or abnormal Montreal Cognitive Assessment. Cognitive recovery was defined as conversion from PSCI 24 months) and cognitive recovery or decline using logistic regression. Models were adjusted for age, sex, education, prior stroke, infarct volume, and study site. Results: We included 2341 patients with 4657 cognitive assessments. PSCI was present in 398/844 patients (47%) 24 months. Cognitive recovery occurred in 64/181 (35%) patients and cognitive decline in 26/287 (9%). The network impact score predicted PSCI in the univariable (OR 1.50, 95%CI 1.34–1.68) and multivariable (OR 1.27, 95%CI 1.10–1.46) GEE model, with similar ORs in the logistic regression models for specified post-stroke intervals. The network impact score was not associated with cognitive recovery or decline. Conclusions: The network impact score is an independent predictor of PSCI. As such, the network impact score may contribute to a more precise and individualized cognitive prognostication in patients with ischemic stroke. Future studies should address if multimodal prediction models, combining the network impact score with demographics, clinical characteristics and other advanced brain imaging biomarkers, will provide accurate individualized prediction of PSCI. A tool for calculating the network impact score is freely available at https://metavcimap.org/features/software-tools/lsm-viewer/.

In this paper, we propose a new algorithm for introducing Coarray Fortran (CAF) into a program that dynamically analyzes the frequency domain in soil–structure interactions. This was implemented in KIESSI-3D, which is frequency domain soil-structure interaction analysis computer code based on finite and infinite element techniques, and the performance of analysis speed based on the number of images (or coarrays) and cores per image was evaluated. For the performance evaluation, we used examples of full-scale nuclear power plant buildings with a shallow foundation model as well as a deep foundation model. In terms of analysis speed, the new KIESSI-3D improved the analysis speed by an average of 2.78 times for the shallow foundation problem and 2.69 times for the deep foundation problem, compared with the existing KIESSI-3D, because of the new algorithm and CAF. Further, as the number of cores and the internal memory size in the computer systems increased, the efficiency of also parallel processing increased.

Abstract Introduction The Meta VCI Map consortium performs meta‐analyses on strategic lesion locations for vascular cognitive impairment using lesion‐symptom mapping. Integration of data from different cohorts will increase sample sizes, to improve brain lesion coverage and support comprehensive lesion‐symptom mapping studies. Methods Cohorts with available imaging on white matter hyperintensities or infarcts and cognitive testing were invited. We performed a pilot study to test the feasibility of multicenter data processing and analysis and determine the benefits to lesion coverage. Results Forty‐seven groups have joined Meta VCI Map (stroke n = 7800 patients; memory clinic n = 4900; population‐based n = 14,400). The pilot study (six ischemic stroke cohorts, n = 878) demonstrated feasibility of multicenter data integration (computed tomography/magnetic resonance imaging) and achieved marked improvement of lesion coverage. Discussion Meta VCI Map will provide new insights into the relevance of vascular lesion location for cognitive dysfunction. After the successful pilot study, further projects are being prepared. Other investigators are welcome to join.

Background and Aims: Systemic inflammation is associated with an increased risk of cognitive impairment and dementia, but the associations between them in stroke patients are less clear. We examined the impact of systemic inflammation represented as the neutrophil-lymphocyte ratio (NLR) on the development of post-stroke cognitive impairment (PSCI) and domain-specific cognitive outcomes 3-month after ischemic stroke.Methods: Using prospective stroke registry data, we consecutively enrolled 345 participants with ischemic stroke whose cognitive functions were evaluated 3-month after stroke. Their cognition was assessed with the Korean version of the Vascular Cognitive Impairment Harmonization Standards and the Korean-Mini Mental Status Examination. PSCI was defined as a z-score of < -2 standard deviations for age, sex, and education adjusted means in at least one cognitive domain. The participants were categorized into five groups according to the quintiles of NLR (lowest NLR, Q1). The cross-sectional association between NLR and PSCI was assessed using multiple logistic regression, adjusting for age, sex, education, vascular risk factors, and stroke type.Results: A total of 345 patients were enrolled. The mean age was 63.0 years and the median NIHSS score and NLR were 2 [1–4] and 2.26 [1.65–2.91], respectively. PSCI was identified in 71 (20.6%) patients. NLR was a significant predictor for PSCI both as a continuous variable (adjusted OR, 1.14; 95% CI, 1.00–1.31) and as a categorical variable (Q5, adjusted OR, 3.26; 95% CI, 1.17–9.08). Patients in the Q5 group (NLR ≥ 3.80) showed significantly worse performance in global cognition and in visuospatial and memory domains.Conclusions: NLR in the acute stage of ischemic stroke was independently associated with PSCI at 3 months after stroke, and high NLR was specifically associated with cognitive dysfunction in the memory and visuospatial domains. Thus, systemic inflammation may be a modifiable risk factor that may influence cognitive outcomes after stroke.

Background: Although controversial, homocysteine (Hcy) and lipid parameters have been associated with particular stroke subtypes. However, there are limited studies concerning the relationship between Hcy and lipid levels in acute ischemic stroke (AIS). We evaluated the impact of Hcy levels on lipid profiles in terms of specific stroke subtypes.Methods: A total of 2,324 patients with first-ever AIS were recruited from two hospitals in South Korea. The exclusion criteria were as follows: (a) pre-stroke modified Rankin scale (mRS) ≥ 1, (b) undetermined or other stroke etiology, and (c) absence of Hcy data. Among the 1,580 eligible patients, the Hcy level was divided into tertile groups. Logistic regression was used to assess association of Hcy levels with lipid levels by stroke subtypes.Results: Significant downward trends in total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) were only observed in patients with small vessel occlusion (SVO) as Hcy increased. In logistic regression analysis, while in patients with SVO subtype, the highest level of Hcy tertiles (OR = 1.648, 95% CI = 1.047–2.594) was associated with the lower HDL level (≤40 mg/dL), the significance disappeared in patients with LAA and CE subtypes.Conclusion: Although our study does not demonstrate causal relationship, we suggest that Hcy might play a mediating role between HDL and SVO stroke development. To clarify the role of Hcy on AIS, this study will provide academic support for designing future research.

Psoralea corylifolia is a medicinal herb that provides advantageous pharmacological effects against vitiligo and skin rash. Former studies have shown that bakuchicin, a furanocoumarin compound from the fruits of P. corylifolia, has therapeutic effects against inflammation, and infection. This study aimed to define the pharmacological effects of bakuchicin on inflammatory responses and lichenification, the major symptoms of atopic dermatitis (AD). To induce AD-like skin inflammation, we exposed the ears of female BALB/c mice to 2, 4-dinitrochlorobenzene (DNCB) and Dermatophagoides farinae (house dust mite) extract (DFE) for 4 weeks. Intragastric administration of bakuchicin attenuated the symptoms of AD-like skin inflammation, as evident by reductions in ear thickness, erythema, and keratosis. Bakuchicin also reversed increases in auricular epidermal and dermal layer thicknesses, and attenuated eosinophil and mast cell infiltration in AD-induced mice. It also suppressed Th2 gene expression as well as that of pro-inflammatory cytokines and chemokines, such as interleukin (IL)-4, IL-13, IL-31, IL-1β, IL-6, CXCL-1, and CCL-17 in the ear tissue. The levels of total and DFE-specific immunoglobulin (Ig)E, and IgG2a in the mice sera were reduced by the bakuchicin. To investigate the effect of bakuchicin on keratinocytes, experiments were performed using HaCaT cells, the representative cell type used in skin disease studies. Tumor necrosis factor-α and interferon-γ were used to activate keratinocytes. Bakuchicin suppressed Th2 gene expression and that of pro-inflammatory cytokines and chemokines; it also suppressed STAT-1 phosphorylation and the nuclear translocation of NF-κB in activated keratinocytes. These results suggest that bakuchicin attenuated AD symptoms, thus suggesting it as a potential therapeutic agent for the treatment of AD.

Background: The recently-proposed National Institute on Aging and Alzheimer’s Association research framework organizes Alzheimer’s disease (AD) biomarkers based on amyloid/tau/neurodegeneration (AT(N)). This study investigated the mediating effect of structural change in brain MRI on changes in cognitive function according to initial AT(N) profiles. Methods: We included 576 subjects (cognitively unimpaired (N = 136), mild cognitive impairment (N = 294), dementia (N = 146)) from the Alzheimer’s disease Neuroimaging Initiative study. The parallel-process latent growth curve model was applied to test the mediational effect of cortical thickness growth trajectory between the initial AT(N) profiles and cognitive growth trajectory. Results: In Alzheimer’s continuum, only the A + T + (N)+ profile showed a mediational effect of the cortical thickness growth trajectory. A + T − (N)− was not sufficient to induce direct or indirect effects on cognitive dysfunction, and A + T + (N)− showed a significant direct path from an altered cortical thickness to cognitive decline. Conclusion: The sequential effect between changes in brain MRI and cognition varied by baseline AT(N) profile, suggesting the dynamic changes in the relationships among biomarkers in the current cascade model.

Background and Purpose: Vitamin D is a predictor of poor outcome for cardiovascular disease. We evaluated whether serum 25-hydroxyvitamin D level was associated with poor outcome in patients with acute ischemic stroke (AIS) using machine learning approach.Materials and Methods: We studied a total of 328 patients within 7 days of AIS onset. Serum 25-hydroxyvitamin D level was obtained within 24 h of hospital admission. Poor outcome was defined as modified Rankin Scale score of 3–6. Logistic regression and extreme gradient boosting algorithm were used to assess association of 25-hydroxyvitamin D with poor outcome. Prediction performances were compared with area under ROC curve and F1 score.Results: Mean age of patients was 67.6 ± 13.3 years. Of 328 patients, 59.1% were men. Median 25-hydroxyvitamin D level was 10.4 (interquartile range, 7.1–14.8) ng/mL and 47.2% of patients were 25-hydroxyvitamin D-deficient (

Background and Purpose Many studies have evaluated the association between weather and stroke, with variable conclusions. Herein we determined the relationships between daily meteorological parameters and acute stroke incidence in South Korea. Methods Patients with acute stroke (2,894) were identified by standard sampling of a nationwide insurance claims database from January to December 2011. We used multiple Poisson regression analyses of stroke incidence and meteorological parameters (mean temperature, diurnal temperature change, temperature differences over the preceding 24 hours, atmospheric pressure, humidity, wind speed, and physiologically equivalent temperature) to calculate the relative risk of stroke incidence associated with meteorological parameters. Results There were no seasonal variations in the incidences of ischemic (2,176) or hemorrhagic (718) stroke. Temperature change during the day was positively correlated with ischemic stroke in men (relative risk [RR] 1.027; 95% confidence interval [CI] 1.006–1.05) and older patients (≥65 years) (RR 1.031, 95% CI 1.011–1.052). Temperature differences over the preceding 24 hours had a negative correlation with all strokes (RR 0.968, 95% CI 0.941–0.996), especially among older women. Diurnal variation of atmospheric pressure was also significantly associated with the incidence of ischemic stroke (age

Background: Post-stroke hyperglycemia is a frequent finding in acute ischemic stroke patients and is associated with poor functional and cognitive outcomes. However, it is unclear as to whether the glycemic gap between the admission glucose and HbA1c-derived estimated average glucose (eAG) is associated with post-stroke cognitive impairment (PSCI). Methods: We enrolled acute ischemic stroke patients whose cognitive functions were evaluated three months after a stroke using the Korean version of the vascular cognitive impairment harmonization standards neuropsychological protocol (K-VCIHS-NP). The development of PSCI was defined as having z-scores of less than −2 standard deviations in at least one cognitive domain. The participants were categorized into three groups according to the glycemic gap status: non-elevated (initial glucose − eAG ≤ 0 mg/dL), mildly elevated (0 mg/dL < initial glucose − eAG < 50 mg/dL), and severely elevated (50 mg/dL ≤ initial glucose − eAG). Results: A total of 301 patients were enrolled. The mean age was 63.1 years, and the median National Institute of Health Stroke Scale (NIHSS) score was two (IQR: 1–4). In total, 65 patients (21.6%) developed PSCI. In multiple logistic regression analyses, the severely elevated glycemic gap was a significant predictor for PSCI after adjusting for age, sex, education level, initial stroke severity, Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, and left hemispheric lesion (aOR: 3.65, p-value = 0.001). Patients in the severely elevated glycemic gap group showed significantly worse performance in the frontal and memory domains. Conclusions: In conclusion, our study demonstrated that an elevated glycemic gap was significantly associated with PSCI three months after a stroke, with preferential involvement of frontal and memory domain dysfunctions.

Background: It is not yet clear whether nutritional status is associated with post-stroke cognitive impairment (PSCI). We examined the geriatric nutritional risk index (GNRI) on the domain-specific cognitive outcomes 3 months after a stroke. Methods: A total of 344 patients with acute ischemic stroke were included for the analysis. The GNRI was calculated as 1.489 × serum albumin (g/L) + 41.7 × admission weight (kg)/ideal body weight (kg) and was dichotomized according to the prespecified cut-off points for no risk and any risks. The primary outcome was PSCI, defined as having adjusted z-scores of less than −2 standard deviations in at least one cognitive domain: executive/activation, memory, visuospatial and language. Multiple logistic regression and linear regression analyses were performed to investigate the association between the GNRI and cognitive outcomes. Results: Seventy (20.3%) patients developed PSCI 3 months after a stroke. The mean GNRI was 106.1 ± 8.6, and 59 (17.2%) patients had low (p-value = 0.03; β = 0.03, p-value = 0.03, respectively). Conclusions: A low GNRI was independently associated with the development of PSCI at 3 months after an ischemic stroke. The GNRI scores were specifically associated with the z-scores of the global cognition and frontal domain cognitive outcomes.

The most feared complication of left ventricular thrombus (LVT) is the occurrence of systemic thromboembolic events, especially in the brain. Herein, we report a patient with severe sepsis who suffered recurrent devastating embolic stroke. Transthoracic echocardiography revealed apical ballooning of the left ventricle with a huge LVT, which had not been observed in chest computed tomography before the stroke. This case emphasizes the importance of serial cardiac evaluation in patients with stroke and severe medical illness.

BACKGROUND:Numerous efforts have been made to identify biomarkers for predicting the progression of dementia in patients with mild cognitive impairment (MCI), and recently, a comprehensive visual rating scale (CVRS) based on magnetic resonance imaging (MRI) has been validated to assess structural changes in the brain of elderly patients. Based on this, the present study investigated the use of CVRS for predicting dementia and elucidated its association with cognitive change in patients with MCI over a three-year follow-up. METHODS:We included 340 patients with MCI with more than one follow-up visit. Data were obtained from the Alzheimer's disease Neuroimaging Initiative study. We assessed all the patients using CVRS and determined their progression to dementia during a follow-up period of over 3 years. The cox proportional hazards model was used to analyze hazard ratios (HRs) of CVRS for disease progression. Further, multiple cognitive measures of the patients over time were fitted using the random effect model to assess the effect of initial CVRS score on subsequent cognitive changes. RESULTS:Of 340 patients, 69 (20.2%) progressed to dementia and the median baseline score (interquartile range) of CVRS significantly differed between stable MCI and progressive MCI (9 (5-13) vs 13 (8-17), p

Several studies have reported that chronic rhinosinusitis (CRS) increases the risk of stroke. The aim of this study is to elucidate the putative association between CRS and stroke (ischemic or hemorrhagic) using large population-based national health insurance data.Using the national cohort study from the Korean Health Insurance Review and Assessment Service, CRS patients (n = 22,959) and control participants (n = 91,836) were selected and matched at a rate of 1:4 (age, sex, income, region, hypertension, diabetes, and dyslipidemia). A Cox-proportional hazard model was used to analyze the hazard ratio (HR) of CRS for hemorrhagic stroke and ischemic stroke. We divided the participants according to age and gender for the subgroup analysis.The HR for hemorrhagic and ischemic stroke was significantly increased in the CRS patients compared to that in the controls (adjusted HR = 2.43, 95% confidence interval [CI] = 2.10-2.80 for hemorrhagic stroke; adjusted HR = 1.76, 95% CI = 1.61-1.92 for ischemic stroke) after adjusting for age, sex, income, region of residence, hypertension, diabetes, dyslipidemia, ischemic heart disease, migraine, chronic kidney disease, depression, sleep disorder, and chronic obstructive pulmonary disorder. In the subgroup analysis, the HR of hemorrhagic stroke was significantly increased in the CRS group regardless of age and gender. The HR of ischemic stroke was also significantly increased in all subgroups of the CRS group.CRS consistently increased the risk of ischemic and hemorrhagic stroke regardless of age and gender.

Systems-based approaches to neuroscience, using network analysis and the human connectome, have been adopted by many researchers by virtue of recent progress in neuroimaging and computational technologies. Various neurological disorders have been evaluated from a network perspective, including stroke, Alzheimer’s disease, Parkinson’s disease, and traumatic brain injury. Until now, dynamic processes after stroke and during recovery were investigated through multimodal neuroimaging techniques. Many studies have shown disruptions in structural and functional connectivity, including in large-scale neural networks, in patients with stroke sequela such as motor weakness, aphasia, hemianopia, neglect, and general cognitive dysfunction. A connectome-based approach might shed light on the underlying mechanisms of stroke sequela and the recovery process, and could identify candidates for individualized rehabilitation programs. In this review, we briefly outline the basic concepts of structural and functional connectivity, and the connectome. Then, we explore current evidence regarding how stroke lesions cause changes in connectivity and network architecture parameters. Finally, the clinical implications of perspectives on the connectome are discussed in relation to the cognitive and behavioral sequela of stroke.

Cerebral small vessel disease (SVD) commonly coexists with large artery atherosclerosis (LAA).We evaluate the effect of SVD on stroke recurrence in patients for ischemic stroke with LAA.We consecutively collected first-ever ischemic stroke patients who were classified as LAA mechanism between Jan 2010 and Dec 2013. Univariate and multivariate Cox analyses were performed to evaluate the association between the 2-year recurrence and demographic, clinical, and radiological factors. To evaluate the impact of SVD and its components on recurrent stroke, we used the Kaplan-Meier analysis. SVD was defined as the presence of severe white matter hyperintensity (WMH) or old lacunar infarction (OLI) or cerebral microbleeds (CMB). We also compared frequency and burden of SVD among recurrent stroke groups with different mechanisms.Among a total of 956 participants, 92 patients had recurrent events. Recurrence group showed a higher frequency of severe WMH, OLI, asymptomatic territorial infarction, and severe stenosis on the relevant vessel in multivariate analysis. The impact of SVD and its components on recurrent stroke was significant in any ischemic recurrent stroke, and the presence of SVD was continuously important in stroke recurrence regardless of its mechanism, including recurrent LAA stroke, recurrent small vessel occlusion stroke, and even recurrent cardioembolic stroke. Additionally, the recurrence rate increased in dose-response manner with the increased number of SVD components.Cerebral SVD is associated with recurrent stroke in patients with LAA. Additionally, it may affect any mechanisms of recurrent stroke and even with a dose response manner.

[This corrects the article DOI: 10.1371/journal.pone.0172210.].

INTRODUCTION:Cerebral microbleed (CMB) is a potent risk factor for overt cerebrovascular disease. Although some studies indicated the possible role of renal dysfunction as a risk factor of CMB, the findings could not be generalized. This study aimed to investigate the association between renal dysfunction and cerebral microbleed (CMB) in neurologically healthy adults. MATERIALS AND METHODS:A total of 2,518 subjects who underwent brain MRI as part of health screening were involved in the study. CMBs were defined as well-demarcated focal areas of low signal intensity with associated blooming on the T2-weighted MRI measuring less than 5mm in diameter. The glomerular filtration rate (GFR) was estimated using the Modification of Diet in Renal Disease formula. Kidney function was classified as normal (≥90), mild (60 to 89.9), moderate (30 to 59.9), and severe (

Acute perturbation of the hippocampus, one of the connector hubs in the brain, is a key step in the pathophysiological cascade of transient global amnesia (TGA). We tested the hypothesis that network efficiency, meaning the efficiency of information exchange over a network, is impaired during the acute stage of TGA. Graph theoretical analysis was applied to resting-state EEG data collected from 21 patients with TGA. The EEG data were obtained twice, once during the acute stage (< 24 hours after symptom onset) and once during the resolved stage (> 2 months after symptom onset) of TGA. Characteristic path lengths and clustering coefficients of functional networks constructed using phase-locking values were computed and normalized as a function of the degree in the delta, theta, alpha, beta 1, beta 2 and gamma frequency bands of the EEG. We investigated whether the normalized characteristic path length (nCPL) and normalized clustering coefficients (nCC) differed significantly between the acute and resolved stages of TGA at each frequency band using the Wilcoxon signed-rank test. For networks where the nCPL or nCC differed significantly between the two stages, we also evaluated changes in the connections of the brain networks. During the acute stage of TGA, the nCPL of the theta band networks with mean degrees of 8, 8.5, 9 and 9.5 significantly increased (P < 0.05). During the acute stage, the lost edges for these networks were mostly found between the anterior (frontal and anterior temporal) and posterior (parieto-occipital and posterior temporal) brain regions, whereas newly developed edges were primarily found between the left and right frontotemporal regions. The nCC of the theta band with a mean degree of 5.5 significantly decreased during the acute stage (P < 0.05). Our results indicate that TGA deteriorates the network efficiency of the theta frequency band. This effect might be related to the desynchronization between the anterior and posterior brain areas.

Jae-Sung Lim1, Hyung-Min Kwon21Department of Neurology, Armed Forces Seoul Hospital, Seoul, Republic of Korea; 2Department of Neurology, Seoul National University Boramae Hospital, Seoul, Republic of KoreaAbstract: With the increasing size of the elderly population and evolving imaging technology, silent brain infarction (SBI) has garnered attention from both the public and the physicians. Over 20% of the elderly exhibit SBI, and the prevalence of SBI increases steadily with age, ie, 30%–40% in those older than 70 years. Well-known cardiovascular risk factors such as hypertension has been identified as a risk factor of SBI (odds ratio [OR] = 3.47) Besides this, blood pressure (BP) reactivity to mental stress, morning BP surges, and orthostatic BP changes have been demonstrated to contribute to the presence of SBI. Further, a metabolic syndrome not only as a whole syndrome (OR = 2.18) but also as individual components could have an influence on SBI. Increased C-reactive protein and interleukin-6, coronary artery disease, body mass index, and alcohol consumption have also been associated with SBI. The ORs and ­possible mechanisms have been discussed in this article. Overt stroke, dementia, depression, and aspiration pneumonia were all associated with SBI. (overt stroke: hazard ratio [HR] = 1.9, 95% confidence interval [CI]; 1.2–2.8; dementia: HR = 2.26, 95% CI: 1.09–4.70). We also looked into their close relationship with SBI in this review.Keywords: silent brain infarction, elderly, risk factors, treatment