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. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: Brain Lang. 2022 Apr 7;229:105112. doi: 10.1016/j.bandl.2022.105112

Environmental Noise, Brain Structure, and Language Development in Children

Katrina R Simon a, Emily C Merz b, Xiaofu He c, Kimberly G Noble a
PMCID: PMC9126644  NIHMSID: NIHMS1796831  PMID: 35398600

Abstract

While excessive noise exposure in childhood has been associated with reduced language ability, few studies have examined potential underlying neurobiological mechanisms that may account for noise-related differences in language skills. In this study, we tested the hypotheses that higher everyday noise exposure would be associated with 1) poorer language skills and 2) differences in language-related cortical structure. A socioeconomically diverse sample of children aged 5–9 (N = 94) completed standardized language assessments. High-resolution T1-weighted magnetic resonance imaging (MRI) scans were acquired, and surface area and cortical thickness of the left inferior frontal gyrus (IFG) and left superior temporal gyrus (STG) were extracted. Language Environmental Analysis (LENA) was used to measure levels of exposure to excessive environmental noise over the course of a typical day (n = 43 with complete LENA, MRI, and behavioral data). Results indicated that children exposed to excessive levels of noise exhibited reduced cortical thickness in the left IFG. These findings add to a growing literature that explores the extent to which home environmental factors, such as environmental noise, are associated with neurobiological development related to language development in children.

Keywords: child development, noise, MRI, language, brain

1. Introduction

Environmental noise is defined as unwanted, unpleasant, or distracting sounds in the environment (Erickson & Newman, 2017; Muzet, 2007), and may impact developmental processes in a myriad of ways. While it is well known that excessive exposure to loud noise can lead to hearing loss, exposure to environmental noise in moderate doses that do not cause hearing damage may nonetheless have a wide range of non-auditory effects in children and adults (Basner et al., 2014; Eggermont, 2017; Pérez-Valenzuela et al., 2018). For example, noise exposure has been linked to lower cognitive performance (Haines et al., 2001), poorer mental health (Dreger et al., 2015), increased blood pressure, and altered cortisol levels (Evans et al., 2001).

Children may be particularly at risk of adverse effects following high noise exposure due to their developing sensory systems, as well as their decreased ability to anticipate stressors and lack of well-developed coping strategies. Further, children in noisy environments may simply lack the independence or ability to leave those environments, especially in settings such as the home or classroom.

Environmental noise exposure may contribute to children’s developing language and reading skills. For one, children may learn to ‘screen out’ speech in noisy environments due to the presence of other stimuli, which may impede their ability to acquire language in the context of social interactions (Fletcher, 1940). Additionally, hearing and learning new words in the context of noisy environments may interfere with the phonological aspects of language learning, potentially impacting the development of language skills (Wightman & Kistler, 2005). Children’s early speech perception and phonological awareness are pivotal to language and reading development (Nittrouer, 2002; Melby-Lervåg et al., 2012; Melvin et al., 2017; Wagner & Torgeson, 1987), and impaired speech perception may mediate the link between noise exposure and language development (Evans & Maxwell, 1997). Indeed, increases in language and reading scores have been observed following the closure of a nearby airport (Hygge et al., 2002), and it has been suggested that excessive noise exposure may be a potential mechanism underlying socioeconomic disparities in language development (Skoe et al., 2013).

Increased exposure to excessive noise places high demands on auditory processing, which in turn may influence the development of neural circuitry associated with language development, a process contingent on being able to hear and parse out phonemes and syllables (White-Schwoch et al., 2015). Further, increased environmental noise may cause disruptions in both the neural processing of individual acoustic sounds (such as phonemes) and the formation of consistent representations of successive events (such as words or sentences) (Ahissar et al., 2006). High levels of environmental noise may therefore lead to disruptions to neural processes associated with sensory and auditory processing, which may in turn contribute to differences in brain regions supporting language processing and skill.

Language and reading development are supported by a widespread left hemispheric network, and structural characteristics of certain regions, including the left inferior frontal gyrus (IFG) and left superior temporal gyrus (STG), have been associated with concurrent and future language and reading abilities (Gauger et al., 1997; Merz et al., 2020; Porter et al., 2011). Additionally, the morphometric qualities (i.e., cortical thickness and surface area) of these regions have been associated with environmental factors more broadly (Noble et al., 2007; Noble et al., 2012; Norbom et al., 2021) and with language input specifically (Merz et al., 2020). For example, differences in the home language environment, including the number of words children hear and the number of conversations they engage in, have been associated with the cortical thickness and surface area of these brain regions (Merz et al., 2020). Further, differences in the home language environment have also been associated with the integrity of the underlying white matter tracts supporting language abilities, as well as language-related brain function in the IFG (Romeo et al, 2018a, Romeo et al., 2018b) Thus, these regions both support language development and are sensitive to verbal auditory experience. However, while excessive exposure to noise is an auditory input that also impacts language development, the extent to which excessive noise exposure is associated with differences in the structure of these regions is less clear.

In animal work, short- and long-term noise exposure has been linked to differences in gene-specific changes in DNA methylation in neural structure in rats (Guo et al., 2017). Rats exposed to long-term environmental noise within a normal hearing range displayed decreases in blood-oxygenation levels in the auditory cortex during a sound processing task, suggesting that long-term, passive exposure to moderate noise levels may adversely affect auditory processing (Lau et al., 2015). In adult humans, noise exposure has been related to atrophy in the frontoparietal network, which has been associated with language, executive function, and memory (Nußbaum et al., 2020). Associations between exposure to long-term environmental noise and brain function supporting speech processing have also been described (Brattico et al., 2005; Kajala et al., 2004). In children with learning problems, background noise has been associated with differences in the cortical representation of speech (Warrier et al., 2004). Further, noise has been related to the neural encoding of speech in children (Anderson et al., 2010). But the extent to which children’s typical daily noise exposure is related to morphometric properties of language-related brain regions in children has not been investigated. Given that children often spend large portions of their day in noisy environments (Bluyssen et al., 2018; Pujol et al., 2014), it is important to gain a better understanding of the potential developmental impacts of noise exposure. Understanding the links between typical daily noise exposure and structural brain development will help elucidate how the auditory environment may “get under the skin” and potentially impact developmental processes. Further, identifying neural markers associated with excessive noise exposure may serve as potential indices useful to intervention and prevention strategies aimed at optimizing developmental trajectories.

1.1. The Current Study

The aims of the current study were to examine 1) whether typical daily environmental noise exposure is related to language skill in children, 2) whether typical daily environmental noise exposure is related to language-related cortical structure. We hypothesized that children exposed to excessive environmental noise exposure would be exhibit 1) poorer language skills and 2) reduced cortical thickness and surface area in the left IFG and STG.

2. Methods

2.1. Participants

Given that participant and procedural information has been described in detail elsewhere (Merz et al., 2020), we briefly describe these methods here. A socioeconomically diverse sample of typically developing children aged 5–9 years (N = 94; 61% female) were recruited from the New York City region to participate in this study. Participant demographics are provided in Table 1. There were 94 total families who completed demographic questionnaires and participated in the language assessment. Of those, 76 children had complete and usable noise measurements, 51 children had structural MRI scans that passed quality control criteria, and 93 children had completed language assessment scores. In total, 43 children had complete data on environmental noise, brain structure, and language skills.

Table 1.

Descriptive statistics for sample characteristics and study variables

M SD Range n
Child age (years) 7.03 1.29 5–9 94
Family income-to-needs ratio 2.68 2.79 0–15 94
Parental education (years) 14.14 2.64 7–20 94
Noise exposure .51 .23 .00–.97 76
L IFG cortical thickness (mm) 2.88 .17 2.51–3.17 51
L IFG surface area (mm2) 3542.3 539.7 2545–5388 51
L STG cortical thickness (mm) 3.02 .16 2.48–3.34 51
L STG surface area (mm2) 3929.01 492.5 2477–5243 51
CTOPP-2 composite score 100.90 12.26 76–130 93
NIH Toolbox Picture Vocabulary score 104.66 12.99 78–153 93
% n
Child sex (female) 60.64 57
Child race/ethnicity
 African American/Black, non-Hispanic/Latino 30.85 29
 Hispanic/Latino 50 47
 White, non-Hispanic/Latino 13.83 13
 Other 5.32 5
Family income below US poverty threshold a 29.79 28
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Note: Parental education reflects educational attainment averaged across parents.

IFG, inferior frontal gyrus; STG, superior temporal gyrus

a

Income-to-needs ratio <1.00.

2.2. Measures

2.2.1. Demographic information

Participants’ age, race, sex, parental educational attainment, number of people in the household, and household income were reported via parent questionnaire. Household income-to-need ratios (ITN) were computed by dividing the household income by the poverty threshold for the size of the family in the year they completed the lab visit. The ITN is a ratio, set by the United Sates Department of Health and Human Services, used to examine annual family income relative to the federal poverty level. An ITN of 1 and below indicates that a family is living in poverty, whereas an ITN above 1 indicates that a family is living above the poverty line. For example, in 2015 (when families were being recruited), the federal poverty level for a family of four was set at $24,250. For a family of four reporting an income of $100,000, the ITN would be calculated as $100,000 / $24,250 = 4.12, indicating that the family’s annual income was 4.12 times above the federal poverty level for a family of that size. Due to expected positive skew, ITN values were log transformed. See Table 1 for descriptive statistics on demographic, ITN, and parent education information for the full sample.

2.2.2. Excessive Noise Exposure

Noise exposure levels were assessed using the LENA Pro digital language processor (DLP), a small device that fits in a child’s shirt pocket and stores up to 16 hours of digitally recorded audio (Xu, Yapanel, & Gray, 2009). While typically used to assess the natural language environments of children and families, studies have also utilized the LENA’s ability to measure noise levels in different environments (Benitez-Barrera et al., 2020; Chow & Shellhaas, 2016; Pineda et al. 2017). Parents were instructed to record eight continuous hours each day for 2 days (weekend days or days when children were primarily at home), amounting to 16 recorded hours per child. Most families (n=62) recorded data entirely on weekends or public-school holidays, with some completing data collection on one weekend day and one weekday (n= 13), and a small number recording on weekdays only (n=5). However, all recordings were examined to ensure children were not in school while wearing LENA recorders, and participants who used LENA incorrectly were excluded from the final sample. The average number of days between the noise recording and the MRI scanning session was 5.80 days (SD = 15.10), with a maximum of 65 days. Upon return of the devices, data were uploaded and analyzed using LENA Advanced Data Extractor (ADEX) (Xu et al., 2008). Average noise levels in decibels (dB), a measure of sound intensity, were extracted from LENA software. Data for each participant were exported into .csv files, where typical LENA measures (adult word count, conversational turn count, and child vocalization count) as well as sound pressure levels in dB were automatically extracted for each 5-minute epoch of the recording.

To understand how much time children spent in environments with potentially harmful levels of noise exposure, we calculated the proportion of time children spent exposed to sound pressure levels greater than 70 dB, the threshold at which noise may begin to be harmful at large doses and at which speech comprehension noticeably decreases (Berglund et al., 1999; EPA, 1974). To calculate this value, we computed the proportion of five-minute epochs out of the total number of five-minute epochs in the recording in which average sound pressure level was greater than 70 dB, as reported by the LENA ADEX. Descriptive information on noise levels is provided in Figure 1.

Figure 1.

Figure 1.

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Distribution of excessive noise exposure. Excessive noise exposure was computed by calculating the proportion of five-minute epochs out of the total number of five-minute epochs in the recording in which average sound pressure level was greater than 70 dB.

We additionally examined how much this measure of noise exposure correlated with other widely used measures provided by LENA software. Excessive noise exposure was positively associated with average hourly adult word count (r =.24, p = .04), average hourly conversational turn count (r = .34, p = .002), and average hourly child vocalization count (r = .49, p < .001). Because conversational turns could be expected to be associated with language-related brain structure and function over and above the effect of quantity of adult words or child vocalizations (Romeo et al., 2018, Merz et al., 2020), all brain analyses controlled for average hourly conversational turn count. As an additional robustness check, we re-ran the same analyses using only the portions of the recordings where no speech was detected, as described below (See Section 2.3).

Most families (66%) had 16 hours of recording time. Three families with < 5 hours of recording time and one family that used the recorder incorrectly were excluded from analyses, for a final total of 76 families with usable LENA data. Recording times ranged from 5.18 to 16.00 hours (M = 14.22, SD = 3.24), and recording length was not correlated with noise exposure (r = .09; p = .40). While the samples captured a relatively short time in the life of the child, data derived from LENA recordings tend to be relatively consistent across days (Gilkerson et al., 2017). Further, families were asked to go about their day as normally as possible while using the LENA device, and to wear them during a typical day at home, rendering it more likely that the samples obtained here are likely to reflect each child’s typical naturalistic auditory home environment.

2.2.3. Language Skills

Language skills were measured using two assessments: composite scores from the Comprehensive Test of Phonological Processing, 2nd edition (CTOPP-2; Wagner et al., 1999), and the Picture Vocabulary subtest of the NIH Toolbox Cognition Battery (NIHTB-CB) (Gershon et al., 2015). The CTOPP 2 is a comprehensive, norm referenced instrument designed to assess phonological processing abilities as prerequisites to reading fluency. The Picture Vocabulary test assesses vocabulary comprehension via auditory comprehension of single words that are graded in difficulty and measured with an auditory word-picture matching paradigm.

For all children, the Elision and Blending Words subtests of the CTOPP-2 were administered. For children aged 5–6 years (n = 49), the Sound Matching subtest was additionally administered and factored into the composite score, as per the scoring manual. Raw scores for the administered subtests of each assessment were standardized based on age and averaged to formulate a CTOPP-2 composite score and standardized Picture Vocabulary score for each child. One child did not complete the entire language assessment, leading to a total of 93 children with CTOPP-2 composite scores and Picture Vocabulary scores. In analyses linking brain structure (cortical thickness and surface area) to language skills, raw language scores were used.

2.2.4. Image Acquisition and Processing

MRI data were acquired on a 3-Tesla General Electric MR750 scanner with a 32-channel head coil at the New York State Psychiatric Institute. During scanning, children watched a movie of their choice. Children completed a high-resolution, T1-weighted fast spoiled gradient echo scan with the following parameters: sagittal acquisition; TR = 7.1 ms; TE = min full; inversion time (TI) = 500 ms; flip angle = 11 degrees; 176 slices; 1.0 mm slice thickness; field of view (FOV) = 25 cm; in plane resolution = 1 9 1 mm. All images were visually inspected for motion artifacts and ghosting, leading to exclusion of 15 participants, and a final sample of 51 usable scans. There was no manual editing of data that were deemed eligible for inclusion.

2.2.4.1. Cortical Thickness and Surface Area

Structural images were processed using standard automated procedures in the FreeSurfer software suite (http://surfer.nmr.mgh.harvard.edu/; version 6.0). These included the removal of non-brain tissue, image intensity normalization, and construction of white/gray matter and gray matter/cerebrospinal fluid boundaries (Dale, Fischl, & Sereno, 1999; Fischl & Dale, 2000). Following cortical surface reconstruction, automated procedures parcellate the cerebral cortex into regions based on gyral and sulcal structure (Desikan et al., 2006; Fischl et al., 2004), using the Desikan-Killiany atlas (Desikan et al., 2006). All images were visually inspected both before and after surface reconstruction by two research assistants to assure data quality. Cortical thickness is computed as the closest distance from the gray matter-white matter boundary to the gray matter-cerebrospinal fluid boundary at each vertex on the tessellated surface. Surface area is computed as the sum of the areas of each tessellation falling within a given region. FreeSurfer cortical thickness and surface area measurements have been shown to be reliable and have been well validated. For the current study, the boundaries of the left IFG (pars opercularis, pars triangularis, and pars orbitalis) and left STG were determined using the Desikan-Killiany atlas. IFG and STG cortical thickness and surface area data were extracted for analysis (Desikan et al., 2006).

2.3. Statistical Analysis

Descriptive analyses were conducted in SPSS (version 27.0, IBM Corp) and are provided in Table 2, and zero-order correlations of our measures of interest are in Table 3. Subsequent analyses were performed in R (R Core Team, 2020) and FreeSurfer software. Multiple linear regression analyses were performed to examine associations between 1) excessive environmental noise exposure and language skills; 2) excessive environmental noise exposure and language-related cortical structure (surface area and cortical thickness of left IFG and STG); and 3) cortical structure and language skills. All initial analyses examining structural brain measures controlled for child age, sex, LENA device recording time, family ITN, and hourly conversational turns. For models predicting cortical thickness, analyses included mean cortical thickness as a covariate to account for whole-brain differences in children. For all regression analyses, Full Information Maximum Likelihood Estimation was used to account for missing data, which were missing at random (Little’s MCAR: χ2(86, N=94) = 105.56 p = .68).

Table 2.

Descriptive statistics for sample characteristics and study variables for analytic sample (n=43)

M SD Range
Child age (years) 7.38 1.17 5–9
Family income-to-needs ratio 2.62 2.77 0–15
Parental education (years) 14.64 2.60 10–20
Noise exposure .51 .23 .12–.88
L IFG cortical thickness (mm) 2.90 .17 2.55–3.17
L IFG surface area (mm2) 3561.9 570.28 2545–5388
L STG cortical thickness (mm) 3.03 .16 2.48–3.34
L STG surface area (mm2) 3903.60 510.3 2477–5243
CTOPP-2 composite score 102.90 10.9 79–130
NIH Toolbox Picture Vocabulary score 106.25 14.67 78–153
% n
Child sex (female) 65.1 28
Child race/ethnicity
 African American/Black, non-Hispanic/Latino 27.90 12
 Hispanic/Latino 44.2 19
 White, non-Hispanic/Latino 12.3 10
 Other 4.7 2
Family income below US poverty threshold a 32.60 14
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Note: Parental education reflects educational attainment averaged across parents.

IFG, inferior frontal gyrus; STG, superior temporal gyrus

a

Income-to-needs ratio <1.00.

Table 3.

Zero-order correlations.

Variable Name 1 2 3 4 5 6 7 8 9 10 11 n
1. Noise Exposure -- -- -- -- -- -- -- -- -- -- -- 75
2. L IFG cortical thickness (mm) −.03 -- -- -- -- -- -- -- -- -- -- 51
3. L IFG surface area (mm 2 ) −.02 −.08 -- -- -- -- -- -- -- -- -- 51
4. L STG cortical thickness (mm) −.11 .50* .02 -- -- -- -- -- -- -- -- 51
5. L STG surface area (mm 2 ) .01 −.17 .56* −.19 -- -- -- -- -- -- -- 51
6. CTOPP-2 composite Score .02 −.15 .11 −.07 .00 -- -- -- -- -- -- 93
7. NIH Toolbox Picture Vocabulary Score .04 −.03 .03 −.08 .01 .26* -- -- -- -- -- 93
8. Family ITN .06 .09 .20 .02 .10 .23* .35* -- -- -- -- 94
9. Hourly CT .36* .02 .24 −.02 .38* −.20 .15 .28* -- -- -- 76
10. Hourly CTC .50* .10 .09 .01 .21 −.18 .10 .19 .84* -- -- 76
11. Hourly AWC .24* −.02 .28 .01 .36* −.02 .22 .20 .27* .78* - 76
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*

p < .05

ITN = income-to-needs ratio; IFG, inferior frontal gyrus; STG, superior temporal gyrus; CT, conversational turns; CTC, child vocalization count; AWC, adult word count.

Post-hoc exploratory whole-brain, vertex-wise analyses were then conducted using FreeSurfer 6.0’s GLM tool to examine whether excessive noise exposure was associated with differences in cortical thickness and surface area outside of our predefined ROIs. Whole-brain analyses used a 10-mm smoothing kernel and cluster-wise correction for multiple comparisons. Monte Carlo null-Z simulations were conducted with the cluster-wise p-value threshold set to .05 and the vertex-wise threshold set to .01.

Because our measure of noise was positively associated with the number of adult words children heard and the amount of speech they produced, we conducted two robustness checks, by re-running the above analyses using only the portion of the recordings in which 1) no adult speech was present, and 2) neither adult nor child speech was present. To do this, we extracted the 5-minute segments of LENA data in which (1) the Adult Word Count (AWC) was zero and (2) both the AWC and Child Vocalization Count (CVC) were zero. From there, we calculated average noise levels, both for periods with no adult speech, and for periods with neither adult nor child speech. We then re-ran the analyses. One child was excluded from both analyses, as there no were segments free of adult words. Three children were excluded from the second analysis due to having no segments that were completely speech-free.

The amount of time children spent in environments with no adult words varied from 0 to 6.58 hours (M=1.16; SD = 1.28), while children spent 0 to 4.08 hours in completely speech-free environments (M = .92; SD = .76). Excessive noise exposure during these periods was correlated with excessive noise exposure during the full recording (adult-speech free: r = .43, p = .007; fully speech-free: r = .51, p < .001). Importantly, however, excessive noise exposure during these periods was not correlated with other LENA measures in the full recording (average hourly adult word count: r = −.085 – −.03, p’s > 0.6; average hourly conversational turns: r = −.157 – −.08, p’s >.3); average hourly child vocalizations: r = −.11 – 0.03; p’s > 0.4).

Analyses using these speech-free noise measures controlled for child age, sex, recording length, mean cortical thickness (when examining ROI cortical thickness as the dependent variable), and family income-to-needs ratio.

3. Results

3.1. Noise Exposure and Language Skills

Excessive noise exposure was not associated with language skills (CTOPP-2 composite Score: β = −.048; p = .68; Picture Vocabulary test: β = .026; p = .83), after adjusting for covariates.

3.2. Noise Exposure and Language-Related Brain Structure

Excessive noise exposure was significantly associated with reduced cortical thickness in the left IFG (β = −.19, p = .045, adjusted R2 change = .03) after controlling for child age, sex, mean cortical thickness, recording time, hourly conversational turn count, and income-to-needs ratio (See Figure 2). However, this result did not pass FDR correction (p = .18, corrected). Excessive noise exposure was not associated with left IFG surface area (β = −.092, p = .52), nor with cortical thickness (β = −.16, p= .37) or surface area (β = −.15, p = .33) in the left STG.

Figure 2.

Figure 2.

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Children with greater levels of excessive noise exposure exhibited reduced cortical thickness in the left inferior frontal gyrus. Child age, sex, mean cortical thickness, device recording time, family ITN, and hourly conversational turns were included as covariates in this model.

When examining brain structure in relation to speech-free noise levels, results remained similar, though only marginally significant. Greater noise exposure during periods with no adult speech was related to reduced cortical thickness in the left IFG (β = −.18, p = .059). During periods with neither adult nor child speech, greater excessive noise exposure was nonsignificantly related to reduced cortical thickness in the left IFG (β = −.15, p = .09). Noise remained unrelated to the surface area of the left IFG, or to the cortical thickness or surface area of the left STG. Of note, children averaged around an hour of speech-free time, in comparison to an average of 14 hours of total time recorded.

3.3. Language-Related Brain Structure and Language Skills

To examine cortical thickness in relation to language skills, we used raw assessment scores and controlled for child age, sex, and family income-to-needs ratio. Cortical thickness and surface area of the left IFG were neither related to raw CTOPP scores (sum of Blending Words and Elision subtests) (CT: β =−.09; p = .62; SA: β = .20, p = .35), nor to Picture Vocabulary raw scores (CT: β = −0.07, p = .56; SA: β =−.05, p = .71). Further, cortical thickness and surface area of the left STG were neither related to raw CTOPP scores (CT: β = .15; p = .48; SA: β = .08; p = .60) nor to Picture Vocabulary raw scores (CT: β =−.10; p = .33; SA: β = −.04; p = .70).

3.4. Post Hoc Analyses

3.4.1. Exploratory Whole Brain Analysis

As an exploratory analysis, we examined the associations between excessive noise exposure and whole brain cortical thickness and cortical surface area. Results indicated that greater noise exposure was significantly associated with greater cortical thickness in one left hemisphere cluster which survived correction for multiple comparisons (MNI coordinates for peak vertex: −44.5, −9.3, 51.5; 3332 vertices; cluster-forming p < .05; cluster-wise p <. 05). The peak coordinate fell within the left precentral gyrus. Covariates included child age, sex, average hourly conversational turns, and device recording time. However, this whole-brain result did not hold when examining either measure of speech-free noise exposure.

3.4.2. Exploratory Mediation Analysis

Although excessive noise exposure was not directly related to language skills in children, we also performed a mediation model to explore whether left IFG cortical thickness indirectly mediated an association between noise and language skills. We tested two models, with raw CTOPP scores and raw NIH Picture Vocabulary Scores as our dependent variable in separate models. In both models, excessive noise exposure served as the independent variable and left IFG cortical thickness served as the mediator variable. Covariates included age, sex, family income-to-needs ratio for all paths. Both the c path (linking noise exposure and left IFG cortical thickness) and the a path (linking noise exposure and IFG cortical thickness) additionally controlled for hourly conversational turns and recording time, with the a path also controlling for mean cortical thickness. Neither bore evidence for an indirect effect, with 95% confidence intervals for both models containing 0 (CTOPP: [−0.033 – 0.055], NIH Picture Vocabulary [−0.016 – 0.048]).

4. Discussion

The goal of this study was to examine associations among excessive environmental noise exposure, language-related brain structure, and language skills in children. We did not find support for our first hypothesis, that greater noise exposure would be associated with poorer language skills in children. However, we found some support for our second hypothesis, in that excessive noise exposure was associated with reduced cortical thickness in the left IFG. However, results were significant before, but not after, adjustment for multiple comparisons. As a robustness check, speech-free noise segments were examined, and results remained similar, with greater speech-free noise associated with reduced cortical thickness in the left IFG. No associations were found with left IFG surface area, nor with left STG structure. Post-hoc exploratory whole-brain analyses revealed a significant association between excessive noise exposure and increased cortical thickness in the left precentral gyrus, but this link was not observed when examining speech-free segments.

Past studies have linked greater noise exposure to lower language skills in children (Clark et al., 2006; Klatte et al., 2017; Maxwell & Evans, 2000). Contrary to our hypothesis, however, we did not find a relationship between excessive noise exposure and language skills in the present sample. It is possible that excessive noise exposure may only be associated with certain language subskills, and that the phonological and vocabulary skills assessed here are less strongly related to noise. Indeed, while Klatte et al., (2016) found associations between noise exposure and reading scores in children, no associations were found between noise and phonological processing or listening comprehension. Another possible explanation might stem from differences in how and for how long noise was measured. For example, in Hygge (2002), researchers measured noise using a dedicated sound measurement device for an entire 24-hour period. In another study, a school was chosen for examination based on predicted noise levels using their location within the airport’s flight contour (Evans & Maxwell 1997). In contrast, our study used LENA technology to measure individual children’s home noise environments over the course of 5–16 hours. It is possible that this recording time period was not long enough to capture the daily variability in excessive noise exposure that may be related to language development. It is also possible that the conditions in which we asked participants to use LENA did not fully reflect children’s average noise exposure, given that children of this age spend a large amount of time at school. At the same time, other aspects of noise, such as type of noise or variability of noise levels, may contribute to behavioral processes (Wass et al., 2019). Other characteristics, such as length of time for which children have lived at their residence, may also be pertinent. Finally, it is possible that the association between noise and language skills depends on the environmental noise level during the assessment of language itself. In the current study, children were tested in quiet testing environments with limited distractors. It is possible that in the context of noise or other environments with higher processing demands, such as a classroom, children exposed to chronically higher noise levels would fare worse (D’Angiulli et al., 2008).

Increased noise exposure may place high demands on auditory processing systems, potentially impacting brain development. The left IFG is responsible for language production and processing as well as phonological processing abilities in children (Nuñez et al., 2011; Poldrack et al., 1999). Decreased cortical thickness in this region during childhood could reflect less gray-matter formation early in development (Hanson et al., 2013), or may be indicative of otherwise altered neurodevelopmental trajectories (Mills & Tamnes, 2014). Structural differences in language-related brain regions have been linked to reading problems, such as dyslexia (Norton et al., 2015; Richlan et al., 2012), reduced language processing skills (Lu et al., 2007), as well as lower socioeconomic circumstances (Mackey et al., 2015; Merz et al., 2020).

Several mechanisms could explain why noise exposure may be associated with left IFG structure in children. Excessive noise exposure is believed to place extra sensory demands on children, which may then contribute to changes in the development of neurobiological mechanisms. In the context of language development, where exposure to language has been found to contribute to children’s own language skills (Rowe, 2012; Weisleder & Fernald, 2013), increased exposure to noise may disrupt the processes through which children learn language, which in turn may be associated with the development of language-related brain structure. Noise exposure over time may also impact how sound is perceived by the brain, with work suggesting that impoverished early auditory experiences may become “embedded” biologically by shaping the neural response to sound (Skoe et al., 2013). Finally, noise is often defined as an environmental stressor, with previous work linking increased noise exposure to physiological and cognitive differences in children and adults (Evans et al., 1997, Wass et al., 2019). It is possible that prefrontal cortical structure varies with noise exposure because of the stress-related aspects of noise exposure, rather than due to auditory/linguistic impacts per se. The higher concentration of glucocorticoid receptors in this region (McEwen & Morrison, 2013) supports this possibility, suggesting that prefrontal brain development may potentially be affected by the increased stress typically reported by those exposed to noisier environments. Exposure to noise has also been documented to be associated with changes in autonomic and behavioral responses to attention-eliciting stimuli, suggesting another way in which noise can get “under the skin” to influence outcomes in cognitive and affective domains (Wass et al., 2019). Though suggestive, however, the lack of significance after correcting for multiple comparisons merits replication with a larger sample size.

A post-hoc exploratory whole-brain analysis revealed a significant association between noise exposure and increased cortical thickness of the left precentral gyrus. However, this result did not hold when examining speech-free noise levels. The precentral gyrus, also referred to as the premotor cortex, has been associated with motor control and is believed to play a role in the planning of complex and coordinated movements. While unexpected, some literature suggests that the precentral cortex may play a role in the language network via its connection to the IFG (Coursen et al., 2017; Catani et al, 2012). Some research has also observed activation within the precentral gyrus during the completion of speech-in-noise and speech perception tasks in adults, (Holmes et al., 2020; Wilson et al., 2004), and increases in gray matter volume in this region has been associated with children’s language delays (Raschle et al., 2017). Finally, we note that noise exposure was associated with increased cortical thickness in precentral gyrus but reduced cortical thickness in the IFG. Replication with a larger sample is clearly warranted to better understand the potential association between noise exposure and the structure of this region.

Surprisingly, we found no significant associations between brain structure in our regions of interest and language skills, despite past evidence of these links (Eckert, 2004; Sowell et al., 2004). This, paired with the lack of a significant association between noise exposure and language skills, raises questions as to whether our language assessments were sensitive to variation in language ability. Although both assessments of language were reliable, valid, standardized measures of language commonly used in this age group, they were not strongly correlated (r = .26), suggesting that other factors may have contributed to variation in language scores, and possibly to the null results found here. This raises the question as to whether different circumstances may moderate the associations between noise and language, and/or between brain structure and language skills. Indeed, some work has shown that socioeconomic factors (Leonard et al., 2019; Noble et al., 2006) or neighborhood safety (Ellwood-Lowe et al., 2021) can moderate the relationship between brain structure or function and cognitive skills.

There are several possible explanations as to why an environmental factor such as noise may be related to neurobiological measures but not cognitive skills. For one, neurobiological differences may emerge before related cognitive or behavioral differences. A second possibility is that children exposed to greater noise levels adapt to their environments or develop different neural mechanisms supporting their language abilities compared to their low-noise peers, leading to observation of neurobiological but not behavioral differences in language skills. A third possibility is that behavioral links may only exist under certain conditions but not others, suggesting the need for larger samples in which interaction analyses with other contributing factors can be performed (see Stevens et al., 2009 and D’Angiulli et al., 2008). Given the limitations of the current study to explore these possible mechanisms, the need to replicate these findings is crucial, in order to better understand the relationships among brain development, language skills, and noise exposure.

Several limitations should be acknowledged when interpreting these findings. First, this study had a cross-sectional and correlational design, which precludes any causal inference. Future studies should focus on studying the longitudinal associations among noise exposure, brain development, and language skills to better understand how noise exposure may be associated with developmental trajectories. Second, while the LENA technology has been widely used, including in assessments of environmental noise levels in various settings (Benítez-Barrera et al., 2020, Caskey & Vohr, 2013; Chow & Shellhaas, 2016), these measures of noise only capture a brief snapshot of children’s environments, and thus these data are only valid to the extent that they reflect a typical day at home for the child. Given that children may spend large portions of their day in other locations, future work should also examine noise levels in other settings, such as classrooms and school environments.

We did not expect our measures of noise exposure to correlate so highly with the child’s language input, including adult word count and conversational turn count. While we attempted to mitigate this by controlling for conversational turns, and then repeating analyses using noise measured during speech-free intervals, we note that the amount of available data dropped dramatically using this approach, and speech-free time varied greatly between participants. In addition, there was substantial variation in the noise levels children were exposed to, with some children spending no time in noisy environments, and others spending nearly their entire recording time in an environment over 70 dB. Despite these limitations, the use of LENA technology is a strength in that it allows researchers to capture naturalistic data on children’s home environmental exposures over relatively long periods, as opposed to measuring noise exposure during short home visits or via self- or parental report methods.

Future work should also examine additional characteristics of noise in relation to brain structure, including type or fluctuation of noise levels throughout the day. The current study focused only on noise levels, and it is possible that different types of noise may be differently associated with brain and language development. Children may be exposed to a wide range of environmental noise both indoors and outdoors, with some types of noise potentially being more disruptive than others (Pujol et al., 2014). Further, more attention should be paid to the timing, intensity, and duration of noise exposure in childhood, to better understand mechanisms through which specific aspects of noise may be associated with children’s development.

Altogether, the present study provides preliminary evidence that noise exposure may be associated with children’s language-related brain development. This is important, given that both noise exposure and structural qualities of language-related brain regions have been associated with concurrent and future language and reading skills. However, given the limitations of this study, further research into the effects of environmental noise exposure on developmental processes is recommended.

Research Highlights:

  1. Environmental noise, brain structure and language skills were measured in children

  2. Higher noise levels were associated with reduced cortical thickness in the L IFG

  3. Levels of noise exposure were not related to SES or children’s language skills

Acknowledgements

This publication was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through grant numbers UL1TR001873 and UL1RR024156, and through the Eunice Kennedy Shriver National Institute of Child Health and Human Development R01HD093707-01. Additional funding was provided by the Gertrude H. Sergievsky Center, Columbia University Medical Center; Teachers College, Columbia University; and a National Institute of Mental Health training grant (T32MH13043). The content is solely the responsibility of the authors and does not necessarily represent the official views of its funding sources. We are grateful to the families who participated in this study. We also thank Elaine Maskus, Pooja Desai, Rehan Rehman, Rachel RouChen Lin, Charles Sisk, Mayra Lemus Rangel, Lexi Paul, Samantha Moffett, Julissa Veras, and Victor Issa Garcia for assisting with data collection, and Melissa A. Giebler for helpful comments on an earlier version of this manuscript.

Footnotes

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We wish to confirm that there are no known conflicts of interest associated with this publication.

The authors declare no conflicts of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request.

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