No abstract available.
Coronary Vessels*

BACKGROUND: White matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) have been considered as a reliable biomarker of small vessel damages. To evaluate the severity of WMHs, it is vital to develop reliable methods to measure the volume of WMHs. We applied open source software to measure WMH volume in the semi-automated way, and tested the reliability and validity by comparing with the commonly used qualitative rating scale. METHODS: Twenty five subjects with variable WMHs were recruited. ANALYZE 10.0 was used for the image processing and volumetric measurement of WMHs. The inhomogeneity and artifacts of signal were corrected with Insight Segmentation and Registration Toolkit in ANALYZE. For the gold standard of the WMH volumetric measurement, threshold method was applied with consensus of manual editing on each slice of the MRI images by two raters. Histogram of the all slices of the Fluid Attenuated Inversion Recovery (FLAIR) MRI was generated to calculate the optimal voxel intensity of threshold, and the lowest voxel threshold was decided as the mean+1.4 SD. The volumes of WMHs were generated by multiplying the area and the thickness of each slice. Inter- and intrarater reliability of the semi-automated volumetric and Scheltens'methods, and the association between the individual methods were analyzed. RESULTS: The semi-automated WMH volume at the threshold of 1.4 SD as well as the gold standard volume was well correlated with the Scheltens' visual scale (r=0.75, p<0.001). The semi-automated volumetry showed the excellent intra-rater (ICC=0.9929; 95% CI, 0.9840-0.9968) and inter-rater reliability (ICC=0.9830; 95% CI, 0.9620-0.9925), superior to the Scheltens' visual rating scale. CONCLUSIONS: The semi-automated volume measurement of the WMHs with Analyze was a valid and a reliable method to quantify subcortical white matter damages of various etiologies.
Artifacts ; Consensus ; Glycosaminoglycans ; Humans ; Magnetic Resonance Imaging ; Reproducibility of Results

BACKGROUND AND PURPOSE: The "closing-in" phenomenon refers to the tendency to copy near or overlap a model while performing figure-copying tasks. The mechanisms underlying the closing-in phenomenon have not been fully elucidated, and previous studies only investigated the mechanisms through neuropsychological tests. We investigated the neuroanatomical correlates of the closing-in phenomenon using voxel-based morphometry (VBM). METHODS: Thirty-eight patients diagnosed with probable Alzheimer's disease (AD) and 21 normal controls were included. All subjects underwent neuropsychological testing to diagnose dementia and magnetization prepared rapid acquisition gradient echo brain magnetic resonance imaging for the voxel-based statistical analysis. The subjects were asked to copy the modified Luria's alternating squares and triangles to quantify the closing-in phenomenon. We applied SPM8 for the VBM analysis to detect gray matter loss associated with the closing-in phenomenon. RESULTS: The patients with probable AD showed a higher closing-in score than that of the normal control subjects (p<0.0001). The VBM analysis revealed more parietal and temporal atrophy in the patients with AD than that in the normal control group. Moreover, atrophy of the orbito-frontal area was associated with the closing-in phenomenon. CONCLUSIONS: The closing-in phenomenon is dysfunction while performing figure-copying tasks and is more common in patients with AD. The analysis of the orbito-frontal area, which is associated with inhibiting primitive reflexes, revealed that the closing-in phenomenon is an imitation behavior commonly observed in patients with frontal lobe damage.
Alzheimer Disease ; Atrophy ; Brain ; Dementia ; Frontal Lobe ; Humans ; Magnetic Resonance Imaging ; Neuropsychological Tests ; Rabeprazole ; Reflex

Background: and Purpose: Early detection of subjective cognitive decline (SCD) due to Alzheimer’s disease (AD) is important for clinical research and effective prevention and management. This study examined if quantitative electroencephalography (qEEG) could be used for early detection of AD in SCD. Methods: Participants with SCD from 6 dementia clinics in Korea were enrolled.18 F-florbetaben brain amyloid positron emission tomography (PET) was conducted for all the participants. qEEG was performed to measure power spectrum and source cortical activity. Results: The present study included 95 participants aged over 65 years, including 26 amyloid PET (+) and 69 amyloid PET (−). In participants with amyloid PET (+), relative power at delta band was higher in frontal (p=0.025), parietal (p=0.005), and occipital (p=0.022) areas even after adjusting for age, sex, and education. Source activities of alpha 1 band were significantly decreased in the bilateral fusiform and inferior temporal areas, whereas those of delta band were increased in the bilateral cuneus, pericalcarine, lingual, lateral occipital, precuneus, posterior cingulate, and isthmus areas. There were increased connections between bilateral precuneus areas but decreased connections between left rostral middle frontal area and bilateral frontal poles at delta band in participants with amyloid PET (+) showed. At alpha 1 band, there were decreased connections between bilateral entorhinal areas after adjusting for covariates. Conclusions SCD participants with amyloid PET (+) showed increased delta and decreased alpha 1 activity. qEEG is a potential means for predicting amyloid pathology in SCD. Further longitudinal studies are needed to confirm these findings.

Background: and Purpose: Voice, reflecting cerebral functions, holds potential for analyzing and understanding brain function, especially in the context of cognitive impairment (CI) and Alzheimer’s disease (AD). This study used voice data to distinguish between normal cognition and CI or Alzheimer’s disease dementia (ADD). Methods: This study enrolled 3 groups of subjects: 1) 52 subjects with subjective cognitive decline; 2) 110 subjects with mild CI; and 3) 59 subjects with ADD. Voice features were extracted using Mel-frequency cepstral coefficients and Chroma. Results: A deep neural network (DNN) model showed promising performance, with an accuracy of roughly 81% in 10 trials in predicting ADD, which increased to an average value of about 82.0%±1.6% when evaluated against unseen test dataset. Conclusions Although results did not demonstrate the level of accuracy necessary for a definitive clinical tool, they provided a compelling proof-of-concept for the potential use of voice data in cognitive status assessment. DNN algorithms using voice offer a promising approach to early detection of AD. They could improve the accuracy and accessibility of diagnosis, ultimately leading to better outcomes for patients.

The aim of the study was to assess the association between usual dietary nutrient intake and obesity in Korean type 2 diabetic patients. We examined 2,832 type 2 diabetic patients from the Korean National Diabetes Program cohort who completed dietary assessment and clinical evaluation in this cross-sectional study. In men, higher dietary fiber intake was associated with a lower odds of being obese (Ptrend = 0.003) and in women, higher protein intake was associated with a lower odds of being obese (Ptrend = 0.03) after adjustment for age, diabetes duration, HbA1c, alcohol drinking, income, education level, and calorie intake. In men, higher fiber intake was associated with lower odds of obesity after further adjustment for diastolic blood pressure, physical activity, and possible confounding nutritional intake and medication. The multivariable adjusted odds ratio for the highest quintile of fiber intake was 0.37 (Ptrend < 0.001). In women, protein intake was not associated with obesity after further adjustment. In conclusion, higher intake of dietary fiber is associated with lower odds of being obese in type 2 diabetic men, suggesting a role for dietary fiber in the management and prevention of obesity in type 2 diabetes (ClinicalTrials.gov: NCT 01212198).
Asian Continental Ancestry Group ; Cohort Studies ; Cross-Sectional Studies ; Demography ; Diabetes Mellitus, Type 2/*complications/diagnosis ; Dietary Fiber ; *Energy Intake ; Female ; Humans ; Male ; Middle Aged ; Obesity/*etiology ; Odds Ratio ; Republic of Korea ; Risk Factors

no abstract available.

Background: and Purpose: Dementia subtypes, including Alzheimer’s dementia (AD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD), pose diagnostic challenges. This review examines the effectiveness of 18 F-Fluorodeoxyglucose Positron Emission Tomography ( 18 F-FDG PET) in differentiating these subtypes for precise treatment and management. Methods: A systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines was conducted using databases like PubMed and Embase to identify studies on the diagnostic utility of 18 F-FDG PET in dementia. The search included studies up to November 16, 2022, focusing on peer-reviewed journals and applying the goldstandard clinical diagnosis for dementia subtypes. Results: From 12,815 articles, 14 were selected for final analysis. For AD versus FTD, the sensitivity was 0.96 (95% confidence interval [CI], 0.88–0.98) and specificity was 0.84 (95% CI, 0.70–0.92). In the case of AD versus DLB, 18F-FDG PET showed a sensitivity of 0.93 (95% CI 0.88-0.98) and specificity of 0.92 (95% CI, 0.70–0.92). Lastly, when differentiating AD from non-AD dementias, the sensitivity was 0.86 (95% CI, 0.80–0.91) and the specificity was 0.88 (95% CI, 0.80–0.91). The studies mostly used case-control designs with visual and quantitative assessments. Conclusions 18 F-FDG PET exhibits high sensitivity and specificity in differentiating dementia subtypes, particularly AD, FTD, and DLB. This method, while not a standalone diagnostic tool, significantly enhances diagnostic accuracy in uncertain cases, complementing clinical assessments and structural imaging.