The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers

Abstract

Latino immigrants that work on farms experience chronic exposures to potential neurotoxicants, such as pesticides, as part of their work. For tobacco farmworkers there is the additional risk of exposure to moderate to high doses of nicotine. Pesticide and nicotine exposures have been associated with neurological changes in the brain. Long-term exposure to cholinesterase-inhibiting pesticides, such as organophosphates and carbamates, and nicotine place this vulnerable population at risk for developing neurological dysfunction. In this study we examined whole-brain connectivity patterns and brain network properties of Latino immigrant workers. Comparisons were made between farmworkers and non-farmworkers using resting-state functional magnetic resonance imaging data and a mixed-effects modeling framework. We also evaluated how measures of pesticide and nicotine exposures contributed to the findings. Our results indicate that despite having the same functional connectivity density and strength, brain networks in farmworkers had more clustered and modular structures when compared to non-farmworkers. Our findings suggest increased functional specificity and decreased functional integration in farmworkers when compared to non-farmworkers. Cholinesterase activity was associated with population differences in community structure and the strength of brain network functional connections. Urinary cotinine, a marker of nicotine exposure, was associated with the differences in network community structure. Brain network differences between farmworkers and non-farmworkers, as well as pesticide and nicotine exposure effects on brain functional connections in this study, may illuminate underlying mechanisms that cause neurological implications in later life.

Keywords: Brain network; Latino immigrant workers; Mixed model; Nicotine; Pesticide; Resting state fMRI.

Figure 2.1. Schematic of different steps

Rs-fMRI data were collected from each study participant. The average time series was determined from 116 anatomical brain regions as defined in the AAL atlas. Each region served as a network node. A correlation matrix was obtained through calculating the Pearson correlation between the average time series from every node pair, with negative correlation set to zero. The adjacency matrix was obtained via binarizing the correlation matrix. The four network metrics including nodal clustering coefficient, global efficiency, degree and overall modularity were extracted from the weighted brain network. These metrics along with exposure measurements including blood AChE and BChE activities, and urinary cotinine levels, farmworker status (FWS), and confounding variables were used as covariates in the two-part mixed-effects modeling framework to assess the relationship of farmworker status (and other covariates) with the probability and strength of functional brain connections. (2-column figure)

Figure 3.1. Connection Probability^* and Connection Strength as functions of clustering coefficient and modularity

This figure was created using coefficients obtained from the probability and strength models (table 3.1) to illustrate how connection probability^* (A and B) and connection strength (C and D) change in farmworkers and non-farmworkers as clustering coefficient and modularity increase from their minimum to their maximum values. A. Connection probability in non-farmworkers was higher and increased at a faster rate than in farmworkers when clustering coefficient increased. B. Connection probability in non-farmworkers decreased as modularity increased; however, connection probability did not have a significant relationship with modularity in farmworkers. C. Connection strength in farmworkers was higher and increased at a faster rate than in non-farmworkers when clustering coefficient increased. D. Connection strength in farmworkers was higher and decreased at a slower rate than in non-farmworkers as modularity increased. *It is important to note that the y-axis in all figures is the log-odds of connection probability. Any change in the log-odds of connection probability reflects a similar change in the connection probability, thus the y-axis was labeled as connection probability instead of log-odds of connection probability for simplicity. (##: Significant relationship.) (2-column color figure)

Figure 3.2. Boxplots for clustering coefficient and modularity in farmworkers and non-farmworkers

Both clustering coefficient (p = 0.0108) and modularity (p = 0.0495) were significantly higher in farmworkers. (single or 1.5-column figure)

Figure 3.3. Boxplots for urinary cotinine levels (ng/ml) and acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) activities (umole/min/ml) in farmworkers and non-farmworkers

Urinary cotinine was significantly different between farmworkers and non-farmworkers (p = 0.0063). However, AChE (p = 0.6598) and BChE (p = 0.6209) were not different between the two groups. (1.5- or 2- column figure)

Figure 4.1. Connection probability as a function of clustering coefficient (A) and connection strength as a function of modularity (B)

A. for any given connection probability, the clustering coefficient in farmworkers is higher than in non-farmworkers (C_FW > C_Non-FW). Similarly, for any given connection strength, clustering coefficient in farmworkers is higher than in non-farmworkers (not shown here). B. for any given connection strength, the overall modularity in farmworkers is higher than in non-farmworkers (M_FW > M_Non-FW). (2-column color figure)

Figure 4.2. Cartoon model of brain networks for farmworkers (A) and non-farmworkers (B)

Each network node represents a brain region and the lines represent functional connections. Although the same brain areas are included in both networks, the overall network connectivity is different. The node color indicates the module membership and the edge thickness represents connection strength. The average connection probability (density) and strength are the same between farmworkers and non-farmworkers (i.e., the total number of brain edges and average strength of present edges are the same in A and B — Each network has 35 edges including 17 strong edges). However, brain networks of farmworkers are more modularly organized and have higher functional specificity and lower intermodular integrity when compared to non-farmworkers (stronger connections are shown with thicker edges). This cartoon model was created for illustrative purposes to better visualize the study results. (2-column color figure)