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

Pesticide exposure and cortical brain activation among farmworkers in Costa Rica

BACKGROUND

Previous epidemiological studies have reported associations of pesticide exposure with poor cognitive function and behavioral problems. However, these findings have relied primarily on neuropsychological assessments. Questions remain about the neurobiological effects of pesticide exposure, specifically where in the brain pesticides exert their effects and whether compensatory mechanisms in the brain may have masked pesticide-related associations in studies that relied purely on neuropsychological measures.

METHODS

We conducted a functional neuroimaging study in 48 farmworkers from Zarcero County, Costa Rica, in 2016. We measured concentrations of 13 insecticide, fungicide, or herbicide metabolites or parent compounds in urine samples collected during two study visits (approximately 3-5 weeks apart). We assessed cortical brain activation in the prefrontal cortex during tasks of working memory, attention, and cognitive flexibility using functional near-infrared spectroscopy (fNIRS). We estimated associations of pesticide exposure with cortical brain activation using multivariable linear regression models adjusted for age and education level.

RESULTS

We found that higher concentrations of insecticide metabolites were associated with reduced activation in the prefrontal cortex during a working memory task. For example, 3,5,6-trichloro-2-pyridinol (TCPy; a metabolite of the organophosphate chlorpyrifos) was associated with reduced activation in the left dorsolateral prefrontal cortex (β = -2.3; 95% CI: -3.9, -0.7 per two-fold increase in TCPy). Similarly, 3-phenoxybenzoic acid (3-PBA; a metabolite of pyrethroid insecticides) was associated with bilateral reduced activation in the dorsolateral prefrontal cortices (β = -3.1; 95% CI: -5.0, -1.2 and -2.3; 95% CI: -4.5, -0.2 per two-fold increase in 3-PBA for left and right cortices, respectively). These associations were similar, though weaker, for the attention and cognitive flexibility tasks. We observed null associations of fungicide and herbicide biomarker concentrations with cortical brain activation during the three tasks that were administered.

CONCLUSION

Our findings suggest that organophosphate and pyrethroid insecticides may impact cortical brain activation in the prefrontal cortex - neural dynamics that could potentially underlie previously reported associations with cognitive and behavioral function. Furthermore, our study demonstrates the feasibility and utility of fNIRS in epidemiological field studies.

Multi-target scenario discovery to plan for sustainable food and land systems in Australia

The development of detailed national pathways towards sustainable food and land systems aims to provide stakeholders with clarity on how long-term goals could be achieved and to reduce roadblocks in the way to making commitments. However, the inability to perfectly capture the relationships between all variables in a system and the unknown probability of future values (deep uncertainty) makes it very difficult to design scenarios that account for the full breadth of system uncertainty. Here we use scenario discovery to systematically explore the effect of different parameter ranges on model outputs, and design resilient pathways to sustainability in which multiple target achievement requires a broad portfolio of solutions. We use a model of the Australian food and land system, the FABLE (Food, Agriculture, Biodiversity, Land-use, Energy) Calculator, to investigate conditions for achieving a sustainable Australian food and land system under scenarios based on the Shared Socioeconomic Pathways (SSP) 1, 2, and 3 narratives. Here we link the FABLE Calculator with a Monte Carlo simulation tool to explore hundreds of thousands of scenarios. This allows us to identify the ranges of systemic drivers that achieve multiple sustainability targets around diets, net forest growth, agricultural water consumption, greenhouse gas emissions, biodiversity conservation, and exports by 2050. Our results show that livestock productivity and density, afforestation, and dietary change are powerful influencers for sustainability target achievement. Around 10% of the SSP1 scenarios could achieve all modelled sustainability targets. However, practically none of the scenarios based on SSP2 and SSP3 narratives could achieve such targets. The results suggest that there are options to achieve a more sustainable and resilient Australian food and land-use system with better socio-economic and environmental outcomes than under current trends. However, its achievement requires significant structural changes and coordinated interventions in several components of the domestic food and land system to increase its resilience and environmental and socio-economic performance. Understanding the bounds within which this system needs to change and operate to achieve sustainability targets will enable greater clarity and flexibility during discussions between decision-makers and stakeholders.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11625-022-01202-2.

Altered Default Mode Network Associated with Pesticide Exposure in Latinx Children from Rural Farmworker Families

Pesticide exposure has been associated with adverse cognitive and neurological effects. However, neuroimaging studies aimed at examining the impacts of pesticide exposure on brain networks underlying abnormal neurodevelopment in children remain limited. It has been demonstrated that pesticide exposure in children is associated with disrupted brain anatomy in regions that make up the default mode network (DMN), a subnetwork engaged across a diverse set of cognitive processes, particularly higher-order cognitive tasks. This study tested the hypothesis that functional brain network connectivity/topology in Latinx children from rural farmworker families (FW children) would differ from urban Latinx children from non-farmworker families (NFW children). We also tested the hypothesis that probable historic childhood exposure to pesticides among FW children would be associated with network connectivity/topology in a manner that parallels differences between FW and NFW children. We used brain networks from functional magnetic resonance imaging (fMRI) data from 78 children and a mixed-effects regression framework to test our hypotheses. We found that network topology was differently associated with the connection probability between FW and NFW children in the DMN. Our results also indicated that, among 48 FW children, historic reports of exposure to pesticides from prenatal to 96 months old were significantly associated with DMN topology, as hypothesized. Although the cause of the differences in brain networks between FW and NFW children cannot be determined using a cross-sectional study design, the observed associations between network connectivity/topology and historic exposure reports in FW children provide compelling evidence for a contribution of pesticide exposure on altering the DMN network organization in this vulnerable population. Although longitudinal follow-up of the children is necessary to further elucidate the cause and reveal the ultimate neurological implications, these findings raise serious concerns about the potential adverse health consequences from developmental neurotoxicity associated with pesticide exposure in this vulnerable population.

Risk Factors for Brain Health in Agricultural Work: A Systematic Review

Certain exposures related to agricultural work have been associated with neurological disorders. To date, few studies have included brain health measurements to link specific risk factors with possible neural mechanisms. Moreover, a synthesis of agricultural risk factors associated with poorer brain health outcomes is missing. In this systematic review, we identified 106 articles using keywords related to agriculture, occupational exposure, and the brain. We identified seven major risk factors: non-specific factors that are associated with agricultural work itself, toluene, pesticides, heavy metal or dust exposure, work with farm animals, and nicotine exposure from plants. Of these, pesticides are the most highly studied. The majority of qualifying studies were epidemiological studies. Nigral striatal regions were the most well studied brain area impacted. Of the three human neuroimaging studies we found, two focused on functional networks and the third focused on gray matter. We identified two major directions for future studies that will help inform preventative strategies for brain health in vulnerable agricultural workers: (1) the effects of moderators such as type of work, sex, migrant status, race, and age; and (2) more comprehensive brain imaging studies, both observational and experimental, involving several imaging techniques.

Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to health outcomes has lagged behind. We have attempted to address this need by developing mixed modeling frameworks that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as a synergistic fusion of multivariate statistical approaches with network science, providing a needed analytic (modeling and inferential) foundation for whole-brain network data. In this chapter we delineate these approaches that have been developed for single-task and multitask (longitudinal) brain network data, illustrate their utility with data applications, detail their implementation with a user-friendly Matlab toolbox, and discuss ongoing work to adapt the methods to (within-task) dynamic network analysis.

Migrant workers occupational health research: an OMEGA-NET working group position paper

Objective

The aims of the study were: (1) to clarify the definitions of “migrant” used in occupational health research; (2) to summarize migrant workers’ industry sectors, occupations and employment conditions; (3) to identify the occupational health and safety services available to migrant workers; (4) to summarize work-related health problems found among migrant workers; (5) to identify the methodological challenges to research into occupational health of migrant workers; and (6) to recommend improvements in migrant occupational health research.

Methods

This position paper was prepared by researchers from several European countries and Australia, working within the EU COST Action OMEGA-NET. The paper drew on two recent systematic reviews on the occupational health of international migrant workers and other literature, and also identified uncertainties and gaps in the research literature. Migrants may, for example, be temporary or permanent, moving for specific jobs migrants or other reasons. Their ethnicity and language capabilities will affect their work opportunities.

Results

The occupational health literature seldom adequately identifies the heterogeneity or characteristics of the migrant group being studied. Migrants tend to work in more physically and mentally demanding environments with higher exposures than native workers. Migrants tend to have an increased risk of physical and mental ill health, but less access to health care services. This has been demonstrated recently by high rates of COVID-19 and less access to health care. There have been a number of cross-sectional studies of migrant health but few long-term cohort studies were identified. Other study designs, such as registry-based studies, surveys and qualitative studies may complement cross-sectional studies. Mixed-methodology studies would be valuable in research on migrants’ occupational health. Language and lack of trust are barriers to migrant research participation.

Conclusion

Targeted research, especially longitudinal, identifying how these economically important but often-vulnerable workers can be best assisted is needed. Researchers should identify the characteristics of the migrant workers that they are studying including visa/migration circumstances (temporary, permanent, undocumented), racial and ethnic characteristics, existing skills and language abilities.

Influence of Heart Rate Variability on Abstinence-Related Changes in Brain State in Everyday Drinkers

Alcohol consumption is now common practice worldwide, and functional brain networks are beginning to reveal the complex interactions observed with alcohol consumption and abstinence. The autonomic nervous system (ANS) has a well-documented relationship with alcohol use, and a growing body of research is finding links between the ANS and functional brain networks. This study recruited everyday drinkers in an effort to uncover the relationship between alcohol abstinence, ANS function, and whole brain functional brain networks. Participants (n = 29), 24-60 years-of-age, consumed moderate levels of alcohol regularly (males 2.4 (±0.26) drinks/day, females 2.3 (±0.96) drinks/day). ANS function, specifically cardiac vagal tone, was assessed using the Porges-Bohrer method for calculating respiratory sinus arrhythmia (PB_RSA). Functional brain networks were generated from resting-state MRI scans obtained following 3-day periods of typical consumption and abstinence. A multi-task mixed-effects regression model determined the influences of HRV and drinking state on functional network connectivity. Results showed differences in the relationship between the strength of network connections and clustering coefficients across drinking states, moderated by PB_RSA. Increases in connection strength between highly clustered nodes during abstinence as PB_RSA increases demonstrates a greater possible range of topological configurations at high PB_RSA values. This novel finding begins to shed light on the complex interactions between typical alcohol abstinence and physiological responses of the central and autonomic nervous system.

Pesticide Toxicity Hazard of Agriculture: Regional and Commodity Hotspots in Australia

While the need to reduce the impacts of pesticide use on the environment is increasingly acknowledged, the existing data on the use of agricultural chemicals are hardly adequate to support this goal. This study presents a novel, spatially explicit, national-scale baseline analysis of pesticide toxicity hazard (the potential for chemicals to do harm). The results show an uneven contribution of land uses and growing regions toward the national aggregate toxicity hazard. A hectare of horticultural crops generates on average ten times more aquatic ecotoxicity hazard and five times more human toxicity hazard than a hectare of broadacre crops, but the higher yields and incomes in horticulture mean that both sectors are similar in terms of environmental efficiency. Livestock is the sector with the least contribution to overall hazard, even when the indirect hazard associated with feed is considered. Metrics such as pesticide use (kg/ha) or spray frequency (sprays/ha), commonly reported in highly aggregated forms, are not linearly related to toxicity hazard and are therefore less informative in driving reductions in impact. We propose toxicity hazard as a more suitable indicator for real-world risk than quantity of pesticide used, especially because actual risk can often be difficult to quantify. Our results will help broaden the discussion around pathways toward sustainability in the land-use sector and identify targeted priorities for action.

Analysis of brain subnetworks within the context of their whole-brain networks

Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.

A mixed-modeling framework for analyzing multitask whole-brain network data

The emerging area of brain network analysis considers the brain as a system, providing profound insight into links between system-level properties and health outcomes. Network science has facilitated these analyses and our understanding of how the brain is organized. While network science has catalyzed a paradigmatic shift in neuroscience, methods for statistically analyzing networks have lagged behind. To address this for cross-sectional network data, we developed a mixed-modeling framework that enables quantifying the relationship between phenotype and connectivity patterns, predicting connectivity structure based on phenotype, simulating networks to gain a better understanding of topological variability, and thresholding individual networks leveraging group information. Here we extend this comprehensive approach to enable studying system-level brain properties across multiple tasks. We focus on rest-to-task network changes, but this extension is equally applicable to the assessment of network changes for any repeated task paradigm. Our approach allows (a) assessing population network differences in changes between tasks, and how these changes relate to health outcomes; (b) assessing individual variability in network differences in changes between tasks, and how this variability relates to health outcomes; and (c) deriving more accurate and precise estimates of the relationships between phenotype and health outcomes within a given task.

Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity

There is a growing interest in using so-called dynamic functional connectivity, as the conventional static brain connectivity models are being questioned. Brain network analyses yield complex network data that are difficult to analyze and interpret. To deal with the complex structures, decomposition/factorization techniques that simplify the data are often used. For dynamic network analyses, data simplification is of even greater importance, as dynamic connectivity analyses result in a time series of complex networks. A new challenge that must be faced when using these decomposition/factorization techniques is how to interpret the resulting connectivity patterns. Connectivity patterns resulting from decomposition analyses are often visualized as networks in brain space, in the same way that pairwise correlation networks are visualized. This elevates the risk of conflating connections between nodes that represent correlations between nodes' time series with connections between nodes that result from decomposition analyses. Moreover, dynamic connectivity data may be represented with three-dimensional or four-dimensional (4D) tensors and decomposition results require unique interpretations. Thus, the primary goal of this article is to (1) address the issues that must be considered when interpreting the connectivity patterns from decomposition techniques and (2) show how the data structure and decomposition method interact to affect this interpretation. The outcome of our analyses is summarized as follows. (1) The edge strength in decomposition connectivity patterns represents complex relationships not pairwise interactions between the nodes. (2) The structure of the data significantly alters the connectivity patterns, for example, 4D data result in connectivity patterns with higher regional connections. (3) Orthogonal decomposition methods outperform in feature reduction applications, whereas nonorthogonal decomposition methods are better for mechanistic interpretation.

A MATLAB toolbox for multivariate analysis of brain networks

Complex brain networks formed via structural and functional interactions among brain regions are believed to underlie information processing and cognitive function. A growing number of studies indicate that altered brain network topology is associated with physiological, behavioral, and cognitive abnormalities. Graph theory is showing promise as a method for evaluating and explaining brain networks. However, multivariate frameworks that provide statistical inferences about how such networks relate to covariates of interest, such as disease phenotypes, in different study populations are yet to be developed. We have developed a freely available MATLAB toolbox with a graphical user interface that bridges this important gap between brain network analyses and statistical inference. The modeling framework implemented in this toolbox utilizes a mixed-effects multivariate regression framework that allows assessing brain network differences between study populations as well as assessing the effects of covariates of interest such as age, disease phenotype, and risk factors on the density and strength of brain connections in global (i.e., whole-brain) and local (i.e., subnetworks) brain networks. Confounding variables, such as sex, are controlled for through the implemented framework. A variety of neuroimaging data such as fMRI, EEG, and DTI can be analyzed with this toolbox, which makes it useful for a wide range of studies examining the structure and function of brain networks. The toolbox uses SAS, R, or Python (depending on software availability) to perform the statistical modeling. We also provide a clustering-based data reduction method that helps with model convergence and substantially reduces modeling time for large data sets.

Connectivity in fMRI: Blind Spots and Breakthroughs

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

Assessment of Pesticide-Related Pollution and Occupational Health of Vegetable Farmers in Benguet Province, Philippines

BACKGROUND

Agriculture accounts for 20% of the national income in the Philippines. In order to boost agricultural activity and prevent crop damage, farmers rely on pesticides for vector control and management.

OBJECTIVES

The present study assessed the pesticide exposure and occupational health of agricultural farmers in the Philippines. The study site is one of the largest vegetable-producing provinces in the Philippines.

METHODS

This study employed both a survey questionnaire and physical health assessment, including a mental state examination. Pesticide exposure was estimated based on the duration of pesticide use, as well as the amount per spray application. The data results were segregated by gender, as women are also heavily engaged in agriculture in this part of the Philippines.

RESULTS

The results showed that pesticide exposure usually occurred during agricultural activities such as spray applications in the field (63.7%), mixing (38.4%), loading (34.1%) and field re-entry (9.7%). The most frequently used pesticides were Tamaron, Selecron, and Dithane. The mean duration of pesticide exposure was 14.23 years for males and 15.4 years for females. The longest used pesticide among males was Sumicidine (16.2 years), and Tamaron for females (18 years). In terms of amount used, the average was 147 ml per spray application for males and 65.5 ml for females. Exposure to pesticides was expressed in number of years and amount used per spray application, and the average exposure of males was 2,024.43 ml/years and 993.55 ml/years for females. Among farmers, 49% complained of being sick due to their work. Of those who became ill, a large percentage (69.8%) did not receive any medical attention. The most prevalent health symptoms were muscle pains (63.3%), muscle weakness (55%), and easy fatigability (52.4%). For the mini-mental state examination, abnormalities were found in 5.4% of males and 13.3% of females. The use of insecticides was associated with weakness, easy fatigability and weight loss.

DISCUSSION

The present study demonstrated frequent and significant duration of pesticide use among farmers in Benguet province, Philippines.

CONCLUSIONS

Pesticide exposure was considerable among the farmers in the present study. The occupational health conditions reported by the study subjects can be linked to their pesticide use. Although this study assessed risk factors associated with general health symptoms, further investigation is needed to determine specific pesticide exposure-health correlations.

PARTICIPANT CONSENT

Obtained.

ETHICS APPROVAL

The study was approved by the Research Ethics Board of the University of the Philippines, Manila, which is recognized and accredited by the Forum for Ethical Review Committees in Asia and the Western Pacific (FERCAP).