Abstract
The language environment to which children are exposed has an impact on later language abilities as well as on brain development; however, it is unclear how early such impacts emerge. This study investigates the effects of children's early language environment and socioeconomic status (SES) on brain structure in infancy at 6 and 30 months of age (both sexes included). We used magnetic resonance imaging to quantify concentrations of myelin in specific fiber tracts in the brain. Our central question was whether Language Environment Analysis (LENA) measures from in-home recording devices and SES measures of maternal education predicted myelin concentrations over the course of development. Results indicate that 30-month-old children exposed to larger amounts of in-home adult input showed more myelination in the white matter tracts most associated with language. Right hemisphere regions also show an association with SES, with older children from more highly educated mothers and exposed to more adult input, showing greater myelin concentrations in language-related areas. We discuss these results in relation to the current literature and implications for future research.
SIGNIFICANCE STATEMENT This is the first study to look at how brain myelination is impacted by language input and socioeconomic status early in development. We find robust relationships of both factors in language-related brain areas at 30 months of age.
Introduction
Children's early language environment is crucial for their emerging language abilities (Weizman and Snow, 2001; Hoff and Naigles, 2002), which are in turn associated with later literacy skills (Rodriguez and Tamis-LeMonda, 2011; Vernon-Feagans et al., 2022). For instance, children exposed to a large quantity of high-quality language input—longer utterances, higher grammatical complexity, more vocabulary diversity—have larger vocabularies (Huttenlocher et al., 2010; Rowe, 2012; Laing and Bergelson, 2019). In addition, children who are exposed to more child-directed speech early in development have faster language-processing abilities (Weisleder and Fernald, 2013), show larger vocabularies (Fernald et al., 2006), and have better language outcomes (Marchman and Fernald, 2008).
Critically, there are large individual differences in the quantity and quality of language input that children receive from their caregivers. This variation may be associated with the caregiver's socioeconomic status (SES; Golinkoff et al., 2019), a complex index of a family's social and financial resources often based on parental education and/or income. Some studies using manual annotation of children's in-home speech exposure report that parents from higher-SES backgrounds talk more and use richer language input with their children (Hart and Risley, 1995; Hoff, 2003; Huttenlocher et al., 2010; Rowe, 2012). Such variations could be because of multiple contextual influences such as high parental stress and economic instability in low-SES families (Hoff, 2006). However, recent research has questioned whether variations in input quantity and quality are strictly associated with SES, because there also seems to be substantial variation in children's language environments within each socioeconomic stratum (Sperry et al., 2019).
These findings have sparked a debate about the strength of the association between SES and children's language experiences. One focus of this debate is differences in the methods used to gather input quantity data. To overcome possible observer effects (Zegiob et al., 1975; Dudley-Marling and Lucas, 2009), a recent meta-analysis examined the strength of the relationship between SES and children's language experiences measured with the Language Environment Analysis (LENA) system (Piot et al., 2022), a recorder and software system that generates automatic analyses of the speech occurring in the child's home environment (Gilkerson et al., 2017). The meta-analysis found quantitative differences of SES on children's home language experiences showing it is possible to capture differences in children's language environments as a function of SES using automatized measures (Piot et al., 2022).
Recent research has also shown links between language exposure and brain development. One study used LENA home audio recordings to measure children's language exposure and a story-listening functional MRI task to measure brain activation (Romeo et al., 2018a). Children between 4 and 6 years of age who had experienced more conversational turns with adults showed greater left inferior frontal activation near Broca's area in the fMRI task. Interestingly, these effects were independent of SES, IQ, and the quantity of adult–child utterances. A second study using diffusion tensor imaging (DTI) techniques (Romeo et al., 2018b) with the same sample of 4- to 6-year-old children also found a relationship between the amount of conversational experience and fractional anisotropy (FA) values in white matter tracts most associated with language including the left arcuate fasciculus (AF) that connects Broca's area with Wernicke's area as well as the superior longitudinal fasciculus (SLF). Once again, these relationships were independent of SES and the quantity of adult language input. Other literature also supports the role of the AF in language skills in adults (Rodríguez-Fornells et al., 2009; López-Barroso et al., 2015; Vaquero et al., 2017) as well as in 4-year-old children (François et al., 2019). The SLF has also been associated with language abilities in adults (Madhavan et al., 2014). The youngest children from these studies were 4 years of age; thus, an important question is whether these relationships hold earlier in development when language abilities are first emerging.
To our knowledge, there is only one study that has looked at relationships among children's language environment, SES, and brain development in infancy. This study used LENA home language input estimates and EEG activity in a diverse sample of 6- to 12-month-old infants. Home language environment, independent of SES, accounted for disparities in early language abilities (Brito et al., 2020). Interestingly, the relationships between language input and brain activity were negative. These negative associations may reflect a positive relationship between the amount of chaos in the home and the amount of language input to children. In particular, children living in high-chaos households who heard more adult words tended to have reduced brain activity (Brito et al., 2020).
The goal of the present study was to investigate the relationships among children's home language input, SES, and brain myelination in language-related brain regions early in development in a group of 6-month-old and 30-month-old children learning British English. We gathered the following three types of data: day-long in-home recordings of language experience of infants using the LENA system; SES information based on maternal education; and brain myelination using the multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT)-MRI protocol (Deoni et al., 2012). We obtained individual myelin water fraction maps in all participants (Deoni and Kolind, 2015) and registered them to a common group space to extract myelin concentrations from the white matter tracts most associated with language processing and cognitive control: the SLF and the AF.
We measured myelin concentration using the mcDESPOT protocol as this approach appears to measure changes in brain myelination more directly than traditional diffusion-weighted imaging, particularly in early development (Deoni et al., 2012; Gilmore et al., 2018). For example, early brain maturation is accompanied not only by the establishment of the myelin sheath itself, but also by the arrival of precursory lipids and proteins, and compartmentalization of water and iron within the oligodendrocytes. Each of these processes can lead to changes in both T1 and T2 relaxation (MacKay et al., 2006), while the myelin water fraction appears to be more sensitive to changes in the embellishment of the myelin sheath itself (Deoni et al., 2012). Furthermore, the mcDESPOT method has been validated using multiple approaches (Hurley et al., 2010; Deoni et al., 2011, 2012; Kitzler et al., 2012; Kolind et al., 2012).
Our emphasis on structural brain changes in early development reflects the very rapid changes in the brain in the first years of life. Brain volume increases dramatically in the first year, with brain volume ∼80% of the adult size by 2 years of age. In terms of white matter growth, myelination in infancy begins in the cerebellum, pons, and internal capsule, and proceeds in a “back-to-front” pattern from the optic radiations to the occipital and parietal lobes and then to the frontal and temporal lobes (Mori et al., 2008; Deoni et al., 2011, 2012).
In an initial analysis, we looked at the relationships between LENA measures (number of adult words, conversational turns, and child vocalizations) and children's SES based on maternal education. The aim was to see whether the LENA output measures followed expected developmental trends across our two age groups. Next, we examined how early brain myelination is related to both language exposure and SES by measuring in-home language experience and structural brain development at 6 and 30 months of age. These ages are particularly important because 6-month-old children have high brain plasticity, relatively little brain myelin, and less experience with language. Thus, language input could have a smaller impact on brain structure at this early age. On the contrary, by 30 months of age, most children are able to understand and produce a large number of words (Frank et al., 2017). At 30 months, therefore, language input might have a strong influence on structural brain development, and there might be stronger associations with contextual factors such as SES. A final set of exploratory analyses considered whole-brain myelination in relation to children's home language experience.
Materials and Methods
Experimental design and preregistration.
This study was modeled after another study looking at the relationships between language input and myelination in the brain (Romeo et al., 2018b). In our case, we extended these results to a younger population. The a priori hypotheses (Hs) and main analyses were preregistered (see OSF preregistration at https://doi.org/10.17605/OSF.IO/SU93B). Our specific Hs were as follows: H1, at both 6 and 30 months, the amount of adult input and measures of conversational experience will be positively related to white matter concentrations within fiber tracts known to be involved in language processing and cognitive control, the SLF and AF (Catani et al., 2005); and H2, measures of conversational experience will be more relevant at older ages as language production increases at 30 months. This should boost the strength of the relationship between conversational turns and white matter concentrations in the SLF and AF. We established that our confirmatory analyses would focus on the relationships among language input, SES, and myelin in the AF and the SLF fiber tracks in both hemispheres. We decided to include both hemispheres because at very early ages, brain function is less lateralized than later in development (Deoni et al., 2015). In a set of exploratory analyses, we planned to look at whole-brain myelination since this is the first study to measure language input, SES, and brain myelination early in development.
Participants.
We collected language home input data for 163 children from the following two age groups: a 6-month-old group (N = 87; 42 girls between 4.28 and 13.77 months of age; mean age, 6.75 months; SD, 1.54); and a 30-month-old group (N = 76; 40 girls between 28.49 and 36.41 months of age; mean age, 30.94 months; SD, 1.85). For a subset of those participants (N = 84 children), we also collected measures of brain myelination using MRI at a similar time point (difference between the age of LENA collection and MRI collection was mean age, 0.73 months; SD, 1.94 months; age range, −4.05 to 7.13 months). This subsample included 38 6-month-olds (14 girls; MRIs collected between 4.93 and 10 months of age) and 46 30-months-olds (22 girls; MRIs collected between 28.61 and 35.15 months of age). These participants were white (N = 79, 94.05%), mixed (N = 4, 4.76%), and African (N = 1, 1.2%). All children were native speakers of British English and were not exposed to another primary language at home. Participants had no history of premature birth, neurologic disorders, or developmental delay (Table 1, details).
Demographic information of the sample
Eight additional children were not included in these analyses because they underwent their LENA recordings when they were much older (3 children) than the other children in their cohort (>15 months of age for the 6-month-old group, and >37 months of age for the 30-months-old group) or they had myelin data but no language input data (5 children). All procedures used in this study were reviewed and approved by the UK NHS Health Research Authority Ethics Committee [IRAS (Integrated Research and Application System) ID 196063]. Parents signed an informed consent form and received £20 for attending the MRI session. Children received a small toy of their choosing and a t-shirt with the laboratory logo for participating. The participants from this study are also part of a larger longitudinal project examining the early development of working memory and executive function. Note that the target sample size for each cohort (6 and 30 months) was 40 based on a power analysis conducted for the larger project (where power was estimated at 0.99 and 0.86 across two sample analyses). Because of the challenges of longitudinal designs and MRI data collection with young children as well as to protect against dropout over time, we oversampled, yielding an N of ∼80 for each cohort.
Socioeconomic status measures.
We gathered information on the socioeconomic background of each participant and their family using a questionnaire that asked the main two caregivers to provide their level of education as well as the family annual household income. Studies have used multiple measures to calculate SES including family income, maternal education, average parental education, or a composite score based on multiple measures (Romeo et al., 2018b). The SES of our sample was relatively homogeneous, especially regarding family income: 72.62% of our sample had an annual household income >29,400 British Pound Sterling (GBP), which was the average household income in the UK in 2019 (UK Office for National Statistics; https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/bulletins/householddisposableincomeandinequality/financialyearending2019), when these data were primarily collected. However, our sample showed more variability in the level of maternal education with 59.52% of mothers having completed a higher education degree (Table 1). Maternal education has been broadly used as a proxy for SES in many studies investigating SES in relation to language development, and it seems to be the component of SES most strongly related to child development outcomes (Pace et al., 2017). Here, we calculated the z score for participants' maternal education as the primary SES variable.
Language input measures.
The linguistic environment of the child was measured using the LENA Pro system (Gilkerson et al., 2017). The LENA system is composed of a recorder and associated analysis software. The small recorder can be worn in a vest by the target child at home, and it can store up to 16 h of audio recordings. The LENA software automatically processes the recordings and estimates the number of words spoken by an adult in the child's vicinity, which is referred to as the adult word count (AWC), the number of vocalizations the target child made or child vocalization count (CVC), and the number of dyadic conversational turns or conversational turn count (CTC), which is defined as a discrete pair of consecutive adult and child utterances in any order, with no more than 5 s of separation. Families took the LENA recorder home on 3 different days when they did not attend nursery. During those days, the child wore the recorder in a specially constructed vest for a maximum of 16 h (in total, we gathered 6208.63 h of LENA recordings, with an average of 15.31 h/d). Each child contributed between 1 and 3 d of recordings (mean, 2.48 d; SD, 0.73). We processed the recordings using the LENA Pro software, which automatically calculated the estimates for each measure (adult words, child vocalizations, and conversational turns). These data were then processed with R (R Core Team, 2021) using an approach similar to that used in previous studies (Romeo et al., 2018b). In particular, for each LENA outcome measure and participant, we calculated the total count for each consecutive 60 min across all LENA days, in 5 min increments. For example, we extracted the total number of adult words that the child was exposed to between 7:00 A.M. and 8:00 A.M., then we calculated the total number of adult words between 7:05 A.M. and 8:05 A.M., and so on. We then selected the hour with the highest number of adult words (i.e., the “maximum hour”). We used this procedure to extract the hour with the maximum adult word count, the hour with the maximum child vocalization count, and the hour with the maximum turn count across the several days of home recordings that each participant provided. This maximum measure was used in all the analyses reported here.
Myelin data acquisition.
The MRI scans were gathered at the Norfolk and Norwich University Hospital. Before scanning, children were brought to a “sleepy room” adjacent to the MRI room to fall asleep (Deoni et al., 2011). This was a special quiet room where children were not disturbed; it included a bed with a comfortable blanket and several children's books, as well as an infant monitor and nightlight. The bed children slept on had a foam mattress on top of a Plexiglas platform that was specially designed to fit into the head coil and scanner bore. Children slept on top of the foam mattress with a “slippy sheet” under the top bedsheet (so the child could be easily shifted once asleep). Parents were encouraged to do their typical bedtime routine in the sleepy room, and then we waited until the child was asleep (which took anywhere from 5 min to 2–3 h). The experimenters then quietly entered the room, adjusted the child so the head was positioned correctly on the Plexiglas platform, lifted the platform onto an MRI-compatible plastic trolley, and then rolled the child into the scanning room. Finally, the child was positioned in the head coil, the child's ears were covered with noise-canceling headphones, and the child was moved into the bore (all while the child was sleeping). An experimenter remained with the child throughout the scan to stop the scan if the child woke up or moved substantially. To maximize success, we additionally used these following strategies: added a sound-insulating insert to the MR bore (Ultra Barrier, American Micro Industries); used electrodynamic headphones (MR Confon); and used customized “quiet” imaging sequences (Deoni et al., 2011). Participants were scanned during natural sleep. Each participant was imaged using a 3 T Discovery 750 W MRI scanner (GE Healthcare) equipped with an 8-channel head coil.
Myelin data protocol (mcDESPOT).
Myelin content was mapped using a multicomponent-driven equilibrium single-pulse observation of T1 and T2 (Deoni et al., 2008). Parameters were as follows: repetition time, 750 ms; echo time, 0.02 ms; inversion time, 650 ms; flip angle, 5°; receiver bandwidth, 244 Hz/voxel; field-of-view, 200 × 200 mm; matrix size, 200 × 200; section thickness, 1 mm. The sequences used as part of the mcDESPOT protocol were as follows: two balanced steady-state free precession (bSSFP) series with phase-cycling increments 0° and 180° to allow for correction of off-resonance artifacts (Deoni, 2011); eight spoiled gradient echo (SPGR) scans collected over different flip angles; and two inversion-recovery SPGR (IR-SPGR) scans for accurate estimation of the B1 transmit field. Further, all mcDESPOT data were acquired in pure sagittal or coronal orientation, with a field of view adjusted for head size and participant orientation, and a matrix size and section thickness chosen to give consistent isotropic resolution of 1.7 × 1.7 × 1.7 mm3. To reduce acoustic noise, these scans were run with reduced gradient amplitudes and slew rates. This resulted in extended scan time. To minimize scan time, mcDESPOT data were acquired with a partial Fourier factor of 0.75 in ky and with an ASSET (array coil spatial sensitivity encoding) parallel imaging factor of 1.5. The full protocol lasted <45 min. A member of the research team was present in the scanner suite to monitor the child at all times. The main motivation to use the mcDESPOT technique was that it is more specific to myelination and less sensitive to other biological factors such as axon packing density, axon caliber, microglia, inflammation, and tissue architecture. Moreover, mcDESPOT allowed the data collection sequence to be quieter than other methods—such as DTI—and therefore, it was less likely to awaken sleeping infants.
Myelin data processing.
First, the SPGR image with the highest flip angle was selected, and the individual SPGR, IR-SPGR, and bSSFP images were all linearly coregistered to that image using flirt from FSL (Jenkinson et al., 2002). This accounted for small amounts of motion during the scans. Nonbrain tissue and background were then removed from the images. Both the main (B0) and transmit (B1) magnetic field inhomogeneities were calculated. Myelin water fraction maps were then calculated in a voxelwise manner for every subject using the three-pool model (Deoni et al., 2013). The resulting images were then aligned to a custom template using ANTS (Advanced Normalization Tools; Avants et al., 2011) and checked for registration quality. Core white matter tract masks were used to extract the values for the regions examined, namely the AF and the SLF so that only voxels contained in these masks were used for analyses. To create the masks, we used a white matter atlas based on the Providence dataset (Deoni et al., 2012), except for the AF mask. For the AF, the white matter tracts were pulled from the atlas created by Figley et al. (2015, 2017), which was based on adult data.
Statistical analyses.
Statistical analyses were conducted using R software version 4.2.1 with the lm function from the stats package (R Core Team, 2021). We performed three sets of analyses. Within each set, linear regression models followed a basic structure. Analysis 1 looked at relationships among the LENA output measures (number of adult words, conversational turns, and child vocalizations) set as the predicted variable, and SES and age group set as the predictor variables. Analysis 2 (confirmatory) used linear regressions to assess whether language input measures predicted myelination in the SLF and AF. Analysis 3 (exploratory) measured the relationship between language input and other brain tracts that have been related to language in previous studies. The model basic structure used in Analyses 2 and 3 set the mean myelin concentration on a specific region as the predicted variable, and LENA measure as predictor variable. The models controlled for SES and age group set as fixed effects and interacting with each other. In all our models, SES was set as a continuous variable and age group was included as a categorical variable (6 vs 30 months). These two age groups refer to the approximate age when the data were collected. This decision was based on the distribution of age in months, which showed two clusters at ∼6 and 30 months, and a gap in between. Age group was contrast coded with 6-month-olds set at −0.5 and 30-month-olds set at 0.5. Child gender was not included in the analyses because we did not find consistent effects during Analysis 1 using the LENA data only, and it did not significantly improve model fit when performing model comparison (AWC: F(4) = 0.496, p = 0.738; CVC: F(4) = 1.544, p = 0.192; or CTC: F(4) = 1.08, p = 0.367). In our exploratory analyses (Analysis 3), we corrected for multiple comparisons, setting our α level for the familywise error at 0.01. Thus, only p values <0.01 are considered significant.
Results
Analysis 1: language exposure at home
Our initial analysis included three linear models, one per LENA measure. The LENA measure was the dependent variable, and SES as well as age group were set as predictor variables interacting with each other. This means that we assumed that different values on SES and age group influence each of the LENA measures differently, depending on the values of the other interacting variables.
The linear model predicting the number of adult words showed main effects of SES and age group (Table 2). As can be seen in Figure 1A, children from families with higher SES scores heard more adult words at home than children from families with lower SES scores. Moreover, the number of adult words decreased by age—older children heard fewer adult words than younger children. The linear model predicting child vocalizations also showed main effects of SES and age group. As can be seen in Figure 1C, children's vocalizations increased as a function of their mother's education. Moreover, children vocalized more with age, as they developed better language skills. Finally, the linear model predicting number of conversational turns showed only a main effect of age group, with older children producing more turns than younger children (Fig. 1D).
Linear regressions estimates for the three LENA outcome measures in relation to SES
A–D, LENA measure showing AWC (A), a child wearing the LENA vest with a LENA device inside a pocket (B), and LENA measures showing CVC (C) and CTC (D). All graphs are split by age group (6 vs 30 months old) and median SES (lower SES, light green; higher SES, dark green) based on maternal education.
Analysis 2: language exposure and myelin in language-related fiber tracts (AF and SLF)
Our second set of analyses used linear models to assess whether the three LENA output measures predict myelination in the SLF and AF fiber tracts. We ran linear models with each language exposure measure (adult words, child vocalizations, and conversational turns) predicting mean myelination in the right and left AF and SLF. Models controlled for age group (6 vs 30 months old) as well as SES (set as a continuous measure based on maternal education z scores).
Results of our confirmatory analyses showed positive main effects of age group on the AF and the SLF for all the language measures, reflecting the increase in brain myelination with age in these white matter tracts. In our first set of models, we found positive relationships between the amount of adult input (AWC) and myelination in the AF and SLF. The models showed an interaction between the amount of adult input and children's age group on the AF and the right SLF, but not on the left SLF (Table 3). In particular, larger amounts of adult word input were positively associated with higher quantities of myelin in the AF and SLF in the 30-month-old group (Fig. 2, middle graphs, darker shaded linear trends). Thus, older children who were exposed to more adult speech had more myelinated language-related fiber tracts. At 6 months of age, this relationship was reversed: infants who were exposed to more adult speech had lower myelin concentrations in the regions of interest (Fig. 2, middle graphs, lighter color shading). This pattern became stronger in the right hemispheric regions as family SES increased. This was indicated by a significant interaction between the number of adult words spoken around the child, age group, and SES on the right AF and right SLF (Table 3). This SES effect can be seen in Figure 2: older children from families with a higher SES score, who were exposed to more adult words, showed greater myelin concentrations in these right hemisphere regions and younger children showed the reversed pattern.
Linear regression estimates for AWC predicting myelination in AF and SLF
Relationships between AWC (adult input) and myelin in the left AF (green), right AF (blue), and right SLF (red) as well as CVC and myelin concentrations on the left SLF (yellow). Dark shading shows data for the 30-month-old group; light shading shows data for the 6-month-old group. Scatter plots are divided by SES using a median split. Brain images were obtained using MRI scans and show examples of myelin concentration in the brain. The fiber tracts of interest are highlighted on each image using the color scheme from the middle graphs.
The second set of linear models examining child vocalizations also showed significant relationships between language and myelin concentration. In particular, we found a significant main effect of child vocalizations on brain myelin concentration in the left SLF (Table 4). As can be seen in Figure 2, more vocalizations were associated with less myelin in the left SLF. Our final set of confirmatory models examined brain myelination and conversational turns. In addition to the main age group effects, we did not find any significant relationships between of number of conversational turns and myelin concentrations in the AF and SLF (Table 5).
Linear regression estimates for CVC predicting myelination in AF and SLF
Linear regression estimates for CTC predicting myelination in AF and SLF
Note that we examined the intersection of the AF and SLF in a preliminary analysis. The overlap found between the AF and SLF was minimal. On the left side, there was a proportional voxel overlap of 0.138; on the right side, there was a proportional voxel overlap of 0.165. As a check, we reran Analysis 2 on the nonoverlapping masks. The results replicate all of our findings from Tables 3, 4, and 5, except for the effects for the right AF, in which the two-way interaction between AWC and age group (p = 0.062) and the three-way interaction among AWC, age group, and SES (p = 0.051) no longer met standard significance levels. Details of these supplementary analyses along with the data and full analysis scripts from this study can be found at https://doi.org/10.17605/OSF.IO/EJ8YA.
Overall, results from the confirmatory analyses showed that the amount of adult language speech that children are exposed to at home is positively associated with brain myelination in the AF and SLF at 30 months. This partially confirms our H1. However, we found negative associations between adult word input and brain myelination in younger children in the 6-month-old group. Our H2 was that conversational experience would positively predict brain myelination at 30 months. This was not supported by our analyses.
Analysis 3: language experience and overall brain myelination
Our last set of analyses aimed to explore the effect of in-home language exposure on overall brain development. We conducted exploratory analyses using a similar set of linear models as in Analysis 2, but now looking at a larger number of brain regions. As in the previous models, we controlled for age group (6 vs 30 months of age) and SES (using a continuous z score based on maternal education). From a list of 21 brain region templates that we had available from the Deoni et al. (2012) study, we selected the 17 brain regions that have been previously associated with language. These areas consisted of the body and genu of the corpus callosum, as well as both the left and right areas of the cerebellum, cingulum, corona radiata, internal capsule, and frontal, parietal, and temporal lobes. We decided to exclude the right and left occipital lobes and the optic radiation because they seemed to be unrelated to language in previous studies. In addition, we only considered maximum adult words per hour (AWC) as a measure of language experience because this was the LENA measure that showed stronger relationships to myelin concentrations in our a priori regions of interest (Analysis 2). Also, recall that in Analysis 3, we set our α level at 0.01 to control the familywise error rate.
All brain areas showed a strong positive age main effect, indicating that myelin concentrations increased with age. In addition, six regions showed significant variations in myelin concentrations below our threshold as a function of adult input and/or SES (Table 6).
Linear regression estimates for AWC predicting myelination in the brain
Results from the left frontal region mimicked findings from the left AF (Fig. 3). In particular, we found positive relationships between myelin concentration and the amount of adult input for the 30-month-old children in the left frontal region, and negative relationships between myelin and adult input for 6-month-old children. Similarly, we found a statistical correspondence between the right frontal region and the right AF in that both regions showed a significant three-way interaction of adult word count by age group by SES. As can be seen in Figure 3, high-SES 30-month-old children showed a positive relationship between myelin concentration and the amount of adult input, while 6-month-old infants showed a negative relationship. There was also a slight negative relationship between myelin concentration and adult input for low-SES 30-month-old children.
Significant relationships were found between adult input (AWC) and myelin concentration in the brain. Brain region and tract overlays are shown on the left; each region and tract prior is highlighted in a different color. Right graphs are divided by SES using a median split, where dark shading shows data from the 30-month-old group and light shading shows data from the 6-month-old group.
This same pattern of results was evident for the right corona radiata, the left internal capsule, and the genu of the corpus callosum, with significant three-way interactions in all cases (Fig. 3, Table 6). The body of the Corpus Callosum showed a similar pattern; however, in this case, there was only a significant interaction between adult word count and SES, with higher-SES children tending toward a positive relationship between myelin concentration and adult input, while lower-SES children showed a negative relationship.
Discussion
In this study, we examined the relationship among language experience, SES, and myelination in the brain early in development. We hypothesized that more conversational turns and adult input would predict brain myelination in language-related areas, particularly at older ages. Toward that aim, we conducted three analyses with the purpose of quantifying the LENA measures (Analysis 1); confirming or refuting our hypotheses (Analysis 2); and, more broadly, exploring relationships among children's language experience, SES, and overall brain myelination (Analysis 3).
Analysis 1 was used as a primary validation of the LENA system in our sample of participants with the variables of interest. We discovered that the number of adult words was related to children's SES, with children with more highly educated mothers being exposed to higher amounts of overall adult input than children with less educated mothers. The quantity of children's vocalizations was also associated with SES: children with more educated mothers produced more vocalizations. This finding was somewhat surprising given that our sample was relatively homogeneous with most children coming from middle-SES and high-SES backgrounds. This indicates that even small differences in SES (in this study indexed by maternal education) can have an effect on the amount of adult input children experience early in life and the number of vocalizations that they produce.
Analysis 2 was the main focus of this study; quantifying the impact of early language experiences on myelination of the AF and SLF white matter tracts. Our findings showed that the amount of adult word input was the only language measure strongly associated with myelin concentration in the AF and SLF. In particular, the concentration of myelin in the AF and SLF was higher with more adult word input at 30 months of age. Therefore, this age group followed the preregistered predicted pattern: more adult input was positively associated with greater myelin concentrations in the left and right AF and SLF. We did not have specific predictions regarding the effects of SES in our sample, as SES did not account for differences in neural connectivity in the brain in previous studies (Romeo et al., 2018b). However, we found relationships among myelin, age, and SES in the right areas of the AF and the SLF with a stronger positive relationship between myelin concentration and adult input for higher-SES 30-month-old children.
For the 6-month-old group, we generally found a negative relationship between the number of adult words and myelin concentration. These negative relationships should be interpreted with caution as myelin concentrations are quite low at the age of 6 months and might be susceptible to noise in the MRI data (Lankford and Does, 2013). Nevertheless, other studies have also found negative relationships between home language input and brain activity in children 6–12 months of age (Brito et al., 2020). These researchers related this effect to more chaos at home. We did not measure chaos at home; thus, future work will be needed to examine these relationships in more detail. Another possibility is that the negative relationships between myelin and adult input at 6 months reflect a delay in myelination in high-functioning infants. Deoni et al. (2016) reported that higher cognitive ability in the first year of life was associated with slower initial development of myelin, followed by a prolonged period of rapid development thereafter. Based on this, they suggested that an early period of slowed myelination may coincide with increased synaptogenesis with a prolonged period of synaptic pruning after the first year. Thus, our findings may reflect slower initial development of myelin in high-functioning 6-month-olds who receive more adult input.
Our results did not show an effect of conversational turns on AF and SLF myelination as previously reported in 4- to 6-year-old children (Romeo et al., 2018b). It is important to note, however, that our study had several methodological differences relative to this prior work. Romeo et al. (2018b) used DTI techniques and individually defined white matter tracts to assess brain myelination. By contrast, we measured average myelin concentration from specific brain regions defined using group templates. Note that it was not possible to acquire both DTI and myelin data in our sample because the mcDESPOT scans took 35–45 min to acquire, at which point many children started to wake up. It is possible, therefore, that the absence of conversational turn effects in our study reflects these methodological differences. It is also possible that conversational turns have an effect on the brain later in development as children learn more language. In fact, studies looking at the relationships between quantity and quality of language input show that children might benefit from different aspects of language input at different time points depending on their language abilities (Rowe, 2012). Early in development, the quantity of language input, which in our study was measured by the number of adult words, seems to be more relevant for children's emerging language skills. In contrast, quality of language input—richness of words, utterance length, and conversational experience—may be more relevant for children at older ages, consistent with effects reported in previous studies (Romeo et al., 2018b). This would explain why we found that the amount of adult input is more strongly predictive of myelin in the AF and SLF at 30 months, while previous research shows that conversational turns are more relevant at 4–6 years of age (Romeo et al., 2018b). Another difference between our findings and prior work is that our results showed effects in both right and left hemispheres for the AF, and only in the right hemisphere for the SLF. This is consistent with work suggesting that the brain is less lateralized for language early in development, with left areas gaining more specialization for language as children gain language skills.
Our results also diverge in that we found SES effects in our sample, with children from more highly educated mothers being exposed to more adult words and showing higher myelin concentrations in the right AF and right SLF. It is possible that early in development, children are more sensitive to the effects of less maternal education than later at 4–6 years of age (although note that Romeo et al., 2018b only measured the left AF and SLF and used a different SES measure). Moreover, it could be that SES effects are more pronounced for the amount of adult speech compared with conversational turns; in fact, we found a main effect of SES on the number of adult words but not on conversational turns in Analysis 1. It is particularly interesting that across our analyses, we found consistent differences with maternal education (especially when related to the amount of adult speech) in a population that is relatively homogeneous. This suggests that SES might impact development even in less diverse contexts. That said, SES is a highly complex construct that should be interpreted carefully, even in a homogeneous population. This is because SES effects are likely to vary across populations and countries, and are based on how SES is captured (i.e., based on income, parental education, or composite scores derived from those measures). Future work will be needed to address these open questions.
We also note that the current study is one piece of a larger longitudinal study that also includes behavioral tasks measuring language skill, attention, and memory. Thus, further analyses using additional measures and at later time points will help disentangle how the findings for 6-month-old and the 30-month-old children are related, and how they might be associated with children's language abilities beyond input and output quantity. Ultimately, we hope to understand how language skill and brain myelination codevelop within individuals and what role language input and SES might play in that path. We also hope to clarify why our SES effects were largely focused in the right hemisphere and how these effects are modulated over development. It is possible that the role of SES changes throughout development as other individual differences and sociocultural factors play out.
Our exploratory analyses looked at possible relationships between language experience and myelin concentrations in a broad range of brain regions. After familywise correction, results showed relationships between the amount of adult input and myelin concentrations in the left and right frontal lobe, the left internal capsule and the right corona radiata, and the body and genu of the corpus callosum. These results largely followed the same pattern as our confirmatory analyses: more adult input was associated with more myelin in the 30-month-old group, and this relationship was reversed in the 6-month-old group. These relationships were more pronounced in 30-month-old children from higher-SES families, with some negative relationships found with 30-month-old children from lower-SES families. It is not clear why we found inverse effects in some brain regions at 30 months of age.
Interestingly, structural brain development in these brain regions has been linked to aspects of language development in prior work. The genu is an early developing part of the corpus callosum. Myelin concentration in this tract has been related to receptive language in early development (O'Muircheartaigh et al., 2014). Similarly, both the genu and body of the corpus callosum have been linked to early cognitive scores measured using the Mullen Scales of Early Learning (Deoni et al., 2016). The structural development of the left internal capsule has also been related to language measures in prior work. In particular, white matter in the left internal capsule (measured using DTI) is related to reading scores in 7- to 10-year-old children (Fletcher et al., 1992; Qiu et al., 2008). Similarly, less white matter in the right corona radiata has been linked to reading dysfluencies in 11-year-old children (Lebel et al., 2019). In this context, it is interesting to note that our study is one of the first to look at myelin and language development before the onset of formal reading; thus, our data suggest a role for these fiber tracts in processing language in prereading children. It would be interesting in this context to expand the number of regions we examined in future work to look at additional brain areas associated with language such as the uncinate fasciculus (Papagno, 2011), the inferior longitudinal fasciculus (Del Tufo et al., 2019), the inferior fronto-occipital fasciculus (Almairac et al., 2015), or the frontal aslant tract (Dick et al., 2019).
Finally, our home language input measures relied on the LENA automated estimates, which are a big advantage over highly time-consuming manual annotations of daylong recordings. However, a limitation of this method (and of this study) is that it is difficult to know precisely what the LENA estimates for adult words are capturing since they might contain both child-directed and overheard speech. Moreover, there are some open questions about the reliability of the LENA estimates, particularly for conversational turns that were found to have low-to-moderate correlations (CTC, r = 0.36) when compared with human transcriptions, as opposed to higher correlation coefficients for adult words (AWC, r = 0.79) and child vocalizations (CVC, r = 0.77; Cristia et al., 2020). Manual transcription techniques are not a feasible option when used over large amounts of daylong recording data. This is why it is important that future research tools similar to the LENA are developed with a focus on even more reliable estimates that can make a distinction between child-directed speech and overheard speech. This is especially important because a recent meta-analysis comparing SES groups in terms of child-directed word counts and overall word counts found a larger effect for child-directed estimates and lower effects for overall estimates, which would be equivalent to the AWC estimate used in our study. The results of this meta-analysis mostly relied on manually annotated naturalistic data rather than on automatic estimates of home input data, such as those derived by the LENA. However, they suggest that SES may influence child-directed speech quantities even more than overall amount of speech (Dailey and Bergelson, 2022). Finally, the qualitative proprieties of children's language exposure also play an important role in language development. Conversational turns capture some of those qualitative aspects; however, they seem to be the less reliable estimates from LENA (Cristia et al., 2020), and they are found in low amounts at early ages since they are highly dependent on children's own productions (a conversational turn necessarily needs a child vocalization). Future studies should look at other qualitative aspects of speech to children—such as word length, vocabulary richness, or word repetition—in combination with myelin. It is possible that some of those qualitative aspects of children's language input are more meaningful at very early ages when children produce a limited set of words and sentences.
In summary, our findings suggest that early in development, the amount of adult speech that children hear is crucial for the myelination of language-related brain regions. Moreover, at these early ages, myelin quantity seems to be sensitive to SES differences based on maternal education in a quite homogeneous population. This study is the first to examine the association among SES, language development, and myelination in the first 2.5 years of life. Therefore, it is essential to conduct further research across more diverse populations to gain a better understanding of the impact of SES on early childhood development.
Footnotes
This work was supported by National Institutes of Health (NIH) Grant R01-HD-083287 to J.P.S., as well as by NIH Grants R01-MH-111578 and P50-HD-103556 to V.M. We thank all of the families and children who participated in this study as well as all the DDLab members, staff, students, and volunteers who helped with this project.
The authors declare no competing financial interests.
- Correspondence should be addressed to John P. Spencer at j.spencer{at}uea.ac.uk
