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Brief diesel exhaust exposure acutely impairs functional brain connectivity in humans: a randomized controlled crossover study

A Correction to this article was published on 23 January 2023

This article has been updated

Abstract

Background

While it is known that exposure to traffic-related air pollution causes an enormous global toll on human health, neurobiological underpinnings therein remain elusive. The study addresses this gap in knowledge.

Methods

We performed the first controlled human exposure study using functional MRI with an efficient order-randomized double-blind crossover study of diesel exhaust (DE) and control (filtered air; FA) in 25 healthy adults (14 males, 11 females; 19–49 years old; no withdrawals). Analyses were carried out using a mixed effects model in FLAME. Z (Gaussianised T/F) statistic images were thresholded non-parametrically using clusters determined by Z > 2.3 and a (corrected) cluster significance threshold of p = 0.05.

Results

All 25 adults went through the exposures and functional MRI imaging were collected. Exposure to DE yielded a decrease in functional connectivity compared to exposure to FA, shown through the comparison of DE and FA in post-exposure measurement of functional connectivity.

Conclusion

We observed short-term pollution-attributable decrements in default mode network functional connectivity. Decrements in brain connectivity causes many detrimental effects to the human body so this finding should guide policy change in air pollution exposure regulation.

Trial registration

University of British Columbia Clinical Research Ethics Board (# H12-03025), Vancouver Coastal Health Ethics Board (# V12-03025), and Health Canada’s Research Ethics Board (# 2012-0040).

Peer Review reports

Background

Exposure to traffic-related air pollution (TRAP) has long been associated with a range of adverse health effects, principally cardiovascular and respiratory [1]. This poses an enormous global burden, in terms of morbidity and lost productivity, as well as deaths estimated at approximately five million per year worldwide [2]. This profound toll is increasingly appreciated as including impacts on the central nervous system, but the data therein remains immature. Further, neurobiological underpinnings of these observations remain elusive, although some preliminary data suggest direct transmission of particles via the olfactory bulb and/or secondary transmission of inflammation likely generated more proximally [35]. Given the profound implications for public health across essentially all communities [6], data that adds overall biologic plausibility and also specific evidence of affected body systems are critically needed in order to support observational data [710]. Therefore, we performed the first controlled human exposure study to TRAP, using an established and safe paradigm of diluted diesel exhaust, that examines changes in functional MRI in an efficient crossover study (namely, diesel exhaust [DE] or filtered air [FA] exposure following light exercise), allowing observation of short-term effects on brain connectivity in this context.

Methods

Participants

A total of 100 MRI acquisitions were obtained in the current study. Twenty-five adult participants were tested immediately pre- and post-exposure to diesel exhaust (DE) and immediately pre- and post-exposure to filtered air (FA) for comparison. All participants were recruited through posters in the community, online notices, and e-mail notifications to the Vancouver Coastal Health Staff List-Serve.

Inclusion criteria for participants were as follows: between 19 and 49 years of age, able to converse in English, healthy, non-smoking, not pregnant or breast-feeding, and without any contraindications for MRI. So long as inclusion criteria were met, the only exclusion criteria was claustrophobia.

Procedure

The study employed a controlled, double-blinded crossover design at the Air Pollution Exposure Lab. Each participant was tested in both the control condition (exposure to FA) and the experimental condition (exposure to DE) with four data acquisitions: (1) pre-FA; (2) post-FA; (3) pre-DE and (4) post-DE. The order of exposure to FA and DE was randomized and counterbalanced across participants, with a two-week delay between conditions. Both participants and individuals involved in collecting the MRI data were blinded to the condition, a technique that has been shown not only nominal but also effective [11].

FA or DE (nominal concentration: 300 µg of particulate matter of 2.5 microns or less [PM2.5]/m3) exposure occurred for 120 min [12]. During exposure, participants cycled on a stationary bicycle at light effort (that which yields ventilation at 15 L/min/m2) for 15 min, during the first quarter of each hour, to maintain a representative level of activity.

Image acquisition

The following MRI protocol was employed pre-FA, post-FA, pre-DE and post-DE for each participant. All images were acquired at BC Children’s Hospital on a 3 Tesla GE Discovery MR750 MRI scanner. A whole-brain anatomical MRI scan was acquired with a T1-weighted FSPGR 3D sequence, with the following parameters: a repetition time (TR) of 8.148 ms, an echo time of 3.172 ms, voxel size of 1 × 1 × 1 mm, and a flip angle of 8°. A functional MRI (fMRI) scan was obtained during resting state (with eyes open or closed). The resting state fMRI scan was 6 min in duration and obtained with a T2*-weighted echo-planar imaging sequence with the following parameters: a repetition time of 2000 ms, an echo time of 19 ms, 180 volumes, 39 slices, and a voxel size of 3 × 3 × 3 mm. Additional task-based scans were obtained following resting state scans, but were ancillary to the hypotheses being tested here.

Functional MRI data analyses

All analysis steps were performed using tools within the Functional MRI of the Brain Software Library (FSL; Analysis Group, FMRIB, Oxford, UK, http://fsl.fmrib.ox.ac.uk) [13]. Non-brain tissue in the raw T1 images was removed using the automated Brain Extraction Tool, followed by manual verification and optimization for each subject.

A seed-based approach was used to examine functional connectivity in the default mode network (DMN) [14]. The FEAT function was used to pre-process the data including skull removal (using the Brain Extraction Tool), motion correction (using MCFLIRT) [15], and highpass temporal filtering (using Gaussian-weighted least-squares straight line fitting with σ = 50.0 s). No smoothing was applied. Registration of the functional data to the high-resolution structural image was carried out using the boundary-based registration algorithm. Registration of the high-resolution structural images to standard space was carried out using FLIRT [15, 16] and then further refined using FLIRT or FNIRT nonlinear registration (optimized for each individual) [17, 18]. Next, the posterior cingulate cortex region of interest (ROI or seed) was registered to individual space. This ROI/seed was created based on ROIs from previous studies and included a 10 voxel spherical ROI centred on the following MNI coordinates: -2, -51, 27 [19, 20]. The FEAT function was used to examine the default mode network posterior cingulate cortex ROI/seed and to regress out the lateral ventricle signal to correct for confounding noise. Specifically, the mean blood oxygen level-dependent signal time series was extracted from the posterior cingulate seed region and used as the model response function in a general linear model analysis. This allowed for examination of functional connectivity in the DMN through the detection of voxels with timeseries that correlate with that measured in the posterior cingulate seed. The time-series statistical analysis was carried out using FILM (FMRIB’s Improved Linear Model) with local autocorrelation correction and correction for motion parameters [21].

Higher-level analyses were carried out using FMRIB’s Local Analysis of Mixed Effects (FLAME), an approach for multisubject and multisession fMRI data analyses [22, 23]. Specifically, this approach allowed for higher-level within group comparisons of resting state functional connectivity in the DMN pre- versus post-DE exposure, pre- versus post-FA exposure, pre-FA versus pre-DE exposure and post-FA versus post-DE exposure (all contrasts were examined bidirectionally). Z (Gaussianised T/F) statistic images were thresholded non-parametrically using clusters determined by Z > 2.3 and a (corrected) cluster significance threshold of p = 0.05 [22, 24] The pre-exposure MRI effectively serves as a baseline for a given individual and, given the crossover design of this study, each individual served as his/her own control, virtually eliminating the concern for confounding by personal characteristics [12].

Results

In the present study, we focused on putative effects of TRAP on the default mode network (DMN), a set of inter-connected cortical brain regions in which activity is maximal at rest or during internal thought engagement. We focused on the DMN, given the preferential vulnerability of this network to aging [25, 26], toxicity [27], and disease states [28, 29].

The 25 participants were 11 female and 14 male, with mean age of 27.4 (s.d. 5.5) years. Exposure conditions were achieved as follows, in terms of PM2.5 as µg/m3, for filtered air (FA): 2.4 and for DE: 289.6; total volatile organic carbons (ppb) for FA: 124.5 and for DE: 1425.0; carbon dioxide (ppm) for FA: 794.1 and for DE: 2098.0; nitrogen dioxide (ppb) for FA: 51.9 and for DE: 283.1.

In the DE group, there were no significant differences in DMN functional connectivity for post- compared to pre-DE exposure (Fig. 1A). By contrast, in the FA group, significantly greater DMN functional connectivity was observed post-exposure relative to pre-exposure, localized in the right middle temporal gyrus and occipital fusiform gyrus (Fig. 1B; Table 1).

Fig. 1
figure 1

Results of group level comparisons (p < 0.05, corrected) with significant regions in red. A represents no significant findings pre- versus post- diesel exhaust. B depicts regions with increased functional connectivity post-filtered air > pre-filtered air. C shows regions with increased functional connectivity pre-diesel exhaust > pre-filtered air. D depicts areas with greater functional connectivity post-filtered air > post-diesel exhaust

Table 1 Functional connectivity post- and pre-filtered air exposure

There were small albeit significant differences observed when comparing groups pre-exposure. Specifically, participants demonstrated greater functional connectivity, pre-DE compared to pre-FA, in the right occipital fusiform gyrus as well as the occipital pole (Fig. 1C; Table 2). However, a more robust pattern of significant differences emerged when groups were compared post-exposure. Participants demonstrated greater functional connectivity in widespread regions of the default mode network following exposure to FA compared to following exposure to DE (Fig. 1D; Table 3). Briefly stated another way, exposure to DE yielded a decrease in functional connectivity compared to exposure to FA.

Table 2 Functional connectivity pre-diesel exhaust exposure and pre-filtered air exposure
Table 3 Functional connectivity post-diesel exhaust exposure and post-filtered air exposure

Discussion

Our study provides the first evidence in humans, from a controlled experiment, of altered brain network connectivity acutely induced by air pollution. The use of this model is important because it is not subject to potential confounding by variables correlated to exposure, a vexing concern common to observational studies. The precise functional impact of the changes seen in fMRI are unknown but are likely modest given the small magnitude of change, as expected with such limited exposure. That said, real-world exposures are often more persistent, particularly in regions of the world for which levels such as those we use are not uncommon. It is hypothesized that chronic exposure is effectively a series of short-term exposures (only one of which our participants were exposed to) that ultimately leads to accumulated deficits through a stress on allostatic load [30, 31], but whether or not this applies to pollution in the neurocognitive realm, while hypothesized [32], requires further study. That being said, our results are consistent with a study of chronic air pollution exposure in Germans [33].

In considering why DE attenuated functional connectivity in the DMN relative to FA, it is worth noting previous studies have demonstrated increased functional connectivity following exercise and the results for the FA condition are consistent with these findings [34, 35]. However, these results were only found when participants were exposed to the FA condition (whereas no significant change in functional connectivity was detected pre-post DE exposure). Therefore, our current results suggest that the brain-related benefits of light exercising (e.g., increased functional connectivity) are not obtained under the DE condition. Although previous observational investigations suggest exposure to air pollutants is associated with decreased functional connectivity [36, 37] the current results are an extension of these findings, given that the DE condition elicited a relative decrease in functional connectivity compared to the FA condition. Our demonstrating this using such directly controlled methodology adds considerably to the plausibility if these previous findings. More precise mechanisms have been elusive to date, though a link to neuroinflammation (difficult to measure directly in the intact human), potentially secondary to particle migration via the olfactory bulb as seen in animal models [38], seems likely.

There are several ways in which decrements in brain connectivity, such as those we demonstrated, might manifest in daily life. Changes in brain connectivity have been associated with decreased working memory [39] and behavioural performance [40], and deterioration in productivity at work (which is also associated with air pollution) [41]. It is also possible that these decrements worsen further in the context of multifaceted exposures not studied here [42].

Conclusion

The current study represents the first controlled human exposure to diesel exhaust investigation using functional MRI. The results of an order-randomized double-blind crossover study of diesel exhaust and control air in healthy adults revealed immediate pollution-attributable declines in default mode network functional connectivity. Change in policy surrounding air pollution exposure has long been driven by a combination of observational and experimental evidence, which together are most compelling especially in the face of interests aggressively opposed to regulation that foster improved air quality. In spite of volumes of existing evidence regarding adverse effects of air pollution, history demonstrates that implicating additional organ systems can augment the already strong evidence and effectively apply further pressure for emissions control in areas lagging in that regard. This data may be informative therein, while deepening the evidence base for direct evidence of neurocognitive effects due to acute exposure to TRAP. As the changes in cognition we have demonstrated may put individuals at risk for impaired vocational performance, this is an important consideration for public health.

Availability of data and materials

The data generated during this study are available from the corresponding author (CC) on reasonable request.

Change history

Abbreviations

DE:

Diesel exhaust

DMN:

Default mode network

FA:

Filtered air

MRI:

Magnetic resonance imaging

ROI:

Region of interest

TRAP:

Traffic-related air pollution

References

  1. Dominski FH, Lorenzetti Branco JH, Buonanno G, Stabile L, Gameiro da Silva M, Andrade A. Effects of air pollution on health: a mapping review of systematic reviews and meta-analyses. Environ Res. 2021;1(201):111487.

    Article  Google Scholar 

  2. Burnett R, Chen H, Szyszkowicz M, Fann N, Hubbell B, Pope CA, et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc Natl Acad Sci. 2018;115(38):9592–7.

    Article  CAS  Google Scholar 

  3. Cipriani G, Danti S, Carlesi C, Borin G. Danger in the Air: Air Pollution and Cognitive Dysfunction. Am J Alzheimers Dis Other Demen. 2018;33(6):333–41.

  4. Costa LG, Cole TB, Coburn J, Chang YC, Dao K, Roque P. Neurotoxicants are in the air: Convergence of human, animal, and in vitro studies on the effects of air pollution on the brain. Biomed Res Int. 2014;2014:736385.

  5. Allen JL, Klocke C, Morris-Schaffer K, Conrad K, Sobolewski M, Cory-Slechta DA. Cognitive Effects of Air Pollution Exposures and Potential Mechanistic Underpinnings. Curr Environ Health Rep. 2017;4(2):180–91.

  6. Zhang X, Chen X, Zhang X. The impact of exposure to air pollution on cognitive performance. Proc Natl Acad Sci U S A. 2018;115:37.

    Article  Google Scholar 

  7. Schikowski T, Altuğ H. The role of air pollution in cognitive impairment and decline. Neurochem Int. 2020;136:104708.

  8. Delgado-Saborit JM, Guercio V, Gowers AM, Shaddick G, Fox NC, Love S. A critical review of the epidemiological evidence of effects of air pollution on dementia, cognitive function and cognitive decline in adult population. Science of the Total Environment. 2021;757:143734.

  9. Tallon LA, Manjourides J, Pun VC, Salhi C, Suh H. Cognitive impacts of ambient air pollution in the National Social Health and Aging Project (NSHAP) cohort. Environ Int. 2017;104:102–9.

  10. Bedi AS, Nakaguma MY, Restrepo BJ, Rieger M. Particle pollution and cognition: Evidence from sensitive cognitive tests in brazil. J Assoc Environ Resour Econ. 2021;8(3).

  11. Carlsten C, Oron AP, Curtiss H, Jarvis S, Daniell W, Kaufman JD. Symptoms in response to controlled diesel exhaust more closely reflect exposure perception than true exposure. PLoS One. 2013;8(12):e83573.

  12. Birger N, Gould T, Stewart J, Miller MR, Larson T, Carlsten C. The Air Pollution exposure laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: characterization and comparison to existing facilities. Inhal Toxicol. 2011;23(4):219–25.

    Article  CAS  Google Scholar 

  13. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(SUPPL. 1):S208–19.

  14. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98(2):676–82.

  15. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17(2):825–41.

  16. Greve DN, Fischl B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage. 2009;48(1):63–72.

  17. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5(2):143–56.

  18. Andersson JLR, Jenkinson M, Smith SM. Non-linear optimisation. FMRIB technical report TR07JA1. In prac. Oxford; 2007.

  19. De Luca M, Beckmann CF, Stefano N de, Matthews PM, Smith SM. fMRI resting state networks define distinct modes of long-distance interactions in the human brain. Neuroimage. 2006;29(4):1359–67.

  20. Uddin LQ, Kelly AMC, Biswal BB, Castellanos FX, Milham MP. Functional Connectivity of Default Mode Network Components: Correlation, Anticorrelation, and Causality. Hum Brain Mapp. 2009;30(2):625–37.

  21. Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage. 2001;14(6):1370–86.

  22. Beckmann CF, Jenkinson M, Smith SM. General multilevel linear modeling for group analysis in FMRI. Neuroimage. 2003;20(2):1052–63.

  23. Woolrich MW, Behrens TEJ, Beckmann CF, Jenkinson M, Smith SM. Multilevel linear modelling for FMRI group analysis using bayesian inference. Neuroimage. 2004;21(4):1732–47.

    Article  Google Scholar 

  24. Worsley Keith. Statistical analysis of activation images. In: Functional MRI: An Introduction to Methods. 2001. p. 251–70.

  25. Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev. 2013;37(3):384–400.

  26. Dennis EL, Thompson PM. Functional brain connectivity using fMRI in aging and Alzheimer’s disease. Neuropsychol Rev. 2014;24(1):49–62.

  27. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, et al. Molecular, structural, and functional characterization of Alzheimer’s disease: Evidence for a relationship between default activity, amyloid, and memory. J Neurosci. 2005;25(34):7709–17.

  28. Damoiseaux JS, Prater KE, Miller BL, Greicius MD. Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol Aging. 2012;33(4):828 e19-828.e30 .

    Article  Google Scholar 

  29. Hafkemeijer A, van der Grond J, Rombouts SARB. Imaging the default mode network in aging and dementia. Biochim Biophys Acta Mol Basis Dis. 2012;1822(3):431–41.

  30. McEwen BS, Stellar E. Stress and the Individual: Mechanisms Leading to Disease. Arch Intern Med. 1993;153(18):2093–101.

  31. Baldasano JM, Valera E, Jiménez P. Air quality data from large cities. Sci Total Environ. 2003;307:1–3.

    Article  Google Scholar 

  32. Thomson EM. Air Pollution, Stress, and Allostatic Load: Linking Systemic and Central Nervous System Impacts. Journal of Alzheimer’s Disease. 2019;69(3):597–614.

  33. Glaubitz L, Stumme J, Lucht S, Moebus S, Schramm S, Jockwitz C, et al. Association between Long-Term Air Pollution, Chronic Traffic Noise, and Resting-State Functional Connectivity in the 1000BRAINS Study. Environ Health Perspect. 2022;130(9):97007.

  34. Li MY, Huang MM, Li SZ, Tao J, Zheng GH, Chen LD. The effects of aerobic exercise on the structure and function of DMN-related brain regions: a systematic review. Int J Neurosci. 2017;127(7):634–49.

  35. Stillman CM, Uyar F, Huang H, Grove GA, Watt JC, Wollam ME, et al. Cardiorespiratory fitness is associated with enhanced hippocampal functional connectivity in healthy young adults. Hippocampus. 2018;28(3):239–47.

  36. Pujol J, Martínez-Vilavella G, Macià D, Fenoll R, Alvarez-Pedrerol M, Rivas I, et al. Traffic pollution exposure is associated with altered brain connectivity in school children. Neuroimage. 2016;129:175–84.

  37. Wong A, Lou W, Ho K fai, Yiu BK fung, Lin S, Chu WC wing, et al. Indoor incense burning impacts cognitive functions and brain functional connectivity in community older adults. Sci Rep. 2020;10(1):7090.

  38. Oberdörster G, Sharp Z, Atudorei V, Elder A, Gelein R, Kreyling W, et al. Translocation of inhaled Ultrafine particles to the brain. Inhal Toxicol. 2004;16(6–7):437–45.

    Article  Google Scholar 

  39. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT. Brain connectivity related to working memory performance. Journal of Neuroscience. 2006;26(51):13338–43.

  40. Sala-Llonch R, Peña-Gómez C, Arenaza-Urquijo EM, Vidal-Piñeiro D, Bargalló N, Junqué C, et al. Brain connectivity during resting state and subsequent working memory task predicts behavioural performance. Cortex. 2012;48(9):1187–96.

  41. Chang TY, Zivin JG, Gross T, Neidell M. The effect of pollution on worker productivity: Evidence from call center workers in China.Am Econ J Appl Econ. 2019;11(1):151–72.

  42. Carlsten C. Synergistic Environmental Exposures and the Airways Capturing Complexity in Humans: An Underappreciated World of Complex Exposures.2018;154(4):918–24.

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Acknowledgements

We thank Health Canada’s Clean Air Regulatory Agenda, particularly Dr. Robin Shutt and Dr. Ling Liu, for support of this project. We also thank Rachel Cliff, Agnes Yuen, and Andrew Lee for help with logistics, recruiting, and carefully running exposures.

Funding

This work was supported by Health Canada’s Clean Air Regulatory Agenda. Funding body provided peer review of the proposed study during the funding application process.

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Contributions

JC and CC contributed to conception and design of the work. JRG, DJP, and AP performed the analysis. All authors contributed to the drafting, revision, and editing of the manuscript and agree to be accountable for all aspect of the present work.

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Correspondence to Chris Carlsten.

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Ethics approval and consent to participate

All participants provided their informed consent. The consent forms and study protocol were approved by the University of British Columbia Clinical Research Ethics Board (#H12-03025), Vancouver Coastal Health Ethics Board (# V12-03025), and Health Canada’s Research Ethics Board (# 2012–0040).

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Not applicable.

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The authors declare that they have no competing interests.

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The original online version of this article was revised: "Daniela J. Polombo should be Daniela J. Palombo.

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Gawryluk, J.R., Palombo, D.J., Curran, J. et al. Brief diesel exhaust exposure acutely impairs functional brain connectivity in humans: a randomized controlled crossover study. Environ Health 22, 7 (2023). https://doi.org/10.1186/s12940-023-00961-4

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Keywords

  • Air pollution
  • Functional magnetic resonance imaging (MRI)
  • Controlled human exposure
  • Environmental health
  • Neuroimaging