Overview

In the US alone, over 600,000 welders are exposed to welding fumes containing a complex mixture of metallic oxides that can cause serious health problems if inhaled. Manganese (Mn) is one of these toxic metals, which may lead to neurotoxicity due to chronic high-exposure settings. High exposure to Mn is known to cause manganism, a condition with psychological, cognitive, and motor deficits like Parkinson's disease (PD). Mn toxicity continues to be widely studied in neurotoxicology. Still, the field has failed to adequately address the role of other metals present in welding fumes. Iron (Fe) is a significant constituent of welding fumes and shares similar chemical characteristics with Mn. It is known that both Mn and Fe compete for the same metal transporter for uptake in the body and brain. Animal studies have demonstrated that the transport and regulation of these two metal ions are intertwined (Ye, Q et al. 2017; Fitsanakis VA, et al. 2010; Garcia SJ, et al. 2006) and that iron loading alters the physiological function of Mn in the brain, further modifying Mn-induced neurological dysfunction (Hansen SL, et al. 2009) . On the other hand, other animal research studies have suggested that co-exposure to Mn and Fe appears to counteract oxidative stress from each other (Sziráki I, et al. 1995; Ye, Q et al. 2017). Furthermore, the vulnerability of different brain regions to Mn and/or Fe is still scarce and unclear. Advances in magnetic resonance imaging (MRI) allow for the exploitation of the paramagnetic properties of Mn and Fe to study the accumulation dynamics of these two metals directly in the human brain.

Due to the strong paramagnetism of Mn, it serves as a contrast agent in magnetic resonance imaging (MRI) by shortening the longitudinal relaxation time T1, or respectively increasing the relaxation rate R1 (R1 = 1/T1). Therefore, lower relaxation times result in higher signal intensities (“hyperintensities”) in T1-weighted (T1-w) imaging (as seen in the pictures in the section above). Historically, most in-vivo studies of Mn neurotoxicity have only focused on the basal ganglia (Dietz et al. 2001; Jiang YM, et al. 2007; Criswell SR, et al. 2012; Lewis MM, et al. 2016). However, several animal studies in non-human primates have provided evidence that Mn also accumulates in frontal white matter (Verina T, et al. 2012; Dorman DC, et al. 2006; Guilarte TR, et al. 2006). 

Because Mn can disperse throughout the brain, we developed a sensitive, whole-brain imaging approach to better understand the spatial distribution of Mn throughout the entire human brain. We hypothesized that R1 is highly sensitive to Mn deposition, and thus can reliably measure the accumulation of Mn beyond the basal ganglia, including in cortical and cerebellar regions and white matter tracts, even under the condition of low-level exposure such as in an occupational setting. 

Using data from our prior studies on typically exposed welders in the US, our group developed the first whole-brain approach to visualize group differences (between controls and welders) in R1 mapping using a statistical image analysis approach similar to that used for functional MRI (fMRI). Our results showed that the spatial distribution of excess brain Mn expands beyond the basal ganglia via white matter tracts to larger brain areas associated with motor and cognitive function (figure on the left). Furthermore, we showed that elevated Mn levels were associated with exposure metrics and lower score performance in motor tests (Monsivais, et al. 2024). 

One of my most recent interest is brain network science. Brain network science or connectomics, studies brain connections to understand how the brain works, how it's organized, and how different regions communicate. By doing so, we can better understand brain health, develop strategies to address brain disorders, and even learn ways to optimize its function.

Network science utilizes graph theory, which represents a system as a graph containing a set of objects, or nodes, connected by edges or links. From this graph, various analyses can be conducted to understand the topology and organization of the network (Telesford QK, et al. 2011). The most common approaches seen in the field of neuroimagig include structural connectivity (using DTI or DWI) or functional connectivity (using fMRI). Changes in morphological properties (such as cortical thickness) of different brain regions that occur during normal development or in brain disorder/diseases can be studied using structural covariance networks. For detailed reviews of these techniques, you can read the following books and articles: Networks of the Brain, Fundametals of Brain Network Analysis, Telesford QK, et al. 2011, Betzel RF, et al. 2023).

One of my projects involves combining MRI relaxometry (R1 and R2*) and network science approaches to try to understand the propagation of excess metals in the brain and investigate the role of affected brain regions on health outcomes. Specifically, MRI relaxometry and network science can provide a powerful approach to studying manganese (Mn) neurotoxicity. 

R1 and R2* mapping can be used to quantify Mn and Fe accumulation in the brain to extract patterns of metal accumulation between two different regions. One way of doing this is by analyzing the relaxometry covariance (at the group level) between welder and controls. To this end, I have developed a novel network-based approach termed MRI “relaxometry covariance matrix” (RCM) analysis to assess the spatial distribution of Mn and identify specific brain regions that, in addition to accumulating Mn (silos), also propagate it to other brain areas (propagators). This can help in identifying brain regions that are most affected by Mn exposure and may correlate with specific symptoms of Mn neurotoxicity. This method is a work in progress, but I presented my preliminary results at the Society of Toxicology (SOT) and the American Association for Physicist in Medicine (AAPM) annual meetings this year.