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Is being a bilingualistic scientist stressful?

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Is being a bilingualistic scientist stressful?

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In my opinion, I don´t think there is a lot of pressure put on you as a scientist trying to expand the world´s knowledge about bilingualism and how it affects our brain.

After further investigation and a little conversation with AI,

I´ve found out, that being a bilingualistic scientist isn´t so stress-free. Especially when this area is so complex. You have to have the knowledge about brain, how does it work, etc.

In many academic fields, including linguinistics and psychology (which bilingualism overlaps with), publishing articles or their researches is a way to to advance one´s career. Research and experminets might be the most stressful part. Because it may not work out, many times. Maybe even 20 times in a row- that might be really demotivating and stressful. At the end of the day, even all the "wasted" time might be worth it and you´ll discover something amazing and helpful for our society.

Thus I don´t think that we should stress even over some experiment or a study. To fix this stressful, anxious feeling, meditation, gratituded and being grounded might relieve your worries. After all we all live on a flying rock.

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Structural changes associated with bilingualism may be expected in (at least) those brain structures involved in bilingualism.

Thus, bilingualism may be expected to affect the structure of cortical and subcortical regions involved in (perhaps among other functions) language learning, processing, and control, as well as the white matter tracts that provide connectivity between these regions.

The reported structural effects of bilingualism in adults (measured as differences between bilingual and monolingual groups, or sometimes in training studies) are most commonly reported in grey matter regions that have been found to underlie such language-related (as well as other) processes.

Moreover, several white matter tracts that provide connectivity between these structures (among others) have also been found to be modified by bilingualism.

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Abstract

Bilingualism affects the structure of the brain in adults, as evidenced by experience-dependent grey and white matter changes in brain structures implicated in language learning, processing, and control. However, limited evidence exists on how bilingualism may influence brain development. We examined the developmental patterns of both grey and white matter structures in a cross-sectional study of a large sample ( n = 711 for grey matter, n  = 637 for white matter) of bilingual and monolingual participants, aged 3-21 years. Metrics of grey matter (thickness, volume, and surface area) and white matter (fractional anisotropy and mean diffusivity) were examined across 41 cortical and subcortical brain structures and 20 tracts, respectively. We used generalized additive modelling to analyze whether, how, and where the developmental trajectories of bilinguals and monolinguals might differ. Bilingual and monolingual participants manifested distinct developmental trajectories in both grey and white matter structures. As compared to monolinguals, bilinguals showed: (a) more grey matter (less developmental loss) starting during late childhood and adolescence, mainly in frontal and parietal regions (particularly in the inferior frontal gyrus pars opercularis, superior frontal cortex, inferior and superior parietal cortex, and precuneus); and (b) higher white matter integrity (greater developmental increase) starting during mid-late adolescence, specifically in striatal-inferior frontal fibers. The data suggest that there may be a developmental basis to the well-documented structural differences in the brain between bilingual and monolingual adults.

Electronic supplementary material

The online version of this article (10.1007/s00429-020-02115-5) contains supplementary material, which is available to authorized users.

Keywords: Bilingualism, Brain development, Grey matter, White matter, Generalized additive models

Introduction

Bilingualism and brain structure in adults

Recent years have seen an emerging interest in the effects that learning and using more than one language have on brain structure (Luk et al. 2020). An increasing number of studies suggest that, as compared to monolinguals, bilingual (or multilingual) adults show structural alterations in cortical regions and subcortical grey matter structures, as well as in white matter tracts that connect these regions (for recent reviews, see Hayakawa and Marian 2019; Pliatsikas 2019). These findings suggest that bilingualism/multilingualism (referred from here onwards as bilingualism) is on par with other types of longer term experience that lead to significant structural brain changes during the acquisition and maintenance of a new skill, such as juggling, the use of novel tools, and navigation (Maguire et al. 2000; Draganski et al. 2004; Quallo et al. 2009; Taubert et al. 2010).

Indeed, the reported structural effects of bilingualism in adults (measured as differences between bilingual and monolingual groups, or sometimes in training studies) are most commonly reported in grey matter regions that have been found to underlie such language-related (as well as other) processes (for details, see Pliatsikas 2019). These regions primary include: frontal and nearby cortex, including the three portions of the left inferior frontal gyrus (IFG), namely, opercularis (IFGop), triangularis (IFGtr), and orbitalis (IFGor), as well as the frontal pole, the middle and superior frontal gyri (MFG and SFG), and the anterior cingulate cortex (ACC); temporal cortex, including the superior, middle, and inferior temporal gyri (STG, MTG and ITG), Heschl's gyrus, the temporal pole, and the hippocampus; and parietal cortex, including the supramarginal gyrus, the angular gyrus, and the superior parietal lobule (Mechelli et al. 2004; Mårtensson et al. 2012; Abutalebi et al. 2014a; Klein et al. 2014; Stein et al. 2014; Kaiser et al. 2015; Olulade et al. 2016; Hämäläinen et al. 2018). Subcortical structures that are affected mainly include the basal ganglia, in particular the caudate nucleus, the putamen, and the globus pallidus, as well as the thalamus (Burgaleta et al. 2016; Pliatsikas et al. 2017; DeLuca et al. 2019a), with some effects also having been reported in the cerebellum (Filippi et al. 2011, 2020; Pliatsikas et al. 2014). Moreover, several white matter tracts that provide connectivity between these structures (among others) have also been found to be modified by bilingualism. These primarily constitute the inferior and superior longitudinal fasciculi (ILF and SLF), the inferior fronto-occipital fasciculus (IFOF), the arcuate and uncinate fasciculi, and the anterior thalamic radiation, as well as the corpus callosum, including the forceps major and the forceps minor (Luk et al. 2011; Pliatsikas et al. 2015; Kuhl et al. 2016; Mamiya et al. 2016; Rossi et al. 2017; Anderson et al. 2018; Singh et al. 2018).

The specific structural effects of bilingualism within adults appear to vary as a function of both experience and age. In young adult bilinguals with limited experience using their second language (L2) (e.g., with limited immersion in second language speaking environments), studies have typically found cortical tissue increases, reflected as increased cortical thickness and/or cortical volumes (we are not aware of any research examining surface area differences as a function of bilingualism in adults). Such increases have been observed in multiple frontal, temporal, and parietal regions in bilinguals as compared to monolinguals (Mechelli et al. 2004; Ressel et al. 2012; Klein et al. 2014; Olulade et al. 2016) as well as in younger adults in language training studies, at least in initial stages (Mårtensson et al. 2012; Bellander et al. 2016). In contrast, young adult bilinguals with limited L2 experience often show limited or no changes in subcortical volumes (Pliatsikas et al. 2017). Rather, subcortical differences seem to be found in more experienced bilinguals (in particular, those with substantial L2 immersion, or in bilingual environments), who show greater volumes than monolinguals in a variety of subcortical structures, especially in the basal ganglia (in the caudate, putamen, and globus pallidus), as well as the thalamus and the cerebellum (Burgaleta et al. 2016; Pliatsikas et al. 2017; DeLuca et al. 2019b). Similarly, white matter effects have not been reported in young adults with limited L2 experience. Instead, experienced bilinguals (again, especially those with substantial immersion/in bilingual environments) seem to show white matter differences, as compared to monolinguals, in particular increases in white matter integrity (generally reported as increases in fractional anisotropy [FA] and/or decreases in mean diffusivity [MD]; see below for definitions) (Pliatsikas et al. 2015; Rahmani et al. 2017). Experienced bilingual young adults, by contrast, generally do not show differences in cortical thickness or volume as compared to monolinguals (Pliatsikas 2019). Somewhat different patterns have been observed in older (and typically longer term) bilingual adults, who generally show (in comparison to age-matched monolinguals) greater grey matter volumes in similar cortical as well as subcortical regions, in addition to greater white matter integrity, again mainly in the same tracts that are found to change in younger adult bilinguals (Luk et al. 2011; Abutalebi et al. 2014a, b; Olsen et al. 2015; Anderson et al. 2018; Borsa et al. 2018; Del Maschio et al. 2019).

What may explain these patterns? A detailed review of the neuroplastic effects of bilingualism (see Pliatsikas 2019) is beyond the scope of this paper. However, the available models that attempt to explain such effects and their underlying mechanisms have certain common denominators, in addition to the claim that any changes result from the bilingual experience (Li et al. 2014; Hernandez et al. 2015, 2018; Abutalebi and Green 2016; Grundy et al. 2017). Based on these accounts, and in addition to the expansion-partial renormalization hypothesis (EPH), which is designed to explain experience-dependent adult grey matter changes in general (Lövdén et al. 2013), Pliatsikas (Pliatsikas 2020) proposed the dynamic restructuring model (DRM) of structural changes due to bilingualism.

First of all, according to the EPH (on which Pliatsikas's DRM is based), the acquisition of a new skill is marked by transient local expansions in the implicated grey matter regions, reflected as increases in cortical thickness or volume (such effects on cortical surface area are not discussed in Lövdén et al. 2013). These grey matter tissue increases are posited to be likely due to the development of new dendritic spines (perhaps as well as neurogenesis). The increases are followed by a slow process of pruning older and idle spines, eventually leading to the gradual reversal of the initial expansions (perhaps even back to baseline), as the newly formed networks become more efficient over time and with practice. Putting this into the context of bilingualism, Pliatsikas ( 2020) suggested that the local grey matter increases reported in relatively inexperienced bilinguals, including those at earlier stages of learning in language training studies, reflect an initial tissue increase in brain structures related to language learning and use. As language use becomes more efficient, these structures are posited to gradually return to baseline. Together, this may explain the patterns described above: cortical thickness and volume increases in less experienced bilinguals (vs. monolinguals) and at early stages of language training in young adults, as well as a lack of such differences in more experienced bilinguals. The emergence of larger subcortical volumes in younger adult bilinguals with greater experience, in particular in the basal ganglia, may be due to their ongoing roles in language control as well as later stages of language learning (proceduralization; Ullman, 2016, 2020); interestingly, in both of these cases one may expect gradual increases as well as eventual (but much later) decreases, as language control becomes more efficient and as more of the language is proceduralised.

Age is also posited to contribute to at least some of the observed structural changes in adult bilinguals. The greater grey matter cortical and subcortical metrics (thickness, volumes) in older adult bilinguals, as compared to older monolinguals (see above), is usually interpreted as greater age-related tissue loss for monolinguals, as a result of "strengthened" local connections becoming more resilient to ageing, which might help explain why these effects in older adults only emerge in experienced bilinguals (Perani and Abutalebi 2015). Much less attention, however, has been devoted to possible age effects during child development, even though much of the bilingual literature described above relates to additional languages learned (at least part) before early adulthood. Nevertheless, it has been suggested that at least some of the grey matter increases observed in adult bilinguals, as compared to monolinguals, might be explained by less pruning (less grey matter tissue loss) in bilinguals during brain development, even though neural mechanisms for this process were not discussed (de Bot 2006).

Again putting this into the context of bilingualism, in the DRM, Pliatsikas ( 2020) argued that increases in white matter integrity should generally emerge in more experienced young adult bilinguals, as has been observed. This could be due to increased optimization of different sorts, including proceduralization and various aspects of language use and control. Along these lines, since bilingualism is a life-long experience (at least for those with substantial L2 immersion or in bilingual environments, who show the clearest white matter changes; see above), the increased integrity might persevere in older age and counteract neurodegeneration, giving rise to the effects reported in older bilinguals (Perani and Abutalebi 2015). We are not aware of any theoretical suggestions that adult bilingual/monolingual differences in white matter integrity can be attributed to bilingual/monolingual differences in how the brain develops during childhood and adolescence.

In sum, the evidence (and the DRM) suggests that, at least in adults, bilingualism-induced neuroplasticity cycles dynamically through stages. Specifically, relatively early regional cortical grey matter increases likely related to initial stages of language learning, use, or control later proceed to cortical grey matter renormalization, subcortical grey matter increases, and increases of white matter integrity, all possibly related to progressively more efficient language learning, use, or control. However, the role of bilingualism in the development of grey and white matter structures prior to adulthood in explaining the observed patterns in adults has, as of yet, barely been discussed. Moreover, as we will see below, there has also been very little (empirical or theoretical) work on structural brain changes due to bilingualism within children and adolescents. This paper addresses these issues.

Brain development in childhood and adolescence

Before reviewing the available evidence regarding structural effects of bilingualism on the developing brain, it is useful to first discuss the more general patterns of brain development in (typical) childhood and adolescence. A note on terminology: for simplicity, in this paper we use "development", "changes" and similar terms to refer to all age-related differences found during childhood and adolescence, including in cross-sectional studies, which constitute the majority of the extant literature on this topic. However, we remind the reader that caution is warranted regarding the inference of developmental trajectories from non-longitudinal data.

Grey matter development from early childhood through adolescence has been studied quite extensively (Giedd 2004; Tamnes and Østby 2018; Vijayakumar et al. 2018; Foulkes and Blakemore 2018). Overall, the evidence suggests that grey matter generally (but not always) decreases (reflecting developmental grey matter loss) between early childhood and early adulthood, across cortical and subcortical structures. However, the (linear or nonlinear) trajectories of these decreases seem to vary as a function of different factors, including which measures are used (volume, thickness, or surface area) and which structures are examined (Fjell et al. 2009). First of all, cortical thickness tends to decrease continuously throughout childhood and adolescence, though with some regional variability (Remer et al. 2017; Tamnes et al. 2017). In contrast, both cortical surface area and cortical volume show clear nonlinear trajectories, with early increases often peaking within the first decade or so, followed by sustained decreases into early adulthood, with the exact shape varying according to the region examined (e.g., peak hippocampal volumes occur somewhat later, around age 15) (Wierenga et al. 2014; Mills et al. 2016; Tamnes et al. 2017). The volumes of subcortical structures seem to show a more varied pattern: whereas some structures show a similar trajectory of increases (peaking around age 15) followed by decreases (e.g., globus pallidus, amygdala, cerebellum), others instead show linear decreases throughout childhood and adolescence to early adulthood (e.g., the caudate nucleus, putamen, and nucleus accumbens, which together form the striatum), though again there is some variability, including as a function of factors such as sex (Tiemeier et al. 2010; Wierenga et al. 2014; Herting et al. 2018). Overall, the mechanisms underlying these different developmental trajectories remain unclear, though various factors likely contribute to them, including the developmental pruning of dendritic and axonal connections as well as increasing myelination (which may be a significant driver of cortical changes in adolescence) (Chechik et al. 1998; Luo and Leary 2005; Whitaker et al. 2016).

With respect to white matter, a substantial literature employing diffusion-weighted imaging has shown continuous increases in white matter integrity from early childhood to early adulthood, generally reflected as increases in FA and/or decreases in MD (Giedd 2004; Qiu et al. 2008; Lebel and Deoni 2018; Lebel et al. 2019). However, these trajectories show variability according to which tract is examined, as well as according to factors such as sex, genetics, and environmental variables (Lebel and Deoni 2018). Evidence points towards increasing myelination during development as an underlying mechanism that may help explain these trajectories, though the exact nature of this process remains unclear (e.g. increased axonal myelination and/or axonal packing, or other factors; for a review, see Beaulieu, 2002).

Bilingualism and brain development

Even with the increasing interest in the effects of bilingualism on brain structure in adults, there has been very little work examining such effects in children or adolescents, let alone investigating whether developmental trajectories of grey or white matter might be affected by bilingualism. This, despite the suggestion that bilingual brain effects in adults may be at least partly explained by developmental bilingual effects (see above; de Bot, 2006), let alone that the influence of bilingualism on brain development is of interest in its own right.

We are aware of four studies of bilingual effects on grey matter in children or adolescents. The first such evidence was presented by Della Rosa and colleagues (2013), who examined 10-year-old children growing up in a multilingual environment. The children were scanned twice, 12 months apart. The study examined changes specifically in cortical volume, and focused on the left inferior parietal lobule. Della Rosa et al. reported that individuals' combined competence of their various spoken languages predicted increases during the time period in the cortical volume of the left inferior parietal lobule, a region that appears to underlie (among other functions) the processing of the meaning and phonology of newly acquired words (Richardson et al. 2010). This is the only longitudinal study on children to date probing aspects of bilingualism or multilingualism on grey matter. However, the study did not include a monolingual control group. Given that 10-year olds typically still learn words even in their native language, which in itself might introduce structural changes (Lee et al. 2007), such a comparison would be necessary to attribute the observed effects to multilingualism.

In another study that tested effects of bilingualism on grey matter, Brito and Noble ( 2018) compared cortical thickness and surface area between monolinguals and bilinguals, aged between 3 and 21 years. Several covariates were included in their analyses, including age, sex, socioeconomic status, and genetic ancestry. Cortical volumes, as well as subcortical structures, were not examined. The study focused on left IFG, left and right MFG, left STG, and ACC. No main effect of bilingualism (vs. monolingualism) was reported. They additionally examined the effect of bilingualism separately in groups of younger (3-11 years) and older (12-21 years) individuals. The potential effect of bilingualism on developmental trajectories of brain structures was not examined (e.g., no direct comparison of bilingual effects between the age groups was reported). No effect of bilingualism was found in the younger age group. However, in the older group, bilinguals showed greater surface area in the ACC, a region that is central to language control (Abutalebi and Green 2016). Brito and Noble ( 2018) interpret their findings as suggesting that there might not be robust structural effects of bilingualism in younger children, though it is important to emphasize that averaging across such a large age range (3-11) may obscure developmental patterns.

Finally, Thieba, Long, Dewey and Lebel ( 2019) compared 3-to-5-year-old children raised in a multilingual environment to children raised in a monolingual environment. The two groups were matched on age, sex, and both maternal education and household income (measures of socioeconomic status). Age effects were not examined. They focused (bilaterally) on three cortical regions of interest (IFGop, IFGtr, IFGor), and moreover performed exploratory analyses on 31 additional cortical (sub)regions, again bilaterally. In all regions, they examined cortical thickness, surface area, and volume. Subcortical structures were not probed. None of their findings survived statistical correction, though in their uncorrected results they reported thicker cortex in the left IFGop and the right caudal MFG, as well as larger volumes in the left caudal ACC, the left caudal MFG, and the right MTG, in the multilingual group as compared to the monolingual group. It is worth noting that the increased cortical thickness in the IFGop and MFG matches the finding of increased cortical thickness for unbalanced (vs. balanced) bilinguals in the same two structures in Archilla-Suerte et al. (2018). This overlap seems consistent with the fact that the participants in Thieba et al. were much younger, and, therefore, had relatively little bilingual experience, just as might be the case for the unbalanced (vs. the balanced) older bilingual children in Archilla-Suerte et al. Therefore, both groups might have been at an earlier stage of bilingualism-induced neuroplasticity in which increased cortical thickness is observed.

The dearth of evidence regarding the effect of bilingualism on brain development is particularly evident in the very limited literature on white matter. We are aware of only two studies on this topic. Mohades and colleagues (2012) compared three groups of 8-11-year-old children: simultaneous bilinguals, sequential bilinguals, and monolinguals. The groups had similar age and sex distributions. The effect of age was not examined. The study focused on FA in four tracts (IFOF, SLF, and two bundles related to the corpus callosum). They reported greater FA values in the left IFOF (which plays important roles in language) for simultaneous bilinguals compared to both sequential bilinguals and monolinguals, as well as in one of the corpus callosum bundles (connecting orbital frontal cortex bilaterally) for monolinguals compared to the two bilingual groups. Interestingly, and despite not emerging as significantly different, the FA values in the left IFOF for sequential bilinguals were intermediate between the values of the two other groups. Additionally, the same participants were scanned again after 2 years (Mohades et al. 2015), in a longitudinal design. The study reported that while all groups showed a significant increase in their FA values in the left IFOF over the 2 years (as expected, given general developmental increases in white matter integrity; see above), the increase was larger for the sequential bilinguals than the other two groups. This appears to be consistent with experience-based increases in white matter integrity due to the bilingual experience. Note that the finding that a larger increase was observed for sequential than simultaneous bilinguals might be partly due to the latter having undergone earlier increases due to their experience.

In sum, there is an emerging literature on the effects of bilingualism on brain structure during development. The findings reveal intriguing overlap both between different developmental groups (e.g., younger bilinguals and somewhat older unbalanced bilinguals), as well as between developmental and adult bilingual effects (see Sect. Bilingualism and brain structure in adults). Overall, the results suggest that similar experience-based effects may be found during development and in adults, and that developmental patterns might in fact help explain adult patterns.

Nevertheless, a number of important gaps remain, including the following. First and perhaps most importantly, there is a dearth of research comparing the developmental trajectories of brain structures between bilinguals and monolinguals, that is, examining interactions between bilingualism/monolingualism and age, including nonlinear effects. Indeed, we are not aware of any studies probing this issue over the course of childhood and adolescence. Such studies seem critical for understanding how development may lead to bilingual effects in adults, let alone for understanding how bilingualism affects the course of development. Second, not all studies have controlled for certain potentially confounding variables (e.g., sex, socioeconomic status). Third, most previous developmental studies probing bilingual effects prior to adulthood have focused on specific brain structures: generally particular grey matter cortical regions, leaving subcortical structures and white matter greatly understudied. Fourth, prior studies have also focused on particular measures that moreover often differ between studies (e.g., only volume, or only cortical thickness and surface area, or only FA). Indeed, we are not aware of any research that has widely examined both grey and white matter within subjects, let alone with all major measures (cortical thickness and surface area, volumes, FA, and MD). Thus, our understanding of the potential effects of bilingualism on brain development is still quite limited. This gap seems to warrant clarification with a more comprehensive study.

The present study

The present study was designed to address these gaps. We analyzed a large existing dataset of children and adolescents (PING, Jernigan et al. 2016), aged 3-21. The dataset included both bilinguals and monolinguals, who were reasonably evenly distributed across the age range. To comprehensively investigate the effect of bilingualism on brain development, we directly compared the two groups' developmental trajectories in a wide range of brain structures. We controlled for a number of potentially confounding variables, including sex, genetic ancestry, and socioeconomic factors (both parental education and household income) (Bakken et al. 2011; Noble et al. 2015; Fan et al. 2015; Romeo et al. 2018; Foulkes and Blakemore 2018). We examined effects bilaterally in a wide set of (both cortical and subcortical) grey matter structures and white matter tracts. For grey matter, we probed cortical thickness, surface area, and volume (with the latter also examined for subcortical structures), while for white matter we examined both FA and MD.

If bilingualism indeed has somewhat analogous effects on the brain in children and adolescents as in adults, then one might expect similar grey and white matter effects of bilingualism during development as in adults, in similar structures, though (in some manner) overlaid or interacting with more general developmental brain trajectories (which are described in Sect. Brain development in childhood and adolescence). Thus, we might expect the following: (a) For cortical grey matter, the increases in cortical thickness and volumes observed in adult bilinguals at earlier stages of the bilingual experience (in training studies or as compared to monolinguals), followed by gradual decreases in more experienced bilinguals, may be reflected during development as initial increases followed by gradual decreases over the course of childhood and adolescence. These patterns should overlap with the general developmental trajectories of continuous decreases in cortical thickness, and nonlinear increases followed by decreases in cortical volume. Thus, the expected bilingual vs. monolingual "increases" would manifest as less steep age-related decreases (less grey matter loss) in cortical thickness, and perhaps volume as well, with these differences eventually disappearing as the trajectories of the two groups gradually converge. The dearth of adult bilingual evidence from cortical surface area precludes clear bilingual vs. monolingual developmental predictions for this measure. (b) By contrast, and again based on the adult bilingual literature, during the course of development we might expect increases in subcortical volumes in bilinguals as compared to monolinguals, but only after bilinguals have experienced a substantial period of bilingualism, perhaps around the same period that bilingual/monolingual differences in cortical thickness and volume cease to be observed. Again, this pattern should overlap with overall developmental trajectories for subcortical structures. (c) Similarly, greater white matter integrity in bilinguals as compared to monolinguals may be expected to emerge only at higher levels of bilingual experience during childhood or adolescence (perhaps around the same time as the subcortical bilingual/monolingual differences emerge), again overlapping the general developmental pattern of increases in white integrity (as measured by FA and/or MD).

In sum, it seems reasonable that over the course of development, one might expect the following: Bilinguals should show increasing cortical thickness and volumes as compared to monolinguals (less steep decreases, that is, less grey matter loss), with no or few concurrent group differences in either subcortical volumes or white matter. In contrast, subcortical volumes and white should show bilingual effects (larger subcortical volumes and increased white matter integrity as compared to monolinguals) mainly at later stages of experience, at about the same time that cortical thickness and volume differences are no longer observed.

Methods

Participants

This study analyzed the dataset provided by the Pediatrics, Imaging, Neurocognition and Genetics (PING) project (Jernigan et al. 2016) (also examined in Brito and Noble 2018). PING is a multi-site collaborative repository comprising demographic, neuroimaging, medical, and cognitive data from 1493 typically developing children, adolescents, and young adults aged between 3 and 21 years. Neuroimaging data for grey matter metrics were available from 1293 participants, and for white matter metrics from 1119 participants. Because this study was concerned with the brain structure and language background of typically developing individuals, we excluded participants with a diagnosis or personal history of ADHD, learning problems, hearing problems, speech and language therapy, meningitis, seizures, head injury, or "unconsciousness". Moreover, we restricted our analysis to participants that reported English as their first language (see below). Any participants with missing data points for key variables in our models (age, sex, parental education, household income, genetic ancestry, scanner site, as well as language background), were excluded. This resulted in final samples of 711 participants with grey matter data and 637 with white matter data. Finally, the participants were split into groups according to whether they spoke language(s) in addition to English (bilinguals), or not (monolinguals); see below. Demographic and other participant-level information for the final sample are provided in Table 1.

Table 1.

Grey matter

White matter

Bilinguals

Monolinguals

Bilinguals

Monolinguals

n
141

570

127

510

n (by age range)

Parental education level

Household income

Data description

Background participant information

A number of measures of key background characteristics (see " Introduction" and Table 1) were used as covariates in the analyses: sex (male/female); scanner site (which of the thirteen scanners employed in the PING data was used for the particular participant); socioeconomic status, included both as level of household income, provided in twelve bands in PING, as well as highest parental education level, provided in seven bands (see Table 1); and genetic ancestry of the participants, provided by PING in the form of six Genetic Ancestry Factors (proportion of European, African, American Indian, East Asian, Oceanian, and Central Asian ancestry for each participant).

The background questionnaire that was administered as part of PING included the following two questions related to language use: (a) " Was English the first language participant learned to speak?" (Yes/No), and (b) " Does participant speak a language other than English? " (Yes/No). Only participants who responded "Yes" to (a) were included. These were subsequently split into bilinguals (potentially including multilinguals) and monolinguals based on their response to (b) (coded in our analyses as bilingualism: yes/no). Note that participants who responded "No" to (a) were excluded, as in this case it was not possible to judge whether they were monolingual or bilingual.

Brain data

PING provides pre-processed measures of brain morphology obtained from a Freesurfer processing pipeline (Fischl 2012); see Jernigan et al. ( 2016) for a detailed description of data collection and processing. We analyzed data from all grey matter structures and white matter tracts available in the PING database (see Table 2). For cortical grey matter, PING provides measures of thickness, volume, and surface area for 33 cortical regions, based on the cortical parcellation described in Desikan and colleagues (2006, Table 1). Further, PING includes volumes of eight subcortical structures and the cerebellum (Fischl et al. 2002). With respect to white matter, PING provides measures of fractional anisotropy (FA) and mean diffusivity (MD), as well as of longitudinal (axial) and transversal (radial) diffusivity, for 20 tracts or tract subdivisions (Hagler et al. 2009; Yendiki et al. 2011). In this study, we focus on FA and MD, since longitudinal and transversal diffusivity can be difficult to interpret (Singh et al. 2018), and indeed are often not reported or discussed in studies probing white matter integrity, including in the bilingual literature (but see Singh et al., for adults). Nevertheless, for the sake of completeness and transparency, since these measures were available in PING, we performed the same analyses on these as on the other metrics (see below), and report significant effects in Supplementary Material; we do not discuss these measures further in the paper.

Table 2.

Cortical regions

Subcortical structures

Frontal pole

Nucleus accumbens

Orbitofrontal cortex-lateral

Caudate nucleus

Orbitofrontal cortex-medial

Putamen

Inferior frontal gyrus-pars opercularis

Pallidum

Inferior frontal gyrus-pars triangularis

Thalamus

Inferior frontal gyrus-pars orbitalis

Ventral diencephalon

Rostral middle frontal gyrus

Amygdala

Caudal middle frontal gyrus

Hippocampus (volumes only, so treated as subcortical)

Superior frontal gyrus

Temporal pole

Cerebellum

Inferior temporal gyrus

Middle temporal gyrus

White matter tracts

Banks of the superior temporal sulcus

Superior longitudinal fasciculus

Superior temporal gyrus

parietal portion

Transverse temporal cortex

temporal portion (arcuate fasciculus)

Inferior parietal cortex

Inferior longitudinal fasciculus

Superior parietal cortex

Inferior frontal-Superior frontal cortex

Supramarginal gyrus

Inferior fronto-occipital fasciculus

Rostral anterior cingulate cortex

Striatal-Inferior frontal cortex

Caudal anterior cingulate cortex

Uncinate fasciculus

Posterior cingulate cortex

Anterior thalamic radiation

Isthmus cingulate cortex

Cingulate cingulum

Entorhinal cortex

Parahippocampal cingulum

Parahippocampal gyrus

Cortico-spinal/pyramidal

Precentral gyrus

Superior cortico-striate

Postcentral gyrus

frontal portion

Paracentral gyrus

parietal portion

Cuneus

Fornix

Precuneus

Fornix excluding fimbria

Fusiform gyrus

Corpus callosum

Lateral occipital cortex

Forceps major

Lingual gyrus

Forceps minor

Pericalcarine cortex

Data analysis

Data were analyzed in R (R Core Team 2014) with generalized additive models (GAMs), using the bam() function in the mcgv package (Wood 2011). GAMs provide the means to fit a nonlinear regression spline, that is, a 'wiggly' curve that consists of the sum of simpler nonlinear functions. Additionally, GAMs prevent overfitting the data by penalising such wiggliness, which is only included when there is sufficient evidence for a particular shape. For these reasons, GAMs are well suited to model a variety of curved shapes, including the nonlinear patterns observed in brain development as a function of age (Chang et al. 2016; Tamnes et al. 2017). Separate GAMs were run for each structure/tract and each metric: cortical thickness, surface area, and volume for grey matter cortical regions; volume for grey matter subcortical structures and the cerebellum; and FA and MD for white matter.

We applied an analytical procedure akin to using a "vibration of effects" approach (Patel et al. 2015). This approach involves running a number of alternate models in which parameters are slightly changed across them. Reliability is assessed in terms of consistency across the models; that is, effects that are consistent across alternate models can be considered to be reliable. In GAMs, the estimation of nonlinear effects, and in particular of nonlinear 'difference curves' (for example, the difference between the effect of age for monolinguals and bilinguals) can be sensitive to the particular specification of parameters in the model, specifically, to what level is chosen as the reference. Thus, in the present study, the comparison reference level for key variables of interest (bilingualism, hemisphere) was changed to generate a set of similar models. For example, for the first-level model (see just below), the GAM for each bilateral structure/tract and metric was run four times, with all combinations of bilingualism (yes/no) and hemisphere (left/right) as reference levels. Following a conservative approach (to minimise type I errors), effects of interest were assessed as reliable only if they were significant across all of these models ( p s < 0.05), and thus were not dependent on which reference level was used for comparison.

For the first-level model ('Model 1' in the 'Code for data analysis' in Supplementary Material), for each bilateral structure/tract (that is, all apart from the corpus callosum and forceps minor and major) and each metric, we first applied a GAM in which we fit a regression spline for the effect of age (for the group and hemisphere that were selected as reference levels), as well as the difference curves corresponding to the age x bilingualism interaction, the age x hemisphere interaction, and the three-way age x bilingualism x hemisphere interaction. This model also included random effects for participant and scanner site, as well as the covariates parental education, household income, and genetic ancestry as linear terms. Sex was not included as a covariate because it was well matched (see Table 1) within each participant group (i.e., for bilinguals and monolinguals, for both the grey and white matter analyses). As described above, this model was run four times (for each structure/tract and metric). If the three-way interaction (age x bilingualism x hemisphere) was significant in all four models, these were followed up by the second-level model (see next paragraph), separately for each hemisphere. If the three-way interaction was not significant in all four models (or if none of the second-level analyses in either hemisphere were significant, following up from a significant three-way interaction), we again ran the second-level model, but analyzing both hemispheres together (as two repeated measures for each participant).

The second-level model ('Model 2′) included a term for bilingualism (as an ordered factor, which allows estimating interactions), a regression spline term for age, and a difference curve for the age x bilingualism interaction, as well as all random effects and covariates included in the first model. This allowed us to assess whether the pattern of age-related changes (that is, the developmental trajectory) differed between bilinguals and monolinguals: either in each hemisphere separately (as follow-ups to reliable three-way interactions), or across the two hemispheres (if the corresponding three-way interaction was not reliable). This model was also applied to the corpus callosum, forceps minor, and forceps major, for which the first-level model was not run. In all instances, the second-level model was run twice, once with each level of bilingualism (monolingual and bilingual) as the reference level. It was considered reliable only if the age by bilingualism interaction was significant for both reference levels.

Finally, for structures/tracts and metrics where the age x bilingualism interaction in the second-level model was reliable (significant for both reference levels of bilingualism), a third-level model ('Model 3′) was run, to unpack this interaction. This model included a main effect of bilingualism, and a regression spline for age for each level of bilingualism (Yes/No) separately.

Results

As described above, the first-level analysis for all structures/tracts and metrics examined the three-way interaction between age, bilingualism, and hemisphere. In only two cases did these models yield reliable three-way interactions (that is, the model was statistically significant across all four reference levels, as described above): for the volume of the putamen and the surface area (SA) of the posterior cingulate cortex. However, the follow-up second-level analyses for these three-way interactions did not yield any significant effects of bilingualism, that is, either main effects of bilingualism or age by bilingualism interactions, for either hemisphere and for either of these structures and metrics; see Table 3.

Table 3.

First-level analysis

Second-level analyses

Age × Bilingualism × Hemisphere

Left hemisphere

Right hemisphere

Bilingualism

Age × Bilingualism

Bilingualism

Age × Bilingualism

Putamen (volume)

**

ns

ns

ns

ns

Posterior cingulate cortex (SA)

*

ns

ns

ns

ns

Following our analysis plan, we then ran second-level models on all structures/tracts and metrics while analysing both hemispheres together (as well as on the corpus callosum, forceps minor, and forceps major), to test for two-way interactions between age and bilingualism, independent of hemisphere. Eighteen of these analyses yielded reliable age by bilingualism interactions (that is, the interaction was statistically significant across both reference levels, as described above). This indicates that in these 18 cases, the developmental trajectories of these metrics for these structures or tracts differed significantly between bilinguals and monolinguals. See Table 4 and Figs. 1, 2. The 18 analyses included 12 cortical regions with cortical thickness as the metric (Table 4, Fig. 2), 4 cortical regions with cortical volume as the metric (Table 4, Fig. 3), 1 cortical region with cortical surface area as the metric (Table 4, Fig. 4), and 1 white matter tract with FA as the metric (Table 4; Fig. 5). Figure 1 labels the cortical regions that showed any reliable age by bilingualism interactions, that is, for cortical thickness, volume, or surface area. Note that a total of 14 cortical regions and 1 white matter tract showed reliable analyses, since 2 cortical regions (the precuneus and superior frontal gyrus) yielded reliable interactions in more than 1 metric; see Table 4. No subcortical structures yielded significant age by bilingualism interactions.

Table 4.

Second-level analysis

Third-level analyses

Bilinguals-
Age

Monolinguals-
Age

Bilingualism

Age × bilingualism

Cortical thickness

Cortical volume

Cortical surface area

Fractional anisotropy

Fig. 1.

The 14 cortical regions that showed reliable age by bilingualism interactions, that is, for any of the three cortical metrics (cortical thickness, volume, and surface area)

Fig. 2.

Cortical regions yielding reliable age by bilingualism interactions for cortical thickness. This displays which cortical regions show significantly different developmental trajectories for cortical thickness between age 3 and 21 for bilinguals versus monolinguals. In panel A, the color map reflects F values of each interaction between ages by bilingualism (based on the smaller F value of the two alternate analyses). In panel B, the developmental trajectories of bilinguals are shown in red, while the trajectories of monolinguals are shown in blue

Fig. 3.

Cortical regions yielding reliable age by bilingualism interactions for cortical volume. This displays which cortical regions show significantly different developmental trajectories for cortical volume between age 3 and 21 for bilinguals versus monolinguals. See Fig. 2 for more information

Fig. 4.

The single cortical region that yielded a reliable age by bilingualism interaction for cortical surface area. This region shows significantly different developmental trajectories for cortical surface area between age 3 and 21 for bilinguals versus monolinguals. See Fig. 2 for more information

Fig. 5.

The single white matter tract that yielded a reliable age by bilingualism interaction, specifically for fractional anisotropy. This tract shows significantly different developmental trajectories for FA between age 3 and 21 for bilinguals versus monolinguals. Panel A adapted, with permission, from Reyes et al. ( 2018)

Discussion

In the present study, we investigated whether and how brain development may be affected by bilingualism-that is, to what extent being bilingual (or multilingual) versus monolingual might influence the developmental trajectories of brain structures during childhood and adolescence. To examine this question, we analyzed a large dataset of individuals, aged 3-21, that included both bilinguals and monolinguals reasonably evenly distributed across the age range (PING) (Jernigan et al. 2016). We analyzed cortical thickness, volume, and surface area for 33 cortical regions, as well as subcortical volumes for 8 structures, for 711 participants; and both fractional anisotropy (FA) and mean diffusivity (MD) for 20 white matter tracts, for 637 participants. The data were analyzed with generalized additive models (GAMs) to reveal the expected nonlinear patterns of brain development while preventing overfitting, while using an analytical procedure akin to a vibration of effects approach to minimize Type I errors.

Interpretation

Our predictions were based on a combination of previous data and models pertaining to the effects of bilingualism on the brain in adults, together with well-documented general effects of development on the brain. Specifically, we expected that, on top of basic brain developmental patterns, during development greater cortical thickness and volumes (e.g., due to less steep developmental decreases for cortical thickness, that is, less grey matter loss) should emerge for bilinguals as compared to monolinguals, followed by gradual convergence of these metrics between the two groups. (The paucity of adult bilingual evidence from cortical surface area precluded predictions regarding this metric.) In contrast, greater bilingual (vs. monolingual) subcortical volumes and white matter integrity were only expected at later stages of bilingualism, around the same time that cortical differences cease to be observed.

The analyses revealed that while both bilinguals and monolinguals showed the expected general developmental patterns of brain structures, they also showed differences in their developmental trajectories.

First, consistent with the broader developmental literature, both groups showed the following patterns (see Table 4 and Figs. 2, 3, 4, 5). Both evidenced continuous decreases in cortical thickness throughout childhood/adolescence (Remer et al. 2017; Tamnes et al. 2017). In both, cortical volume and cortical surface area generally showed more nonlinear trajectories, often with early increases or stability followed by decreases (Wierenga et al. 2014; Mills et al. 2016; Tamnes et al. 2017). And in both groups, white matter integrity increased continuously with age (Lebel and Deoni 2018; Lebel et al. 2019). Overall, this demonstrates that the patterns observed here follow expected trajectories of brain development.

Second, and of greater interest here, the two groups also showed developmental differences, which were moreover strikingly similar across measures and structures (Table 4 and Figs. 2, 3, 4, 5). As expected based on the adult literature (see above), greater cortical thickness and cortical volume (less developmental grey matter loss) were observed for bilinguals as compared to monolinguals across multiple cortical regions, with this deviation becoming apparent around late childhood to early puberty (in most cases about age 10-13 for cortical thickness and 12-14 for cortical volume). Moreover, both for cortical thickness and volume, this pattern continued in most regions; though in some cortical areas the values for bilinguals and monolinguals reconverged in early adulthood, about age 20-21. Additionally, across cortical regions, a very early and unexpected difference between the groups was observed: starting from around age 3, the lowest age in our sample, bilinguals displayed lower cortical thickness and volumes than monolinguals, with these values only converging between the groups from around mid to late childhood to early puberty. However, given the relatively small sample size of the bilingual group at the youngest ages (Table 1), this finding should be treated with caution. Interestingly, though we did not have clear predictions for cortical surface area (due to the dearth of studies examining this metric in bilinguals; see above), the one region showing bilingual/monolingual differences in surface area showed a pattern somewhat similar to that observed for cortical volumes, in particular during early childhood. In sum, the bilinguals consistently showed lower values for cortical metrics than monolinguals until mid to late childhood or early puberty, at which point they generally diverged again in showing larger values (especially for cortical thickness and volume), with this bilingual increase continuing into early adulthood, or in some cases reconverging.

With respect to white matter, one tract showed bilingual/monolingual differences, that is, striatal-inferior frontal fibers for FA. Strikingly, this again yielded the same pattern as cortical regions, namely lower FA values from age 3 for bilinguals than monolinguals, with the groups gradually converging, till the bilinguals began to show larger values from about age 16. Overall, this suggests a greater increase for bilinguals than monolinguals in white matter integrity over the course of development.

The pattern of results suggests consistency not only in the developmental trajectories of the different metrics, but also in which parts of the brain show effects. In particular, almost all of the bilingual/monolingual by age interactions were found for frontal or parietal structures. This held across all three cortical metrics (thickness, volume, surface area) and the one implicated white matter tract (which connects the striatum and inferior frontal cortex). Only two regions yielded effects outside these two lobes (one in the temporal lobe, one in the occipital lobe), neither of which were among the strongest interactions observed.

How might the implication of frontal and parietal structures in bilingual/monolingual developmental differences, as well as striatal-inferior frontal structural connectivity, be interpreted? Though interpretation must be made with caution, due to the risk of reverse inference (Poldrack 2006, 2011), this pattern appears to be consistent with the involvement of particular circuits (networks).

First of all, according to certain 'dual-stream' views, language and audition (as well as vision) are processed by a ventral stream that is closely related to temporal cortex and underlies aspects of auditory object identification and meaning, together with a dorsal stream that projects from parietal (mainly inferior parietal and nearby cortex) to frontal regions (mainly (pre)motor and IFG pars opercularis) and is primarily involved in sensory-motor integration and articulatory functions (Hickok and Poeppel 2007; Rauschecker and Scott 2009). In the present study, the implication both of inferior parietal regions (supramarginal gyrus and inferior parietal) and motor and related regions (precentral, paracentral, as well as posterior portions of superior frontal and perhaps caudal middle frontal) as well as IFG pars opercularis, thus appears to be consistent with changes in the dorsal stream in the developing bilingual brain. Indeed, the supramarginal gyrus, pars opercularis, and superior frontal gyrus showed particularly reliable differences between developmental trajectories for bilinguals and monolinguals (Figs. 2 and 3). The involvement of the dorsal stream would be consistent with evidence suggesting increased contributions from this stream during at least reading in bilinguals as compared to monolinguals (Parker Jones et al. 2012; Bakhtiari et al. 2014), findings that have been attributed to increased articulatory competition in bilinguals (Parker Jones et al. 2012). A similar argument could also be made for increased competition among the different grammars in bilinguals, particularly given the dependence of grammar on pars opercularis and nearby premotor cortex (Friederici 2011). Nevertheless, bilingual-based developmental changes in the dorsal stream do not seem to full explain the observed patterns, not only because changes in other structures were also observed, but additionally because the supramarginal gyrus showed greater effects than more posterior aspects of inferior parietal cortex, which have generally been the focus in language-related dorsal stream models (Hickok and Poeppel 2007; Rauschecker and Scott 2009).

Second, it is possible that the pattern could (also) be due in part to changes in the procedural memory circuit that appears to underlie language (Ullman 2004, 2020). Both the basal ganglia (in particular the striatum) and frontal cortex (in particular (pre)motor regions and the IFG pars opercularis), as well as parietal regions, play key roles in this circuit, which is implicated in the learning, representation, and use of both first and second language (whereas dorsal stream models focus on first language). Procedural memory may underlie multiple portions of language, including grammar, speech-sound representations, articulation, and more generally aspects of both speech production and speech perception (Ullman 2020; Ullman et al. 2020). Thus, the greater involvement of this system in bilinguals than monolinguals would not be surprising, given the need for all these aspects of language to be supported in two languages in bilinguals, as compared to one in monolinguals. The involvement of the striatal-inferior frontal tract is particularly striking, especially given that it yielded the largest bilingual/monolingual by age interaction (see Fig. 5), that is, the greatest difference in developmental trajectories between bilinguals and monolinguals. Interestingly, given the suggestion that the learning of dorsal stream parieto-frontal circuits may depend importantly on procedural memory (Ullman 2004; Ullman et al. 2020), the findings here may be interpreted as implicating both dorsal stream and procedural memory functions as two sides of the same coin.

However, the above circuits and functions might not fully explain the bilingual/monolingual developmental changes observed here. In particular, although the precuneus was found to show different developmental trajectories between the groups in all three cortical measures (thickness, volume, surface area), it is not importantly implicated in two of the three circuits and associated functions above, and is not generally implicated with a primary role in executive functions. So what might (additionally) explain its involvement here? The precuneus has been implicated in various functions, perhaps most notably aspects of visuo-spatial processing and declarative memory (Cavanna and Trimble 2006). Intriguingly, some evidence suggests that the precuneous underlies not only visuo-spatial processing, but also the linguistic processing of spatial relations (Wallentin et al. 2008), though it remains unclear why this function should be more engaged in the bilingual than monolingual developing brain. In contrast, declarative memory is clearly involved in both first and second language (working closely together with procedural memory), in particular for lexical knowledge, but also for various other aspects of language, including grammar and speech-sound representations (Ullman 2020; Ullman et al. 2020). Thus, learning, representing, and processing two (or more) languages may engage declarative memory more than one language does. This view is also consistent with the finding here that the posterior parietal and inferior temporal cortex, both of which play important roles in declarative memory and associated functions (Ullman 2016; Tagarelli et al. 2019), show bilingual/monolingual differences in their developmental trajectories. Nevertheless, it remains unclear why other declarative memory structures, in particular the hippocampus and other medial temporal lobe structures, were not involved here-though interestingly, the hippocampus has been found to be enlarged in adult second language learners (Mårtensson et al. 2012), remains plastic in active adult bilinguals (DeLuca et al. 2019b) and its volume declines at a slower rate in older bilinguals as compared to older monolinguals (Li et al. 2017; Voits et al. 2020).

Overall, the pattern of structures showing different developmental trajectories between the two groups seems largely consistent with prior studies of the bilingual brain. In adults, bilingual/monolingual differences have been found mainly in frontal (and the ACC), temporal, and parietal regions, as well as the basal ganglia (mainly the striatum) and thalamus, and a number of (mainly cortico-cortical) white matter tracts (see Sect. " Bilingualism and brain structure in adults"). In children, the small number of studies (which, however, focused on specific structures) have mainly implicated frontal and nearby cortex (IFG pars opercularis, middle frontal gyrus, the ACC), temporal cortex (STG), parietal cortex (inferior parietal), the basal ganglia (striatum), and tracts related to frontal cortex. The present study, which examined a large sample size across multiple structures and a range of measures, extends and further specifies our understanding of bilingual/monolingual differences in development. In particular, while it confirms the involvement of IFG pars opercularis, middle frontal gyrus, inferior parietal cortex, and (at least connections with) the basal ganglia in the developing bilingual brain, it suggests that certain other structures may also play differential roles in bilingual and monolingual development, in particular, though not only, in frontal and parietal regions.

Implications, limitations, and conclusion

The study has a number of implications and limitations. The findings suggest that bilinguals' and monolingual's brains differ even during development. This clearly extends bilingual/monolingual brain differences from adults to the developing brain. The results suggest that the apparent resilience of the bilingual brain to aging (see introduction) may begin already during development, reinforcing the view that any beneficial effects of bilingualism on brain structure may require long-term experience using the two languages (Perani and Abutalebi 2015).

The finding that these patterns overlap to a fair extent with previously observed bilingual/monolingual brain differences in adults both validates the current findings with respect to the adult literature and suggests that some adult brain differences already emerge during childhood and adolescence. This is not in fact surprising given that the vast majority of studies of the adult bilingual brain examined bilinguals who learned more than one language prior to adulthood (Luk and Pliatsikas 2016; García-Pentón et al. 2016; Hayakawa and Marian 2019; Pliatsikas 2019). Nevertheless, the findings also indicate that during development, bilingual/monolingual differences are not exactly the same as those observed in adulthood. In particular, the results suggest that some bilingual/monolingual brain differences found during development disappear by early adulthood, while others appear soon thereafter. Indeed, we saw here that certain bilingual/monolingual differences that emerged by late childhood to early puberty (in particular, greater cortical thickness or volume for bilinguals in a number of regions, likely due to less grey matter loss during development) stabilized, reduced, or even disappeared by early adulthood. Additionally, certain findings that appear to be consistent in the adult literature in more experienced bilinguals were not found or were sparsely observed in the present study, in particular greater bilingual than monolingual subcortical and cerebellar volumes (not observed here) and greater white matter integrity (found in one tract).

Overall, these patterns seem in line with our predictions, namely that developing bilinguals without extensive experience should show greater cortical thickness and volume as compared to monolinguals, though these differences should then gradually decrease or disappear around the same time as they begin to show increased volumes for subcortical structures and greater white matter integrity. Interestingly, the lack of any bilingual/monolingual differences in subcortical volumes and the cerebellum, and the involvement of only one white matter tract, seems to be consistent with the finding that most cortical regions did not show full reconvergence between the groups by the oldest age in the sample, namely 21. These findings thus appear to jibe both with the broad adult literature (regarding both which structures are implicated and their changes over the course of bilingualism) and with our predictions for bilingual/monolingual development differences based in part on this literature.

Nevertheless, a number of limitations suggest the need for further studies. First, we did not predict the finding that at very early ages the bilinguals appeared to show lower values than monolinguals, indeed for nearly all metrics for all structures. It is not clear what may account for this pattern, though the relatively small sample size of bilinguals at these ages suggests treating this finding with caution. Second, the PING database did not contain detailed information regarding the ages of acquisition of bilinguals' languages, nor the amount or type of language experience they had. The very early bilingual/monolingual effects suggest that the children at this age were exposed to two (or more) languages from an early point, indicating that at least a portion of the sample experienced early ages of acquisition. This also suggests the possibility-though this is speculative-that the rest of the sample might also have experienced quite early exposures to their languages, given that the older children and adolescents may have been drawn from similar subject pools at the same testing sites. This possibility is supported by the observed trajectories of different bilingual/monolingual patterns at different ages, which (as we have argued above) appear to be consistent with experience-based changes over time with similar ages of acquisition across the participants. Nevertheless, the age of exposure of the older children in this sample was not known, and thus future research should examine whether the findings here replicate with populations with clearly documented early ages of exposure, as well as investigating developmental trajectories with later exposures. Third, it may be argued that further measures against type I errors could have been taken. Indeed, we did not correct for multiple comparisons. This decision was taken due to the fact that our approach attempted to balance the likelihood of type I and type II errors, and was already conservative in that it was designed to reduce type I errors by virtue of an analytical procedure akin to a vibration of effects approach, with only those effects with significant results across all alternative analyses being reported. Additionally, the high degree of consistency in the observed patterns in the relative trajectories of bilingual and monolingual brain measures suggests that the reported findings are not spurious. Together, this suggests that type I errors are not prevalent in the findings reported here. Finally, we emphasize that the present study (like the vast majority of developmental studies across large age ranges) is cross-sectional, so caution is warranted in extrapolating to actual developmental patterns within subjects. Nevertheless, we suggest that our findings can provide a solid foundation for well-designed longitudinal developmental studies.

In sum, the present study suggests that bilinguals and monolinguals differ in quite consistent ways regarding the developmental trajectories of brain structures from early childhood to early adulthood. Thus, the study clearly extends previous observations of bilingual/monolingual brain differences in adults to development. The evidence presented here suggests that, as compared to monolinguals, bilinguals show more grey matter (less developmental loss) starting around late childhood and adolescence, mainly in frontal and parietal regions, as well as increased white matter integrity (greater developmental increase) starting in mid-late adolescence, specifically in fibers connecting the striatum and inferior frontal cortex. The findings not only suggest that there may be a developmental basis for some of the structural brain differences found between bilingual and monolingual adults, but also indicate that some bilingual/monolingual differences may occur in the developing but not the adult brain. Overall, the data indicate that the bilingual brain does indeed differ from the monolingual brain, and that this difference begins to be apparent even during development.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Acknowledgements

We thank Peter Molfese for helpful feedback on the paper, and Anny Reyes for providing the image of our white matter tract of interest.

Author contribution

The study was conceived and designed by Christos Pliatsikas and Michael Ullman. Data preparation and analysis were performed by Lotte Meteyard, João Veríssimo, and Vincent DeLuca. The first draft of the manuscript was written by Christos Pliatsikas and subsequent drafts co-authored by Christos Pliatsikas and Michael Ullman. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

This research was funded by a Franklin Research Grant from the American Philosophical Society and a Small Grant from the Experimental Psychology Society (UK), both awarded to C.P., and National Science Foundation BCS 1439290 to M.T.U.

Availability of data and material

Data obtained from a freely available dataset

Compliance with ethical standards

Conflicts of interest

None.

Ethics approval

N/A (anonymous data obtained from a freely available dataset).

Consent to participate

N/A (see above).

Consent for publication

N/A (see above).

Code availability

Analysis code provided in Electronic Supplementary Information.

Footnotes

Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Lotte Meteyard and João Veríssimo denote equal contribution.

Contributor Information

Christos Pliatsikas, Email: c.pliatsikas@reading.ac.uk.

Michael T. Ullman, Email: michael@georgetown.edu

References

1. de Bot K (2006) The plastic bilingual brain: synaptic pruning or growth? commentary on Green et al. Lang Learn 56:127-132. 10.1111/j.1467-9922.2006.00358.x

2. Pliatsikas C. Multilingualism and brain plasticity. In: Schwieter J, editor. The Handbook of the Neuroscience of Multilingualism. Hoboken: Wiley- Blackwell; 2019. pp. 230-251. [Google Scholar]

3. R Core Team (2014) nlme: linear and nonlinear mixed effects models

4. Ullman MT (2020) The Declarative/Procedural Model: A Neurobiologically-Motivated Theory of First and Second Language. In: VanPatten B, Keating GD, Wulff S (eds) Theories in Second Language Acquisition, 3rd edition. Routledge, 3rd edn. Routledge

5. Ullman MT (2016) The Declarative / Procedural Model : A Neurobiological Model of Language. In: Hickok G, Small SA (eds) The Neurobiology of Language. Elsevier, pp 953-68

6. Voits T, Robson H, Rothman J, Pliatsikas C (2020) The effects of bilingualism on the structure of the hippocampus and on memory performance in ageing bilinguals. PsyArXiv. 10.31234/osf.io/8hcxz

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

Data Availability Statement

Data obtained from a freely available dataset

Articles from Brain Structure & Function are provided here courtesy of Springer

# data[].attributes.attachment_link.title
The effect of bilingualism on brain development from early childhood to young adulthood

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Bilingualism affects the structure of the brain in adults, as evidenced by experience-dependent grey and white matter changes in brain structures implicated in language learning, processing, and control. However, limited evidence exists on how ...

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The base of this step is the article The Cognitive Benefits of Being Bilingual' by Victoria Marian and Anthony Shook (2012). Over the past few decades, technological advances have allowed researchers to peer deeper into the brain to investigate how bilingualism interacts with and changes the cognitive and neurological systems.

Cognitive consequences of bilingualism

Research has overwhelmingly shown that when a bilingual person uses one language, the other is active at the same time. This is called language co-activation. A Russian-English bilingual person asked to pick up a marker from a set of objects looks more at a stamp than someone who doesn't know Russian, as the Russian word for stamp, "marka", sounds like 'marker'. Language co-activation can lead to difficulties. For example, it can take longer for bilingual speakers to name pictures and it can increase tip-of-the-tongue states, situations in which you're unable to find the right word, although you remember specific details about it.

The constant juggling of two languages creates a need to control how much a person accesses a language at any given time. This is an important skill from a communicative standpoint, as it is hard to understand a message if your other language always interferes. A bilingual speaker uses his control mechanisms every time he speaks or listens. This constant practice strengthens the control mechanisms and changes the associated brain regions.

Bilingual people often perform better on tasks that require conflict management, as they are better than monolinguals in ignoring irrelevant information. This is called inhibitory control. In stroop tasks people see a word and are asked to name the color of the word's font, instead of reading the word. They see for example the word 'yellow' printed in green and their task is to say: 'green'. This (classic) task is often performed better by bilingual people than by monolingual people, as bilingual people can use their inhibitory control to ignore the word en focus on the font color. Moreover, bilinguals are better than monolinguals in switching between tasks.

Stroop task © Nevit Dilmen

Changes in neurological processing and structure

Studies suggest that bilingual advantages in executive function are not limited to the brain's language network. Bilingual people show increased activation in the brain region associated with cognitive skills like attention and inhibition. For example, bilinguals are proven to be better than monolinguals in encoding the fundamental frequency of sounds in the presence of background noise. So, in a noisy restaurant, it will be easier for a bilingual person than for a monolingual person to encode what the other person is saying.

Moreover, it appears that bilingual experience not only changes the way neurological structures process information, but may also alter the neurological structures themselves. Higher proficiency in a second language as well as earlier acquisition of that language, correlates with higher gray matter volume in the left inferior parietal cortex. This is the part of the brain where language switching is managed.

The Parietal cortex (in yellow) (Wikipedia)

Improvements in learning

Being bilingual can have tangible practical benefits. The improvements in cognitive and sensory processing driven by bilingual experience may help a person to better process information, leading to a clearer signal for learning. The bilingual language-learning advantage may be rooted in the ability to focus on information about the new language while reducing interference from the languages they already know. This ability would allow bilingual people to access newly learned words more easily, leading to larger gains in vocabulary than those experienced by monolingual people who aren't as skilled at inhibiting competing information.

The benefits associated with bilingualism seem to start early - researchers have shown positive influence of bilingualism on attention and conflict management in infants as young as seven months.

The cognitive and neurological benefits of bilingualism also extend into older adulthood. Bilingualism is a means of fending off a natural decline of cognitive function and maintaining cognitive reserve: the efficient utilisation of brain networks to enhance brain function during ageing. Older bilingual people enjoy improved memory and executive control relative to older monolingual people.

In a study of more than 200 bilingual and monolingual patients with Alzheimer's disease, bilingual patients reported showing initial symptoms five years later than monolinguals did. Likewise, bilingual patients were diagnosed more than four years later than monolingual patients.

Conclusion

To summarise, the cognitive and neurological benefits of bilingualism extend from early childhood to old age as the brain more efficiently processes information and staves off cognitive decline. The attention and ageing benefits discussed above aren't exclusive to people who were raised bilingual; they are also seen in people who learn a second language later in life. Bilingual people enjoy advantages: they have enriched cognitive control, it's likely that they have improved metalinguistic awareness, as well as better memory, visual-spatial skills and even creativity. There are also social benefits from being bilingual. For example, having the ability to explore a culture through its native tongue or talking to someone with whom you might otherwise never be able to communicate.

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This is a summary of Marian and Shook (2012) investigation into how bilingualism interacts with and changes the cognitive and neurological systems.

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The persona who is probably most related to bilingualism and brain development is Prof. Lera Boroditsky. She is a Professor of Cognitive Science at the University of California, where she has made significant contributions to the field of cognitive science. Professor Boroditsky's research primarily centers on the relationship between language, cognition, and culture. She explores how language can influence and shape the way people perceive, think, and reason about the world.Her work often delves into the concept of linguistic relativity, which suggests that the language we speak can impact our perception of reality. Boroditsky has conducted studies examining how different languages affect people's understanding of concepts like time, space, and even moral reasoning.

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Lera Boroditsky (born c.1976) is a cognitive scientist and professor in the fields of language and cognition. She is one of the main contributors to the theory of linguistic relativity. She is a Searle Scholar, a McDonnell Scholar, recipient of a National Science Foundation Career award, and an American Psychological Association Distinguished Scientist. She is Professor of Cognitive Science at the University of California, San Diego. She previously served on the faculty at Massachusetts Institute of Technology and at Stanford.

Boroditsky was born in Belarus to a Jewish family. When she was 12 years old, her family emigrated to the United States, where she learned to speak English as her fourth language. As a teenager she began thinking about the degree to which language differences could shape an argument and exaggerate the differences between people. She received her B.A. degree in cognitive science at Northwestern University in 1996. She went to graduate school at Stanford University, where she obtained her Ph.D. in cognitive psychology in 2001. She worked under Gordon Bower who was her thesis advisor at Stanford. Boroditsky also conducted research at Stanford University.

She became an assistant professor in the department of brain and cognitive sciences at MIT before she was hired by Stanford in 2004. Gordon Bower says: "It's exceedingly rare for us to hire back our own graduate students. She brought a very high IQ and a tremendous ability for penetrating analysis." At Stanford, she was an assistant professor of psychology, philosophy, and linguistics.

Boroditsky is professor of cognitive science at the University of California, San Diego (UCSD). She studies language and cognition, focusing on interactions between language, cognition, and perception. Her research combines insights and methods from linguistics, psychology, neuroscience, and anthropology.

Her work has provided new insights into the controversial question of whether the languages we speak shape the way we think (Linguistic relativity). She uses powerful examples of cross-linguistic differences in thought and perception that stem from syntactic or lexical differences between languages. Her papers and lectures have influenced the fields of psychology, philosophy, and linguistics in providing evidence and research against the notion that human cognition is largely universal and independent of language and culture.

She was named a Searle Scholar and has received several awards for her research, including an NSF CAREER award, the Marr Prize from the Cognitive Science Society, and the McDonnell Scholar Award.

In addition, Boroditsky gives popular science lectures to the general public, and her work has been covered in news and media outlets. Boroditsky talks about how all the languages differ from one another, whether in grammatical differences or contain different sounds, vocabulary, or patterns. Boroditsky studies how the languages we speak shape the way we think.

Boroditsky is known for her research relating to cognitive science, how language affects the way we think, and other linguistic related topics. One of her main research topics focuses on how people with different linguistic backgrounds act or have different behaviors when exposed to certain events. On the individual level, Boroditsky is interested in how the languages we speak influence and shape the way we think.

She has done studies comparing English to other native speakers of a different language and seeing the differences in the way they think and act given a certain scenario. For example, English and Russian differentiate between cups and glasses. In Russian, the difference between a cup and a glass is based on its shape instead of its material as in English.

Another example of her work is how she highlighted the difference in the organization of time and space from English to Mandarin. In her article "Does language shape thought? Mandarin and English speakers' conceptions of time" Boroditsky has argued for a weak version of linguistic relativity, providing a ground for it through her cross-language studies on verb tenses carried out with English and Mandarin speakers. She argues that English speakers conceive time in a way that is analogous to their conception of spatial horizontal movement, whereas native Mandarin speakers associate it with vertical movement. She has also stated that these differences do not totally determine conceptualization, since it is possible for the speakers of a language to be taught to think like the speakers of other languages do, without needing to learn any such language. Therefore, and according to Boroditsky, mother tongues may have an effect on cognition, but it is not determining.

A study published in 2000, observed that "the processing of the concrete domain of space could modulate the processing of the abstract domain of time, but not the other way around." The frequent use of a mental metaphor connects it to the abstract concept and helps the mind to store non-concrete informations in the long-term memory. Boroditsky has also done research on metaphors and their relation to crime. Her work has suggested that some conventional and systematic metaphors influence the way people reason about the issues they describe. For instance, previous work has found that people were more likely to want to fight back against a crime "beast" by increasing the police force but more likely to want to diagnose and treat a crime "virus" through social reform.

Publications (partial list)

Thibodeau PH, Boroditsky L (2015) Measuring Effects of Metaphor in a Dynamic Opinion Landscape. PLoS ONE 10(7): e0133939. doi: 10.1371/journal.pone.0133939

Thibodeau PH, Boroditsky L (2013) Natural Language Metaphors Covertly Influence Reasoning. PLoS ONE 8(1): e52961. doi: 10.1371/journal.pone.0052961

Boroditsky, L. (2003), "Linguistic relativity", in Nadel, L. (ed.), Encyclopedia of cognitive science, London: Macmillan, pp. 917-922

Boroditsky, L.; Schmidt, L.; Phillips, W. (2003), "Sex, syntax, and semantics", in Gentner, D.; Goldin-Meadow, S. (eds.), Language in mind: Advances in the study of language and thought, Cambridge, MA: MIT Press, pp. 61-80

Boroditsky, L. & Ramscar, M. (2002). The roles of body and mind in abstract thought. Psychological Science, 13(2), 185-188.

Boroditsky, L. (2001). Does language shape thought? Mandarin and English speakers' conceptions of time. Cognitive Psychology, 43(1), 1-22.

Boroditsky, L. (2000). Metaphoric Structuring: Understanding time through spatial metaphors. Cognition, 75(1), 1-28.

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Lera Boroditsky (born c.1976) is a cognitive scientist and professor in the fields of language and cognition. She is one of the main contributors to the theory of linguistic relativity. She is a Searle Scholar, a McDonnell Scholar, recipient of a National Science Foundation Career award, and an American Psychological Association Distinguished Scientist.

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Listen to this episode from Matt D'Elia Is Confused on Spotify. Dr. Lera Boroditsky - 'Beast Or Virus?' - Matt talks to renowned cognitive scientist Dr. Lera Boroditsky about one of his favorite topics and the root of all confusion: language.

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Lera Boroditsky | ❝What do you think about being persistent in learning process?❞

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Interview with Prof. Lera Boroditsky ⬆️

Boroditsky was born in Belarus to a Jewish family. When she was 12 years old, her family emigrated to the United States, where she learned to speak English as her fourth language. As a teenager she began thinking about the degree to which language differences could shape an argument and exaggerate the differences between people.

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Lera Boroditsky | ❝What do you think about being persistent in learning process?❞

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Time Is Shaped by Language | Lera Boroditsky

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How language shapes the way we think | Lera Boroditsky

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HOW TO LEARN LANGUAGES EFFECTIVELY | Matyáš Pilin | TEDxYouth@ECP

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The effect of bilingualism on brain development from early childhood to young adulthood

Abstract

Bilingualism affects the structure of the brain in adults, as evidenced by experience-dependent grey and white matter changes in brain structures implicated in language learning, processing, and control. However, limited evidence exists on how bilingualism may influence brain development. We examined the developmental patterns of both grey and white matter structures in a cross-sectional study of a large sample ( n = 711 for grey matter, n  = 637 for white matter) of bilingual and monolingual participants, aged 3-21 years. Metrics of grey matter (thickness, volume, and surface area) and white matter (fractional anisotropy and mean diffusivity) were examined across 41 cortical and subcortical brain structures and 20 tracts, respectively. We used generalized additive modelling to analyze whether, how, and where the developmental trajectories of bilinguals and monolinguals might differ. Bilingual and monolingual participants manifested distinct developmental trajectories in both grey and white matter structures. As compared to monolinguals, bilinguals showed: (a) more grey matter (less developmental loss) starting during late childhood and adolescence, mainly in frontal and parietal regions (particularly in the inferior frontal gyrus pars opercularis, superior frontal cortex, inferior and superior parietal cortex, and precuneus); and (b) higher white matter integrity (greater developmental increase) starting during mid-late adolescence, specifically in striatal-inferior frontal fibers. The data suggest that there may be a developmental basis to the well-documented structural differences in the brain between bilingual and monolingual adults.

Introduction

Bilingualism and brain structure in adults

Recent years have seen an emerging interest in the effects that learning and using more than one language have on brain structure (Luk et al. 2020). An increasing number of studies suggest that, as compared to monolinguals, bilingual (or multilingual) adults show structural alterations in cortical regions and subcortical grey matter structures, as well as in white matter tracts that connect these regions (for recent reviews, see Hayakawa and Marian 2019; Pliatsikas 2019). These findings suggest that bilingualism/multilingualism (referred from here onwards as bilingualism) is on par with other types of longer term experience that lead to significant structural brain changes during the acquisition and maintenance of a new skill, such as juggling, the use of novel tools, and navigation (Maguire et al. 2000; Draganski et al. 2004; Quallo et al. 2009; Taubert et al. 2010).

Indeed, the reported structural effects of bilingualism in adults (measured as differences between bilingual and monolingual groups, or sometimes in training studies) are most commonly reported in grey matter regions that have been found to underlie such language-related (as well as other) processes (for details, see Pliatsikas 2019). These regions primary include: frontal and nearby cortex, including the three portions of the left inferior frontal gyrus (IFG), namely, opercularis (IFGop), triangularis (IFGtr), and orbitalis (IFGor), as well as the frontal pole, the middle and superior frontal gyri (MFG and SFG), and the anterior cingulate cortex (ACC); temporal cortex, including the superior, middle, and inferior temporal gyri (STG, MTG and ITG), Heschl's gyrus, the temporal pole, and the hippocampus; and parietal cortex, including the supramarginal gyrus, the angular gyrus, and the superior parietal lobule (Mechelli et al. 2004; Mårtensson et al. 2012; Abutalebi et al. 2014a; Klein et al. 2014; Stein et al. 2014; Kaiser et al. 2015; Olulade et al. 2016; Hämäläinen et al. 2018). Subcortical structures that are affected mainly include the basal ganglia, in particular the caudate nucleus, the putamen, and the globus pallidus, as well as the thalamus (Burgaleta et al. 2016; Pliatsikas et al. 2017; DeLuca et al. 2019a), with some effects also having been reported in the cerebellum (Filippi et al. 2011, 2020; Pliatsikas et al. 2014). Moreover, several white matter tracts that provide connectivity between these structures (among others) have also been found to be modified by bilingualism. These primarily constitute the inferior and superior longitudinal fasciculi (ILF and SLF), the inferior fronto-occipital fasciculus (IFOF), the arcuate and uncinate fasciculi, and the anterior thalamic radiation, as well as the corpus callosum, including the forceps major and the forceps minor (Luk et al. 2011; Pliatsikas et al. 2015; Kuhl et al. 2016; Mamiya et al. 2016; Rossi et al. 2017; Anderson et al. 2018; Singh et al. 2018).

The specific structural effects of bilingualism within adults appear to vary as a function of both experience and age. In young adult bilinguals with limited experience using their second language (L2) (e.g., with limited immersion in second language speaking environments), studies have typically found cortical tissue increases, reflected as increased cortical thickness and/or cortical volumes (we are not aware of any research examining surface area differences as a function of bilingualism in adults). Such increases have been observed in multiple frontal, temporal, and parietal regions in bilinguals as compared to monolinguals (Mechelli et al. 2004; Ressel et al. 2012; Klein et al. 2014; Olulade et al. 2016) as well as in younger adults in language training studies, at least in initial stages (Mårtensson et al. 2012; Bellander et al. 2016). In contrast, young adult bilinguals with limited L2 experience often show limited or no changes in subcortical volumes (Pliatsikas et al. 2017). Rather, subcortical differences seem to be found in more experienced bilinguals (in particular, those with substantial L2 immersion, or in bilingual environments), who show greater volumes than monolinguals in a variety of subcortical structures, especially in the basal ganglia (in the caudate, putamen, and globus pallidus), as well as the thalamus and the cerebellum (Burgaleta et al. 2016; Pliatsikas et al. 2017; DeLuca et al. 2019b). Similarly, white matter effects have not been reported in young adults with limited L2 experience. Instead, experienced bilinguals (again, especially those with substantial immersion/in bilingual environments) seem to show white matter differences, as compared to monolinguals, in particular increases in white matter integrity (generally reported as increases in fractional anisotropy [FA] and/or decreases in mean diffusivity [MD]; see below for definitions) (Pliatsikas et al. 2015; Rahmani et al. 2017). Experienced bilingual young adults, by contrast, generally do not show differences in cortical thickness or volume as compared to monolinguals (Pliatsikas 2019). Somewhat different patterns have been observed in older (and typically longer term) bilingual adults, who generally show (in comparison to age-matched monolinguals) greater grey matter volumes in similar cortical as well as subcortical regions, in addition to greater white matter integrity, again mainly in the same tracts that are found to change in younger adult bilinguals (Luk et al. 2011; Abutalebi et al. 2014a, b; Olsen et al. 2015; Anderson et al. 2018; Borsa et al. 2018; Del Maschio et al. 2019).

What may explain these patterns? A detailed review of the neuroplastic effects of bilingualism (see Pliatsikas 2019) is beyond the scope of this paper. However, the available models that attempt to explain such effects and their underlying mechanisms have certain common denominators, in addition to the claim that any changes result from the bilingual experience (Li et al. 2014; Hernandez et al. 2015, 2018; Abutalebi and Green 2016; Grundy et al. 2017). Based on these accounts, and in addition to the expansion-partial renormalization hypothesis (EPH), which is designed to explain experience-dependent adult grey matter changes in general (Lövdén et al. 2013), Pliatsikas (Pliatsikas 2020) proposed the dynamic restructuring model (DRM) of structural changes due to bilingualism.

First of all, according to the EPH (on which Pliatsikas's DRM is based), the acquisition of a new skill is marked by transient local expansions in the implicated grey matter regions, reflected as increases in cortical thickness or volume (such effects on cortical surface area are not discussed in Lövdén et al. 2013). These grey matter tissue increases are posited to be likely due to the development of new dendritic spines (perhaps as well as neurogenesis). The increases are followed by a slow process of pruning older and idle spines, eventually leading to the gradual reversal of the initial expansions (perhaps even back to baseline), as the newly formed networks become more efficient over time and with practice. Putting this into the context of bilingualism, Pliatsikas ( 2020) suggested that the local grey matter increases reported in relatively inexperienced bilinguals, including those at earlier stages of learning in language training studies, reflect an initial tissue increase in brain structures related to language learning and use. As language use becomes more efficient, these structures are posited to gradually return to baseline. Together, this may explain the patterns described above: cortical thickness and volume increases in less experienced bilinguals (vs. monolinguals) and at early stages of language training in young adults, as well as a lack of such differences in more experienced bilinguals. The emergence of larger subcortical volumes in younger adult bilinguals with greater experience, in particular in the basal ganglia, may be due to their ongoing roles in language control as well as later stages of language learning (proceduralization; Ullman, 2016, 2020); interestingly, in both of these cases one may expect gradual increases as well as eventual (but much later) decreases, as language control becomes more efficient and as more of the language is proceduralised.

Age is also posited to contribute to at least some of the observed structural changes in adult bilinguals. The greater grey matter cortical and subcortical metrics (thickness, volumes) in older adult bilinguals, as compared to older monolinguals (see above), is usually interpreted as greater age-related tissue loss for monolinguals, as a result of "strengthened" local connections becoming more resilient to ageing, which might help explain why these effects in older adults only emerge in experienced bilinguals (Perani and Abutalebi 2015). Much less attention, however, has been devoted to possible age effects during child development, even though much of the bilingual literature described above relates to additional languages learned (at least part) before early adulthood. Nevertheless, it has been suggested that at least some of the grey matter increases observed in adult bilinguals, as compared to monolinguals, might be explained by less pruning (less grey matter tissue loss) in bilinguals during brain development, even though neural mechanisms for this process were not discussed (de Bot 2006).

Again putting this into the context of bilingualism, in the DRM, Pliatsikas ( 2020) argued that increases in white matter integrity should generally emerge in more experienced young adult bilinguals, as has been observed. This could be due to increased optimization of different sorts, including proceduralization and various aspects of language use and control. Along these lines, since bilingualism is a life-long experience (at least for those with substantial L2 immersion or in bilingual environments, who show the clearest white matter changes; see above), the increased integrity might persevere in older age and counteract neurodegeneration, giving rise to the effects reported in older bilinguals (Perani and Abutalebi 2015). We are not aware of any theoretical suggestions that adult bilingual/monolingual differences in white matter integrity can be attributed to bilingual/monolingual differences in how the brain develops during childhood and adolescence.

In sum, the evidence (and the DRM) suggests that, at least in adults, bilingualism-induced neuroplasticity cycles dynamically through stages. Specifically, relatively early regional cortical grey matter increases likely related to initial stages of language learning, use, or control later proceed to cortical grey matter renormalization, subcortical grey matter increases, and increases of white matter integrity, all possibly related to progressively more efficient language learning, use, or control. However, the role of bilingualism in the development of grey and white matter structures prior to adulthood in explaining the observed patterns in adults has, as of yet, barely been discussed. Moreover, as we will see below, there has also been very little (empirical or theoretical) work on structural brain changes due to bilingualism within children and adolescents. This paper addresses these issues.

Brain development in childhood and adolescence

Before reviewing the available evidence regarding structural effects of bilingualism on the developing brain, it is useful to first discuss the more general patterns of brain development in (typical) childhood and adolescence. A note on terminology: for simplicity, in this paper we use "development", "changes" and similar terms to refer to all age-related differences found during childhood and adolescence, including in cross-sectional studies, which constitute the majority of the extant literature on this topic. However, we remind the reader that caution is warranted regarding the inference of developmental trajectories from non-longitudinal data.

Grey matter development from early childhood through adolescence has been studied quite extensively (Giedd 2004; Tamnes and Østby 2018; Vijayakumar et al. 2018; Foulkes and Blakemore 2018). Overall, the evidence suggests that grey matter generally (but not always) decreases (reflecting developmental grey matter loss) between early childhood and early adulthood, across cortical and subcortical structures. However, the (linear or nonlinear) trajectories of these decreases seem to vary as a function of different factors, including which measures are used (volume, thickness, or surface area) and which structures are examined (Fjell et al. 2009). First of all, cortical thickness tends to decrease continuously throughout childhood and adolescence, though with some regional variability (Remer et al. 2017; Tamnes et al. 2017). In contrast, both cortical surface area and cortical volume show clear nonlinear trajectories, with early increases often peaking within the first decade or so, followed by sustained decreases into early adulthood, with the exact shape varying according to the region examined (e.g., peak hippocampal volumes occur somewhat later, around age 15) (Wierenga et al. 2014; Mills et al. 2016; Tamnes et al. 2017). The volumes of subcortical structures seem to show a more varied pattern: whereas some structures show a similar trajectory of increases (peaking around age 15) followed by decreases (e.g., globus pallidus, amygdala, cerebellum), others instead show linear decreases throughout childhood and adolescence to early adulthood (e.g., the caudate nucleus, putamen, and nucleus accumbens, which together form the striatum), though again there is some variability, including as a function of factors such as sex (Tiemeier et al. 2010; Wierenga et al. 2014; Herting et al. 2018). Overall, the mechanisms underlying these different developmental trajectories remain unclear, though various factors likely contribute to them, including the developmental pruning of dendritic and axonal connections as well as increasing myelination (which may be a significant driver of cortical changes in adolescence) (Chechik et al. 1998; Luo and Leary 2005; Whitaker et al. 2016).

With respect to white matter, a substantial literature employing diffusion-weighted imaging has shown continuous increases in white matter integrity from early childhood to early adulthood, generally reflected as increases in FA and/or decreases in MD (Giedd 2004; Qiu et al. 2008; Lebel and Deoni 2018; Lebel et al. 2019). However, these trajectories show variability according to which tract is examined, as well as according to factors such as sex, genetics, and environmental variables (Lebel and Deoni 2018). Evidence points towards increasing myelination during development as an underlying mechanism that may help explain these trajectories, though the exact nature of this process remains unclear (e.g. increased axonal myelination and/or axonal packing, or other factors; for a review, see Beaulieu, 2002).

Bilingualism and brain development

Even with the increasing interest in the effects of bilingualism on brain structure in adults, there has been very little work examining such effects in children or adolescents, let alone investigating whether developmental trajectories of grey or white matter might be affected by bilingualism. This, despite the suggestion that bilingual brain effects in adults may be at least partly explained by developmental bilingual effects (see above; de Bot, 2006), let alone that the influence of bilingualism on brain development is of interest in its own right.

We are aware of four studies of bilingual effects on grey matter in children or adolescents. The first such evidence was presented by Della Rosa and colleagues (2013), who examined 10-year-old children growing up in a multilingual environment. The children were scanned twice, 12 months apart. The study examined changes specifically in cortical volume, and focused on the left inferior parietal lobule. Della Rosa et al. reported that individuals' combined competence of their various spoken languages predicted increases during the time period in the cortical volume of the left inferior parietal lobule, a region that appears to underlie (among other functions) the processing of the meaning and phonology of newly acquired words (Richardson et al. 2010). This is the only longitudinal study on children to date probing aspects of bilingualism or multilingualism on grey matter. However, the study did not include a monolingual control group. Given that 10-year olds typically still learn words even in their native language, which in itself might introduce structural changes (Lee et al. 2007), such a comparison would be necessary to attribute the observed effects to multilingualism.

In another study that tested effects of bilingualism on grey matter, Brito and Noble ( 2018) compared cortical thickness and surface area between monolinguals and bilinguals, aged between 3 and 21 years. Several covariates were included in their analyses, including age, sex, socioeconomic status, and genetic ancestry. Cortical volumes, as well as subcortical structures, were not examined. The study focused on left IFG, left and right MFG, left STG, and ACC. No main effect of bilingualism (vs. monolingualism) was reported. They additionally examined the effect of bilingualism separately in groups of younger (3-11 years) and older (12-21 years) individuals. The potential effect of bilingualism on developmental trajectories of brain structures was not examined (e.g., no direct comparison of bilingual effects between the age groups was reported). No effect of bilingualism was found in the younger age group. However, in the older group, bilinguals showed greater surface area in the ACC, a region that is central to language control (Abutalebi and Green 2016). Brito and Noble ( 2018) interpret their findings as suggesting that there might not be robust structural effects of bilingualism in younger children, though it is important to emphasize that averaging across such a large age range (3-11) may obscure developmental patterns.

Finally, Thieba, Long, Dewey and Lebel ( 2019) compared 3-to-5-year-old children raised in a multilingual environment to children raised in a monolingual environment. The two groups were matched on age, sex, and both maternal education and household income (measures of socioeconomic status). Age effects were not examined. They focused (bilaterally) on three cortical regions of interest (IFGop, IFGtr, IFGor), and moreover performed exploratory analyses on 31 additional cortical (sub)regions, again bilaterally. In all regions, they examined cortical thickness, surface area, and volume. Subcortical structures were not probed. None of their findings survived statistical correction, though in their uncorrected results they reported thicker cortex in the left IFGop and the right caudal MFG, as well as larger volumes in the left caudal ACC, the left caudal MFG, and the right MTG, in the multilingual group as compared to the monolingual group. It is worth noting that the increased cortical thickness in the IFGop and MFG matches the finding of increased cortical thickness for unbalanced (vs. balanced) bilinguals in the same two structures in Archilla-Suerte et al. (2018). This overlap seems consistent with the fact that the participants in Thieba et al. were much younger, and, therefore, had relatively little bilingual experience, just as might be the case for the unbalanced (vs. the balanced) older bilingual children in Archilla-Suerte et al. Therefore, both groups might have been at an earlier stage of bilingualism-induced neuroplasticity in which increased cortical thickness is observed.

The dearth of evidence regarding the effect of bilingualism on brain development is particularly evident in the very limited literature on white matter. We are aware of only two studies on this topic. Mohades and colleagues (2012) compared three groups of 8-11-year-old children: simultaneous bilinguals, sequential bilinguals, and monolinguals. The groups had similar age and sex distributions. The effect of age was not examined. The study focused on FA in four tracts (IFOF, SLF, and two bundles related to the corpus callosum). They reported greater FA values in the left IFOF (which plays important roles in language) for simultaneous bilinguals compared to both sequential bilinguals and monolinguals, as well as in one of the corpus callosum bundles (connecting orbital frontal cortex bilaterally) for monolinguals compared to the two bilingual groups. Interestingly, and despite not emerging as significantly different, the FA values in the left IFOF for sequential bilinguals were intermediate between the values of the two other groups. Additionally, the same participants were scanned again after 2 years (Mohades et al. 2015), in a longitudinal design. The study reported that while all groups showed a significant increase in their FA values in the left IFOF over the 2 years (as expected, given general developmental increases in white matter integrity; see above), the increase was larger for the sequential bilinguals than the other two groups. This appears to be consistent with experience-based increases in white matter integrity due to the bilingual experience. Note that the finding that a larger increase was observed for sequential than simultaneous bilinguals might be partly due to the latter having undergone earlier increases due to their experience.

In sum, there is an emerging literature on the effects of bilingualism on brain structure during development. The findings reveal intriguing overlap both between different developmental groups (e.g., younger bilinguals and somewhat older unbalanced bilinguals), as well as between developmental and adult bilingual effects (see Sect. Bilingualism and brain structure in adults). Overall, the results suggest that similar experience-based effects may be found during development and in adults, and that developmental patterns might in fact help explain adult patterns.

Nevertheless, a number of important gaps remain, including the following. First and perhaps most importantly, there is a dearth of research comparing the developmental trajectories of brain structures between bilinguals and monolinguals, that is, examining interactions between bilingualism/monolingualism and age, including nonlinear effects. Indeed, we are not aware of any studies probing this issue over the course of childhood and adolescence. Such studies seem critical for understanding how development may lead to bilingual effects in adults, let alone for understanding how bilingualism affects the course of development. Second, not all studies have controlled for certain potentially confounding variables (e.g., sex, socioeconomic status). Third, most previous developmental studies probing bilingual effects prior to adulthood have focused on specific brain structures: generally particular grey matter cortical regions, leaving subcortical structures and white matter greatly understudied. Fourth, prior studies have also focused on particular measures that moreover often differ between studies (e.g., only volume, or only cortical thickness and surface area, or only FA). Indeed, we are not aware of any research that has widely examined both grey and white matter within subjects, let alone with all major measures (cortical thickness and surface area, volumes, FA, and MD). Thus, our understanding of the potential effects of bilingualism on brain development is still quite limited. This gap seems to warrant clarification with a more comprehensive study.

The present study

The present study was designed to address these gaps. We analyzed a large existing dataset of children and adolescents (PING, Jernigan et al. 2016), aged 3-21. The dataset included both bilinguals and monolinguals, who were reasonably evenly distributed across the age range. To comprehensively investigate the effect of bilingualism on brain development, we directly compared the two groups' developmental trajectories in a wide range of brain structures. We controlled for a number of potentially confounding variables, including sex, genetic ancestry, and socioeconomic factors (both parental education and household income) (Bakken et al. 2011; Noble et al. 2015; Fan et al. 2015; Romeo et al. 2018; Foulkes and Blakemore 2018). We examined effects bilaterally in a wide set of (both cortical and subcortical) grey matter structures and white matter tracts. For grey matter, we probed cortical thickness, surface area, and volume (with the latter also examined for subcortical structures), while for white matter we examined both FA and MD.

If bilingualism indeed has somewhat analogous effects on the brain in children and adolescents as in adults, then one might expect similar grey and white matter effects of bilingualism during development as in adults, in similar structures, though (in some manner) overlaid or interacting with more general developmental brain trajectories (which are described in Sect. Brain development in childhood and adolescence). Thus, we might expect the following: (a) For cortical grey matter, the increases in cortical thickness and volumes observed in adult bilinguals at earlier stages of the bilingual experience (in training studies or as compared to monolinguals), followed by gradual decreases in more experienced bilinguals, may be reflected during development as initial increases followed by gradual decreases over the course of childhood and adolescence. These patterns should overlap with the general developmental trajectories of continuous decreases in cortical thickness, and nonlinear increases followed by decreases in cortical volume. Thus, the expected bilingual vs. monolingual "increases" would manifest as less steep age-related decreases (less grey matter loss) in cortical thickness, and perhaps volume as well, with these differences eventually disappearing as the trajectories of the two groups gradually converge. The dearth of adult bilingual evidence from cortical surface area precludes clear bilingual vs. monolingual developmental predictions for this measure. (b) By contrast, and again based on the adult bilingual literature, during the course of development we might expect increases in subcortical volumes in bilinguals as compared to monolinguals, but only after bilinguals have experienced a substantial period of bilingualism, perhaps around the same period that bilingual/monolingual differences in cortical thickness and volume cease to be observed. Again, this pattern should overlap with overall developmental trajectories for subcortical structures. (c) Similarly, greater white matter integrity in bilinguals as compared to monolinguals may be expected to emerge only at higher levels of bilingual experience during childhood or adolescence (perhaps around the same time as the subcortical bilingual/monolingual differences emerge), again overlapping the general developmental pattern of increases in white integrity (as measured by FA and/or MD).

In sum, it seems reasonable that over the course of development, one might expect the following: Bilinguals should show increasing cortical thickness and volumes as compared to monolinguals (less steep decreases, that is, less grey matter loss), with no or few concurrent group differences in either subcortical volumes or white matter. In contrast, subcortical volumes and white should show bilingual effects (larger subcortical volumes and increased white matter integrity as compared to monolinguals) mainly at later stages of experience, at about the same time that cortical thickness and volume differences are no longer observed.

Methods

Participants

This study analyzed the dataset provided by the Pediatrics, Imaging, Neurocognition and Genetics (PING) project (Jernigan et al. 2016) (also examined in Brito and Noble 2018). PING is a multi-site collaborative repository comprising demographic, neuroimaging, medical, and cognitive data from 1493 typically developing children, adolescents, and young adults aged between 3 and 21 years. Neuroimaging data for grey matter metrics were available from 1293 participants, and for white matter metrics from 1119 participants. Because this study was concerned with the brain structure and language background of typically developing individuals, we excluded participants with a diagnosis or personal history of ADHD, learning problems, hearing problems, speech and language therapy, meningitis, seizures, head injury, or "unconsciousness". Moreover, we restricted our analysis to participants that reported English as their first language (see below). Any participants with missing data points for key variables in our models (age, sex, parental education, household income, genetic ancestry, scanner site, as well as language background), were excluded. This resulted in final samples of 711 participants with grey matter data and 637 with white matter data. Finally, the participants were split into groups according to whether they spoke language(s) in addition to English (bilinguals), or not (monolinguals); see below. Demographic and other participant-level information for the final sample are provided in Table 1.

Data description

Background participant information

A number of measures of key background characteristics (see " Introduction" and Table 1) were used as covariates in the analyses: sex (male/female); scanner site (which of the thirteen scanners employed in the PING data was used for the particular participant); socioeconomic status, included both as level of household income, provided in twelve bands in PING, as well as highest parental education level, provided in seven bands (see Table 1); and genetic ancestry of the participants, provided by PING in the form of six Genetic Ancestry Factors (proportion of European, African, American Indian, East Asian, Oceanian, and Central Asian ancestry for each participant).

The background questionnaire that was administered as part of PING included the following two questions related to language use: (a) " Was English the first language participant learned to speak?" (Yes/No), and (b) " Does participant speak a language other than English? " (Yes/No). Only participants who responded "Yes" to (a) were included. These were subsequently split into bilinguals (potentially including multilinguals) and monolinguals based on their response to (b) (coded in our analyses as bilingualism: yes/no). Note that participants who responded "No" to (a) were excluded, as in this case it was not possible to judge whether they were monolingual or bilingual.

Brain data

PING provides pre-processed measures of brain morphology obtained from a Freesurfer processing pipeline (Fischl 2012); see Jernigan et al. ( 2016) for a detailed description of data collection and processing. We analyzed data from all grey matter structures and white matter tracts available in the PING database (see Table 2). For cortical grey matter, PING provides measures of thickness, volume, and surface area for 33 cortical regions, based on the cortical parcellation described in Desikan and colleagues (2006, Table 1). Further, PING includes volumes of eight subcortical structures and the cerebellum (Fischl et al. 2002). With respect to white matter, PING provides measures of fractional anisotropy (FA) and mean diffusivity (MD), as well as of longitudinal (axial) and transversal (radial) diffusivity, for 20 tracts or tract subdivisions (Hagler et al. 2009; Yendiki et al. 2011). In this study, we focus on FA and MD, since longitudinal and transversal diffusivity can be difficult to interpret (Singh et al. 2018), and indeed are often not reported or discussed in studies probing white matter integrity, including in the bilingual literature (but see Singh et al., for adults). Nevertheless, for the sake of completeness and transparency, since these measures were available in PING, we performed the same analyses on these as on the other metrics (see below), and report significant effects in Supplementary Material; we do not discuss these measures further in the paper.

Data analysis

Data were analyzed in R (R Core Team 2014) with generalized additive models (GAMs), using the bam() function in the mcgv package (Wood 2011). GAMs provide the means to fit a nonlinear regression spline, that is, a 'wiggly' curve that consists of the sum of simpler nonlinear functions. Additionally, GAMs prevent overfitting the data by penalising such wiggliness, which is only included when there is sufficient evidence for a particular shape. For these reasons, GAMs are well suited to model a variety of curved shapes, including the nonlinear patterns observed in brain development as a function of age (Chang et al. 2016; Tamnes et al. 2017). Separate GAMs were run for each structure/tract and each metric: cortical thickness, surface area, and volume for grey matter cortical regions; volume for grey matter subcortical structures and the cerebellum; and FA and MD for white matter.

We applied an analytical procedure akin to using a "vibration of effects" approach (Patel et al. 2015). This approach involves running a number of alternate models in which parameters are slightly changed across them. Reliability is assessed in terms of consistency across the models; that is, effects that are consistent across alternate models can be considered to be reliable. In GAMs, the estimation of nonlinear effects, and in particular of nonlinear 'difference curves' (for example, the difference between the effect of age for monolinguals and bilinguals) can be sensitive to the particular specification of parameters in the model, specifically, to what level is chosen as the reference. Thus, in the present study, the comparison reference level for key variables of interest (bilingualism, hemisphere) was changed to generate a set of similar models. For example, for the first-level model (see just below), the GAM for each bilateral structure/tract and metric was run four times, with all combinations of bilingualism (yes/no) and hemisphere (left/right) as reference levels. Following a conservative approach (to minimise type I errors), effects of interest were assessed as reliable only if they were significant across all of these models ( p s < 0.05), and thus were not dependent on which reference level was used for comparison.

For the first-level model ('Model 1' in the 'Code for data analysis' in Supplementary Material), for each bilateral structure/tract (that is, all apart from the corpus callosum and forceps minor and major) and each metric, we first applied a GAM in which we fit a regression spline for the effect of age (for the group and hemisphere that were selected as reference levels), as well as the difference curves corresponding to the age x bilingualism interaction, the age x hemisphere interaction, and the three-way age x bilingualism x hemisphere interaction. This model also included random effects for participant and scanner site, as well as the covariates parental education, household income, and genetic ancestry as linear terms. Sex was not included as a covariate because it was well matched (see Table 1) within each participant group (i.e., for bilinguals and monolinguals, for both the grey and white matter analyses). As described above, this model was run four times (for each structure/tract and metric). If the three-way interaction (age x bilingualism x hemisphere) was significant in all four models, these were followed up by the second-level model (see next paragraph), separately for each hemisphere. If the three-way interaction was not significant in all four models (or if none of the second-level analyses in either hemisphere were significant, following up from a significant three-way interaction), we again ran the second-level model, but analyzing both hemispheres together (as two repeated measures for each participant).

The second-level model ('Model 2′) included a term for bilingualism (as an ordered factor, which allows estimating interactions), a regression spline term for age, and a difference curve for the age x bilingualism interaction, as well as all random effects and covariates included in the first model. This allowed us to assess whether the pattern of age-related changes (that is, the developmental trajectory) differed between bilinguals and monolinguals: either in each hemisphere separately (as follow-ups to reliable three-way interactions), or across the two hemispheres (if the corresponding three-way interaction was not reliable). This model was also applied to the corpus callosum, forceps minor, and forceps major, for which the first-level model was not run. In all instances, the second-level model was run twice, once with each level of bilingualism (monolingual and bilingual) as the reference level. It was considered reliable only if the age by bilingualism interaction was significant for both reference levels.

Finally, for structures/tracts and metrics where the age x bilingualism interaction in the second-level model was reliable (significant for both reference levels of bilingualism), a third-level model ('Model 3′) was run, to unpack this interaction. This model included a main effect of bilingualism, and a regression spline for age for each level of bilingualism (Yes/No) separately.

Results

As described above, the first-level analysis for all structures/tracts and metrics examined the three-way interaction between age, bilingualism, and hemisphere. In only two cases did these models yield reliable three-way interactions (that is, the model was statistically significant across all four reference levels, as described above): for the volume of the putamen and the surface area (SA) of the posterior cingulate cortex. However, the follow-up second-level analyses for these three-way interactions did not yield any significant effects of bilingualism, that is, either main effects of bilingualism or age by bilingualism interactions, for either hemisphere and for either of these structures and metrics; see Table 3.

Following our analysis plan, we then ran second-level models on all structures/tracts and metrics while analysing both hemispheres together (as well as on the corpus callosum, forceps minor, and forceps major), to test for two-way interactions between age and bilingualism, independent of hemisphere. Eighteen of these analyses yielded reliable age by bilingualism interactions (that is, the interaction was statistically significant across both reference levels, as described above). This indicates that in these 18 cases, the developmental trajectories of these metrics for these structures or tracts differed significantly between bilinguals and monolinguals. See Table 4 and Figs. 1, 2. The 18 analyses included 12 cortical regions with cortical thickness as the metric (Table 4, Fig. 2), 4 cortical regions with cortical volume as the metric (Table 4, Fig. 3), 1 cortical region with cortical surface area as the metric (Table 4, Fig. 4), and 1 white matter tract with FA as the metric (Table 4; Fig. 5). Figure 1 labels the cortical regions that showed any reliable age by bilingualism interactions, that is, for cortical thickness, volume, or surface area. Note that a total of 14 cortical regions and 1 white matter tract showed reliable analyses, since 2 cortical regions (the precuneus and superior frontal gyrus) yielded reliable interactions in more than 1 metric; see Table 4. No subcortical structures yielded significant age by bilingualism interactions.

The 14 cortical regions that showed reliable age by bilingualism interactions, that is, for any of the three cortical metrics (cortical thickness, volume, and surface area)

Cortical regions yielding reliable age by bilingualism interactions for cortical thickness. This displays which cortical regions show significantly different developmental trajectories for cortical thickness between age 3 and 21 for bilinguals versus monolinguals. In panel A, the color map reflects F values of each interaction between ages by bilingualism (based on the smaller F value of the two alternate analyses). In panel B, the developmental trajectories of bilinguals are shown in red, while the trajectories of monolinguals are shown in blue

Cortical regions yielding reliable age by bilingualism interactions for cortical volume. This displays which cortical regions show significantly different developmental trajectories for cortical volume between age 3 and 21 for bilinguals versus monolinguals. See Fig. 2 for more information

The single cortical region that yielded a reliable age by bilingualism interaction for cortical surface area. This region shows significantly different developmental trajectories for cortical surface area between age 3 and 21 for bilinguals versus monolinguals. See Fig. 2 for more information

The single white matter tract that yielded a reliable age by bilingualism interaction, specifically for fractional anisotropy. This tract shows significantly different developmental trajectories for FA between age 3 and 21 for bilinguals versus monolinguals. Panel A adapted, with permission, from Reyes et al. ( 2018)

Discussion

In the present study, we investigated whether and how brain development may be affected by bilingualism-that is, to what extent being bilingual (or multilingual) versus monolingual might influence the developmental trajectories of brain structures during childhood and adolescence. To examine this question, we analyzed a large dataset of individuals, aged 3-21, that included both bilinguals and monolinguals reasonably evenly distributed across the age range (PING) (Jernigan et al. 2016). We analyzed cortical thickness, volume, and surface area for 33 cortical regions, as well as subcortical volumes for 8 structures, for 711 participants; and both fractional anisotropy (FA) and mean diffusivity (MD) for 20 white matter tracts, for 637 participants. The data were analyzed with generalized additive models (GAMs) to reveal the expected nonlinear patterns of brain development while preventing overfitting, while using an analytical procedure akin to a vibration of effects approach to minimize Type I errors.

Interpretation

Our predictions were based on a combination of previous data and models pertaining to the effects of bilingualism on the brain in adults, together with well-documented general effects of development on the brain. Specifically, we expected that, on top of basic brain developmental patterns, during development greater cortical thickness and volumes (e.g., due to less steep developmental decreases for cortical thickness, that is, less grey matter loss) should emerge for bilinguals as compared to monolinguals, followed by gradual convergence of these metrics between the two groups. (The paucity of adult bilingual evidence from cortical surface area precluded predictions regarding this metric.) In contrast, greater bilingual (vs. monolingual) subcortical volumes and white matter integrity were only expected at later stages of bilingualism, around the same time that cortical differences cease to be observed.

The analyses revealed that while both bilinguals and monolinguals showed the expected general developmental patterns of brain structures, they also showed differences in their developmental trajectories.

First, consistent with the broader developmental literature, both groups showed the following patterns (see Table 4 and Figs. 2, 3, 4, 5). Both evidenced continuous decreases in cortical thickness throughout childhood/adolescence (Remer et al. 2017; Tamnes et al. 2017). In both, cortical volume and cortical surface area generally showed more nonlinear trajectories, often with early increases or stability followed by decreases (Wierenga et al. 2014; Mills et al. 2016; Tamnes et al. 2017). And in both groups, white matter integrity increased continuously with age (Lebel and Deoni 2018; Lebel et al. 2019). Overall, this demonstrates that the patterns observed here follow expected trajectories of brain development.

Second, and of greater interest here, the two groups also showed developmental differences, which were moreover strikingly similar across measures and structures (Table 4 and Figs. 2, 3, 4, 5). As expected based on the adult literature (see above), greater cortical thickness and cortical volume (less developmental grey matter loss) were observed for bilinguals as compared to monolinguals across multiple cortical regions, with this deviation becoming apparent around late childhood to early puberty (in most cases about age 10-13 for cortical thickness and 12-14 for cortical volume). Moreover, both for cortical thickness and volume, this pattern continued in most regions; though in some cortical areas the values for bilinguals and monolinguals reconverged in early adulthood, about age 20-21. Additionally, across cortical regions, a very early and unexpected difference between the groups was observed: starting from around age 3, the lowest age in our sample, bilinguals displayed lower cortical thickness and volumes than monolinguals, with these values only converging between the groups from around mid to late childhood to early puberty. However, given the relatively small sample size of the bilingual group at the youngest ages (Table 1), this finding should be treated with caution. Interestingly, though we did not have clear predictions for cortical surface area (due to the dearth of studies examining this metric in bilinguals; see above), the one region showing bilingual/monolingual differences in surface area showed a pattern somewhat similar to that observed for cortical volumes, in particular during early childhood. In sum, the bilinguals consistently showed lower values for cortical metrics than monolinguals until mid to late childhood or early puberty, at which point they generally diverged again in showing larger values (especially for cortical thickness and volume), with this bilingual increase continuing into early adulthood, or in some cases reconverging.

With respect to white matter, one tract showed bilingual/monolingual differences, that is, striatal-inferior frontal fibers for FA. Strikingly, this again yielded the same pattern as cortical regions, namely lower FA values from age 3 for bilinguals than monolinguals, with the groups gradually converging, till the bilinguals began to show larger values from about age 16. Overall, this suggests a greater increase for bilinguals than monolinguals in white matter integrity over the course of development.

The pattern of results suggests consistency not only in the developmental trajectories of the different metrics, but also in which parts of the brain show effects. In particular, almost all of the bilingual/monolingual by age interactions were found for frontal or parietal structures. This held across all three cortical metrics (thickness, volume, surface area) and the one implicated white matter tract (which connects the striatum and inferior frontal cortex). Only two regions yielded effects outside these two lobes (one in the temporal lobe, one in the occipital lobe), neither of which were among the strongest interactions observed.

How might the implication of frontal and parietal structures in bilingual/monolingual developmental differences, as well as striatal-inferior frontal structural connectivity, be interpreted? Though interpretation must be made with caution, due to the risk of reverse inference (Poldrack 2006, 2011), this pattern appears to be consistent with the involvement of particular circuits (networks).

First of all, according to certain 'dual-stream' views, language and audition (as well as vision) are processed by a ventral stream that is closely related to temporal cortex and underlies aspects of auditory object identification and meaning, together with a dorsal stream that projects from parietal (mainly inferior parietal and nearby cortex) to frontal regions (mainly (pre)motor and IFG pars opercularis) and is primarily involved in sensory-motor integration and articulatory functions (Hickok and Poeppel 2007; Rauschecker and Scott 2009). In the present study, the implication both of inferior parietal regions (supramarginal gyrus and inferior parietal) and motor and related regions (precentral, paracentral, as well as posterior portions of superior frontal and perhaps caudal middle frontal) as well as IFG pars opercularis, thus appears to be consistent with changes in the dorsal stream in the developing bilingual brain. Indeed, the supramarginal gyrus, pars opercularis, and superior frontal gyrus showed particularly reliable differences between developmental trajectories for bilinguals and monolinguals (Figs. 2 and 3). The involvement of the dorsal stream would be consistent with evidence suggesting increased contributions from this stream during at least reading in bilinguals as compared to monolinguals (Parker Jones et al. 2012; Bakhtiari et al. 2014), findings that have been attributed to increased articulatory competition in bilinguals (Parker Jones et al. 2012). A similar argument could also be made for increased competition among the different grammars in bilinguals, particularly given the dependence of grammar on pars opercularis and nearby premotor cortex (Friederici 2011). Nevertheless, bilingual-based developmental changes in the dorsal stream do not seem to full explain the observed patterns, not only because changes in other structures were also observed, but additionally because the supramarginal gyrus showed greater effects than more posterior aspects of inferior parietal cortex, which have generally been the focus in language-related dorsal stream models (Hickok and Poeppel 2007; Rauschecker and Scott 2009).

Second, it is possible that the pattern could (also) be due in part to changes in the procedural memory circuit that appears to underlie language (Ullman 2004, 2020). Both the basal ganglia (in particular the striatum) and frontal cortex (in particular (pre)motor regions and the IFG pars opercularis), as well as parietal regions, play key roles in this circuit, which is implicated in the learning, representation, and use of both first and second language (whereas dorsal stream models focus on first language). Procedural memory may underlie multiple portions of language, including grammar, speech-sound representations, articulation, and more generally aspects of both speech production and speech perception (Ullman 2020; Ullman et al. 2020). Thus, the greater involvement of this system in bilinguals than monolinguals would not be surprising, given the need for all these aspects of language to be supported in two languages in bilinguals, as compared to one in monolinguals. The involvement of the striatal-inferior frontal tract is particularly striking, especially given that it yielded the largest bilingual/monolingual by age interaction (see Fig. 5), that is, the greatest difference in developmental trajectories between bilinguals and monolinguals. Interestingly, given the suggestion that the learning of dorsal stream parieto-frontal circuits may depend importantly on procedural memory (Ullman 2004; Ullman et al. 2020), the findings here may be interpreted as implicating both dorsal stream and procedural memory functions as two sides of the same coin.

However, the above circuits and functions might not fully explain the bilingual/monolingual developmental changes observed here. In particular, although the precuneus was found to show different developmental trajectories between the groups in all three cortical measures (thickness, volume, surface area), it is not importantly implicated in two of the three circuits and associated functions above, and is not generally implicated with a primary role in executive functions. So what might (additionally) explain its involvement here? The precuneus has been implicated in various functions, perhaps most notably aspects of visuo-spatial processing and declarative memory (Cavanna and Trimble 2006). Intriguingly, some evidence suggests that the precuneous underlies not only visuo-spatial processing, but also the linguistic processing of spatial relations (Wallentin et al. 2008), though it remains unclear why this function should be more engaged in the bilingual than monolingual developing brain. In contrast, declarative memory is clearly involved in both first and second language (working closely together with procedural memory), in particular for lexical knowledge, but also for various other aspects of language, including grammar and speech-sound representations (Ullman 2020; Ullman et al. 2020). Thus, learning, representing, and processing two (or more) languages may engage declarative memory more than one language does. This view is also consistent with the finding here that the posterior parietal and inferior temporal cortex, both of which play important roles in declarative memory and associated functions (Ullman 2016; Tagarelli et al. 2019), show bilingual/monolingual differences in their developmental trajectories. Nevertheless, it remains unclear why other declarative memory structures, in particular the hippocampus and other medial temporal lobe structures, were not involved here-though interestingly, the hippocampus has been found to be enlarged in adult second language learners (Mårtensson et al. 2012), remains plastic in active adult bilinguals (DeLuca et al. 2019b) and its volume declines at a slower rate in older bilinguals as compared to older monolinguals (Li et al. 2017; Voits et al. 2020).

Overall, the pattern of structures showing different developmental trajectories between the two groups seems largely consistent with prior studies of the bilingual brain. In adults, bilingual/monolingual differences have been found mainly in frontal (and the ACC), temporal, and parietal regions, as well as the basal ganglia (mainly the striatum) and thalamus, and a number of (mainly cortico-cortical) white matter tracts (see Sect. " Bilingualism and brain structure in adults"). In children, the small number of studies (which, however, focused on specific structures) have mainly implicated frontal and nearby cortex (IFG pars opercularis, middle frontal gyrus, the ACC), temporal cortex (STG), parietal cortex (inferior parietal), the basal ganglia (striatum), and tracts related to frontal cortex. The present study, which examined a large sample size across multiple structures and a range of measures, extends and further specifies our understanding of bilingual/monolingual differences in development. In particular, while it confirms the involvement of IFG pars opercularis, middle frontal gyrus, inferior parietal cortex, and (at least connections with) the basal ganglia in the developing bilingual brain, it suggests that certain other structures may also play differential roles in bilingual and monolingual development, in particular, though not only, in frontal and parietal regions.

Implications, limitations, and conclusion

The study has a number of implications and limitations. The findings suggest that bilinguals' and monolingual's brains differ even during development. This clearly extends bilingual/monolingual brain differences from adults to the developing brain. The results suggest that the apparent resilience of the bilingual brain to aging (see introduction) may begin already during development, reinforcing the view that any beneficial effects of bilingualism on brain structure may require long-term experience using the two languages (Perani and Abutalebi 2015).

The finding that these patterns overlap to a fair extent with previously observed bilingual/monolingual brain differences in adults both validates the current findings with respect to the adult literature and suggests that some adult brain differences already emerge during childhood and adolescence. This is not in fact surprising given that the vast majority of studies of the adult bilingual brain examined bilinguals who learned more than one language prior to adulthood (Luk and Pliatsikas 2016; García-Pentón et al. 2016; Hayakawa and Marian 2019; Pliatsikas 2019). Nevertheless, the findings also indicate that during development, bilingual/monolingual differences are not exactly the same as those observed in adulthood. In particular, the results suggest that some bilingual/monolingual brain differences found during development disappear by early adulthood, while others appear soon thereafter. Indeed, we saw here that certain bilingual/monolingual differences that emerged by late childhood to early puberty (in particular, greater cortical thickness or volume for bilinguals in a number of regions, likely due to less grey matter loss during development) stabilized, reduced, or even disappeared by early adulthood. Additionally, certain findings that appear to be consistent in the adult literature in more experienced bilinguals were not found or were sparsely observed in the present study, in particular greater bilingual than monolingual subcortical and cerebellar volumes (not observed here) and greater white matter integrity (found in one tract).

Overall, these patterns seem in line with our predictions, namely that developing bilinguals without extensive experience should show greater cortical thickness and volume as compared to monolinguals, though these differences should then gradually decrease or disappear around the same time as they begin to show increased volumes for subcortical structures and greater white matter integrity. Interestingly, the lack of any bilingual/monolingual differences in subcortical volumes and the cerebellum, and the involvement of only one white matter tract, seems to be consistent with the finding that most cortical regions did not show full reconvergence between the groups by the oldest age in the sample, namely 21. These findings thus appear to jibe both with the broad adult literature (regarding both which structures are implicated and their changes over the course of bilingualism) and with our predictions for bilingual/monolingual development differences based in part on this literature.

Nevertheless, a number of limitations suggest the need for further studies. First, we did not predict the finding that at very early ages the bilinguals appeared to show lower values than monolinguals, indeed for nearly all metrics for all structures. It is not clear what may account for this pattern, though the relatively small sample size of bilinguals at these ages suggests treating this finding with caution. Second, the PING database did not contain detailed information regarding the ages of acquisition of bilinguals' languages, nor the amount or type of language experience they had. The very early bilingual/monolingual effects suggest that the children at this age were exposed to two (or more) languages from an early point, indicating that at least a portion of the sample experienced early ages of acquisition. This also suggests the possibility-though this is speculative-that the rest of the sample might also have experienced quite early exposures to their languages, given that the older children and adolescents may have been drawn from similar subject pools at the same testing sites. This possibility is supported by the observed trajectories of different bilingual/monolingual patterns at different ages, which (as we have argued above) appear to be consistent with experience-based changes over time with similar ages of acquisition across the participants. Nevertheless, the age of exposure of the older children in this sample was not known, and thus future research should examine whether the findings here replicate with populations with clearly documented early ages of exposure, as well as investigating developmental trajectories with later exposures. Third, it may be argued that further measures against type I errors could have been taken. Indeed, we did not correct for multiple comparisons. This decision was taken due to the fact that our approach attempted to balance the likelihood of type I and type II errors, and was already conservative in that it was designed to reduce type I errors by virtue of an analytical procedure akin to a vibration of effects approach, with only those effects with significant results across all alternative analyses being reported. Additionally, the high degree of consistency in the observed patterns in the relative trajectories of bilingual and monolingual brain measures suggests that the reported findings are not spurious. Together, this suggests that type I errors are not prevalent in the findings reported here. Finally, we emphasize that the present study (like the vast majority of developmental studies across large age ranges) is cross-sectional, so caution is warranted in extrapolating to actual developmental patterns within subjects. Nevertheless, we suggest that our findings can provide a solid foundation for well-designed longitudinal developmental studies.

In sum, the present study suggests that bilinguals and monolinguals differ in quite consistent ways regarding the developmental trajectories of brain structures from early childhood to early adulthood. Thus, the study clearly extends previous observations of bilingual/monolingual brain differences in adults to development. The evidence presented here suggests that, as compared to monolinguals, bilinguals show more grey matter (less developmental loss) starting around late childhood and adolescence, mainly in frontal and parietal regions, as well as increased white matter integrity (greater developmental increase) starting in mid-late adolescence, specifically in fibers connecting the striatum and inferior frontal cortex. The findings not only suggest that there may be a developmental basis for some of the structural brain differences found between bilingual and monolingual adults, but also indicate that some bilingual/monolingual differences may occur in the developing but not the adult brain. Overall, the data indicate that the bilingual brain does indeed differ from the monolingual brain, and that this difference begins to be apparent even during development.

Availability of data and material

Data obtained from a freely available dataset

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Acknowledgements

We thank Peter Molfese for helpful feedback on the paper, and Anny Reyes for providing the image of our white matter tract of interest.

Funding

This research was funded by a Franklin Research Grant from the American Philosophical Society and a Small Grant from the Experimental Psychology Society (UK), both awarded to C.P., and National Science Foundation BCS 1439290 to M.T.U.

Author information

Author notes

Lotte Meteyard and João Veríssimo denote equal contribution.

Authors and Affiliations

1. School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AL, UK

Christos Pliatsikas & Lotte Meteyard

2. Facultad de Lenguas y Educación, Universidad Nebrija, Madrid, Spain

Christos Pliatsikas

3. Potsdam Research Institute for Multilingualism, University of Potsdam, Potsdam, Germany

João Veríssimo

4. School of Psychology, University of Birmingham, Birmingham, B15 2SA, UK

Vincent DeLuca

5. Departments of Neuroscience and Neurology, Georgetown University, Washington, DC, USA

Kyle Shattuck

6. Department of Neuroscience, Georgetown University, Washington, DC, 20057-1464, USA

Michael T. Ullman

Contributions

The study was conceived and designed by Christos Pliatsikas and Michael Ullman. Data preparation and analysis were performed by Lotte Meteyard, João Veríssimo, and Vincent DeLuca. The first draft of the manuscript was written by Christos Pliatsikas and subsequent drafts co-authored by Christos Pliatsikas and Michael Ullman. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Christos Pliatsikas or Michael T. Ullman.

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Analysis code provided in Electronic Supplementary Information.

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Pliatsikas, C., Meteyard, L., Veríssimo, J. et al. The effect of bilingualism on brain development from early childhood to young adulthood. Brain Struct Funct 225, 2131-2152 (2020). https://doi.org/10.1007/s00429-020-02115-5
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Keywords

Bilingualism

Brain development

Grey matter

White matter

Generalized additive models

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The effect of bilingualism on brain development from early childhood to young adulthood - Brain Structure and Function

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Bilingualism affects the structure of the brain in adults, as evidenced by experience-dependent grey and white matter changes in brain structures implicated in language learning, processing, and control. However, limited evidence exists on how bilingualism may influence brain development.

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Even when you're fluent in two languages, it can be a challenge to switch back and forth smoothly between them. It's common to mangle a split verb in Spanish, use the wrong preposition in English, or lose sight of the connection between the beginning and end of a long German sentence. So - does mastering a second language hone our multitasking skills or merely muddle us up?

CREDIT: JAMES PROVOST (CC BY-ND)

Psycholinguist Mark Antoniou

Western Sydney University

This debate has been pitting linguists and psychologists against one another since the 1920s, when many experts thought that bilingual children were fated to suffer cognitive impairments later in life. But the science has marched on. In the Annual Review of Linguistics, psycholinguist Mark Antoniou of Western Sydney University in Australia outlines how bilingualism - as he defines it, using at least two languages in your daily life - might benefit our brains, especially as we age. He addresses how best to teach languages to children and lays out evidence that multiple-language use on a regular basis may help delay the onset of Alzheimer's disease. This conversation has been edited for length and clarity.
What are the benefits of bilingualism?
I'm interested in the interaction between language-learning and cognition - the mental processes of the brain. The cognitive benefits of bilingualism can begin from experiences very early in childhood and can persist throughout life.

The first main advantage involves what's loosely referred to as executive function. This describes skills that allow you to control, direct and manage your attention, as well as your ability to plan. It also helps you ignore irrelevant information and focus on what's important. Because a bilingual person has mastery of two languages, and the languages are activated automatically and subconsciously, the person is constantly managing the interference of the languages so that she or he doesn't say the wrong word in the wrong language at the wrong time.

The brain areas responsible for that are also used when you're trying to complete a task while there are distractions. The task could have nothing to do with language; it could be trying to listen to something in a noisy environment or doing some visual task. The muscle memory developed from using two languages also can apply to different skills.
Where are these benefits expressed in the brain?
Executive functions are the most complex brain functions - the most "human" functions that separate us from apes and other animals. They're often observed in parts of the brain that are the newest, in evolutionary terms: the prefrontal cortex, which is responsible for advanced processing; the bilateral supramarginal gyri, which play a role in linking words and meanings; and the anterior cingulate. Studies show that the bilingual experience alters the structure of these areas.

First of all, we see increases in gray matter volume. The brain is made up of cells called neurons, which each have a cell body and little branching connections called dendrites. Gray matter refers to how many cell bodies and dendrites there are. Bilingual experience makes gray matter denser, so you have more cells. This is an indication of a healthier brain.

Bilingualism also affects white matter, a fatty substance that covers axons, which are the main projections coming out from neurons to connect them to other neurons. White matter allows messages to travel fast and efficiently across networks of nerves and to the brain. Bilingualism promotes the integrity of white matter as you age. It gives you more neurons to play with, and it strengthens or maintains the connections between them so that communication can happen optimally.
Can teaching children two languages delay or confuse their understanding?
These myths about bilingualism date back to studies in the US and the UK from the First and Second World Wars. They were seriously flawed studies involving children from war-torn countries: refugees, orphans and, in some cases, even children who were in concentration camps. Their schooling had been disrupted for years. They may have suffered traumas, and then they participated in these studies with tests measuring their verbal language abilities.

Unsurprisingly, they scored very poorly on these tests. Did the researchers attribute the poor scores to post-traumatic stress disorder (PTSD)? They probably didn't even know what that was. No, instead they attributed it to the children's bilingualism.

It wasn't until the 1960s, when a really important study was published by Elizabeth Peal and Wallace Lambert at McGill University in Montreal, that views started to shift. Their findings showed that not only do bilingual children not have a cognitive delay or mental retardation but that their bilingualism actually has some cognitive benefits.

In addition to executive function, bilingual individuals and children show advantages in metalinguistic awareness. This is the ability to think about language as abstract units and associations. A good example is the letter H, which is associated with the sound "he" in English, with "n" as in "nickel" in Russian, and with the vowel sound "e" in Greek. There's nothing special about H that makes it have to have a "he" sound; a bilingual person understands this more readily than a monolingual person does.

What do the skeptics argue?
The original findings about bilingual advantages to executive function in the 1960s generated a lot of excitement and media interest. Perhaps the advantages were overstated or misinterpreted. Not every bilingual person is going to have a healthier brain than every monolingual person. We're talking about general, population-level trends.

We see evidence of bilingual advantages in children, but not always. And as we move into young adults, say, in their 20s, it becomes more difficult to detect these advantages. This makes sense in terms of brain maturation: When you're a child, your brain is still developing, but when you reach young adulthood, your brain is at its peak, so bilingualism doesn't give you much extra.
Learning languages as a child is different than doing so later in life, right?
It depends. For a long time, it was thought that the only way to really learn a language was to do it early. It was thought that after adolescence, you couldn't learn a language perfectly. You were always going to be accented. But we now know that that's not true, because there are many people who learn languages as adults, and they learn them very well. So this has led us to reexamine what it is about learning a language during childhood that makes it different from adulthood.

Is your brain more ready and more flexible - what we call more "plastic" - when you're a child, and then it becomes more rigid and fixed as an adult? Or is it that the conditions of language-learning are different when you're a child, in terms of the amount and type of input you receive, how much slack you're afforded and how much encouragement others give you? An adult who is working two jobs and going to language classes at 7 o'clock at night has a different type of acquisition than a child constantly receiving input from the mother, grandmother, father or other primary caregiver.

Ultimately, the difference between language-learning in children and adults is probably some combination of the two: plasticity and conditions. There are also individual differences. If you put different people in the same situation, some people will flourish and others will struggle.
Does a bilingual brain age differently than a monolingual one?
We know from studies that starting at the age of about 25, your brain starts to decline, in terms of working memory, efficiency, processing speed, those kinds of things. As you age, these declines become steeper. The argument is that as we get into older age, bilingualism puts the brakes on and makes that decline less steep. Evidence from older adults is the strongest kind supporting a bilingual advantage. (The second strongest comes from children.)

When you look at bilingual individuals who have suffered neurodegeneration, their brains look damaged. From their brain scans, you'd think these people should be more forgetful, or that they shouldn't be coping as well as they are. But that's not the case. A bilingual brain can compensate for brain deterioration by using alternative brain networks and connections when original pathways have been destroyed. Researchers call this theory "cognitive compensation" and conclude that it occurs because bilingualism promotes the health of both gray and white matter.

Could learning a language later in life keep Alzheimer's at bay?
That is a working hypothesis. We're doing studies where we teach a foreign language to people aged 65 and up with the goal of promoting healthy brain function, even at such a late point in life. What we're testing is: Can we help people in old age by using language-learning? Does that give you some benefit in terms of a "use it or lose it" approach to brain health?

The initial signs are encouraging. Preliminary data look good. It seems that learning a language in later life results in positive cognitive outcomes.

Because language-learning and use is so complex - arguably the most complex behavior we human beings engage in - it involves many levels. You have speech sounds, syllables, words, grammar, sentences, syntax. There's so much going on; it really is a workout for a wide brain network. And those areas of the brain overlap with the ones in which aging adult brains show decline or neurological pathological disease. As a result, we argue that learning a second language would be an optimal activity to promote healthy aging.

But not enough studies have been done to settle this once and for all. And we don't know any of the details. How much language experience is needed? Does it matter which languages you learn? Do you need to achieve a certain level of proficiency? We don't have answers to these questions.

Bilingualism, the Brain and Society: A live conversation with leading experts
Watch now
Does speaking two languages enrich the brain? How do class and race influence the labeling of bilingualism as good or bad? Watch now to explore the neuroscience and social context of bilingualism.

What advice do you have for parents raising bilingual children?
My advice would be to be encouraging and patient. Bilingual children have a tougher task than those learning only a single language. They're learning two sets of vocabulary and speech sounds. It can be challenging for those of us living in a country with a dominant language to establish a functional purpose for the second language. A child needs to feel that the language is practical and has a use. Grandparents are great for this, and so is living in a community where there are cultural events or schools where children can be immersed in the second language.

Another concern parents bring up is worrying that their child might be mixing the languages. Don't worry about what we refer to as "code mixing." It's a perfectly normal part of bilingual development. They're not confused. It's thought to be a sign of bilingual proficiency or competence to mix up the languages.
What other research are you doing in this area?
I'm interested in trying to understand why sometimes we see a bilingual effect, and other times we don't. In one article, I proposed that maybe the language pairing matters. If you speak two distant languages, like Mandarin Chinese and English, would that result in similar types of brain changes as speaking two closely related languages, like German and English?

Maybe if the languages are closely related, they're competing more and you have a harder job of separating them, to avoid using the wrong word at the wrong time. Maybe if they're more distant, then you can't rely on prior knowledge from learning the first one to learn the second. In that case, you're starting from scratch with the second language, and that's more effortful at the initial learning stages. But once you've learned the two languages, perhaps there's less competition.

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How a second language can boost the brain

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Being bilingual benefits children as they learn to speak - and adults as they age

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Why Bilingualism Makes For a Healthier Brain

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Listen to this episode from Magnetic Memory Method Podcast on Spotify. Are you a polyglot? No, I don't mean a ghost that walks around the attic making loud noises and moving objects. That's a poltergeist! A polyglot, on the other hand, is a more tame individual who is fluent in several languages.