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. Author manuscript; available in PMC: 2023 Dec 20.
Published in final edited form as: Transl Issues Psychol Sci. 2023 Aug 24;9(4):364–379. doi: 10.1037/tps0000362

Cortical Thickness Is Related to Variability in Heritage Bilingual Language Proficiency

My V H Nguyen 1, Kelly A Vaughn 2, Hannah Claussenius-Kalman 1, Pilar Archila-Suerte 1, Arturo E Hernandez 1
PMCID: PMC10732586  NIHMSID: NIHMS1951422  PMID: 38125719

Abstract

Research suggests that bilingual experience is associated with gray matter changes, such that initial language gains are associated with expansion and language expertise is associated with renormalization. Previous studies on language proficiency development primarily focused on between-subjects, quasiexperimental comparisons of monolinguals and bilinguals. This study proposes a new paradigm to examine language expertise and cortical thickness within heritage bilinguals (n = 215), as well as between bilinguals and monolinguals (n = 145), using data combined from eight previous magnetic resonance imaging studies. In general, results highlight variability within bilinguals, finding relationships between cortical thickness and English proficiency that are relatively consistent within monolinguals, but inconsistent within bilinguals. In all participants, higher levels of proficiency in English—monolinguals’ only language and bilinguals’ second but stronger language—were negatively related to cortical thickness. In bilinguals, higher proficiency in the weaker, albeit first learned, language was positively related to cortical thickness. Moreover, there was an interaction between language group and English proficiency in predicting cortical thickness, such that the relationship between proficiency and thickness was stronger in monolinguals than in bilinguals. Findings also demonstrate that the regions associated with language expertise differ between bilinguals and monolinguals. Future directions for cognitive-developmental neuroscience research in bilinguals are suggested, particularly the longitudinal examination of cortical changes in relation to bilingual experiences.

Keywords: cortical thickness, language, language proficiency, gray matter, bilingualism

Historically, research has tried to understand how language networks in the brain develop in monolinguals (i.e., people who know a single language), but increasingly, research is focusing on language network development in bilinguals (i.e., people who know two languages). Bilingualism is associated with plasticity in the human brain, especially gray matter changes (Birdsong, 2018; Mechelli et al., 2004). The dynamic restructuring model (DRM) describes the pattern of cortical, subcortical, and white matter changes that occur with increasing second language (L2) exposure (Pliatsikas, 2020). This model describes cortical gray matter growth in the early stages of L2 exposure; with increased language experience, cortical renormalization occurs.

It must be noted that while the DRM (Pliatsikas, 2020) draws evidence from a variety of studies with simultaneous or sequential speakers with varying exposure backgrounds and age of acquisition (AoA), evidence is usually not from populations of bilingual heritage learners who are immersed in a societal L2 environment. For instance, in the United States, 13.2% of the population 5 years and over reported speaking Spanish in addition to English (United States Census Bureau, 2020). Individuals who grow up in Spanish-speaking homes and who have some degree of proficiency in Spanish are considered Spanish heritage speakers (Valdés, 2014), and they may experience a complex language environment that could contribute to changes in the gray matter. A phenomenon seen in heritage speakers is that they usually exhibit a more dominant L2—the societal language, which is English in the United States—and a weaker first or native language (L1)—the home language, for example, Spanish (Veltman, 2014). It is unclear how this experience would shape the brain of a heritage language speaker, and models such as DRM currently do not make predictions about this population or about the relationship between the proficiency of each language and cortical change. Therefore, the current article explores language proficiency and cortical thickness in a less studied population: Spanish heritage speakers in the United States.

Proficiency and Cortical Thickness in Monolinguals

When viewing language learning from a skill-learning perspective, language learning in monolinguals throughout development may be consistent with the expansion and renormalization model (ERM; Wenger et al., 2017). The ERM states that early stages of skill learning are associated with an increase in gray matter (i.e., expansion), after which additional improvements in task performance are associated with a decrease in gray matter (i.e., renormalization). Perhaps monolinguals follow this trajectory; and by adulthood, greater language proficiency should be associated with thinner cortices, signifying the more automatic nature of the process in comparison to children first learning a language. For instance, the sensorimotor hypothesis considers language learning as a procedural learning-based process that relies on subcortical areas early in life and executive function-related areas such as the frontal cortex later in development (Hernandez & Li, 2007). However, this model lacks predictions about the link between cortical thickness and language proficiency in adulthood. In addition, prior research has not established evidence for the renormalization of the cortex following language learning, so it is unclear if we would observe a proficiency effect in the monolinguals’ cortex. Proficiency, measured by language comprehension and production, could serve as a proxy for language skill or performance in language tasks. Indeed, adult monolinguals showed variation in language proficiency and processing of their native language (Pakulak & Neville, 2010). Hence, the current article explored the relationship between proficiency and cortical thickness in a sample of monolinguals.

Proficiency and Cortical Thickness in Heritage Bilinguals

Beyond the typical development of language found in monolinguals, bilinguals exhibit experience-dependent changes in the brain in relation to language learning. Findings in adults suggest a relationship between L2 proficiency and cortical changes (Li et al., 2014). The initial increase in gray matter metrics is positively related to an increase in language proficiency (Grogan et al., 2009; Mechelli et al., 2004), and this is particularly evident in short-term L2 training studies. Engaging in training (ranging from 3 to 10 months of a foreign language) was associated with an increase in gray matter volume and cortical thickness in frontal and temporal regions associated with language control, although they were primarily conducted with native speakers of an L1 learning an L2 (Hosoda et al., 2013; Mårtensson et al., 2012; Stein et al., 2012; Wong et al., 2008). Regardless, findings appeared to show a role of proficiency independent from the length of exposure (Mechelli et al., 2004), and the effect of proficiency could be due to changes in the engagement of the language network and the attentional control resources in bilinguals (Li et al., 2014).

In research on L2 learning, this is usually both the weaker and the later-learned language. However, a special situation arises when L2 is learned at an early age and becomes the more dominant language (Birdsong, 2014). In extreme cases such as adoptees, individuals sometimes appear to lose the ability to recognize their native language, although they are more sensitive to the lost L1 than those without any knowledge of this language (Choi et al., 2017; Hyltenstam et al., 2009; Norrman & Bylund, 2016; Park, 2015; Rajagopal et al., 2013). In the context of the United States, many bilinguals become more dominant in English, the L2, while retaining some proficiency in their L1 (Veltman, 2014). While previous research has examined this relationship in native speakers with a weaker L2, none has examined the pattern of language dominance that is the most prominent in the United States—L2 dominance. Even though this pattern of dominance exists outside of the United States and had been somewhat incorporated in L3 language research with findings showing that receiving early exposure to multiple languages facilitates bilinguals’ learning of L3 (Hofer & Jessner, 2019; Huang, 2018; Kopečková, 2018), the interplay and role of these languages were not explored in the brain. Indeed, it is important to understand whether this different pattern of language dominance leads to differences in cortical structure in relation to each of the known languages. The setting of the current study thus allows us to consider a unique pattern of language proficiency and explore how brain structure reflects language skills for a more recently learned, stronger L2 compared to a lifelong, weaker L1 within the same individuals and can help extend understanding about the complexity of the bilingual brain.

The Present Study

The current study examined the association between cortical thickness and language proficiency among bilinguals and monolinguals. Given the lack of prior models that can sufficiently explain this relationship in both monolinguals and heritage language speakers, we base some of our hypotheses on the ERM (Wenger et al., 2017) and the DRM (Pliatsikas, 2020), which state that improvements and exposures are associated with increases in gray matter for those in early stages of language learning but with decreases in gray matter for those with extensive language experience.

According to the DRM, early phases of exposure to a novel language are associated with synaptogenesis, and therefore cortical expansion. Later on, increased immersion and experience are associated with consolidation, in which the brain undergoes pruning, leading to a decrease in cortical thickness (Pliatsikas, 2020). While these models do not directly apply to language proficiency, we expect there would be a negative correlation between cortical thickness and English proficiency for both monolingual young adults—English being their native and only language—and heritage Spanish speakers—English being their second but more dominant language. In terms of the relationship between cortical thickness and Spanish proficiency in our sample of bilinguals—Spanish being the first learned but less dominant language, it is unclear from prior models whether a first-learn but weaker language would conform to the expansion or the normalization phase of language exposure in heritage learners. However, as the DRM (Pliatsikas, 2020) characterized the different stages of neural changes based on language exposure and experience such as usage, we cautiously hypothesize that Spanish may be negatively correlated with thickness, as this language might also be used less by heritage speakers. This may lend support to the dynamic developmental bilingualism framework, which characterizes a complex interaction of individual language expertise and environment (Claussenius-Kalman et al., 2021).

Cortical thickness was chosen as the measure of gray matter because of its experience-dependent development. Gray matter volume is a combination of cortical thickness and gyrification; research suggests that cortical thickness develops based on experience, while gyrification is established prenatally (Williams et al., 2018; but see also Del Maschio et al.’s work [2019] on bilingualism and gyrification). Therefore, the current study’s investigation of language proficiency focused on cortical thickness as an experience-dependent measure of gray matter volume. Notably, prior research suggests that socioeconomic status (SES) is related to cortical thickness at language relation regions (Piccolo et al., 2016) and differs between monolinguals and bilinguals (Brito & Noble, 2018). SES, proxied by measures such as family income or parent education, reflects the available resources to provide an enriched environment for the child, or parenting style and quality of parent–child development (Brooks-Gunn & Markman, 2005; Duncan & Magnuson, 2012; Johnson et al., 2016). Thus, SES may indirectly affect brain development and can be reflected in cortical thickness—an experience-dependent measure. For this reason, it is important to include SES as a control variable as it may account for differences found between groups, and this can add to the translational value of findings. Age of the second language acquisition (L2 AoA) is related to cortical thickness within bilinguals (Claussenius-Kalman et al., 2020). Moreover, the duration of language exposure and typical brain development may also affect cortical thickness in both monolinguals and bilinguals (DeLuca et al., 2020). Thus, we included SES, AoA, and chronological age as covariates in the current study. By controlling for these factors, we were able to observe the effects of language proficiency in L1 and L2 in a group of heritage Spanish–English bilinguals.

Method

Participants

The data for this study were combined from eight magnetic resonance imaging (MRI) studies with an original sample size of 386 participants. Some participants were removed because of the following issues: brain scan reconstruction issues (six scans), technical MRI scanning issues (five scans), brain trauma (one scan), and incomplete information for the covariates included in the general linear models (i.e., age of English acquisition, English language proficiency, Spanish language proficiency, SES). The remaining sample consisted of 215 heritage Spanish–English bilinguals and 145 participants English monolinguals.

Participants were recruited through the University of Houston SONA system—which allows psychology students to earn class credit for participating in research studies, flyers placed around the campus, and announcements made in psychology classes. All participants gave informed consent in compliance with the University of Houston Institutional Review Board that approved the protocol for this study. Demographic information is reported in Table 1. Bilingual participants on average had higher proficiency in English than in Spanish and lower English proficiency than monolinguals. Participants were between 18 and 45 years of age. Bilinguals and monolinguals did not differ in age (see the online supplemental materials for additional figures). Only 138 of the 145 monolinguals reported their age. Bilinguals and monolinguals did not differ in gender.

Table 1.

Summary of demographic characteristics

Bilinguals
(n = 215)		 Monolinguals
(n = 145)		 Comparison Estimates
M SD M SD
English Proficiency 73% 9% 81% 7% t(348.19) = −9.46, p < .001
Spanish Proficiency 70% 14% a t(214) = 3.13, p = .002
English AoA 7.44 5.30
Age (range = 18 – 45) 23.17 4.58 23.31 4.94 t(275.95) = −0.27, p = .79
SES 2.65 1.44 4.32 .94 t(268.73) = −11.67, p < .001
n n
Gender χ2(2) = 4.34, p = .11
 Female 138 95
 Male 62 32
 Did not report 15 18
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Note.

a

Comparison between Spanish and English proficiency within bilinguals

SES

In terms of SES (measured as parental education on a scale of 1–6; 1 = some elementary school or less than elementary school, 2 = some high school or less than high school, 3 = high school graduate, 4 = some college, 5 = college graduate, 6 = advanced degree), monolinguals came from higher SES backgrounds than bilinguals. Only 177 bilinguals and 100 monolinguals reported SES.1 SES was significantly correlated with Spanish proficiency (r = .20, p = .006) but not with English proficiency in bilinguals (r = .13, p = .08), and was uncorrelated with English proficiency for monolinguals (r = .05, p = .63).

Language Proficiency Measures

Because these data were combined from eight different studies, language proficiency was assessed using the Woodcock–Muñoz Language Survey-Revised picture vocabulary subtest along with either the passage comprehension subtest or listening comprehension subtest (Woodcock et al., 2010) and/or the Boston Naming Test (Goodglass et al., 1983). Performance on these measures was calculated as a proportion (total correct/total possible), and when participants completed more than one of these measures, their percentages for each measure were averaged to create a total measure of language proficiency. All participants completed English measures, and bilingual participants also completed Spanish measures (36 and 328 participants completed the Boston Naming Test and Woodcock–Muñoz Language Survey-Revised, respectively; for more details about the measures completed, see Claussenius-Kalman et al., 2020).

MRI Parameters

Anatomical MRI scans were collected at Baylor College of Medicine’s Core for Advanced Magnetic Resonance Imaging (formerly the Human Neuroimaging Laboratory). Anatomical images were T1-weighted scans collected on a 3T Magnetom Trio scanner using a magnetization-prepared rapid gradient-echo sequence with the following parameters2: Orientation = transversal, field of view = 245 mm, TR = 1,200 ms, TE = 2.66 ms, flip angle = 12°, voxel size = 0.48 mm × 0.48 mm × 1.0 mm, and images were reconstructed from 192 slices. One scan was collected from each participant. Collecting this single scan took 4 min and 30 s.

Statistics

All analyses were conducted using FreeSurfer image analysis suite (Version 5.3.0, https://surfer.nmr.mgh.harvard.edu/). This software was used to process each participant’s T1-weighted MRI scan using surface-based morphometry in order to create measures of cortical thickness for each vertex along the surface of the brain (Fischl & Dale, 2000). Scans were reconstructed using the automated preprocessing steps included in the recon-all command in FreeSurfer. These steps include motion correction, intensity normalization, Talairach transformation, skull stripping, volumetric registration, volumetric labeling, white matter segmentation, smoothing (10 FWHM), and cortical parcellation. After these automated reconstruction procedures were conducted, each brain was manually checked and, when necessary, the pial and white matter surfaces were edited to ensure that the program correctly distinguished between gray and white matter. Once each brain was deemed to be correctly segmented, the resulting images were normalized to an average template (fsaverage, based on Talairach space) using the qcache command and were analyzed using the Query, Design, Estimate, Contrast software, which conducts general linear models on surface-based measures such as cortical thickness, surface area, and volume (https://surfer.nmr.mgh.harvard.edu/).

Four two-tailed general linear models were conducted in each hemisphere to analyze the data for the current study, each with cortical thickness as the outcome variable. The final sample included in the analyses included 177 bilinguals and 95 monolinguals. The first model used English language proficiency as the predictor with SES and age as the covariates for monolinguals’ MRIs. The second model used Spanish proficiency as a predictor with English proficiency, English AoA, age, and SES as covariates for bilinguals’ MRIs. The third model used English proficiency as a predictor with Spanish proficiency, English AoA, age, and SES as covariates for bilinguals’ MRIs. The last model used English proficiency and language group (monolingual vs. bilinguals) as the predictors with age and SES as the covariates. These models allowed for an understanding of the independent relationships between language proficiency and cortical thickness in the only language (English for monolinguals), the first learned but weaker language (Spanish for bilinguals), and the second learned but more dominant language (English for bilinguals).3 A Monte Carlo simulation was applied to each general linear model to correct for multiple comparisons when determining significant clusters. Beta values and variance were extracted from the significant clusters using the mri_segstats command on the F.mgh and gammavar.mgh files included in the Query, Design, Estimate, Contrast output.

Results

Language Proficiency and Cortical Thickness in Monolinguals

For monolinguals, after controlling for SES and age, English proficiency was negatively correlated with cortical thickness in 12 different clusters in the left and right frontal, parietal, and temporal lobes (Figure 1; Table 2).

Figure 1.

Figure 1

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Cortical Thickness Correlations With English Proficiency Controlling for SES and Age in English Monolinguals

Note. Blue (left bar) indicates a negative correlation. This figure only has negative correlation, with brighter shade meaning thinner cortex. The left and right images are lateral views of the relationships in the left and right hemispheres, respectively. All results presented have been Monte Carlo-corrected (n = 95). SES = socioeconomic status.

Table 2.

Cortical Thickness Correlation with Language Proficiency in Monolinguals (n = 95)

Analyses Annotation Beta CW p Var SD F VtxMax Size (mm2) TalX TalY TalZ NVtxs BA
Left Hemisphere superior parietal −5.165 0.038 0.172 0.022 11.534 43071 396.31 −14.9 −48.6 60.2 959 7
superior frontal −5.147 0.000 0.175 0.079 11.824 58354 6604.75 −7.6 45.9 38.4 12993 8
inferior parietal −5.096 0.000 0.170 0.072 10.801 78289 1608.96 −42.6 −56.9 41.8 3810 39
lateral orbitofrontal −4.068 0.000 0.172 0.028 10.363 110293 839.91 −18 46 −13.9 1138 11
inferior parietal −3.943 0.042 0.217 0.050 11.026 142290 387.32 −34.9 −76.2 32.3 620 39
Right Hemisphere superior frontal −5.734 0.000 0.185 0.051 3.697 123029 2605.69 8.1 40.2 33.5 4445 9
precentral −4.442 0.001 0.184 0.059 2.833 137887 688.45 18.3 −11 57.1 1524 6
supramarginal −4.388 0.001 0.254 0.058 2.953 159207 735.21 61.1 −34.9 30.9 1672 40
posterior cingulate −4.285 0.040 0.115 0.019 2.528 99371 400.29 8.3 0.2 37.9 974 32
inferior parietal −3.87 0.000 0.253 0.089 1.706 74409 864.38 40.4 −65.1 38.4 1729 39
superior parietal −3.609 0.009 0.159 0.025 1.848 126215 522.68 17.5 −66.2 44.5 997 7
inferior temporal −3.441 0.025 0.229 0.031 1.699 97909 438.74 53.4 −16.7 −27.2 689 20
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Notes. CW = cluster-wise, VtxMax = vertex max, Tal = Talairach, NVtxs = number of vertices, BA = Brodmann area

Language Proficiency and Cortical Thickness in Heritage Bilinguals

For bilinguals, Spanish proficiency was positively correlated with cortical thickness in nine clusters in the left and right hemispheres in the frontal and parietal regions (Figure 2; Table 3); and negatively correlated with cortical thickness in the pericalcarine. For the same participants, English proficiency was negatively correlated with cortical thickness in two clusters in the left and right hemispheres in the frontal regions (Figure 3; Table 3).

Figure 2.

Figure 2

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Cortical Thickness Correlations With Spanish Proficiency Controlling for English Proficiency, English AoA, Age, and SES in Bilinguals

Note. Red/yellow (right bar) indicates a negative correlation. This figure only has positive correlation, with brighter shade meaning thicker cortex. The left and right images are lateral views of the relationships in the left and right hemispheres, respectively. All results presented have been Monte Carlo-corrected (n = 177). AoA = age of acquisition; SES = socioeconomic status.

Table 3.

Cortical Thickness Correlation with Language Proficiency in Bilinguals (n = 177)

Annotation Beta CW p Var SD F VtxMax Size (mm2) TalX TalY TalZ NVtxs BA
Spanish Proficiency
Left Hemisphere supramarginal 4.725 0.004 0.081 0.017 3.607 85651 566.46 −59.3 −47.4 25.3 1285 39
lingual −4.538 0.028 0.029 0.007 3.267 91159 419.19 −9.2 −90.3 0 517 18
superior frontal 4.308 0.000 0.064 0.013 2.715 152787 1720.34 −6.4 11.9 57.4 3109 6
precuneus 3.391 0.002 0.065 0.013 1.451 71488 644.43 −7.3 −62.1 51.4 1505 7
precentral 2.933 0.006 0.058 0.013 1.165 76077 535.76 −44.9 −1 41.1 941 6
Right Hemisphere superior frontal 5.134 0.000 0.015 0.026 3.338 63820 1569.49 8.8 20.9 55.3 2988 6
superior parietal 5.097 0.001 0.010 0.028 3.492 78204 690.78 13.5 −59.6 58.7 1444 7
supramarginal 5.057 0.024 0.014 0.046 3.865 13006 444.7 56.4 −35.6 36.8 877 40
caudal middle frontal 3.675 0.000 0.011 0.025 1.813 152445 1139.43 38.6 14.4 48.3 1987 8
English Proficiency
Left Hemisphere superior frontal −3.667 0.003 0.0594 0.0073 9.7676 83322 586.11 −8.3 44.7 20.3 943 9
Right Hemisphere superior frontal −3.55 0.000 0.089 0.015 9.549 63940 901.37 8.2 17.1 53.9 1740 6
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Notes. CW = cluster-wise, VtxMax = vertex max, Tal = Talairach, NVtxs = number of vertices, BA = Brodmann area

Figure 3.

Figure 3

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Cortical Thickness Correlations With English Proficiency Controlling for Spanish Proficiency, English AoA, Age, and SES in Bilinguals

Note. Blue (left bar) indicates a negative correlation. This figure only has negative correlation, with brighter shade meaning thinner cortex. The left and right images are lateral views of the relationships in the left and right hemispheres, respectively. All results presented have been Monte Carlo-corrected. AoA = age of acquisition; SES = socioeconomic status.

Within- and Between-Subject Comparisons

While the within-subject analyses allow for the characterization of patterns within each language group, and the relationship between English proficiency and cortical thickness appeared consistent across monolinguals and bilinguals, it is unclear whether this pattern is identical across language groups. Thus, the last model explored the main and interaction effects of English proficiency and language group on cortical thickness, controlling for SES and age. For all participants, English proficiency was negatively correlated with cortical thickness in six left frontal and parietal clusters and nine right frontal, temporal, and parietal clusters (Table 4; Figure 4). There was no main effect of language group on cortical thickness. However, there was a language group by English proficiency interaction with cortical thickness in two frontal clusters in the left hemisphere (Table 4; Figures 5 and 6). Specifically, within monolinguals, English proficiency negatively correlated with thickness; however, in bilinguals, this relationship does not appear to be evident.

Table 4.

Between-subjects Comparison of English Proficiency and Cortical Thickness (n = 272)

Analyses Annotation Beta p-value Var SD F VtxMax Size (mm2) TalX TalY TalZ NVtxs BA
Main Effect of English Proficiency
Left Hemisphere precentral −7.659 0.000 0.227 0.093 12.593 65461 6961.3 −23.9 −11.8 55.2 13939 6
inferior parietal −6.622 0.000 0.254 0.084 12.178 78289 2710.15 −42.6 −56.9 41.8 5810 39
pars opercularis −6.213 0.005 0.150 0.033 13.056 4896 555.26 −50.2 11.3 3.9 1052 44
rostral middle frontal −4.806 0.000 0.178 0.044 10.365 99317 1927.54 −32.7 47.7 6.1 2809 10
superior parietal −4.47 0.000 0.182 0.052 10.034 133627 1295.32 −15.5 −48.3 60.1 3052 7
Right Hemisphere superior frontal −7.272 0.000 0.234 0.084 11.616 149401 5894.45 8.3 39.3 32.1 11030 8
inferior parietal −4.696 0.000 0.288 0.086 11.118 29430 2348.44 52 −47.3 39.7 4927 39
rostral middle frontal −4.623 0.029 0.194 0.054 9.863 109429 428.86 41.7 24.5 29.7 768 9
precuneus −4.601 0.000 0.210 0.050 10.360 58739 1220.11 7.1 −66.2 47.6 2248 7
superior temporal −3.921 0.007 0.253 0.067 10.022 89001 544.97 65 −29.2 9.6 1276 22
pars triangularis −3.771 0.032 0.183 0.030 8.969 145594 418.73 52.8 26.1 3.7 725 45
caudal middle frontal −3.617 0.014 0.198 0.044 9.506 48651 485.12 34.5 3.8 44.7 859 6
rostral middle frontal −3.611 0.038 0.141 0.017 9.025 77249 403.32 38.1 50.6 4.3 580 10
inferior temporal −3.293 0.013 0.282 0.027 9.167 8040 492.35 53.3 −28.7 −16.7 785 20
Interaction between Language Group and English Proficiency
Left Hemisphere lateral orbitofrontal 4.457 0.045 0.208 0.035 11.675 83977 382.59 −21.2 48.5 −13.2 486 11
caudal middle frontal 4.304 0.000 0.211 0.115 9.180 80338 1887.91 −35.6 8.7 32.2 3822 8
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Notes. VtxMax = vertex max, Tal = Talairach, NVtxs = number of vertices, BA = Brodmann area

Figure 4.

Figure 4

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Cortical Thickness Correlations With English Proficiency in All Participants, Controlling for SES and Age

Note. Blue (left bar) indicates a negative correlation. This figure only has negative correlation, with brighter shade meaning thinner cortex. The left and right images are lateral views of the relationships in the left and right hemispheres, respectively. All results presented have been Monte Carlo-corrected (n = 272). SES = socioeconomic status.

Figure 5.

Figure 5

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English Proficiency Interacts With Language Group to Predict Left-Hemispheric Cortical Thickness in All Participants, Controlling for SES and Age

Note. Red/yellow (right bar) indicates a stronger relationship between English proficiency and cortical thickness in monolinguals compared to bilinguals. This figure only have positive correlation, with brighter shade meaning stronger relationship. All results presented have been Monte Carlo-corrected. SES = socioeconomic status.

Figure 6.

Figure 6

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English Proficiency and Language Group Interaction Plots in Predicting Left-Hemispheric Cortical Thickness in All Participants, Controlling for SES and Age at the Caudal Middle Frontal Gyrus

Note. Proficiency was calculated as a percentage, with range from 0 to 1. SES = socioeconomic status.

Discussion

The current study investigated the relationship between language proficiency and cortical thickness—an experience-dependent measure of gray matter volume. We expected a negative correlation between cortical thickness and English proficiency in both bilinguals and monolinguals. We cautiously expected a negative relationship between cortical thickness and Spanish proficiency in heritage bilinguals as it is the first but weaker language, and there is a lack of research characterizing such a relationship. The current study examined a less studied group—heritage speakers in an L2 societal language environment—in exploring the relationship between language proficiency and cortical thickness, which has not been studied extensively in the literature on bilingualism in comparison to other metrics such as the AoA.

English Proficiency and Cortical Thickness

In both the within-group and between-group analyses, English proficiency was negatively correlated with cortical thickness in frontal and parietal regions for both monolinguals and heritage bilinguals, with English being the only language for monolinguals and the second learned but stronger language for bilinguals. This finding fits to a certain extent with the ERM (Wenger et al., 2017) and can help extend the DRM (Pliatsikas, 2020); particularly, as language expertise increases, the brain regions that are associated with language for a particular person first expand to accommodate the new skill and then renormalize as the brain becomes more automatized in the process of using this skill—in this case, using English as an adult.

English proficiency had a relatively consistent negative relationship with the cortical thickness at Brodmann areas 9 [BA9] in the left and right hemispheres, which correspond to the dorsolateral prefrontal cortex. This region has been implicated in speech and language processing due to its connectivity with the perisylvian language network (Hertrich et al., 2021). In addition, English proficiency is negatively correlated with cortical thickness in BA39 and BA40. These areas are considered to be part of Wernicke’s area, a region that plays a large role in both the production and comprehension of language (Binder, 2015). Similarly, English proficiency is negatively correlated with cortical thickness at BA6—the premotor cortex—and BA7—the visuomotor coordination—in both hemispheres, which are relevant for the production and comprehension of spoken and written language (Ardila et al., 2017; Friederici & Gierhan, 2013). Therefore, our findings provide some evidence showing that language proficiency is negatively related to the cortical thickness of several language-related regions both in monolingual and bilingual young adults.

In the within-group analyses, monolinguals also showed a negative relationship between English proficiency and cortical thickness after controlling for SES in the orbitofrontal area [BA11]. This region was shown to have a connection with language-related regions in the inferior frontal gyrus, the angular gyri, and the supramarginal gyri and might be linked to the language system (Du et al., 2020). In the between-group direct comparison, there was a language group by proficiency interaction primarily in left frontal cortical thickness, such that the relationship between English language proficiency and cortical thickness is stronger in monolinguals than in bilinguals in left BA8 and BA11 (orbitofrontal). It seems that even though monolinguals and bilinguals in our sample have command of the same language, they may utilize slightly different parts of the brain to process it.

Spanish Proficiency and Cortical Thickness

An interesting result emerged for heritage bilinguals. Unlike the findings with the first language (English) in monolinguals, the proficiency in the first language (Spanish) of our sample of bilinguals was positively correlated with cortical thickness in frontal and parietal regions. This follows to a certain extent the expansion phase of the ERM (Wenger et al., 2017), in which an increase in proficiency is associated with the thickening of the cortex. However, current models such as the DRM (Pliatsikas, 2020) cannot account for these results as it pertains to a first learned but weaker language.

In heritage bilinguals, Spanish proficiency was bilaterally positively correlated with BA7 and BA8 cortical thickness. In addition, a positive relationship was also found at the left BA39 (Wernicke’s) and the right BA6 (premotor). These are regions largely related to language production. For heritage speakers, a common experience is receptive bilingualism, where they are able to understand both languages but usually speak the dominant language (in this case, English) in all situations, leading to a receptive–expressive gap in L1 vocabulary scores (Giguere & Hoff, 2020), which was part of our measures of proficiency. It is possible that our findings of the positive association between Spanish proficiency and cortical thickness in language production-related areas reflect this. Finally, cortical thickness at BA18 (secondary visual cortex) is negatively associated with Spanish proficiency. This region is recruited when processing written language, although the relationship here is unclear. In general, heritage bilinguals’ process of language development may differ in meaningful ways from monolinguals. Specifically, heritage bilinguals first learn the home language natively, but then almost completely switch to learning and using the societal language as they enter the school system, unlike monolinguals who had a more continuous process of language learning. Future research should examine these findings in populations of bilinguals with diverse proficiency levels and language experience to further understand the relationship between language proficiency and cortical thickness in bilingual young adults.

The current study focused on language proficiency as a predictor of cortical thickness; however, it is unclear how and when these changes occur during development. Particularly, previous research conducted in our lab has identified relationships between second/majority language proficiency and cortical thickness in frontal and temporal regions at ages 6–10 (Archila-Suerte et al., 2018) and at ages 9–10 (Vaughn et al., 2021); however, we identified this relationship in frontal and parietal regions in the current sample of adults. Prior research suggests the role of the ventral stream—connecting frontal and temporal regions—in mapping sound to meaning and the dorsal stream—connecting frontal and parietal regions—in mapping sounds to articulation (Hickok & Poeppel, 2004). It is possible that when children are learning both languages, they recruit the frontal and temporal regions for phonetic and semantic learning, while adults have a more advanced language representation and recruit the frontal and parietal regions to use language appropriately. Moreover, we also found frontal and parietal regions associated with English usage relative to the other language in bilingual children (Vaughn et al., 2021), which further suggests the role of these regions in language and cognitive control processes. Perhaps for adults, measures of language skills are partially capturing their ability to effectively control and use language, which explains its association with frontal and parietal regions instead of temporal regions.

The current findings somewhat align with the neuroemergentism perspective, which posits that previous experiences and the developmental stage of the brain influence the reorganization of interaction among brain regions associated with skill learning (Hernandez et al., 2018, 2019). Taken together with prior findings in various age groups (Archila-Suerte et al., 2018; Vaughn et al., 2021), the current research provides support for the dynamic developmental bilingualism framework, which suggests that language outcomes emerge through the interaction of individual expertise (i.e., aptitude for language learning) and ecosystem (i.e., language and learning environment; Claussenius-Kalman et al., 2021). However, there is a gap in the literature concerning the mechanism of change in cortical thickness in relation to language skill development both in adolescence and before schooling. While there is cross-sectional research on neural development of bilinguals (Pliatsikas et al., 2020), there is a lack of research and theory characterizing how bilinguals at differing stages of development may recruit different cortical regions to process multiple languages. The current research helped extend the DRM framework to consider proficiency as a measure of language experience, as well as imply the role of language dominance concerning experience-dependent changes in the bilingual brain. Future research should examine these relationships longitudinally in order to develop a complete picture of the language-related changes in cortical thickness.

Limitations and Future Directions

The current study, while contributing to further understanding of the bilingual brain, may lack generalizability due to the unique nature of the sample. Our findings are limited by the lack of a comparison group of bilingual participants who are dominant in their L1 (i.e., native English speakers learning Spanish as L2). In addition, those who had missing SES data differed from those who did not, and while the difference is small and might be statistically significant due to the large sample size, it is important for future research to explore the role of SES and its interaction with language and cortical thickness in the bilingual brain. Moreover, the study is limited by the field’s understanding of cortical thickness. While researchers can agree that cortical thickness is an experience-dependent measure of gray matter, it is unclear whether increases and decreases in cortical thickness are a result of neurogenesis, synaptic changes, or glial changes (Pliatsikas, 2020; Wenger et al., 2017). Technological advances are needed to better understand the microstructural changes in the cortex that accompany learning. Some researchers suggest that gray matter changes may be a proxy for concurrent white matter changes (Sowell et al., 2004), but others suggest that white matter changes alone cannot explain gray matter changes during development (Tamnes et al., 2010). Finally, models such as the DRM theorizes about bilingual neural changes and length of exposure. Hence, future research can explore the relationship between bilingual usage and cortical measures while controlling for L2 AoA and proficiency.

Conclusions

The current study examines language proficiency within and between subjects in relation to cortical thickness. The results suggest that relationships between language expertise and cortical thickness fit within the ERM (Wenger et al., 2017) and can extend the DRM (Pliatsikas, 2020) to include aspects of proficiency as a proxy for language experience while taking into account the unique experience of heritage bilinguals. In general, an increase in proficiency in the only language for monolingual and the second but stronger language for heritage bilinguals is associated with the thinning of the cortex, while an increase in proficiency in the first but weaker language for heritage bilinguals is associated with the thickening of the cortex. These results highlight variability within bilinguals, their difference from monolinguals, as well as the dynamic nature of language system organization/cortical representation, and future research is needed to understand how these relationships develop.

Supplementary Material

Supplementary Material

What is the significance of this article for the general public?

This study highlights neural variability among bilinguals, their difference from monolinguals, as well as the dynamic nature of language system organization/cortical representation by examining the relationship between language proficiency and cortical thickness. Notably, proficiency in the first-learn but weaker language positively correlated to cortical thickness. Future research is needed to understand how these complex relationships between bilingual experience and neural structure develop.

Acknowledgments

Research reported in this publication was supported by the National Institutes of Health to the National Institute on Aging (Award R21AG063537) as well as to the National Institutes of Child Health and Human Development (Award P50HD052117). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

The authors declare no conflicts of interest.

1

Missingness in SES was correlated with Spanish proficiency in bilinguals, such that those in the final sample had slightly higher SES than those who did not report SES (b = .06, p = .005, η2 = .04) with a small to medium effect. English proficiency did not correlate with missingness in SES in either group. In addition, missingness in SES was correlated with cortical thickness, such that those who reported SES had slightly thicker cortex than those who did not across various regions in the brain.

2

Studies conducted between 2007 and 2014.

3

While sex has been showed to correlate to neural metric, there were no gender differences between monolinguals and bilinguals, gender was unrelated to English or Spanish proficiency. Thus, any potential gender effect of cortical thickness would not affect the relationship between proficiency and cortical thickness. For these reasons, sex was not included as covariates in the current model.

References

  1. Archila-Suerte P, Woods EA, Chiarello C, & Hernandez AE (2018). Neuroanatomical profiles of bilingual children. Developmental Science, 21(5), Article e12654. 10.1111/desc.12654 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Ardila A, Bernal B, & Rosselli M (2017). Should Broca’s area include Brodmann area 47? Psicothema, 29(1), 73–77. 10.7334/psicothema2016.11 [DOI] [PubMed] [Google Scholar]
  3. Binder JR (2015). The Wernicke area. Neurology, 85(24), 2170–2175. 10.1212/WNL.0000000000002219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Birdsong D (2014). Dominance and age in bilingualism. Applied Linguistics, 35(4), 374–392. 10.1093/applin/amu031 [DOI] [Google Scholar]
  5. Birdsong D (2018). Plasticity, variability and age in second language acquisition and bilingualism. Frontiers in Psychology, 9, Article 81. 10.3389/fpsyg.2018.00081 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brito NH, & Noble KG (2018). The independent and interacting effects of socioeconomic status and dual-language use on brain structure and cognition. Developmental Science, 21(6), Article e12688. 10.1111/desc.12688 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brooks-Gunn J, & Markman LB (2005). The contribution of parenting to ethnic and racial gaps in school readiness. The Future of Children, 15(1), 139–168. 10.1353/foc.2005.0001 [DOI] [PubMed] [Google Scholar]
  8. Choi J, Broersma M, & Cutler A (2017). Early phonology revealed by international adoptees’ birth language retention. Proceedings of the National Academy of Sciences of the United States of America, 114(28), 7307–7312. 10.1073/pnas.1706405114 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Claussenius-Kalman H, Hernandez AE, & Li P (2021). Expertise, ecosystem, and emergentism: Dynamic developmental bilingualism. Brain and Language, 222, Article 105013. 10.1016/j.bandl.2021.105013 [DOI] [PubMed] [Google Scholar]
  10. Claussenius-Kalman H, Vaughn KA, Archila-Suerte P, & Hernandez AE (2020). Age of acquisition impacts the brain differently depending on neuroanatomical metric. Human Brain Mapping, 41(2), 484–502. 10.1002/hbm.24817 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Del Maschio N, Fedeli D, Sulpizio S, & Abutalebi J (2019). The relationship between bilingual experience and gyrification in adulthood: A cross-sectional surface-based morphometry study. Brain and Language, 198, Article 104680. 10.1016/j.bandl.2019.104680 [DOI] [PubMed] [Google Scholar]
  12. DeLuca V, Rothman J, Bialystok E, & Pliatsikas C (2020). Duration and extent of bilingual experience modulate neurocognitive outcomes. NeuroImage, 204, Article 116222. 10.1016/j.neuroimage.2019.116222 [DOI] [PubMed] [Google Scholar]
  13. Du J, Rolls ET, Cheng W, Li Y, Gong W, Qiu J, & Feng J (2020). Functional connectivity of the orbitofrontal cortex, anterior cingulate cortex, and inferior frontal gyrus in humans. Cortex, 123, 185–199. 10.1016/j.cortex.2019.10.012 [DOI] [PubMed] [Google Scholar]
  14. Duncan GJ, & Magnuson K (2012). Socioeconomic status and cognitive functioning: Moving from correlation to causation. Wiley Interdisciplinary Reviews: Cognitive Science, 3(3), 377–386. 10.1002/wcs.1176 [DOI] [PubMed] [Google Scholar]
  15. Fischl B, & Dale AM (2000). Measuring the thickness of the human cerebral cortex. NeuroImage, 97(20), 11050–11055. 10.1073/pnas.200033797 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Friederici AD, & Gierhan SME (2013). The language network. Current Opinion in Neurobiology, 23(2), 250–254. 10.1016/j.conb.2012.10.002 [DOI] [PubMed] [Google Scholar]
  17. Giguere D, & Hoff E (2020). Home language and societal language skills in second-generation bilingual adults. International Journal of Bilingualism, 24(5–6), 1071–1087. 10.1177/1367006920932221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Goodglass H, Kaplan E, & Weintraub S (1983). Boston naming test. Lea & Febiger. [Google Scholar]
  19. Grogan A, Green DW, Ali N, Crinion JT, & Price CJ (2009). Structural correlates of semantic and phonemic fluency ability in first and second languages. Cerebral Cortex, 19(11), 2690–2698. 10.1093/cercor/bhp023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hernandez AE, Claussenius-Kalman HL, Ronderos J, Castilla-Earls AP, Sun L, Weiss SD, & Young DR (2019). Neuroemergentism: A framework for studying cognition and the brain. Journal of Neurolinguistics, 49, 214–223. 10.1016/j.jneuroling.2017.12.010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hernandez AE, Claussenius-Kalman HL, Ronderos J, & Vaughn KA (2018). Symbiosis, parasitism and bilingual cognitive control: A neuroemergentist perspective. Frontiers in Psychology, 9, Article 2171. 10.3389/fpsyg.2018.02171 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hernandez AE, & Li P (2007). Age of acquisition: Its neural and computational mechanisms. Psychological Bulletin, 133(4), 638–650. 10.1037/0033-2909.133.4.638 [DOI] [PubMed] [Google Scholar]
  23. Hertrich I, Dietrich S, Blum C, & Ackermann H (2021). The role of the dorsolateral prefrontal cortex for speech and language processing. Frontiers in Human Neuroscience, 15, Article 645209. 10.3389/fnhum.2021.645209 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hickok G, & Poeppel D (2004). Dorsal and ventral streams: A framework for understanding aspects of the functional anatomy of language. Cognition, 92(1–2), 67–99. 10.1016/j.cognition.2003.10.011 [DOI] [PubMed] [Google Scholar]
  25. Hofer B, & Jessner U (2019). Multilingualism at the primary level in South Tyrol: How does multilingual education affect young learners’ metalinguistic awareness and proficiency in L1, L2 and L3? The Language Learning Journal, 47(1), 76–87. 10.1080/09571736.2016.1195865 [DOI] [Google Scholar]
  26. Hosoda C, Tanaka K, Nariai T, Honda M, & Hanakawa T (2013). Dynamic neural network reorganization associated with second language vocabulary acquisition: A multimodal imaging study. The Journal of Neuroscience, 33(34), 13663–13672. 10.1523/JNEUROSCI.0410-13.2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Huang KJ (2018). On bilinguals’ development of metalinguistic awareness and its transfer to L3 learning: The role of language characteristics. International Journal of Bilingualism, 22(3), 330–349. 10.1177/1367006916681081 [DOI] [Google Scholar]
  28. Hyltenstam K, Bylund E, Abrahamsson N, & Park HS (2009). Dominant-language replacement: The case of international adoptees. Bilingualism: Language and Cognition, 12(2), 121–140. 10.1017/S1366728908004008 [DOI] [Google Scholar]
  29. Johnson SB, Riis JL, & Noble KG (2016). State of the art review: Poverty and the developing brain. Pediatrics, 137(4), Article e20153075. 10.1542/peds.2015-3075 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kopečková R (2018). Exploring metalinguistic awareness in L3 phonological acquisition: The case of young instructed learners of Spanish in Germany. Language Awareness, 27(1–2), 153–166. 10.1080/09658416.2018.1432629 [DOI] [Google Scholar]
  31. Li P, Legault J, & Litcofsky KA (2014). Neuroplasticity as a function of second language learning: Anatomical changes in the human brain. Cortex, 58, 301–324. 10.1016/j.cortex.2014.05.001 [DOI] [PubMed] [Google Scholar]
  32. Mårtensson J, Eriksson J, Bodammer NC, Lindgren M, Johansson M, Nyberg L, & Lövdén M (2012). Growth of language-related brain areas after foreign language learning. NeuroImage, 63(1), 240–244. 10.1016/j.neuroimage.2012.06.043 [DOI] [PubMed] [Google Scholar]
  33. Mechelli A, Crinion JT, Noppeney U, O’Doherty J, Ashburner J, Frackowiak RS, & Price CJ (2004). Structural plasticity in the bilingual brain: Proficiency in a second language and age at acquisition affect grey-matter density. Nature, 431(7010), Article 757. 10.1038/431757a [DOI] [PubMed] [Google Scholar]
  34. Norrman G, & Bylund E (2016). The irreversibility of sensitive period effects in language development: Evidence from second language acquisition in international adoptees. Developmental Science, 19(3), 513–520. 10.1111/desc.12332 [DOI] [PubMed] [Google Scholar]
  35. Pakulak E, & Neville HJ (2010). Proficiency differences in syntactic processing of monolingual native speakers indexed by event-related potentials. Journal of Cognitive Neuroscience, 22(12), 2728–2744. 10.1162/jocn.2009.21393 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Park HS (2015). Korean adoptees in Sweden: Have they lost their first language completely? Applied Psycholinguistics, 36(4), 773–797. 10.1017/S0142716413000507 [DOI] [Google Scholar]
  37. Piccolo LR, Merz EC, He X, Sowell ER, & Noble KG (2016). Age-related differences in cortical thickness vary by socioeconomic status. PLoS One, 11(9), Article e0162511. 10.1371/journal.pone.0162511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Pliatsikas C (2020). Understanding structural plasticity in the bilingual brain: The dynamic restructuring model. Bilingualism: Language and Cognition, 23(2), 459–471. 10.1017/S1366728919000130 [DOI] [Google Scholar]
  39. Pliatsikas C, Meteyard L, Veríssimo J, DeLuca V, Shattuck K, & Ullman MT (2020). The effect of bilingualism on brain development from early childhood to young adulthood. Brain Structure and Function, 225(7), 2131–2152. 10.1007/s00429-020-02115-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Rajagopal A, Holland SK, Walz NC, Staat MA, Altaye M, & Wade S (2013). A functional magnetic resonance imaging study of language function in international adoptees. The Journal of Pediatrics, 163(5), 1458–1464. 10.1016/j.jpeds.2013.06.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, & Toga AW (2004). Longitudinal mapping of cortical thickness and brain growth in normal children. The Journal of Neuroscience, 24(38), 8223–8231. 10.1523/JNEUROSCI.1798-04.2004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Stein M, Federspiel A, Koenig T, Wirth M, Strik W, Wiest R, Brandeis D, & Dierks T (2012). Structural plasticity in the language system related to increased second language proficiency. Cortex, 48(4), 458–465. 10.1016/j.cortex.2010.10.007 [DOI] [PubMed] [Google Scholar]
  43. Tamnes CK, Østby Y, Fjell AM, Westlye LT, Due-Tønnessen P, & Walhovd KB (2010). Brain maturation in adolescence and young adulthood: Regional age-related changes in cortical thickness and white matter volume and microstructure. Cerebral Cortex, 20(3), 534–548. 10.1093/cercor/bhp118 [DOI] [PubMed] [Google Scholar]
  44. United States Census Bureau. (2020). 2020 American community survey 5-year estimates: Language spoken at home. https://data.census.gov/cedsci/table?q=LanguageSpoken+at+Home&tid=ACSST5Y2020.S1601
  45. Valdés G (2014). Heritage language students: Profiles and possibilities. In Peyton JK, Ranard DA, & McGinnis S (Eds.), Handbook of heritage, community, and Native American languages in the United States: Research, policy, and educational practice (pp. 27–35). Center for Applied Linguistics & Delta Systems. 10.4324/9780203122419 [DOI] [Google Scholar]
  46. Vaughn KA, Nguyen MVH, Ronderos J, & Hernandez AE (2021). Cortical thickness in bilingual and monolingual children: Relationships to language use and language skill. NeuroImage, 243, Article 118560. 10.1016/j.neuroimage.2021.118560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Veltman C (2014). Language shift in the United States (Vol. 34). Walter de Gruyter GmbH & Co KG. [Google Scholar]
  48. Wenger E, Brozzoli C, Lindenberger U, & Lövdén M (2017). Expansion and renormalization of human brain structure during skill acquisition. Trends in Cognitive Sciences, 21(12), 930–939. 10.1016/j.tics.2017.09.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Williams VJ, Juranek J, Cirino P, & Fletcher JM (2018). Cortical thickness and local gyrification in children with developmental dyslexia. Cerebral Cortex, 28(3), 963–973. 10.1093/cercor/bhx001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Wong PCM, Warrier CM, Penhune VB, Roy AK, Sadehh A, Parrish TB, & Zatorre RJ (2008). Volume of left Heschl’s gyrus and linguistic pitch learning. Cerebral Cortex, 18(4), 828–836. 10.1093/cercor/bhm115 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Woodcock RW, Muñoz-Sandoval AF, Alvarado CG, Ruef ML, & Schrank FA (2010). Woodcock-Muñoz Language Survey—Revised. Houghton Mifflin Harcourt. [Google Scholar]

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