Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children

Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children View ORCID Profile Mikołaj Compa , Yarema Mysak , Clemens Baumbach , Paulina Lewandowska , Aleksandra Domagalik , Bartosz Kossowski , Katarzyna Kaczmarek-Majer , Anna Degórska , Krzysztof Skotak , Katarzyna Sitnik-Warchulska , Małgorzata Lipowska , Bernadetta Izydorczyk , James Grellier , Iana Markevych , Marcin Szwed doi: https://doi.org/10.1101/2025.11.26.25341043 Mikołaj Compa 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 2 Doctoral School of Social Sciences, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mikołaj Compa For correspondence: mikolaj.compa{at}doctoral.uj.edu.pl Yarema Mysak 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Clemens Baumbach 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 3 Institute and Clinic for Occupational , Social and Environmental Medicine, LMU University Hospital , LMU Munich, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paulina Lewandowska 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 2 Doctoral School of Social Sciences, Jagiellonian University , Krakow, Poland 4 Faculty of Psychology, SWPS University of Social Sciences and Humanities , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aleksandra Domagalik 5 Centre for Brain Research, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bartosz Kossowski 6 Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katarzyna Kaczmarek-Majer 7 Institute of Environmental Protection-National Research Institute , Warsaw, Poland 8 Systems Research Institute, Polish Academy of Sciences , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anna Degórska 7 Institute of Environmental Protection-National Research Institute , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Krzysztof Skotak 7 Institute of Environmental Protection-National Research Institute , Warsaw, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Katarzyna Sitnik-Warchulska 9 Institute of Applied Psychology, Faculty of Management and Social Communication, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Małgorzata Lipowska 10 Institute of Psychology, University of Gdansk , Gdansk, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Bernadetta Izydorczyk 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site James Grellier 11 European Centre for Environment and Human Health, University of Exeter Medical School , Penryn, United Kingdom Find this author on Google Scholar Find this author on PubMed Search for this author on this site Iana Markevych 1 Institute of Psychology, Jagiellonian University , Krakow, Poland 12 Health and quality of life in a green and sustainable environment, SRIPD, Medical University of Plovdiv , Plovdiv, Bulgaria 13 Environmental Health Division, Research Institute at Medical University of Plovdiv, Medical University of Plovdiv , Plovdiv, Bulgaria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marcin Szwed 1 Institute of Psychology, Jagiellonian University , Krakow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: m.szwed{at}uj.edu.pl Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background The development of inhibitory control, a core component of cognitive control, can be influenced by environmental factors. We investigated whether exposure to particulate matter with diameter ≤ 10 μm (PM 10 ) and nitrogen dioxide (NO 2 ) across different life-time periods is related to the neural correlates of inhibitory control in 10-to 13-year-old children from southern Poland. Methods Task functional magnetic resonance imaging (task fMRI) measures brain activity while participants perform specific cognitive or behavioral tasks. We investigated inhibition using a Go/NoGo task during task fMRI and tested associations between neural correlates of inhibitory control and exposure to air pollution during prenatal, early-life, and current life periods. The study population comprised children from the NeuroSmog study with Attention-Deficit/Hyperactivity Disorder (ADHD, n=143) and their typically developing peers (n = 385). Results Higher current exposure to PM 10 was significantly associated with reduced brain activation during response inhibition in key cognitive control networks, including the dorsolateral prefrontal cortex and anterior cingulate cortex. We did not observe significant interactions between our participants’ ADHD diagnosis and their exposure to air pollution. Conclusions Long-term exposure to air pollution was associated with impairments in brain function related to inhibition in both ADHD children and their typically developing peers. Our findings add novel, pertinent evidence to the growing body of research indicating that air pollution negatively impacts the development of executive function in children and suggests that the same mechanisms that underlie pollution’s effects on the brain may also lead to the increased incidence of ADHD. Introduction Air pollution is the leading environmental risk to human health (Brauer et al., 2024). Exposure to air pollutants is related to increased rates of cardiovascular and pulmonary diseases such as stroke, elevated blood pressure, and chronic obstructive pulmonary disease (De Bont et al., 2022; Duan et al., 2020; Konduracka & Rostoff, 2022). Inhalation of air pollutants can also negatively impact the central nervous system ( Morrel et al., 2025 ), an effect that may be explained by a dysregulated immune response. One of its mechanisms involves pollutants activating microglia, which triggers chronic neuroinflammation (R. Babadjouni et al., 2018 ; R. M. Babadjouni et al., 2017 ; Elder et al., 2004 ; Habre et al., 2018 ; Li et al., 2017 ; Wilson et al., 2010 ). Over time, this sustained inflammatory state can damage neurons and decrease overall brain network activity. Exposure to air pollution has been linked with poorer performance in several cognitive tasks ( Compa et al., 2023 ; Forns et al., 2016 , 2017 ; Gignac et al., 2021 , 2022 ; Guxens et al., 2014 ; Saenen et al., 2016 ; Sunyer et al., 2015 , 2017 ). Chronic exposure to particulate matter (PM) and NO 2 is associated with poorer sustained and selective attention ( Saenen et al., 2016 ), slower cognitive development trajectories in working memory that persist for years ( Forns et al., 2017 ; Sunyer et al., 2015 ), and more behavioral problems in schoolchildren ( Forns et al., 2016 ). The effects may begin even before birth, as prenatal exposure to NO 2 has been linked to delayed psychomotor development ( Guxens et al., 2014 ). Short-term and exposure to nitrogen dioxide (NO 2 ) has also been associated with attention deficits in schoolchildren, such as slower response times and more errors ( Sunyer et al., 2017 ), and with less efficient executive attention in both typically developing (TD) children and children with Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental condition characterized by persistent patterns of inattention, hyperactivity, and impulsivity ( Compa et al., 2023 ). In addition, multiple studies have found associations between exposure to air pollution and the incidence of ADHD ( Abid et al., 2014 ; Kim et al., 2021 ; Markevych et al., 2018 ; Thygesen et al., 2020 ), further demonstrating the negative effects of exposure to air pollution on cognitive development. Detrimental effects of exposure to air pollution have been observed not only in strictly behavioral studies, but also in neuroimaging studies. However, the effects on brain structure, while investigated in multiple studies, have turned out to be often inconsistent (see recent systematic review by Morrel et al. (2025) . Effects on brain function on the other hand, offer a clearer and more consistent picture, suggesting that alterations in brain activity could be a better indicator of negative effects of exposure to air pollution. Several studies indicate that air pollution disrupts the maturation of large-scale brain networks by interfering with normal integration and segregation processes ( Cotter et al., 2023 ; Gawryluk et al., 2023 ; Pujol et al., 2016 ; Zundel et al., 2024b ). Integration involves strengthening connections within networks, whereas segregation reflects the pruning of connections between networks—both essential for typical neurodevelopment ( Compa et al., 2023 ; Forns et al., 2016 , 2017 ; Gignac et al., 2021 , 2022 ). Higher exposure to pollutants such as PM2.5 has also been linked to elevated connectivity between the default mode network (DMN) and task-positive systems, suggesting impaired differentiation of these networks and delayed maturation ( Cotter et al., 2023 ; Pujol et al., 2016 ; Zundel et al., 2024a ). Acute diesel exhaust exposure can also reduce within-DMN connectivity ( Gawryluk et al., 2023 ), and long-term pollution exposure appears to blunt the normal age-related strengthening of this network (Zundel et al., 2024). Although some of these effects may reflect compensatory reorganization, the overall evidence indicates that air pollution adversely alters the development of multiple brain networks. Neuroimaging offers several methods of studying of how air pollution impacts the brain. Another method that is particularly relevant yet still underused in this field is task-based functional magnetic resonance imaging (task fMRI). Task fMRI measures brain activity while participants perform specific cognitive or behavioral tasks and allows one to identify neural systems engaged is a particular mental process. The present study aimed to fill this critical evidence gap by examining associations between exposure to air pollution and task-related brain activity related to inhibitory control. To achieve this aim, we employed a classic Go/NoGo task during functional Magnetic Resonance Imaging (fMRI) to measure brain activity related to inhibition, an important component of cognitive control and executive attention ( Badre, 2025 ; Matsumoto & Tanaka, 2004 ). Similar to other, pre-registered studies on this cohort ( Lewandowska et al., 2025 ) we chose three time-windows of long-term exposure to air pollution – prenatal, early-life (0 to 4 years), and current (long-term exposure at current home address) (at time of recruitment). Prenatal and early-life periods are critical for the development of the central nervous system; we expected that the effects of exposure to air pollution during these time windows could have the largest impact ( Guxens et al., 2018 ; Rivas et al., 2019 ). Finally, we explored whether the impact of air pollution on brain activity during the Go/NoGo attentional task is dependent on ADHD status. By testing for such an interaction effect, we aimed to determine if the brains of individuals with ADHD, an at-risk population, are more sensitive to exposure to air pollution than the brains of their TD peers. Methods Study area and population Participants of the NeuroSmog study ( Markevych et al., 2021 ) were recruited in the years 2020–2022 among children aged 10–13 years living in 18 towns located in the southern part of Poland. Towns were specifically chosen for their diverse air pollution levels and population sizes. Children at risk of ADHD diagnosis were recruited using convenience sampling and were referred to the study by local psychologists, while likely TD children were recruited using random sampling. Children with intellectual disability, neurological or psychiatric disorders, or other serious medical conditions were excluded. We also excluded children with a gestational age less than 35 weeks or birthweight smaller than 2000 grams. To be included, children and their parents had to be fluent in the Polish language and to attend a school in one of the study towns for at least a year prior to recruitment. Each child was subjected to psychological evaluation, including a full ADHD diagnosis. All testing was done by trained clinical child psychologists. Details on child selection and evaluation procedures are described in Markevych et al. (2021) . Initially, we recruited 741 children (217 with ADHD diagnosis and 524 TD). To create the analytic sample, we removed data of children who did not complete MRI scanning, performed overly poorly during the scanning task, who were outliers in air pollution or fMRI data, or who lacked data on demographics or exposure to air pollution. This resulted in a sample comprising 545 participants for analyses on current long-term exposure to air pollution and a sample comprising 528 participants for analyses on pregnancy and early-life exposure to air pollution ( Figure 1 ). Download figure Open in new tab Figure 1. A flowchart showing the construction of the two analytic samples. Numbers in parentheses represent the number of children with ADHD diagnosis. Abbreviations: fMRI, functional Magnetic Resonance Imaging; AP, Air Pollution; ROI, region of interest. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the Institute of Psychology, Jagiellonian University, Kraków, Poland (#KE_24042019A). Written informed consent was obtained from the legal guardians, and all children gave their written informed assent. The Clinical Trials Identifier is NCT04574414 . Air pollution exposure assessment Estimates of mean air pollution concentrations were derived from hybrid land use regression (LUR) models. The resulting raster maps of mean NO 2 and mean PM 10 concentrations in the study area had a spatial resolution of 125 m × 125 m and were available for each month from 2007 to 2012 and for each year from 2013 to 2018. Following the statistical approach outlined by de Hoogh et al., 2018 , stepwise forward linear regression models were applied. These models were used to estimate air pollution levels based on ground measurements and influencing predictive factors from multiple sources. The main groups of predictor variables considered in the LUR models included: (i) traffic and residential emissions of air pollutants from dispersion models and (ii) land-use data from the CORINE land cover database ( European Environment Agency, 2018 ) and the Polish Database of Topographic Objects (BDOT10k) ( Sęp & Sikora, 2018 ). Significant predictor variables were included in the model if their addition resulted in an increased adjusted R 2 relative to the previous step. The LUR models were validated using 5-fold cross-validation. The adjusted R 2 values indicating the model fits for 2018 were 0.85 for NO 2 and 0.64 for PM 10 . For the years spanning the early-life period (2007–2017), the average adjusted R 2 was 0.82 (with range 0.64−0.92) for the NO 2 models and 0.66 (with range 0.43−0.84) for the PM 10 models. Current long-term exposure to air pollution was defined as the annual mean air pollution concentration of 2018 at the address where the child was living at the time of recruitment. Prenatal exposure was calculated by averaging the monthly mean air pollution concentrations at the mother’s pregnancy address or addresses over the last two trimesters of the pregnancy as these are the periods most relevant for brain development. The start of pregnancy was determined by subtracting the reported gestational age from the child’s birthdate; in cases of missing gestational age, 40 weeks were assumed. Early-life exposure was calculated by averaging the monthly and/or annual mean air pollution concentrations over the child’s home addresses from birth to the child’s fourth birthday. MRI procedure Mock scanner training To ensure the best possible quality of MRI images, we employed a mock scanner protocol in our study ( Suzuki et al., 2023 ). Children underwent a 15-minute mock scanner session to get prepared for a real MRI. They first acclimated to the loud noise and confined space. Using a motion sensor with visual feedback, they practiced lying still. They then completed a three-stage task training, progressing from practice with feedback to a final test requiring 80% accuracy without feedback. This process ensured that each child was comfortable and ready for the actual scan. Go/NoGo task During fMRI, each child performed a Go/NoGo task. The task performed in the scanner was based on the Human Connectome Project’s Conditioned Approach Response Inhibition Task (CARIT) task ( Somerville et al., 2018 ). Participants were asked to react to Go stimuli and to ignore NoGo stimuli. Each stimulus was displayed for 600 ms. The interstimulus interval was jittered and lasted between 1 and 4.5 seconds. The task included 68 (74%) Go and 24 (26%) NoGo stimuli. In total, two runs of the task were completed, each lasting 4 minutes and 19 seconds. The number of Go stimuli preceding a NoGo stimulus, known as prepotency, was set to 2, 3, and 4, and distributed randomly throughout the task. The Go/NoGo task generates four types of events: a reaction to a Go stimulus is called a hit; a non-reaction to a NoGo stimulus is a correct rejection; a reaction to a NoGo stimulus is a commission error; and a non-reaction to a Go stimulus is an omission error. Image acquisition All MRI data were acquired on the same Siemens Magnetom Skyra 3T scanner with a 64-channel head coil. Scans were performed at the Małopolska Centre of Biotechnology, Jagiellonian University in Krakow, Poland. Anatomical scans were acquired using the T1-weighted sequence to perform detailed alignment between the structural and functional scans. In this sequence, repetition time (TR) was 2500 ms, echo time (TE) was 2.9 ms, the flip angle was 8°, voxel size was 1 mm × 1 mm × 1 mm with 50% distance factor, and Field of View (FoV) was set to 256 mm. Slices were acquired in the interleaved sequence with the GRAPPA acceleration factor set to 2 ( Feinberg et al., 2010 ; Moeller et al., 2010 ; Xu et al., 2013 ). Two T2*-weighted sequences were used to acquire functional scans. In each sequence, TR was 800 ms, TE was 30 ms, and the flip angle was set to 52°. Voxel size dimensions were 2.4 mm × 2.4 mm × 2.4 mm, with FoV set to 210 mm. Sixty slices were collected in an interleaved fashion using the multiband acceleration factor of 6. Each of the two runs of the sequence lasted 4 minutes and 19 seconds. Both anatomical and functional sequences used anterior-posterior phase encoding direction. Both sequences were adapted from the ABCD study ( Casey et al., 2018 ). Preprocessing of MRI data Functional and structural data were preprocessed using fMRIPrep version 23.2.0 ( Esteban et al., 2019 ) a Nipype-based tool ( Gorgolewski et al., 2011 ). fMRIPrep’s preprocessing pipeline included skull-stripping, motion correction, slice-timing correction, co-registration to the participant’s anatomical image, and normalization to Montreal Neurological Institute (MNI) standard space. fMRI data was smoothed using a 7.2 mm kernel as implemented in the SPM12 toolbox ( Wellcome Trust Centre for Neuroimaging, 2014 ). A comprehensive description of the pipeline can be found in Section 1 of the Supplementary materials. Statistical analysis Identification of brain activity related to successful inhibition For each participant and voxel, the observed blood-oxygenation-level-dependent (BOLD) signal time-series was modeled as a function of several regressors using a General Linear Model (GLM), as implemented in the SPM12 software package. Event-specific regressors for hits, correct rejections, commission errors, and omission errors were created by convolving the event-specific SPM12 canonical hemodynamic response functions (HRF). In addition to these event-specific regressors, the model included six motion parameters and the signal from the cerebrospinal fluid (CSF) as nuisance regressors. To identify brain activity related to successful inhibition, we computed, for each participant and voxel, t-contrast images of correct rejections versus hits as the difference between their respective coefficient estimates from the GLM. Positive contrast values represent the magnitude of the successful inhibition effect on brain activity. In a second step, these individual-level contrast images were used in a group-level one-sample t-test. This test was performed at each voxel to determine if the group mean effect was significantly different from zero. To correct for multiple comparisons, we applied a standard SPM12 cluster-level family-wise error (FWE) correction based on Random Field Theory (RFT). This procedure involved two steps: first, we formed voxel clusters by grouping together voxels that shared a face and had uncorrected t-test p < 0.001. Second, RFT was used to produce cluster-level FWE-corrected p-values based on cluster extent. Only clusters with p < 10 -6 were considered as showing significant activation related to inhibition and kept as candidates for further analysis. ROI selection Studies that used the Go/NoGo task report several brain regions involved in inhibition ( Aron et al., 2014 ; Botvinick et al., 2001 ; Cai et al., 2014 ; Corbetta & Shulman, 2002 ; Criaud et al., 2020 ; MacDonald, 2000 ; Mostofsky & Simmonds, 2008 ; Simmonds et al., 2008 ). Hence, from our previously identified clusters (see paragraph above) we selected clusters spatially overlapping with brain regions reported in this literature and use those overlapping regions of interest (ROIs) in this study. This procedure resulted in 11 ROIs from the following brain regions: bilateral supramarginal gyrus, bilateral anterior cingulate cortex (ACC), bilateral dorsolateral prefrontal cortex (DLPFC), right hippocampus, right superior parietal lobule, bilateral anterior insula, and right pre-supplementary motor cortex (Pre-SMA). Each ROI was centered on its cluster’s peak activation voxel. Table 2 lists the ROIs and their centers’ coordinates. Analysis of exposure to air pollution and ROI activation To derive a single value that would represent a participant’s inhibition-related activation in a given ROI, we placed virtual 6 mm spheres around the ROI’s center and computed the average correct-rejection-versus-hit t-contrast over all the voxels within the sphere. This resulted in one value per participant and ROI. These values were then used as dependent variables in separate, ROI-specific linear regressions. The primary predictor of interest in those regressions was air pollution exposure for each pollutant (NO 2 and PM 10 ) and for each time window. Covariates were selected as described in our previous research ( Compa et al., 2023 ; Lewandowska et al., 2025 ; Szwed et al., 2025 ) and included sex, exact age in years, ADHD status, and town size (“large” if ≥ 90,000 inhabitants, otherwise “small”, which is in line with the NeuroSmog study’s design (Markevych et al., 2022)). We also ran a sensitivity analysis with an additional adjustment for socioeconomical status (SES) approximated by the parents’ minimum level of education (“low” if primary or vocational education, “medium” if high school and additional training, “high” if Bachelor’s degree or higher). To assess the robustness of these models to the specific definition of the individual ROI, we repeated the full analysis using data extracted with two alternative methods: using larger spheres (8 mm and 10 mm) and using average t-contrasts computed over voxels from the entire anatomical brain region containing the ROI’s center, e.g., the ACC. Outliers were removed from the dataset based on visual inspection of histograms and scatterplots generated for each continuous variable. Generalized additive models were fitted and plotted to visually check for possible nonlinear relationships between ROI variables and exposures to air pollution while adjusting for sex, age, ADHD status, and town size ( Hastie, 2017 ). Residual analysis was done using R’s DHARMa package ( Hartig, 2017 ). Finally, we also ran models that included a two-way interaction for ADHD status and exposure to air pollution. To correct for multiple comparisons, we controlled the False Discovery Rate (FDR) at level α = 0.05 using the Benjamini-Hochberg procedure. Results Descriptive characteristics of the analytic sample The study participants in the analytic sample had a mean age of 11.5 years, 228 of them were female, and 150 were diagnosed with ADHD ( Table 1 ). Regarding the educational level, 42% were from families with a high minimum level of parental education. The mean concentrations of current, prenatal, and early-life PM 10 were 36.5 μg/m 3 , 45.4 μg/m 3 , and 45.1 μg/m 3 , respectively ( Table 2 ). The mean concentrations of current, prenatal, and early-life NO 2 were 19.2 μg/m 3 , 23.8 μg/m 3 , and 24.47 μg/m 3 , respectively. View this table: View inline View popup Table 1. Descriptive statistics of the analytical sample. Abbreviations: ADHD, Attention Deficit/Hyperactivity Disorder; TD, typically developing; SD, standard deviation. Brain clusters with inhibition-related activation Correct-rejection-versus-hit (i.e. correct NoGo vs. correct Go) contrast identified widespread and highly significant activation of the frontoparietal cognitive control network and the salience network ( Table 3 ; Figure 3 , upper panel). Significant clusters included bilateral activation in the Anterior Insula and a large medial cluster that encompassed the ACC and Pre-SMA. Additional activations were identified in the bilateral DLPFC and the bilateral Supramarginal Gyrus. Finally, a significant cluster was also observed in the Hippocampus. Download figure Open in new tab Figure 3. Maps of brain activity seen in the correct-rejection-versus-hit contrast from the Go/NoGo task (upper row). Voxels are color coded by their group-level t-values. The bottom row shows the placement of the 6 mm ROI spheres over whose voxels the average t-contrasts for the ROI analysis were computed. Abbreviation: DLPFC, dorsolateral prefrontal cortex. Threshold (upper panel): p<0.001 voxel-wise, p<10 -6 FWE cluster-wise. View this table: View inline View popup Download powerpoint Table 2. Mean concentrations of air pollutants by time period in μg/m 3 . Abbreviations: PM 10 , Particulate matter with a diameter ≤ 10μm; NO 2 , Nitrogen dioxide; SD, standard deviation. View this table: View inline View popup Download powerpoint Table 3. Clusters showing inhibition-related activation (correct rejection/NoGo vs hit i.e. correct Go). For each cluster of voxels, the anatomical brain region, hemisphere, and number of voxels are listed, as well as the MNI coordinates and t-value of the cluster voxel with peak activation. Abbreviations: Hemisphere L/R, Left and Right; ACC, Anterior Cingulate Cortex; Pre-SMA, pre-supplementary motor area; DLPFC, dorsolateral prefrontal cortex; MNI, Montreal Neurological Institute. Exposure to air pollution and ROI activation We found statistically significant negative relationships between current PM 10 exposure and inhibition-related brain activity within several ROIs, specifically the bilateral DLPFC, bilateral ACC, bilateral anterior insula, bilateral superior parietal lobule, and bilateral supramarginal gyrus (Table 4 and Table S1). Furthermore, a significant negative effect of early-life PM 10 exposure on brain activity was observed within the Pre-SMA. Download figure Open in new tab Figure 4. Beta estimates and 95% confidence intervals for the association between exposure to air pollution and inhibition-related brain activity for 11 regions of interest (ROIs). To test the robustness these findings we ran several sensitivity analyses a) varying the size and the definition method for the ROIs and b) adding minimum parental education, a proxy for socioeconomic status to the models. All effects remained stable in models with ROI activations computed using alternative ROI sizes (Supplementary Figure S1 and S2). The only exception was the effect of early-life exposure to PM 10 on the activity within the Pre-SMA, which lost significance when minimum parental education was included in the statistical model (Supplementary Figure S3) and when brain activity was computed over an ROI from all voxels from the entire anatomical region (Supplementary Figure S4. Finally, none of the models/regions showed any significant interaction effect between ADHD status and exposure to air pollution (Supplementary Figure S5; all interaction p > 0.75). Discussion Our study aimed to investigate the effects of long-term exposure to air pollution on brain activity during an inhibition fMRI task in 10-to 13-year-old children with and without ADHD. We found that higher current PM 10 exposure was associated with decreased activity in key regions within the frontoparietal and salience networks—brain systems critical for executive functions and response inhibition ( Aron et al., 2014 ; Botvinick et al., 2001 ; Cai et al., 2014 ; Corbetta & Shulman, 2002 ; Criaud et al., 2020 ; MacDonald, 2000 ; Mostofsky & Simmonds, 2008 ; Simmonds et al., 2008 ). While other effects were mostly absent, we did observe a non-significant yet continuous pattern of decreased brain activity with higher early-life PM 10 exposure, especially in the Pre-SMA, where the effect seemed to be the largest. The Pre-SMA is known to be involved in response selection and inhibition processes ( Panek et al., 2025 ; Simmonds et al., 2008 ) and its decreased activation could explain the behavioral findings of less efficient conflict processing ( Compa et al., 2023 ; Rivas et al., 2019 ), which may reflect impaired motor decisions ( Panek et al., 2025 ). Concurrently, decreased activation within the salience and frontoparietal networks could lead to difficulties in task engagement and the ability to focus, respectively. Comparison with existing literature and future directions To our knowledge, this is the first study linking exposure to air pollution and task-related brain activity. Most studies linking brain function and air pollution have focused on resting-state networks or structural abnormalities. While we did not perform connectivity analyses, we found decreased activity in similar attentional regions that other studies have reported as disrupted at rest (e.g., Cotter et al., 2024; Kusters et al., 2025 ). This consistency suggests that these networks are broadly vulnerable to air pollution’s effects. Based on these findings and in accordance with studies regarding effects of exposure to air pollution on brain functional connectivity ( Cotter et al., 2023 ; Gawryluk et al., 2023 ; Kusters et al., 2025 ), we hypothesize that connectivity within the frontoparietal and other attentional networks could also be decreased with higher exposures to air pollution. Consequently, a possible future direction in our data analysis will be to perform a network connectivity analysis within the frontoparietal and salience networks. Decreased connectivity within these task-positive networks could be related to less efficient information processing. Possible biological mechanisms Our findings on decreased brain activity associated with exposure to air pollution can be explained by several plausible biological mechanisms through which air pollutants affect the brain. Pollutants can enter the brain either by crossing the blood-brain barrier via the bloodstream or through a direct pathway from the nasal passages to the olfactory bulb and prefrontal cortex ( Gale et al., 2020 ; Manzano-Covarrubias et al., 2023 ; Wright & Ding, 2016 ). Once in the brain, they can trigger a cascade of neurotoxic events. Particulate matter and other pollutants can activate microglia, the brain’s innate immune cells (R. Babadjouni et al., 2018 ; R. M. Babadjouni et al., 2017 ; Herting et al., 2024 ). Once activated, microglia become a source of pro-inflammatory cytokines and also generate reactive oxygen species (ROS) ( Block & Calderón-Garcidueñas, 2009 ). This combined inflammatory–oxidative response can damage neurons and disrupt large-scale networks. microglia-driven inflammation and ROS production could contribute to the decrease in brain activity we found. Prolonged oxidative stress can impair cellular components and compromise neuronal integrity, potentially leading to functional reductions in affected brain areas. In addition, particulate matter can weaken vascular barriers. Liu et al., (2021) demonstrated that exposure increases blood–brain barrier (BBB) permeability and vascular inflammatory signaling, particularly under reduced cerebral perfusion. Such BBB disruption facilitates the entry of circulating toxins and inflammatory molecules into the brain, amplifying neuroinflammatory and oxidative damage These biological mechanisms are supported by studies showing that exposure to air pollution is linked to structural damage to the brain, including reduced gray and white matter volume ( Erickson et al., 2020 ; Huuskonen et al., 2021 ; Lubczyńska et al., 2020 ; Sukumaran et al., 2023 ; Szwed et al., 2025 ) and altered functional brain connectivity ( Cotter et al., 2023 ; Gawryluk et al., 2023 ; Glaubitz et al., 2022; Pujol et al., 2016 ; Zundel et al., 2024) (see Morrel et al., 2025 for a systematic review of both). Our findings on reduced task-related brain activity align with this body of evidence, suggesting that these structural and functional changes may be the underlying cause of the less efficient information processing observed in our study. Air pollution, attention, the brain and ADHD Multiple studies have reported links between exposure to air pollution and ADHD incidence in children ( Kim et al., 2021 ; Markevych et al., 2018 ; Min & Min, 2017 ; Rosi et al., 2023 ; Thygesen et al., 2020 ). In the present study, we sought to examine whether ADHD diagnosis moderates the relationship between air pollution and brain activity during the Go/NoGo task and to test the hypothesis that children with ADHD, an at-risk population might be more vulnerable to detrimental effects of air pollution. We did not find any significant interaction terms for ADHD status and exposure to air pollution. On the contrary, consistent with another study from the same cohort ( Lewandowska et al., 2025 ) we found that the brains of children with ADHD diagnosis and their typically developing peers were similarly affected. We therefore propose that the same mechanisms that underlie pollution’s effects on the brain and its attentional circuits may lead to the increased incidence of ADHD. Strengths & limitations Our study has several strengths. Firstly, we used a randomly stratified sample of TD children, thus limiting selection bias. Secondly, all children’s MRI scans were acquired at one scanner site, which eliminated inter-scanner variability. Finally, we obtained complete lifelong addresses and used state-of-the-art air pollution modelling techniques to obtain maps of air pollution concentrations at a high spatial and temporal resolution. This study also has some limitations. Firstly, MRI data was not available for all the children enrolled in the study, due to low quality of image acquisition and participants resigning during MRI scanning. Secondly, since air pollution modelling was only done for the study area, we lacked exposure data for children whose families had lived outside the study area during the prenatal or early-life periods. This was also the reason why the sample size for analyses of current air pollution exposure was slightly larger. Conclusions Our study found that higher long-term exposure to PM 10 is associated with decreased brain activity in regions involved in key attentional and executive networks. These findings add novel, pertinent evidence to the growing body of research showing that air pollution can impair executive function development in children ( Compa et al., 2023 ; Forns et al., 2016 , 2017 ; Gignac et al., 2021 , 2022 ; Guxens et al., 2014 ; Saenen et al., 2016 ; Sunyer et al., 2015 , 2017 ). We propose that these impairments may arise from disrupted information processing within the frontoparietal and salience networks, which are critical for executive and attentional control. Moving beyond existing structural and resting-state studies, our work provides critical insights into how air pollution affects executive processes in children. Funding Supported by the “NeuroSmog: Determining the impact of air pollution on the developing brain” (Nr. POIR.04.04.00-1763/18-00) grant implemented as part of the TEAM-NET programme of the Foundation for Polish Science, co-financed from EU resources obtained from the European Regional Development Fund under the Smart Growth Operational Programme, also by a grant from the Priority Research Area (“Anthropocene”) under the Strategic Programme Excellence Initiative at Jagiellonian University, and by a grant from the National Science Centre, Poland (grant no. K/NCN/000514), all of which were obtained by Marcin Szwed. Iana Markevych’s and Clemens Baumbach’s time on this publication was supported by the National Science Centre, Poland (grant number 2024/55/B/HS6/01202). Iana Markevych was also partially supported by the “Strategic research and innovation program for the development of Medical University – Plovdiv” N◦ BG-RRP-2.004-0007-C01, Establishment of a network of research higher schools, National plan for recovery and resilience, financed by the European Union – NextGenerationEU. The aforementioned funding sources had no involvement in the design of the study, collection, analysis and interpretation of data, writing of the report, and decision to submit the article for publication. CRediT authorship contribution statement Mikołaj Compa: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization Yarema Mysak: Data curation Aleksandra Domagalik-Pittner: Writing – review & editing, Investigation, Resources Bartosz Kossowski: Software Paulina Lewandowska: Writing – review & editing Clemens Baumbach : Data curation, Writing – Review & Editing Katarzyna Kaczmarek-Majer : Software, Resources Krzysztof Skotak : Resources Anna Degórska : Software, Resources Katarzyna Sitnik-Warchulska : Investigation, Resources Małgorzata Lipowska : Investigation, Resources Bernadetta Izydorczyk : Investigation, Resources James Grellier : Writing – review & editing Iana Markevych : Resources, Data curation, Writing – review & editing, Supervision Marcin Szwed : Conceptualization, Methodology, Resources, Writing – review & editing, Funding acquisition, Supervision Data Availability All data produced in the present study are available upon reasonable request to the authors Acknowledgments We are very grateful to all of the children and their parents for their participation in the study. We thank our partner schools for their help in recruiting the typically developing group of children. We also extend our gratitude to the field psychologists, who identified children with ADHD and performed psychological testing on all children. The efforts of the study team in providing technical, logistic, administrative, and communication support were invaluable. We also thank the group of our Master’s students who helped in the acquisition of MRI data, as well as our MRI technicians for their hard work. Bibliography 1. ↵ Abid , Z. , Roy , A. , Herbstman , J. B. , & Ettinger , A. S. ( 2014 ). Urinary polycyclic aromatic hydrocarbon metabolites and attention/deficit hyperactivity disorder, learning disability, and special education in U.S. children aged 6 to 15 . Journal of Environmental and Public Health , 2014 , 628508 . doi: 10.1155/2014/628508 OpenUrl CrossRef 2. ↵ Aron , A. R. , Robbins , T. W. , & Poldrack , R. A. ( 2014 ). Inhibition and the right inferior frontal cortex: One decade on . Trends in Cognitive Sciences , 18 ( 4 ), 177 – 185 . doi: 10.1016/j.tics.2013.12.003 OpenUrl CrossRef PubMed Web of Science 3. ↵ Babadjouni , R. M. , Hodis , D. M. , Radwanski , R. , Durazo , R. , Patel , A. , Liu , Q. , & Mack , W. J. ( 2017 ). Clinical effects of air pollution on the central nervous system; a review . Journal of Clinical Neuroscience , 43 , 16 – 24 . doi: 10.1016/j.jocn.2017.04.028 OpenUrl CrossRef PubMed 4. ↵ Babadjouni , R. , Patel , A. , Liu , Q. , Shkirkova , K. , Lamorie-Foote , K. , Connor , M. , Hodis , D. M. , Cheng , H. , Sioutas , C. , Morgan , T. E. , Finch , C. E. , & Mack , W. J. ( 2018 ). Nanoparticulate matter exposure results in neuroinflammatory changes in the corpus callosum . PLOS ONE , 13 ( 11 ), e0206934 . doi: 10.1371/journal.pone.0206934 OpenUrl CrossRef PubMed 5. ↵ Badre , D. ( 2025 ). Cognitive Control . Annual Review of Psychology , 76 ( 1 ), 167 – 195 . doi: 10.1146/annurev-psych-022024-103901 OpenUrl CrossRef 6. ↵ Block , M. L. , & Calderón-Garcidueñas , L. ( 2009 ). Air pollution: Mechanisms of neuroinflammation and CNS disease . Trends in Neurosciences , 32 ( 9 ), 506 – 516 . doi: 10.1016/j.tins.2009.05.009 OpenUrl CrossRef PubMed Web of Science 7. ↵ Botvinick , M. M. , Braver , T. S. , Barch , D. M. , Carter , C. S. , & Cohen , J. D. ( 2001 ). Conflict Monitoring and Cognitive Control . Psychological Review , 108 ( 3 ), 624 – 652 . doi: 10.1037//0033-295X.I08.3.624 OpenUrl CrossRef PubMed Web of Science 8. ↵ Cai , W. , Ryali , S. , Chen , T. , Li , C.-S. R. , & Menon , V. ( 2014 ). Dissociable Roles of Right Inferior Frontal Cortex and Anterior Insula in Inhibitory Control: Evidence from Intrinsic and Task-Related Functional Parcellation, Connectivity, and Response Profile Analyses across Multiple Datasets . The Journal of Neuroscience , 34 ( 44 ), 14652 – 14667 . doi: 10.1523/JNEUROSCI.3048-14.2014 OpenUrl Abstract / FREE Full Text 9. ↵ Casey , B. J. , Cannonier , T. , Conley , M. I. , Cohen , A. O. , Barch , D. M. , Heitzeg , M. M. , Soules , M. E. , Teslovich , T. , Dellarco , D. V. , Garavan , H. , Orr , C. A. , Wager , T. D. , Banich , M. T. , Speer , N. K. , Sutherland , M. T. , Riedel , M. C. , Dick , A. S. , Bjork , J. M. , Thomas , K. M. , … Dale , A. M. ( 2018 ). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites . Developmental Cognitive Neuroscience , 32 (May 2017), 43 – 54 . doi: 10.1016/j.dcn.2018.03.001 OpenUrl CrossRef PubMed 10. ↵ Compa , M. , Baumbach , C. , Kaczmarek-Majer , K. , Buczyłowska , D. , Gradys , G. O. , Skotak , K. , Degórska , A. , Bratkowski , J. , Wierzba-Łukaszyk , M. , Mysak , Y. , Sitnik-Warchulska , K. , Lipowska , M. , Izydorczyk , B. , Grellier , J. , Asanowicz , D. , Markevych , I. , & Szwed , M. ( 2023 ). Air pollution and attention in Polish schoolchildren with and without ADHD . Science of the Total Environment , 892 . doi: 10.1016/j.scitotenv.2023.164759 OpenUrl CrossRef 11. ↵ Corbetta , M. , & Shulman , G. L. ( 2002 ). Control of goal-directed and stimulus-driven attention in the brain . Nature Reviews Neuroscience , 3 ( 3 ), 201 – 215 . doi: 10.1038/nrn755 OpenUrl CrossRef PubMed Web of Science 12. ↵ Cotter , D. L. , Campbell , C. E. , Sukumaran , K. , McConnell , R. , Berhane , K. , Schwartz , J. , Hackman , D. A. , Ahmadi , H. , Chen , J.-C. , & Herting , M. M. ( 2023 ). Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence . Environment International , 177 , 108001 . doi: 10.1016/j.envint.2023.108001 OpenUrl CrossRef PubMed 13. ↵ Criaud , M. , Wulff , M. , Alegria , A. A. , Barker , G. J. , Giampietro , V. , & Rubia , K. ( 2020 ). Increased left inferior fronto-striatal activation during error monitoring after fMRI neurofeedback of right inferior frontal cortex in adolescents with attention deficit hyperactivity disorder . NeuroImage: Clinical , 27 (May). doi: 10.1016/j.nicl.2020.102311 OpenUrl CrossRef 14. ↵ de Hoogh , K. , Chen , J. , Gulliver , J. , Hoffmann , B. , Hertel , O. , Ketzel , M. , Bauwelinck , M. , van Donkelaar , A. , Hvidtfeldt , U. A. , Katsouyanni , K. , Klompmaker , J. , Martin , R. V. , Samoli , E. , Schwartz , P. E. , Stafoggia , M. , Bellander , T. , Strak , M. , Wolf , K. , Vienneau , D. , … Hoek , G. ( 2018 ). Spatial PM2.5, NO2, O3 and BC models for Western Europe – Evaluation of spatiotemporal stability . Environment International , 120 , 81 – 92 . doi: 10.1016/J.ENVINT.2018.07.036 OpenUrl CrossRef PubMed 15. ↵ Elder , A. , Gelein , R. , Finkelstein , J. , Phipps , R. , Frampton , M. , Utell , M. , Kittelson , D. B. , Watts , W. F. , Hopke , P. , Jeong , C.-H. , Kim , E. , Liu , W. , Zhao , W. , Zhuo , L. , Vincent , R. , Kumarathasan , P. , & Oberdörster , G. ( 2004 ). On-road exposure to highway aerosols. 2. Exposures of aged, compromised rats . Inhalation Toxicology , 16 Suppl 1 , 41 – 53 . doi: 10.1080/08958370490443222 OpenUrl CrossRef PubMed 16. ↵ Erickson , L. D. , Gale , S. D. , Anderson , J. E. , Brown , B. L. , & Hedges , D. W. ( 2020 ). Association between Exposure to Air Pollution and Total Gray Matter and Total White Matter Volumes in Adults: A Cross-Sectional Study . Brain Sciences , 10 ( 3 ), 164 . doi: 10.3390/brainsci10030164 OpenUrl CrossRef PubMed 17. ↵ Esteban , O. , Markiewicz , C. , Blair , R. W. , Moodie , C. , Isik , A. I. , Erramuzpe Aliaga , A. , Kent , J. , Goncalves , M. , DuPre , E. , Snyder , M. , Oya , H. , Ghosh , S. , Wright , J. , Durnez , J. , Poldrack , R. , & Gorgolewski , K. J. ( 2019 ). fMRIPrep: A robust preprocessing pipeline for functional MRI . Nature Methods , 16 , 111 – 116 . doi: 10.1038/s41592-018-0235-4 OpenUrl CrossRef PubMed 18. ↵ European Environment Agency . ( 2018 ). CORINE land cover 2018 (raster 100 m), europe, 6-yearly . doi: 10.2909/960998c1-1870-4e82-8051-6485205ebbac OpenUrl CrossRef 19. ↵ Feinberg , D. A. , Moeller , S. , Smith , S. M. , Auerbach , E. , Ramanna , S. , Glasser , M. F. , Miller , K. L. , Ugurbil , K. , & Yacoub , E. ( 2010 ). Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging . PLoS ONE , 5 ( 12 ), e15710 . doi: 10.1371/journal.pone.0015710 OpenUrl CrossRef PubMed 20. ↵ Forns , J. , Dadvand , P. , Esnaola , M. , Alvarez-Pedrerol , M. , López-Vicente , M. , Garcia-Esteban , R. , Cirach , M. , Basagaña , X. , Guxens , M. , & Sunyer , J. ( 2017 ). Longitudinal association between air pollution exposure at school and cognitive development in school children over a period of 3.5 years . Environmental Research . doi: 10.1016/J.ENVRES.2017.08.031 OpenUrl CrossRef 21. ↵ Forns , J. , Dadvand , P. , Foraster , M. , Alvarez-Pedrerol , M. , Rivas , I. , López-Vicente , M. , Suades-Gonzalez , E. , Garcia-Esteban , R. , Esnaola , M. , Cirach , M. , Grellier , J. , Basagaña , X. , Querol , X. , Guxens , M. , Nieuwenhuijsen , M. J. , & Sunyer , J. ( 2016 ). Traffic-Related air pollution, noise at school, and behavioral problems in barcelona schoolchildren: A cross-sectional study . Environmental Health Perspectives , 124 ( 4 ), 529 – 535 . doi: 10.1289/ehp.1409449 OpenUrl CrossRef 22. ↵ Gale , S. D. , Erickson , L. D. , Anderson , J. E. , Brown , B. L. , & Hedges , D. W. ( 2020 ). Association between exposure to air pollution and prefrontal cortical volume in adults: A cross-sectional study from the UK biobank . Environmental Research , 185 (March), 109365 . doi: 10.1016/j.envres.2020.109365 OpenUrl CrossRef 23. ↵ Gawryluk , J. R. , Palombo , D. J. , Curran , J. , Parker , A. , & Carlsten , C. ( 2023 ). Brief diesel exhaust exposure acutely impairs functional brain connectivity in humans: A randomized controlled crossover study . Environmental Health , 22 ( 1 ), 7 . doi: 10.1186/s12940-023-00961-4 OpenUrl CrossRef PubMed 24. ↵ Gignac , F. , Barrera-Gómez , J. , Persavento , C. , Solé , C. , Tena , È. , López-Vicente , M. , Foraster , M. , Amato , F. , Alastuey , A. , Querol , X. , Llavador , H. , Apesteguia , J. , Júlvez , J. , Couso , D. , Sunyer , J. , & Basagaña , X. ( 2021 ). Short-term effect of air pollution on attention function in adolescents (ATENC!Ó): A randomized controlled trial in high schools in Barcelona, Spain . Environment International , 156 , 106614 . doi: 10.1016/J.ENVINT.2021.106614 OpenUrl CrossRef PubMed 25. ↵ Gignac , F. , Righi , V. , Toran , R. , Paz Errandonea , L. , Ortiz , R. , Mijling , B. , Naranjo , A. , Nieuwenhuijsen , M. , Creus , J. , & Basagaña , X. ( 2022 ). Short-term NO2 exposure and cognitive and mental health: A panel study based on a citizen science project in Barcelona, Spain . Environment International , 164 , 107284 . doi: 10.1016/J.ENVINT.2022.107284 OpenUrl CrossRef PubMed 26. ↵ Gorgolewski , K. , Burns , C. D. , Madison , C. , Clark , D. , Halchenko , Y. O. , Waskom , M. L. , & Ghosh , S. S. ( 2011 ). Nipype: A flexible, lightweight and extensible neuroimaging data processing framework in Python . Frontiers in Neuroinformatics , 5 , 13 . doi: 10.3389/fninf.2011.00013 OpenUrl CrossRef PubMed 27. ↵ Guxens , M. , Garcia-Esteban , R. , Giorgis-Allemand , L. , Forns , J. , Badaloni , C. , Ballester , F. , Beelen , R. , Cesaroni , G. , Chatzi , L. , De Agostini , M. , De Nazelle , A. , Eeftens , M. , Fernandez , M. F. , Fernández-Somoano , A. , Forastiere , F. , Gehring , U. , Ghassabian , A. , Heude , B. , Jaddoe , V. W. V. , … Sunyer , J. ( 2014 ). Air pollution during pregnancy and childhood cognitive and psychomotor development: Six european birth cohorts . Epidemiology , 25 ( 5 ), 636 – 647 . doi: 10.1097/EDE.0000000000000133 OpenUrl CrossRef PubMed 28. ↵ Guxens , M. , Lubczyńska , M. J. , Muetzel , R. L. , Dalmau-Bueno , A. , Jaddoe , V. W. V. , Hoek , G. , van der Lugt , A. , Verhulst , F. C. , White , T. , Brunekreef , B. , Tiemeier , H. , & El Marroun , H. ( 2018 ). Air Pollution Exposure During Fetal Life, Brain Morphology, and Cognitive Function in School-Age Children . Biological Psychiatry , 84 ( 4 ), 295 – 303 . doi: 10.1016/j.biopsych.2018.01.016 OpenUrl CrossRef PubMed 29. ↵ Habre , R. , Zhou , H. , Eckel , S. P. , Enebish , T. , Fruin , S. , Bastain , T. , Rappaport , E. , & Gilliland , F. ( 2018 ). Short-Term Effects of Airport-Associated Ultrafine Particle Exposure on Lung Function and Inflammation in Adults with Asthma . Environment International , 118 , 48 – 59 . doi: 10.1016/j.envint.2018.05.031 OpenUrl CrossRef PubMed 30. ↵ Hartig , F . ( 2017 ). Package ‘DHARMa.’ Vienna , Austria : R Development Core Team . 31. ↵ Hastie , T. J. ( 2017 ). Generalized Additive Models . Statistical Models in S , 249 – 307 . doi: 10.1201/9780203738535-7 OpenUrl CrossRef 32. ↵ Herting , M. M. , Bottenhorn , K. L. , & Cotter , D. L. ( 2024 ). Outdoor air pollution and brain development in childhood and adolescence . Trends in Neurosciences , 47 ( 8 ), 593 – 607 . doi: 10.1016/j.tins.2024.06.008 OpenUrl CrossRef 33. ↵ Huuskonen , M. T. , Liu , Q. , Lamorie-Foote , K. , Shkirkova , K. , Connor , M. , Patel , A. , Montagne , A. , Baertsch , H. , Sioutas , C. , Morgan , T. E. , Finch , C. E. , Zlokovic , B. V. , & Mack , W. J. ( 2021 ). Air Pollution Particulate Matter Amplifies White Matter Vascular Pathology and Demyelination Caused by Hypoperfusion . Frontiers in Immunology , 12 , 785519 . doi: 10.3389/fimmu.2021.785519 OpenUrl CrossRef PubMed 34. ↵ Kim , S. S. , Vuong , A. M. , Dietrich , K. N. , & Chen , A. ( 2021 ). Proximity to traffic and exposure to polycyclic aromatic hydrocarbons in relation to Attention Deficit Hyperactivity Disorder and Conduct Disorder in U.S. children . International Journal of Hygiene and Environmental Health , 232 , 113686 . doi: 10.1016/j.ijheh.2020.113686 OpenUrl CrossRef PubMed 35. ↵ Kusters , M. S. W. , Granés , L. , Petricola , S. , Tiemeier , H. , Muetzel , R. L. , & Guxens , M. ( 2025 ). Exposure to residential air pollution and the development of functional connectivity of brain networks throughout adolescence . Environment International , 196 , 109245 . doi: 10.1016/j.envint.2024.109245 OpenUrl CrossRef PubMed 36. Lei , T. , Liao , X. , Chen , X. , Zhao , T. , Xu , Y. , Xia , M. , Zhang , J. , Xia , Y. , Sun , X. , Wei , Y. , Men , W. , Wang , Y. , Hu , M. , Zhao , G. , Du , B. , Peng , S. , Chen , M. , Wu , Q. , Tan , S. , … He , Y. ( 2022 ). Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence . Cerebral Cortex , 32 ( 5 ), 1024 – 1039 . doi: 10.1093/cercor/bhab263 OpenUrl CrossRef PubMed 37. ↵ Lewandowska , P. , Bajada , C. J. , Mysak , Y. , Domagalik , A. , Kossowski , B. , Baumbach , C. , Kaczmarek-Majer , K. , Degórska , A. , Skotak , K. , Sitnik-Warchulska , K. , Lipowska , M. , Izydorczyk , B. , Grellier , J. , Markevych , I. , & Szwed , M. ( 2025 ). The Impact of Early Life Exposure to Air Pollution on the Brain: A Diffusion MRI Study in 10–13-Year-Old Children With and Without ADHD Diagnosis . Human Brain Mapping , 46 ( 14 ), e70306 . doi: 10.1002/hbm.70306 OpenUrl CrossRef 38. ↵ Li , W. , Dorans , K. S. , Wilker , E. H. , Rice , M. B. , Ljungman , P. L. , Schwartz , J. D. , Coull , B. A. , Koutrakis , P. , Gold , D. R. , Keaney , J. F. , Vasan , R. S. , Benjamin , E. J. , & Mittleman , M. A. ( 2017 ). Short-term Exposure to Ambient Air Pollution and Biomarkers of Systemic Inflammation: The Framingham Heart Study . Arteriosclerosis, Thrombosis, and Vascular Biology , 37 ( 9 ), 1793 – 1800 . doi: 10.1161/ATVBAHA.117.309799 OpenUrl Abstract / FREE Full Text 39. ↵ Liu , Q. , Shkirkova , K. , Lamorie-Foote , K. , Connor , M. , Patel , A. , Babadjouni , R. , Huuskonen , M. , Montagne , A. , Baertsch , H. , Zhang , H. , Chen , J.-C. , Mack , W. J. , Walcott , B. P. , Zlokovic , B. V. , Sioutas , C. , Morgan , T. E. , Finch , C. E. , & Mack , W. J. ( 2021 ). Air Pollution Particulate Matter Exposure and Chronic Cerebral Hypoperfusion and Measures of White Matter Injury in a Murine Model . Environmental Health Perspectives , 129 ( 8 ), 087006 . doi: 10.1289/EHP8792 OpenUrl CrossRef PubMed 40. ↵ Lubczyńska , M. J. , Muetzel , R. L. , El Marroun , H. , Basagaña , X. , Strak , M. , Denault , W. , Jaddoe , V. W. V. , Hillegers , M. , Vernooij , M. W. , Hoek , G. , White , T. , Brunekreef , B. , Tiemeier , H. , & Guxens , M. ( 2020 ). Exposure to Air Pollution during Pregnancy and Childhood, and White Matter Microstructure in Preadolescents . Environmental Health Perspectives , 128 ( 2 ), 027005 . doi: 10.1289/EHP4709 OpenUrl CrossRef PubMed 41. ↵ MacDonald , A. W. ( 2000 ). Dissociating the Role of the Dorsolateral Prefrontal and Anterior Cingulate Cortex in Cognitive Control . Science , 288 ( 5472 ), 1835 – 1838 . doi: 10.1126/science.288.5472.1835 OpenUrl Abstract / FREE Full Text 42. ↵ Manzano-Covarrubias , A. L. , Yan , H. , Luu , M. D. A. , Gadjdjoe , P. S. , Dolga , A. M. , & Schmidt , M. ( 2023 ). Unravelling the signaling power of pollutants . Trends in Pharmacological Sciences , 44 ( 12 ), 917 – 933 . doi: 10.1016/j.tips.2023.09.002 OpenUrl CrossRef 43. ↵ Markevych , I. , Orlov , N. , Grellier , J. , Kaczmarek-Majer , K. , Lipowska , M. , Sitnik-Warchulska , K. , Mysak , Y. , Baumbach , C. , Wierzba-Łukaszyk , M. , Soomro , M. H. , Compa , M. , Izydorczyk , B. , Skotak , K. , Degórska , A. , Bratkowski , J. , Kossowski , B. , Domagalik , A. , & Szwed , M. ( 2021 ). NeuroSmog: Determining the Impact of Air Pollution on the Developing Brain: Project Protocol . International Journal of Environmental Research and Public Health , 19 ( 1 ), 310 . doi: 10.3390/ijerph19010310 OpenUrl CrossRef 44. ↵ Markevych , I. , Tesch , F. , Datzmann , T. , Romanos , M. , Schmitt , J. , & Heinrich , J. ( 2018 ). Outdoor air pollution, greenspace, and incidence of ADHD: A semi-individual study . The Science of the Total Environment , 642 , 1362 – 1368 . doi: 10.1016/j.scitotenv.2018.06.167 OpenUrl CrossRef PubMed 45. ↵ Matsumoto , K. , & Tanaka , K. ( 2004 ). Conflict and Cognitive Control . Science , 303 ( 5660 ), 969 – 970 . doi: 10.1126/science.1094733 OpenUrl Abstract / FREE Full Text 46. ↵ Min , J. , & Min , K. ( 2017 ). Exposure to ambient PM10 and NO2 and the incidence of attention-deficit hyperactivity disorder in childhood . Environment International , 99 , 221 – 227 . doi: 10.1016/j.envint.2016.11.022 OpenUrl CrossRef PubMed 47. ↵ Moeller , S. , Yacoub , E. , Olman , C. A. , Auerbach , E. , Strupp , J. , Harel , N. , & Uğurbil , K. ( 2010 ). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI . Magnetic Resonance in Medicine , 63 ( 5 ), 1144 – 1153 . doi: 10.1002/mrm.22361 OpenUrl CrossRef PubMed 48. ↵ Morrel , J. , Dong , M. , Rosario , M. A. , Cotter , D. L. , Bottenhorn , K. L. , & Herting , M. M. ( 2025 ). A systematic review of air pollution exposure and brain structure and function during development . Environmental Research , 275 , 121368 . doi: 10.1016/j.envres.2025.121368 OpenUrl CrossRef 49. ↵ Mostofsky , S. H. , & Simmonds , D. J. ( 2008 ). Response inhibition and response selection: Two sides of the same coin . Journal of Cognitive Neuroscience , 20 ( 5 ), 751 – 761 . doi: 10.1162/jocn.2008.20500 OpenUrl CrossRef PubMed Web of Science 50. ↵ Panek , B. , Asanowicz , D. , & Van Der Lubbe , R. ( 2025 ). Attention– and action-related oscillatory dynamics in a visuomotor network . Cerebral Cortex , 35 ( 10 ), bhaf276 . doi: 10.1093/cercor/bhaf276 OpenUrl CrossRef PubMed 51. ↵ Pujol , J. , Martínez-Vilavella , G. , Macià , D. , Fenoll , R. , Alvarez-Pedrerol , M. , Rivas , I. , Forns , J. , Blanco-Hinojo , L. , Capellades , J. , Querol , X. , Deus , J. , & Sunyer , J. ( 2016 ). Traffic pollution exposure is associated with altered brain connectivity in school children . NeuroImage , 129 , 175 – 184 . doi: 10.1016/j.neuroimage.2016.01.036 OpenUrl CrossRef PubMed 52. ↵ Rivas , I. , Basagaña , X. , Cirach , M. , López-Vicente , M. , Suades-González , E. , Garcia-Esteban , R. , Álvarez-Pedrerol , M. , Dadvand , P. , & Sunyer , J. ( 2019 ). Association between early life exposure to air pollution and working memory and attention . Environmental Health Perspectives , 127 ( 5 ). doi: 10.1289/EHP3169 OpenUrl CrossRef PubMed 53. ↵ Rosi , E. , Crippa , A. , Pozzi , M. , De Francesco , S. , Fioravanti , M. , Mauri , M. , Molteni , M. , Morello , L. , Tosti , L. , Metruccio , F. , Clementi , E. , & Nobile , M. ( 2023 ). Exposure to environmental pollutants and attention-deficit/hyperactivity disorder: An overview of systematic reviews and meta-analyses . Environmental Science and Pollution Research , 30 ( 52 ), 111676 – 111692 . doi: 10.1007/s11356-023-30173-9 OpenUrl CrossRef 54. ↵ Saenen , N. D. , Provost , E. B. , Viaene , M. K. , Vanpoucke , C. , Lefebvre , W. , Vrijens , K. , Roels , H. A. , & Nawrot , T. S. ( 2016 ). Recent versus chronic exposure to particulate matter air pollution in association with neurobehavioral performance in a panel study of primary schoolchildren . Environment International , 95 , 112 – 119 . doi: 10.1016/j.envint.2016.07.014 OpenUrl CrossRef PubMed 55. ↵ Sęp , M. , & Sikora , A. ( 2018 ). The validity of the bdot10k database on the example of the town of tarnobrzeg . Journal of Civil Engineering, Environment and Architecture , XXXV( 65 ), 141 – 150 . doi: 10.7862/rb.2018.15 OpenUrl CrossRef 56. ↵ Simmonds , D. J. , Pekar , J. J. , & Mostofsky , S. H. ( 2008 ). Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent . Neuropsychologia , 46 ( 1 ), 224 – 232 . doi: 10.1016/j.neuropsychologia.2007.07.015 OpenUrl CrossRef PubMed Web of Science 57. ↵ Somerville , L. H. , Bookheimer , S. Y. , Buckner , R. L. , Burgess , G. C. , Curtiss , S. W. , Dapretto , M. , Elam , J. S. , Gaffrey , M. S. , Harms , M. P. , Hodge , C. , Kandala , S. , Kastman , E. K. , Nichols , T. E. , Schlaggar , B. L. , Smith , S. M. , Thomas , K. M. , Yacoub , E. , Van Essen , D. C. , & Barch , D. M. ( 2018 ). The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds . NeuroImage , 183 , 456 – 468 . doi: 10.1016/j.neuroimage.2018.08.050 OpenUrl CrossRef PubMed 58. ↵ Sukumaran , K. , Cardenas-Iniguez , C. , Burnor , E. , Bottenhorn , K. L. , Hackman , D. A. , McConnell , R. , Berhane , K. , Schwartz , J. , Chen , J.-C. , & Herting , M. M. ( 2023 ). Ambient fine particulate exposure and subcortical gray matter microarchitecture in 9– and 10-year-old children across the United States . iScience , 26 ( 3 ), 106087 . doi: 10.1016/j.isci.2023.106087 OpenUrl CrossRef PubMed 59. ↵ Sunyer , J. , Esnaola , M. , Alvarez-Pedrerol , M. , Forns , J. , Rivas , I. , López-Vicente , M. , Suades-González , E. , Foraster , M. , Garcia-Esteban , R. , Basagaña , X. , Viana , M. , Cirach , M. , Moreno , T. , Alastuey , A. , Sebastian-Galles , N. , Nieuwenhuijsen , M. , & Querol , X. ( 2015 ). Association between Traffic-Related Air Pollution in Schools and Cognitive Development in Primary School Children: A Prospective Cohort Study . PLoS Medicine , 12 ( 3 ). doi: 10.1371/journal.pmed.1001792 OpenUrl CrossRef PubMed 60. ↵ Sunyer , J. , Suades-González , E. , García-Esteban , R. , Rivas , I. , Pujol , J. , Alvarez-Pedrerol , M. , Forns , J. , Querol , X. , & Basagaña , X. ( 2017 ). Traffic-related Air Pollution and Attention in Primary School Children . Epidemiology , 28 ( 2 ), 181 – 189 . doi: 10.1097/EDE.0000000000000603 OpenUrl CrossRef PubMed 61. ↵ Suzuki , A. , Yamaguchi , R. , Kim , L. , Kawahara , T. , & Ishii-Takahashi , A. ( 2023 ). Effectiveness of mock scanners and preparation programs for successful magnetic resonance imaging: A systematic review and meta-analysis . Pediatric Radiology , 53 ( 1 ), 142 – 158 . doi: 10.1007/s00247-022-05394-8 OpenUrl CrossRef PubMed 62. ↵ Szwed , M. , De Jesus , A. V. , Kossowski , B. , Ahmadi , H. , Rutkowska , E. , Mysak , Y. , Baumbach , C. , Kaczmarek-Majer , K. , Degórska , A. , Skotak , K. , Sitnik-Warchulska , K. , Lipowska , M. , Grellier , J. , Markevych , I. , & Herting , M. M. ( 2025 ). Air pollution and cortical myelin T1w/T2w ratio estimates in school-age children from the ABCD and NeuroSmog studies . Developmental Cognitive Neuroscience , 73 , 101538 . doi: 10.1016/j.dcn.2025.101538 OpenUrl CrossRef PubMed 63. ↵ Thygesen , M. , Holst , G. J. , Hansen , B. , Geels , C. , Kalkbrenner , A. , Schendel , D. , Brandt , J. , Pedersen , C. B. , & Dalsgaard , S. ( 2020 ). Exposure to air pollution in early childhood and the association with Attention-Deficit Hyperactivity Disorder . Environmental Research , 183 . doi: 10.1016/j.envres.2019.108930 OpenUrl CrossRef 64. ↵ Wellcome Trust Centre for Neuroimaging . ( 2014 ). Statistical parametric mapping: SPM12 . http://www.fil.ion.ucl.ac.uk/spm/ 65. Wierenga , L. M. , van den Heuvel , M. P. , van Dijk , S. , Rijks , Y. , de Reus , M. A. , & Durston , S. ( 2015 ). The development of brain network architecture . Human Brain Mapping , 37 ( 2 ), 717 – 729 . doi: 10.1002/hbm.23062 OpenUrl CrossRef PubMed 66. ↵ Wilson , D. W. , Aung , H. H. , Lame , M. W. , Plummer , L. , Pinkerton , K. E. , Ham , W. , Kleeman , M. , Norris , J. W. , & Tablin , F. ( 2010 ). Exposure of mice to concentrated ambient particulate matter results in platelet and systemic cytokine activation . Inhalation Toxicology , 22 ( 4 ), 267 – 276 . doi: 10.3109/08958370903278069 OpenUrl CrossRef PubMed Web of Science 67. ↵ Wright , J. , & Ding , Y. ( 2016 ). Pathophysiological effects of particulate matter air pollution on the central nervous system . Environmental Disease , 1 ( 3 ), 85 . doi: 10.4103/2468-5690.191932 OpenUrl CrossRef 68. ↵ Xu , J. , Moeller , S. , Auerbach , E. J. , Strupp , J. , Smith , S. M. , Feinberg , D. A. , Yacoub , E. , & Uğurbil , K. ( 2013 ). Evaluation of slice accelerations using multiband echo planar imaging at 3 T . NeuroImage , 83 , 991 – 1001 . doi: 10.1016/j.neuroimage.2013.07.055 OpenUrl CrossRef PubMed Web of Science 69. ↵ Zundel , C. G. , Ely , S. , Brokamp , C. , Strawn , J. R. , Jovanovic , T. , Ryan , P. , & Marusak , H. A. ( 2024a ). Particulate matter exposure and default mode network equilibrium during early adolescence . Brain Connectivity , 14 ( 6 ), 307 – 318 . doi: 10.1089/brain.2023.0072 OpenUrl CrossRef PubMed 70. ↵ Zundel , C. G. , Ely , S. , Brokamp , C. , Strawn , J. R. , Jovanovic , T. , Ryan , P. , & Marusak , H. A. ( 2024b ). Particulate Matter Exposure and Default Mode Network Equilibrium During Early Adolescence . Brain Connectivity , 14 ( 6 ), 307 – 318 . doi: 10.1089/brain.2023.0072 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted November 27, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children Mikołaj Compa , Yarema Mysak , Clemens Baumbach , Paulina Lewandowska , Aleksandra Domagalik , Bartosz Kossowski , Katarzyna Kaczmarek-Majer , Anna Degórska , Krzysztof Skotak , Katarzyna Sitnik-Warchulska , Małgorzata Lipowska , Bernadetta Izydorczyk , James Grellier , Iana Markevych , Marcin Szwed medRxiv 2025.11.26.25341043; doi: https://doi.org/10.1101/2025.11.26.25341043 Share This Article: Copy Citation Tools Long-term exposure to air pollution impacts activity in brain regions involved in inhibitory control in 10 to 13- year old children Mikołaj Compa , Yarema Mysak , Clemens Baumbach , Paulina Lewandowska , Aleksandra Domagalik , Bartosz Kossowski , Katarzyna Kaczmarek-Majer , Anna Degórska , Krzysztof Skotak , Katarzyna Sitnik-Warchulska , Małgorzata Lipowska , Bernadetta Izydorczyk , James Grellier , Iana Markevych , Marcin Szwed medRxiv 2025.11.26.25341043; doi: https://doi.org/10.1101/2025.11.26.25341043 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Epidemiology Subject Areas All Articles Addiction Medicine (567) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4412) Dentistry and Oral Medicine (443) Dermatology (380) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1505) Epidemiology (15205) Forensic Medicine (30) Gastroenterology (1119) Genetic and Genomic Medicine (6575) Geriatric Medicine (666) Health Economics (994) Health Informatics (4511) Health Policy (1365) Health Systems and Quality Improvement (1608) Hematology (537) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15904) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (666) Neurology (6573) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1139) Occupational and Environmental Health (954) Oncology (3319) Ophthalmology (968) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (662) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5423) Public and Global Health (9205) Radiology and Imaging (2191) Rehabilitation Medicine and Physical Therapy (1367) Respiratory Medicine (1191) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fed255afee4300f',t:'MTc3OTI5OTAzMA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();