Impact of air pollution on Neurite Orientation Dispersion and Density metrics (NODDI) in 10–13-year-old children with and without ADHD diagnosis

Impact of air pollution on Neurite Orientation Dispersion and Density metrics (NODDI) in 10–13-year-old children with and without ADHD diagnosis | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Impact of air pollution on Neurite Orientation Dispersion and Density metrics (NODDI) in 10–13-year-old children with and without ADHD diagnosis View ORCID Profile Paulina Lewandowska , View ORCID Profile Claude J. Bajada , View ORCID Profile Mikołaj Compa , Yarema Mysak , View ORCID Profile Aleksandra Domagalik , Bartosz Kossowski , Clemens Baumbach , Katarzyna Kaczmarek-Majer , Anna Degórska , Krzysztof Skotak , Katarzyna Sitnik-Warchulska , Małgorzata Lipowska , Bernadetta Izydorczyk , James Grellier , Iana Markevych , Marcin Szwed doi: https://doi.org/10.1101/2025.11.03.686321 Paulina Lewandowska 1 Institute of Psychology, Jagiellonian University , Cracow, Poland 2 Doctoral School of Social Sciences, Jagiellonian University , Cracow, Poland 3 Department of Psychology, SWPS University , Cracow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Paulina Lewandowska For correspondence: p.lewandowska{at}doctoral.uj.edu.pl m.szwed{at}uj.edu.pl Claude J. Bajada 4 Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta , Malta Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claude J. Bajada Mikołaj Compa 1 Institute of Psychology, Jagiellonian University , Cracow, Poland 2 Doctoral School of Social Sciences, Jagiellonian University , Cracow, Poland 3 Department of Psychology, SWPS University , Cracow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mikołaj Compa Yarema Mysak 1 Institute of Psychology, Jagiellonian University , Cracow, 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 , Cracow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Aleksandra Domagalik 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 Clemens Baumbach 1 Institute of Psychology, Jagiellonian University , Cracow, Poland 7 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 Katarzyna Kaczmarek-Majer 8 Institute of Environmental Protection-National Research Institute , Warsaw, Poland 9 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 8 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 8 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 10 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 11 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 , Cracow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site James Grellier 12 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 , Cracow, Poland 13 Health and quality of life in a green and sustainable environment, SRIPD, Medical University of Plovdiv , Plovdiv, Bulgaria 14 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 , Cracow, Poland Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: p.lewandowska{at}doctoral.uj.edu.pl m.szwed{at}uj.edu.pl Abstract Full Text Info/History Metrics Preview PDF ABSTRACT Air pollution is a significant risk factor for adverse neurodevelopmental outcomes in children. While studies have linked pollutants to changes in brain structure, specific effects on white matter microstructure remain inconclusive. Neurite Orientation Dispersion and Density Imaging (NODDI) provide two nuanced, separate white matter measures that serve as proxies for neurite density and cell-body organization. We used NODDI to examine potential associations between prenatal, early life and current exposure to nitrogen dioxide (NO 2 ) and particulate matter with diameter < 10 micrometers (PM 10 ) and white matter microstructure in school-aged children. We also explored whether ADHD diagnosis moderated these associations. We observed several negative associations between both NO 2 and PM 10 exposure and neurite density across various white-matter fibers and exposure windows, but none of the associations were statistically significant after correcting for multiple comparisons. In this analysis, we did not find any statistically significant associations between long-term exposure to PM 10 and NO 2 and white matter microstructural integrity as measured by NODDI. Our results highlight the challenge of detecting modest environmental impacts on the brain and underscore the need for larger, more targeted studies to confirm these preliminary trends. 1. INTRODUCTION Air pollution is one of the leading environmental contributors to the disease burden worldwide ( Global Burden of Disease Collaborative Network, 2024 ). Exposure to airborne pollutants, such as fine particulate matter (PM) and nitrogen dioxide (NO 2 ) has been consistently linked to respiratory ( Duan et al., 2020 ; Paciência et al., 2022 ) and cardiovascular ( de Bont et al., 2022 ; Konduracka & Rostoff, 2022 ) diseases. A growing body of research has highlighted the effects of air pollution on the nervous system, particularly exposure during critical periods of childhood development ( Cory-Slechta et al., 2023 ; Herting et al., 2024 ; Morrel et al., 2025 ; Parenteau et al., 2024 ). Children are especially vulnerable to air pollution due to greater effective exposure from physiological and behavioral factors, such as higher inhalation volume per body weight ( Bateson & Schwartz, 2008 ). This heightened biological sensitivity means that as pollutants cross the blood-brain barrier, they can disrupt the maturing brain via mechanisms like neuroinflammation and microglial activation, leading to potential long-term consequences ( Cory-Slechta et al., 2023 ; Morris et al., 2021 ). Consistent with these mechanisms, childhood exposure to air pollution has been linked to cognitive deficits, particularly in attention and executive functioning ( Compa et al., 2023 ; Forns et al., 2016 ; Sunyer et al., 2015 ). This link is further solidified by growing evidence connecting early life exposure to traffic-related air pollution with an increased risk of Attention Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental condition characterized by these specific impairments ( Liu et al., 2023 ; Thygesen et al., 2020 ). Although growing evidence suggests that air pollution can affect cognitive development and behavior, our understanding of the underlying neural pathways remains limited ( Cory-Slechta et al., 2023 ; Herting et al., 2024 ; Parenteau et al., 2024 ; Polemiti et al., 2024 ; Szwed et al., 2025 ). Studies employing structural Magnetic Resonance Imaging (MRI) indicate that exposure to pollutants such as particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM 2.5 ) and with a diameter of less than 10 micrometers (PM 10 ), NO 2 , and polycyclic aromatic hydrocarbons (PAHs) ( Mortamais et al., 2017 ; Yang et al., 2025 ) are associated with changes in cortical volume, thickness, and surface area ( Guxens et al., 2018 ; Herting et al., 2019 ; Kusters et al., 2025 ; Pujol, Martínez-Vilavella, et al., 2016 ). However, findings are inconsistent: the reported effects are often small, and the specific cortical or subcortical regions implicated differ substantially across studies ( Morrel et al., 2025 ). In addition to analyses of structural MRI, a small but growing body of research has used resting-state functional MRI (rs-fMRI) to explore the impact of air pollution on brain network organization ( Cotter et al., 2023 ; López-Vicente et al., 2025 ; Pujol, Fenoll, et al., 2016 ; Zundel et al., 2024 ). Most recent longitudinal studies are beginning to shed light on how these exposures affect the maturation of brain networks over time ( Cotter et al., 2023 ; López-Vicente et al., 2025 ). The majority of studies reporting associations between air pollution exposure and altered white matter have relied on metrics from Diffusion Tensor Imaging (DTI) ( Binter et al., 2022 ; Kusters et al., 2024 ; Lubczyńska et al., 2020 ; Pujol, Martínez-Vilavella, et al., 2016 ). While the majority of such studies typically report statistically significant associations between air pollution exposures and altered white matter ( Binter et al., 2022 ; Burnor et al., 2021 ; Kusters et al., 2024 ; Lewandowska et al., 2025 ; Lubczyńska et al., 2020 ; Pujol, Fenoll, et al., 2016 ; Pujol, Martínez-Vilavella, et al., 2016 ), their findings are limited by a lack of biological specificity. DTI metrics like fractional anisotropy (FA) are informative, they provide only limited insight into the precise microstructural properties of the brain tissue; for example, FA conflates multiple features such as neurite density, myelination, and fiber organization, leading to potentially ambiguous interpretation ( Jones et al., 2013 ). More advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), and fixel-based analysis (FBA), offer a more nuanced characterization of white matter microstructure compared to a conventional tensor model ( Burnor et al., 2021 ; Cotter et al., 2024 ). While studies on the Adolescent Brain Child Development (ABCD) cohort have successfully used RSI to identify microstructural alterations linked to air pollution ( Burnor et al., 2021 ; Cotter et al., 2024 ), our own previous work on the Neurosmog cohort found no significant associations using FBA ( Lewandowska et al., 2025 ). This discrepancy suggests either that the effects are different in the two cohorts, or that the detection of any underlying biological effect is sensitive to the specific microstructural properties captured by different diffusion models. To address this issue, the present study employs NODDI, an advanced method of diffusion modelling, not yet applied in this research context. It has already been used in developmental research to track brain maturation in children ( Lynch et al., 2020 ; Mah et al., 2017 ; Vaher et al., 2022 ) and proved to be a sensitive metric of microstructural changes across early life. However, its application in the field of environmental neuroimaging remains scarce. Conducting such research is particularly important as evidence indicates that exposure to air pollution can increase the risk for the incidence of ADHD and its associated symptoms (Bernardina Dalla et al., 2022; Liu et al., 2023 ). In the present study we therefore applied NODDI to examine potential associations between air pollution exposure and white matter microstructure in children with ADHD diagnosis and their typically developing peers. We hypothesized that higher exposure to air pollution would be associated with altered neurite density and orientation dispersion in atlas-based white matter regions. Guided by our prior work on the same population ( Lewandowska et al., 2025 ), we focused on NO 2 and PM 10 exposure during the first four years of life and conducted an additional analysis of the prenatal period ( Binter et al., 2022 ; Bové et al., 2019 ; Crooijmans et al., 2024 ). As an exploratory step, we also examined associations between NODDI metrics and current long-term exposure. Prior studies have highlighted the potential links between air pollution exposure and attentional deficits ( Compa et al., 2023 ; Liu et al., 2023 ; Thygesen et al., 2020 ) suggesting that there is a possibility that both factors may independently or jointly influence brain structure. Additionally, structural brain differences between children with and without ADHD in both white and grey matter have been reported ( Bernanke et al., 2022 ; McAlonan et al., 2007 ; Nakao et al., 2011 ). Also, given that ADHD is linked to baseline structural alterations in white matter ( Connaughton et al., 2022 ; Fuelscher et al., 2021 ) and may represent a vulnerability factor, we also hypothesized that ADHD diagnosis moderates these associations. 2. MATERIALS & METHODS Our analysis was preregistered on the Open Science Framework (OSF) registry as a project entitled “Impact of prenatal and early life exposure of air pollution on NODDI metrics in Polish children: diffusion MRI study.” ( Lewandowska, 2025 ). We used data from the NeuroSmog study ( Markevych et al., 2021 ), which was conducted according to the guidelines of the Declaration of Helsinki and 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 of all children enrolled in the study, and all children also gave their own written informed assent. The Clinical Trials Identifier is NCT04574414 . 2.1 Study population and sample size The final sample consisted of 388 children from the NeuroSmog cohort ( Markevych et al., 2021 ). The children were aged 10–13 years (mean age = 11.3, SD = 0.78; 161 female) and either had a clinical diagnosis of ADHD or were typically-developing (TD) children. In the NeuroSmog study, the children in the TD group were defined as participants without an ADHD diagnosis, intellectual disability, or any known neurological, comorbid psychiatric, or other serious medical conditions ( Markevych et al., 2021 ). The TD children were recruited via random stratified sampling from schools in 18 towns. Study towns were selected based on varying population sizes and air pollution levels. In parallel, children with an ADHD diagnosis were recruited from the same 18 towns using convenience sampling, with referrals from local psychological counseling centers, school psychologists, and medical professionals, or through direct parental application. From the 741 children enrolled in the NeuroSmog study (demographic details in Supplementary Materials, Supplemental Methods 1 , Table 1 S), 714 children completed the MRI scanning session. Children with a complete diffusion-weighted imaging sequence consisted of 578 and these images underwent rigorous quality control processes. After completion of all quality control, preprocessing and registration steps (see section 2.4 Diffusion data preprocessing and quality control ), the final sample of 388 participants was established ( Table 1 ). For a comprehensive description of the inclusion/exclusion criteria and recruitment details, please refer to the NeuroSmog protocol paper and previous published analyses ( Compa et al., 2023 ; Markevych et al., 2021 ). View this table: View inline View popup Download powerpoint Table 1. Descriptive statistics of the study sample 2.2 Air pollution exposure assessment To estimate individual-level exposure to air pollution, we first collected lifelong residential address histories for each child, provided by their legal guardians ( Markevych et al., 2021 ). For this study, we successfully developed reliable exposure estimates for PM 10 and NO 2 . Monthly average concentration maps for these pollutants were generated at 125m resolution using hybrid Land Use Regression (LUR) models ( de Hoogh et al., 2013 ; Szwed et al., 2025 ), as detailed in the Supplementary Materials (Supplemental Methods 2). The prenatal exposure period was estimated by averaging the monthly exposure across the second and third trimester of pregnancy, periods most crucial for fetal brain development. The start of each pregnancy was calculated by subtracting the gestational age from the child’s birth date. For cases where gestational age was not available, we assumed a duration of 40 weeks. Early life exposure corresponds to the average of annual exposure estimated within the first four years of children’s life (later referred to as ‘years 0-4’). Current exposure (years 2020-2022) was assessed by assigning modeled annual average concentrations to each child’s residential address. The models used were based on 2018 monitoring data, as these were the most up-to-date available. 2.3 MRI data acquisition MRI data were acquired at the Małopolska Centre of Biotechnology, Jagiellonian University in Kraków, Poland, using Siemens MAGNETOM Skyra 3T MRI scanner (Erlangen, Germany) with a 64-channel head coil. Children were instructed to remain awake and to stay as still as possible during the scanning process. Each child completed a mock scanning session before the actual scan to familiarize them with the procedure and ensure their comfort. A spin-echo diffusion weighted echo planar imaging (DW_EPI) sequence was applied with TE=101 ms, TR=3800 ms, flip angle = 78 deg, and voxel size = 2 x 2 x 2 mm 3 . We used anterior-posterior (AP) phase coding directions and 118 volumes in total with b-values of 500, 1250, 2500 (n = 18, n = 36, n = 53) along with 11 images applying no diffusion gradient (b-value = 0). To correct for susceptibility-induced distortions, we also acquired 4 images with a b-value = 0 using posterior-anterior (PA) phase coding. For the purpose of registration steps (see Masks registration to diffusion space section) we have also used T1-weighted images with a resolution of 1 x 1 x 1 mm 3 , TE=2.88 ms, TR=2500 ms, and flip angle = 8 deg. The acquisition sequences for T1-weighted data were adopted from the ABCD project (Casey et al., 2018). Further information on the neuroimaging data acquisition is incorporated in the NeuroSmog protocol paper ( Markevych et al., 2021 ). 2.4 Diffusion data preprocessing and quality control From the initially recruited participants (N = 741) we obtained complete diffusion-weighted imaging (DWI) data for 578 children ( Figure 1 ). Each scan was visually inspected to identify any structural abnormalities. Twelve participants were excluded due to cerebellar malformations, and an additional 15 were excluded due to image acquisition issues. This resulted in 551 DWI scans included in the preprocessing pipeline. Following, the diffusion images were matched with their corresponding T1-weighted structural scans to ensure proper registration (for detailed description see section Masks registration to diffusion space in the text below). Download figure Open in new tab Figure 1. The chart illustrates the number of participants retained at each stage of data processing, starting from initial recruitment to the final sample included in the analyses. Manual review involved excluding images with visible distortions unresolvable during preprocessing or any identified brain malformations. The matching of diffusion with T1 data refers to the co-registration process used to define anatomical regions within the diffusion space. AP: Air Pollution. Data preprocessing was conducted using the FSL eddy tool ( Andersson & Sotiropoulos, 2016 ) which corrects for subject motion and eddy current-induced distortions. Susceptibility-induced off-resonance fields were estimated from spin-echo EPI images acquired with opposing phase-encoding directions ( Andersson et al., 2003 ), and incorporated into eddy to jointly model susceptibility, motion, and eddy current effects ( Andersson & Sotiropoulos, 2016 ). Slice-level signal loss due to motion during diffusion encoding was identified and replaced using Gaussian Process predictions ( Andersson et al., 2016 ). Intra-volume motion artifacts—wherein misaligned slices compromise volumetric integrity— were corrected using slice-to-volume alignment ( Andersson et al., 2017 ). Subject movement-related variations in susceptibility-induced distortions were modeled using a Taylor series expansion around the first volume of each scan ( Andersson et al., 2018 ). Quality control (QC) was performed using the eddy QC framework ( Bastiani et al., 2019 ), which provides both subject-level and study-level metrics. Following the study-wise QC protocol implemented in SQUAD, participants with more than one flagged QC index were classified as outliers and excluded from further analysis (n = 78) (see Supplemental Methods 3 for a detailed description of the QC indices). 2.4.1 Selection of white matter ROIs Due to the limited and inconsistent findings in the existing literature, we adopted a comprehensive, exploratory approach for our region-of-interest (ROI) selection rather than focusing on a small number of pre-selected tracts. To achieve this, we based our analysis on a well-validated, standard-space ICBM-DTI-81 white matter labels atlas ( Mori et al., 2005 ), which parcellates the white matter into distinct tracts and regions. The atlas consists of 81 white matter tracts including brainstem tracts, projection fibers, association fibers, and commissural fibers. The list of the full list of the white matter tracts is available at http://www.bmap.ucla.edu/portfolio/atlases/ICBM_DTI-81_Atlas/ For this study, all available white matter regions from this atlas were selected as our ROIs to provide a thorough and systematic investigation of potential pollution-related effects across the entire white matter architecture. 2.4.2 NODDI estimations We implemented the NODDI model to gain specific insight into tissue microstructure. NODDI resolves the diffusion signal by modeling three compartments in each voxel: intra-neurite, extra-neurite, and isotropic free water. This approach yields the Neurite Density Index (NDI), which reflects the packing density of axons and dendrites, and the Orientation Dispersion Index (ODI), which quantifies the coherence of neurite orientations, and the Free Water Fraction (FWF), which estimates the proportion of the voxel occupied by non-restricted water, such as cerebrospinal fluid (CSF). Figure 2 provides a schematic illustration of the NODDI decomposition for a representative white matter voxel ( Figure 2 ). Download figure Open in new tab Figure 2. The figure shows how a single white matter voxel is decomposed into three compartments: intra-neurite (water within axons), extra-neurite (water outside axons), and free water. From this, NODDI derives two biophysical metrics: 1) NDI (Neurite Density Index): The volume fraction of the intra-neurite compartment relative to the total tissue volume, defined as the sum of the intra- and extra-neurite spaces (a measure of density); 2) ODI (Orientation Dispersion Index): A measure from 0 to 1 representing the degree of fanning or dispersion of neurite orientation. Low values indicate highly aligned, parallel fibers (e.g., major white matter tracts), while high values indicate complex or crossing fibers (e.g., gray matter). Estimation of microstructural brain parameters was conducted using the NODDI model (Zhang et al., 2012), implemented via the CUDIMOT ( Hernandez-Fernandez et al., 2019 ) toolbox. NODDI parameter estimation was performed using the Watson distribution for modeling specific NODDI metrics. Voxel-wise estimation was carried out using CUDA-accelerated routines with default priors and convergence settings optimized for large-scale application. The model fitting resulted in parameter maps for orientation dispersion and neurite density in native diffusion space. Summary maps were retained for the statistical analyses. All NODDI maps were visually inspected for estimation artifacts and outlier regions prior to ROI extraction within each subject. 2.4.4 Masks registration to diffusion space For each subject, ROIs were defined using the ICBM-DTI-81 white matter labels atlas ( Mori et al., 2005 ). Then these masks were transformed into each subject’s native diffusion space, as follows. First, the subject’s T1-weighted anatomical image was registered to the standard MNI-152 1mm brain template. This was accomplished using a linear registration with FSL’s FLIRT ( Andersson et al., 2007 ; Jenkinson et al., 2012 ), followed by a non-linear registration with FNIRT ( Andersson et al., 2007 ; Jenkinson et al., 2012 ) to account for individual brain morphology. This process yielded a high-precision warp field from T1 space to MNI space. Next, the inverse of this transformation was applied to each standard-space ROI mask, bringing it into the subject’s native T1 space. The resulting masks were visually inspected against the structural T1 image to confirm accurate alignment. A separate affine registration was then performed using FLIRT ( Andersson et al., 2007 ; Jenkinson et al., 2012 ) to map the skull-stripped T1 image to the subject’s b=0 diffusion image, where the b=0 image was extracted from the first volume of the diffusion-weighted series. Finally, the MNI-to-T1 transformation (derived by inverting the T1-to-MNI warp) and the T1-to-b0 affine transformation were concatenated into a single, combined warp. This transformation was applied to each of the original MNI space ROI masks in a single interpolation step, directly placing the masks into the subject’s native diffusion space. The quality of the final mask alignment was verified by overlaying the binarized ROIs on the diffusion image. NODDI-derived metrics were subsequently calculated within these registered ROIs, ensuring that all analyses were performed in identically defined anatomical regions across subjects. Graphic representation of the mask registration is presented in the Supplementary Materials (Supplemental Methods 4, Figure 2S ). 2.5 Statistical analysis All statistical analyses were conducted in R, version 4.4.2. (R Core Team et al., 2024 ). As a first step, we performed diagnostic checks to assess the distribution of all the variables and to examine the linearity of associations. We verified all statistical assumptions before conducting the analysis. Model residuals were diagnosed using the DHARMa package ( Hartig & Hartig, 2020 ). Based on these diagnostics, we identified non-normally distributed residuals from regression and therefore decided to apply permutation testing to all models using the lmPerm package ( Torchiano & Wheeler, 2025 ), which provides a robust alternative to classical parametric inference. We treated the brain measures as the outcomes (dependent variables) and air pollution as the primary predictor (independent variable). Associations between every dependent variable and the main predictor (air pollution) were assessed by separate models. We have run all the models based on the formula below: We included an interaction term for air pollution and ADHD status to explore whether the ADHD status moderated the association between air pollution exposure and specific brain outcomes. Exact age in years and sex were included as covariates as they are known to influence brain maturation ( Genc et al., 2018 ; Simmonds et al., 2014 ). We also included socioeconomic status (represented by minimal parental education with levels low, medium, and high) as a covariate. This represents a deviation from our preregistered analysis plan. We made this decision on the basis of peer-review of a previous manuscript ( Lewandowska et al., 2025 ) which highlighted the importance of controlling for this potential confounder. 3. RESULTS A detailed description and a graphical representation of the NODDI measures are given in Figure 2 in the Methods section ( 2.4.2 NODDI estimations ). 3.1 Associations between NODDI measures and prenatal and early life air pollution exposures For NDI, our analysis revealed several main effects for various brain regions in relation to air pollution exposure, which were significant before corrections for multiple comparisons ( Table 2 ). Negative associations were found between NDI and prenatal exposure to NO 2 in the left cingulum (β = −0.0005, p = 0.028). For early-life exposure (years 0-4), NDI was negatively associated with NO 2 in the left anterior limb of the internal capsule (β = −0.0004, p = 0.049), the left cingulum (β = −0.0006, p = 0.028), and the right external capsule (β = −0.0005, p = 0.038). Early-life PM 10 exposure was also negatively associated with NDI in the right anterior corona radiata (β = −0.0006, p = 0.045), left cingulum (β = −0.0007, p = 0.020), right inferior fronto-occipital fasciculus (β = −0.0004, p = 0.016), left posterior limb of the internal capsule (β = −0.0005, p = 0.017), and left superior fronto-occipital fasciculus (β = −0.0009, p = 0.018). None of these associations remained significant after controlling the false discovery rate (FDR) at α = 0.05 using the Benjamini-Hochberg procedure. We found no statistically significant main effects of air pollutants on ODI in these models. View this table: View inline View popup Download powerpoint Table 2. Table shows associations between prenatal and early life exposures to NO 2 and NODDI measures that were significant at α = 0.05 before multiple-testing correction. The last column shows the p-value for the respective model’s interaction term for NO 2 and ADHD status. 3.2 Additional analysis - associations with current air pollution exposure Furthermore, negative associations were observed between NDI and current exposure to NO 2 in the left anterior limb of the internal capsule (β = −0.0005, p = 0.008) and the right external capsule (β = - 0.0004, p = 0.0235) ( Table 3 ). Current exposure to PM 10 was negatively associated with NDI in the left anterior limb of the internal capsule (β = −0.0006, p = 0.032), the right inferior fronto-occipital fasciculus (β = −0.0005, p = 0.024), and the left superior fronto-occipital fasciculus (β = −0.001, p = 0.015). No significant main effects of air pollutants were found for ODI in these models. None of the associations were statistically significant after controlling the FDR at α = 0.05. View this table: View inline View popup Download powerpoint Table 3. Table shows associations between prenatal and early life exposures to PM 10 and NODDI measures that were significant at α = 0.05 before multiple-testing correction. The last column shows the p-value for the respective model’s interaction term for PM 10 and ADHD status. Additionally, several models revealed significant interaction effects between air pollution exposure and ADHD diagnosis, however these were not significant when controlling the FDR at α = 0.05. 4. DISCUSSION We aimed to investigate potential relationships between prenatal, early life, and current exposure to PM 10 and NO 2 and white matter microstructure in children aged 10-13. We used NODDI, which allowed for estimating neurite density and orientation dispersion metrics within atlas-based white matter tracts. We found several statistically significant associations between air pollution exposure and the neurite density metric across various white matter regions. However, after correcting for multiple comparisons, neither these associations nor the ADHD moderation analyses remained statistically significant. To our knowledge, this is the first study to investigate potential associations between air pollution exposure and NODDI metrics referring to intracellular and extracellular spaces. We had hypothesized that air pollution would be associated with localized microstructural alterations, based on previous work using RSI in the ABCD cohort ( Burnor et al., 2021 ). However, our null findings are consistent with our own prior work on the same dataset, which found associations with global DTI metrics but not with the more specific FBA metrics ( Lewandowska et al., 2025 ). This pattern suggests that while global metrics from single-tensor models like DTI may be sensitive enough to detect widespread, subtle effects, the more granular metrics from multi-compartment models like NODDI may require greater statistical power to isolate such changes, especially in a modest-sized sample. Several factors are likely to contribute to this difficulty. First, our final sample (n=388) is substantially smaller than those used in large-scale studies like the ABCD cohort, limiting statistical power. NODDI’s biophysical modeling also required higher data quality standards; specifically, the necessary matching of T1 and diffusion images for accurate registration resulted in a smaller final sample than in our previous DTI/FBA analyses ( Lewandowska et al., 2025 ). Finally, the multiple-comparisons correction required by our broad, atlas-based approach set a high statistical threshold, which may have obscured subtle regional effects. Although no associations withstood multiple-testing correction, the pattern of the uncorrected findings may offer an exploratory basis for future, more targeted research. For instance, several uncorrected associations were observed in tracts relevant to attention, which aligns with the proposed neurobiological mechanisms linking air pollution to attentional deficits. Furthermore, the direction of these exploratory associations (e.g., lower NDI with higher exposure) is consistent with RSI findings ( Burnor et al., 2021 ). However, it should be acknowledged that the failure of these associations to survive statistical correction may also reflect a true null finding, indicating no effect. We therefore present these observations not as conclusive evidence, but as exploratory data that could aid in narrowing hypotheses for future studies with more statistical power. The observed reductions in neurite density within the anterior corona radiata and the left posterior limb of the internal capsule could indicate a potential disruption of large-scale brain networks. For example, the associations in the corona radiata align with previous research linking these areas to the focusing, execution, and shifting of attention, which are all core deficits in ADHD ( Stave et al., 2017 ). The findings in the posterior limb of the internal capsule could further suggest that air pollution may impair critical sensorimotor pathways and broader brain connectivity. Furthermore, the left cingulum, a region with uncorrected associations with prenatal and early-life NO 2 and early-life PM 10 , is a key component of the default mode network ( Burks et al., 2017 ; Skandalakis et al., 2020 ) and is proven to be associated with cognitive functioning, such as sustaining attention and working memory ( Takahashi et al., 2010 ). This association between cingulum and air pollution exposure aligns with a previous study exploring air pollution and brain microstructure ( Burnor et al., 2021 ). Additional uncorrected results pointed to a similar pattern of reduced neurite density associated with both early-life and current air pollution exposure (NO 2 and PM 10 ) in tracts such as the superior fronto-occipital fasciculus (SFOF), the right external capsule (EC), and the left anterior limb of the internal capsule (ALIC). A similar negative association was observed between neurite density and both early-life and current PM 10 exposure in the right inferior fronto-occipital fasciculus (IFOF). The IFOF is a critical white matter tract responsible for higher-order cognition, serving as the primary pathway for semantic processing by connecting sensory cortices with the prefrontal lobe to enable comprehension, abstract thought, and executive control ( Duffau et al., 2013 ). Though not statistically robust after correction, these collective findings suggest that airborne pollutants may have a subtle neurotoxic effect on the microstructural development of white matter tracts essential for executive function and attention. A reduction in neurite density within these tracts implies a structural deficit that could lower the efficiency of information transfer. Our results could mean that no localized white-matter effects exist in this particular study population, however, they could also be taken as circumstantial evidence for a subtle effect of air pollution on white matter microstructure. Although no single association withstood correction for multiple comparisons, the consistent pattern of negative associations between pollutant exposure and neurite density across numerous tracts suggests that robust, widespread effects may not be present, but cellular-level alterations could be occurring. We hypothesize that these effects reflect changes at the cellular level, rather than broader bundle-wide alterations, a conclusion supported by the divergence of these NODDI results from our previous FBA findings ( Lewandowska et al., 2025 ). This interpretation is biologically plausible, as exposure to air pollution has been suggested to induce neuroinflammatory processes, including microglial activation and oxidative stress ( Burnor et al., 2021 ). It also aligns with findings from other imaging modalities that suggest air pollution can alter the cellular composition of white matter, potentially by expanding the population of glial cells ( Burnor et al., 2021 ). The lack of statistically significant results after correction highlights the need for larger longitudinal studies to confirm these trends and elucidate the precise mechanisms through which air pollution may impact neurodevelopment. Our study has a number of considerable strengths, which are rooted in its design approach specifically for air pollution analyses and high-quality data. We examined a population characterized by high PM exposures and large exposure ranges, which allowed for a powerful assessment of air pollution’s effects. Our exposure assessment combined comprehensive lifelong address histories with state-of-the-art air pollution models that provided high spatiotemporal resolution (see section Supplemental Methods in Supplementary Materials). Finally, the cohort design, which included a population of children with ADHD, along with the use of a multiple b-value diffusion MRI protocol to perform NODDI analysis, provides a comprehensive look into the complex relationship between air pollution and brain development. In addition, the analyses were prepared in the native space of each participant by registering the masks exported from the standard space to native space using T1 images. This allowed for analyses to be performed in identically defined anatomical regions across subjects. This study is not without limitations. First, as noted, the exploratory nature of the analysis with a large number of models required multiple-testing correction, which increased the threshold for statistical significance and may have led to an underestimation of the true number of affected brain regions. Second, the study did not include PM 2.5 exposure data, a critical pollutant consistently linked to adverse health effects, which limits the comprehensiveness of our findings. While PM 10 and PM 2.5 are highly correlated in our study area (r = 0.9, correlation in monitoring station data) ( Szwed et al., 2025 ), the smaller particle size of PM 2.5 may have a stronger impact due to its greater potential for infiltrating the body’s organs, including the brain. Finally, we could not isolate the effects of specific chemical mixtures within the measured air pollutants, such as PAHs ( Yang et al., 2025 ). While our findings link general air pollution metrics to brain changes, the specific neurotoxicants driving these associations remain unknown. A sophisticated mixture-based approach represents an important future direction but it is only recently beginning to be applied in our field of neuroscience ( Bottenhorn et al., 2024 ). 5. CONCLUSIONS The present study aimed to establish the impact of long-term PM 10 and NO 2 exposure across different time windows on white matter in the brains of children aged 10-13 years. Our analyses revealed several significant associations across different time windows and white matter tracts; however, no associations remained significant after correcting for multiple comparisons. Despite the lack of robust findings, the analysis revealed numerous significant results prior to correction, suggesting that an underlying biological effect may exist, but their detection was challenged by the exploratory nature of the analysis. Consequently, these findings serve as preliminary evidence, highlighting the importance of future research focused on specific white matter regions implicated in attentional networks. 7. AUTHOR CONTRIBUTION Paulina Lewandowska: Conceptualization; data curation; formal analysis; visualization; writing-original draft; writing - review and editing Mikołaj Compa: formal analysis Claude Bajada: Conceptualization; formal analysis; writing - review and editing, supervision Yarema Mysak: Data curation; software Aleksandra Domagalik : Data acquisition, writing - review and editing Bartosz Kossowski : Data acquisition Clemens Baumbach: Data curation; software; writing - review and editing Katarzyna Kaczmarek-Majer: Air pollution modelling Anna Degórska: Air pollution modelling Krzysztof Skotak: Air pollution modelling Katarzyna Sitnik-Warchulska: Data acquisition; subject diagnosis Małgorzata Lipowska: Data acquisition; subject diagnosis Bernadetta Izydorczyk: Data acquisition; subject diagnosis James Grellier: Conceptualization; writing – review and editing Iana Markevych: Conceptualization, data curation, writing – review and editing Marcin Szwed: Funding acquisition, conceptualization, data acquisition, writing - original draft; writing-review and editing, supervision 8. FUNDING RESOURCES Supported by “NeuroSmog: Determining the impact of air pollution on the developing brain” (Nr. POIR.04.04.00-1763/18-00) grant to M.S. 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, by a grant from the Priority Research Area (“Anthropocene”) under the Strategic Programme Excellence Initiative at Jagiellonian University to M.S. and by a National Science Centre, Poland (grant no. K/NCN/000514) to M.S. Iana Markevych’s time on this publication was supported by the National Science Centre, Poland (grant number 2023/50/E/HS6/00102) and 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. 9. DATA AVAILABILITY STATEMENT We welcome individual data inquiries for NeuroSmog data, and engage to provide data on a case-to-case basis. Due to European Union General Data Protection Rules on sharing of sensitive data, NeuroSmog data sharing is possible on the basis of bilateral data sharing agreements ratified by the Jagiellonian University and the inquirer’s institution. Please direct inquiries to m.szwed{at}uj.edu.pl . NeuroSmog data sharing guidelines are expected to be published online in 2026 at https://neurosmog.psychologia.uj.edu.pl/dla-naukowcow/for-scientists 6. ACKNOWLEDGMENTS We are very grateful to all of the children and their parents for their participation in the study, partner schools for helping us to reach out to children assigned to the control group, field psychologists for identifying children with ADHD and performing psychological testing on all children, and the study team for technical, logistic, administrative, and communication effort. 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