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OBJECTIVES The purpose of this study was to compare the risk factors associated with anterior cruciate ligament (ACL) injuries in elite soccer and taekwondo players by examining landing error scoring system (LESS) scores and limb symmetry index (LSI). METHODS A total of 20 male athletes (10 soccer players and 10 taekwondo players) were evaluated. Participants completed a drop-landing task, and their landing mechanics were evaluated using the LESS. Muscle activation was measured with surface electrodes placed on the rectus femoris and biceps femoris. LSI values were calculated as a percentage based on muscle activation data, using the ratio of the nondominant limb to the dominant limb, to assess asymmetry. RESULTS Significant differences were observed between the two groups in knee valgus angle at initial contact (p=0.005), knee joint displacement (p=0.024), and overall impression (p=0.011). Notably, the soccer players demonstrated a significantly higher LESS score (total score, p=0.024) compared to the taekwondo players. However, no significant differences in LSI were found between the two groups. CONCLUSIONS The findings suggest that elite male soccer players may benefit from targeted interventions, such as neuromuscular control training, to enhance joint stability and correct landing mechanics, compared to taekwondo players.

Efforts to achieve carbon neutrality, which aims to reduce the net carbon emissions to zero by decreasing carbon emissions from human activities and increasing carbon absorption, are actively underway. Additionally, the search for clean energy alternatives to fossil fuels has become a global research trend. This paper presents research on metal–air batteries, focusing on the development of energy supply technologies that do not generate carbon emissions during power generation and require less space for power generation compared to existing renewable energy sources. The proposed Mg–air battery (MAB) in this study uses magnesium as the metal anode and theoretically offers a maximum open-circuit voltage of 3.1 V and a high energy density of 6.8 kWh/kg. While previous research has primarily focused on designing small-capacity cells and maximizing the performance of metal anodes, this study differentiates itself by designing a large-capacity MAB cell and optimizing its electrical performance. For the large-capacity cell design, the weight, shape, and size of the anode were designed based on MAB performance factors, and research was conducted on manufacturing methods to optimize the performance of the air cathode. Furthermore, to enhance usability and extend the lifespan of the MAB cell, it was designed to allow electrolyte circulation, and the electrolyte circulation performance was verified through simulations of fluid flow within the cell. Based on the study of the power performance of the newly designed large-capacity MAB cell, the feasibility of constructing a kW-class system using multiple Mg–air battery cell stacks was confirmed.

Purpose There are various variables such as exercise posture, exercise intensity, number of repetitions, and rest time of training for muscle strength development, and these variables are intended to stimulate muscle activity. The purpose of this study was to examine the effects of muscle activation according to grip thickness in pull-up exercise. Methods Eleven healthy men were randomly crossover design assigned to pull-up exercise (concentric: 1-s, eccentric: 1-s, 2-s/repetition) to failure. Surface electromyography (EMG) was recorded from the forearm flexors/extensors, biceps brachii, trapezius middle/ lower and latissimus dorsi for muscle activation. Using the resulting EMG data, which were filtered of electromyogram artifacts, we calculated the root mean squares (RMS). Results Dependent-sample t-test produced a result, muscle activity in forearm flexors (p

In this paper, we present an electric field analysis for the optimal structural design of lightning rods for high performance with a charge transfer system (CTS). In the case of a conventional rod that is produced with an empirical design and structure without quantitative data because the design is structurally very simple, only the materials and radius of curvature of the lightning rod to concentrate the electric field at the tip part of the rod are considered. Recently, the development of new types of lightning rods, such as early streamer emission (ESE) and charge transfer system (CTS), has been introduced through simulation analysis and experiments, but detailed specifications and information about the optimal design and structure have not been fully reported. In this paper, we performed an electric field analysis of the structures and materials for the optimal structural design of lightning rods with a function of CTS through computer software analysis with consideration for the radius of curvature, the size of corona ring, and optimal position (X-axis and Y-axis) of the floating electrode. For optimal structural design of lightning rods based on electric field analysis, we used a source of lightning voltage with 1.2/50 µs based on a double exponential equation. The results revealed that the electric field on the relaxation part decreases as the radius of curvature and corona ring increases. For the radius of curvature, the electric field first decreases and then increases with increasing radius of curvature and reaches a minimum at 7 mm and a maximum above 8 mm. For the case of the corona ring, the electric field decreases with increasing corona ring, and the optimal size of the corona ring was selected as 4 mm; the size of the 4 mm corona ring uniformly formed the electric field both at the tip part of the ground current collector and the corona ring. For the electric field concentration part, we found that the optimal X-axis position of the floating electrode and the Y-axis position between the ionizer conductor and floating electrode are 7 mm and 0.1 mm, respectively. These simulation results in this paper are expected to provide useful information for the design of optimized CTS-type lightning rods.

In this paper, we present the optimized air space of the lightning protection rod (SK-AOR380) with the function of a charge transfer system (CTS). For evaluation of CTS in the laboratory setting, some studies have focused on the modification of the structure and shape of the CTS; the air space is designed (>2 mm) as an empirical design without quantitative data. However, in this paper, we have focused on the air space between the ionizer conductor and current collector to control the inception and occurrence position of corona discharge in air insulation. This is because the performance, such as the initial corona discharge inception of CTS, is determined by the air space. The simulation analysis was performed in a narrow, micro-sized air space as a first step, where the air space was reduced to the extent possible for simulation. To evaluate the performance of SK-AOR380 according to the narrow air space, we considered the numerical analysis method. The fundamental equations consist of Poisson’s equation and the charge continuity equation. Poisson’s equation for electric fields is a fully coupled numerical model based on the charge continuity equations for a positively charged ion, negatively charged ion, and free electron. Fowler–Nordheim electron emission was employed for the boundary condition at the surface of the ionizer conductor. To simulate the corona discharge behavior under standard lightning impulse voltage, we used a source of lightning voltage with 1.2/50 μs based on a double exponential equation; the corona discharge behaviors (electric field distribution, free electron density, positive and negative ion density) were investigated dependent on each time step (0.5, 1 and 1.2 μs) until 3.5 μs. The results revealed that the characteristics graph of free electron density, positive and negative ion density showed similar trends, with lightning impulse voltage increasing with increasing time steps until 1.2 μs and each density resulted in a decreasing trend from 1.2 μs to 3.5 μs. The SK-AOR380 is improved with a decreasing air space in terms of electric field distribution, electron, and ion density. In other words, the 0.0005 mm air space created a non-uniform electric field distribution with a large field enhancement, causing ionization to initiate corona discharge. In addition, in the case of a 0.0005 mm air space, the electric field and electron density are increased by 1.3 and 1.9 times, respectively, than that of 0.001 mm. However, there was no longer a significant difference under 0.0005 mm in the simulation results. To improve the CTS, we suggest the air space between the ionizer conductor and current collector should be less than 2 mm than that of conventional CTS from our research work.

During the off-season, soccer players in Korea attend the winter training season (WTS) to build running stamina for the next season. For young soccer players, proper recovery time is needed to prevent injury or muscle damage. In this study, urinary metabolites in young players after 1, 5, and 10 days of the WTS were analyzed using nuclear magnetic resonance spectroscopy (NMR) combined with multivariate analysis to suggest appropriate recovery times for improving their soccer skills. After NMR analysis of the urine samples obtained from young players, 79 metabolites were identified, and each group (1, 5, or 10 days after WTS) was separated from the before the WTS group in the target profiling analysis using partial least squares-discriminant analysis (PLS-DA). Of these, 15 metabolites, including 1-methylnicotinamide, 3-indoxylsulfate, galactarate, glutamate, glycerol, histamine, methylmalonate, maltose, N-phenylacetylglycine, trimethylamine, urea, 2-hydroxybutyrate, adenine, alanine, and lactate, were significantly different than those from before the WTS and were mainly involved in the urea, purine nucleotide, and glucose-alanine cycles. In this study, most selected metabolites increased 1 day after the WTS and then returned to normal levels. However, 4 metabolites, adenine, 2-hydroxybutyrate, alanine, and lactate, increased during the 5 days of recovery time following the WTS. Based on excess ammonia, adenine, and lactate levels in the urine, at least 5 days of recovery time can be considered appropriate.

Although Alzheimer’s disease (AD) is the most common neurodegenerative disease, there are still no drugs available to treat or prevent AD effectively. Here, we examined changes in levels of selected proteins implicated in the pathogenesis of AD using plasma samples of control subjects and patients with cognition impairment. To precisely categorize the disease, fifty-six participants were examined with clinical cognitive tests, amyloid positron emission tomography (PET) scan, and white matter hyperintensities scored by magnetic resonance imaging. Plasma cathepsin D levels of the subjects were examined by immunoblotting and enzyme-linked immunosorbent assay (ELISA). Correlation of plasma cathepsin D levels with AD-related factors and clinical characteristics were examined by statistical analysis. By analyzing quantitative immunoblot and ELISA, we found that the plasma level of cathepsin D, a major lysosomal protease, was decreased in the group with amyloid plaque deposition at the brain compared to the control group. The level of plasma cathepsin D was negatively correlated with clinical dementia rating scale sum of boxes (CDR-SB) scores. In addition, our integrated multivariable logistic regression model suggests the high performance of plasma cathepsin D level for discriminating AD from non-AD. These results suggest that the plasma cathepsin D level could be developed as a diagnostic biomarker candidate for AD.

This paper presents a case study on the problem of loading air containers in air express carriers motivated from DHL and Air Hong Kong. The problem is to determine the containers to be loaded and the locations of the loaded containers in an aircraft while maintaining stability of the aircraft. The objective of the problem is to maximize the revenue obtained from delivering containers. We present an integer programming model to represent and optimally solve the problem. Computational experiments done on a number of randomly generated test instances show that the integer program can be a viable tool for generating loading plans in the companies since optimal or near-optimal solutions for the test instances are obtained within a reasonable amount of computation time.

Abstract Background Cholesterol plays important roles in β-amyloid (Aβ) metabolism and atherosclerosis. However, the relationships of plasma cholesterol levels with Aβ and cerebral small vessel disease (CSVD) burdens are not fully understood in Asians. Herein, we investigated the relationships between plasma cholesterol profile components and Aβ and CSVD burdens in a large, non-demented Korean cohort. Methods We enrolled 1,175 non-demented participants (456 with unimpaired cognition [CU] and 719 with mild cognitive impairment [MCI]) aged ≥ 45 years who underwent Aβ PET at the Samsung Medical Center in Korea. We performed linear regression analyses with each cholesterol (low-density lipoprotein cholesterol [LDL-c], high-density lipoprotein cholesterol [HDL-c], and triglyceride) level as a predictor and each image marker (Aβ uptake on PET, white matter hyperintensity [WMH] volume, and hippocampal volume) as an outcome after controlling for potential confounders. Results Increased LDL-c levels (β = 0.014 to 0.115, p = 0.013) were associated with greater Aβ uptake, independent of the APOE e4 allele genotype and lipid-lowering medication. Decreased HDL-c levels (β = − 0.133 to − 0.006, p = 0.032) were predictive of higher WMH volumes. Increased LDL-c levels were also associated with decreased hippocampal volume (direct effect β = − 0.053, p = 0.040), which was partially mediated by Aβ uptake (indirect effect β = − 0.018, p = 0.006). Conclusions Our findings highlight that increased LDL-c and decreased HDL-c levels are important risk factors for Aβ and CSVD burdens, respectively. Furthermore, considering that plasma cholesterol profile components are potentially modified by diet, exercise, and pharmacological agents, our results provide evidence that regulating LDL-c and HDL-c levels is a potential strategy to prevent dementia.

Abstract Several programs are widely used for clinical and research purposes to automatically quantify the degree of amyloid deposition in the brain using positron emission tomography (PET) images. Given that very few studies have investigated the use of Heuron, a PET image quantification software approved for clinical use, this study aimed to compare amyloid deposition values quantified from 18F-flutemetamol PET images using PMOD and Heuron. Amyloid PET data obtained from 408 patients were analysed using each quantitative program; moreover, the standardized uptake value ratios (SUVRs) of target areas were obtained by dividing the standardized uptake value (SUV) of the target region by the SUV of cerebellar grey matter as a reference. Compared with PMOD, Heuron yielded significantly higher SUVRs for all target areas (paired sample t-test, p

Abstract Amyloid PET imaging has been crucial for detecting the accumulation of amyloid beta (Aβ) deposits in the brain and to study Alzheimer’s disease (AD). We performed a genome-wide association study on the largest collection of amyloid imaging data (N = 13,409) to date, across multiple ethnicities from multicenter cohorts to identify variants associated with brain amyloidosis and AD risk. We found a strong APOE signal on chr19q.13.32 (top SNP: APOE ɛ4; rs429358; β = 0.35, SE = 0.01, P = 6.2 × 10–311, MAF = 0.19), driven by APOE ɛ4, and five additional novel associations (APOE ε2/rs7412; rs73052335/rs5117, rs1081105, rs438811, and rs4420638) independent of APOE ɛ4. APOE ɛ4 and ε2 showed race specific effect with stronger association in Non-Hispanic Whites, with the lowest association in Asians. Besides the APOE, we also identified three other genome-wide loci: ABCA7 (rs12151021/chr19p.13.3; β = 0.07, SE = 0.01, P = 9.2 × 10–09, MAF = 0.32), CR1 (rs6656401/chr1q.32.2; β = 0.1, SE = 0.02, P = 2.4 × 10–10, MAF = 0.18) and FERMT2 locus (rs117834516/chr14q.22.1; β = 0.16, SE = 0.03, P = 1.1 × 10–09, MAF = 0.06) that all colocalized with AD risk. Sex-stratified analyses identified two novel female-specific signals on chr5p.14.1 (rs529007143, β = 0.79, SE = 0.14, P = 1.4 × 10–08, MAF = 0.006, sex-interaction P = 9.8 × 10–07) and chr11p.15.2 (rs192346166, β = 0.94, SE = 0.17, P = 3.7 × 10–08, MAF = 0.004, sex-interaction P = 1.3 × 10–03). We also demonstrated that the overall genetic architecture of brain amyloidosis overlaps with that of AD, Frontotemporal Dementia, stroke, and brain structure-related complex human traits. Overall, our results have important implications when estimating the individual risk to a population level, as race and sex will needed to be taken into account. This may affect participant selection for future clinical trials and therapies.

Abstract Background A growing body of evidence shows differences in the prevalence of cardiometabolic syndrome (CMS) and dementia based on gender and ethnicity. However, there is a paucity of information about ethnic- and gender-specific CMS effects on brain age. We investigated the different effects of CMS on brain age by gender in Korean and British cognitively unimpaired (CU) populations. We also determined whether the gender-specific difference in the effects of CMS on brain age changes depending on ethnicity. Methods These analyses used de-identified, cross-sectional data on CU populations from Korea and United Kingdom (UK) that underwent brain MRI. After propensity score matching to balance the age and gender between the Korean and UK populations, 5759 Korean individuals (3042 males and 2717 females) and 9903 individuals from the UK (4736 males and 5167 females) were included in this study. Brain age index (BAI), calculated by the difference between the predicted brain age by the algorithm and the chronological age, was considered as main outcome and presence of CMS, including type 2 diabetes mellitus (T2DM), hypertension, obesity, and underweight was considered as a predictor. Gender (males and females) and ethnicity (Korean and UK) were considered as effect modifiers. Results The presence of T2DM and hypertension was associated with a higher BAI regardless of gender and ethnicity (p

Aggregation of misfolded tau in the brain is a major pathological feature common in various neurodegenerative disorders known as tauopathies, including Alzheimer’s disease, progressive supranuclear palsy, corticobasal syndrome, and dementia with Lewy bodies. Tauopathies are collection of diseases with varied overlapping symptoms and complicated manifestations. Consequently, it is crucial to be able to assess tau deposits in vivo. Over the past decade, tau-specific radioligands for positron emission tomography (PET) have been developed and tested, including first-generation compounds (e.g., 18F-THK5317, 18F-THK5351, 18F-AV1451, and 11C-PBB3) and second-generation compounds (18F-MK-6240, 18F-RO-948, and 18F-PI-2620). With the recent advances of tau PET tracers, assessing the pattern of tau deposition in vivo is possible. These methods will allow accurate diagnosis of tauopathies and monitoring of disease progression. In this mini review, we summarize current findings from studies using tau PET tracers regarding neuropathological characteristics, clinical implications, and potential applications of tau PET. We also discuss methodological considerations for appropriate use of these technologies and discuss what has been learned from these findings.

Abstract Background Accessible and cost-effective diagnostic tools are urgently needed to accurately quantify blood biomarkers to support early diagnosis of Alzheimer’s disease (AD). In this study, we investigated the ability of plasma amyloid-beta (Aβ)42/Aβ40 ratio measured by an antibody-free mass-spectrometric (MS) method, ABtest-MS, to detect early pathological changes of AD. Methods This cohort study included data from the baseline and 2-year follow-up visits from the Fundació ACE Healthy Brain Initiative (FACEHBI) study. Plasma Aβ42/Aβ40 was measured with ABtest-MS and compared to 18F-Florbetaben PET as the reference standard (cutoff for early amyloid deposition of 13.5 centiloids). Cross-validation was performed in an independent DPUK-Korean cohort. Additionally, associations of plasma Aβ42/Aβ40 with episodic memory performance and brain atrophy were assessed. Results The FACEHBI cohort at baseline included 200 healthy individuals with subjective cognitive decline (SCD), of which 36 (18%) were Aβ-PET positive. Plasma Aβ42/Aβ40 levels were significantly lower in Aβ-PET positive individuals (median [interquartile range, IQR], 0.215 [0.203–0.236]) versus Aβ-PET negative subjects (median [IQR], 0.261 [0.244–0.279]) (P < .001). Plasma Aβ42/Aβ40 was significantly correlated with Aβ-PET levels (rho = −0.390; P < .001) and identified Aβ-PET status with an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI], 0.80–0.93). A cutoff for the Aβ42/Aβ40 ratio of 0.241 (maximum Youden index) yielded a sensitivity of 86.1% and a specificity of 80.5%. These findings were cross-validated in an independent DPUK-Korean cohort (AUC 0.86 [95% CI 0.77–0.95]). Lower plasma Aβ42/Aβ40 ratio was associated with worse episodic memory performance and increased brain atrophy. Plasma Aβ42/Aβ40 at baseline predicted clinical conversion to mild cognitive impairment and longitudinal changes in amyloid deposition and brain atrophy at 2-year follow-up. Conclusions This study suggests that plasma Aβ42/Aβ40, as determined by this MS-based assay, has potential value as an accurate and cost-effective tool to identify individuals in the earliest stages of AD, supporting its implementation in clinical trials, preventative strategies and clinical practice.

Abstract Background We investigated the relevance of various imaging markers for the clinical trajectory of cerebral amyloid angiopathy (CAA) patients in a memory clinic. Methods A total of 226 patients with probable CAA were included in this study with a mean follow-up period of 3.5 ± 2.7 years. Although all had more than one follow-up visit, 173 underwent follow-up Mini-Mental Status Examination (MMSE) and Clinical Dementia Rating Sum of Boxes (CDR-SB) ranging from 2 to 15 time points. Among 226, 122 patients underwent amyloid-β (Aβ) PET imaging. The prevalence of intracerebral hemorrhage (ICH) and its imaging predictors was investigated. The effects of CAA imaging markers and Aβ PET positivity on longitudinal cognition based on the MMSE and CDR-SB were evaluated using mixed effects models. Results During the follow-up, 10 (4.4%) patients developed ICH: cortical superficial siderosis (cSS; hazard ratio [HR], 6.45) and previous lobar ICH (HR, 4.9), but lobar cerebral microbleeds (CMBs) were not predictors of ICH development. The presence of CMIs (p = 0.045) and Aβ positivity (p = 0.002) were associated with worse MMSE trajectory in CAA patients. Regarding CDR-SB trajectory, only Aβ positivity was marginally associated with worse longitudinal change (p = 0.050). Conclusion The results of the present study indicated that various imaging markers in CAA patients have different clinical relevance and predictive values for further clinical courses.

Amyloid-beta (Aβ) is a pathological hallmark of Alzheimer’s disease (AD). We aimed to identify genes related to Aβ uptake in the Korean population and investigate the effects of these novel genes on clinical outcomes, including neurodegeneration and cognitive impairments. We recruited a total of 759 Korean participants who underwent neuropsychological tests, brain magnetic resonance imaging, 18F-flutemetamol positron emission tomography, and microarray genotyping data. We performed gene-based association analysis, and also performed expression quantitative trait loci and network analysis. In genome-wide association studies, no single nucleotide polymorphism (SNP) passed the genome-wide significance threshold. In gene-based association analysis, six genes (LCMT1, SCRN2, LRRC46, MRPL10, SP6, and OSBPL7) were significantly associated with Aβ standardised uptake value ratio in the brain. The three most significant SNPs (rs4787307, rs9903904, and rs11079797) on these genes are associated with the regulation of the LCMT1, OSBPL7, and SCRN2 genes, respectively. These SNPs are involved in decreasing hippocampal volume and cognitive scores by mediating Aβ uptake. The 19 enriched gene sets identified by pathway analysis included axon and chemokine activity. Our findings suggest novel susceptibility genes associated with the uptake of Aβ, which in turn leads to worse clinical outcomes. Our findings might lead to the discovery of new AD treatment targets.

BackgroundNon-alcoholic fatty liver disease (NAFLD) is known to be associated with a high risk of clinically diagnosed Alzheimer’s disease (AD). Additionally, the prevalence of NAFLD and AD is higher in elderly females than in males. However, a sex-specific association between NAFLD and amyloid-beta (Aβ) deposition remains unclear. Therefore, we investigated the sex-specific relationship between NAFLD and Aβ deposition in a large-sized cohort of cognitively unimpaired (CU) individuals.MethodsWe enrolled 673 (410 [60.9%] females and 263 [39.1%] males) CU individuals aged ≥45 years who underwent Aβ positron emission tomography (PET). The presence of NAFLD, assessed using the hepatic steatosis index, and the severity of NAFLD, assessed using the Fibrosis-4 index, were considered predictors. Aβ deposition on PET was considered as an outcome.ResultsFemales had a higher frequency of NAFLD than males (48 and 23.2%, p

Background and objectivesAlzheimer’s disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD.MethodsIn total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis.ResultsWe observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia.ConclusionOur findings provide important evidence for sex-specific alterations in the structural brain network related to AD.

Abstract Background The standard Centiloid (CL) method was proposed to harmonize and quantify global 18F-labeled amyloid beta (Aβ) PET ligands using MRI as an anatomical reference. However, there is need for harmonizing and quantifying regional Aβ uptakes between ligands using CT as an anatomical reference. In the present study, we developed and validated a CT-based regional direct comparison of 18F-florbetaben (FBB) and 18F-flutemetamol (FMM) Centiloid (rdcCL). Methods For development of MRI-based or CT-based rdcCLs, the cohort consisted of 63 subjects (20 young controls (YC) and 18 old controls (OC), and 25 participants with Alzheimer’s disease dementia (ADD)). We performed a direct comparison of the FMM-FBB rdcCL method using MRI and CT images to define a common target region and the six regional VOIs of frontal, temporal, parietal, posterior cingulate, occipital, and striatal regions. Global and regional rdcCL scales were compared between MRI-based and CT-based methods. For clinical validation, the cohort consisted of 2245 subjects (627 CN, 933 MCI, and 685 ADD). Results Both MRI-based and CT-based rdcCL scales showed that FMM and FBB were highly correlated with each other, globally and regionally (R 2 = 0.96~0.99). Both FMM and FBB showed that CT-based rdcCL scales were highly correlated with MRI-based rdcCL scales (R 2 = 0.97~0.99). Regarding the absolute difference of rdcCLs between FMM and FBB, the CT-based method was not different from the MRI-based method, globally or regionally (p value = 0.07~0.95). In our clinical validation study, the global negative group showed that the regional positive subgroup had worse neuropsychological performance than the regional negative subgroup (p < 0.05). The global positive group also showed that the striatal positive subgroup had worse neuropsychological performance than the striatal negative subgroup (p < 0.05). Conclusions Our findings suggest that it is feasible to convert regional FMM or FBB rdcSUVR values into rdcCL scales without additional MRI scans. This allows a more easily accessible method for researchers that can be applicable to a variety of different conditions.

Abstract Background The relationship of specific body composition in the thighs and brain amyloid-beta (Aβ) deposition remained unclear, although there were growing evidence that higher muscle and fat mass in thighs had a protective effect against cardiometabolic syndromes. To determine whether muscle mass and fat mass in the thighs affected amyloid-beta (Aβ) positivity differently in relation to gender, we investigated the association of muscle mass and fat mass with Aβ positivity using positron emission tomography (PET) in individuals without dementia. Methods We recruited 240 participants (134 [55.8%] males, 106 [44.2%] females) without dementia ≥45 years of age who underwent Aβ PET, bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DEXA) scans of the hip in the health promotion center at Samsung Medical Center in Seoul, Korea. Lower extremity skeletal muscle mass index (LASMI) was measured using BIA, and gluteofemoral fat percentage (GFFP) was estimated using DEXA scans of the hip. We investigated the associations of LASMI and GFFP with Aβ positivity using logistic regression analyses after controlling for age, APOE4 genotype, and cognitive stage. Results Higher muscle mass in the thighs, measured as LASMI (odds ratio [OR]=0.27, 95% confidence interval [CI] 0.08 to 0.84, p=0.031) was associated with a lesser risk of Aβ positivity in only females. Higher fat mass in the thighs, measured as GFFP (OR=0.84, 95% CI 0.73 to 0.95, p=0.008) was associated with a lesser risk of Aβ positivity in only males. However, the association between LAMSI (p for interaction= 0.810), GFFP (p for interaction= 0.075) and Aβ positivity did not significantly differ by gender. Furthermore, LAMSI only negatively correlated with centiloid (CL) values in females (r=−0.205, p=0.037), and GFFP only negatively correlated with CL values only in males (r=−0.253, p=0.004). Conclusions Our findings highlight the importance of recognizing that gender differences exist with respect to the specific body composition to potentially protect against Aβ deposition. Therefore, our results may help in designing gender-specific strategies for controlling body composition to prevent Aβ deposition.

BackgroundWe aimed to investigate the incidence of dementia by age and year as well as the population-attributable fractions (PAFs) for known dementia risk factors in Republic of Korea.MethodsA 12-year, nationwide, population-based, retrospective cohort study was conducted. We used customized health information from the National Health Insurance Service (NHIS) data from 2002 to 2017. We analyzed age- and sex-adjusted incidence rates and PAF of dementia for each risk factor such as depression, diabetes, hemorrhagic stroke, ischemic stroke, hypertension, osteoporosis and physical inactivity using Levin’s formula.ResultsOf the 794,448 subjects in the dementia-free cohort, 49,524 (6.2%) developed dementia. Dementia incidence showed annual growth from 1.56 per 1,000 person-years in 2006 to 6.94 per 1,000 person-years in 2017. Of all dementia cases, 34,544 subjects (69.8%) were female and 2,479 subjects (5.0%) were early onset dementia. AD dementia accounted for 66.5% of the total dementia incidence. Considering relative risk and prevalence, physical inactivity attributed the greatest to dementia (PAF, 8.1%), followed by diabetes (PAF, 4.2%), and hypertension (PAF, 2.9%). Altogether, the significant risk factors increased the risk of dementia by 18.0% (overall PAF).ConclusionWe provided the incidence of dementia and PAFs for dementia risk factors in Republic of Korea using a 12-year, nationwide cohort. Encouraging lifestyle modifications and more aggressive control of risk factors may effectively prevent dementia.

IntroductionThe prevalence of Alzheimer’s disease (AD) and other dementias is increasing; therefore, identifying individuals at risk for dementia is crucial. Traditional neuropsychological assessments are expensive and time-consuming; however, computerized cognitive testing is becoming popular in clinical and research settings, particularly during the COVID-19 pandemic. This study aimed to investigate the correlation between the computerized cognitive test, Inbrain cognitive screening test (CST), and the traditional neuropsychological battery, the consortium to establish a registry for Alzheimer’s disease assessment packet (CERAD-K).MethodsWe enrolled 166 participants from five districts in Republic of Korea, including cognitively unimpaired individuals and those with mild cognitive impairment (MCI) diagnosed by experienced neurologists. We used the Inbrain CST and CERAD-K to evaluate the cognitive function of the participants, and the scores of each subtest of the Inbrain CST and CERAD-K were compared.ResultsA significant correlation was found between the Inbrain CST and CERAD-K subtests. Furthermore, multivariate analysis revealed a significant correlation between the Inbrain CST and the CERAD-K test pairs after adjusting for age, educational level, and sex.DiscussionIn conclusion, this study demonstrates that the Inbrain CST is a reliable tool for detecting cognitive impairment in cognitively unimpaired individuals and patients with MCI, because it has a high correlation and agreement with CERAD-K. Therefore, the Inbrain CST can be a useful, time-efficient, and cost-effective computer-based cognitive test for individuals at risk for cognitive impairment.

Abstract Cortical atrophy is measured clinically according to established visual rating scales based on magnetic resonance imaging (MRI). Although brain MRI is the primary imaging marker for neurodegeneration, computed tomography (CT) is also widely used for the early detection and diagnosis of dementia. However, they are seldom investigated. Therefore, we developed a machine learning algorithm for the automatic estimation of cortical atrophy on brain CT. Brain CT images (259 Alzheimer’s dementia and 55 cognitively normal subjects) were visually rated by three neurologists and used for training. We constructed an algorithm by combining the convolutional neural network and regularized logistic regression (RLR). Model performance was then compared with that of neurologists, and feature importance was measured. RLR provided fast and reliable automatic estimations of frontal atrophy (75.2% accuracy, 93.6% sensitivity, 67.2% specificity, and 0.87 area under the curve [AUC]), posterior atrophy (79.6% accuracy, 87.2% sensitivity, 75.9% specificity, and 0.88 AUC), right medial temporal atrophy (81.2% accuracy, 84.7% sensitivity, 79.6% specificity, and 0.88 AUC), and left medial temporal atrophy (77.7% accuracy, 91.1% sensitivity, 72.3% specificity, and 0.90 AUC). We concluded that RLR-based automatic estimation of brain CT provided a comprehensive rating of atrophy that can potentially support physicians in real clinical settings.

Abstract Background Decreased visual acuity (VA) is reported to be a risk factor for dementia. However, the association between VA and cortical thickness has not been established. We investigated the association between VA and cortical thickness in cognitively normal adults. Method We conducted a cross-sectional, single-center cohort study with cognitively normal adults (aged ≥ 45) who received medical screening examinations at the Health Promotion Center at Samsung Medical Center. Subjects were categorized as bad (VA ≤ 20/40), fair (20/40 < VA ≤ 20/25), and good (VA > 20/25) VA group by using corrected VA in the Snellen system. Using 3D volumetric brain MRI, cortical thickness was calculated using the Euclidean distance between the linked vertices of the inner and outer surfaces. We analyzed the association between VA and cortical thickness after controlling for age, sex, hypertension, diabetes, dyslipidemia, intracranial volume, and education level. Results A total of 2756 subjects were analyzed in this study. Compared to the good VA group, the bad VA group showed overall thinner cortex (p = 0.015), especially in the parietal (p = 0.018) and occipital (p = 0.011) lobes. Topographical color maps of vertex-wise analysis also showed that the bad VA group showed a thinner cortex in the parieto-temporo-occipital area. These results were more robust in younger adults (aged 45 to 65) as decreased VA was associated with thinner cortex in more widespread regions in the parieto-temporo-occipital area. Conclusion Our results suggest that a thinner cortex in the visual processing area of the brain is related to decreased visual stimuli.

Abstract Background While numerous neuroimaging studies have demonstrated that glaucoma is associated with smaller volumes of the visual cortices in the brain, only a few studies have linked glaucoma with brain structures beyond the visual cortices. Therefore, the objective of this study was to compare brain imaging markers and neuropsychological performance between individuals with and without glaucoma. Methods We identified 64 individuals with glaucoma and randomly selected 128 age-, sex-, and education level-matched individuals without glaucoma from a community-based cohort. The study participants underwent 3 T brain magnetic resonance imaging and neuropsychological assessment battery. Regional cortical thickness and subcortical volume were estimated from the brain images of the participants. We used a linear mixed model after adjusting for potential confounding variables. Results Cortical thickness in the occipital lobe was significantly smaller in individuals with glaucoma than in the matched individuals (β = − 0.04 mm, P = 0.014). This did not remain significant after adjusting for cardiovascular risk factors (β = − 0.02 mm, P = 0.67). Individuals with glaucoma had smaller volumes of the thalamus (β = − 212.8 mm3, P = 0.028), caudate (β = − 170.0 mm3, P = 0.029), putamen (β = − 151.4 mm3, P = 0.051), pallidum (β = − 103.6 mm3, P = 0.007), hippocampus (β = − 141.4 mm3, P = 0.026), and amygdala (β = − 87.9 mm3, P = 0.018) compared with those without glaucoma. Among neuropsychological battery tests, only the Stroop color reading test score was significantly lower in individuals with glaucoma compared with those without glaucoma (β = − 0.44, P = 0.038). Conclusions We found that glaucoma was associated with smaller volumes of the thalamus, caudate, putamen, pallidum, amygdala, and hippocampus.

BackgroundThe genetic basis of amyloid β (Aβ) deposition in subcortical vascular cognitive impairment (SVCI) is still unknown. Here, we investigated genetic variants involved in Aβ deposition in patients with SVCI.MethodsWe recruited a total of 110 patients with SVCI and 424 patients with Alzheimer’s disease-related cognitive impairment (ADCI), who underwent Aβ positron emission tomography and genetic testing. Using candidate AD-associated single nucleotide polymorphisms (SNPs) that were previously identified, we investigated Aβ-associated SNPs that were shared or distinct between patients with SVCI and those with ADCI. Replication analyses were performed using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Rush Memory and Aging Project cohorts (ROS/MAP).ResultsWe identified a novel SNP, rs4732728, which showed distinct associations with Aβ positivity in patients with SVCI (Pinteraction = 1.49 × 10–5); rs4732728 was associated with increased Aβ positivity in SVCI but decreased Aβ positivity in ADCI. This pattern was also observed in ADNI and ROS/MAP cohorts. Prediction performance for Aβ positivity in patients with SVCI increased (area under the receiver operating characteristic curve = 0.780; 95% confidence interval = 0.757–0.803) when rs4732728 was included. Cis-expression quantitative trait loci analysis demonstrated that rs4732728 was associated with EPHX2 expression in the brain (normalized effect size = −0.182, P = 0.005).ConclusionThe novel genetic variants associated with EPHX2 showed a distinct effect on Aβ deposition between SVCI and ADCI. This finding may provide a potential pre-screening marker for Aβ positivity and a candidate therapeutic target for SVCI.

BackgroundAlthough the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status.MethodsWe recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach.ResultsA total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (−) and focal (+) Aβ group, G(−)F(+) group]. The G(−)F(+) group showed worse performance in memory impairment (p

BackgroundCerebrotendinous xanthomatosis (CTX) is a rare autosomal recessive lipid storage disease caused by a mutation in the CYP27A1 gene. Due to the disruption of bile acid synthesis leading to cholesterol and cholestanol accumulation, CTX manifests as premature cataracts, chronic diarrhea, and intellectual disability in childhood and adolescence. This report presents a case of CTX with an unusual phenotype of behavioral variant frontotemporal dementia (bvFTD) in middle age.Case presentationA 60-year-old woman presented with behavioral and personality changes. She showed disinhibition, such as hoarding and becoming aggressive over trifles; compulsive behavior, such as closing doors; apathy; and dietary change. The patient showed a progressive cognitive decline and relatively sparing memory and visuospatial function. She had hyperlipidemia but no family history of neurodegenerative disorders. Initial fluid-attenuated inversion recovery (FLAIR) images showed a high signal in the periventricular area, and brain spectroscopy showed hypoperfusion in the frontal and temporal lobes, mimicking bvFTD. However, on physical examination, xanthomas were found on both the dorsum of the hands and the Achilles tendons. Hyperactive deep tendon reflexes in the bilateral biceps, brachioradialis, and knee and positive Chaddock signs on both sides were observed. Four years later, FLAIR images showed symmetrical high signals in the bilateral dentate nuclei of the cerebellum. Her serum cholestanol (12.4 mg/L; normal value ≤6.0) and 7α,12α-dihydroxycholest-4-en-3-one (0.485 nmol/mL; normal value ≤0.100) levels were elevated. A novel likely pathogenic variant (c.1001T>A, p.Met334Lys) and a known pathogenic variant (c.1420C>T, p.Arg474Trp) of the CYP27A1 gene were found in trans-location. The patient was diagnosed with CTX and prescribed chenodeoxycholic acid (750 mg/day).ConclusionsThis report discusses the case of a middle-aged CTX patient with an unusual phenotype of bvFTD. A novel likely pathogenic variant (c.1001T>A, p.Met334Lys) was identified in the CYP27A1 gene. Early diagnosis is important because supplying chenodeoxycholic acid can prevent CTX progression.

ObjectivesEfforts to prevent Alzheimer’s disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts.MethodsA total of 407 CU individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model.ResultsGrowth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the “declining group.” In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: −0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: −0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: −4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model).ConclusionOur study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.

BackgroundEarly detection of β-amyloid (Aβ) accumulation, a major biomarker for Alzheimer’s disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aβ for predicting Aβ deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aβ is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aβ and CSF Aβ levels for Aβ PET positivity.MethodsWe recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed.ResultsWhen predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p

Objective Lateral temporal lobe epilepsy (LTLE) has been diagnosed in only a small number of patients; therefore, its surgical outcome is not as well-known as that of mesial temporal lobe epilepsy. We aimed to evaluate the long-term (5 years) and short-term (2 years) surgical outcomes and identify possible prognostic factors in patients with LTLE. Methods This retrospective cohort study was conducted between January 1995 and December 2018 among patients who underwent resective surgery in a university-affiliated hospital. Patients were classified as LTLE if ictal onset zone was in lateral temporal area. Surgical outcomes were evaluated at 2 and 5 years. We subdivided based on outcomes and compared clinical and neuroimaging data including cortical thickness between two groups. Results Sixty-four patients were included in the study. The mean follow-up duration after the surgery was 8.4 years. Five years after surgery, 45 of the 63 (71.4%) patients achieved seizure freedom. Clinically and statistically significant prognostic factors for postsurgical outcomes were the duration of epilepsy before surgery and focal cortical dysplasia on postoperative histopathology at the 5-year follow-up. Optimal cut-off point for epilepsy duration was eight years after the seizure onset (odds ratio 4.375, p-value = 0.0214). Furthermore, we propose a model for predicting seizure outcomes 5 years after surgery using the receiver operating characteristic curve and nomogram (area under the curve = 0.733; 95% confidence interval, 0.588–0.879). Cortical thinning was observed in ipsilateral cingulate gyrus and contralateral parietal lobe in poor surgical group compared to good surgical group (p-value < 0.01, uncorrected). Conclusions The identified predictors of unfavorable surgical outcomes may help in selecting optimal candidates and identifying the optimal timing for surgery among patients with LTLE. Additionally, cortical thinning was more extensive in the poor surgical group.

BackgroundBrain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.ObjectiveThis study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.MethodsThis study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.ResultsOur proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.ConclusionOur unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

Structural changes in the brain due to Alzheimer’s disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer’s Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.

Introduction: Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cerebral cortex atrophy. In this study, we used sparse canonical correlation analysis (SCCA) to identify associations between single nucleotide polymorphisms (SNPs) and cortical thickness in the Korean population. We also investigated the role of the SNPs in neurological outcomes, including neurodegeneration and cognitive dysfunction. Methods: We recruited 1125 Korean participants who underwent neuropsychological testing, brain magnetic resonance imaging, positron emission tomography, and microarray genotyping. We performed group-wise SCCA in Aβ negative (-) and Aβ positive (+) groups. In addition, we performed mediation, expression quantitative trait loci, and pathway analyses to determine the functional role of the SNPs. Results: We identified SNPs related to cortical thickness using SCCA in Ab negative and positive groups and identified SNPs that improve the prediction performance of cognitive impairments. Among them, rs9270580 was associated with cortical thickness by mediating Ab uptake, and three SNPs (rs2271920, rs6859, rs9270580) were associated with the regulation of CHRNA2, NECTIN2, and HLA genes. Conclusion: Our findings suggest that SNPs potentially contribute to cortical thickness in AD, which in turn leads to worse clinical outcomes. Our findings contribute to the understanding of the genetic architecture underlying cortical atrophy and its relationship with AD. [ABSTRACT FROM AUTHOR]

Abstract We developed machine learning (ML) algorithms to predict abnormal tau accumulation among patients with prodromal AD. We recruited 64 patients with prodromal AD using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Supervised ML approaches based on the random forest (RF) and a gradient boosting machine (GBM) were used. The GBM resulted in an AUC of 0.61 (95% confidence interval [CI] 0.579–0.647) with clinical data (age, sex, years of education) and a higher AUC of 0.817 (95% CI 0.804–0.830) with clinical and neuropsychological data. The highest AUC was 0.86 (95% CI 0.839–0.885) achieved with additional information such as cortical thickness in clinical data and neuropsychological results. Through the analysis of the impact order of the variables in each ML classifier, cortical thickness of the parietal lobe and occipital lobe and neuropsychological tests of memory domain were found to be more important features for each classifier. Our ML algorithms predicting tau burden may provide important information for the recruitment of participants in potential clinical trials of tau targeting therapies.

ObjectiveLanguage function test-specific neural substrates in Korean patients with primary progressive aphasia (PPA) might differ from those in other causes of dementia and English-speaking PPA patients. We investigated the correlation between language performance tests and cortical thickness to determine neural substrates in Korean patients with PPA.Materials and methodsNinety-six patients with PPA were recruited from the memory clinic. To acquire neural substrates, we performed linear regression using the scores of each language test as a predictor, cortical thickness as an outcome and age, sex, years of education, and intracranial volume as confounders.ResultsPoor performance in each language function test was associated with lower cortical thickness in specific cortical regions: (1) object naming and the bilateral anterior to mid-portion of the lateral temporal and basal temporal regions; (2) semantic generative naming and the bilateral anterior to mid-portion of the lateral temporal and basal temporal regions; (3) phonemic generative naming and the left prefrontal and inferior parietal regions; and (4) comprehension and the left posterior portion of the superior and middle temporal regions. In particular, the neural substrates of the semantic generative naming test in PPA patients, left anterior to mid-portion of the lateral and basal temporal regions, quite differed from those in patients with other causes of dementia.ConclusionOur findings provide a better understanding of the different pathomechanisms for language impairments among PPA patients from those with other causes of dementia.

PurposeMany epidemiological studies suggest that lower education levels and vascular risk factors increase the likelihood of developing Alzheimer's disease dementia (ADD) and subcortical vascular dementia (SVaD). However, whether the brain-battering hypothesis can explain the relationship between education levels and the clinical diagnosis of dementia remains controversial. The objective of this study was to investigate whether vascular risk factors mediate the association between education level and the diagnosis of amyloid-beta positive (Aβ+) ADD and amyloid-beta negative (Aβ-) SVaD.MethodsWe analyzed 376 participants with Aβ normal cognition (Aβ- NC), 481 with Aβ+ ADD, and 102 with Aβ- SVaD. To investigate the association of education level and vascular risk factors with these diagnoses, multivariable logistic regression analysis was used, with age, sex, and APOE ε4 carrier status used as covariates. Path analysis was performed to investigate the mediation effects of hypertension on the diagnosis of Aβ- SVaD.ResultsThe Aβ- SVaD group (7.9 ± 5.1 years) had lower education levels than did the Aβ- NC (11.8 ± 4.8 years) and Aβ+ ADD (11.2 ± 4.9 years) groups. The frequencies of hypertension and diabetes mellitus were higher in the Aβ- SVaD group (78.4 and 32.4%, respectively) than in the Aβ- NC (44.4 and 20.8%) and Aβ+ ADD (41.8 and 15.8%, respectively) groups. Increased education level was associated with a lower risk of Aβ- SVaD [odds ratio (OR) 0.866, 95% confidence interval (CI), 0.824–0.911], but not Aβ+ ADD (OR 0.971, 95% CI 0.940–1.003). The frequency of hypertension was associated with a higher risk of developing Aβ- SVaD (OR 3.373, 95% CI, 1.908–5.961), but not Aβ+ ADD (OR 0.884, 95% CI, 0.653–1.196). In the path analysis, the presence of hypertension partially mediated the association between education level and the diagnosis of Aβ- SVaD.ConclusionOur findings revealed that education level might influence the development of Aβ- SVaD through the brain-battering hypothesis. Furthermore, our findings suggest that suitable strategies, such as educational attainment and prevention of hypertension, are needed for the prevention of Aβ- SVaD.

PurposeAmnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer’s disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients’ conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient.MethodsWe prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients.ResultsAmong the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient.ConclusionWe were able to propose a predictive algorithm for each aMCI individual’s conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.

ObjectivesThe relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.MethodsWe retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.ResultsDecreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.ConclusionOur findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control.

“Reserve” refers to the individual clinical differences in response to a neuropathological burden. We explored the behavioral reserve (BR) and associated neural substrates in 40 participants with behavioral variant frontotemporal dementia (bvFTD) who were assessed with the frontal behavioral inventory (FBI) and magnetic resonance imaging. Because neuroimaging abnormality showed a high negative correlation with the FBI negative (but not positive) symptom scores, we developed a linear model only to calculate the nBR (BR for negative symptoms) marker using neuroimaging abnormalities and the FBI score. Participants were divided into high nBR and low nBR groups based on the nBR marker. The FBI negative symptom score was lower in the high nBR group than in the low nBR group having the same neuroimaging abnormalities. However, the high nBR group noted a steeper decline in cortical atrophy and showed less atrophy in the left frontotemporal cortices than the low nBR group. In addition, the fractional anisotropy (FA) values were greater in the high nBR than in the low nBR group, except in the sensory-motor and occipital areas. We identified an nBR-related functional network composed of bilateral frontotemporal areas and the left occipital pole. We propose the concept of BR in bvFTD, and these findings can help predict the disease progression.

ObjectiveAnalyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as “center effects,” and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).MethodsFor MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.ResultsFirst, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.ConclusionThrough comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer’s disease or Parkinson’s disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.

Abstract To characterize the course of Alzheimer’s disease (AD) over a longer time interval, we aimed to construct a disease course model for the entire span of the disease using two separate cohorts ranging from preclinical AD to AD dementia. We modelled the progression course of 436 patients with AD continuum and investigated the effects of apolipoprotein E ε4 (APOE ε4) and sex on disease progression. To develop a model of progression from preclinical AD to AD dementia, we estimated Alzheimer’s Disease Assessment Scale-Cognitive Subscale 13 (ADAS-cog 13) scores. When calculated as the median of ADAS-cog 13 scores for each cohort, the estimated time from preclinical AD to MCI due to AD was 7.8 years and preclinical AD to AD dementia was 15.2 years. ADAS-cog 13 scores deteriorated most rapidly in women APOE ε4 carriers and most slowly in men APOE ε4 non-carriers (p

Abstract Background The retina and the brain share anatomic, embryologic, and physiologic characteristics. Therefore, retinal imaging in patients with brain disorders has been of significant interest. Using optical coherence tomography angiography (OCTA), a novel quantitative method of measuring retinal vasculature, we aimed to evaluate radial peripapillary capillary (RPC) network density and retinal nerve fiber layer (RNFL) thickness in cognitively impaired patients and determine their association with brain imaging markers. Methods In this prospective cross-sectional study, a total of 69 patients (138 eyes) including 29 patients with amyloid-positive Alzheimer’s disease-related cognitive impairment (ADCI), 25 patients with subcortical vascular cognitive impairment (SVCI), and 15 amyloid-negative cognitively normal (CN) subjects were enrolled. After excluding eyes with an ophthalmologic disease or poor image quality, 117 eyes of 60 subjects were included in the final analyses. Retinal vascular [capillary density (CD) of the radial peripapillary capillary (RPC) network] and neurodegeneration markers [retinal nerve fiber layer (RNFL) thickness at four quadrants] were measured using OCTA and OCT imaging. Brain vascular (CSVD score) and neurodegeneration markers (cortical thickness) were assessed using 3D brain magnetic resonance imaging. The CD and RNFL thickness and their correlation with brain imaging markers were investigated. Results The SVCI group showed lower CD in the temporal quadrant of the RPC network compared to the CN group (mean (SD), 42.34 (6.29) vs 48.45 (7.08); p = 0.001). When compared to the ADCI group, the SVCI showed lower CD in the superior quadrant (mean (SD), 60.14 (6.42) vs 64.15 (6.39); p = 0. 033) as well as in the temporal quadrant (ADCI 45.76, SVCI 42.34; p = 0.048) of the RPC network. The CD was negatively correlated with CSVD score in the superior (B (95%CI), − 0.059 (− 0.097 to − 0.021); p = 0.003) and temporal (B (95%CI), − 0.048 (− 0.080 to − 0.017); p = 0.003) quadrants of the RPC network. RNFL thickness did not differ among the groups nor did it correlate with cortical thickness. Conclusions and relevance The microvasculature of the RPC network was related to the CSVD burden. However, the RNFL thickness did not reflect cerebral neurodegeneration. Noninvasive and rapid acquisition of the OCTA image might have the potential to be used as a screening tool to detect CSVD.

The concept of cognitive reserve (CR) has been proposed as a protective factor that modifies the effect of brain pathology on cognitive performance. It has been characterized through CR proxies; however, they have intrinsic limitations. In this study, we utilized two different datasets containing tau, amyloid PET, and T1 magnetic resonance imaging. First, 91 Alzheimer’s disease (AD) continuum subjects were included from Alzheimer’s Disease Neuroimaging Initiative 3. CR was conceptualized as the residual between actual cognition and estimated cognition based on amyloid, tau, and neurodegeneration. The proposed marker was tested by the correlation with CR proxy and modulation of brain pathology effects on cognitive function. Second, longitudinal data of baseline 53 AD spectrum and 34 cognitively unimpaired (CU) participants in the MEMORI dataset were analyzed. CR marker was evaluated for the association with disease conversion rate and clinical progression. Applying our multimodal CR model, this study demonstrates the differential effect of CR on clinical progression according to the disease status and the modulating effect on the relationship between brain pathology and cognition. The proposed marker was associated with years of education and modulated the effect of pathological burden on cognitive performance in the AD spectrum. Longitudinally, higher CR marker was associated with lower disease conversion rate among prodromal AD and CU individuals. Higher CR marker was related to exacerbated cognitive decline in the AD spectrum; however, it was associated with a mitigated decline in CU individuals. These results provide evidence that CR may affect the clinical progression differentially depending on the disease status.

The general population is exposed to numerous environmental pollutants, and it remains unclear which pollutants affect the brain, accelerating brain aging and increasing the risk of dementia. The Environmental-Pollution-Induced Neurological Effects study is a multi-city prospective cohort study aiming to comprehensively investigate the effect of different environmental pollutants on brain structures, neuropsychological function, and the development of dementia in adults. The baseline data of 3,775 healthy elderly people were collected from August 2014 to March 2018. The eligibility criteria were age ≥50 years and no self-reported history of dementia, movement disorders, or stroke. The assessment included demographics and anthropometrics, laboratory test results, and individual levels of exposure to air pollution. A neuroimaging sub-cohort was also recruited with 1,022 participants during the same period, and brain magnetic resonance imaging and neuropsychological tests were conducted. The first follow-up environmental pollutant measurements will start in 2022 and the follow-up for the sub-cohort will be conducted every 3-4 years. We have found that subtle structural changes in the brain may be induced by exposure to airborne pollutants such as particulate matter 10 μm or less in diameter (PM10), particulate matter 2.5 μm or less in diameter (PM2.5) and Mn10, manganese in PM10; Mn2.5, manganese in PM2.5. PM10, PM2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

Abstract Background Although amyloid PET of typical Alzheimer’s disease (AD) shows diffuse ß-amyloid (Aß) deposition, some patients show focal deposition. The clinical significance of this focal Aß is not well understood. We examined the clinical significance of focal Aß deposition in terms of cognition as well as Aß and tau cerebrospinal fluid (CSF) levels. We further evaluated the order of Aß accumulation by visual assessment. Methods We included 310 subjects (125 cognitively unimpaired, 125 mild cognitive impairment, and 60 AD dementia) from 9 referral centers. All patients underwent neuropsychological tests and 18F-flutemetamol (FMM) PET. Seventy-seven patients underwent CSF analysis. Each FMM scan was visually assessed in 10 regions (frontal, precuneus and posterior cingulate, lateral temporal, parietal, and striatum of each hemisphere) and was classified into three groups: No-FMM, Focal-FMM (FMM uptake in 1–9 regions), and Diffuse-FMM (FMM uptake in all 10 regions). Results 53/310 (17.1%) subjects were classified as Focal-FMM. The cognitive level of the Focal-FMM group was better than that of Diffuse-FMM group and worse than that of No-FMM group. Among the Focal-FMM group, those who had FMM uptake to a larger extent or in the striatum had worse cognitive levels. Compared to the Diffuse-FMM group, the Focal-FMM group had a less AD-like CSF profile (increased Aß42 and decreased t-tau, t-tau/Aß42). Among the Focal-FMM group, Aß deposition was most frequently observed in the frontal (62.3%) and least frequently observed in the striatum (43.4%) and temporal (39.6%) regions. Conclusions We suggest that focal Aß deposition is an intermediate stage between no Aß and diffuse Aß deposition. Furthermore, among patients with focal Aß deposition, those who have Aß to a larger extent and striatal involvement show clinical features close to diffuse Aß deposition. Further longitudinal studies are needed to evaluate the disease progression of patients with focal Aß deposition.

Background: Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict ADspecific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Methods: Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Results: Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Conclusion: Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D. [ABSTRACT FROM AUTHOR]

Abstract Background THK5351 and flortaucipir tau ligands have high affinity for paired helical filament tau, yet diverse off-target bindings have been reported. Recent data support the hypothesis that THK5351 binds to monoamine oxidase B (MAO-B) expressed from reactive astrocytes and that flortaucipir has an affinity toward MAO-A and B; however, pathological evidence is lacking. We performed a head-to-head comparison of the two tau ligands in a sporadic Creutzfeldt-Jakob disease (CJD) patient and performed an imaging-pathological correlation study. Case presentation A 67-year-old man visited our clinic a history of 6 months of rapidly progressive dementia, visual disturbance, and akinetic mutism. Diffusion-weighted imaging showed cortical diffusion restrictions in the left temporo-parieto-occipital regions. 18F-THK5351 PET, but not 18F-flortaucipir PET showed high uptake in the left temporo-parieto-occipital regions, largely overlapping with the diffusion restricted areas. Cerebrospinal fluid analysis was weakly positive for 14–3-3 protein and pathogenic prion protein was found. The patient showed rapid cognitive decline along with myoclonic seizures and died 13 months after his first visit. A post-mortem study revealed immunoreactivity for PrPsc, no evidence of neurofibrillary tangles, and abundant astrocytosis which was reactive for MAO-B antibody. Conclusions Our findings add pathological evidence that increased THK5351 uptake in sporadic CJD patients might be caused by an off-target binding driven by its high affinity for MAO-B.