1. Whole-brain R1 predicts manganese exposure and biological effects in welders.
- Author
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Edmondson DA, Yeh CL, Hélie S, and Dydak U
- Subjects
- Adult, Age Factors, Air Pollutants, Occupational metabolism, Brain Chemistry, Humans, Magnetic Resonance Imaging, Male, Manganese metabolism, Metal Workers, Middle Aged, Models, Biological, Movement Disorders diagnosis, Movement Disorders metabolism, Support Vector Machine, Thalamus diagnostic imaging, Thalamus metabolism, Welding, Young Adult, gamma-Aminobutyric Acid analysis, Air Pollutants, Occupational toxicity, Brain metabolism, Manganese toxicity, Manganese Poisoning metabolism, Occupational Exposure
- Abstract
Manganese (Mn) is a neurotoxicant that, due to its paramagnetic property, also functions as a magnetic resonance imaging (MRI) T1 contrast agent. Previous studies in Mn toxicity have shown that Mn accumulates in the brain, which may lead to parkinsonian symptoms. In this article, we trained support vector machines (SVM) using whole-brain R1 (R1 = 1/T1) maps from 57 welders and 32 controls to classify subjects based on their air Mn concentration ([Mn]
Air ), Mn brain accumulation (ExMnBrain ), gross motor dysfunction (UPDRS), thalamic GABA concentration (GABAThal ), and total years welding. R1 was highly predictive of [Mn]Air above a threshold of 0.20 mg/m3 with an accuracy of 88.8% and recall of 88.9%. R1 was also predictive of subjects with GABAThal having less than or equal to 2.6 mM with an accuracy of 82% and recall of 78.9%. Finally, we used an SVM to predict age as a method of verifying that the results could be attributed to Mn exposure. We found that R1 was predictive of age below 48 years of age with accuracies ranging between 75 and 82% with recall between 94.7% and 76.9% but was not predictive above 48 years of age. Together, this suggests that lower levels of exposure (< 0.20 mg/m3 and < 18 years of welding on the job) do not produce discernable signatures, whereas higher air exposures and subjects with more total years welding produce signatures in the brain that are readily identifiable using SVM.- Published
- 2020
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