Cohort Profile: The Environmental-Pollution-Induced Neurological EFfects (EPINEF) study, a multicenter cohort study of Korean adults

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 (PM_2.5) and Mn₁₀, manganese in PM₁₀; Mn_2.5, manganese in PM_2.5. PM₁₀, PM_2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

Machine Learning for the Prediction of Amyloid Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Amyloid-β (Aβ) evaluation in amnestic mild cognitive impairment (aMCI) patients is important for predicting conversion to Alzheimer's disease. However, Aβ evaluation through Aβ positron emission tomography (PET) is limited due to high cost and safety issues.

OBJECTIVE

We therefore aimed to develop and validate prediction models of Aβ positivity for aMCI using optimal interpretable machine learning (ML) approaches utilizing multimodal markers.

METHODS

We recruited 529 aMCI patients from multiple centers who underwent Aβ PET. We trained ML algorithms using a training cohort (324 aMCI from Samsung medical center) with two-phase modelling: model 1 included age, gender, education, diabetes, hypertension, apolipoprotein E genotype, and neuropsychological test scores; model 2 included the same variables as model 1 with additional MRI features. We used four-fold cross-validation during the modelling and evaluated the models on an external validation cohort (187 aMCI from the other centers).

RESULTS

Model 1 showed good accuracy (area under the receiver operating characteristic curve [AUROC] 0.837) in cross-validation, and fair accuracy (AUROC 0.765) in external validation. Model 2 led to improvement in the prediction performance with good accuracy (AUROC 0.892) in cross validation compared to model 1. Apolipoprotein E genotype, delayed recall task scores, and interaction between cortical thickness in the temporal region and hippocampal volume were the most important predictors of Aβ positivity.

CONCLUSION

Our results suggest that ML models are effective in predicting Aβ positivity at the individual level and could help the biomarker-guided diagnosis of prodromal AD.

The effect of body fatness on regional brain imaging markers and cognitive function in healthy elderly mediated by impaired glucose metabolism

Brain atrophy is related to vascular risk factors and can increase cognitive dysfunction risk. This community-based, cross-sectional study investigated whether glucose metabolic disorders due to body fatness are linked to regional changes in brain structure and a decline in neuropsychological function in cognitively healthy older adults. From 2016 to 2019, 429 participants underwent measurements for cortical thickness and subcortical volume using 3 T magnetic resonance imaging and for cognitive function using the neuropsychological screening battery. The effects of body fatness mediated by impaired glucose metabolism on neuroimaging markers and cognitive function was investigated using partial least square structural equation modeling. Total grey matter volume (β = -0.020; bias-corrected (BC) 95% confidence interval (CI) = -0.047 to -0.006), frontal (β = -0.029; BC 95% CI = -0.063 to -0.005) and temporal (β = -0.022; BC 95% CI = -0.051 to -0.004) lobe cortical thickness, and hippocampal volume (β = -0.029; BC 95% CI = -0.058 to -0.008) were indirectly related to body fatness. Further, frontal/temporal lobe thinning was associated with recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001) and delayed recall for visual information (β = -0.005; BC 95% CI = -0.013 to -0.001/β = -0.005; BC 95% CI = -0.013 to -0.001). Additionally, the smaller the hippocampal volume, the lower the score in recognition memory (β = -0.005; BC 95% CI = -0.012 to -0.001), delayed recall for visual information (β = -0.005; BC 95% CI = -0.012 to -0.001), and verbal learning (β = -0.008; BC 95% CI = -0.017 to -0.002). Our findings indicate that impaired glucose metabolism caused by excess body fatness affects memory decline as well as regional grey matter atrophy in elderly individuals with no neurological disease.

Association Between Visceral Fat and Brain Cortical Thickness in the Elderly: A Neuroimaging Study

Background

Despite emerging evidence suggesting that visceral fat may play a major role in obesity-induced neurodegeneration, little evidence exists on the association between visceral fat and brain cortical thickness in the elderly.

Purpose

We aimed to examine the association between abdominal fat and brain cortical thickness in a Korean elderly population.

Methods

This cross-sectional study included elderly individuals without dementia (n = 316). Areas of visceral fat and subcutaneous fat (cm²) were estimated from computed tomography scans. Regional cortical thicknesses (mm) were obtained by analyzing brain magnetic resonance images. Given the inverted U-shaped relationship between visceral fat area and global cortical thickness (examined using a generalized additive model), visceral fat area was categorized into quintiles, with the middle quintile being the reference group. A generalized linear model was built to explore brain regions associated with visceral fat. The same approach was used for subcutaneous fat.

Results

The mean (standard deviation) age was 67.6 (5.0) years. The highest quintile (vs. the middle quintile) group of visceral fat area had reduced cortical thicknesses in the global [β = -0.04 mm, standard error (SE) = 0.02 mm, p = 0.004], parietal (β = -0.04 mm, SE = 0.02 mm, p = 0.01), temporal (β = -0.05 mm, SE = 0.02 mm, p = 0.002), cingulate (β = -0.06 mm, SE = 0.02 mm, p = 0.01), and insula lobes (β = -0.06 mm, SE = 0.03 mm, p = 0.02). None of the regional cortical thicknesses significantly differed between the highest and the middle quintile groups of subcutaneous fat area.

Conclusion

The findings suggest that a high level of visceral fat, but not subcutaneous fat, is associated with a reduced cortical thickness in the elderly.

Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population

BACKGROUND

Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population.

METHODS

One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs.

RESULTS

In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain.

CONCLUSION

The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations.

Early-stage Parkinson's disease: Abnormal nigrosome 1 and 2 revealed by a voxelwise analysis of neuromelanin-sensitive MRI

Previous pathologic studies evaluated the substantia nigra pars compacta (SNpc) of a limited number of idiopathic Parkinson's disease (IPD) patients with relatively longer disease durations. Therefore, it remains unknown which region of the SNpc is most significantly affected in early‐stage IPD. We hypothesized that a voxelwise analysis of thin‐section neuromelanin‐sensitive MRI (NM‐MRI) may help determine the significantly affected regions of the SNpc in early‐stage IPD and localize these areas in each nigrosome on high‐spatial‐resolution susceptibility map‐weighted imaging (SMwI). Ninety‐six healthy subjects and 50 early‐stage IPD patients underwent both a 0.8 × 0.8 × 0.8 mm³ NM‐MRI and a 0.5 × 0.5 × 1.0 mm³ multi‐echo gradient‐recalled echo imaging for SMwI. Both NM‐MRI and SMwI templates were created by using image data from the 96 healthy subjects. Permutation‐based nonparametric tests were conducted to investigate spatial differences between the two groups in NM‐MRI, and the results were displayed on both NM‐MRI and SMwI templates. The posterolateral and anteromedial regions of the SNpc in NM‐MRI were significantly different between the two groups, corresponding to the nigrosome 1 and nigrosome 2 regions, respectively, on the SMwI template. There were the areas of significant spatial difference in the hypointense SN on SMwI between early‐stage IPD patients and healthy subjects. These areas on SMwI were slightly greater than those on NM‐MRI, including the areas showing group difference on NM‐MRI. Our voxelwise analysis of NM‐MRI suggests that two regions (nigrosome 1 and nigrosome 2) of the SNpc are separately affected in early‐stage IPD.

Effects of Alzheimer's and Vascular Pathologies on Structural Connectivity in Early- and Late-Onset Alzheimer's Disease

Early- and late-onset Alzheimer's disease (AD) patients often exhibit distinct features. We sought to compare overall white matter connectivity and evaluate the pathological factors (amyloid, tau, and vascular pathologies) that affect the disruption of connectivity in these two groups. A total of 50 early- and 38 late-onset AD patients, as well as age-matched cognitively normal participants, were enrolled and underwent diffusion-weighted magnetic resonance imaging to construct fractional anisotropy-weighted white matter connectivity maps. [¹⁸F]-THK5351 PET, [¹⁸F]-Flutemetamol PET, and magnetic resonance imaging were used for the evaluation of tau and related astrogliosis, amyloid, and small vessel disease markers (lacunes and white matter hyperintensities). Cluster-based statistics was performed for connectivity comparisons and correlation analysis between connectivity disruption and the pathological markers. Both patient groups exhibited significantly disrupted connectivity compared to their control counterparts with distinct patterns. Only THK retention was related to connectivity disruption in early-onset AD patients, and this disruption showed correlations with most cognitive scores, while late-onset AD patients had disrupted connectivity correlated with amyloid deposition, white matter hyperintensities, and lacunes in which only a few cognitive scores showed associations. These findings suggest that the pathogenesis of connectivity disruption and its effects on cognition are distinct between EOAD and LOAD.

Voxel-wise partial volume correction method for accurate estimation of tissue sodium concentration in 23 Na-MRI at 7 T

Sodium is crucial for the maintenance of cell physiology, and its regulation of the sodium-potassium pump has implications for various neurological conditions. The distribution of sodium concentrations in tissue can be quantitatively evaluated by means of sodium MRI (²³ Na-MRI). Despite its usefulness in diagnosing particular disease conditions, tissue sodium concentration (TSC) estimated from ²³ Na-MRI can be strongly biased by partial volume effects (PVEs) that are induced by broad point spread functions (PSFs) as well as tissue fraction effects. In this work, we aimed to propose a robust voxel-wise partial volume correction (PVC) method for ²³ Na-MRI. The method is based on a linear regression (LR) approach to correct for tissue fraction effects, but it utilizes a 3D kernel combined with a modified least trimmed square (3D-mLTS) method in order to minimize regression-induced inherent smoothing effects. We acquired ²³ Na-MRI data with conventional Cartesian sampling at 7 T, and spill-over effects due to the PSF were considered prior to correcting for tissue fraction effects using 3D-mLTS. In the simulation, we found that the TSCs of gray matter (GM) and white matter (WM) were underestimated by 20% and 11% respectively without correcting tissue fraction effects, but the differences between ground truth and PVE-corrected data after the PVC using the 3D-mLTS method were only approximately 0.6% and 0.4% for GM and WM, respectively. The capability of the 3D-mLTS method was further demonstrated with in vivo ²³ Na-MRI data, showing significantly lower regression errors (ie root mean squared error) as compared with conventional LR methods (p < 0.001). The results of simulation and in vivo experiments revealed that 3D-mLTS is superior for determining under- or overestimated TSCs while preserving anatomical details. This suggests that the 3D-mLTS method is well suited for the accurate determination of TSC, especially in small focal lesions associated with pathological conditions.

Identification of Heterogeneous Subtypes of Mild Cognitive Impairment Using Cluster Analyses Based on PET Imaging of Tau and Astrogliosis

Background: Mild cognitive impairment (MCI) is a condition with diverse causes and clinical outcomes that can be categorized into subtypes. [¹⁸F]THK5351 has been known to detect reactive astrogliosis as well as tau which is accompanied by neurodegenerative changes. Here, we identified heterogeneous groups of MCI patients using THK retention patterns and a graph theory approach, allowing for the comparison of risk of progression to dementia in these MCI subgroups.

Methods: Ninety-seven participants including 60 MCI patients and individuals with normal cognition (NC, n = 37) were included and undertook 3T MRI, [¹⁸F]THK5351 PET, and detailed neuropsychological tests. [¹⁸F]Flutemetamol PET was also performed in 62 participants. We calculated similarities between MCI patients using their regional standardized uptake value ratio of THK retention in 75 ROIs, and clustered subjects with similar retention patterns using the Louvain method based on the modularity of the graph. The clusters of patients identified were compared with an age-matched control group using a general linear model. Dementia conversion was evaluated after a median follow-up duration of 34.6 months.

Results: MCI patients were categorized into four groups according to their THK retention patterns: (1) limbic type; (2) diffuse type; (3) sparse type; and (4) AD type (retention pattern as in AD). Subjects of the limbic type were characterized by older age, small hippocampal volumes, and reduced verbal memory and frontal/executive functions. Patients of the diffuse type had relatively large vascular burden, reduced memory capacity and some frontal/executive functions. Co-morbidity and mortality were more frequent in this subgroup. Subjects of the sparse type were younger and declined only in terms of visual memory and attention. No individuals in this subgroup converted to dementia. Patients in the AD type group exhibited the poorest cognitive function. They also had the smallest hippocampal volumes and the highest risk of progression to dementia (90.9%).

Conclusion: Using cluster analyses with [¹⁸F]THK5351 retention patterns, it is possible to identify clinically-distinct subgroups of MCI patients and those at greater risk of progression to dementia.

Extended approach or usage of nasoseptal flap is a risk factor for olfactory dysfunction after endoscopic anterior skullbase surgery: results from 928 patients in a single tertiary center

BACKGROUND

The aim of this study was to compare olfactory function change in patients who underwent endoscopic skull-base surgery.

METHODOLOGY

A total of 928 patients were included in this retrospective study. Olfactory function was measured using the non- validated Likert scale (0â€"100), the Cross-Cultural Smell Identification Test (CC-SIT) and the butanol threshold test (BTT). Patients were divided into two groups: an endoscopic trans-sellar approach group (ETA, n = 768) and an extended endoscopic endonasal approach group (EEEA, n = 160). The ETA group was sub-divided into Nasoseptal flap (NSF) and no NSF groups.

RESULTS

Non-validated olfactory function significantly worsened in the EEEA and ETA-NSF groups compared with that in the ETA- no NSF group for at least 6 months post-operatively. Validated olfactory impairment (BTT and CC-SIT) was also significantly worse in the EEEA and NSF groups compared with that in the ETA-no NSF group 3 months post-operatively. Additionally, the degrees of non-validated and validated olfactory deterioration were not significantly different between the EEEA and ETA-NSF groups. We also found that CC-SIT score changes were significantly impaired in tuberculum sellae meningioma patients than in craniopharyn- gioma patients.

CONCLUSIONS

We conclude that NSF was the key factor that led to olfactory impairment after endoscopic skull-base surgery.

Long-Term Ambient Air Pollution Exposures and Brain Imaging Markers in Korean Adults: The Environmental Pollution-Induced Neurological EFfects (EPINEF) Study

BACKGROUND

Only a limited number of neuroimaging studies have explored the effects of ambient air pollution in adults. The prior studies have investigated only cortical volume, and they have reported mixed findings, particularly for gray matter. Furthermore, the association between nitrogen dioxide (NO2) and neuroimaging markers has been little studied in adults.

OBJECTIVES

We investigated the association between long-term exposure to air pollutants (NO2, particulate matter (PM) with aerodynamic diameters of ≤10μm (PM10) and ≤2.5μm (PM2.5), and neuroimaging markers.

METHODS

The study included 427 men and 530 women dwelling in four cities in the Republic of Korea. Long-term concentrations of PM10, NO2, and PM2.5 at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images. A generalized linear model was used, adjusting for potential confounders.

RESULTS

A 10-μg/m3 increase in PM10 was associated with reduced thicknesses in the frontal [-0.02mm (95% CI: -0.03, -0.01)] and temporal lobes [-0.06mm (95% CI: -0.07, -0.04)]. A 10-μg/m3 increase in PM2.5 was associated with a thinner temporal cortex [-0.18mm (95% CI: -0.27, -0.08)]. A 10-ppb increase in NO2 was associated with reduced thicknesses in the global [-0.01mm (95% CI: -0.01, 0.00)], frontal [-0.02mm (95% CI: -0.03, -0.01)], parietal [-0.02mm (95% CI: -0.03, -0.01)], temporal [-0.04mm (95% CI: -0.05, -0.03)], and insular lobes [-0.01mm (95% CI: -0.02, 0.00)]. The air pollutants were also associated with increased thicknesses in the occipital and cingulate lobes. Subcortical structures associated with the air pollutants included the thalamus, caudate, pallidum, hippocampus, amygdala, and nucleus accumbens.

DISCUSSION

The findings suggest that long-term exposure to high ambient air pollution may lead to cortical thinning and reduced subcortical volume in adults. https://doi.org/10.1289/EHP7133.

A Three-Dimensional Sensor to Recognize Amyloid-β in Blood Plasma of Patients

Detecting amyloid beta (Aβ) in unpurified blood to diagnose Alzheimer's disease (AD) is challenging owing to low concentrations of Aβ and the presence of many other substances in the blood. Here, we propose a 3D sensor for AD diagnosis using blood plasma, with pairs of 3D silicon micropillar electrodes with a comprehensive circuit configuration. The sensor is developed with synthesized artificial peptide and impedance analysis based on a maximum signal-to-noise ratio. Its sensitivity and selectivity were verified using an in vitro test based on samples of human blood serum, which showed its feasibility for application in diagnosis of AD by testing blood plasma of the AD patient. The 3D sensor is designed to improve reliability by checking the impedance of each pair multiple times via constructing a reference pair and a working pair on the same sensor. Therefore, we demonstrate the ability of the 3D sensor to recognize cases of AD using blood plasma and introduce its potential as a self-health care sensor for AD patients.

Association of Subcortical Structural Shapes With Tau, Amyloid, and Cortical Atrophy in Early-Onset and Late-Onset Alzheimer's Disease

The objectives of this study were to compare the topographical subcortical shape and to investigate the effects of tau or amyloid burden on atrophic patterns in early onset Alzheimer's disease (EOAD) and late-onset Alzheimer's disease (LOAD). One hundred and sixty-one participants (53 EOAD, 44 LOAD, 33 young controls, and 31 older controls) underwent [18F]THK5351 positron emission tomography (PET), [18F]flutemetamol (FLUTE) PET, and 3T MRI scans. We used surface-based analysis to evaluate subcortical structural shape, permutation-based statistics for group comparisons, and Spearman's correlations to determine associations with THK, FLUTE, cortical thickness, and neuropsychological test results. When compared to their age-matched controls, EOAD patients exhibited shape reduction in the bilateral amygdala, hippocampus, caudate, and putamen, while in LOAD patients, the bilateral amygdala and hippocampus showed decreased shapes. In EOAD, widespread subcortical shrinkage, with less association of the hippocampus, correlated with THK retention and cortical thinning, while in LOAD patients, subcortical structures were limited which had significant correlation with THK or mean cortical thickness. Subcortical structural shape showed less correlation with FLUTE global retention in both EOAD and LOAD. Multiple cognitive domains, except memory function, correlated with the bilateral amygdala, caudate, and putamen in EOAD patients, while more restricted regions in the subcortical structures were correlated with neuropsychological test results in LOAD patients. Subcortical structures were associated with AD hallmarks in EOAD. However, the correlation was limited in LOAD. Moreover, relationship between subcortical structural atrophy and cognitive decline were quite different between EOAD and LOAD. These findings suggest that the effects of Alzheimer's pathologies on subcortical structural changes in EOAD and LOAD and they may have different courses of pathomechanism.

A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FP_avg) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FP_avg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach

Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detection and identification of CMBs in Magnetic Resonance (MR) images is a very challenging task due to their wide distribution throughout the brain, small sizes, and the high degree of visual similarity between CMBs and CMB mimics such as calcifications, irons, and veins. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two-channel input for the regional-based YOLO method. This enables YOLO to learn more reliable and representative hierarchal features and hence achieve better detection performance. The proposed work was independently trained and evaluated using high and low in-plane resolution data, which contained 72 subjects with 188 CMBs and 107 subjects with 572 CMBs, respectively. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and 78.85% and an average number of false positives per subject (FPavg) of 52.18 and 155.50 throughout the five-folds cross-validation for both the high and low in-plane resolution data, respectively. These findings outperformed results by previously utilized techniques such as 3D fast radial symmetry transform, producing fewer FPavg and lower computational cost. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42 and 1.89 for the high and low in-plane resolution data, respectively. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.

Association between exposure to polycyclic aromatic hydrocarbons and brain cortical thinning: The Environmental Pollution-Induced Neurological EFfects (EPINEF) study

BACKGROUND

Although some studies have suggested that exposure to polycyclic aromatic hydrocarbons (PAHs) induces neurodevelopmental disturbances in children and neurodegeneration in animals, the neurotoxic effect of PAH exposure is unclear in adults. The aim was to examine the associations of PAH exposure with brain structure and neuropsychological function in adults without known neurological diseases.

METHODS

This study included 421 men and 528 women dwelling in four cities in the Republic of Korea. Urinary concentrations of four PAH metabolites (1-hydroxypyrene, 2-naphthol, 1-hydroxyphenanthrene, and 2-hydroxyfluorene) were obtained. Participants underwent brain 3 T magnetic resonance imaging and neuropsychological tests. Cortical thickness and volume were estimated using the region-of-interest method. Separate generalized linear models were constructed for each sex, adjusting for age, years of education, cohabitation status, income, tobacco use, alcohol consumption, and vascular risk factors.

RESULTS

The mean (standard deviation) age was 68.3 (6.6) years in men and 66.4 (6.1) years in women. In men, those in quartile 4 (versus quartile 1, the lowest) of urinary 2-naphthol concentration had cortical thinning in the global (β = -0.03, P = .02), parietal (β = -0.04, P = .01), temporal (β = -0.06, P < .001), and insular lobes (β = -0.05, P = .02). Higher quartiles of urinary 2-naphthol concentration were associated with cortical thinning in the global (P = .01), parietal (P = .004), temporal (P < .001), and insular lobes (P = .01). In women, those in quartile 4 (versus quartile 1) of urinary 1-hydroxypyrene concentration had cortical thinning in the frontal (β = -0.03, P = .006) and parietal lobes (β = -0.03, P = .003). Higher quartiles of urinary 1-hydroxypyrene concentration were associated with cortical thinning in the frontal (P = .006) and parietal lobes (P = .001). In both sexes, verbal learning and memory scores significantly declined with an increase in quartile of urinary 1-hydroxypyrene concentration.

CONCLUSIONS

PAH exposure was associated with cortical thinning and decline in verbal learning and memory function in cognitively healthy adults. This suggests PAHs as an environmental risk factor for neurodegeneration.

Marriage as a social tie in the relation of depressive symptoms attributable to air pollution exposure among the elderly

BACKGROUND

Air pollution is a risk factor for depression or depressive symptoms. However, few studies have examined an effect modifier as a protective factor against depressive symptoms associated with air pollution, including social support. Notably, less is known about a married relationship in the association between exposure to air pollution and depressive symptoms among the elderly.

METHODS

This study included 2122 marrieds and 607 non-marrieds, recruited in 2014-2017 from different regions of South Korea. Depressive symptoms were measured by the Korean version of the Geriatric Depression Scale Short Form (SGDS-K). After adjustment for potential confounders using propensity score of being assigned to the marrieds, we examined the extent of whether the effects of exposure to air pollutants (PM₁₀, PM_2.5, and NO₂) on depressive symptoms were different between marrieds and non-marrieds. Subgroup analyses by gender and residence area were also performed.

RESULTS

Marrieds than non-marrieds were less likely to have depressive symptoms and had smaller SGDS-K associated with increased exposure to PM₁₀ and PM_2.5 concentrations, respectively. After stratification of subjects by gender and residence area, the interaction term appeared to be significant among men and the non-metropolitan group, indicating the protective effect of married relationships on depressive symptoms attributable to air pollution exposure in them.

LIMITATIONS

Although we adjusted the propensity score, our findings might be confounded by the contextual effect associated with married relationships.

CONCLUSIONS

A married relationship, as a social tie, may attenuate the effect of exposure to air pollution on depressive symptoms among the elderly. Nonetheless, additional research is worthwhile to explore the extent of other social relationships in the association between air pollution exposure and depressive symptoms.

[¹⁸F]THK5351 PET Imaging in Patients with Mild Cognitive Impairment

Background and Purpose

Mild cognitive impairment (MCI) is a condition with diverse clinical outcomes and subgroups. Here we investigated the topographic distribution of tau in vivo using the positron emission tomography (PET) tracer [¹⁸F]THK5351 in MCI subgroups.

Methods

This study included 96 participants comprising 38 with amnestic MCI (aMCI), 21 with nonamnestic MCI (naMCI), and 37 with normal cognition (NC) who underwent 3.0-T MRI, [¹⁸F]THK5351 PET, and detailed neuropsychological tests. [¹⁸F]flutemetamol PET was also performed in 62 participants. The aMCI patients were further divided into three groups: 1) verbal-aMCI, only verbal memory impairment; 2) visual-aMCI, only visual memory impairment; and 3) both-aMCI, both visual and verbal memory impairment. Voxel-wise statistical analysis and region-of-interest -based analyses were performed to evaluate the retention of [¹⁸F]THK5351 in the MCI subgroups. Subgroup analysis of amyloid-positive and -negative MCI patients was also performed. Correlations between [¹⁸F]THK5351 retention and different neuropsychological tests were evaluated using statistical parametric mapping analyses.

Results

[¹⁸F]THK5351 retention in the lateral temporal, mesial temporal, parietal, frontal, posterior cingulate cortices and precuneus was significantly greater in aMCI patients than in NC subjects, whereas it did not differ significantly between naMCI and NC participants. [¹⁸F] THK5351 retention was greater in the both-aMCI group than in the verbal-aMCI and visualaMCI groups, and greater in amyloid-positive than amyloid-negative MCI patients. The cognitive function scores were significantly correlated with cortical [¹⁸F]THK5351 retention.

Conclusions

[¹⁸F]THK5351 PET might be useful for identifying distinct topographic patterns of [¹⁸F]THK5351 retention in subgroups of MCI patients who are at greater risk of the progression to Alzheimer's dementia.

Gender Difference in the Effects of Outdoor Air Pollution on Cognitive Function Among Elderly in Korea

Background/Aim: Given a fast-growing aging population in South Korea, the prevalence of cognitive impairment in elderly is increasing. Despite growing evidence of air pollution exposure as one of the risk factors for declining cognition, few studies have been conducted on gender difference in the relation of cognitive function associated with outdoor air pollution. The aim of this study is to investigate the effect modification of gender difference in the association between cognitive function and air pollutant exposure (PM₁₀, PM_2.5−10, and NO₂).

Methods: The study focused on elderly, and the resulting sample included 1,484 participants aged 55 and older with no neurologic diseases, recruited from the four regions in Korea (Seoul, Incheon, Pyeongchang, and Wonju). We used the Mini-Mental State Examination (MMSE) score (with the conventional cut-off point “23–24”) to assess cognitive decline as the primary outcome of the study. Air pollution data used in this study were based on the 5-year average of predicted PM₁₀ and NO₂ concentrations, as well as the 2015 average PM_2.5 concentration. Additionally, a survey questionnaire was utilized to obtain information about general health assessment. To explore gender differences in the effects of air pollution exposure on cognitive function, we used penalized logistic regression, negative binomial regression, and generalized linear mixed model analyses. Subgroup analyses were also performed by the geographic location of residence (metropolitan vs. non-metropolitan).

Results: We found that women than men had a higher risk for decreased cognitive function associated with increased exposure to PM₁₀ and PM_2.5−10, respectively, even after adjustments for confounding factors (OR 1.01 [95%CI 1.00-1.03] in PM₁₀; OR 1.03 [95%CI 1.01–1.07] in PM_2.5−10). After stratification by metropolitan status, we also found that the adverse effect of NO₂ exposure on cognitive function was higher in women than men [OR 1.02 [95%CI 1.00–1.05] in metropolitan; OR 1.12 [95%CI 1.04–1.20] in non-metropolitan]. Notably, the magnitude of the effect sizes was greater among those in non-metropolitan regions than metropolitan ones.

Conclusions: Although our findings suggest that the adverse effects of outdoor air pollution on cognitive function appeared to be higher in women than men, this should be tentatively reflected due to some limitations in our results. While additional research is warranted to confirm or dispute our results, our findings suggest an indication of the need for developing and implementing prevention or interventions with a focus on elderly women with increased risk for air pollution exposure.

A Nomogram for Predicting Amyloid PET Positivity in Amnestic Mild Cognitive Impairment

BACKGROUND

Most clinical trials focus on amyloid-β positive (Aβ+) amnestic mild cognitive impairment (aMCI), but screening failures are high because only a half of patients with aMCI are positive on Aβ PET. Therefore, it becomes necessary for clinicians to predict which patients will have Aβ biomarker.

OBJECTIVE

We aimed to compare clinical factors, neuropsychological (NP) profiles, and apolipoprotein E (APOE) genotype between Aβ+ aMCI and Aβ-aMCI and to develop a clinically useful prediction model of Aβ positivity on PET (PET-Aβ+) in aMCI using a nomogram.

METHODS

We recruited 523 aMCI patients who underwent Aβ PET imaging in a nation-wide multicenter cohort. The results of NP measures were divided into following subgroups: 1) Stage (Early and Late-stage), 2) Modality (Visual, Verbal, and Both), 3) Recognition failure, and 4) Multiplicity (Single and Multiple). A nomogram for PET-Aβ+ in aMCI patients was constructed using a logistic regression model.

RESULTS

PET-Aβ+ had significant associations with NP profiles for several items, including high Clinical Dementia Rating Scale Sum of Boxes score (OR 1.47, p = 0.013) and impaired memory modality (impaired both visual and verbal memories compared with visual only, OR 3.25, p = 0.001). Also, presence of APOEɛ4 (OR 4.14, p < 0.001) was associated with PET-Aβ+. These predictors were applied to develop the nomogram, which showed good prediction performance (C-statistics = 0.79). Its prediction performances were 0.77/0.74 in internal/external validation.

CONCLUSIONS

The nomogram consisting of NP profiles, especially memory domain, and APOEɛ4 genotype may provide a useful predictive model of PET-Aβ+ in patients with aMCI.

Topographical Heterogeneity of Alzheimer's Disease Based on MR Imaging, Tau PET, and Amyloid PET

Alzheimer’s disease (AD) patients are known to have heterogeneous clinical presentation and pathologic patterns. We hypothesize that AD dementia can be categorized into subtypes based on multimodal imaging biomarkers such as magnetic resonance imaging (MRI), tau positron emission tomography (PET), and amyloid PET. We collected 3T MRI, ¹⁸F-THK5351 PET, and ¹⁸F-flutemetamol (FLUTE) PET data from 83 patients with AD dementia [Clinical Dementia Rating (CDR) ≤1] and 60 normal controls (NC), and applied surface-based analyses to measure cortical thickness, THK5351 standardized uptake value ratio (SUVR) and FLUTE SUVR for each participant. For the patient group, we performed an agglomerative hierarchical clustering analysis using the three multimodal imaging features on the vertices (n = 3 × 79,950). The identified AD subtypes were compared to NC using general linear models adjusting for age, sex, and years of education. We mapped the effect size within significant cortical regions reaching a corrected p-vertex <0.05 (random field theory). Our surface-based multimodal framework has revealed three distinct subtypes among AD patients: medial temporal-dominant subtype (MT, n = 44), parietal-dominant subtype (P, n = 19), and diffuse atrophy subtype (D, n = 20). The topography of cortical atrophy and THK5351 retention differentiates between the three subtypes. In the case of FLUTE, three subtypes did not show distinct topographical differences, although cortical composite retention was significantly higher in the P type than in the MT type. These three subtypes also differed in demographic and clinical features. In conclusion, AD patients may be clustered into three subtypes with distinct topographical features of cortical atrophy and tau deposition, although amyloid deposition may not differ across the subtypes in terms of topography.

Quantitative assessment of phased array coils with different numbers of receiving channels in terms of signal-to-noise ratio and spatial noise variation in magnetic resonance imaging

The neuroimaging of humans using 7T magnetic resonance imaging (MRI) has been conducted using phased array (PA) coils with different numbers of receiving channels. PA coils with a high number of channels may offer parallel imaging (PI) with a high reduction (R)-factor, which is enabled via under-sampling and coil geometry (g) factor, increasing the radiofrequency signal sensitivity provided by a small coil. The goals of this study were to assess and validate the coil performance of PA coils with different numbers of receiver (Rx)-channels in and to propose the coil selection guidelines by visualizing 7T brain images. The combined magnetic flux density (||B₁||) distributions of four configurations of PA coils—4-, 8-, 12-, and 16-channel Rx-only mode under the local transmit (Tx) mode of birdcage coils—were evaluated using electromagnetic (EM) calculations. These four configurations of PA coils and a local Tx coil were designed and built for a 7T MRI experiment. For 7T brain imaging experiments, all PA coils with (w/) and without (w/o) R-factors were compared in terms of signal-to-noise ratio (SNR) and spatial noise variation (SNV). EM simulation results clearly demonstrated that PA coils with a high number of Rx channels showed more homogeneously distributed ||B₁|| fields than a PA coils with a low number of Rx coils. The results of this study demonstrate that a collection of smaller surface coils can contribute to high RF signal sensitivity in terms of the anatomical coverage of the brain and may facilitate PI. With further improvement in coil technology, researchers and clinicians will be provided with PA coils with different numbers of channels, which can ensure the optimum SNR and PI benefits for 7T brain MR imaging.