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. Author manuscript; available in PMC: 2026 Jun 27.
Published in final edited form as: Neurotoxicology. 2026 Mar 7;114:103417. doi: 10.1016/j.neuro.2026.103417

The unique effects of chronic exposure to ambient fine particulate matter and indoor radon on subcortical brain morphology in youth

Brittany K Taylor 1,2,3, Monica N Clarke-Smith 1,2, Grace E Parolek 1,2, Saige C Rasmussen 1,2, Sarah L Greenwood 1,2, Haley R Pulliam 1,2,a, OgheneTejiri V Smith 1,2,b, John P Wilson 4, Aaron van Donkelaar 5, Chi Li 5, Siyuan Shen 5, Randall V Martin 5, Beau MacDonald 4, Teresa K Monreal 6, Lauren E Salminen 6
PMCID: PMC13019485  NIHMSID: NIHMS2156210  PMID: 41802605

Abstract

Exposure to environmental toxins can have marked impacts on brain structure and function, particularly among children and adolescents for whom vulnerability to toxic exposures is the greatest. Herein, we explored whether there were unique and interactive effects of exposures to two ubiquitous toxins, namely indoor radon and ambient outdoor PM2.5, on sensitive subcortical brain morphology in a sample of youth. Sixty-three adolescents ages 12-to-17 years-old underwent 3T MRI scans from which we measured total bilateral subcortical gray matter volume for each of seven nuclei. Parents of participants also completed a home radon test, and provided a home address from which we derived 5-year average PM2.5 concentrations within a 1-kilometer squared area surrounding their home via spatiotemporal modeling. We computed chronic exposure indices for both PM2.5 and radon based on the measured concentrations multiplied by the duration of time that the child had lived in the current residence. Using hierarchical regression modeling, we found that youth had significantly larger nucleus accumbens (b = 50.473, β = .324, pFDR = .037) and amygdala volumes (b = 114.141, β = .249, pFDR = .047) as a function of increasing home radon exposure. We did not detect any significant associations between subcortical morphology and PM2.5 exposure, nor did we find any interactive effects of radon and PM2.5. These findings suggest an important potential effect of long-term home radon exposure on the morphology of the extended amygdala circuitry, and impress the need for further study into the functional implications of these structural aberrations in youth.

Keywords: subcortical, gray matter volume, inhaled toxins, adolescent development, extended amygdala

1. Introduction

The public at large is increasingly aware of the sweeping health effects of exposure to environmental toxins and toxicants, including long-term consequences for cardiovascular, respiratory, and neurological health, among others (ACOG Committee, 2013; Brender et al., 2011; Carpenter and Bushkin-Bedient, 2013; Sangkham et al., 2024). With respect to brain health, numerous reports indicate that greater exposure to common inhaled air pollutants like fine particulate matter (PM2.5) is linked with a greater lifetime risk of neurodegenerative diseases in older adulthood (Calderón-Garcidueñas et al., 2016; Cristaldi et al., 2022; Jankowska-Kieltyka et al., 2021), as well as neurodevelopmental and mental health outcomes throughout the earlier end of the lifespan (Braithwaite et al., 2019; Myhre et al., 2018; Parenteau et al., 2024). A smaller, but recently building body of work also suggests that long-term exposure to naturally-occurring toxins like indoor radon may carry similar risks for neurodegenerative disease in older age (Lehrer et al., 2017; Momcilovic et al., 2001; Momcilović et al., 1999; Zhang et al., 2022), and may have impacts on neurocognitive functioning in youth (Pulliam et al., 2025, 2024; Taylor et al., 2024). In light of this collection of findings, there has been a growing focus on the role of inhaled toxins and toxicants on patterns of neurodevelopment during childhood and adolescence to better understand the ontogeny of these neurocognitive health manifestations across the lifespan.

Indeed, youth are particularly sensitive to the effects of environmental exposures (Bearer, 1995; Falck et al., 2015; Parenteau et al., 2024), and early life exposure to common pollutants like PM2.5 has been associated with alterations to structural brain morphology in childhood and adolescence (Binter et al., 2022a; Calderón-Garcidueñas et al., 2011; Morrel et al., 2025). These effects are believed to arise through a combination of pathways including indirect inflammatory and oxidative stress mechanisms, as well as direct effects on the brain as PM2.5 can cross the blood-brain-barrier and directly induce neuronal and glial cell death and neuroinflammation (Cristaldi et al., 2022; Sangkham et al., 2024). Many of these studies have focused on subcortical morphology in particular given 1) the notable susceptibility of subcortical nuclei to environmental insults (Beauchaine et al., 2019), and 2) the pivotal role of subcortical nuclei in a multitude of brain networks implicated in mental health outcomes (Sukumaran et al., 2023). Subcortical nuclei are believed to be particularly sensitive to environmental exposures due to the combination of their dominant neurotransmitter systems being highly vulnerable to toxin-related oxidative stress processes, and the tendency for specific components like heavy metals to selectively accumulate in subcortical tissues (Binter et al., 2022b; de Prado Bert et al., 2018).

Despite relatively consistent findings in adults suggesting that greater PM2.5 exposure is associated with smaller subcortical volumes (Balboni et al., 2022; Calderón-Garcidueñas et al., 2022; Cho et al., 2020; Hedges et al., 2020; Power et al., 2018), the data in children are far more mixed. Several large-scale birth cohort studies and meta analyses suggest that greater PM2.5 exposure (including investigations of specific components of PM2.5) in children is linked with smaller caudate, amygdala, and hippocampal volumes (Kusters et al., 2025; Morrel et al., 2025; Sukumaran et al., 2025), whereas others report increased volumes in regions like the putamen and amygdala (Binter et al., 2022a; Morrel et al., 2025). Still others report no associations between subcortical volumes and PM2.5 exposure in children (Balboni et al., 2022). While the field has put great effort into understanding the effects of PM2.5 on the brain, investigations into the effects of indoor radon exposure on subcortical brain morphometry are far more limited.

Radon is a ubiquitous, radioactive gas that commonly accumulates in homes and other structures, and is most widely known for its carcinogenic effects on the respiratory tract (Chen, 2013; Clement and Ogino, 2018; United States Environmental Protection Agency, 2016). As a form of ionizing radiation, radon and its decay products (e.g., polonium [Po-210], lead [Pb-210], bismuth [Bi-210]) avidly bind to other particles in the air, like those that comprise PM2.5, and readily enter the body through inhalation releasing damaging alpha, beta, and gamma radioactivity on surrounding tissues (Blomberg et al., 2020; Li et al., 2018). Studies have shown that radon and its decay products can impact the human body systemically, and do in fact impact the brain both directly (Harley and Robbins, 2022; Kendall and Smith, 2002) and indirectly via mechanisms like inflammation and oxidative stress (Lumniczky et al., 2017; Nie et al., 2012). Studies of postmortem brain tissues acquired from deceased older adults with dementia suggest that radon and its decay products selectively accumulate in tissues throughout the brain, though the greatest depositions were detected in the gray matter of the amygdala and hippocampus (Momcilović et al., 2006). Further, a study of gross brain volumes in a sample of healthy children showed that greater radon exposure is linked with overall decreased gray matter volume (Smith et al., 2024), though the study did not specifically examine subcortical morphology.

Overall, there remains a clear gap in the literature regarding not only the unique individual effects of PM2.5 and indoor radon on structural brain morphology in youth, but also their potential combined and/or interactive effects. The goal of the current study was to explore these links in a sample of neurotypically developing children and adolescents (i.e., individuals with no known mental health diagnoses, neurodevelopmental disorders, or other conditions that may affect the central nervous system). Participants underwent a structural MRI scan, completed home radon testing, and provided detailed address information from which robust estimates of ambient PM2.5 exposure were computed using state-of-the-art spatiotemporal modeling techniques. Based on the collection of literature, we broadly hypothesized that subcortical volumes would be reduced as a function of exposure to both PM2.5 and radon, with evidence for unique effects of each. Further, we hypothesized that the interactive effect of both exposures may exacerbate these associations, such that youth with greater exposure to both inhaled toxins would show the greatest reductions in subcortical volumes. We anticipated that the most robust effects would be detected in the amygdala and hippocampus.

2. Methods

2.1. Participants

A total of 76 youth were recruited from the broader Omaha, Nebraska, USA metro area between June 2023 and August 2024. Participants ranged in age from 12.03 to 17.97 years-old (M = 14.988 years, SD = 1.666) and were generally representative of the local demographics of the area (53.9% males; 90.8% Not Hispanic or Latino, 9.2% Hispanic or Latino; 78.9% White, 14.5% Black or African American, 3.9% Asian, 2.6% American Indian/Alaska Native). Exclusionary criteria for the study were a history of head trauma, epilepsy, neurodevelopmental disorders, or other conditions affecting the central nervous system, current use of substances or medications known to alter brain function, and presence of any non-removable ferromagnetic implants or materials (e.g., orthodontics). These criteria were confirmed by the parent via an initial phone screening, and again during the consent process. Parents of the youth provided signed informed consent, and the youth provided written assent prior to beginning any study procedures. This protocol was approved by the Boys Town National Research Hospital Institutional Review Board (protocol #22-22-XP). All procedures and protocols were performed in compliance with relevant laws, institutional guidelines, and the Declaration of Helsinki.

During their first visit, participants’ height and weight were measured. These measurements, along with sex and age, were used to compute body mass index (BMI) in accordance with CDC guidelines. Several participants declined exact measurements of height and/or weight (n = 3); thus, we were not able to compute their BMI for use in further analyses. These participants were excluded from the final sample.

2.2. Environmental Measurement and Exposure Quantification

2.2.1. Home Radon Measurements

Families were provided with a commercial long-term home radon testing kit (https://www.radon.com/). The test kit is a standard alpha-track radon detector that is placed in the lowest livable level of the home for 30 days in accordance with instructions provided by the vendor. After the testing period, the kit is placed in a box and sent in for processing at a commercial lab. Our lab and the family each received a copy of the home radon results. In the case that a result exceeded the EPA action limit for mitigation (4 pCi/L), the principal investigator (BKT) contacted the family to ensure they understood the results and provided additional information on radon safety and local resources. Several participants did not successfully complete home radon tests, and thus were excluded from further analyses (n = 7).

In addition to completing the radon test kit, parents completed a questionnaire probing information about how long the child had lived in the home, the construction of their home, and other details to help characterize home radon exposure. We used the information about how long the child had lived in the home to compute a radon exposure index. Specifically, each child’s individual radon exposure index was computed as the natural log of the measured home radon concentration multiplied by the duration that the child had lived in the home (in years), plus one to account for the natural log transform of values that were originally less than 1, thus avoiding negative values in the resultant distribution (see equation 1 below). This radon exposure index (REI) was used as a measure of cumulative exposure in subsequent analyses exploring the associations between chronic home radon exposure and neural dynamics. Note that this exposure index is highly concordant with the International Commission on Radiological Protection gold standard computation for cumulative radon dose metrics (Clement and Ogino, 2018; Marsh et al., 2010), as demonstrated in prior publications in independent study samples (Taylor et al., 2024, 2023).

Radon exposure index=ln(([time lived in home]∗[home radon concentration])+1) (1)

2.2.2. PM2.5 Measurements

PM2.5 was characterized as part of the ENIGMA-Environment consortium using satellite-derived data (SatPM) from the Washington University in St. Louis Atmospheric Composition Analysis Group (version V5.GL.05.02; Hammer et al., 2023; van Donkelaar et al., 2021). This PM2.5 dataset combines information from satellite retrievals of aerosol optical depth and GEOS-Chem chemical transport modelling with ground-based PM2.5 observations within a hybrid geophysical-statistical framework, providing an observationally constrained, spatially complete PM2.5 surface. 5-year average levels of ground-level PM2.5 were estimated within around 1 km2 area centered on the home address of each participant. At the time of estimation, data were modeled using the most recently available version, which provided PM2.5 concentrations through 2023. Address sharing for geocoding purposes was an optional aspect of the study consent process, and several families opted out of address sharing. Participants for whom we could not acquire these PM2.5 measurements due to missing address information were excluded from further analyses (n = 2). For comparability with the cumulative radon exposure measurements, we computed a PM2.5 index by multiplying the robust estimates of local PM2.5 by the duration of time that the child had lived in the home (determined from the home radon questionnaire described above). The resultant values were natural log transformed for use in further analyses (see equation 2 below).

PM2.5index=ln([time lived in home]∗[5-year average PM2.5concentration]) (2)

2.3. MRI Data Acquisition and Subcortical Volume Measurements

Structural T1-weighted MR images were acquired using a Siemens Prisma 3 T MRI scanner with a 32-channel head coil and a MP-RAGE sequence with the following parameters: TR =2400 ms; TE =1.94 ms; flip angle = 8°; FOV = 256 mm; slice thickness = 1 mm (no gap); voxel size = 1 × 1 × 1 mm. These structural images were segmented in FreeSurfer (version 7.4.1) following the standardized ENIGMA protocols for quality control and segmentation (https://enigma.ini.usc.edu/protocols/imaging-protocols/; (Hibar et al., 2015). Briefly, seven bilateral subcortical structures were identified according to the Desikan-Killiany atlas and visually inspected for segmentation success. One participant was excluded for poor segmentation quality at this stage of analysis. Gray matter volumes per region of interest were extracted, and distributions of values were inspected for outliers in accordance with ENIGMA procedures. Because we did not have specific hypotheses about lateralized effects, left and right subcortical volumes were summed within each region of interest. Individual outliers were excluded pairwise (not listwise) from further analyses based on volume values that exceeded ± 3 standard deviations from the group mean within a given subcortical region. In addition to subcortical parcellation, we acquired total intracranial volume (ICV) measurements per person.

2.4. Statistical Analysis

To test our hypotheses that subcortical volumes may be related to exposure to both PM2.5 and home radon exposures, we computed a series of hierarchical regression analyses. For each analysis, a given total subcortical volume was modeled as the dependent variable and predicted by a set of independent variables including critical demographic control variables in the first step (age, sex, BMI, and ICV), then adding the PM2.5 index in the second step, and finally adding the REI in the third step. The decision to enter PM2.5 first and radon second was based on prior literature suggesting that radon primarily impacts the body by binding to particulates which are then inhaled (e.g., Blomberg et al., 2020; Li et al., 2018); thus, accounting for the effects of PM2.5 first seemed the most logical and well-justified option. We examined the change in R2 at each step of the model to determine whether PM2.5 and/or REI significantly predicted variance in subcortical volumes above and beyond more general demographic variables. We did also attempt to fit a fourth-level model adding in the interaction between REI and PM2.5 index, however none of the interaction terms across models reached statistical significance (uncorrected ps = .128 to .962). Thus, we proceeded with just the three-step models excluding the interaction term in favor of model parsimony.

volume=age+sex+BMI+ICV Model 1:
volume=age+sex+BMI+ICV+PM2.5index Model 2:
volume=age+sex+BMI+ICV+PM2.5index+REI Model 3:
volume=age+sex+BMI+ICV+PM2.5index+REI+PM2.5index∗REI Model 4:

Finally, we ran a set of follow-up sensitivity analyses. We first re-ran the hierarchical models per subcortical region of interest reversing the order of entry for REI and PM2.5. Finally, we ran the hierarchical models using ICV as the independent variable, rather than as a predictor. Again, this was done with both orders of entry for the two toxins of interest.

Given the potential for multicollinearity among variables in the models, we inspected variance inflation factor (VIF) values at each step of the modeling procedure to ensure that values were all within a reasonable range. In accordance with conservative recommendations in the field, VIF values ≥ 5 were considered indicators of multicollinearity and would be an indication that the model parameters may be unreliable or misspecified (Shrestha, 2020). To account for potential inflation of error with the number of models tested, we applied false-discovery rate (FDR) corrections to all of the models that showed statistically significant effects (α = .05). Analyses were conducted in JASP (version 0.95.2).

3. Results

3.1. Descriptive Statistics

Thirteen participants were excluded from final analyses due to missing data on one or more variables of interest (missing radon results: n = 7; missing PM2.5 results: n = 2, poor quality MRI data: n = 1; missing BMI: n = 3). The final evaluable sample was comprised of 63 children and adolescents ages 12.03 to 17.97 years-old (M = 14.996 years, SD = 1.616; 52.4% males; 92.1% Not Hispanic or Latino, 7.9% Hispanic or Latino; 82.5% White, 12.7% Black or African American, 3.2% Asian, 1.6% American Indian/Alaska Native). Using available data, we computed Mann-Whitney U tests and chi-squared tests to determine whether children excluded from analyses differed from the evaluable sample on any predictors of interest or demographic variables. Those excluded from analyses did not significantly differ from the evaluable sample on the basis of age (Z = −0.034, p = .972), home radon concentration (Z = −0.294, p = .769), 5-year average PM2.5 concentration (Z = −0.350, p = .727), duration of residence in the current dwelling (Z = −0.034, p = .258), BMI (Z = −0.024, p = .981), or total intracranial volume (Z = −0.636, p = .525). Additionally, there were no group differences on the basis of sex (χ2 = 0.364, p = .546), race (χ2 = 3.487, p = .322), or ethnicity (χ2 = 0.715, p = .630). Descriptive statistics for all variables of interest in the evaluable sample are listed in Table 1, and correlations are shown in Figure 1. As mentioned, several individuals were excluded as outliers in volume measurements for specific subcortical extractions and were excluded pairwise (not listwise) from further analyses. The final sample size for each subcortical volume acquired is also listed in Table 1.

Table 1.

Descriptive statistics for all variables of interest (N = 63 unless otherwise listed).

M SD Min Max
Age (years) 14.996 1.616 12.030 17.970
BMI 23.778 5.950 14.500 50.900
radon concentration (pCi/L) 4.886 3.095 1.000 12.900
REI 3.359 1.041 0.220 5.380
PM2.5 average (μg/m3) 7.748 0.401 6.020 8.500
PM2.5 index 8.038 1.022 4.140 9.350
Brain volumes (mm3) N
nucleus accumbens 60 1280.003 162.565 957.900 1653.000
amygdala 62 3683.624 473.565 2485.400 4875.700
thalamus 63 1266.991 1544.253 13714.700 19929.500
caudate 62 7985.910 852.253 6035.800 9983.000
putamen 61 10904.953 1118.978 8487.400 13478.400
pallidum 61 4153.612 395.556 3325.900 5127.700
hippocampus 63 8786.064 883.641 7141.900 10892.500
ICV 63 1512063.492 197901.562 1070000.000 1930000.000
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Note: The number of participants with valid data after outlier exclusions for each subcortical brain volume measure is noted in the table. “BMI” = body mass index; “ICV” = intracranial volume; “PM2.5 index” = an exposure index of particulate matter computed based on the 5-year average of PM2.5 within 1 km2 of the home multiplied by the duration of time the child has lived in that home, natural log transformed for normality; “REI” = an exposure index of radon computed based on the measured concentration of home radon multiplied by the duration of time the child has lived in that home, natural log transformed for normality

Figure 1: Correlations among variables of interest.

Figure 1:

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Correlation heatmap showing the strength of correlations among all variables of interest acquired from the final evaluable sample. Pearson correlation values are listed within each cell. Red hues indicate positive bivariate correlations, blue hues indicate negative bivariate relationships, and white indicates a zero correlation (i.e., no bivariate relationship). Correlations that are significant at the p < .05 (uncorrected) level are also outlined in a dark blue box. Several participants’ subcortical volume measurements were missing due to outlier values, thus correlations were computed pairwise (not listwise) for all available data points. Note: “BMI” = body mass index; “ICV” = intracranial volume; “NAc” = nucleus accumbens “PM2.5 average” = the 5-year average of PM2.5 within 1 km2 of the home; “PM2.5 index” = an exposure index of particulate matter computed based on the 5-year average of PM2.5 within 1 km2 of the home multiplied by the duration of time the child has lived in that home, natural log transformed for normality; “REI” = an exposure index of radon computed based on the measured concentration of home radon multiplied by the duration of time the child has lived in that home, natural log transformed for normality; “Sex” was dummy coded as “0 = male” and “1 = female”

Home radon levels ranged widely across the study sample, with the overall mean exceeding the EPA recommended action limit for mitigation (4.0 pCi/L; United States Environmental Protection Agency, 2016). In fact, 54.0% of the evaluable sample’s homes tested above this level. In contrast, mean annual PM2.5 levels across the study sample were consistently below the EPA standard of 9.0 μg/m3 (Environmental Protection Agency, 2024). Youth in the current study lived in their current dwellings for an average of 8.510 years (SD = 4.676).

3.2. Relationships between Inhaled Toxins and Subcortical Volumes

To test our hypothesis that subcortical volumes may be related to exposure to both PM2.5 and home radon concentrations, we computed a series of hierarchical regression analyses. For each analysis, a given subcortical volume was modeled as the dependent variable and predicted by a set of independent variables including critical demographic control variables in the first step (age, sex, BMI, and ICV), then adding the PM2.5 index in the second step, and finally adding REI in the third step. As noted previously, we did attempt to fit a fourth-level model including the interaction between REI and PM2.5 index, however the models were not statistically significant. Thus, we report only the results for the three-level models (without the interaction term) herein. We examined the change in R2 at each step of the model to determine whether PM2.5 and/or REI significantly predicted variance in subcortical volumes above and beyond more general demographic variables. Additionally, we inspected VIF values for potential instances of multicollinearity in our models. The maximum VIF value detected across all models was 1.692, thus no multicollinearity adjustments were necessary across any models.

Among the set of seven regions investigated, two showed significant effects of REI on subcortical volumes. Specifically, REI was linked with significantly larger total nucleus accumbens (NAc) volumes (b = 50.473, β = .324, pFDR = .037) as well as larger total amygdala volumes (b = 114.141, β = .249, pFDR = .047), accounting for all other variables (see Figure 2). Full model results for the NAc and amygdala are shown in Table 2. Interestingly, there were no statistically significant links between PM2.5 and any subcortical volumes (uncorrected ps = .163 to .896), nor were there any other significant links between REI and any other assessed subcortical volumes (uncorrected ps = .243 to .585). Results for models predicting volumes for the thalamus, caudate, putamen, pallidum, and hippocampus are shown in Supplementary Tables S1-S5.

Figure 2: Associations between radon exposure and subcortical gray matter volumes.

Figure 2:

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Scatterplots showing the statistically significant (pFDR < .05) relationships between the radon exposure index (REI and nucleus accumbens volumes (left) and amygdala volumes (right). Shaded areas show the 95% confidence intervals around the correlation lines. Plotted volumes and REI values are residuals accounting for age, sex, BMI, ICV, and PM2.5 exposure.

Table 2.

Model results for the nucleus accumbens and amygdala

Nucleus Accumbens
 Amygdala
R2 F p ΔR2 p p FDR R2 F p ΔR2 p p FDR
Mode1 1 .307 6.094 <.001 – – – .563 18.330 <.001 – – –
Model 2 .336 5.459 <.001 .029 .133 .190 .563 14.410 <.001 .000 .941 .941
Model 3 .399 5.857 <.001 .063 .022 .037 .600 13.740 <.001 .037 .028 .047
b SE β t p p FDR b SE β t p p FDR
Age −32.387 11.360 −.317 −2.851 .006 .015 −7.264 26.233 −0.25 −0.277 .783 .870
sex −11.888 38.086 −.037 −0.312 .756 .840 −263.721 88.006 −.278 −2.997 .004 .010
ICV 3.486e−4 9.615e−5 .414 3.626 <.001 <.001 0.001 2.166e−4 .615 6.804 <.001 <.001
BMI −1.911 3.042 −.071 −0.628 .533 .666 3.770 7.194 .048 0.524 .602 .753
PM2.5 −2.827 21.610 −.018 −0.131 .896 .896 −72.068 50.923 −.157 −1.415 .163 .233
REI 50.473 21.43 .324 2.355 .022 .037 114.141 50.524 .249 2.259 .028 .047
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Model 1: Age, Sex, ICV, and BMI as predictor variables

Model 2: Age, Sex, ICV, BMI, and PM2.5 index as predictor variables

Model 3: Age, Sex, ICV, BMI, PM2.5 index, and REI as predictor variables

Note: “BMI” = body mass index; “ICV” = intracranial volume; “PM2.5” = an exposure index of particulate matter computed based on the 5-year average of PM2.5 within 1 km2 of the home multiplied by the duration of time the child has lived in that home; “REI” = an exposure index of radon computed based on the measured concentration of home radon multiplied by the duration of time the child has lived in that home; “Sex” was dummy coded as “0 = male” and “1 = female”

3.3. Follow-Up Sensitivity Analyses

Next, we ran a series of follow-up analyses to determine the robustness of the findings. First, we re-ran all hierarchical regressions entering REI at the second step and PM2.5 in the third step, thereby swapping the order of variable inputs. Consistent with the original models, there were no significant relationships between either REI or PM2.5 and volumes of the thalamus, caudate, putamen, pallidum, or hippocampus (uncorrected ps = .150 to .939). Also consistent with our original findings, examination of the model of NAc volumes showed that adding REI in the second step significantly improved model fit (ΔR2 = .091, b = 48.799, β = .313, pFDR = .009), whereas adding PM2.5 in the third step did not improve the model (ΔR2 < .001, b = −2.827, β = −.018, pFDR = .896). However, model results did not hold as strongly for the amygdala volumes. Adding REI to the model in the second step trended toward an improved fit, but did not reach statistical significance, (ΔR2 = .015, b = 71.301, β = .156, pFDR = .129), as was the case when adding PM2.5 in the third step (ΔR2 = .015, b = −72.068, β = −.157, pFDR = .210).

Finally, we ran the hierarchical regression with total ICV as the independent variable, as opposed to a single subcortical volume. Regardless of the order of entry for REI and PM2.5, neither toxin was significantly associated with total ICV (uncorrected ps = .138 to .948; see Supplementary Table S6).

4. Discussion

The goal of the current study was to characterize the relationship between subcortical brain morphology in youth and multiple inhaled toxins, namely PM2.5 and home radon. We hypothesized that both PM2.5 and indoor radon exposure would have unique contributions to subcortical volumes, but that the overall pattern would suggest decreased gray matter volumes with increased exposure to both toxins. We further anticipated an exacerbating effect of the interaction between REI and PM2.5. To our surprise, we did not detect a significant interaction effect between PM2.5 and radon exposure on any of the measured subcortical volumes. Further, in opposition to our original hypotheses, we found that greater radon exposure was linked with larger NAc volumes. Radon exposure was additionally associated with larger amygdala volumes, but only when we first accounted for PM2.5 exposure. We did not detect any significant links between subcortical volumes and PM2.5 exposure in our current study sample. Here, we discuss the implications of these findings.

Our major finding was that radon exposure was associated with larger volumes in two subcortical nuclei, namely the NAc and amygdala. This came as a surprise in light of prior work suggesting that radon exposure is associated with smaller total gray matter volume in youth (Smith et al., 2024), though this earlier study was non-specific and did not probe subcortical nuclei separately. Additional works exploring the effects of other forms of ionizing radiation on the brain, including that resulting from medical imaging and treatments among patients with brain tumors, have also suggested that more extreme doses of radiation can result in gray matter volume loss (e.g., Kumar et al., 2025; Parihar et al., 2015; Parihar and Limoli, 2013). However, the radiation dosing in these studies tends to be orders of magnitude greater than what an individual experiences in their ambient environment. This is critical because careful rodent modeling work suggests that the effects of ionizing radiation on the brain can vary significantly under different dosing conditions (Acharya et al., 2015). For example, some work has shown that acute low doses of radiation (< 1 Gy) can induce proliferative effects on cellular architectures in the central nervous system, with evidence for increased dendritic spine growth (Betlazar et al., 2016). Although this gray matter proliferation may seem potentially neuroprotective, these studies do generally find that radiation-induced dendritic outgrowth tends to be functionally and morphologically distorted, and more likely to be related to neurocognitive disorders than any protective neuroplastic effects (Kumar et al., 2025). It is possible that in the present study, the apparent radon-related increases in amygdala and NAc volumes may be indicative of some of these radiation-related dendritic proliferations. However, more fine-grained work is required to definitively decipher the composition of the gray matter measured herein, for example through postmortem histology.

An alternative explanation of the increased volumes, particularly for the NAc, is that radon exposure may be related to reduced normative gray matter pruning in this developmental sample. A hallmark of normative neurodevelopment is the reduction of gray matter associated with the refinement of brain networks (Dima et al., 2021; Frangou et al., 2021; Kharitonova et al., 2013; Taylor et al., 2020). In our data, we did detect a negative association between age and NAc volumes, suggesting that older youth tended to have smaller volumes (i.e., reduced gray matter). It is possible that radon exposure may be disruptive to this process and perhaps prevent or disrupt normative patterns of NAc pruning. Disruptions to the development of gray matter morphology have been highlighted as a function of other influences like mental health symptoms, traffic-related air pollution exposure, and other factors (Morrel et al., 2025; Taylor et al., 2020). Additional works should explore potential alterations to developmental trajectories in longitudinal studies.

Interestingly, studies have shown that the effects of acute focal irradiation of brain tissues does have cascading effects beyond the targeted region, with evidence suggesting functional reorganization of circuitries throughout extended networks (Zaer et al., 2022). This is of particular interest for our present findings because the NAc and amygdala are intimately coupled both structurally and functionally (Amurthur et al., 2025; Holmgren and Wills, 2021; Koob, 2009; Salgado and Kaplitt, 2015). Amygdala-NAc circuitry plays an important role in reward, motivation, and learning, and aberrations in the morphology and functioning of this circuitry are frequently implicated in psychiatric conditions including anxiety, depression, and perhaps most commonly, addiction and compulsive behaviors (Floresco, 2015; Lubman et al., 2008; Rosenberg et al., 2025; Xu et al., 2020). In youth specifically, studies frequently report that larger amygdala and NAc volumes are linked with weight gain and obesity (García-García et al., 2020; Perlaki et al., 2018), and this is believed to be in part due to aberrations in reward sensitivity and motivation associated with subcortical morphology and functioning (Rapuano et al., 2020; Zaugg et al., 2022). Future studies may seek to extend our current findings into functional domains to determine whether any radon-related increases in amygdala and NAc morphology might be linked to heightened reward sensitivity, aberrations in functional and structural connectivity, and subsequent behavioral and health outcomes.

To our surprise, we did not detect any statistically significant associations between PM2.5 and subcortical volumes. Examination of the effects in our models suggested generally negative associations (i.e., greater PM2.5 exposure associated with smaller volumes), but the effects were highly variable and cannot be reliably or meaningfully interpreted in the present study. This wide variability is, however, reflective of the broader literature which has reported varied effects of PM2.5 on subcortical morphology in youth in particular (Balboni et al., 2022; Binter et al., 2022a; Kusters et al., 2025; Morrel et al., 2025; Sukumaran et al., 2025). Some of this variability may be due to differences in the composition of PM2.5 across regions from which cohort studies were conducted. For example, a study examining geographic variability in components of PM2.5 across the United States demonstrated significant variability in the amounts of sulfates and nitrates comprising PM2.5 across 187 assessed counties (Bell et al., 2007). Studies that more discretely examine links between unique components of PM2.5 and brain morphology have shown specificity in which components tend to have the strongest associations with gray matter metrics. Sukumaran et al. (2025) showed that exposure to potassium, a common component of PM2.5 originating from biomass burning, was associated with smaller volumes of the multiple subcortical areas including the right hippocampus, left amygdala, and bilateral caudate in the Adolescent Brain Cognitive Development (ABCD) study cohort. In tandem, widespread cortical thickness and surface area metrics were associated more closely with other elements specific to crustal materials and vehicle traffic sources (Sukumaran et al., 2025). Similarly, studies in rodent models also suggest that specific metals that comprise PM2.5 can have differential impacts throughout the brain, with the NAc particularly sensitive to heavy metals like iron, copper, and others (Cory-Slechta et al., 2019). It is possible that examination of more specific components of PM2.5 might have yielded significant effects in the present study. Future work should continue to disentangle the potential unique contributions of multiple components of PM2.5 on brain morphology.

Another explanation for our null results regarding unique effects of PM2.5 on subcortical volumes is that the current study sample may have been underpowered, or that the relative dosing of PM2.5 was too low to yield detectable effects. Many of the studies to-date that report effects of ambient environmental PM2.5 on brain morphology in youth have been done in large cohort studies that are orders of magnitude larger than the study sample herein (for a review, see Morrel et al., 2025). Further, many of these large-scale studies have been done in major urban areas that tend to have higher average annual PM2.5 concentrations due to numerous urban factors (e.g., greater density of traffic, concentrated industrial sources, etc.). The maximum 5-year average PM2.5 in the present study was 8.5 μg/m3, which is below the US EPA recommendations for safe maximum 3-year average dosing (9 μg/m3; Environmental Protection Agency, 2024). Prior works suggest that the effects of PM2.5 on brain morphology tend to be small even under conditions of higher PM2.5 dosing, and thus require larger samples to be reliably detected. We acknowledge that our study sample is rather modest, however the results reported do provide a strong foundation on which to build future investigations. Additionally, although our measured PM2.5 concentrations were generally below US EPA standards, they notably exceeded guidelines from the World Health Organization (5μg/m3 average annual dose; World Health Organization, 2021). Larger-scale studies that account for multiple types of toxins and toxicants at varying doses are necessary to effectively characterize the unique and combinatory effects of co-occurring toxin exposures on sensitive brain development.

We did not detect any significant radon-by-PM2.5 interactions in the current study, which was surprising in light of prior work suggesting that radon and its decay products commonly bind to fine particulates in the air (Blomberg et al., 2020; Li et al., 2018). However, this null finding may be an artifact of the discrepant sources of measurement for radon and PM2.5 in the present study. Radon concentrations were quantified in the individual’s indoor home environment, whereas PM2.5 was estimated within a 1 km2 area surrounding the home address using spatiotemporal modeling techniques. It is possible that the estimated outdoor PM2.5 may not have been representative of PM2.5 in the indoor environment to which the measured radon may bind. Using more similar measurement techniques to estimate the effects of these two environmental exposures may yield more precise and informative effects on individual-level brain morphology. For instance, using wearable devices (e.g., Lin et al., 2020) or other home-based measurement tools to assess actual indoor home PM2.5 concentrations may be beneficial for future studies exploring fine-grained effects of these concurrent toxin exposures on brain health. Still, it is well-worth noting that the effects of radon exposure on amygdala volumes in the current study were only detectable when we first accounted for the effects of PM2.5, suggesting that there may have been a true interactive effect that we simply were not able to detect with our statistical thresholding in the current study and the small sample size. Future works should continue to probe these possible interactive effects in larger study samples.

With respect to home radon concentrations, our data were well-aligned with expected local norms for indoor radon levels as reported by the local Department of Health and Human Services (Nebraska Department of Health and Human Services, 2020), and the US EPA (United States Environmental Protection Agency, 2016). The counties from which youth in the current study were recruited have been designated “zone 1” radon areas by the US EPA, indicating that the agency expects at least half of all indoor settings test at or above the EPA action limit. Indeed, in our study, just over half of all homes tested registered at or above 4.0 pCi/L. Approximately one-third of all counties across the country are designated as “zone 1” radon zones, with many of these counties spanning primarily the Great Plains and northern states, and spanning through the rust belt and into New England states. Overall, an estimated 1 in 15 homes in the US are expected to register at or above the EPA action limit (United States Environmental Protection Agency, 2016). The current study sample offered a wide representation of home radon levels, offering potentially good generalizability of the effects of varying degrees of home radon exposure on brain morphology. However, further work in larger samples, including from individuals living in “zone 2” and “zone 3” designated areas would be advantageous to determine the full generalizability of the current findings to other areas.

Before closing, it is important to address limitations of the current study. First, this work was done in a modest-sized study sample from a single metro area. It is likely that our study sample was underpowered to detect any significant effects of PM2.5, and the composition of PM2.5 to which this study cohort was exposed may not align exactly with that of cohorts from other regions. Further, the local region from which we derived the study sample is a region of the country that is notoriously high in indoor radon, which may not be representative of other areas of the country. It would be worthwhile to replicate the current study in a larger, geographically diverse sample of youth to determine the extent to which both indoor radon and ambient outdoor PM2.5 may impact brain morphology during this pivotal developmental window. Second, this observational study was not designed to evaluate the mechanisms by which either radon or PM2.5 might impact the brain. Future works should expand on this study by incorporating sensitive measurements of biological mediators that may explain some of these environment-brain associations (e.g., inflammation, oxidative stress), as well as histological measurements of potential direct infiltration of PM2.5 and radon particles beyond the blood-brain-barrier (Cristaldi et al., 2022; Lumniczky et al., 2017; Sangkham et al., 2024). Additionally, this study only examined the effects of radon and PM2.5 exposure on a sample of adolescents. Research suggests that younger children may be more, or perhaps differentially vulnerable to the deleterious effects of toxin exposures compared to adolescents (Bearer, 1995; Falck et al., 2015). Expanding on this work by exploring these exposures in younger children would be advantageous for fully understanding these effects across potentially sensitive windows of development. Finally, we took the approach of exploring the effects of chronic exposure to toxins based on measurements from the participants’ current residence. Although we used the most robust measurements possible, there will always remain questions about variability in toxin levels over seasons and years, and exposures in previously-inhabited residences (Bell et al., 2007; Stanley et al., 2019). Future prospective birth cohorts would benefit from taking both PM2.5 and regular home radon measurements to more fully characterize the individual’s full lifespan exposure history, rather than using retrospective estimations as was done in the current study.

4.1. Conclusions

To conclude, the present study explored whether there are unique or interactive effects of two ubiquitous toxins, namely indoor radon and ambient outdoor PM2.5, on subcortical brain volumes in a sample of healthy youth. Although we did not detect any significant links between PM2.5 and subcortical morphology, we found that indoor radon exposure was associated with larger NAc and amygdala volumes. The findings suggest a key, unique link between brain morphology and a prevalent but widely understudied inhaled toxin. Radon exposure has most commonly been associated with lifetime risk for lung cancer, but our findings contribute to a growing body of literature suggesting critical impacts on the central nervous system even in youth. Our data provide a critical foundation for future studies that can ascertain the functional implications of these radon-related aberrations in subcortical structure, and continued investigation into other aspects of brain morphology.

Supplementary Material

1

Highlights.

  • The adolescent brain is highly sensitive to environmental toxins

  • Subcortical gray matter is particularly vulnerable to toxic exposures

  • We studied the effects of two common toxins on adolescent subcortical morphology

  • Radon exposure was associated with larger amygdala and nucleus accumbens volumes

  • The data suggest possible sensitivity of reward networks to common toxin exposures

Acknowledgements:

We would like to thank all of the participants and their families, without whom this work would not be possible.

Funding:

This work was funded by the National Institutes of Health, specifically the National Institute of General Medical Sciences (P20-GM14461 to BKT) and the National Institute of Environmental Health Sciences (R21-ES035146 to BKT, and R01-ES033961 to LES). The funders had no part in the study design, analysis, interpretation, or writing of this report.

Declaration of interests

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Brittany K. Taylor reports financial support was provided by National Institute of Environmental Health Sciences. Brittany K. Taylor reports financial support was provided by National Institute of General Medical Sciences. Lauren E. Salminen reports financial support was provided by National Institute of Environmental Health Sciences. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Competing Interests

All authors declare no competing interests.

Author CRediT Statement:

BKT: conceptualization, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, validation, visualization, writing - original draft, writing – review and editing

MNC-S: data curation, formal analysis, validation, writing – review and editing

GEP: data curation, writing – review and editing

SCR: data curation, writing – review and editing

SLG: data curation, writing – review and editing

HRP: data curation, writing – review and editing

OTVS: data curation, writing – review and editing

JPW: formal analysis, validation, writing – review and editing

RM: formal analysis, validation, writing – review and editing

SS: data curation, writing – review and editing

AvD: data curation, writing – review and editing

CL: data curation, writing – review and editing

BM: methodology, formal analysis, validation, writing – review and editing

TKM: project administration, writing – review and editing

LES: conceptualization, supervision, project administration, writing – review and editing

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Data Availability

The data reported herein are available on reasonable request via the Collaborative Informatics and Neuroimaging Suite (COINS; https://coins.trendscenter.org/).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS2156210-supplement-1.docx (32KB, docx)

Data Availability Statement

The data reported herein are available on reasonable request via the Collaborative Informatics and Neuroimaging Suite (COINS; https://coins.trendscenter.org/).

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