Gray matter microstructural alterations in manganese-exposed welders: a preliminary neuroimaging study

Effects of mixed metal exposure on MRI metrics in basal ganglia

Welding fumes contain various metals. Past studies, however, mainly focused on Manganese (Mn)-related neurotoxicity. This study investigated welding-related mixed metal exposure effects on MRI metrics in the basal ganglia (BG) and their dose-response relationship. Subjects with (N = 23) and without (N = 24) a welding exposure history were examined. Metal exposure was estimated with an exposure history questionnaire and whole blood metal levels. T1 (weighted-intensity and relaxation time; estimates of brain Mn accumulation), diffusion tensor imaging (axial [AD], mean [MD], radial diffusivity, and fractional anisotropy [FA]; estimates of microstructural differences) metrics in BG (caudate nucleus, putamen, and globus pallidus [GP]), and voxel-based morphometry (for volume) were examined and related with metal exposure measures. Compared with controls, welders showed higher GP R1 (1/T1; P = 0.034) but no differences in blood metal and T1-weighted (T1W) values in any ROIs (P's > 0.120). They also had higher AD and MD values in the GP (P's < 0.033) but lower FA values in the putamen (P = 0.039) with no morphologic differences. In welders, higher blood Mn and Vanadium (V) levels predicted higher BG R1 and T1W values (P's < 0.015). There also were significant overall metal mixture effects on GP T1W and R1 values. Moreover, GP AD and MD values showed nonlinear associations with BG T1W values: They increased with increasing T1W values only above certain threshold of T1 values. The current findings suggest that Mn and V individually but also metal mixtures jointly predict GP T1 signals that may in turn contribute to altered DTI metrics in the BG after certain exposure threshold levels.

Gray matter morphometric biomarkers for distinguishing manganese-exposed welders from healthy adults revealed by source-based morphometry

BACKGROUND

Chronic overexposure to manganese (Mn) may result in neurotoxicity, which is characterized by motor and cognitive dysfunctions. This study aimed to utilize multivariate source-based morphometry (SBM) to explore the biomarkers for distinguishing Mn-exposed welders from healthy controls (HCs).

METHODS

High-quality 3D T1-weighted MRI scans were obtained from 45 Mn-exposed full-time welders and 33 age-matched HCs in this study. After extracting gray matter structural covariation networks by SBM, multiple classic interaction linear models were applied to investigate distinct patterns in welders compared to HCs, and Z-transformed loading coefficients were compared between the two groups. A receiver operating characteristic (ROC) curve was used to identify potential biomarkers for distinguishing Mn-exposed welders from HCs. Additionally, we assessed the relationships between clinical features and gray matter volumes in the welders group.

RESULTS

A total of 78 subjects (45 welders, mean age 46.23±4.93 years; 33 HCs, mean age 45.55±3.40 years) were evaluated. SBM identified five components that differed between the groups. These components displayed lower loading weights in the basal ganglia, thalamus, default mode network (including the lingual gyrus and precuneus), and temporal lobe network (including the temporal pole and parahippocampus), as well as higher loading weights in the sensorimotor network (including the supplementary motor cortex). ROC analysis identified the highest classification power in the thalamic network.

CONCLUSIONS

Altered brain structures might be implicated in Mn overexposure-related disturbances in motivative modulation, cognitive control and information integration. These results encourage further studies that focus on the interaction mechanisms, including the basal ganglia network, thalamic network and default mode network. Our study identified potential neurobiological markers in Mn-exposed welders and illustrated the utility of a multivariate method of gray matter analysis.

Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network

OBJECTIVES

Reliable detection of disease-specific atrophy in individual T1w-MRI by voxel-based morphometry (VBM) requires scanner-specific normal databases (NDB), which often are not available. The aim of this retrospective study was to design, train, and test a deep convolutional neural network (CNN) for single-subject VBM without the need for a NDB (CNN-VBM).

MATERIALS AND METHODS

The training dataset comprised 8945 T1w scans from 65 different scanners. The gold standard VBM maps were obtained by conventional VBM with a scanner-specific NDB for each of the 65 scanners. CNN-VBM was tested in an independent dataset comprising healthy controls (n = 37) and subjects with Alzheimer's disease (AD, n = 51) or frontotemporal lobar degeneration (FTLD, n = 30). A scanner-specific NDB for the generation of the gold standard VBM maps was available also for the test set. The technical performance of CNN-VBM was characterized by the Dice coefficient of CNN-VBM maps relative to VBM maps from scanner-specific VBM. For clinical testing, VBM maps were categorized visually according to the clinical diagnoses in the test set by two independent readers, separately for both VBM methods.

RESULTS

The VBM maps from CNN-VBM were similar to the scanner-specific VBM maps (median Dice coefficient 0.85, interquartile range [0.81, 0.90]). Overall accuracy of the visual categorization of the VBM maps for the detection of AD or FTLD was 89.8% for CNN-VBM and 89.0% for scanner-specific VBM.

CONCLUSION

CNN-VBM without NDB provides a similar performance in the detection of AD- and FTLD-specific atrophy as conventional VBM.

CLINICAL RELEVANCE STATEMENT

A deep convolutional neural network for voxel-based morphometry eliminates the need of scanner-specific normal databases without relevant performance loss and, therefore, could pave the way for the widespread clinical use of voxel-based morphometry to support the diagnosis of neurodegenerative diseases.

KEY POINTS

• The need of normal databases is a barrier for widespread use of voxel-based brain morphometry. • A convolutional neural network achieved a similar performance for detection of atrophy than conventional voxel-based morphometry. • Convolutional neural networks can pave the way for widespread clinical use of voxel-based morphometry.

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

Background

Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease.

Objective

This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance.

Methods

Metal exposure history, whole blood metal, and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume, cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, correlation, multiple linear, Bayesian kernel machine regression, and mediation analyses were employed to examine effects of single- or mixed-metal predictor(s) and their interactions on MTL structural and neuropsychological metrics; and on the path from metal exposure to neuropsychological consequences.

Results

Compared to controls, exposed subjects had higher blood Cu, Fe, K, Mn, Pb, Se, and Zn levels (p's<0.026) and poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Exposed subjects displayed higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in entorhinal and parahippocampal cortices (p's<0.033), but not morphological differences. Long-term mixed-metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p=0.023). Higher whole blood Mn and Cu predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007) without overall metal mixture or interaction effects.

Discussion

Mixed metal exposure predicted MTL structural and neuropsychological features that are similar to Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate the path for environmental exposure to Alzheimer's disease-related health outcomes.

Identification of histone acetylation modification sites in the striatum of subchronically manganese-exposed rats

Aim: To explore the specific histone acetylation sites and oxidative stress-related genes that are associated with the pathogenesis of manganese toxicity. Methods: We employed liquid chromatography-tandem mass spectrometry and bioinformatics analysis to identify acetylated proteins in the striatum of subchronic manganese-intoxicated rats. Results: We identified a total of 12 differentially modified histone acetylation sites: H3K9ac, H3K14ac, H3K18ac, H3K56ac and H3K79ac were upregulated and H3K27ac, H3K36ac, H4K91ac, H4K79ac, H4K31ac, H2BK16ac and H2BK20ac were downregulated. Additionally, we found that CAT, SOD1 and SOD2 might be epigenetically regulated and involved in the pathogenesis of manganism. Conclusion: This study identified histone acetylation sites and oxidative stress-related genes associated with the pathogenesis of manganese toxicity, and these findings are useful in the search for potential epigenetic targets for manganese toxicity.

A prognostic nomogram based on log odds of positive lymph nodes to predict the overall survival in biliary neuroendocrine neoplasms (NENs) patients after surgery

BACKGROUND

The prognosis of biliary neuroendocrine neoplasms (NENs) patients is affected by the status of metastatic lymph nodes. The purpose of this study was to explore the prognostic value of the log odds of positive lymph nodes (LODDS) and develop a novel nomogram to predict the overall survival (OS) in biliary NENs patients.

METHODS

A total of 125 patients with histologically confirmed biliary NENs were selected from the Surveillance, Epidemiology and End Results (SEER) database and further divided into training and validation cohorts. The discrimination and calibration of the nomogram were evaluated using the concordance index (C-index), the area under the time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration plots. The net benefits and clinical utility of the nomogram were quantified and compared with those of the SEER staging system using decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). The risk stratifications of the nomogram and the SEER staging system were compared.

RESULTS

LODDS showed the highest accuracy in predicting OS for biliary NENs. The C-index (0.789 for the training cohort and 0.890 for the validation cohort) and the time-dependent AUC (> 0.7) indicated the satisfactory discriminative ability of the nomogram. The calibration plots showed a high degree of consistency. The DCA, NRI, and IDI indicated that the nomogram performed significantly better than the SEER staging system.

CONCLUSION

A novel LODDS-incorporated nomogram was developed and validated to assist clinicians in evaluating the prognosis of biliary NENs patients.