{"paper_id":"123f3d28-674f-484a-b7ca-68db6b371374","body_text":"1 \nTitle \nDoes bilingualism buffer genetic predispositions to reading difficulties through \nalterations of structural interhemispheric connectivity? An ABCDⓇ Study. \n \nAuthor names \nMarie Lalliera,b, Cristina Rius-Manaua, 23andMe Research Teamc, Amaia Carrión-Castilloa,b \n \nAuthor Affiliations \na. Basque Center on Cognition, Brain, and Language (BCBL), 20009 Donostia-San \nSebastian, Spain \nb. IKERBASQUE. Basque Foundation for Science, 48009 Bilbao, Spain \nc. 23andMe Research Institute, Palo Alto, CA USA 94306 \n \nCorresponding authors: \nm.lallier@bcbl.eu, a.carrion@bcbl.eu  \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n2 \nAbstract \nHere, we test the hypothesis that early sustained exposure to complex bilingual \nenvironments can positively affect reading development by altering structural \ninterhemispheric connectivity via the corpus callosum (CC). Interhemispheric connectivity \nhas been shown to be inefficient in dyslexia, but also to support compensatory pathways \nwhen genetic risk for reading difficulties is present, by enabling the preserved right \nhemisphere to support a dysfunctional left hemisphere. Mediation models were conducted \non children aged 9–10 years (with a 2-year follow-up assessment) from the Adolescent Brain \nCognitive Development database (N>10,000). Polygenic scores (PGS) for dyslexia and \ncognitive performance and continuous bilingualism indices were used as predictors, with \nreading aloud as the outcome.  Bilingualism showed a positive effect on reading partially \nmediated by the anterior CC, independently of overall brain size. In contrast, genetic \npredispositions to reading difficulties influenced reading primarily through overall brain size \nrather than CC connectivity specifically. These two pathways were independent, suggesting \nthat bilingual experience and genetic risk operate through distinct neuroanatomical \nmechanisms. These findings suggest that recurrent early exposure to complex bilingual \nenvironments may shape the brain’s structural connectivity toward a more balanced and \nintegrated bilateral frontal organisation. The results highlight potential brain compensatory \npathways induced by environmental experiences that may support more efficient reading \ndevelopment and mitigate risks for developmental dyslexia. \n \nKeywords \nCorpus Callosum, Bilingualism, Genetic risks, Dyslexia, Reading development, \nCompensation. \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n3 \n1. Introduction \nAmongst the cognitive functions that a child must develop during their first years of \nschooling, reading is by far one of the most complex. Because a child’s brain is highly plastic \nand adaptable to environmental influences, childhood experiences have a profound impact \non shaping and optimising the properties and configurations of neural networks, including \nthose supporting reading development (Dehaene, 2011; Dehaene, Cohen, Morais, & \nKolinsky, 2015; Perry, 2002) . Reading strongly depends on the adequate development of \nphonological abilities, which have been a cornerstone theoretical construct in the study of \ndyslexia (Ramus & Szenkovits, 2008; Snowling M. , 1998) . Evidence shows that \nphonological (dis)abilities mediate the link between the complex dyslexia phenotype and its \ngenetic bases (Moll, et al., 2014) , and for this reason, they are susceptible to be influenced \nby linguistic environmental factors and modulate reading outcomes  (Landerl, et al., 2013; \nSnowling M. J., 2008) . Therefore, the early acquisition of two phonological systems in early \nbilingualism might significantly imp act the organisation of the reading brain, changing the \ntrajectory of reading acquisition and determining the extent of its success. \nBilingual contexts make early language and phonological acquisition particularly complex, \nas young learners have to adapt and develop optimal strategies to navigate linguistic \nvariability, uncertainty and conflict. Learning two or more linguistic systems is now widely \nrecognized to lead to neurocognitive adaptations affecting both brain function and structure \n(Felton, et al., 2017; Pliatsikas, 2019; Pliatsikas, et al., 2020; Amoruso, et al., 2024) . \nAmongst these effects, bilingualism has been associated with reduced classical phonological \nand attentional control spatial asymmetries or dominance  \n(Hausmann, Durmusoglu, Yazgan, & Güntürkün, 2004; Hull & Vaid, 2006; Hull & Vaid, \n2007; Marzecová, Asanowicz, Krivá, & Wodniecka, 2012) , indexed by reduced processing \nadvantages for stimuli presented to the contralateral side of the dominant hemisphere, \nnamely the left hemisphere (LH) for phonological operations, and the right hemisphere (RH) \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n4 \nfor attentional control processes. These neural adaptations might arise because of the need \nto select and switch between languages, which create perceptual, attentional and cognitive \ndemands that are unique to bilingual language use (Blanco-Elorrieta & Pylkkänen, 2018; \nGreen & Abutalebi, 2013).  \nNotably, research has shown that tasks with high complexity and attentional demands \ntrigger the activity of LH frontal regions (Friederici, Fiebach, Schlesewsky, Bornkessel, & \nCramon, 2005; Roskies, Fiez, Balota, Raichle, & Petersen, 2001; Swick, Ashley, Turken, & \nU, 2008) , involved in both phonological and attentional control networks (Diveica, et al., \n2023), and play a critical supporting role when the RH-dominant attentional network reaches \nmaximal engagement (Hirose, et al., 2012) . Interestingly, young bilinguals show a \nhyperreactivity and sensitivity of LH frontal regions to increased attentional demands and \ncomplexity (Arredondo, Hu, Satterfield, & Kovelman, 2016; Arredondo, Aslin, & Werker, \n2021), which may contribute to shapin g a bilateral “phonological attentional control network” \nready to handle the complex demands of bilingual environments (Green & Abutalebi, 2013; \nKroll & Bialystok, 2013).  \nThis aligns well with observations that, as task complexity increases, a gradual transition \nis observed going from lateralised and segregated networks towards more integrated or \nbilateral networks characterised by longer distance and stronger interhemispheric \nconnectivity (Kitzbichler, Henson, Smith, Nathan, & Bullmore, 2011) . This hemispheric \ncooperative benefit is mostly observed in the genu (Davis, Kragel, Madden, & Cabeza, 2011; \nDavis & Cabeza, 2015) , the a nterior section of the corpus callosum (CC), a major white \nmatter tract connecting the two hemispheres. These neural connectivity adaptations are \nfurther supported by behavioural evidence showing that when a task is sufficiently \nchallenging, hemispheric cooperation is recruited to enhance performance (Belger & Banich, \n1998; Hughes, Upshaw, Macaulay, & Rutherford, 2016; Weissman & Banich, 2000) , \nresulting in a “rebalancing” of normally dominant and lateralised networks. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n5 \nRecently, Klimovich-Gray et al. (2026) proposed a theoretical framework explaining how \nsuch rebalancing and strengthening interhemispheric cooperation could underlie successful \nand r esilient neural speech processing adaptations in dyslexia. They suggest that the \nclassically observed bilateral, less left -lateralised, phonological and speech networks in \ndyslexia - reflecting RH overactivation (Hoeft, et al., 2007; Pugh, et al., 2000) - often viewed \nas an impairment, may, in some cases, reflect successful compensatory strategies that help \nmanage the high demands imposed by a dysfunctional LH. The authors argue that these \nsuccessful cases of atyp ical bilateral neural organisation will partly depend on the active \nengagement of the CC:  an overactivated RH would only serve as a n optimal compensatory \nstrategy if its activity is transferred to the challenged LH for support (e.g., (Molinaro, \nLizarazu, Lallier, Bourguignon, & Carreiras, 2016; Yu, et al., 2020) ). This hypothesis implies \nexplaining why altered structural interhemispheric connectivity - mostly in the posterior \nsplenium CC section, but also the g enu - predicts low phonological and reading skills (e.g., \n(Dougherty, et al., 2007; Frye, et al., 2008; Rumsey, et al., 1996; Swanson, et al., 2015; \nRobichon, Bouchard, Démonet, & Habib, 2000; Plessen, 2002)) and could be viewed as part \nof causal accounts of reading deficits. \nHere, we adopt an “adaptive” view of the CC where it may either passively suffer from \nweakened signals sent from a dysfunctional hemisphere - the LH in the case of dyslexia - or \nsupport dysfunction through the active (but also more costly) transfer from the preserved \nhemisphere - the RH in dyslexia. The follow-up task for research is therefore to identify \nwhich factor(s) may contribute to favouring the active use of interhemispheric connectivity as \nan effective compensatory strategy that strengthens the reading networks. \nIn the present study, we examine early bilingualism as an environmental factor that may \nultimately alter interhemispheric connectivity to create a rebalanced and resilient reading \nnetwork. In line with this, plenty of evidence shows that experience with bilingual \nenvironments alters functional and structural brain interhemispheric connectivity mainly in \nanterior (Bice, Yamasaki, & Prat, 2020; Fedeli, Del Maschio, Sulpizio, Rothman, & Abutalebi, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n6 \n2021; Luk, Bialystok, Craik, & Grady, 2011; Mohades, et al., 2012; Pliatsikas, \nMoschopoulou, & Saddy, 2015; Schlegel, Rudelson, & Tse, 2012)  but also the posterior \n(Bice, Yamasaki, & Prat, 2020; Pliatsikas, Moschopoulou, & Saddy, 2015; Pereira Soares, \nKubota, Rossi, & Rothman, 2021)  CC regions. Moreover, bilinguals seem to exhibit a more \nbalanced and efficient allocation of attentional res ources (Bialystok & Craik, 2022; Phelps & \nBozic, 2024), which could also reflect the “greater openness” of young bilinguals when they \nexplore their environment (Singh, Kalashnikova, & Quinn, 2023) . The CC might have a role \nto play in this bilingual attentional openness, since it contributes to the optimal attentional \norientation across hemifields, thus hemispheres (Chechlacz, Humphreys, Sotiropoulos, \nKennard, & Cazzoli, 2015; Pollmann, Maertens, Cramon, Lepsien, & Hugdahl, 2002; \nPollmann, 2010), and to reduced lateralisation (Andrulyte, et al., 2024). \nThis is particularly well illustrated by studies using the dichotic listening paradigm \n(Kimura, 1961) , in which hemispheric dominance for phonological processing and the \nefficiency of interhemispheric connectivity can be indirectly meas ured with behavioural \nreadouts. In this paradigm, participants hear different syllables presented simultaneously to \nboth ears and are either instructed to report either the one they heard best (measuring \nbottom-up attentional orientation) or the one presented in a specific ear (top-down attentional \norientation). In this task, right -ear syllables are generally easier to report because they are \nprocessed directly by the dominant LH. Left -ear syllables, however, are more complex to \nreport as they must cross th rough the CC (Pollmann, Maertens, Cramon, Lepsien, & \nHugdahl, 2002) from the non-dominant RH to the dominant LH to be processed linguistically \n(Steinmann, et al., 2017; Steinmann, et al., 2018) . Individuals with high degree of bilingual \nuse and exposure have been shown to exhibit increased left -ear reports (Ershaid, 2026; \nLallier, Peréz -Navarro, & Ordin, 2024)  interpreted as efficient interhemispheric RH -to-LH \nconnectivity. Most importantly this attentional rebalance was associated with better reading \nand phonological skills in bilingual children and adults reports (Ershaid, 2026; Lallier, Peréz-\nNavarro, & Ordin, 2024)  and with protective effects against family risks of dyslexia in \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n7 \nmonolinguals (Hakvoort, et al., 2016) . This evidence also aligns with reports showing less \nsevere phonological deficits in bilingual individuals with dyslexia (Lallier, Thierry, Barr, \nCarreiras, & Tainturier, 2018; Lallier, Peréz -Navarro, & Ordin, 2024; Balboni, Kepinska, \nBerthele, & Golestani, 2025).  \nImportantly, reading ability is continuously distributed in the population, with dyslexia \nrepresenting the lower tail of this distribution rather than a categorically distinct condition \n(Shaywitz, Escobar, Shaywitz, Fletcher, & Makuch, 1992; Pennington, 2006) . This \ndimensional view implies that the neural mechanisms proposed to underlie reading \ndifficulties and associated compensatory strategies in clinically diagnosed individuals, \nincluding structural interhemispheric pathways, may be detectable along the continuum of \ngenetic liability in the general population, thus, even below the clinical threshold \n(Pennington, 2006). \nGenetic studies also support a liability model: reading and spelling outcomes and \nassociated cognitive traits (e.g. phonological awareness) in the general population are \nstrongly genetically correlated with dyslexia (Doust, et al., 2022). Genome-Wide Association \nStudies (GWAS) have begun to identify genetic loci associated with dyslexia (Doust, et al., \n2022; Gialluisi, et al., 2020)  and reading abilities  (Eising, et al., 2022; Price, et al., 2022) , \nenabling the derivation of individual -level polygenic scores (PGS) that aggregate genetic \neffects into a single genetic predictor for a given trait  (Belsky & Harden, 2019; Maier, \nVisscher, Robinson, & Wray, 2017). PGSs based on the presence of a self-reported dyslexia \ndiagnosis have been shown to predict reading performance (Doust, et al., 2022; Bicona, et \nal., 2025; Carrion-Castillo, Carreiras, & Lallier, 2025).  \nInterestingly, PGSs derived from broader and less reading-specific traits, such as \ncognitive performance (i.e., intelligence) or educational attainment, have been shown to \nexplain a larger proportion of (i) variance in reading outcomes (Procopio, et al., 2024; \nCarrion-Castillo, Carreiras, & Lallier, 2025)  and (ii) associated neural structural organisat ion \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n8 \n(Carrión-Castillo, Paz-Alonso, & Carreiras, 2023) , than dyslexia and reading -based PGSs. \nCognitive performance PGS effects on reading were found to be mainly mediated by the \nglobal brain measure of to tal left  cortical surface area (Carrión-Castillo, Paz -Alonso, & \nCarreiras, 2023) . In addition, a meta -analysis of neuroimaging studies identified smaller \noverall brain volume as the most robustly replicated structural fi nding in dyslexia (Ramus, \nAltarelli, Jednoróg, Zhao, & Scotto di Covella, 2018), an effect that persisted after controlling \nfor IQ. Therefore, it is still unclear whether part of the genetic influence linked to general \ncognitive performance on reading operates through global brain measures, reading -specific \ncircuitry, or both. Whether PGSs specific to dyslexia also modulate this global brain pathway \nremains to be established.  \nRecently, both cognitive performance and dyslexia PGSs were used to investigate how \nenvironmental experiences (bilingualism, socio-economic status, etc) modulate genetic \ninfluences on reading outcomes. Carrión-Castillo et al. (2025) found a positive effect of \nbilingualism for reading acquisition that seemed to operate across the genetic risk continuum \nat the population level, benefiting individuals both at high and low genetic risks. Which brain \npathway(s) mediate this positive relat ionship is still unclear and is the focus of the present \nstudy.  \nHere, we adopt a gene-environment perspective to investigate how genetic \npredispositions for reading difficulties - linked to less efficiently connected neural systems \n(Paulesu, et al., 1996; Turker, Kuhnke, Jiang, & Hartwigsen, 2023)  possibly including \nalterations of the splenium and the genu (e.g., (Dougherty, et al., 2007; Sun, et al., 2017)  - \nand bilingualism may both shape the structure of the CC to influence reading outcomes, \nindependently from global brain measures. To do so, we explored the Adolescent Brain \nCognitive Development (ABCD Ⓡ) database which provides genetic and environmental data \nof thousands of children across the United States (Jernigan & Brown, 2018) . We \nhypothesised that long -term recurrent exposure to complex dual -language environments \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n9 \nwould play a key role in molding structural interhemispheric architecture in the brain that is \nbeneficial for learning to read. We expected reading-related PGS and bilingualism to directly \ninfluence reading performance, as shown previously, and these associations to be mediated \nby CC structural variations, with positive (bilingualism) and detrimental (genetic risk, PGS) \nconsequences for reading skills. The structural properties of the genu were expected to be \nmore strongly modulated by bilingualism than reading-related PGS, whereas the splenium \nwas predicted to be more strongly influenced by PGS than bilingualism. Based on previous \nresearch in this sample (Carrion-Castillo, Carreiras, & Lallier, 2025) , we did not expect the \ndirect effects of bilingualism on reading to s trongly interact with genetic risks, but we did not \nhave clear predictions regarding how or whether these two factors would interact to \nmodulate the putative mediating effect of the CC on reading skills (see Figure 1).  \n—---- insert Figure 1 here —---- \n2. Methods  \n2.1 Participants \nThe participants were part of the ABCD Ⓡ study ( https://abcdstudy.org/) (Jernigan & \nBrown, 2018). At baseline (i.e., first timepoint) they included 11,886 children ages 9 to 10 \nfrom the United States recruited and tested between September 2016 and August 2018 \n(Garavan, et al., 2018) . The data was acquired in 21 research centres with the consent of \nthe participant’s parents and following the guidelines of the Declar ation of Helsinki. \nParticipants included in the database had data acquired for all the independent variables \ndefined in the experimental design section. Exclusion criteria, including lack of English \nproficiency, intellectual, medical, neurological or senso ry impairments, and absence of first \nMRI scanning session (Acosta-Rodriguez, et al., 2024).  \nThe current study analysed separately the full baseline sample (N ∼ 11,878) and the 2 -\nyear follow-up timepoint from the ABCD Curated Annual Release 4.0. (DOI: \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n10 \n10.15154/1523041) and the Genotyping Data from the ABCD Curated Annual Release 3.0 \n(NDA Study 901; DOI: 10.15154/1519007).  \n2.1.1 Analysis subsets \nThe full sample included all participants (baseline: N = 11,007, mean age=9.9 years, \nrange=8.9-11; 2-year follow-up: N = 9,693, mean age=12, range=10.6-13.8). Three \nadditional partially overlapping subsets were defined for sensitivity analyses (Table S1): the \nfull unrelated sample  was derived by retaining one child per family unit (based on \nrel_family_id), to avoid non-independence due to related individuals (baseline: N = 9,036; 2-\nyear follow-up: N = 7,924). The European ancestry sample  was defined by restricting to \nparticipants within 6 SDs of the European ancestry centroid on the first two genetic principal \ncomponents (PCs), to minimise population stratification in PGS analyses (baseline: N = \n5,740; 2-year follow-up: N = 5,235) (as in (Carrion-Castillo, Carreiras, & Lallier, 2025) ). The \nEuropean ancestry unrelated sample combined both filters, retaining unrelated individuals of \nEuropean ancestry (baseline: N = 4,716; 2-year follow-up: N = 4,286). \n    Primary mediation analyses for bilingualism predictors were conducted in the full \nsample to maximise statistical power and generalisability. Primary mediation analyses for \nPGS were conducted in the European ancestry unrelated sample to minimise population \nstratification and family-level non-independence. \n2.2 Variables  \nAll variables included in the current study are listed and described in Table S2, with \nderived variables defined in Table S3. Extreme outliers were removed for all continuous \nvariables (±7 SD from the mean). Table S4 provides their descriptive statistics per timepoint \nand subset. \n2.2.1 Reading outcome measure  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n11 \nWe used the uncorrected reading variable from the NIH Toolbox Oral Reading \nRecognition Test®, an adaptive test that assesses reading aloud (Gershon, et al., 2014; \nLuciana, et al., 2018).  \n2.2.2 Bilingual indexes predictors \nParticipant bilingualism was quantified using questionnaires completed by both parents and \nchildren, covering demographic, acculturation, school, and home environmental factors. \nWhile English proficiency was a prerequisite for study participation, questionnaires were \nadministered in both English and Spanish. We operationalized bilingualism through two \nmetrics: a continuous \"bilingualism continuum\" index and a \"bilingual degree\" variable for the \nbilingual subset. \nBilingualism Continuum Index  \nThis metric reflects the spectrum of linguistic exposure and serves as our main operational \ndefinition of bilingualism. We first computed specific sub-scores for proficiency balance, \nlanguage preference, and environmental exposure from selected questionnaire items \n(Tables S2, S3). The items incorporated the following dimensions: language proficiency, \nlanguage usage, linguistic exposure (home and school), parental experience. Then, the \nscore was computed as an absolute weighted average of responses to selected \nquestionnaire items, ranging from 0 (completely monolingual environment) to 4 (maximally \nbilingual environment). See Table S3 for the specific weighting and scoring for each variable.\n  \nBilingual Degree Index \nThis index was derived by recalculating the aforementioned scores exclusively in \nparticipants who said they were able to understand or speak a language other than English, \nthereby excluding monolingual participants. \n2.2.3 PGS predictors \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n12 \nThe PGS is an individual-level variable, computed from a GWAS study, that represents \nthe weighted summation of variants or single nucleotide polymorphisms (SNPs) associated \nwith the trait multiplied by their regression coefficient as a measure of effect of the SNP \nwithin a specific trait (Maier et al. 2018). We used the dyslexia (Doust, et al., 2022)  and \ncognitive performance (Lee, et al., 2018)  GWAS summary statistics to compute ind ividual \nPGS for the target ABCD Ⓡ dataset using PRS -CS (Ge, Chen, Ni, Feng, & Smoller, 2019)  \n(see (Carrion-Castillo, Carreiras, & Lallier, 2025) for details on the procedure). \n2.2.4 Brain mediator measures \nWe used both macrostructural (e.g., volumes) and microstructural (e.g., diffusion tensor \nimaging) data derived from the ABCDⓇ database’s tabulated data, extracted from a T1 -\nweighted sequence (1 mm isotropic voxels), and a diffusion-weighted MRI sequence, \nobtained from the scanning sessions in a 3T MRI scanner (General Electric 750, Philips, \nSiemens) (Casey, et al., 2018). \nMacrostructural MRI data were extracted using the standard morphometric pipeline in \nFreeSurfer 5.3.0, which includes quality control. CC volumes were obtained from the “aseg” \nsegmentation atlas, including anterior, mid-anterior, central, mid-posterior, and posterior \nsegments. Intracranial volume (ICV) was also extracted as part of the “aseg” subcortical \natlas to obtain a proxy for overall brain size (Hyatt, et al., 2020).   \nDiffusion tensor imaging microstructural values were calculated from the CC ROI \ndefined with AtlasTrack (Hagler, et al., 2008)  using linear estimation on log -transformed \ndiffusion-weighted signals (Hagler, et al., 2019) . Averaged weighted measures included \nfractional anisotropy (FA), longitudinal diffusivity (LD), transvers e diffusivity (TD), and mean \ndiffusivity (MD). Additionally, we included a tractography-derived macrostructural measure of \nthe total CC, namely total fiber bundle volume computed from diffusion-weighted MRI. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n13 \n2.3 Data Analysis \nWe considered the baseline data for our primary analyses; 2-year follow-up analyses were \nperformed to assess longitudinal consistency and sensitivity of findings. Only participants \nwith complete data on all variables of interest were included in each analysis; no imputation \nwas performed.  \n2.3.1 Correlation matrices \nAn exploratory correlation analysis was conducted to inform mediator selection, using \nBonferroni correction for multiple comparisons. The selection criteria were: (i) significant \ncorrelations with reading outcomes, and (ii) for variables meeting this criterion, theoretical \nrelevance to prioritise among candidates from the same structure (i.e. CC subregions).  \n \n2.3.2 Mediation analyses \nWe first performed mixed effects models using the lme4 package (Bates, Mächler, Bolker, & \nWalker, 2015) to define direct effects between each predictor and outcomes. \nThen, we conducted simple mediation analyses using the mediation package \n(version v4.5.0) (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014), with 10,000 quasi -\nBayesian simulations. In all models, age and sex were included as fixed -effect covariates \nand site as a random effect to account for the nested data structure. To control for global \nbrain size, ICV was included as a covariate in both the mediator and outcome models. For \nthe PGS analyses, genetic ancestry PCs were included as additional covariates to account \nfor population stratification (Patterson, Price, & Reich, 2006) . These PCs were deriv ed \nbased on genotype data within each full and European ancestry subsets separately (see \n(Carrión-Castillo, Paz -Alonso, & Carreiras, 2023) ). To control for family -wise error rate \nacross the 48 mediation tests (4 predictors × 4 mediators × 3 outcomes), Bonferroni \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n14 \ncorrection was applied. The threshold for statistical significance was adjusted to α = 0.05/48 \n= 0.001. Results are presented with both raw and corrected p-values in Tables S5-S8. \nAll sensitivity analyses are specified in section 2.3.4 and Table S1. In short, to \nassess robustness to global brain size adjustment, secondary mediation analyses were \nrepeated with CC mediator measures normalised by ICV, and without ICV adjustment (Dick, \net al., 2021). Family-level relatedness could not be modelled as an additional random effect \nin the mediation; to address this, analyses were repeated in “ unrelated” subsets, and a \nsensitivity analysis incorporating family as a random effect (instea d of site) was conducted \nfor the primary finding using lme4 directly. Sensitivity analyses were not corrected for \nmultiple comparisons given their exploratory nature, but are reported for transparency. \nAnalyses were replicated with lavaan (v0.6- 15) (Rosseel, 2012)  with the MLR \nestimator with cluster-robust standard errors, and study site as a cluster variable, to provide \na bridge to the structural equation modelling framework described below. The mediation \npackage results are reported as primary given its more complete handling of the nested data \nstructure through mixed-effects models than the lavaan models. \n2.3.3 Structural equation modelling (SEM) \nTo extend the simple mediation analyses and examine all predictors and mediators \nsimultaneously, we fitted a near-saturated SEM in lavaan (Rosseel, 2012). This model was \nfit for the baseline timepoint and included all predictor and mediator variables (except the \n“bilingual degree” variable, which was not significant in any of the mediation models) and \nreading as outcome. ICV was modelled as an endogenous variable predicted by all three \npredictors, and was additionally included as a covariate in the anterior CC and posterior CC \nequations. Residual covariance between anterior CC and posterior CC was freely estimated \nto account for shared variance between adjacent subregion measures. \nThe model was estimated using the MLR estimator with standard errors robust to \nnon-normality. Indirect effects were tested using the delta method approximation, as \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n15 \nbootstrap confidence intervals cannot be combined with cluster-robust standard errors in \nlavaan. Age and sex were included as covariates in all equations, and study site was \nspecified as a clustering variable to obtain cluster-robust standard errors. No additional \ncorrection for multiple comparisons was applied to SEM path coefficients, as the model was \nestimated simultaneously and serves to confirm patterns observed in the primary mediation \nanalyses rather than test independent hypotheses. Robustness checks were run with \nadditional covariates (Table S1, Figure S1, section 2.3.4 Sensitivity analyses).  \nModel fit was evaluated using SRMR, which remains interpretable in near-saturated \nmodels (df=2). CFI, RMSEA, and TLI are not reported as they are known to perform poorly \nwith very low degrees of freedom (df = 2) (Kenny, Kaniskan, & McCoach, 2014) and produce \nuninterpretable values in the current model. Model adequacy and robustness of fin dings \nwere therefore primarily evaluated based on SRMR and the consistency of parameter \nestimates across model specifications.  \n2.3.4 Sensitivity analyses (Table S1) \nSensitivity analyses examined robustness across the following dimensions: \n1. Sample composition: all analyses were repeated in four subsets differing in ancestry \nstratification and relatedness filtering (Tables S1, S4). Primary results on bilingualism \neffects on reading are reported for the full sample, and for PGS effects for the \nEuropean unrelated sample. The SEM was conducted in the full sample to retain \nsufficient power when modelling bilingualism and PGS effects simultaneously. \n2. Given evidence that covariate selection can substantially alter structural-behavioral \nassociations (Hyatt, et al., 2020), we examined the robustness of our findings across \nthree analytical approaches: unadjusted models (NOadj), models with ICV as a \ncovariate (ICVcov), and models using brain volume normalized by ICV (ICVnorm). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n16 \n3. Analytic approach: mediation analyses were replicated using lavaan to confirm \nrobustness to estimation framework (see Section 2.3.2). \n4. Ancestry PCs and SES: SEM models were re-run adding ancestry principal \ncomponents (PC1 –PC10) and socioeconomic covariates (household income, \nparental education). \n5. To assess specificity of findings to reading, analyses were repeated with control \ncognitive outcomes vocabulary (NIH Toolbox Picture Vocabulary Test ) (Gershon, et \nal., 2014) and non-verbal reasoning (WISC-V Matrix Reasoning) (Wechsler, 2014) as \noutcomes (Luciana, et al., 2018). \nA full overview of all analysis specifications is provided in Table S1. \nThe main hypotheses of this study were preregistered in OSF: https://osf.io/hcf53 (Rius-\nManau, Lallier, & Carrion -Castillo, 2026). Deviations from the pre -registered analyses are \ndetailed in the Supplementary materials (Supplementary annex A). \n3. Results \nDescriptive statistics for all variables and subsets are presented in Table S4. The results of \nthe correlation analyses (Figures S2 and S3) led to the selection of anterior and posterior CC \nvolumes and ICV, and the total CC fiber bundle volume (diffusion-weighted MRI). \n3.1 Direct effects of the predictors on reading (ADE paths in Tables S5-S8) \nBilingualism → Reading:  Bilingualism continuum scores were positively and robustly \nassociated with reading across all timepoints ( baseline std. β ≈ 0.033-0.037, unadjusted p < \n.0001; year 2 follow up: std. β ≈ 0.0548–0.0575, unadjusted p < .0001; Table S5) and \nsubsets (Tables S6-S8). The bilingualism degree score computed within the bilingual \nsubsample only (N = 3,767 at baseline and year 2 follow up: N=2,654) showed nominal \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n17 \nassociations with reading in some subsets and timepoints that did not survive correction for \nmultiple comparisons (Table S5-S8). \nRegarding the control outcomes (vocabulary and non-verbal reasoning), bilingualism \ncontinuum showed no significant effect on non-verbal reasoning in any subset, and only a \nnominally significant effect on vocabulary in the full European ancestry subset at baseline \nthat did not replicate in the unrelated subset. A negative association between bilingualism \ndegree and non-verbal reasoning was nominally significant in the full samples with \nbilingualism degree scores (std. β = −0.035 to −0.045, p = .009–.032, N=3,735 to 3,133), but \nwas absent in European ancestry subsets. \nPGSs → Reading:  Both PGSs showed strong and consistent effects in all subsets (Tables \nS5-S8): CP PGS was positively (std. β ≈ 0.203 -0.263, all unadjusted p < .0001), and \nDyslexia PGS was negatively (std. β ≈ −0.145 to −0.195, all unadjusted p < .0001) \nassociated with reading. \nRegarding the control outcomes, CP PGS also showed strong and consistent positive effects \non both vocabulary (std. β = 0.15-0.223, p < .0001) and non-verbal reasoning (std. β = 0.12-\n0.148, p < .0001) across subsets and timepoints. Dyslexia PGS effects showed mostly \nnominally significant negative effects on vocabulary in the European ancestry subsets at \nbaseline and follow- up (std. β = -0.058 - -0.011, p < .0001 –0.2548) but weaker effects for \nnon-verbal reasoning which did not survive in the European unrelated subset (Table S8). \n3.2 Mediation analyses \nFull results are presented in Supplementary Tables S6 –S8 and in Figure 2. Point estimates \nshowed near-perfect agreement across methods (92.5% agreement on statistical \nsignificance of indirect effects; Supplementary Annex B), supporting the robustness of \nfindings across estimation frameworks (Table S9). \n—---- insert Figure 2 here —---- \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n18 \n3.2.1 Mediation analyses for the prediction of bilingualism to reading \nThere was no significant mediation for bilingualism degree (only bilingual participants) (Table \nS5, Figures S4, S5), which showed high kurtosis across subsets (Table S4). For bilingualism \ncontinuum (including both monolinguals and bilinguals), analyses revealed a significant \nindirect effect on reading via the anterior CC volume at baseline, before and after ICV \nadjustment (Figure 2A, Table S5). Specifically, after including ICV as a covariate, \nbilingualism continuum was negatively associated with anterior CC volume (std. A-path = -\n0.0319, p = 0.007), and anterior CC volume was negatively associated with reading (std. B-\npath = - 0.0354, p<0.0001), yielding a significant positive indirect effect (std. indirect β = \n0.0013, 95% CI [0.0004, 0.0024], p = 6x10-4) (Table S5). Strikingly, this indirect effect was \nnegative and significant without ICV control (std. indirect β = -0.0018, 95% CI [-0.0031, -\n0.0007], p<0.0001; std B-path = 0.0511, p<0.0001), reflecting the zero-order negative \ncorrelation between bilingualism and anterior CC volume (r = -0.04, p=3x10-5, Figure S2) \nand leading to a reversed effect after ICV was partialled out. This indirect effect remained \nsignificant across ICV handling specifications, and when family ID was modelled as a \nrandom effect instead of study site (p = 0.048 to <0.0001, Table S10).  \nRobustness analyses in other subsets across the two timepoints showed that this indirect \neffect was not significant at the 2-year follow-up (mean age 12, Table S4) in neither subset \n(Tables S5-S8; with ICV adjustment: full sample std. indirect β =0.0004 , p =0.176, \nN=6,796). However, it was maintained in the unrelated full sample at baseline (N = 8,840, \nTable S6), with both ICV-covariate and ICV-normalised specifications, but not after adjusting \nfor ancestry PCs (Table S9, Figures S5-S6). In the full European ancestry subset at baseline \n(N = 5,389, Table S7), this indirect effect was nominally significant with ICV as a covariate \n(std. indirect β = 0.0011, p = .034) and with ICV normalisation (std. indirect β = 0.00117, p = \n.014). In the unrelated European subset (N = 4,635, Table S8), no significant indirect effect \nwas observed (all p > .10). The full robustness analysis of the indirect effect of bilingualism \non reading through the anterior CC across 36 model specifications is presented in Figure S6. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n19 \nControl outcomes (vocabulary and non-verbal reasoning):  There were no significant \nmediations for bilingualism degree. Anterior CC (unadjusted for ICV or ICV-normalised) \nsignificantly mediated the effect of bilingualism on non-verbal reasoning and vocabulary \n(Table S5), but not when including ICV as a covariate (Figures S7A, S8A). These effects \nwere not present in the European ancestry subsets (Tables S7,S8). \n3.2.2 Mediation analyses for the prediction of PGS to reading \nPGS→CC →Reading: The total CC fiber bundle volume showed significant indirect effects for \nboth PGS ( CP PGS: std. indirect β = 0.0068 , p <0.0001; Dyslexi a PGS: std. indirect β = -\n0.005, p <0.0001, Table S8). However, these indirect effects were no longer significant after \nincluding ICV as a covariate or after ICV-normalised CC volume (all p > .05). This pattern \nwas consistent across subsets (Tables S5-S8, S9). There was a nominally significant \nindirect effect of the PGS on reading through the anterior CC (baseline ICV-normalised: \np=0.026) that was affected by ICV handling and subset specifications (Tables S5-S9). \nPGS→ICV→Reading: Both CP PGS and Dyslexia PGS showed significant indirect effects on \nreading via ICV. Specifically, higher CP PGS was associated with larger ICV (std. A-path at \nbaseline= 0.0868, p = 4.1x10-13; year 2=0.0809, p=5.1x10- 08), which in turn was positively \nassociated with reading (std. B-path at baseline= 0.1516, p = 1.4x10-19; year 2= 0.1646, \np=2.8x10-16), yielding a significant positive indirect effect (std . indirect β at baseline= \n0.0131, 95% CI [0.0089, 0.0179], p<0.0001; year 2=0.0133, 95% CI [0.0079, 0.0194] , \np<0.0001). Conversely, higher Dyslexia PGS was associated with smaller ICV (std. A-path \nat baseline = -0.0435, p = 0.0003; year 2=-0.0355 , p=0.0165), yielding a significant negative \nindirect effect on reading via ICV (std. indirect β at baselin e=-0.0074, 95% CI [-0.0117, -\n0.0033], p = 0.0004; year2=-0.0069, 95% CI [-0.0124, -0.0012], p=0.0148). These patterns \nwere consistent across all subsets (Tables S5-S9) and timepoints (Figure S4). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n20 \nControl outcomes (vocabulary and non-verbal reasoning): CP PGS showed significant total \neffects and ICV-mediated indirect effects for vocabulary (Figure S7C, Table S8) and non-\nverbal intelligence (Figure S8C, Table S8). Dyslexia PGS total effects were nominally \nsignificant, though marginal ICV-mediated indirect effects were observed for both \n(vocabulary: std. indirect β at baseline =-0.007, p =0.0004, year 2=-0.0062, p<0.0194 ; non-\nverbal reasoning: std. indirect β =-0.0048 , p=0.0002) (Table S8, Figures S7C, S8C). \n \n3.3 Structural equation modelling (SEM) \nThe model is presented in Figure 3 including all predictors and mediators tested in the \nprevious analyses, except for bilingual degree that was not significant in any of the models. \nOverall, the model fit for the primary SEM was acceptable: SRMR = 0.059. CFI and RMSEA \nare not interpreted given the near-saturated model structure (df = 2, see Methods). R² values \nfor the endogenous variables were: ICV = 0.24 (variance explained by PGS and bilingualism \npredictors), CC anterior = 0.03 and CC posterior = 0.03 (variance explained by predictors \nand ICV), and reading = 0.16 (variance explained by all predictors and mediators). \n—---- insert Figure 3 here —---- \nDirect effects \nThe direct effects of the bilingualism and PGS predictors on reading were stable across SEM \nmodel specifications with and without ICV, and additional covariate adjustment (e.g. CP PGS \nstd. β=0.16-0.26, Dyslexia PGS std. β= -0.10 - -0.116; bilingualism continuum std. β =0.040-\n0.064; full results in Table S11). \nIndirect effects \nBilingualism→CC→Reading: The indirect effect of bilingualism via anterior CC reported in the \nsimple mediation analyses was nominally significant in the full sample (std. indirect β (SE) = \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n21 \n0.002 (0.001), p = .0346), also after adjusting for SES covariates (Table S11). However, it \ndisappeared with ancestry PC inclusion (p = .26). \nPGSs→CC→Reading: The CP PGS showed a nominally significant indirect effect via anterior \nCC in the model without ancestry PCs (std. β = −0.002, SE = 0.001, p = .017), but this effect \ndid not survive PC adjustment. No significant indirect effect via posterior CC was observed. \nPGSs→ICV→Reading:  In the primary model (with ICV and no ancestry PCs), the CP PGS \nshowed a robust indirect effect on reading via ICV (std. β = 0.026, SE = 0.004, p = <0.001). \nThis effect was stable across all model specifications. The Dyslexia PGS indirect effect via \nICV was not significant in the primary model ( std. β = −0.003, SE = 0.001, p = .072) but was \nnominally significant on all other specifications (Table S11). \nControl outcomes (vocabulary and non-verbal reasoning) (Tables S12 - S13).  \nAnalyses confirmed robust direct effects of CP PGS and significant indirect effects via ICV  \n(vocabulary: std. β = 0.026, SE=0.005, p <0.001; WISC -V: std. β = 0.018, SE=0.0028, p \n<0.001). The direct effect of Dyslexia PGS, and indirect effect through ICV were nominally \nsignificant for some model specification only (Tables S12, S13). Additionally, indirect effects \nof both PGS via posterior CC emerged in these models, strongest for vocabulary, but also \npresent for non-verbal reasoning (vocabulary: std. β = 0.003, SE=0.0009, p <0.001; WISC-V: \nstd. β = 0.003, SE=0.001, p = 0.012). This indirect ef fect was attenuated but present after \nadjusting for ancestry PCs, ICV and SES (Tables S12, S13). \n \nDiscussion \nBy integrating genetic, brain and behavioural data, we attempted to provide a possible \nmechanistic understanding of how the early and recurrent exposure to bilingual \nenvironments may sharpen the resilience of the brain structure to genetic predispositions to \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n22 \nreading difficulties. Our hypothesis more specifically focused on the CC, which has been \nproposed to hold a critical role in contributing to the formation of a less lateralised and \nrebalanced resilient reading network (Klimovich-Gray, Bozic, Molinaro, & Lallier, 2026). \n1. Global brain size (but not the CC) mediates the influence of genetic \npredispositions to reading difficulties   \nBoth PGS predicted reading performance (negatively for Dyslexia PGS and positively for CP \nPGS), as reported in previous studies (Carrión-Castillo, Paz -Alonso, & Carreiras, 2023; \nCarrion-Castillo, Carreiras, & Lallier, 2025) . Here, we showed that this effect was present in \nthe full subsets, which included heterogeneous ancestries, as the ABCD study is diverse by \ndesign, aiming to obtain a representative sample from the general US population (Garavan, \net al., 2018) . Effect sizes were somewhat larger in the European ancestry subsets, \nconsistent with the known reduction in PGS predictive validity in ancestrally diverse samples \ndue to GWAS discovery bias (Duncan, et al., 2019; Martin, et al., 2017) , though the overlap \nin intervals suggests robustness to ancestry composition. \nThese direct effects were found to be mediated by the total CC volume before \nadjusting for ICV, but not after ICV controls. This reflects shared variance between CC \nvolume mediation and global brain size, rather than a neurogenetic interhemispheric \npathway to reading outcomes. Indeed, part of both the CP and the Dyslexia PGS direct \neffects on reading were mediated by a positive effect of ICV on reading, although less \nconsistent and smaller in magnitude for Dyslexia PGS. Crucially, these indirect effects were \nrobust after controlling for socioeconomic background and to analytical choices, and were \nfound for both study timepoints. These findings are in line with the consistent overall smaller \nICV measures in dyslexia (Ramus, Altarelli, Jednoróg, Zhao, & Scotto di Covella, 2018) , as \nwell as with the indirect effect of CP PGS on reading through total LH cortical surface area in \nthis sample (Carrión-Castillo, Paz -Alonso, & Carreiras, 2023) . Interestingly, the SEM \nanalyses revealed that the direct effects of both PGS and indirect effects through ICV on \nreading rema ined partially independent when estimated simultaneously. Overall, these \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n23 \nresults suggest that both reading-related PGS have an influence on ICV (positive for CP, \nnegative for Dyslexia) which in turn robustly modulates reading-specific and non-reading-\nspecific cognitive outcomes (with some variation between SEM and mediation analyses for \ncontrol outcomes). Notably, an indirect pathway from Dyslexia PGS through ICV more \nspecific to reading might capture variance in core processes underlying dyslexia, such as \ngrapheme-to-phoneme mappings, independent of oral vocabulary and nonverbal IQ. \nContrary to our predictions, there was no robust indirect effect of PGS on reading \nthrough the CC, as effects disappeared when controlling for ICV. This suggests that, rather \nthan deficit-based neurogenetic mechanisms, the previously reported links between CC and \ndyslexia (e.g. (Vandermosten, Poelmans, Sunaert, Ghesquière, & Wouters, 2013) ) may \nreflect compensatory strategies that we hypoth esise are promoted by complex experiences, \nsuch as exposure to bilingual environments. \n \n2. The positive effects of bilingualism on reading are partly mediated through the \nanterior CC, independently of global brain size and genetic predispositions to \nreading difficulties. \nWe found a highly robust direct positive effect of our bilingualism-continuum index on \nreading - including both monolingual and bilingual children (note that this effect was absent \nin smaller samples composed solely of bilingual participants) - across both time points, in \nboth mediation and SEM analyses, replicating the findings of Carrión-Castillo et al. (2025) \nwhile using a continuous rather than dichotomous operationalisation of bilingualism : the \ngreatest reading benefits were seen in children exposed to the most bilingual environments. \nThis replicates previous behavioral findings in Basque –Spanish Grade 1 bilinguals (Lallier, \nPeréz-Navarro, & Ordin, 2024) , showing that bilinguals predominantly exposed to dual -\nlanguage contexts demonstrated more advanced (lexical) reading skills than bilinguals \nmainly exposed to single -language environments. This highlights the importance of taking \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n24 \ninto account the heterogeneity of bilingual experiences to characterise their effects on \nneurocognitive outcomes (see (Blanco-Elorrieta & Pylkkänen, 2018; DeLuca, Rothman, \nBialystok, & Pliatsikas, 2020). \nAlthough a priori planned, we chose not to directly test the interaction between PGS \nand bilingualism since they predicted reading through independent neural paths (ICV for \nPGS, CC for bilingualism). Of note, Carrión-Castillo et al., (2025) found a weak nominal \ngenes x environment interaction in this ABCD sample which tended to show stronger \nprotective effects for bilinguals with the highest risks of developing reading difficulties. In any \ncase, these findings suggest that  learning multiple languages boosts reading development \nacross the whole risk continuum. \nCritically and as predicted, the direct effect of bilingualism on reading was mediated \nby the CC, its anterior region specifically, in the main analysis using the full sample. This \nfinding supports our hypothesis that early exposure to complex bilingual environments \npromotes the active engagement of frontal interhemispheric connectivity to create a \nrebalanced attentional control network. Importantly, this effect was robust despite controlling \nfor global brain size (also in both European samples), and was relatively specific to reading, \nespecially in the mediation analyses. This indirect effect was attenuated with ancestry \nadjustment, smaller sample sizes, and more conservative estimation approaches (e.g., only \nnominally significant and less specific to reading in the SEM analyses), calling for replication \nin other bilingual populations in which linguistic diversity and genetic ancestry are less \nconfounded (see (Ershaid, 2026; Lallier, Thierry, Barr, Carreiras, & Tainturier, 2018; Lallier, \nPeréz-Navarro, & Ordin, 2024)). \nThe selective mediation of the anterior (not posterior) CC independently of ICV rules out a \ngeneral whole-brain structural origin. This specific mediation aligns with frontal regions \nmodulations reported across bilingual infants, children, and adults (Arredondo, Hu, \nSatterfield, & Kovelman, 2016; Arredondo, Aslin, & Werker, 2021; D’Souza & D’Souza, \n2016), that also depend on task complexity (Davis, Kragel, Madden, & Cabeza, 2011; Davis \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n25 \n& Cabeza, 2015) . Surprisingly, bilingualism was associated with reduced anterior CC, \ndespite positive effects on reading, consistent with previous work in this ABCD sample \nshowing that favorable perinatal condi tions were linked to prolonged CC development in \npresence of better cognitive outcomes (Wu, et al., 2025) . This potentially protracted CC \ndevelopment may stem from an increased need to rely simultaneously on opposing inhibitory \nand excitatory callosal forces (see (Bloom & Hynd, 2005; Knaap & Ham, 2011) ) imposed by \ncomplex bilingual environments. Indeed, recurrent reliance on effortful, top-down cooperative \nstrategies between left an d right frontal regions in bilinguals could effectively go “against” \nfunctional lateralisation principles that rely on interhemispheric inhibition, and may slightly \nprotract anterior CC development. However, over time, these strategies may become more \nautomated and more readily deployed at a lower cost, enhancing processing efficiency and \nsupporting the resolution of difficulties, thereby manifesting as a benefit rather than a \ndetriment. This could explain why a positive mediation path from bilingualism to reading was \nobserved, despite appearing negative before controlling for brain size.  \nFunctionally, these adaptive frontal strategies might reflect the reliance on high-order \noral language skills (such as morphosyntactic predictions, see Klimovich-Gray et al., 2026) \nwhich may protect against family risks of dyslexia and their associated phonological \ndifficulties (Snowling M. J., 2008) . It remains unclear whether and how bilingualism -induced \nanterior CC effects could also i nfluence posterior callosal connections (see (Ronderos, Zuk, \nHernandez, & Vaughn, 2024) ), directly linked to phonological and reading development \n(Dougherty, et al., 2007; Swanson, et al., 2015) and protective factors in dyslexia (Yu, et al., \n2020). \nAn important remaining question is when hemispheric rebalance becomes beneficial \nfor reading development and whether positive effects emerge differently for bilinguals with \nlanguage pairs varying on phonological distance. Here, we showed that bilingual \nhemispheric rebalancing supports reading in a sample mainly composed of languages with \nhighly distinct phonemic repertoires (inferred Spanish-English bilingualism: over 80% for \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n26 \nchildren and over 73% for parents), but similar effects were observed several times using a \nbehavioral index of hemispheric rebalance (dichotic listening) in Basque-Spanish bilinguals \nwith highly similar phonological repertoires (Ershaid, 2026; Lallier, Peréz -Navarro, & Ordin, \n2024). This suggests benefits across language pairs and cultural contexts, although future \nresearch should examine these differences more directly. \n \n3. Beyond the CC as the only mediator of the link between bilingualism and \nreading \nIt is important to stress that anterior CC volume is unlikely to be the primary pathway through \nwhich bilingualism influences reading. Indeed, the direct effect of bilingualism on reading \nwas robust across all specifications and substantially larger than the indirect pathway \nthrough the anterior CC. Accordingly, in the SEM, there was a limited variance in CC volume \nexplained by the predictors and covariates, consistent with the implication of other \nstructures.  \nIntrahemispheric connectivity might play a particularly important role in this respect, \nas bilingual experience has been repeatedly linked to changes in intrinsic functional and \nstructural intrahemispheric connectivity (Fedeli, Del Maschio, Sulpizio, Rothman, & \nAbutalebi, 2021; Sulpizio, Del Maschio, Del Mauro, Fedeli, & Abutalebi, 2020; Luk, Bialystok, \nCraik, & Grady, 2011; Pliatsikas, Moschopoulou, & Saddy, 2015; Schlegel, Rudelson, & Tse, \n2012; Mohades, et al., 2012; Hämäläinen, Sairanen, Leminen, & Lehtonen, 2017; Singh, et \nal., 2017). Lower reading skills have also been associated with reduced LH asymmetry of \nthe FA of the arcuate fascicus (AF) - a white matter tract part of the superior longitudinal \nfasciculus (SLF) linking temporo -parietal to frontal regions of the reading and attentional \ncontrol networks (Meisler & Gabrieli, 2022; Thiebaut de Schotten, et al., 2011; \nVandermosten, Poelmans, Sunaert, Ghesquière, & Wouters, 2013; Zhao, Thiebaut de \nSchotten, Altarelli, Dubois, & Ramus, 2016) - asymmetry that might be influenced by the CC \nitself (Andrulyte, Demirkan, Branzi, Bonnett, & Keller, 2026) . We initially explored the \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n27 \ncorrelation between the FA asymmetry index of the whole SLF, as provided by the ABCD \ndatabase (including SLF I, II, III, and AF) (Janelle, Iorio-Morin, D’amour, & Fortin, 2022), and \nreading skills, but found no significant relationship  (see Figures S2,S3) . As this lack of \ncorrelation could not be attributed to the asymmetry of specific subsegments, we did not \npursue further analyses. Future research should further explore the role of the AF/SLF, but \nalso investigate how subcortical and cortical grey matter structures implicated in dyslexia \n(e.g., IFG, cerebellum) may be modulated by bilingualism (Marin-Marin, Costumero, Ávila, & \nPliatsikas, 2022; Pliatsikas, et al., 2020) to influence reading trajectories. \n \n3.4 Conclusion \nThis study offers a nuanced mechanistic understanding of how early bilingual exposure \ninteracts with genetic predispositions to shape the brain’s resilience to reading difficulties. \nContrary to the persistent myth that multilingualism exacerbates reading difficulties, our \nfindings demonstrate that bilingual environments may provide a protective, independent \nbuffer against genetic risks for dyslexia through altering interhemispheric connectivity. While \ngenetic predispositions primarily influence reading outcomes through global brain size, \nbilingualism seems to operate through a distinct neuroanatomical pathway: the anterior CC. \nUltimately, these results validate bilingualism as a powerful environmental factor that can \nfoster neurocognitive resilience. We speculate that these frontal structural effects contribute \nto a more efficient allocation of normally effortful attentional resources across hemispheres, \nthereby supporting phonological processing and mitigating reading difficulties. These \nfindings open potential avenues for research to test whether leveraging this environmental \nlinguistic factor through educational policies could promote resilience to reading difficulties \nacross the entire ability spectrum. \n  \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n28 \n4. Author contributions: CRediT \nConceptualization: M.L., A.C.-C., Data curation: C.R., 23andMe, A.C.-C, Methodology: \nM.L., A.C.-C, Formal analysis: C.R., A.C.-C. Funding acquisition: A.C.-C., M.L. Supervision: \nM.L., A.C.-C. Visualization: C.R., A.C.C. Writing – original draft: M.L., C.R., A.C.C. Writing – \nreview & editing: M.L., C.R., A.C.C. \n5. Author information \n23andMe research team \nAdam Auton, Alan Kwong, Anjali J. Shastri, Barry Hicks, Catherine H. Weldon, David A. \nHinds, Emily DelloRusso, Emily M. Rios, Joyce Y. Tung, Kahsaia de Brito, Katelyn Kukar \nBond, Keng-Han Lin, Matthew H. McIntyre, Matthew J. Kmiecik, Qiaojuan Jane Su, Robert \nK. Bell, Sayantan Das, Shubham Saini, Stella Aslibekyan, Vinh Tran, Wanwan Xu, Alisa P. \nLehman, Noura S. Abul-Husn, R. Ryanne Wu, Rebecca M. K. Berns, Ruth I. Tennen, Stacey \nB. Detweiler, Aditya Ambati, Anna Guan, Bertram L. Koelsch, Chris German, Éadaoin \nHarney, Ethan M. Jewett, G. David Poznik, James R. Ashenhurst, Jingran Wen, Peter R. \nWilton, Steven J. Micheletti, and William A. Freyman. \n6. Declaration of competing interest \nThe 23andMe Research Team is currently employed by the 23andMe Research Institute, \na California non-profit public benefit corporation. Some research was initiated/conducted \nwhile 23andMe, Inc. operated as a for-profit entity; The 23andMe Research Team may have \nheld stock or stock options in 23andMe, Inc. during that period. \n7. Acknowledgments \nA. C-C. received funding from the Spanish Ministry of Science and Innovation and the \nAgencia Estatal de Investigación through Ayudas Ramón y Cajal (RYC2022- 035511-I). M.L. \nis supported by the Spanish Ministry of Science and Innovation (grant no. PID2022-\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n29 \n136989OB-I00 and ERC- 2024-COG 101170337 BIBALANCE). The BCBL acknowledges \nfunding from the Basque Government through the BERC 2022-2025 program, and by the \nSpanish State Research Agency through The BCBL Severo Ochoa excellence accreditation, \nCEX2020-001010-S. The funders had no role in study design, data collection and analysis, \ndecision to publish or preparation of the manuscript. We thank the research participants and \nemployees of 23andMe Research Institute. for making this work possible.  \nData used in the preparation of this article were obtained from the ABCD Ⓡ Study \n(https://abcdstudy.org/) and are held in the National Institute of Mental Health (NIMH) Data \nArchive. This is a multisite, longitudinal study designed to recruit more than 10,000 children \naged 9– 10 and follow them over 10 years into early adulthood. The ABCD Ⓡ Study is \nsupported by the NIH and additional federal partners under award numbers U01DA041022, \nU01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, \nU01DA041120, U01DA041134, U01DA041148, U01DA041156, U01D A041174, \nU24DA041123 and U24DA041147. A full list of supporters is available at \nhttps://abcdstudy.org/federal-partners/\n. A listing of participating sites and a complete listing \nof the study investigators can be found at https://abcdstudy.org/principal-investigators/. \n8. Code availability \nThe custom code associated with this study is publicly available at  \nhttps://git.bcbl.eu/ENDD/MS-reading-IHC-ABCD/. \n \n9. Data availability \nABCDⓇ data are publicly available through the National Institute of Mental Health (NIHM) \nData Archive (https://data-archive.nimh.nih.gov/abcd). GWAS summary statistics used in this \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n30 \nstudy are available from the NHGRI-EBI GWAS Catalog \nhttps://www.ebi.ac.uk/gwas/downloads/summary-statistics). 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It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n46 \n11. Figure legends \n \nFigure 1. (A) Conceptual model: illustrating the hypothesised relationships between \nbilingualism, polygenic scores (PGS), corpus callosum (CC) structure, and reading \noutcomes. ( B) Study design workflow.  Summary of the analytical steps and statistical \napproaches used; see Figure S1 and Table S1 for detailed specifications. \n \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n47 \nFigure 2. Forest plots showing direct and indirect effects of bilingualism and \npolygenic score on reading. Mediations are shown for (A) anterior corpus callosum (CC) \nand (B) intracranial volume (ICV). Results are shown for the full sample at baseline \n(maximum N =10,708, varying by analysis due to missing data) , using the mediation \npackage. Note that primary PGS analyses were conducted in the European ancestry \nunrelated subset (maximum N = 4,636); results for this subset are reported in \nSupplementary Figure S5, Table S8. Effect sizes are standardised (outcome and mediator z-\nscored prior to model fitting). Faded points indicate non-si gnificant effects (p ≥ .05, \nunadjusted); error bars represent 95% bootstrap confidence intervals (10,000 simulations, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n48 \nnot necessarily symmetric around the point estimate). Sensitivity analyses across ICV \nspecifications and sample subsets are reported in Supplementary Figure S5. \n \n \nFigure 3. Structural equation model path diagram, full sample. Results shown for the full \nsample at baseline (N = 9,634); full results in Table S11. Nodes represent observed \nvariables; paths show standardised coefficients (standard error). Solid lines indicate \nsignificant paths (p < .05); dashed lines indicate non-significant paths. Line width is \nproportional to effect size. Model estimated using MLR with cluster-robust standard errors \n(site as clustering variable). ICV was modelled as an endogenous mediator for polygenic \nscore effects and as a covariate in CC subregion equations (ICV → CC paths not shown for \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint \n\n \n49 \nclarity); CC anterior and CC posterior residual covariance was freely estimated (~~). All \nequations included age and sex as covariates. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 7, 2026. ; https://doi.org/10.64898/2026.04.07.716864doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}