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.

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 (ß = -0.03, P = .02), parietal (ß = -0.04, P = .01), temporal (ß = -0.06, P < .001), and insular lobes (ß = -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 (ß = -0.03, P = .006) and parietal lobes (ß = -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.

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

Computer-aided diagnosis for classifying benign versus malignant thyroid nodules based on ultrasound images: A comparison with radiologist-based assessments.

PURPOSE: To develop a semiautomated computer-aided diagnosis (cad) system for thyroid cancer using two-dimensional ultrasound images that can be used to yield a second opinion in the clinic to differentiate malignant and benign lesions. METHODS: A total of 118 ultrasound images that included axial and longitudinal images from patients with biopsy-confirmed malignant (n = 30) and benign (n = 29) nodules were collected. Thyroid cad software was developed to extract quantitative features from these images based on thyroid nodule segmentation in which adaptive diffusion flow for active contours was used. Various features, including histogram, intensity differences, elliptical fit, gray-level co-occurrence matrixes, and gray-level run-length matrixes, were evaluated for each region imaged. Based on these imaging features, a support vector machine (SVM) classifier was used to differentiate benign and malignant nodules. Leave-one-out cross-validation with sequential forward feature selection was performed to evaluate the overall accuracy of this method. Additionally, analyses with contingency tables and receiver operating characteristic (ROC) curves were performed to compare the performance of cad with visual inspection by expert radiologists based on established gold standards. RESULTS: Most univariate features for this proposed cad system attained accuracies that ranged from 78.0% to 83.1%. When optimal SVM parameters that were established using a grid search method with features that radiologists use for visual inspection were employed, the authors could attain rates of accuracy that ranged from 72.9% to 84.7%. Using leave-one-out cross-validation results in a multivariate analysis of various features, the highest accuracy achieved using the proposed cad system was 98.3%, whereas visual inspection by radiologists reached 94.9% accuracy. To obtain the highest accuracies, "axial ratio" and "max probability" in axial images were most frequently included in the optimal feature sets for the authors' proposed cad system, while "shape" and "calcification" in longitudinal images were most frequently included in the optimal feature sets for visual inspection by radiologists. The computed areas under curves in the ROC analysis were 0.986 and 0.979 for the proposed cad system and visual inspection by radiologists, respectively; no significant difference was detected between these groups. CONCLUSIONS: The use of thyroid cad to differentiate malignant from benign lesions shows accuracy similar to that obtained via visual inspection by radiologists. Thyroid cad might be considered a viable way to generate a second opinion for radiologists in clinical practice.