The Routledge Handbook of Second Language Acquisition and Neurolinguistics

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DOI: 10.4324/9781003190912-18

SECOND LANGUAGE

ACQUISITION AND

NEUROPLASTICITY

Insights from the Dynamic Restructuring Model

Michal Korenar and Christos Pliatsikas

Introduction

Learning and using a second language (L2) has been shown to have effects that extend beyond lan-

guage use per se. Notably, these include well- documented effects in the function and the structure of

the brain, which support and relate to effects observed in behavior. The literature on functional and

structural neuroplasticity that is induced by L2 learning remains relatively limited, and not without its

controversies, which usually relate to the methods that have been used and the characteristics of the

tested samples. The present chapter focuses on the available evidence for structural brain adaptations,

and relates this to contemporary models for experience- based neuroplasticity, with the aim of offering

a unifying explanation for seemingly variable patterns documented in previous studies on structural

brain changes induced by L2 acquisition.

This chapter is organized as follows: after a general overview of theories of patterns of experience-

based neuroplasticity on brain structure, evidence for adaptations that are induced by bilingualism is

summarized and explained on the basis of the dynamic restructuring model (DRM; Pliatsikas, 2020),

a recent model in the field. This is followed by an extensive overview of evidence from L2 training

studies and longitudinal language learning studies. This evidence is evaluated against the predictions

of the DRM. The chapter concludes with theoretical and methodological suggestions about how this

emerging field should move forward.

Experience- Based Neuroplasticity

It has been firmly established that the brain is a highly plastic and adaptable organ. The ability of our

brains to change is a crucial part of our healthy ontogenesis, forming us into unique individuals and

helping us to achieve our goals effectively (Lindenberger et al., 2017). Brain alterations occur when

we face influential changes in our environment: for example, after a brain injury or disease, when

we must increase the efficiency of our behavior, or during acquisition of a new skill (Lindenberger

& L�vd�n, 2019). As for the latter, when we acquire demanding skills we also often must face new,

emergent tasks, which brings new challenges to our cognitive processes. This creates a mismatch

between the functional supply of the brain structure and how demanding we experience the skill at

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hand (Wenger et al., 2017). To address this mismatch, the brain adapts its structure at the neuronal

level in various ways to deal with these demands, usually by increasing the means for effective com-

munication between brain regions. For example, skill acquisition has been shown to increase the

volume of the grey matter. Grey matter is a collective term for the neurons’ cell bodies (or somata,

see Figure 14.1). Cell bodies carry out most of the processing and synthesizing of information that is

received in the form of electric impulses by the dendrites, small extensions on the surface of the cell

bodies. Dendrites contain dendritic spines, which form synapses with neighboring neurons. Thus,

dendrites constitute the major avenues for the communication of information. Cognitively challen-

ging experiences trigger the creation of new dendritic spines in order to provide more synapses, i.e.,

communication avenues, eventually leading to volumetric growth of structures responsible for the

given task. Skill acquisition has also been shown to affect the integrity of the white matter of the

brain. White matter is the collective term for the neurons’ axon� single cable- like structures that

transfer electrical signals from the cell body to other neurons via synapses that are formed at the

axon terminals. Axons are usually insulated by myelin, a lipid- rich protein that helps to transfer

the electric signal at higher rates, which also gives the axons their white color. Skill acquisition has

been shown to increase the availability of myelin in implicated white matter tracts, suggesting that

white matter reorganizes to provide better communication between regions, eventually providing the

means for efficient delivery of the information required for the new skill. Therefore, these processes

enable brain regions involved in the acquisition of new skills to respond to the altered environmental

demands effectively (Wenger et al., 2017). The ability of the human brain to adapt when acquiring

and mastering a new skill is termed experience- dependent neuroplasticity (L�vd�n et al., 2013).

Three basic principles are critical in experienced- based neuroplasticity (Kleim & Jones, 2008):

(i) for an experience to trigger structural brain changes, the increased cognitive demands must be high

enough to exceed the possibilities of the existing neural resources; (ii) the duration and continuity of

such experiences are co- determining factors for the changes to occur, and for the time- course within

which they happen; and (iii) the changes occur in brain areas that subserve the behavior relevant for

the task at hand.

Figure 14.1 Neuron Anatomy.

Note: Created with BioRender.com

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These predictions have been repeatedly confirmed in animal research (Crawford et al., 2020; Mesa-

Gresa et al., 2021) and in human studies that used structural brain imaging, comparing brain archi-

tecture before and after engagement in a new or newly demanding experience (De Sousa Fernandes

et al., 2020; Teixeira- Machado et al., 2019). For example, London taxi drivers have been reported

to have an enlarged region in the posterior hippocampus, which was linked to them maintaining and

continuously navigating an elaborate mental map of the city (Woollett & Maguire, 2011). Further

evidence of experience- related grey matter increases in the relevant brain areas comes from a variety

of other populations including medical students (Draganski et al., 2006), musicians (Wenger et al.,

2021), and, crucially, bilinguals (M�rtensson et al., 2012).

Importantly, it would be simplistic to expect that the immense amount of knowledge and skills that

humans acquire throughout their lives will lead to continuous increases of brain volumes. Such a view

is also inconsistent with evolutionary principles that posit that nature’s solution to efficient progress

is not never- ending growth, but rather the selection of the best candidates among many candidates

and elimination of the less suitable ones (Lindenberger & L�vd�n, 2019; Wenger et al., 2017). This

principle has also recently crystallized within the field of experience- dependent neuroplasticity into

the expansion- renormalization model1 (Lindenberger & L�vd�n, 2019; L�vd�n et al., 2013; Wenger

et al., 2017).

According to this model (Figure 14.2), experience- related changes often follow a three- phased

trajectory of expansion, selection, and renormalization. First, the brain reacts to a newly emergent

demanding task by expanding neuronal resources, such as the availability of synapses in structures

responsible for the given task, which leads to an overall increase of the relevant regional brain

volumes. Continuous practice in the new task creates opportunities for the brain to explore which

of the newly built neural resources are most suitable and effective to achieve the targeted behavior.

Figure 14.2 Expansion- Renormalization Model.

Note: The illustration depicts the relationship between grey matter changes and behavioral performance according to the

expansion- renormalization model (Wenger et al., 2017).

Created with BioRender.com

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On the behavioral level, the performance in the concerned task increases with training until it hits

a ceiling of efficiency, after which it stabilizes, at which point, the structural volumes in the related

brain regions are posited to renormalize, sometimes even completely back to the levels prior to

learning. This potentially reflects the changes on the microscopic level. Namely, among the expanded

network of neuronal connections, the most efficient ones are selected, whereas the superfluous ones

are eliminated through a process of so- called synaptic pruning.

In all, skill acquisition may trigger a non- linear trajectory of initial volumetric increases of

implicated brain structures, followed by decreases as a behaviorally optimal neural circuitry is being

selected (Wenger et al., 2017).

Bilingualism and the Brain

As already mentioned, the basic principles of neuroplasticity have mainly been applied to instances

of skill acquisition, usually after intensive training and/ or long- term application of a particular skill.

If that is the case, would the same principles apply to cognitively challenging long- term experiences?

One such experience can be bilingualism, which is a cognitively demanding experience that requires

constant monitoring of the environment for linguistic cues and constant inhibition of the non- target

languages (e.g., Kroll et al., 2012; for more on cognitive control in the context of L2, see Guo & Ma,

this volume). Moreover, bilingualism very often is a life- long experience, in which people engage

continuously, which means that the above skills need to be continuously exercised in the long term to

achieve increasing efficiency in language control, steadily more fluent communication, and growing

language proficiency.

Based on the above assumption, it is not surprising that an emerging body of literature has reported

changes in the structure of a range of cortical and subcortical regions and the connections between

them as an underlying effect of bilingualism (see Tao et al., 2021 for a recent review). These are

regions that have been particularly shown to be activated in tasks related to language acquisition,

processing, and control, using predominantly functional magnetic resonance imaging (fMRI) (for

more on this methodology, see Kousaie & Klein, this volume). Specifically, brain activation linked to

bilingual language control has been observed in brain structures and networks that typically underlie

cognitive control (Anderson et al., 2018; Garbin et al., 2010), as summarized in the next section of

this chapter. Given the principles of experience- dependent neuroplasticity (L�vd�n et al., 2013), the

regions subserving the highly demanding cognitive control processes during bilingual language use

are also potential targets for expected structural adaptations. This also presumes that, if bilingualism

follows these principles, different groups of bilinguals with different experiences may yield different

results that may correspond to nonlinear effects. Indeed, this has been the case in the field of bilin-

gualism (see Pliatsikas, 2019 for a review).

The Dynamic Restructuring Model

If the principles of the expansion- renormalization model hold for bilingualism, it is unlikely that

increasing efficiency in bilingual language use should result in linearly corresponding increases of

neural tissue in the relevant brain regions. Recently, a model of bilingualism- related brain changes

has been proposed that incorporated assumptions of non- linearity: the dynamic restructuring model

(DRM, Pliatsikas, 2020). The DRM brought together evidence from existing studies on structural

brain changes in various bilingual populations with qualitatively and quantitatively different sets of

experiences (for more on structural imaging methodology, see Rossi et al., this volume). According

to the DRM, structural brain adaptations are governed by the ways bilinguals use their languages, the

contexts in which they operate, and by the timing of their language experiences (see also Li et al.,

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2014). Such an approach is consistent with the new development in the field of bilingualism where

researchers aim to identify which sets of bilingual experiences give rise to consistent neurocognitive

effects (e.g., Navarro- Torres et al., 2021), Therefore, the DRM evaluated the existing evidence

according to the various bilingual factors and experiences that have been hypothesized to have effects

on cognitive demands, and set forth three interesting observations (Pliatsikas et al., 2020).

First, different groups of bilinguals who had long- standing experience in bilingual language use

and were using both languages frequently in their daily lives (e.g., immersed bilinguals who live in

an L2- speaking environment, interpreters, and translators) had similar patterns of structural changes

irrespective of the onset of L2 acquisition, including simultaneous, early sequential, and late sequen-

tial bilinguals. The reported effects spanned largely similar effects on the shape and volumes of

subcortical structures (Burgaleta et al., 2016; Pliatsikas et al., 2017), and properties of white matter

tracts connecting regions that subserve language processing and cognitive control (Garc�a- Pent�n

et al., 2014; Mohades et al., 2012). Second, effects in cortical grey matter pertained predominantly to

sequential bilinguals who had limited to no immersion in bilingual environments, but also to elderly

adults with a history of lifetime use of multiple languages (e.g., Voits et al., 2022). Third, grey matter

reductions were observed in interpreters relatively to bilinguals who were not trained as interpreters,

even though interpreters’ routine is marked by exceptional language control and switching demands

(Elmer et al., 2014).

Based on these findings, the DRM inferred that structural brain changes brought about by bilin-

gualism are dynamic, likely following phases of increases and decreases throughout the bilingual

experiential trajectory. The observed direction of these effects and their magnitude will then likely

differ depending on where on the trajectory the measured bilingual is positioned with respect to L2

learning and use. Ultimately, the DRM incorporates the principle of expansion- renormalization to

propose testable predictions of bilingualism- induced non- linear brain adaptations. The model consists

of three stages during which qualitatively different structural adaptations of grey and white matter are

observed depending on the duration, intensity, and quality of exposure to the L2.

During the initial exposure, the first stage, vocabulary learning, and the need to control between

lexical alternatives for the same concepts, sets off additional demands on cognitive control. This stage

induces cortical grey matter changes in regions related to executive control and short- term memory,

such as caudate nuclei, inferior frontal gyrus (IFG), anterior cingulate cortex (ACC), and medial

frontal gyrus (MFG), and a network of regions linked to semantic and phonological learning, namely

hippocampus, inferior parietal lobe (IPL), superior parietal lobe (SPL), anterior temporal lobe (ATL),

anterior temporal gyrus (ATG), and Heschl’s gyrus (HG).

The second stage, consolidation, is marked by the lack of effects on grey matter typical for the ini-

tial stage as indicated by the absence of effects in the cortex, the caudate nucleus, and hippocampus,

accompanied by expansions in other subcortical structures, including the basal ganglia and the thal-

amus in highly proficient bilinguals with prolonged engagement with both languages (Pliatsikas,

2020). The absence of effects in the cortex and the caudate nucleus has been interpreted as evidence

for reversion of the expansions (or renormalization to baseline) of the regions that expanded in the

initial stage, a sign that the brain has reached the required level of optimization in learning new

words and controlling for competing lexical alternatives. Conversely, at this stage, bilinguals face

increased needs for differentiating between semantic, phonological, and grammatical alternatives.

Concurrently, they need to monitor the situations when to use each language and, if necessary,

suppress the non- target language. Bilinguals at this stage demonstrated significant restructuring of

the left putamen (Abutalebi et al., 2013; Berken et al., 2016) and globus pallidus (Burgaleta et al.,

2016), which have been implicated in articulatory control and phonological selection (Pliatsikas et al.,

2017). Also, the increased lexical selection needs during production at this stage leads to expansion

of thalamic volumes, which is assumed to enable a more efficient selection mechanism (Abutalebi &

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Green, 2016). The acquired skill to switch and differentiate between two languages more efficiently

is reflected in increased connectivity between some of the regions underlying the cognitive demands

at the initial stage. The increased structural connectivity is indexed by reductions in diffusivity, which

reflects greater connectivity, of white matter tracts connecting IFG, MFG, superior temporal gyrus

(STG), middle temporal gyrus (MTG), and supramarginal gyrus (SMG) (DeLuca et al., 2020). These

white matter tracts include the inferior fronto- occipital fasciculus (IFOF) (Kuhl et al., 2016), anterior

thalamic radiation (ATR) (Cummine & Boliek, 2013), and superior longitudinal fasciculus (SLF)

(Mamiya et al., 2016). Also, reduced diffusivity is reported in corpus callosum (CC), which is another

white matter tract involved in cognitive control that is strongly connected to ACC (Coggins et al.,

2004). In sum, the consolidation stage is characterized by shifting the focus of neuroplastic change

from the regions affected at the initial stage, which are now renormalizing, to subcortical regions and

the white matter, which now start undergoing restructuring.

The final stage, peak efficiency, predicts adaptations in the most experienced groups of bilinguals,

such as professional interpreters. Efficient and automatic language control stemming from longstanding

and intensive training in interpreting practices results in maximally efficient connectivity and leads to

increases in connectivity within the cerebellum (Van de Putte et al., 2018). Importantly, the caudate

nucleus is thought to renormalize at this stage (Elmer et al., 2014). Furthermore, frontal white matter

structural connectivity decreases while white matter diffusivity of anterior regions is posited to

increase. This indicates a shift in reliance from prefrontal to posterior regions. However, this stage

has received less attention, and it remains unclear whether other parts of the bilingual brain would

also adapt to deal with both growing bilingual experience and changing demands (Pliatsikas, 2020).

L2 Acquisition and Brain Restructuring

The DRM describes specific trajectories for restructuring of both grey and white matter, which differ

from each other and appear to happen over extended periods of time. Given that most evidence for

the classical models of experience- based neuroplasticity has emerged typically from studies looking

at the acquisition of a particular skill, it follows that similar patterns would be observed in studies

looking at the effects of language acquisition. This section reviews the available evidence on grey and

white matter from a handful of studies that tested individuals in language training settings (excluding

training for interpreters and translators that goes typical language learning, for more information

on this topic, see Hervais- Adelman & Babcock, 2020; or Pliatsikas, 2020). Special attention here is

given to recent studies that have not been viewed through the lens of the DRM so far (see Pliatsikas

(2020) for a consideration of previous language training studies through the lense of the DRM).

Grey Matter

A large part of the beginning of the journey to learn a L2 consists of learning a new vocabulary. This

brings an array of challenges, such as learning new phonological rules and putting them in practice to

pronounce the words correctly. Moreover, beginning L2 learners need to assign the newly sounding

lexical units to their meaning. The newly acquired words in the L2 denote conceptual representations

of the items and situations from the world around us. It is important to realize that every language sets

distinctive boundaries of the semantic representations of its language units (see Casaponsa & Thierry,

this volume). This means that learners of a new language need to build a new semantic representation

of concepts with which they may be familiar but which also map onto words in each language in often

very different ways (Neumann et al., 2018).

The challenges of word acquisition, learning how to pronounce the words in a foreign language

and building new semantic representations, would lead to increases in the volume of the relevant

grey matter regions, predominantly in the left hemisphere, as predicted for the initial stage of the

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DRM. Indeed, the earliest study in the field (Osterhout et al., 2008) showed that training in L2

leads to increases of the SMG, an anatomical component of the IPL, which subserves the semantic

phonological encoding of newly acquired words into the knowledge system. Such effects have been

replicated in a study investigating structural brain changes in children who underwent foreign lan-

guage training (Della Rosa et al., 2013).

Bilinguals need to constantly resolve a mental conflict in their minds in choosing the appropriate

language. This process requires cognitive control, which triggers changes in prefrontal regions that

enable it, i.e., IFG, MFG, and ACC. These grey matter regions were shown to increase in volume in

monolinguals trained to read words in three different languages (Stein et al., 2012), which is indi-

cative of the need to develop effective cognitive control processes already in the initial stages of

language learning. Notably, these effects may not be specific to the spoken language modality: in a

study testing participants five times over an eight- month training course on American Sign Language,

Banaszkiewicz and colleagues (Banaszkiewicz et al., 2021) reported increasing grey matter volume

in the IFG, which peaked towards the end of the training program.

Additional language learning has also been shown to affect the structure of other regions of the

brain, beyond those subserving cognitive control. For example, Legault and colleagues (2019a)

reported an increase in grey matter thickness of the STG after a 20- day training course in Mandarin

Chinese vocabulary, a language that differentiates words by tone. This is significant as the STG

is an important node for phonological processing with a particular importance for tonal languages

(Liang & Du, 2018). This suggests that properties of the L2 may influence which regions undergo

neuroplasticity in the context of language acquisition in line with the predictions for the consolidation

stage of the DRM.

Another important region that increases in volume as a function of language training is the hippo-

campus (Bellander et al., 2016; M�rtensson et al., 2012). These changes have been traditionally

linked to the importance of this structure in learning and memory (Voits et al., 2022). However,

none of the brain structures, including the hippocampus, IFG, and STG, underwent any significant

grey matter changes in the span of an eleven- week L2 training course in older adults (Nilsson et al.,

2021). Nilsson and colleagues interpreted these results as an indication of the gradual loss of the

brain’s ability to adapt its structure as we age. Such findings echo behavioral studies that also did

not find effects of bilingual experiences in seniors on inhibitory control (Ant�n et al., 2016) and

switching (Ramos et al., 2017; but see Bak et al., 2014). It also prompts the DRM and other models

of neuroplasticity to incorporate the age at which a new experience is exercised into the models’

predictions (effects of age on L2 are further discussed in Fromont, this volume).

As mentioned above, the duration of an experience is an important factor when predicting struc-

tural brain changes. However, the training studies mentioned only report the effects of relatively short

duration language training; to the best of our knowledge, only two studies have looked at the effects

of longer periods of language acquisition (Legault et al., 2019b; Liu et al., 2021). Liu and colleagues

(2021) acquired data from Chinese college students majoring in English (L2) at two time- points one

year apart, and reported that grey matter volumes of left ACC and right IFG decreased after a year of

English learning. In contrast, another longitudinal study on learners of L2 Spanish found increases of

cortical thickness in ACC and MTG after two semesters of intermediate language courses (Legault

et al., 2019b). These discrepancies can be explained by the differences in the cumulative number of

hours of language training in both studies; participants in Liu et al.’s spent approximately three times

longer in the language course than those in Legault et al. Such an explanation is also rooted in the

prediction of the DRM’s consolidation phase that sustained and intensive engagement with the L2

will eventually lead to volumetric decreases of initially expanded brain structures. Another important

note pertains to the language proficiency at the moment of entering an L2 training, which can serve

as a proxy of the level of engagement in the L2 (but see Deluca et al., 2019 for arguments against this

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concept). The participants in Lui et al.’s study were already relatively proficient in their L2, which

presumes that some levels of volumetric increase would already be in place. In contrast, a study on

complete bilingual novices revealed that the volume of the left IFG, STG/ SMG, and ACC were posi-

tively related to the vocabulary size of the trained language (Hosoda et al., 2013). Taken together,

findings from studies studying long- term training support the view that a long- enough window of

opportunity to exercise the new skill will eventually lead to selection of the most efficient brain

networks whereas the superfluous ones will be eliminated (see Korenar et al., 2023a and 2023b for a

recent direct evidence of this principle).

In all, the reviewed evidence for grey matter suggests that individuals at earlier stages of language

learning tend to show volumetric increases in regions related to language acquisition and control,

which recalls the predictions of the DRM for an initial stage of restructuring. Importantly, these

effects seem to revert (possibly renormalize) in cases of prolonged periods of acquisition, recalling

the consolidation stage of the DRM.

White Matter

Adaptations in the volume and shape of the grey matter regions have been interpreted to reflect

accommodation of the additional information that is being acquired by the brain during the course of

language acquisition. However, because the brain is an interconnected system, it is also necessary to

account for the effects of language training on the structural connectivity between these regions, an

index of which is the integrity of myelin in implicated white matter tracts. This is usually measured

with indices such as Fractional Anisotropy (FA) and Radial Diffusivity (RD), measures of water dif-

fusivity in the brain. Higher FA reflects lower diffusivity, usually interpreted as increased amounts

of myelin, which provides more efficient structural connectivity between brain regions. In contrast,

RD measures water diffusivity perpendicular (i.e., crosswise) to axonal fibres. Increases in RD index

relative loss of myelin, and as a result, greater diffusivity. Evidence of reductions in diffusivity after

skill training have been interpreted as structural reorganization that addresses increased demands for

communication between brain regions. In this vein, training studies on participants acquiring an L2

have reported increases in integrity in white matter tracts that subserve phonological processing, such

as arcuate fasciculus (AF) and SLF as will be discussed below.

Xiang et al. (2015) investigated white matter after a six- week long language course of Dutch

in the Netherlands. The authors reported increases in FA around the left AF, suggesting increased

connectivity. Importantly, high levels of white matter connectivity in this tract shifted to the right

hemisphere once participants became more proficient, and back to the left side of the brain with fur-

ther increased L2 proficiency. Such observations highlight the dynamicity of white matter changes

following training in a L2 and that the demands put on the language- related regions can change as a

function of efficiency in bilingual language use.

Schlegel et al. (2012) provided evidence that L2 learning induces increases in interhemispheric

connectivity, indexed by increases of the FA in the CC, an effect that has been explained by increased

cognitive control demands. In this study, participants were tested on nine different occasions, a month

apart, during completion of L2 training. Additionally, the study reported gradual increases in left

hemispheric white matter tracts connecting regions subserving language comprehension and pro-

duction such as the IFG, caudate nucleus, and STG, as well as right hemispheric tracts that are

linked to these language- related brain areas. Increases in connectivity between the IFG and caudate

nucleus were also reported in a 16- week long training study (Hosoda et al., 2013). Crucially, when

the participants of that study were tested again a year after the program finished, during which time

they did not use their newly acquired language, the originally reported effects in the white matter had

disappeared. Mamiya et al. (2016) reported that the time participants spent in an immersive language

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program was positively related to FA in bilateral SLF, and negatively to RD of this tract. However,

the effects observed during the immersion program disappeared after the program ended. Such results

suggest that the effects of bilingualism on white matter are conditioned by the sustained L2 use.

Therefore, the increased efficiency that led to restructuring of the white matter is no longer needed

once a bilingual stops using the L2. As a result, the relevant white matter effects revert to baseline.

Although most of the time, white matter changes in the above- mentioned studies pertain to

months of L2 training, it has been reported that even an hour of intensive vocabulary training leads

to decreased white matter diffusivity (Hofstetter et al., 2017). This potentially hints at a powerful

reorganization of the brain triggered by sudden and intensive demands linked to L2 learning. By

contrast, a recent study on the effects of language training in older participants did not reveal any

significant white matter changes even after much longer period of L2 training (Nilsson et al., 2021).

In line with the lack of grey matter effects discussed earlier, this suggests that the progressive loss of

plasticity in older age pertains also to the adaptability of white matter.

In all, the evidence for white matter changes in training studies generally shows increases after

some period of training, which do not seem to renormalize. Viewed through the lens of the DRM,

the absence of renormalization of white matter indicates either that these learners have not reached

peak efficiency yet or that those increases in white matter integrity are crucial even at high levels of

language expertise.

Conclusions Regarding the Dynamic Restructuring Model

The DRM posits that the duration and intensity of language learning experiences govern the emer-

gence and character of structural effects in the brain. We reviewed the available evidence from L2

learning studies on structural changes in grey and white matter. The findings support the view that

such adaptations are dynamic and yet highly regular when viewed through the prism of experience-

based neuroplasticity. The dynamicity of these effects in the grey matter is reflected in results reporting

both increases and decreases of the regional brain volumes. On the other hand, evidence suggests that

white matter increases (seemingly subsequent to grey matter adaptations) but does not renormalize

(although the structural increases seem to disappear if the L2 is not used). In all, these findings offer

support for the DRM that effects of L2 acquisition on brain structure will be dynamic, following an

expansion- renormalization trajectory. These effects are likely conditioned by the quantity but also by

the quality of bilingual training and experiences.

Future Directions

This chapter shows that longitudinal and training studies in the field of L2 learning have been invalu-

able in informing the DRM on the neurological impact of weeks to years of bilingual language use.

Indeed, such designs enable the control of extraneous variables more effectively, and they appear to

be especially relevant for revealing arguably rather subtle effects of L2 acquisition on the brain out-

side of immersive settings. However, commonly used designs that administer two imaging sessions

(one pre- and one post- language training) cannot provide insights on the non- linear, dynamic trajec-

tory of bilingualism- induced structural brain changes. Therefore, future studies should engage with

more complex longitudinal study designs with measurements at multiple points in time to unravel

progression of such changes.

Furthermore, we mentioned that phonological language characteristics impact structural

brain changes. But obviously, languages do not differ only in their phonology. The level of simi-

larity between languages, i.e., typological proximity has been proposed as a modulating factor of

levels of cognitive control (Puig- Mayenco et al., 2020). The effects of linguistic proximity on our

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neurocognition could be addressed in a study which investigates groups of bilinguals with a constant

first language and variably distant L2 (e.g. Czech- Slovak bilinguals vs. Czech- English bilinguals vs.

Czech- Vietnamese bilinguals). Furthermore, a part of the effect might stem from cultural differences

rather than linguistic ones (Treffers- Daller et al., 2020). A possible way to disentangle the effects of

languages and cultures is to study various bilingual communities that use the same languages but

differ in their sociocultural backgrounds (i.e., comparing Turkish- English bilinguals living in the

UK coming from Turkey with those coming from Cyprus). The neurocognitive effects of different

language and cultural pairs is a question that deserves further investigation, with potential import-

ance for the DRM and related models, such as the framework unifying the bilingual experience tra-

jectories (DeLuca et al., 2020), or the bilingual anterior- to- posterior and subcortical shift (Grundy

et al., 2017).

Note

1 Note that this model is called differently in the studies we refer to; i.e., the exploration– selection– refinement

model (Lindenberger & L�vd�n, 2019); expansion- partial renormalization hypothesis (L�vd�n et al., 2013);

expansion- renormalization model (Wenger et al., 2017).

Further Readings

This article presents a theoretical model predicting brain changes induced by simultaneous interpreters.

Hervais- Adelman, A., & Babcock, L. (2020). The neurobiology of simultaneous interpreting: Where extreme

language control and cognitive control intersect. Bilingualism, 23(4), 740– 751. https:// doi.org/ 10.1017/

S13667 2891 9000 324

This study evidences the need to consider that the brain changes brought about bilingualism can be non- linear.

Korenar, M., Treffers- Daller, J., & Pliatsikas, C. (2023). Dynamic effects of bilingualism on brain structure map

onto general principles of experience- based neuroplasticity. Scientific Reports, 13(1), 3428. https:// doi.org/

10.1038/ s41 598- 023- 30326- 3

An accessible summary of the principles of the experience- based neuroplasiticity.

Wenger, E., Brozzoli, C., Lindenberger, U., & L�vd�n, M. (2017). Expansion and renormalization of human

brain structure during skill acquisition. Trends in Cognitive Sciences, 21(12), 930– 939. https:// doi.org/

10.1016/ j.tics.2017.09.008

Acknowledgments

This research has received funding from the European Union‘s Horizon2020 research and innovation programme

under the Marie Skłodowska Curie grant agreement No 765556. This publication was supported by the grant

GAUK No. 368120, realized at the Faculty of Arts, Charles University.

References

Abutalebi, J., & Green, D.W. (2016). Neuroimaging of language control in bilinguals: Neural adaptation and

reserve. Bilingualism, 19(4), 689– 698. https:// doi.org/ 10.1017/ S13667 2891 6000 225

Abutalebi, J., Rosa, P.A. Della, Castro Gonzaga, A.K., Keim, R., Costa, A., & Perani, D. (2013). The role of the

left putamen in multilingual language production. Brain and Language, 125(3), 307– 315. https:// doi.org/

10.1016/ j.bandl.2012.03.009

Anderson, J.A.E., Chung- Fat- Yim, A., Bellana, B., Luk, G., & Bialystok, E. (2018). Language and cognitive

control networks in bilinguals and monolinguals. Neuropsychologia, 117, 352– 363. https:// doi.org/ 10.1016/

j.neuro psyc holo gia.2018.06.023

Ant�n, E., Fern�ndez Garc�a, Y., Carreiras, M., & Du�abeitia, J.A. (2016). Does bilingualism shape inhibitory con-

trol in the elderly? Journal of Memory and Language, 90, 147– 160. https:// doi.org/ 10.1016/ j.jml.2016.04.007

Bak, T.H., Vega- Mendoza, M., & Sorace, A. (2014). Never too late? An advantage on tests of auditory attention

extends to late bilinguals. Frontiers in Psychology, 5, 485. https:// doi.org/ 10.3389/ fpsyg.2014.00485

Page 11

Second Language Acquisition and Neuroplasticity

201

Banaszkiewicz, A., Matuszewski, J., Bola, Ł., Szczepanik, M., Kossowski, B., Rutkowski, P., Szwed, M.,

Emmorey, K., Jednor�g, K., & Marchewka, A. (2021). Multimodal imaging of brain reorganization in hearing

late learners of sign language. Human Brain Mapping, 42(2), 384– 397. https:// doi.org/ 10.1002/ hbm.25229

Bellander, M., Berggren, R., M�rtensson, J., Brehmer, Y., Wenger, E., Li, T.Q., Bodammer, N.C., Shing, Y.L.,

Werkle- Bergner, M., & L�vd�n, M. (2016). Behavioral correlates of changes in hippocampal gray matter

structure during acquisition of foreign vocabulary. NeuroImage, 131, 205– 213. https:// doi.org/ 10.1016/ j.neu

roim age.2015.10.020

Berken, J.A., Gracco, V.L., Chen, J.K., & Klein, D. (2016). The timing of language learning shapes brain struc-

ture associated with articulation. Brain Structure and Function, 221(7), 3591– 3600. https:// doi.org/ 10.1007/

s00 429- 015- 1121- 9

Burgaleta, M., Sanju�n, A., Ventura- Campos, N., Sebastian- Galles, N., & �vila, C. (2016). Bilingualism at the

core of the brain. Structural differences between bilinguals and monolinguals revealed by subcortical shape

analysis. NeuroImage, 125, 437– 445. https:// doi.org/ 10.1016/ j.neu roim age.2015.09.073

Fromont, L. (this volume). Age and proficiency in second language neurocognition. In K. Morgan- Short and J.G.

van Hell (Eds.), The Routledge handbook of second language acquisition and neurolinguistics. Routledge.

Casaponsa, A., & Thierry, G. (this volume). Linguistic relativity and second language. In K. Morgan- Short

and J.G. van Hell (Eds.), The Routledge handbook of second language acquisition and neurolinguistics.

Routledge.

Coggins, P.E., Kennedy, T.J., & Armstrong, T.A. (2004). Bilingual corpus callosum variability. 89, 69– 75.

https:// doi.org/ 10.1016/ S0093- 934X(03)00299- 2

Crawford, L.E., Knouse, L.E., Kent, M., Vavra, D., Harding, O., LeServe, D., Fox, N., Hu, X., Li, P., Glory, C., &

Lambert, K.G. (2020). Enriched environment exposure accelerates rodent driving skills. Behavioural Brain

Research, 378, 112309. https:// doi.org/ 10.1016/ j.bbr.2019.112 309

Cummine, J., & Boliek, C. A. (2012). Understanding white matter integrity stability for bilinguals on language

status and reading performance. Brain Structure and Function, 218(2), 595– 601. https:// doi.org/ 10.1007/ s00

429- 012- 0466- 6

de Sousa Fernandes, M.S., Ord�nio, T.F., Santos, G.C., Santos, L.E., Calazans, C.T., Gomes, D.A., & Santos,

T.M. (2020). Effects of physical exercise on neuroplasticity and brain function: A systematic review in human

and animal Studies. Neural Plasticity, 2020, 1– 21. https:// doi.org/ 10.1155/ 2020/ 8856 621

Della Rosa, P.A., Videsott, G., Borsa, V.M., Canini, M., Weekes, B.S., Franceschini, R., & Abutalebi, J. (2013).

A neural interactive location for multilingual talent. Cortex, 49(2), 605– 608. https:// doi.org/ 10.1016/ j.cor

tex.2012.12.001

Deluca, V., Rothman, J., & Pliatsikas, C. (2019). Linguistic immersion and structural effects on the bilingual

brain: A longitudinal study. Bilingualism, 22(5), 1160– 1175. https:// doi.org/ 10.1017/ S13667 2891 8000 883

DeLuca, V., Segaert, K., Mazaheri, A., & Krott, A. (2020). Understanding bilingual brain function and struc-

ture changes? U bet! A unified bilingual experience trajectory model. Journal of Neurolinguistics, 56, 1– 29.

https:// doi.org/ 10.1016/ j.jne urol ing.2020.100 930

Draganski, B., Gaser, C., Kempermann, G., Kuhn, H.G., Winkler, J., B�chel, C., & May, A. (2006). Temporal and

spatial dynamics of brain structure changes during extensive learning. The Journal of Neuroscience, 26(23),

6314– 6317. https:// doi.org/ 10.1523/ JNEURO SCI.4628- 05.2006

Elmer, S., H�nggi, J., & J�ncke, L. (2014). Processing demands upon cognitive, linguistic, and articula-

tory functions promote grey matter plasticity in the adult multilingual brain: Insights from simultaneous

interpreters. Cortex, 54(1), 179– 189. https:// doi.org/ 10.1016/ j.cor tex.2014.02.014

Garbin, G., Sanjuan, A., Forn, C., Bustamante, J.C., Rodriguez- Pujadas, A., Belloch, V., Hernandez, M., Costa,

A., & �vila, C. (2010). Bridging language and attention: Brain basis of the impact of bilingualism on cogni-

tive control. NeuroImage, 53(4), 1272– 1278. https:// doi.org/ 10.1016/ j.neu roim age.2010.05.078

Garc�a- Pent�n, L., P�rez Fern�ndez, A., Iturria- Medina, Y., Gillon- Dowens, M., & Carreiras, M. (2014).

Anatomical connectivity changes in the bilingual brain. NeuroImage, 84, 495– 504. https:// doi.org/ 10.1016/

j.neu roim age.2013.08.064

Grundy, J.G., Anderson, J.A., & Bialystok, E. (2017). Neural correlates of cognitive processing in monolinguals

and bilinguals. Annals of the New York Academy of Sciences, 1396(1), 183– 201. https:// doi.org/ 10.1111/

nyas.13333

Guo, M., & Ma, F. (this volume). Cognitive control in second language neurocognition. In K. Morgan- Short

and J.G. van Hell (Eds.), The Routledge handbook of second language acquisition and neurolinguistics.

Routledge.

Hervais- Adelman, A., & Babcock, L. (2020). The neurobiology of simultaneous interpreting: Where extreme

language control and cognitive control intersect. Bilingualism, 23(4), 740– 751. https:// doi.org/ 10.1017/

S13667 2891 9000 324

Page 12

Michal Korenar and Christos Pliatsikas

202

Hofstetter, S., Friedmann, N., & Assaf, Y. (2017). Rapid language- related plasticity: microstructural changes

in the cortex after a short session of new word learning. Brain Structure and Function, 222(3), 1231– 1241.

https:// doi.org/ 10.1007/ s00 429- 016- 1273- 2

Hosoda, C., Tanaka, K., Nariai, T., Honda, M., & Hanakawa, T. (2013). Dynamic neural network reorgan-

ization associated with second language vocabulary acquisition: A multimodal imaging study. Journal of

Neuroscience, 33(34), 13663– 13672. https:// doi.org/ 10.1523/ JNEURO SCI.0410- 13.2013

Kleim, J.A., & Jones, T.A. (2008). Principles of experience- dependent neural plasticity: Implications for rehabili-

tation after brain damage. Journal of Speech, Language, and Hearing Research, 51(1), 225– 239. https:// doi.

org/ 10.1044/ 1092- 4388(2008/ 018)

Korenar, M., Treffers-Daller, J., & Pliatsikas, C. (2023a). Brain structure adapts dynamically to highly

demanding bilingual experiences: Insights from interpreters and translators. Ampersand, 11, 100148. https://

doi.org/10.1016/j.amper.2023.100148

Korenar, M., Treffers- Daller, J., & Pliatsikas, C. (2023b). Dynamic effects of bilingualism on brain structure map

onto general principles of experience- based neuroplasticity. Scientific Reports, 13(1), 3428. https:// doi.org/

10.1038/ s41 598- 023- 30326- 3

Kousaie, S., & Klein, D. (this volume). Using functional neuroimaging to investigate second language organiza-

tion. In K. Morgan- Short and J.G. van Hell (Eds.), The Routledge handbook of second language acquisition

and neurolinguistics. Routledge.

Kroll, J.F., Dussias, P.E., Bogulski, C.A., & Kroff, J.R. (2012). Juggling two languages in one mind. Psychology

of Learning and Motivation, 56, 229– 262. https:// doi.org/ 10.1016/ b978- 0- 12- 394 393- 4.00007- 8

Kuhl, P.K., Stevenson, J., Corrigan, N.M., Van den Bosch, J.J.F., Can, D.D., & Richards, T. (2016). Neuroimaging

of the bilingual brain: Structural brain correlates of listening and speaking in a second language. Brain and

Language, 162, 1– 9. https:// doi.org/ 10.1016/ j.bandl.2016.07.004

Legault, J., Fang, S.Y., Lan, Y.J., & Li, P. (2019a). Structural brain changes as a function of second language

vocabulary training: Effects of learning context. Brain and Cognition, 134, 90– 102. https:// doi.org/ 10.1016/

j.bandc.2018.09.004

Legault, J., Grant, A., Fang, S.Y., & Li, P. (2019b). A longitudinal investigation of structural brain changes during

second language learning. Brain and Language, 197, 104661. https:// doi.org/ 10.1016/ j.bandl.2019.104 661

Li, P., Legault, J., & Litcofsky, K.A. (2014). Neuroplasticity as a function of second language learning: Anatomical

changes in the human brain. Cortex, 58, 301– 324. https:// doi.org/ 10.1016/ j.cor tex.2014.05.001

Liang, B., & Du, Y. (2018). The functional neuroanatomy of lexical tone perception: An activation likelihood

estimation meta- analysis. Frontiers in Neuroscience, 12, 1– 17. https:// doi.org/ 10.3389/ fnins.2018.00495

Lindenberger, U., & L�vd�n, M. (2019). Brain plasticity in human lifespan development: The exploration–

selection– refinement model. Annual Review of Developmental Psychology, 1(1), 197– 222. https:// doi.org/

10.1146/ annu rev- devps ych- 121 318- 085 229

Lindenberger, U., Wenger, E., & L�vd�n, M. (2017). Towards a stronger science of human plasticity. Nature

Reviews Neuroscience, 18(5), 261– 262. https:// doi.org/ 10.1038/ nrn.2017.44

Liu, C., Jiao, L., Timmer, K., & Wang, R. (2021). Structural brain changes with second language learning: A

longitudinal voxel- based morphometry study. Brain and Language, 222, 105015. https:// doi.org/ 10.1016/

j.bandl.2021.105 015

L�vd�n, M., Wenger, E., M�rtensson, J., Lindenberger, U., & B�ckman, L. (2013). Structural brain plasticity in

adult learning and development. Neuroscience and Biobehavioral Reviews, 37(9), 2296– 2310. https:// doi.

org/ 10.1016/ j.neubio rev.2013.02.014

Mamiya, P.C., Richards, T.L., Coe, B.P., Eichler, E.E., & Kuhl, P.K. (2016). Brain white matter structure and

COMT gene are linked to second- language learning in adults. Proceedings of the National Academy of

Sciences of the United States of America, 113(26), 7249– 7254. https:// doi.org/ 10.1073/ pnas.160 6602 113

M�rtensson, J., Eriksson, J., Bodammer, N. C., Lindgren, M., Johansson, M., Nyberg, L., & L�vd�n, M. (2012).

Growth of language- related brain areas after foreign language learning. NeuroImage, 63(1), 240– 244. https://

doi.org/ 10.1016/ j.neu roim age.2012.06.043

Mesa- Gresa, P., Ramos- Campos, M., & Redolat, R. (2021). Behavioral impact of experience based on envir-

onmental enrichment: Influence of age and duration of exposure in male NMRI mice. Developmental

Psychobiology, 63(5), 1071– 1081. https:// doi.org/ 10.1002/ dev.22093

Mohades, S.G., Struys, E., Van Schuerbeek, P., Mondt, K., Van De Craen, P., & Luypaert, R. (2012). DTI reveals

structural differences in white matter tracts between bilingual and monolingual children. Brain Research,

1435, 72– 80. https:// doi.org/ 10.1016/ j.brain res.2011.12.005

Navarro- Torres, C.A., Beatty- Mart�nez, A.L., Kroll, J. ., & Green, D.W. (2021). Research on bilingualism as dis-

covery science. Brain and Language, 222, 105014. https:// doi.org/ 10.1016/ j.bandl.2021.105 014

Page 13

Second Language Acquisition and Neuroplasticity

203

Neumann, N., Domin, M., Erhard, K., & Lotze, M. (2018). Voxel- based morphometry in creative writers: Grey

matter increase in a prefronto- thalamic- cerebellar network. European Journal of Neuroscience, 48(1), 1647–

1653. https:// doi.org/ 10.1111/ ejn.13952

Nilsson, J., Berggren, R., Garz�n, B., Lebedev, A.V., & L�vd�n, M. (2021). Second language learning in older

adults: Effects on brain structure and predictors of learning success. Frontiers in Aging Neuroscience, 13,

1– 13. https:// doi.org/ 10.3389/ fnagi.2021.666 851

Osterhout, L., Poliakov, A., Inoue, K., McLaughlin, J., Valentine, G., Pitkanen, I., Frenck- Mestre, C., &

Hirschensohn, J. (2008). Second- language learning and changes in the brain. Journal of Neurolinguistics,

21(6), 509– 521. https:// doi.org/ 10.1016/ j.jne urol ing.2008.01.001

Pliatsikas, C. (2020). Understanding structural plasticity in the bilingual brain: The dynamic restructuring model.

Bilingualism: Language and Cognition, 23(2), 459– 471. https:// doi.org/ 10.1017/ S13667 2891 9000 130

Pliatsikas, C., DeLuca, V., Moschopoulou, E., & Saddy, J.D. (2017). Immersive bilingualism reshapes the core

of the brain. Brain Structure and Function, 222(4), 1785– 1795. https:// doi.org/ 10.1007/ s00 429- 016- 1307- 9

Pliatsikas, C., DeLuca, V., & Voits, T. (2019). The many shades of bilingualism: Language experiences modu-

late adaptations in brain structure. Language Learning, 70(S2), 133– 149. https:// doi.org/ 10.1111/ lang.12386

Puig- Mayenco, E., Gonz�lez Alonso, J., & Rothman, J. (2020). A systematic review of transfer studies in third

language acquisition. Second Language Research, 36(1), 31– 64. https:// doi.org/ 10.1177/ 02676 5831 8809 147

Ramos, S., Fern�ndez Garc�a, Y., Ant�n, E., Casaponsa, A., & Du�abeitia, J.A. (2017). Does learning a language

in the elderly enhance switching ability? Journal of Neurolinguistics, 43, 39– 48. https:// doi.org/ 10.1016/ j.jne

urol ing.2016.09.001

Rossi, E., Voits, T., & DeLuca, V. (this volume). Using structural neuroimaging to investigate second language.

In K. Morgan- Short and J.G. van Hell (Eds.), The Routledge handbook of second language acquisition and

neurolinguistics. Routledge.

Schlegel, A.A., Rudelson, J.J., & Tse, P.U. (2012). White matter structure changes as adults learn a second lan-

guage. Journal of Cognitive Neuroscience, 24(8), 1664– 1670. https:// doi.org/ 10.1162/ jocn_ a_ 00 240

Stein, M., Federspiel, A., Koenig, T., Wirth, M., Strik, W., Wiest, R., Brandeis, D., & Dierks, T. (2012). Structural

plasticity in the language system related to increased second language proficiency. Cortex, 48(4), 458– 465.

https:// doi.org/ 10.1016/ j.cor tex.2010.10.007

Tao, L., Wang, G., Zhu, M., & Cai, Q. (2021). Bilingualism and domain- general cognitive functions from a

neural perspective: A systematic review. Neuroscience and Biobehavioral Reviews, 125, 264– 295. https:// doi.

org/ 10.1016/ j.neubio rev.2021.02.029

Teixeira- Machado, L., Arida, R.M., & de Jesus Mari, J. (2019). Dance for neuroplasticity: A descriptive sys-

tematic review. Neuroscience and Biobehavioral Reviews, 96, 232– 240. https:// doi.org/ 10.1016/ j.neubio

rev.2018.12.010

Treffers- Daller, J., Ongun, Z., Hofweber, J., & Korenar, M. (2020). Explaining individual differences in execu-

tive functions performance in multilinguals: The impact of code- switching and alternating between multicul-

tural identity styles. Frontiers in Psychology, 11. https:// doi.org/ 10.3389/ fpsyg.2020.561 088

Van de Putte, E., De Baene, W., Garc�a- Pent�n, L., Woumans, E., Dijkgraaf, A., & Duyck, W. (2018). Anatomical

and functional changes in the brain after simultaneous interpreting training: A longitudinal study. Cortex, 99,

243– 257. https:// doi.org/ 10.1016/ j.cor tex.2017.11.024

Voits, T., Robson, H., Rothman, J., & Pliatsikas, C. (2022). The effects of bilingualism on hippocampal

volume in ageing bilinguals. Brain Structure and Function, 227(3), 979– 994. https:// doi.org/ 10.1007/ s00

429- 021- 02436- z

Wenger, E., Brozzoli, C., Lindenberger, U., & L�vd�n, M. (2017). Expansion and renormalization of human

brain structure during skill acquisition. Trends in Cognitive Sciences, 21(12), 930– 939. https:// doi.org/

10.1016/ j.tics.2017.09.008

Wenger, E., Papadaki, E., Werner, A., K�hn, S., & Lindenberger, U. (2021). Observing plasticity of the auditory

system: Volumetric decreases along with increased functional connectivity in aspiring professional musicians.

Cerebral Cortex Communications, 2(2), 1– 14. https:// doi.org/ 10.1093/ tex com/ tgab 008

Woollett, K., & Maguire, E.A. (2011). Acquiring “the knowledge” of London’s layout drives structural brain

changes. Current Biology, 21(24), 2109– 2114. https:// doi.org/ 10.1016/ j.cub.2011.11.018

Xiang, H., van Leeuwen, T.M., Dediu, D., Roberts, L., Norris, D.G., & Hagoort, P. (2015). L2- proficiency-

dependent laterality shift in structural connectivity of brain language pathways. Brain Connectivity, 5(6),

349– 361. https:// doi.org/ 10.1089/ brain.2013.0199