Prenatal Brain Connectivity and Postnatal Language: How Familial Risk and Prenatal Speech Exposure Shape Early Language Skills

Prenatal Brain Connectivity and Postnatal Language: How Familial Risk and Prenatal Speech Exposure Shape Early Language Skills | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prenatal Brain Connectivity and Postnatal Language: How Familial Risk and Prenatal Speech Exposure Shape Early Language Skills Cristina Cara, Matteo Canini, Claudia Oprandi, Nicolò Pecco, Paolo Ivo Cavoretto, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6113185/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The maturation of the auditory-language brain network begins before birth, driven by gene-environment interactions. We investigated the association between familial and environmental factors and the foetal development of this network, as well as the predictive value of this association for postnatal language outcomes. Using prenatal resting-state fMRI, we examined 25 foetuses to identify functional connectivity within the auditory-language network. Postnatal language was assessed longitudinally between 1-3 years using the Bayley-III scale. Familial risk for language disorders and prenatal speech exposure were quantified using a newly developed questionnaire. The analysis in foetuses identified an auditory-language network. In this network, foetuses with higher speech exposure exhibited increased connectivity between left-hemisphere regions and decreased connectivity between homologous right-hemisphere regions. Higher familial risk was linked to reduced connectivity within the left language network. Regression analyses revealed that prenatal functional connectivity between insula, caudate nucleus, and rolandic operculum significantly predicted postnatal language. These findings underscore the critical role of genetic and environmental influences in functionally shaping the foetal auditory-language network, with lasting impacts on early language development. By integrating prenatal brain connectivity, familial risk, and speech exposure, this study provides new insights into prenatal language neurodevelopment, highlighting its importance for future language capabilities. *Marco Tettamanti & Pasquale Anthony Della Rosa contributed equally. Biological sciences/Neuroscience/Cognitive neuroscience/Dyslexia Biological sciences/Neuroscience/Cognitive neuroscience/Language Biological sciences/Neuroscience/Diseases of the nervous system/Developmental disorders Biological sciences/Psychology/Human behaviour foetal neurodevelopment early language acquisition functional connectivity auditory and language networks familial risk language disorders Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Language developmental trajectories are influenced by both genetic and environmental factors1–3. Familial clustering and twin studies provided evidence that developmental language and reading disorders are highly heritable1,4. Genetic influences extend beyond pathological conditions, as inheritability has also been observed in normotypical language development for various abilities encompassing reading, spelling, phonology, syntax, and articulation5–7. The amount of environmental linguistic stimulation and exposure in childhood has also been shown to influence language skills development8. Genes and environment likely impact language development indirectly, by shaping the structural and functional brain network, which in turn shapes cognitive and behavioural profiles in early infancy and childhood. Language-related genes, as well as environmental factors, participate in early neurodevelopmental processes including axonal growth, neural migration, and myelination9,10, as well as functional connectivity11. As for environmental factors, a recent study has shown that functional connectivity in the language network in infancy is affected by the amount of adult-infant conversations12. Multiple lines of evidence indicate that the impact of genetic and environmental factors on functional connectivity within the auditory and language network begins as early as the foetal stage9,13. Behavioural observations and neuroimaging studies in newborns have suggested that prenatal exposure to auditory and speech stimuli could shape the infants' postnatal brain and behavioural responses, including speech perception and language acquisition abilities13–15. However, evidence gained from observations in newborns has only indirect pertinence to infer prenatal neurodevelopmental processes. Recent advancements in foetal multimodal neuroimaging have greatly enhanced our opportunities to directly investigate prenatal brain development. In a recent review, we extensively described the structural and functional maturation trajectories of the auditory and language networks in foetuses15. The development of peripheral and subcortical brain structures forming the auditory pathways begins in the first trimester of gestation and approaches maturation in foetuses at term16. Foetal brain response to sounds has been recorded in the auditory cortex through magnetoencephalography as early as 27 gestational weeks (GW) and throughout the third trimester of gestation15,17. Cortical folding and gyrification of perisylvian language regions start around 20 GW and undergo significant growth between 24 and 36 GW15,18,19. Structural connections between these regions can be detected as early as 26 GW, with full maturation only occurring in the early years after birth20,21. Concurrently, whole-brain foetal functional connectivity starts to emerge at 26GW, with fronto-temporal connections developing between 29 and 36 GW22. The prenatal period represents a critical epoch in the establishment of early brain connectivity, which is essential for infant´s survival and adaptation after birth23. Resting-state fMRI (rs-fMRI) evidence suggested that the foetal brain shows a modular functional architecture including, among others, primordial sensorimotor and auditory-language networks24,25. These functional modules gradually evolve across perinatal development into the regular set of functional networks in the adult human brain. This process establishes a blueprint for cognitive development23, that sets the stage for future cognitive and behavioral capabilities. Language development entails both resistance to developmental perturbations, such as gene mutations, and sensitivity to environmental changes26, in striving to achieve neurotypical homeostatic brain network states27. The objective of this study was to leverage measures of foetal functional connectivity, as proxies for the assessment of gene-environment interactions with the auditory and language networks, and the influence of these interactions on language outcomes in early infancy. To achieve this goal, we investigated a sample of 25 healthy foetuses which were scanned with rs-fMRI to collect functional connectivity measures. This sample was followed-up longitudinally with the assessment of language abilities within the first three years after birth, by means of the standardized Bayley-III test battery28. We also designed a novel structured questionnaire that we administered to parents in this sample to assess their children's familial susceptibility to language disorders, as well as, retrospectively, the amount of linguistic stimulation during gestation. We first analyzed the foetal functional connectivity data to examine the presence of a primordial auditory-language functional network in our cohort15,25. We then sought to determine the association of functional connectivity strength in the auditory-language network with familial susceptibility to language disorders, as well as with the amount of linguistic stimulation during gestation. In the final step, we utilized a stepwise linear regression analysis on the postnatal Bayley-III language outcomes to assess which prenatal brain “gene-environment sensitive” functional connections can significantly predict language behaviour in early infancy. 2. Materials and Methods 2.1 Experimental sample Twenty-five pregnant mothers participated in the present study. They were recruited at San Raffaele Hospital, Milan, Italy during regular pregnancy monitoring. Inclusion criteria were as follow: i) healthy foetuses in the late foetal period, which is characterized by significant structural and functional changes in the organization of cerebral connections29; ii) normal foetal anatomy and growth assessed through foetal ultrasound; iii) low-risk for major chromosomal disorders by first trimester combined screening with or without non-invasive prenatal screening by means of cell-free fetal DNA testing; iv) no sign of foetal neurodevelopmental abnormality nor brain parenchymal signal alterations acknowledged by means of structural MRI investigation. All foetuses were subsequently born at term, without complications, had normal neonatological assessment at birth, and showed no major health problems in the first year of postnatal life. The final sample included 25 foetuses (13M, 12F). The average gestational age of the foetuses at the time of MRI session was 32.5 GW (SD = 1.8, range = 27-35; Supplementary Materials: Table S1). This study and all its experiments were conducted in full accordance with ethical guidelines and regulations, including the World Medical Association Declaration of Helsinki and subsequent revisions. The research protocol was reviewed and approved by the Ethics Committee of the San Raffaele Hospital (protocol code: RF-2016-02364081; Register of Opinions Number: 51/INT/2018; date of approval: 04/05/2018). Accordingly, all women provided written informed consent prior to foetal MR scanning. 2.2 Family background questionnaire The Family background questionnaire (FBQ) is a parent report instrument designed to gather comprehensive information on child’s risks for developmental language and learning disorders based on familial and prenatal exposure factors. In order to fulfil this aim, the FBQ collects data related to familiar history of language and learning disorders and attempts to retrospectively quantify the amount of prenatal exposure to linguistic stimuli. Information on the validation of the FBQ is provided in the Supplementary Materials (Methods S1). The questionnaire consists of two main parts: the first part (FBQ1) was administered either by phone or in person to the parents (required time for administration was approximately 15 minutes), and included three main sections: 2.2.1 FBQ1 - General information The first section was adapted from the factsheet of the MacArthur–Bates Communicative Development Inventories30 and provides general information about the child and the nuclear family, parental educational and socioeconomic statuses, and child's postnatal exposure to mono- or multilingual contexts. 2.2.2 FBQ1 - Familial susceptibility for language frailties The second section of the FBQ1 explores the child’s familial susceptibility to neurodevelopmental language frailties. Parents are asked whether any first-degree family member (i.e., parents, siblings) ever had undiagnosed difficulties related to: 1) reading; 2) writing; 3) spelling and letter recognition; 4) language production and articulation; 5) speech delay. The responses to this section were summarized by means of a binary score for each subject. A score of 1 was assigned if at least one first-degree relative had one or more frailties in the domains under investigation, while a score of 0 was assigned if no frailties were reported in the family (Supplementary Materials: Figure S1). 2.2.3 FBQ1 – Familial susceptibility for language impairments The third section of the FBQ1 further explores the child’s familial susceptibility to neurodevelopmental language impairments. Parents were asked whether any nuclear or extended family member (i.e., also including cousins, aunts and uncles, grandparents) ever had a diagnosis of the following disorders: 1) dyslexia; 2) language disorders; 3) articulatory or phonological disorders; 4) dysgraphia; 5) dysortographia; 6) stuttering. The responses to this section were summarized by means of a binary score for each subject. A score of 1 was assigned if at least one family member had one or more diagnoses, while a score of 0 was assigned if no diagnoses were reported (Supplementary Materials: Figure S1). 2.2.4 FBQ2 The second part of the questionnaire (FBQ2) unfolded in two sections. Following an oral presentation to the parents by a designated researcher, the FBQ2 was sent via email and. both parents were asked to complete both sections. 2.2.4.1 FBQ2 – Parental self-assessed language- and speech-related difficulties The first section of the FBQ2 includes self-reported questions aimed at characterizing the parents’ cognitive profiles by means of three items: i) “ I am fast at reading a book’s page ”, ii) “ I have difficulties in putting sounds together during word pronunciation ”, iii) “ I make mistakes in taking notes ”. Both parents were asked to rate each item on a 5-point Likert scale (0 = strongly disagree; 4 = completely agree). The aim of this section is to explore more subtle signs of language- and speech-related difficulties without explicitly referring to language or learning disorders. Following reverse scoring for the first item, the raw scores of the three different items were summed to compute an overall median-split score for each subject (i.e., 1 ≥ median; 0 < median) (Supplementary Materials: Figure S1). Three subjects had missing data for this variable, due to failure to return the questionnaire: For these subjects, the overall median-split score was imputed based on the two familial scores for language frailties and impairments, resulting in a score equal to 0 for all three subjects. 2.2.4.2 FBQ2 – Environmental prenatal speech exposure The second section of the FBQ2 investigated the amount of speech stimulation provided by the parents to the child during pregnancy by means of two distinct items: i) "During pregnancy, I talked to the baby" , ii) " During pregnancy, I used to read story tales aloud" . Both parents were asked to rate each item on a 4-point Likert scale (0=never; 3=always). The aim of this section is to retrospectively quantify the child’s prenatal exposure to speech. 2.2.5 Aggregate familial risk score The items described above in sections 2.2.2, 2.2.3, and 2.2.4.1 were combined together into an aggregate familial risk score (Supplementary Materials: Figure S1), measuring the child familial predisposition to neurodevelopmental language and learning disorders. More specifically, the binary scores obtained from the three familial variables were summed together. The aggregate score ranged from 0 to 3, reflecting the subject’s familial risk for developing language disorders. The aggregate familial risk score was used as a variable of interest in the group-level analyses. 2.2.6. Aggregate prenatal speech exposure score For each subject, an aggregate score was calculated by averaging the raw data from both parents for the two items included in the second section of FBQ2 (section 2.2.4.2; Supplementary Materials: Figure S2). This aggregate prenatal speech exposure score was used as a variable of interest in the group-level analyses. Missing data for three subjects were replaced with the sample’s mode. In addition, the mother was asked to rate the noise level of her home and workplace environments at the time of pregnancy on a 4-point Likert scale ranging from 0 (very silent) to 3 (very noisy). The raw scores of these items were averaged to compute an aggregate individual score reflecting the child's prenatal exposure to noise, which was used as a nuisance covariate in group-level analyses. 2.3 Postnatal behavioural assessment Children's linguistic skills were assessed by a developmental neuropsychologist (C.O.) with the Bayley-III scale for infant and toddler development28. The Bayley-III measures developmental functioning in children between 1 and 42 months across various domains, including language28. Specifically for purposes of this study, we used age-corrected total language scores computed as the sum of the expressive and receptive linguistic scores obtained from the Bayley-III language scale. Children were tested between 1 and 3 years of age (mean age at time of testing = 22.2 months, SD = 7.7, range = 12-38; Table S1). 2.4 Prenatal MRI data acquisition Foetal MR scanning was performed on a Philips Achieva 1.5 T scanner, using a 16 channels body coil. All pregnant women were asked not to eat within 2.5 h preceding the MR scanning. Foetal rs-fMRI consisted of GE EPI scans (TR = 2000 ms, TE = 30ms, acquisition voxel size 2.81 × 2.86 × 3 mm, 25 slices, slice gap = 0). Each rs-fMRI scan consisted of 60 volumes. Four to six consecutive rs-fMRI sessions (i.e., 240-360 volumes, covering from 8 to 12 min of continuous brain activity at rest) were acquired for each subject depending on the condition of each pregnant woman during MR scanning and the quality of the scans. Foetal structural scans consisted of a T2 Single Shot Turbo Spin Echo scan on the axial, sagittal and coronal planes of the foetus (TR = 8000 ms, TE = 125 ms, voxel size 1.17 x 2.76 x 3 mm, 25 slices) for a total scanning duration time of 17s. All foetuses showed no sign of foetal neurodevelopmental abnormality nor brain parenchymal signal alterations acknowledged by a neuroradiologist (C.B.) on structural MRI scans. 2.5 Foetal rs-fMRI Image Pre-Processing Foetal scans were processed using the Resting-State Fetal functional MRI (RS-FetMRI) preprocessing pipeline (https://github.com/NicoloPecco/RS-FetMRI). The RS-FetMRI is divided into the following preprocessing steps: a) rs-fMRI volume reorientation and origin set on the anterior commissure; b) 1st-pass masking step with a GWsession-specific mask to remove the majority of the maternal abdominal tissue; c) within-session realignment step with a binary tissue-weighting mask which binds motion estimation only to inner-brain portions of the foetal brain; d) 1st-pass scrubbing through ART (https://www.nitrc.org/projects/artifact_detect); e) segmentation of session-specific functional reference volumes to derive session-specific inner-brain masks in subjects anatomy space based on registration with seven “best-fit” GW-specific brain foetal tissue and structure maps; f) between-session mean functional reference volume calculation on session-specific masked functional reference scans; g) between-session 2nd-pass realignment of all session-specific masked functional volumes; h) 2nd-pass between-session scrubbing procedure through ART and estimation of frame-to-frame estimation of motion (FD) and signal intensity (DVARS) changes; i) calculation of deformation parameters through SPM’s unified segmentation–normalization algorithm31 based on spatial registration with specific brain foetal tissue and structure maps for warping the between-session mean functional reference volume to the median-sample group-based atlas space; j) application of deformation parameters to between-session masked functional volumes in order to warp all volumes in the rs-fMRI time-series to GW median-sample group-based atlas space; k) smoothing normalized between-session masked volumes using an isotropic gaussian filter kernel with full width at half maximum (FWHM) 4mm. 2.6 Functional connectivity analysis Smoothed and normalized resting-state image volumes were analysed with the CONN functional connectivity toolbox v22a, running on Matlab32. A component based noise correction method (CompCor33) was first implemented for rs-fMRI time-series denoising of white matter and cerebrospinal fluid. After nuisance regression, data were band-pass filtered at 0.01-0.08 Hz. Regions of interest (ROI) for functional connectivity analysis were defined on a 33 GW foetal brain atlas34. We selected fourty-two ROI (Figure 1) encompassing the foetal auditory, language, and sensorimotor networks, according to previous literature15,25. At the first level, Pearson's correlation coefficients were computed for each possible pair of ROI. Fisher's transformation was applied to convert Pearson's coefficients to z-score coefficients. This procedure generated a correlation matrix for each subject reflecting the ROI-to-ROI functional connectivity between each pair of ROI. 2.6.1 Data-driven resting-state network identification At the second (group) level, the ROI-to-ROI connectivity matrices was explored to investigate functional module organization in the foetal brain through a multivariate parametric approach implemented in CONN. Functional networks were derived using a data-driven hierarchical clustering procedure with a complete-likage method. The weighting factor was set to 0.05, prioritizing ROI-to-ROI functional similarity over anatomical proximity32. 2.7 Familial risk and prenatal speech exposure associations with functional connectivity The data-driven network identification analysis revealed the presence of unilateral auditory-language networks in both hemispheres of the foetal brain (see Results). We thus investigated the association between the FBQ variables and functional connectivity within the auditory-language networks. For this purpose, the familial risk scores and the prenatal speech exposure scores were correlated with the functional connectivity values between all possible pairs of ROI within the left (n = 45) and the right (n = 55) auditory-language networks. Spearman correlations were first performed. Statistical significance was determined using a threshold of p < 0.05, and confidence interval were computed through bootstrapping (iterations = 10000). Significant effects at this step were used for subsequent regression analyses. 2.8 Stepwise regression model with functional connectivity proxies to predict postnatal language outcomes. From the previous step, we selected ROI-to-ROI functional connections that exhibited significant correlation with familial risk or prenatal speech exposure, surviving the bootstrapping correction (see Results). These functional connectivity values were included as explanatory variables in a stepwise multiple regression analysis to assess model significance and to identify the most significant predictors of the Bayley-III language total composite outcomes. Stepwise regression was chosen due to its ability to add or remove predictors based on statistical criteria, allowing the identification of variables that contribute the most to the model. The criteria for variable entry into the model were based on the probability of F-to-enter set at 0.05 and the probability of F-to-remove set at 0.10. 3. Results 3.1 FBQ - Variable distribution The combination of the three familial risk variables (language frailties, language impairments, parental self-assessed language- and speech-related difficulties) into an aggregate score ranging from 0 to 3, with higher values indicating higher risk levels, yielded 9 subjects with a score of 0, 9 subjects with a score of 1, 4 subjects with a score of 2, and 3 subjects with a score of 3 (Figure 2A; See Supplementary Materials, Results S1, for further details on the individual familial risk scores). The two items investigating prenatal speech exposure were combined into an aggregate score ranging from 0.5 to 2.75 (mean=1.36, SD=0.64), with higher values indicating a greater exposure to speech stimuli during pregnancy (Figure 2B). Importantly, there was no significant sample-wise association between the aggregate familial risk score and the aggregate prenatal speech exposure scores, as shown by a correlation analysis on the two FBQ variables (Spearman coefficient = -0.19, p = 0.37). 3.2 Bayley-III language scale - Score distribution The sample mean for the total score of the Bayley-III language scale was 16.96 SU (SD = 5.3, range = 4-28; Supplementary Materials: Figure S5). Data range and distribution showed a certain degree of variability, which reflects interindividual heterogeneity in children's language trajectories. This diversity renders our sample particularly suitable for studying individual differences in language development. 3.3 Functional connectivity - Data-driven resting-state network identification The hierarchical clustering analysis revealed the emergence of unilateral auditory-language networks in the left and right hemispheres of the foetal brain. Both networks included subcortical and cortical regions, including thalamus, putamen, caudate nucleus, Heschl’s gyrus, superior and middle temporal gyri, superior temporal pole, insula, rolandic operculum, and inferior frontal gyrus. The right network alone also included the orbital middle frontal gyrus (Figure 3). 3.4 Familial risk and environmental score associations with functional Connectivity Familial risk was negatively correlated with functional connectivity in the “left insula - left rolandic operculum” connection (Spearman's rho = -0.5, p = 0.01, bootstrap 95% CI [-0.75, -0.12]), as well as in the “left insula - left caudate” connection (Spearman's rho = -0.47, p = 0.02, bootstrap 95% CI [-0.75, -0.03]) (Figure 4). These effects were also significant in a partial correlation model in which prenatal speech exposure and prenatal noise exposure were entered as nuisance variables (Table 1). The prenatal exposure score positively correlated with functional connectivity in two connections, namely “left rolandic operculum - left middle temporal gyrus” (Spearman's rho = 0.55, p = 0.004, bootstrap 95% CI [0.28, 0.75]), and “left rolandic operculum - left superior temporal pole” (Spearman's rho = 0.6, p = 0.001, bootstrap 95% CI [0.29, 0.79]). There was also a negative correlation in the connection “right orbital middle frontal gyrus - right Heschl’s gyrus” (Spearman's rho = -0.45, p = 0.02, bootstrap 95% CI [-0.74, -0.05]) (Figure 4). These effects were also significant in a partial correlation model in which familial risk and prenatal noise exposure were entered as nuisance variables (Table 2). 3.5 Prenatal functional connectivity association with postnatal language development The two “familial risk” and the three “prenatal exposure” functional connectivity proxies (section 3.4; Tables 1 and 2) were fed as explanatory variables into a stepwise regression model to highlight predictive relations with the postnatal Bayley-III language outcomes. Out of the five explanatory variables, two accounted for significant effects. The first variable was the “left insula - left rolandic operculum” “familial risk” connection, which explained 16.88% of the variance in the Bayley-III language total composite outcomes (R² = .169, Adjusted R² = .133), and showed a statistically significant regression (F(1, 23) = 4.67, p = .041). The stepwise addition of the “left insula - left caudate” “familial risk” connection improved the model's explanatory power, accounting for 33.78% of the variance (R² = .338, Adjusted R² = .278), leading to a significant increase in the model's explanatory power, F(2, 22) = 5.61, p = .011. Due to the stepwise addition of the “left insula - left caudate” “familial risk” connection, the standard error of the estimate decreased from 13.998 to 12.775, indicating improved accuracy of the predictions. In the final stepwise model including both connections, the “left insula - left rolandic operculum” “familial risk” connection had a significant positive association with the Bayley-III language total composite outcomes (B = 27.09, SE = 9.32, β = .522, t = 2.91, p = .008), indicating that an increase in “left insula - left rolandic operculum” connectivity is associated with an increase in postnatal language abilities. In turn, the “left insula - left caudate” “familial risk” connection had a significant negative association with the Bayley-III language total composite outcomes (B = -25.34, SE = 10.69, β = -.426, t = -2.37, p = .027), suggesting that an increase in connection strength is associated with a decrease in postnatal language abilities. In order to find further support for the significant predictive relationship between familial risk and the Bayley-III language score emerged from the stepwise regression model, we tested the correlation between the two variables. A post-hoc Spearmans’ correlation (bootstrapping, n = 10000) revealed a significant negative association (rho = -0.42, p = 0.03, 95% CI [-0.7, -0.003]). 4. Discussion Language acquisition is one of the major achievements in early childhood and represents an essential requirement to succeed in social and educational contexts35,36. Early language skills are indeed reliable predictors of later literacy proficiency, academic attainments, and career outcome35,37. Conversely, language disorders often lead to educational struggles, including specific learning disabilities38. Given this context, there is a pressing need to identify biomarkers for predicting language developmental trajectories in the early stages of development. A growing body of research has demonstrated that the structural and functional brain systems underlying auditory and language processing develop prenatally15. Early brain development trajectories are driven by both genetic and environmental factors11. Understanding the multiple links between genetic and familial influences, prenatal environment, and brain maturation, and how these different variables shape individual differences in language development is one of the major challenges in cognitive neuroscience. The foetal brain, with its rapidly forming neural networks, offers a unique window to understand how early connectivity patterns can act as intermediaries linking genetic predisposition and environmental stimuli to postnatal cognitive outcome. In the current study, we aimed to provide some insights into these issues through a longitudinal approach that involved the acquisition of prenatal rs-fMRI data, followed by postnatal language assessments by means of the Bayley-III language scale28 within the first three years of life. The hereditary background related to familiar vulnerabilities or disorders of language and cognition, and the environmental input, that is the amount of exposure to speech during the prenatal period, were retrospectively reconstructed by means of a novel instrument that we developed ad hoc, named the Family Background Questionnaire (FBQ). The first result of our study is that the data-driven hierarchical clustering analysis of foetal resting-state functional connectivity confirmed the presence of an auditory-language network in the third trimester of foetal development. Unilateral networks were present in both hemispheres encompassing subcortical structures, Heschl’s gyrus, middle and superior temporal cortices, inferior frontal regions, insula, and rolandic operculum. These results are in line with previous rs-fMRI evidence. Subcortico-cortical functional connectivity along the auditory pathway23 as well as local connectivity within temporal regions39 start to develop around 24-25 GW. Thomason and colleagues24 have observed functional connectivity in a left-lateralized fronto-temporal network including language-related regions in foetuses older than 31 GW. A recent study25 conducted on a large sample of foetuses between 21 and 40 GW found functional connectivity in a left-lateralized fronto-temporal-insular module which strongly overlaps with the one we found, and included Heschl’s gyrus, mid and superior temporal cortices, inferior frontal regions, insula, rolandic operculum, and the anterior cingulum. A recent study described the development of the language network from 30 GW to 1 month after birth, and in particular it reported a significant increase in functional connectivity within the language network occurring between 31 and 35 GW40. Considering this body of evidence, we can conclude that our study successfully replicated previous findings, indicating that functional networks supporting auditory and language processing are already established between 27 and 35 GW. The FBQ was conceived as a retrospective tool for determining which children may be at risk of developing language processing difficulties based on familial and prenatal speech exposure factors. The outcome of the expert FBQ validation by means of content and face validity assessments (Supplementary Materials) highlighted that this instrument is endowed with enough sensitivity to capture at least part of the intricate relationships between familial and environmental background and early postnatal language developmental trajectories. Thus, we correlated the familial risk and environmental exposure FBQ scores with foetal functional connectivity values across all possible ROI pairs within the identified left and right auditory-language networks. The results revealed a negative correlation between familial risk scores and strength of the two connections “left insula - left rolandic operculum” and “left insula - left caudate nucleus”. Previous research has consistently demonstrated that infants and children at risk for developmental language and learning disorders exhibit abnormal functional connectivity patterns, reduced functional brain activation, and white matter disorganization compared to controls41–46. Our results extend and anticipate the developmental time frame of these associations to the prenatal period, by showing that they may already be present in the foetal brain. Neuroimaging genetic evidence showed that children with a risk allele for dyslexia have reduced functional and structural connectivity between fronto-temporal language regions compared to children without any risk alleles47. The FBQ variable related to prenatal speech exposure showed a positive correlation with foetal functional connectivity in the two region pairs “left rolandic operculum - left middle temporal gyrus” and “left rolandic operculum - left superior temporal pole”, and a negative correlation in the “right orbital middle frontal gyrus - right Heschl’s gyrus” pair. Thus, foetuses exposed to a greater amount of speech stimuli developed stronger functional connectivity between regions of the left auditory-language network, and weaker connectivity between regions of the right hemisphere. Recent studies demonstrated that the amount of domestic language interactions between infants and adults affects the infants' functional connectivity in posterior temporal regions of the language network12, and predict the myelination of language-related white matter tracts measured at 2 years48. A review paper has recently examined the relationship between other environmental factors, such as the socio-economic status, and structural and functional brain development49. Children and adolescents with higher socio-economic status have thicker cortex50 and larger surface areas51,52 compared to their peers with lower socio-economic status49. These effects are observed in multiple subcortical and cortical regions, including middle temporal and inferior frontal language structures51,52. Functional connectivity between limbic structures and cortical regions has been found to be reduced in children with lower socio-economic status49,53. However, results in this field are not always consistent across studies, and some authors claimed that the influence of environmental variables on brain development may vary depending on developmental age49. In line with the overall hypothesis of our study, we evaluated whether prenatal brain functional connections showing associations with familial risk and environmental speech exposure can predict the postnatal development of linguistic skills, through a stepwise regression model. Our results revealed prediction of language developmental outcomes in infancy pivoted on prenatal connectivity of the insular cortex with the caudate nucleus and the rolandic operculum. First, these results are in line with the known relevance of the insula for the language system. The insula is a key node of a functional system including the subcortical basal ganglia-cerebellum complex, the inferior frontal gyrus, and sensorimotor cortices, and underlying speech production54. Accordingly, on the one hand, the insulo-opercular cortices play a crucial role in phonological rehearsal for learning new words55; on the other hand lesions to the insular cortex and the caudate nucleus result in a plethora of language disturbances, including impaired word finding and object naming, repetition and perseveration errors, verbal and phonemic paraphasias56. Second, the insula is a phylogenetically-old neurodevelopmental pivot, playing a central role in the maturation of the whole neocortical mantle57. Structurally, the insular cortex reaches maturation around 30 GW, and establishes an extensive pattern of connections with both subcortical structures (including the thalamus, putamen, and caudate nucleus), and all major neocortical processing sites related to both sensorimotor and higher-order processing58. On this basis, the anterior insula plays a crucial role in the emergence and consolidation of the functional dynamic regulation between task passive and task active networks59, promoting the development of executive control and adaptive cognitive and behavioural repertoires27,60. Particularly, the development of emotional self-regulatory capability throughout infancy is pivotal for the instantiation and tuning of pre- and proto-linguistic communicative interactions of the newborn with the caregivers, and represents the groundings on which proper language development occurs60,61 Thus, the functional development of insular connectivity exerts both a direct effect on language development, as well as an indirect one through its crucial contribution in tuning of functional networks underlying adaptive cognitive and behavioural processes. It is worth highlighting that, amongst the plethora of regions of interest and connections included in our analyses, insular connectivity emerged as the only predictor of language outcomes. This is interesting when considering that modulation of insular functional connectivity patterns has been previously reported by studies investigating the impact of psychological62–64, environmental63, and lifestyle65 factors in foetuses, as well as in neonates born preterm66 and with a higher familial risk of developing autism40. Our work aligns with these findings and suggests that familial risk for language disorders represents another relevant neurodevelopmental factor affecting the functional gridline of the developing language system, and hence language outcomes in infancy. This body of evidence is consistent with the centrality of the insula for the development of the brain’s functional architecture. During the late foetal period, the insula, together with the sensorimotor cortices, acts as a major functional hub triggering transient bursting activity propagation, and thus underlying the maturation of large scale brain networks67. Similarly to what happens for white matter bundles68 the insula could reach a ‘functional activity peak’ at this developmental stage, making it a particularly vulnerable target of genetic as well as environmental factors. Thus, within the conceptual framework of our study, the insular functional gridline represents a particularly sensitive proxy for the identification of effects related to familial and environmental factors at the prenatal developmental stage. 5. Conclusions, limitations, and future directions The current study represents a first attempt to link familial and prenatal exposure factors with foetal functional connectivity and postnatal language development trajectories. We showed that higher exposure to speech stimuli during pregnancy is associated with stronger prenatal functional connectivity between regions of the left auditory-language network. Moreover, we showed that familiarity for language disorders is associated with lower functional connectivity between the left caudate nucleus and insula, and between the left insula and rolandic operculum. Functional connectivity in these connections is in turn associated with language development outcomes in early childhood. These results are promising and support the idea that prenatal functional connectivity, prenatal speech exposure, and familial risk variables could serve as potential markers for predicting early language development trajectories and disorders. However, an important limitation of this study is the relatively small sample size. While these findings offer valuable insights into early brain and language development, the generalizability to a broader population may be limited. Future confirmative studies in larger samples will be required. Future studies may also take advantage of a more detailed characterization of prenatal environmental variables, by gathering more comprehensive data on the type, amount, and frequency of auditory and speech stimulation that reaches the foetus during pregnancy, either retrospectively and taking advantage of the FBQ as in the present study, or eventually directly in prospective longitudinal studies. Crucially, longitudinal assessments of language abilities up to school-age may evaluate whether these multimodal factors can predict the insurgence of developmental language and learning disorders, thus opening the way to more timely supportive interventions. Declarations Acknowledgments We thank the mothers and infants who made this study possible, as well as their families, for completing the Family Background Questionnaire. We also thank all the doctors of the Department of Obstetrics and Gynaecology of the San Raffaele Hospital and their staff for their support and for allowing us to recruit women for the study. Finally, we thank the MRI technicians of the Department of Neuroradiology of the San Raffaele Hospital for their help during the acquisition of fetal MRI data. This work was supported by the Italian Ministry of Health’s “Ricerca Finalizzata 2016” (grant number RF-2016-02364081; Principal Investigator: Dr. Pasquale Anthony Della Rosa). Author contributions CC, MCanini, CO, MT and PADR conceptualized the study. MCandiani, AF and PADR acquired funding for the study. CC, NP, PIC, MCandiani, CB and AF provided software and resources. CC, MCanini, CO, PIC, CB and PADR collected the data. CC, MCanini, NP, PIC, CB, MT and PADR analyzed the data. CC, MCanini, CO, MT and PADR wrote the original manuscript draft. CC, MCanini, PIC, CB, MT and PADR reviewed and edited the manuscript. Competing interests The authors declare no competing interests. Data availability statement The data that support the findings of this study cannot be made openly available due to confidentiality restrictions. Anonymized data will be made available upon reasonable request from Pasquale Anthony Della Rosa ( [email protected] ), in accordance with the Ethics Committee of the San Raffaele Hospital, Milan, Italy. References Mountford, H. S. & Newbury, D. F. The Genetics of Language Acquisition. in International Handbook of Language Acquisition (eds. Horst, J. S. & Von Koss Torkildsen, J.) 33–50 (Routledge, New York, NY: Routledge, [2019] | Series: Routledge, 2019). doi:10.4324/9781315110622-3. Onnis, L., Truzzi, A. & Ma, X. Language development and disorders: Possible genes and environment interactions. Res. Dev. Disabil. 82 , 132–146 (2018). Rowe, M. L. & Weisleder, A. Language Development in Context. Annu. Rev. Dev. Psychol. 2 , 201–223 (2020). Grigorenko, E. L. Speaking Genes or Genes for Speaking? Deciphering the Genetics of Speech and Language. J. Child Psychol. Psychiatry 50 , 116–125 (2009). Andreola, C. et al. The heritability of reading and reading-related neurocognitive components: A multi-level meta-analysis. Neurosci. Biobehav. Rev. 121 , 175–200 (2021). Kovas, Y. et al. Genetic Influences in Different Aspects of Language Development: The Etiology of Language Skills in 4.5-Year-Old Twins. Child Dev. 76 , 632–651 (2005). Stromswold, K. The Heritability of Language: A Review and Metaanalysis of Twin, Adoption, and Linkage Studies. Language 77 , 647–723 (2001). Ferjan Ramírez, N., Lytle, S. R., Fish, M. & Kuhl, P. K. Parent coaching at 6 and 10 months improves language outcomes at 14 months: A randomized controlled trial. Dev. Sci. 22 , e12762 (2019). Eising, E. et al. A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. Mol. Psychiatry 24 , 1065–1078 (2019). Galaburda, A. M., LoTurco, J., Ramus, F., Fitch, R. H. & Rosen, G. D. From genes to behavior in developmental dyslexia. Nat. Neurosci. 9 , 1213–1217 (2006). Richmond, S., Johnson, K. A., Seal, M. L., Allen, N. B. & Whittle, S. Development of brain networks and relevance of environmental and genetic factors: A systematic review. Neurosci. Biobehav. Rev. 71 , 215–239 (2016). King, L. S., Camacho, M. C., Montez, D. F., Humphreys, K. L. & Gotlib, I. H. Naturalistic Language Input is Associated with Resting-State Functional Connectivity in Infancy. J. Neurosci. 41 , 424–434 (2021). Gervain, J. Plasticity in early language acquisition: the effects of prenatal and early childhood experience. Curr. Opin. Neurobiol. 35 , 13–20 (2015). Dehaene-Lambertz, G. et al. Functional organization of perisylvian activation during presentation of sentences in preverbal infants. Proc. Natl. Acad. Sci. U. S. A. 103 , 14240–14245 (2006). Ghio, M., Cara, C. & Tettamanti, M. The prenatal brain readiness for speech processing: A review on foetal development of auditory and primordial language networks. Neurosci. Biobehav. Rev. 128 , 709–719 (2021). Granier‐Deferre, C., Ribeiro, A., Jacquet, A.-Y. & Bassereau, S. Near-term fetuses process temporal features of speech. Dev. Sci. 14 , 336–352 (2011). Schleussner, E. & Schneider, U. Developmental changes of auditory-evoked fields in fetuses. Exp. Neurol. 190 , 59–64 (2004). Dubois, J. et al. Mapping the early cortical folding process in the preterm newborn brain. Cereb. Cortex N. Y. N 1991 18 , 1444–1454 (2008). Habas, P. A. et al. Early Folding Patterns and Asymmetries of the Normal Human Brain Detected from in Utero MRI. Cereb. Cortex 22 , 13–25 (2012). Khan, S. et al. Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images. NeuroImage 185 , 593–608 (2019). Martino, J. et al. Analysis of the subcomponents and cortical terminations of the perisylvian superior longitudinal fasciculus: a fiber dissection and DTI tractography study. Brain Struct. Funct. 218 , 105–121 (2013). Jakab, A. et al. Fetal functional imaging portrays heterogeneous development of emerging human brain networks. Front. Hum. Neurosci. 8 , (2014). Canini, M. et al. Subcortico-Cortical Functional Connectivity in the Fetal Brain: A Cognitive Development Blueprint. Cereb. Cortex Commun. 1 , (2020). Thomason, M. E. et al. Intrinsic Functional Brain Architecture Derived from Graph Theoretical Analysis in the Human Fetus. PLOS ONE 9 , e94423 (2014). Turk, E. et al. Functional Connectome of the Fetal Brain. J. Neurosci. 39 , 9716–9724 (2019). Bourguignon, N. J., Nadig, A. & Valois, D. The Biolinguistics of Autism: Emergent Perspectives. Biolinguistics 6 , 124–165 (2012). Della Rosa, P. A. et al. The effects of the functional interplay between the Default Mode and Executive Control Resting State Networks on cognitive outcome in preterm born infants at 6 months of age. Brain Cogn. 147 , 105669 (2021). Bayley, N. Bayley Scales of Infant and Toddler Development, Third Edition. https://doi.org/10.1037/t14978-000 (2012). Kostović, I., Radoš, M., Kostović-Srzentić, M. & Krsnik, Ž. Fundamentals of the Development of Connectivity in the Human Fetal Brain in Late Gestation: From 24 Weeks Gestational Age to Term . J Neuropathol Exp Neurol 80 , 393–414 (2021). MacArthur-Bates Communicative Development Inventories. https://mb-cdi.stanford.edu/. Ashburner, J. & Friston, K. J. Unified segmentation. NeuroImage 26 , 839–851 (2005). Whitfield-Gabrieli, S. & Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2 , 125–141 (2012). Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI. NeuroImage 37 , 90–101 (2007). Gholipour, A. et al. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci. Rep. 7 , 476 (2017). Bornstein, M. H., Hahn, C.-S., Putnick, D. L. & Pearson, R. M. Stability of core language skill from infancy to adolescence in typical and atypical development. Sci. Adv. 4 , eaat7422 (2018). Pace, A., Alper, R., Burchinal, M. R., Golinkoff, R. M. & Hirsh-Pasek, K. Measuring success: Within and cross-domain predictors of academic and social trajectories in elementary school. Early Child. Res. Q. 46 , 112–125 (2019). Hohm, E., Jennen-Steinmetz, C., Schmidt, M. H. & Laucht, M. Language development at ten months. Eur. Child Adolesc. Psychiatry 16 , 149–156 (2007). Catts, H. W., Bridges, M. S., Little, T. D. & Tomblin, J. B. Reading Achievement Growth in Children With Language Impairments. J. Speech Lang. Hear. Res. 51 , 1569–1579 (2008). Schöpf, V., Kasprian, G., Brugger, P. C. & Prayer, D. Watching the fetal brain at ‘rest’. Int. J. Dev. Neurosci. 30 , 11–17 (2012). Scheinost, D. et al. Functional connectivity for the language network in the developing brain: 30 weeks of gestation to 30 months of age. Cereb. Cortex 32 , 3289–3301 (2022). Cantiani, C. et al. Reduced left-lateralized pattern of event-related EEG oscillations in infants at familial risk for language and learning impairment. NeuroImage Clin. 22 , 101778 (2019). Langer, N. et al. White Matter Alterations in Infants at Risk for Developmental Dyslexia. Cereb. Cortex N. Y. N 1991 27 , 1027–1036 (2017). Packman, A. et al. White matter connectivity in neonates at risk of stuttering: Preliminary data. Neurosci. Lett. 781 , 136655 (2022). Raschle, N. M., Zuk, J. & Gaab, N. Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. Proc. Natl. Acad. Sci. 109 , 2156–2161 (2012). Vandermosten, M., Hoeft, F. & Norton, E. Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis. Curr. Opin. Behav. Sci. 10 , (2016). Yu, X. et al. Patterns of Neural Functional Connectivity in Infants at Familial Risk of Developmental Dyslexia. JAMA Netw. Open 5 , e2236102 (2022). Skeide, M. A. et al. Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children. NeuroImage 118 , 414–421 (2015). Huber, E., Corrigan, N. M., Yarnykh, V. L., Ferjan Ramírez, N. & Kuhl, P. K. Language Experience during Infancy Predicts White Matter Myelination at Age 2 Years. J. Neurosci. 43 , 1590–1599 (2023). Tooley, U. A., Bassett, D. S. & Mackey, A. P. Environmental influences on the pace of brain development. Nat. Rev. Neurosci. 22 , 372–384 (2021). Lawson, G. M., Duda, J. T., Avants, B. B., Wu, J. & Farah, M. J. Associations between children’s socioeconomic status and prefrontal cortical thickness. Dev. Sci. 16 , 641–652 (2013). McDermott, C. L. et al. Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology. J. Neurosci. 39 , 1365–1373 (2019). Noble, K. G. et al. Family income, parental education and brain structure in children and adolescents. Nat. Neurosci. 18 , 773–778 (2015). Hanson, J. L. et al. Resting state coupling between the amygdala and ventromedial prefrontal cortex is related to household income in childhood and indexes future psychological vulnerability to stress. Dev. Psychopathol. 31 , 1053–1066 (2019). Eickhoff, S. B., Heim, S., Zilles, K. & Amunts, K. A systems perspective on the effective connectivity of overt speech production. Philos. Trans. R. Soc. Math. Phys. Eng. Sci. 367 , 2399–2421 (2009). Veroude, K., Norris, D. G., Shumskaya, E., Gullberg, M. & Indefrey, P. Functional connectivity between brain regions involved in learning words of a new language. Brain Lang. 113 , 21–27 (2010). Kreisler, A. et al. The anatomy of aphasia revisited. Neurology 54 , 1117–1123 (2000). Afif, A., Bouvier, R., Buenerd, A., Trouillas, J. & Mertens, P. Development of the human fetal insular cortex: study of the gyration from 13 to 28 gestational weeks. Brain Struct. Funct. 212 , 335–346 (2007). Ghaziri, J. et al. Subcortical structural connectivity of insular subregions. Sci. Rep. 8 , 8596 (2018). Uddin, L. Q., Kinnison, J., Pessoa, L. & Anderson, M. L. Beyond the Tripartite Cognition–Emotion–Interoception Model of the Human Insular Cortex. J. Cogn. Neurosci. 26 , 16–27 (2014). Canini, M. et al. Functional connectivity markers of prematurity at birth predict neurodevelopmental outcomes at 6, 12, 24, and 36 months. International Journal of Behavioral Development (2025) Topping, K., Dekhinet, R. & Zeedyk, S. Parent–infant interaction and children’s language development. Educ Psychol 33 , 391–426 (2013). Canini, M. et al. Maternal anxiety-driven modulation of fetal limbic connectivity designs a backbone linking neonatal brain functional topology to socio-emotional development in early childhood. J. Neurosci. Res. 101 , 1484–1503 (2023). Thomason, M. E. et al. Prenatal lead exposure impacts cross-hemispheric and long-range connectivity in the human fetal brain. NeuroImage 191 , 186–192 (2019). Wu, Y., De Asis-Cruz, J. & Limperopoulos, C. Brain structural and functional outcomes in the offspring of women experiencing psychological distress during pregnancy. Mol. Psychiatry 29 , 2223–2240 (2024). Thomason, M. E. et al. Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity. Dev. Cogn. Neurosci. 51 , 101000 (2021). Smyser, C. D. et al. Resting-state network complexity and magnitude are reduced in prematurely born infants. Cereb Cortex 26 , 322–333 (2016). Arichi, T. et al. Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI. eLife 6 , e27814 (2017). Kostović, I., Kostović-Srzentić, M., Benjak, V., Jovanov-Milošević, N. & Radoš, M. Developmental dynamics of radial vulnerability in the cerebral compartments in preterm infants and neonates. (2014). Tables Table 1 . Correlations between functional connectivity and familial risk. Functional connectivity and aggregate familial risk score Connection rho p Bootstrap CI 95% Partial correlation model rho p L Insula – L Caudate -0.47 0.018 -0.75 -0.03 -0.47 0.02 L Insula – L Rolandic Operculum -0.5 0.011 -0.75 -0.12 -0.47 0.02 Spearman's rho coefficient and the associated p-values are reported. Confidence intervals (CI) were computed using bootstrap (n = 10000). The two rightmost columns report rho and p for the partial correlation model with prenatal speech exposure and prenatal noise exposure as nuisance variables. L = left. Table 2. Correlations between functional connectivity and prenatal speech exposure. Functional connectivity and prenatal speech exposure score Connection rho p Bootstrap CI 95% Partial correlation model rho p L Rolandic Operculum – L Middle Temporal 0.55 0.004 0.28 0.75 0.51 0.01 L Rolandic Operculum – L Superior Temporal Pole 0.6 0.001 0.29 0.79 0.57 0.004 R Middle Orbital Frontal - R Heschl Gyrus -0.45 0.02 -0.74 -0.05 -0.43 0.04 Spearman's rho coefficient and the associated p-values are reported. Confidence intervals (CI) were computed using bootstrap (n = 10000). The two rightmost columns report rho and p for the partial correlation model with famililal risk and prenatal noise exposure as nuisance variables. L = left; R = right. Additional Declarations No competing interests reported. Supplementary Files CaraCetalsupplmat.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 05 May, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 24 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers agreed at journal 18 Mar, 2025 Reviewers invited by journal 18 Mar, 2025 Editor assigned by journal 10 Mar, 2025 Submission checks completed at journal 06 Mar, 2025 First submitted to journal 06 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6113185","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":431576084,"identity":"00fa04ea-d914-4edf-bc5b-de61a8c2c0a6","order_by":0,"name":"Cristina Cara","email":"","orcid":"","institution":"CIMeC - Center for Mind/Brain Sciences, University of Trento, Italy; Faculty of Mathematics and Natural Sciences, Heinrich Heine University Düsseldorf, Düsseldorf, 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Milan, Italy","correspondingAuthor":false,"prefix":"","firstName":"Pasquale","middleName":"Anthony Della","lastName":"Rosa*","suffix":""}],"badges":[],"createdAt":"2025-02-26 12:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6113185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6113185/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-17531-y","type":"published","date":"2025-09-26T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78951616,"identity":"cf1eb33d-9492-40be-b890-e6b4f59ec3ab","added_by":"auto","created_at":"2025-03-21 09:06:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":492869,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegions of interest. \u003c/strong\u003eRegions of interest selected in the left and right hemisphere for functional connectivityanalysis. All regions were taken from a 33 GW fetal brain atlas34. L, left; R, right.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/2d6407f6e720ac6922052ae9.png"},{"id":78952419,"identity":"02188c23-bf2d-40db-9786-f9596dbb0d09","added_by":"auto","created_at":"2025-03-21 09:14:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":199686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFamily background questionnaire (FBQ) aggregate scores. \u003c/strong\u003eThe plots show the sample distribution of, A) the aggregate familial risk score; B) the aggregate environmental, prenatal speech exposure score.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/122f37262e2ed82c21532f09.png"},{"id":78951614,"identity":"cb4bf0bc-1e07-4acb-8011-acbcbc2cef83","added_by":"auto","created_at":"2025-03-21 09:06:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":674098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAuditory-language networks in the fetal brain. \u003c/strong\u003eRegions of the auditory-language networks in the left and right hemisphere resulting from the functional connectivity analysis. All regions were included in both hemispheres, except for the orbital part of the middle frontal gyrus, which was only present on the right. The displayed regions of interest were taken from a 33 GW fetal brain atlas34. L, left; R, right.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/18ad21c2d3be66b01a7aa4f9.png"},{"id":78952420,"identity":"7199a549-b36c-46c3-a8c7-7371fafc55e5","added_by":"auto","created_at":"2025-03-21 09:14:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":282909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between functional connectivity and FBQ variables. \u003c/strong\u003eThe scatterplots in the top row show the correlations between familial risk (x-axis) and ROI-to-ROI functional connectivity (y-axis). The scatterplots in the bottom row show the correlations between prenatal speech exposure (x-axis) and ROI-to-ROI functional connectivity (y-axis).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/f6c34b11a4dcf70d2e28bfab.png"},{"id":92430798,"identity":"200c9885-11bb-4041-95ea-2b51114de0cc","added_by":"auto","created_at":"2025-09-29 16:07:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2505004,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/25db6c48-91df-4979-a0f8-0dd644391bab.pdf"},{"id":78951620,"identity":"2798202c-a334-415c-bbef-4e2f90a7ed5d","added_by":"auto","created_at":"2025-03-21 09:06:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4287980,"visible":true,"origin":"","legend":"","description":"","filename":"CaraCetalsupplmat.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6113185/v1/34df1c3195cf6fa062d9f2ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prenatal Brain Connectivity and Postnatal Language: How Familial Risk and Prenatal Speech Exposure Shape Early Language Skills","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLanguage developmental trajectories are influenced by both genetic and environmental factors1\u0026ndash;3. Familial clustering and twin studies provided evidence that developmental language and reading disorders are highly heritable1,4. Genetic influences extend beyond pathological conditions, as inheritability has also been observed in normotypical language development for various abilities encompassing reading, spelling, phonology, syntax, and articulation5\u0026ndash;7. The amount of environmental linguistic stimulation and exposure in childhood has also been shown to influence language skills development8.\u003c/p\u003e\n\u003cp\u003eGenes and environment likely impact language development indirectly, by shaping the structural and functional brain network, which in turn shapes cognitive and behavioural profiles in early infancy and childhood. Language-related genes, as well as environmental factors, participate in early neurodevelopmental processes including axonal growth, neural migration, and myelination9,10, as well as functional connectivity11. As for environmental factors, a recent study has shown that functional connectivity in the language network in infancy is affected by the amount of adult-infant conversations12.\u003c/p\u003e\n\u003cp\u003eMultiple lines of evidence indicate that the impact of genetic and environmental factors on functional connectivity within the auditory and language network begins as early as the foetal stage9,13. Behavioural observations and neuroimaging studies in newborns have suggested that prenatal exposure to auditory and speech stimuli could shape the infants\u0026apos; postnatal brain and behavioural responses, including speech perception and language acquisition abilities13\u0026ndash;15. However, evidence gained from observations in newborns has only indirect pertinence to infer prenatal neurodevelopmental processes.\u003c/p\u003e\n\u003cp\u003eRecent advancements in foetal multimodal neuroimaging have greatly enhanced our opportunities to directly investigate prenatal brain development. In a recent review, we extensively described the structural and functional maturation trajectories of the auditory and language networks in foetuses15. The development of peripheral and subcortical brain structures forming the auditory pathways begins in the first trimester of gestation and approaches maturation in foetuses at term16. Foetal brain response to sounds has been recorded in the auditory cortex through magnetoencephalography as early as 27 gestational weeks (GW) and throughout the third trimester of gestation15,17. Cortical folding and gyrification of perisylvian language regions start around 20 GW and undergo significant growth between 24 and 36 GW15,18,19. Structural connections between these regions can be detected as early as 26 GW, with full maturation only occurring in the early years after birth20,21. Concurrently, whole-brain foetal functional connectivity starts to emerge at 26GW, with fronto-temporal connections developing between 29 and 36 GW22.\u003c/p\u003e\n\u003cp\u003eThe prenatal period represents a critical epoch in the establishment of early brain connectivity, which is essential for infant\u0026acute;s survival and adaptation after birth23. Resting-state fMRI (rs-fMRI) evidence suggested that the foetal brain shows a modular functional architecture including, among others, primordial sensorimotor and auditory-language networks24,25. These functional modules gradually evolve across perinatal development into the regular set of functional networks in the adult human brain. This process establishes a blueprint for cognitive development23, that sets the stage for future cognitive and behavioral capabilities. Language development entails both resistance to developmental perturbations, such as gene mutations, and sensitivity to environmental changes26, in striving to achieve neurotypical homeostatic brain network states27.\u003c/p\u003e\n\u003cp\u003eThe objective of this study was to leverage measures of foetal functional connectivity, as proxies for the assessment of gene-environment interactions with the auditory and language networks, and the influence of these interactions on language outcomes in early infancy. To achieve this goal, we investigated a sample of 25 healthy foetuses which were scanned with rs-fMRI to collect functional connectivity measures. This sample was followed-up longitudinally with the assessment of language abilities within the first three years after birth, by means of the standardized Bayley-III test battery28. We also designed a novel structured questionnaire that we administered to parents in this sample to assess their children\u0026apos;s familial susceptibility to language disorders, as well as, retrospectively, the amount of linguistic stimulation during gestation.\u003c/p\u003e\n\u003cp\u003eWe first analyzed the foetal functional connectivity data to examine the presence of a primordial auditory-language functional network in our cohort15,25. We then sought to determine the association of functional connectivity strength in the auditory-language network with familial susceptibility to language disorders, as well as with the amount of linguistic stimulation during gestation. In the final step, we utilized a stepwise linear regression analysis on the postnatal Bayley-III language outcomes to assess which prenatal brain \u0026ldquo;gene-environment sensitive\u0026rdquo; functional connections can significantly predict language behaviour in early infancy.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e2.1 Experimental sample\u003c/p\u003e\n\u003cp\u003eTwenty-five pregnant mothers participated in the present study. They were recruited at San Raffaele Hospital, Milan, Italy during regular pregnancy monitoring. Inclusion criteria were as follow: i) healthy foetuses in the late foetal period, which is characterized by significant structural and functional changes in the organization of cerebral connections29; ii) normal foetal anatomy and growth assessed through foetal ultrasound; iii) low-risk for major chromosomal disorders by first trimester combined screening with or without non-invasive prenatal screening by means of cell-free fetal DNA testing; iv) no sign of foetal neurodevelopmental abnormality nor brain parenchymal signal alterations acknowledged by means of structural MRI investigation. All foetuses were subsequently born at term, without complications, had normal neonatological assessment at birth, and showed no major health problems in the first year of postnatal life. The final sample included 25 foetuses (13M, 12F). The average gestational age of the foetuses at the time of MRI session was 32.5 GW (SD = 1.8, range = 27-35; Supplementary Materials: Table S1).\u003c/p\u003e\n\u003cp\u003eThis study and all its experiments were conducted in full accordance with ethical guidelines and regulations, including the World Medical Association Declaration of Helsinki and subsequent revisions. The research protocol was reviewed and approved by the Ethics Committee of the San Raffaele Hospital (protocol code: RF-2016-02364081; Register of Opinions Number: 51/INT/2018; date of approval: 04/05/2018). Accordingly, all women provided written informed consent prior to foetal MR scanning.\u003c/p\u003e\n\u003cp\u003e2.2 Family background questionnaire\u003c/p\u003e\n\u003cp\u003eThe Family background questionnaire (FBQ) is a parent report instrument designed to gather comprehensive information on child’s risks for developmental language and learning disorders based on familial and prenatal exposure factors. In order to fulfil this aim, the FBQ collects data related to familiar history of language and learning disorders and attempts to retrospectively quantify the amount of prenatal exposure to linguistic stimuli. Information on the validation of the FBQ is provided in the Supplementary Materials (Methods S1).\u003c/p\u003e\n\u003cp\u003eThe questionnaire consists of two main parts: the first part (FBQ1) was administered either by phone or in person to the parents (required time for administration was approximately 15 minutes), and included three main sections:\u003c/p\u003e\n\u003cp\u003e2.2.1 FBQ1 - General information\u003c/p\u003e\n\u003cp\u003eThe first section was adapted from the factsheet of the MacArthur–Bates Communicative Development Inventories30\u0026nbsp;and provides general information about the child and the nuclear family, parental educational and socioeconomic statuses, and child's postnatal exposure to mono- or multilingual contexts.\u003c/p\u003e\n\u003cp\u003e2.2.2 FBQ1 - Familial susceptibility for language frailties\u003c/p\u003e\n\u003cp\u003eThe second section of the FBQ1 explores the child’s familial susceptibility to neurodevelopmental language frailties. Parents are asked whether any first-degree family member (i.e., parents, siblings) ever had undiagnosed difficulties related to: 1) reading; 2) writing; 3) spelling and letter recognition; 4) language production and articulation; 5) speech delay.\u003c/p\u003e\n\u003cp\u003eThe responses to this section were summarized by means of a binary score for each subject. A score of 1 was assigned if at least one first-degree relative had one or more frailties in the domains under investigation, while a score of 0 was assigned if no frailties were reported in the family (Supplementary Materials: Figure S1).\u003c/p\u003e\n\u003cp\u003e2.2.3 FBQ1 – Familial susceptibility for language impairments\u003c/p\u003e\n\u003cp\u003eThe third section of the FBQ1 further explores the child’s familial susceptibility to neurodevelopmental language impairments. Parents were asked whether any nuclear or extended family member (i.e., also including cousins, aunts and uncles, grandparents) ever had a diagnosis of the following disorders: 1) dyslexia; 2) language disorders; 3) articulatory or phonological disorders; 4) dysgraphia; 5) dysortographia; 6) stuttering.\u003c/p\u003e\n\u003cp\u003eThe responses to this section were summarized by means of a binary score for each subject. A score of 1 was assigned if at least one family member had one or more diagnoses, while a score of 0 was assigned if no diagnoses were reported (Supplementary Materials: Figure S1).\u003c/p\u003e\n\u003cp\u003e2.2.4 FBQ2\u003c/p\u003e\n\u003cp\u003eThe second part of the questionnaire (FBQ2) unfolded in two sections. Following an oral presentation to the parents by a designated researcher, the FBQ2 was sent via email and. both parents were asked to complete both sections.\u003c/p\u003e\n\u003cp\u003e2.2.4.1 FBQ2 – Parental self-assessed language- and speech-related difficulties\u003c/p\u003e\n\u003cp\u003eThe first section of the FBQ2 includes self-reported questions aimed at characterizing the parents’ cognitive profiles by means of three items: i) “\u003cem\u003eI am fast at reading a book’s page\u003c/em\u003e”, ii) “\u003cem\u003eI have difficulties in putting sounds together during word pronunciation\u003c/em\u003e”, iii) “\u003cem\u003eI make mistakes in taking notes\u003c/em\u003e”. Both parents were asked to rate each item on a 5-point Likert scale (0 = strongly disagree; 4 = completely agree). The aim of this section is to explore more subtle signs of language- and speech-related difficulties without explicitly referring to language or learning disorders.\u003c/p\u003e\n\u003cp\u003eFollowing reverse scoring for the first item, the raw scores of the three different items were summed to compute an overall median-split score for each subject (i.e., 1 ≥ median; 0 \u0026lt; median) (Supplementary Materials: Figure S1). Three subjects had missing data for this variable, due to failure to return the questionnaire: For these subjects, the overall median-split score was imputed based on the two familial scores for language frailties and impairments, resulting in a score equal to 0 for all three subjects.\u003c/p\u003e\n\u003cp\u003e2.2.4.2 FBQ2 – Environmental prenatal speech exposure\u003c/p\u003e\n\u003cp\u003eThe second section of the FBQ2 investigated the amount of speech stimulation provided by the parents to the child during pregnancy by means of two distinct items: i) \u003cem\u003e\"During pregnancy, I talked to the baby\"\u003c/em\u003e, ii) \"\u003cem\u003eDuring pregnancy, I used to read story tales aloud\"\u003c/em\u003e. Both parents were asked to rate each item on a 4-point Likert scale (0=never; 3=always). The aim of this section is to retrospectively quantify the child’s prenatal exposure to speech.\u003c/p\u003e\n\u003cp\u003e2.2.5 Aggregate familial risk score\u003c/p\u003e\n\u003cp\u003eThe items described above in sections 2.2.2, 2.2.3, and 2.2.4.1 were combined together into an aggregate familial risk score (Supplementary Materials: Figure S1), measuring the child familial predisposition to neurodevelopmental language and learning disorders. More specifically, the binary scores obtained from the three familial variables were summed together. The aggregate score ranged from 0 to 3, reflecting the subject’s familial risk for developing language disorders. The aggregate familial risk score was used as a variable of interest in the group-level analyses.\u003c/p\u003e\n\u003cp\u003e2.2.6. Aggregate prenatal speech exposure score\u003c/p\u003e\n\u003cp\u003eFor each subject, an aggregate score was calculated by averaging the raw data from both parents for the two items included in the second section of FBQ2 (section 2.2.4.2; Supplementary Materials: Figure S2).\u003c/p\u003e\n\u003cp\u003eThis aggregate prenatal speech exposure score was used as a variable of interest in the group-level analyses. Missing data for three subjects were replaced with the sample’s mode.\u003c/p\u003e\n\u003cp\u003eIn addition, the mother was asked to rate the noise level of her home and workplace environments at the time of pregnancy on a 4-point Likert scale ranging from 0 (very silent) to 3 (very noisy). The raw scores of these items were averaged to compute an aggregate individual score reflecting the child's prenatal exposure to noise, which was used as a nuisance covariate in group-level analyses.\u003c/p\u003e\n\u003cp\u003e2.3 Postnatal behavioural assessment\u003c/p\u003e\n\u003cp\u003eChildren's linguistic skills were assessed by a developmental neuropsychologist (C.O.) with the Bayley-III scale for infant and toddler development28. The Bayley-III measures developmental functioning in children between 1 and 42 months across various domains, including language28. Specifically for purposes of this study, we used age-corrected total language scores computed as the sum of the expressive and receptive linguistic scores obtained from the Bayley-III language scale. Children were tested between 1 and 3 years of age (mean age at time of testing = 22.2 months, SD = 7.7, range = 12-38; Table S1).\u003c/p\u003e\n\u003cp\u003e2.4 Prenatal MRI data acquisition\u003c/p\u003e\n\u003cp\u003eFoetal MR scanning was performed on a Philips Achieva 1.5 T scanner, using a 16 channels body coil. All pregnant women were asked not to eat within 2.5 h preceding the MR scanning. Foetal rs-fMRI consisted of GE EPI scans (TR = 2000 ms, TE = 30ms, acquisition voxel size 2.81 × 2.86 × 3 mm, 25 slices, slice gap = 0). Each rs-fMRI scan consisted of 60 volumes. Four to six consecutive rs-fMRI sessions (i.e., 240-360 volumes, covering from 8 to 12 min of continuous brain activity at rest) were acquired for each subject depending on the condition of each pregnant woman during MR scanning and the quality of the scans. Foetal structural scans consisted of a T2 Single Shot Turbo Spin Echo scan on the axial, sagittal and coronal planes of the foetus (TR = 8000 ms, TE = 125 ms, voxel size 1.17 x 2.76 x 3 mm, 25 slices) for a total scanning duration time of 17s. All foetuses showed no sign of foetal neurodevelopmental abnormality nor brain parenchymal signal alterations acknowledged by a neuroradiologist (C.B.) on structural MRI scans.\u003c/p\u003e\n\u003cp\u003e2.5 Foetal rs-fMRI Image Pre-Processing\u003c/p\u003e\n\u003cp\u003eFoetal scans were processed using the Resting-State Fetal functional MRI (RS-FetMRI) preprocessing pipeline (https://github.com/NicoloPecco/RS-FetMRI). The RS-FetMRI is divided into the following preprocessing steps: a) rs-fMRI volume reorientation and origin set on the anterior commissure; b) 1st-pass masking step with a GWsession-specific mask to remove the majority of the maternal abdominal tissue; c) within-session realignment step with a binary tissue-weighting mask which binds motion estimation only to inner-brain portions of\u0026nbsp;the foetal brain; d) 1st-pass scrubbing through ART (https://www.nitrc.org/projects/artifact_detect); e)\u0026nbsp;segmentation of session-specific functional reference volumes to derive session-specific inner-brain masks in subjects anatomy space based on registration with seven “best-fit” GW-specific brain foetal tissue and structure maps; f) between-session mean functional reference volume calculation on session-specific masked functional reference scans; g) between-session 2nd-pass realignment of all session-specific masked functional volumes; h) 2nd-pass between-session scrubbing procedure through ART and estimation of frame-to-frame estimation of motion (FD) and signal intensity (DVARS) changes; i) calculation of deformation parameters through SPM’s unified segmentation–normalization algorithm31 based on spatial registration with specific brain foetal tissue and structure maps for warping the between-session mean functional reference volume to the median-sample group-based atlas space; j) application of deformation parameters to between-session masked functional volumes in order to warp all volumes in the rs-fMRI time-series to GW median-sample group-based atlas space; k) smoothing normalized between-session masked volumes using an isotropic gaussian filter kernel with full width at half maximum (FWHM) 4mm.\u003c/p\u003e\n\u003cp\u003e2.6 Functional connectivity analysis\u003c/p\u003e\n\u003cp\u003eSmoothed and normalized resting-state image volumes were analysed with the CONN functional connectivity toolbox v22a, running on Matlab32. A component based noise correction method (CompCor33) was first implemented for rs-fMRI time-series denoising of white matter and cerebrospinal fluid. After nuisance regression, data were band-pass filtered at 0.01-0.08 Hz. Regions of interest (ROI) for functional connectivity analysis were defined on a 33 GW foetal brain atlas34. We selected fourty-two ROI (Figure 1) encompassing the foetal auditory, language, and sensorimotor networks, according to previous literature15,25. At the first level, Pearson's correlation coefficients were computed for each possible pair of ROI. Fisher's transformation was applied to convert Pearson's coefficients to z-score coefficients. This procedure generated a correlation matrix for each subject reflecting the ROI-to-ROI functional connectivity between each pair of ROI.\u003c/p\u003e\n\u003cp\u003e2.6.1 Data-driven resting-state network identification\u003c/p\u003e\n\u003cp\u003eAt the second (group) level, the ROI-to-ROI connectivity matrices was explored to investigate functional module organization in the foetal brain through a multivariate parametric approach implemented in CONN. Functional networks were derived using a data-driven hierarchical clustering procedure with a complete-likage method. The weighting factor was set to 0.05, prioritizing ROI-to-ROI functional similarity over anatomical proximity32.\u003c/p\u003e\n\u003cp\u003e2.7 Familial risk and prenatal speech exposure associations with functional connectivity\u003c/p\u003e\n\u003cp\u003eThe data-driven network identification analysis revealed the presence of unilateral auditory-language networks in both hemispheres of the foetal brain (see Results). We thus investigated the association between the FBQ variables and functional connectivity within the auditory-language networks. For this purpose, the familial risk scores and the prenatal speech exposure scores were correlated with the functional connectivity values between all possible pairs of ROI within the left (n = 45) and the right (n = 55) auditory-language networks. Spearman correlations were first performed. Statistical significance was determined using a threshold of p \u0026lt; 0.05, and confidence interval were computed through bootstrapping (iterations = 10000). Significant effects at this step were used for subsequent regression analyses.\u003c/p\u003e\n\u003cp\u003e2.8 Stepwise regression model with functional connectivity proxies to predict postnatal language outcomes.\u003c/p\u003e\n\u003cp\u003eFrom the previous step, we selected ROI-to-ROI functional connections that exhibited significant correlation with familial risk or prenatal speech exposure, surviving the bootstrapping correction (see Results). These functional connectivity values were included as explanatory variables in a stepwise multiple regression analysis to assess model significance and to identify the most significant predictors of the Bayley-III language total composite outcomes. Stepwise regression was chosen due to its ability to add or remove predictors based on statistical criteria, allowing the identification of variables that contribute the most to the model. The criteria for variable entry into the model were based on the probability of F-to-enter set at 0.05 and the probability of F-to-remove set at 0.10.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 FBQ - Variable distribution\u003c/p\u003e\n\u003cp\u003eThe combination of the three familial risk variables (language frailties, language impairments, parental self-assessed language- and speech-related difficulties) into an aggregate score ranging from 0 to 3, with higher values indicating higher risk levels, yielded 9 subjects with a score of 0, 9 subjects with a score of 1, 4 subjects with a score of 2, and 3 subjects with a score of 3 (Figure 2A; See Supplementary Materials, Results S1, for further details on the individual familial risk scores).\u003c/p\u003e\n\u003cp\u003eThe two items investigating prenatal speech exposure were combined into an aggregate score ranging from 0.5 to 2.75 (mean=1.36, SD=0.64), with higher values indicating a greater exposure to speech stimuli during pregnancy (Figure 2B).\u003c/p\u003e\n\u003cp\u003eImportantly, there was no significant sample-wise association between the aggregate familial risk score and the aggregate prenatal speech exposure scores, as shown by a correlation analysis on the two FBQ variables (Spearman coefficient = -0.19, p = 0.37).\u003c/p\u003e\n\u003cp\u003e3.2 Bayley-III language scale - Score distribution\u003c/p\u003e\n\u003cp\u003eThe sample mean for the total score of the Bayley-III language scale was 16.96 SU (SD = 5.3, range = 4-28; Supplementary Materials: Figure S5). Data range and distribution showed a certain degree of variability, which reflects interindividual heterogeneity in children's language trajectories. This diversity renders our sample particularly suitable for studying individual differences in language development.\u003c/p\u003e\n\u003cp\u003e3.3 Functional connectivity - Data-driven resting-state network identification\u003c/p\u003e\n\u003cp\u003eThe hierarchical clustering analysis revealed the emergence of unilateral auditory-language networks in the left and right hemispheres of the foetal brain. Both networks included subcortical and cortical regions, including thalamus, putamen, caudate nucleus, Heschl’s gyrus, superior and middle temporal gyri, superior temporal pole, insula, rolandic operculum, and inferior frontal gyrus. The right network alone also included the orbital middle frontal gyrus (Figure 3).\u003c/p\u003e\n\u003cp\u003e3.4\u0026nbsp;Familial risk and environmental score associations with functional Connectivity\u003c/p\u003e\n\u003cp\u003eFamilial risk was negatively correlated with functional connectivity in the “left insula - left rolandic operculum” connection (Spearman's rho = -0.5, p = 0.01, bootstrap 95% CI [-0.75, -0.12]), as well as in the “left insula - left caudate” connection (Spearman's rho = -0.47, p = 0.02, bootstrap 95% CI [-0.75, -0.03]) (Figure 4). These effects were also significant in a partial correlation model in which prenatal speech exposure and prenatal noise exposure were entered as nuisance variables (Table 1). The prenatal exposure score positively correlated with functional connectivity in two connections, namely “left rolandic operculum - left middle temporal gyrus” (Spearman's rho = 0.55, p = 0.004, bootstrap 95% CI [0.28, 0.75]), and “left rolandic operculum - left superior temporal pole” (Spearman's rho = 0.6, p = 0.001, bootstrap 95% CI [0.29, 0.79]). There was also a negative correlation in the connection “right orbital middle frontal gyrus - right Heschl’s gyrus” (Spearman's rho = -0.45, p = 0.02, bootstrap 95% CI [-0.74, -0.05]) (Figure 4). These effects were also significant in a partial correlation model in which familial risk and prenatal noise exposure were entered as nuisance variables (Table 2).\u003c/p\u003e\n\u003cp\u003e3.5 Prenatal functional connectivity association with postnatal language development\u003c/p\u003e\n\u003cp\u003eThe two “familial risk” and the three “prenatal exposure” functional connectivity proxies (section 3.4; Tables 1 and 2) were fed as explanatory variables into a stepwise regression model to highlight predictive relations with the postnatal Bayley-III language outcomes. Out of the five explanatory variables, two accounted for significant effects.\u003c/p\u003e\n\u003cp\u003eThe first variable was the “left insula - left rolandic operculum” “familial risk” connection, which explained 16.88% of the variance in the Bayley-III language total composite outcomes (R² = .169, Adjusted R² = .133), and showed a statistically significant regression (F(1, 23) = 4.67, p = .041). The stepwise addition of the “left insula - left caudate” “familial risk” connection improved the model's explanatory power, accounting for 33.78% of the variance (R² = .338, Adjusted R² = .278), leading to a significant increase in the model's explanatory power, F(2, 22) = 5.61, p = .011. Due to the stepwise addition of the “left insula - left caudate” “familial risk” connection, the standard error of the estimate decreased from 13.998 to 12.775, indicating improved accuracy of the predictions.\u003c/p\u003e\n\u003cp\u003eIn the final stepwise model including both connections, the “left insula - left rolandic operculum” “familial risk” connection had a significant positive association with the Bayley-III language total composite outcomes (B = 27.09, SE = 9.32, β = .522, t = 2.91, p = .008), indicating that an increase in “left insula - left rolandic operculum” connectivity is associated with an increase in postnatal language abilities. In turn, the “left insula - left caudate” “familial risk” connection had a significant negative association with the Bayley-III language total composite outcomes (B = -25.34, SE = 10.69, β = -.426, t = -2.37, p = .027), suggesting that an increase in connection strength is associated with a decrease in postnatal language abilities.\u003c/p\u003e\n\u003cp\u003eIn order to find further support for the significant predictive relationship between familial risk and the Bayley-III language score emerged from the stepwise regression model, we tested the correlation between the two variables. A post-hoc Spearmans’ correlation (bootstrapping, n = 10000) revealed a significant negative association (rho = -0.42, p = 0.03, 95% CI [-0.7, -0.003]).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLanguage acquisition is one of the major achievements in early childhood and represents an essential requirement to succeed in social and educational contexts35,36. Early language skills are indeed reliable predictors of later literacy proficiency, academic attainments, and career outcome35,37. Conversely, language disorders often lead to educational struggles, including specific learning disabilities38. Given this context, there is a pressing need to identify biomarkers for predicting language developmental trajectories in the early stages of development. A growing body of research has demonstrated that the structural and functional brain systems underlying auditory and language processing develop prenatally15. Early brain development trajectories are driven by both genetic and environmental factors11. Understanding the multiple links between genetic and familial influences, prenatal environment, and brain maturation, and how these different variables shape individual differences in language development is one of the major challenges in cognitive neuroscience. The foetal brain, with its rapidly forming neural networks, offers a unique window to understand how early connectivity patterns can act as intermediaries linking genetic predisposition and environmental stimuli to postnatal cognitive outcome.\u003c/p\u003e\n\u003cp\u003eIn the current study, we aimed to provide some insights into these issues through a longitudinal approach that involved the acquisition of prenatal rs-fMRI data, followed by postnatal language assessments by means of the Bayley-III language scale28\u0026nbsp;within the first three years of life. The hereditary background related to familiar vulnerabilities or disorders of language and cognition, and the environmental input, that is the amount of exposure to speech during the prenatal period, were retrospectively reconstructed by means of a novel instrument that we developed ad hoc, named the Family Background Questionnaire (FBQ). The first result of our study is that the data-driven hierarchical clustering analysis of foetal resting-state functional connectivity confirmed the presence of an auditory-language network in the third trimester of foetal development. Unilateral networks were present in both hemispheres encompassing subcortical structures, Heschl\u0026rsquo;s gyrus, middle and superior temporal cortices, inferior frontal regions, insula, and rolandic operculum. These results are in line with previous rs-fMRI evidence. Subcortico-cortical functional connectivity along the auditory pathway23\u0026nbsp;as well as local connectivity within temporal regions39\u0026nbsp;start to develop around 24-25 GW. Thomason and colleagues24\u0026nbsp;have observed functional connectivity in a left-lateralized fronto-temporal network including language-related regions in foetuses older than 31 GW. A recent study25\u0026nbsp;conducted on a large sample of foetuses between 21 and 40 GW found functional connectivity in a left-lateralized fronto-temporal-insular module which strongly overlaps with the one we found, and included Heschl\u0026rsquo;s gyrus, mid and superior temporal cortices, inferior frontal regions, insula, rolandic operculum, and the anterior cingulum. A recent study described the development of the language network from 30 GW to 1 month after birth, and in particular it reported a significant increase in functional connectivity within the language network occurring between 31 and 35 GW40.\u0026nbsp;Considering this body of evidence, we can conclude that our study successfully replicated previous findings, indicating that functional networks supporting auditory and language processing are already established between 27 and 35 GW.\u003c/p\u003e\n\u003cp\u003eThe FBQ was conceived as a retrospective tool for determining which children may be at risk of developing language processing difficulties based on familial and prenatal speech exposure factors. The outcome of the expert FBQ validation by means of content and face validity assessments (Supplementary Materials) highlighted that this instrument is endowed with enough sensitivity to capture at least part of the intricate relationships between familial and environmental background and early postnatal language developmental trajectories. Thus, we correlated the familial risk and environmental exposure FBQ scores with foetal functional connectivity values across all possible ROI pairs within the identified left and right auditory-language networks. The results revealed a negative correlation between familial risk scores and strength of the two connections \u0026ldquo;left insula - left rolandic operculum\u0026rdquo; and \u0026ldquo;left insula - left caudate nucleus\u0026rdquo;. Previous research has consistently demonstrated that infants and children at risk for developmental language and learning disorders exhibit abnormal functional connectivity patterns, reduced functional brain activation, and white matter disorganization compared to controls41\u0026ndash;46. Our results extend and anticipate the developmental time frame of these associations to the prenatal period, by showing that they may already be present in the foetal brain. Neuroimaging genetic evidence showed that children with a risk allele for dyslexia have reduced functional and structural connectivity between fronto-temporal language regions compared to children without any risk alleles47.\u003c/p\u003e\n\u003cp\u003eThe FBQ variable related to prenatal speech exposure showed a positive correlation with foetal functional connectivity in the two region pairs \u0026ldquo;left rolandic operculum - left middle temporal gyrus\u0026rdquo; and \u0026ldquo;left rolandic operculum - left superior temporal pole\u0026rdquo;, and a negative correlation in the \u0026ldquo;right orbital middle frontal gyrus - right Heschl\u0026rsquo;s gyrus\u0026rdquo; pair. Thus, foetuses exposed to a greater amount of speech stimuli developed stronger functional connectivity between regions of the left auditory-language network, and weaker connectivity between regions of the right hemisphere. Recent studies demonstrated that the amount of domestic language interactions between infants and adults affects the infants\u0026apos; functional connectivity in posterior temporal regions of the language network12, and predict the myelination of language-related white matter tracts measured at 2 years48. A review paper has recently examined the relationship between other environmental factors, such as the socio-economic status, and structural and functional brain development49. Children and adolescents with higher socio-economic status have thicker cortex50\u0026nbsp;and larger surface areas51,52\u0026nbsp;compared to their peers with lower socio-economic status49. These effects are observed in multiple subcortical and cortical regions, including middle temporal and inferior frontal language structures51,52. Functional connectivity between limbic structures and cortical regions has been found to be reduced in children with lower socio-economic status49,53. However, results in this field are not always consistent across studies, and some authors claimed that the influence of environmental variables on brain development may vary depending on developmental age49.\u003c/p\u003e\n\u003cp\u003eIn line with the overall hypothesis of our study, we evaluated whether prenatal brain functional connections showing associations with familial risk and environmental speech exposure can predict the postnatal development of linguistic skills, through a stepwise regression model. Our results revealed prediction of language developmental outcomes in infancy pivoted on prenatal connectivity of the insular cortex with the caudate nucleus and the rolandic operculum. First, these results are in line with the known relevance of the insula for the language system. The insula is a key node of a functional system including the subcortical basal ganglia-cerebellum complex, the inferior frontal gyrus, and sensorimotor cortices, and underlying speech production54. Accordingly, on the one hand, the insulo-opercular cortices play a crucial role in phonological rehearsal for learning new words55; on the other hand lesions to the insular cortex and the caudate nucleus result in a plethora of language disturbances, including impaired word finding and object naming, repetition and perseveration errors, verbal and phonemic paraphasias56. Second, the insula is a phylogenetically-old neurodevelopmental pivot, playing a central role in the maturation of the whole neocortical mantle57. Structurally, the insular cortex reaches maturation around 30 GW, and establishes an extensive pattern of connections with both subcortical structures (including the thalamus, putamen, and caudate nucleus), and all major neocortical processing sites related to both sensorimotor and higher-order processing58. On this basis, the anterior insula plays a crucial role in the emergence and consolidation of the functional dynamic regulation between task passive and task active networks59, promoting the development of executive control and adaptive cognitive and behavioural repertoires27,60. Particularly, the development of emotional self-regulatory capability throughout infancy is pivotal for the instantiation and tuning of pre- and proto-linguistic communicative interactions of the newborn with the caregivers, and represents the groundings on which proper language development occurs60,61\u0026nbsp;Thus, the functional development of insular connectivity exerts both a direct effect on language development, as well as an indirect one through its crucial contribution in tuning of functional networks underlying adaptive cognitive and behavioural processes.\u003cbr\u003e\u0026nbsp;It is worth highlighting that, amongst the plethora of regions of interest and connections included in our analyses, insular connectivity emerged as the only predictor of language outcomes. This is interesting when considering that modulation of insular functional connectivity patterns has been previously reported by studies investigating the impact of psychological62\u0026ndash;64, environmental63, and lifestyle65 factors in foetuses, as well as in neonates born preterm66 and with a higher familial risk of developing autism40. Our work aligns with these findings and suggests that familial risk for language disorders represents another relevant neurodevelopmental factor affecting the functional gridline of the developing language system, and hence language outcomes in infancy. This body of evidence is consistent with the centrality of the insula for the development of the brain\u0026rsquo;s functional architecture. During the late foetal period, the insula, together with the sensorimotor cortices, acts as a major functional hub triggering transient bursting activity propagation, and thus underlying the maturation of large scale brain networks67. Similarly to what happens for white matter bundles68 the insula could reach a \u0026lsquo;functional activity peak\u0026rsquo; at this developmental stage, making it a particularly vulnerable target of genetic as well as environmental factors. Thus, within the conceptual framework of our study, the insular functional gridline represents a particularly sensitive proxy for the identification of effects related to familial and environmental factors at the prenatal developmental stage.\u003c/p\u003e"},{"header":"5. Conclusions, limitations, and future directions","content":"\u003cp\u003eThe current study represents a first attempt to link familial and prenatal exposure factors with foetal functional connectivity and postnatal language development trajectories. We showed that higher exposure to speech stimuli during pregnancy is associated with stronger prenatal functional connectivity between regions of the left auditory-language network. Moreover, we showed that familiarity for language disorders is associated with lower functional connectivity between the left caudate nucleus and insula, and between the left insula and rolandic operculum. Functional connectivity in these connections is in turn associated with language development outcomes in early childhood. These results are promising and support the idea that prenatal functional connectivity, prenatal speech exposure, and familial risk variables could serve as potential markers for predicting early language development trajectories and disorders. However, an important limitation of this study is the relatively small sample size. While these findings offer valuable insights into early brain and language development, the generalizability to a broader population may be limited. Future confirmative studies in larger samples will be required. Future studies may also take advantage of a more detailed characterization of prenatal environmental variables, by gathering more comprehensive data on the type, amount, and frequency of auditory and speech stimulation that reaches the foetus during pregnancy, either retrospectively and taking advantage of the FBQ as in the present study, or eventually directly in prospective longitudinal studies. Crucially, longitudinal assessments of language abilities up to school-age may evaluate whether these multimodal factors can predict the insurgence of developmental language and learning disorders, thus opening the way to more timely supportive interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the mothers and infants who made this study possible, as well as their families, for completing the Family Background Questionnaire. We also thank all the doctors of the Department of Obstetrics and Gynaecology of the San Raffaele Hospital and their staff for their support and for allowing us to recruit women for the study. Finally, we thank the MRI technicians of the Department of Neuroradiology of the San Raffaele Hospital for their help during the acquisition of fetal MRI data.\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Italian Ministry of Health’s “Ricerca Finalizzata 2016” (grant number RF-2016-02364081; Principal Investigator: Dr. Pasquale Anthony Della Rosa).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCC, MCanini, CO, MT and PADR conceptualized the study. MCandiani, AF and PADR acquired funding for the study. CC, NP, PIC, MCandiani, CB and AF provided software and resources. CC, MCanini, CO, PIC, CB and PADR collected the data. CC, MCanini, NP, PIC, CB, MT and PADR analyzed the data. CC, MCanini, CO, MT and PADR wrote the original manuscript draft. CC, MCanini, PIC, CB, MT and PADR reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study cannot be made openly available due to confidentiality restrictions. Anonymized data will be made available upon reasonable request from Pasquale Anthony Della Rosa ([email protected]), in accordance with the Ethics Committee of the San Raffaele Hospital, Milan, Italy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMountford, H. S. \u0026amp; Newbury, D. F. The Genetics of Language Acquisition. in \u003cem\u003eInternational Handbook of Language Acquisition\u003c/em\u003e (eds. Horst, J. S. \u0026amp; Von Koss Torkildsen, J.) 33\u0026ndash;50 (Routledge, New York, NY: Routledge, [2019] | Series: Routledge, 2019). doi:10.4324/9781315110622-3.\u003c/li\u003e\n\u003cli\u003eOnnis, L., Truzzi, A. \u0026amp; Ma, X. Language development and disorders: Possible genes and environment interactions. \u003cem\u003eRes. Dev. Disabil.\u003c/em\u003e \u003cstrong\u003e82\u003c/strong\u003e, 132\u0026ndash;146 (2018).\u003c/li\u003e\n\u003cli\u003eRowe, M. L. \u0026amp; Weisleder, A. Language Development in Context. \u003cem\u003eAnnu. Rev. Dev. Psychol.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 201\u0026ndash;223 (2020).\u003c/li\u003e\n\u003cli\u003eGrigorenko, E. L. Speaking Genes or Genes for Speaking? Deciphering the Genetics of Speech and Language. \u003cem\u003eJ. Child Psychol. Psychiatry\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 116\u0026ndash;125 (2009).\u003c/li\u003e\n\u003cli\u003eAndreola, C. \u003cem\u003eet al.\u003c/em\u003e The heritability of reading and reading-related neurocognitive components: A multi-level meta-analysis. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e121\u003c/strong\u003e, 175\u0026ndash;200 (2021).\u003c/li\u003e\n\u003cli\u003eKovas, Y. \u003cem\u003eet al.\u003c/em\u003e Genetic Influences in Different Aspects of Language Development: The Etiology of Language Skills in 4.5-Year-Old Twins. \u003cem\u003eChild Dev.\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 632\u0026ndash;651 (2005).\u003c/li\u003e\n\u003cli\u003eStromswold, K. The Heritability of Language: A Review and Metaanalysis of Twin, Adoption, and Linkage Studies. \u003cem\u003eLanguage\u003c/em\u003e \u003cstrong\u003e77\u003c/strong\u003e, 647\u0026ndash;723 (2001).\u003c/li\u003e\n\u003cli\u003eFerjan Ram\u0026iacute;rez, N., Lytle, S. R., Fish, M. \u0026amp; Kuhl, P. K. Parent coaching at 6 and 10 months improves language outcomes at 14 months: A randomized controlled trial. \u003cem\u003eDev. Sci.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, e12762 (2019).\u003c/li\u003e\n\u003cli\u003eEising, E. \u003cem\u003eet al.\u003c/em\u003e A set of regulatory genes co-expressed in embryonic human brain is implicated in disrupted speech development. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1065\u0026ndash;1078 (2019).\u003c/li\u003e\n\u003cli\u003eGalaburda, A. M., LoTurco, J., Ramus, F., Fitch, R. H. \u0026amp; Rosen, G. D. From genes to behavior in developmental dyslexia. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 1213\u0026ndash;1217 (2006).\u003c/li\u003e\n\u003cli\u003eRichmond, S., Johnson, K. A., Seal, M. L., Allen, N. B. \u0026amp; Whittle, S. Development of brain networks and relevance of environmental and genetic factors: A systematic review. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e71\u003c/strong\u003e, 215\u0026ndash;239 (2016).\u003c/li\u003e\n\u003cli\u003eKing, L. S., Camacho, M. C., Montez, D. F., Humphreys, K. L. \u0026amp; Gotlib, I. H. Naturalistic Language Input is Associated with Resting-State Functional Connectivity in Infancy. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e41\u003c/strong\u003e, 424\u0026ndash;434 (2021).\u003c/li\u003e\n\u003cli\u003eGervain, J. Plasticity in early language acquisition: the effects of prenatal and early childhood experience. \u003cem\u003eCurr. Opin. Neurobiol.\u003c/em\u003e \u003cstrong\u003e35\u003c/strong\u003e, 13\u0026ndash;20 (2015).\u003c/li\u003e\n\u003cli\u003eDehaene-Lambertz, G. \u003cem\u003eet al.\u003c/em\u003e Functional organization of perisylvian activation during presentation of sentences in preverbal infants. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 14240\u0026ndash;14245 (2006).\u003c/li\u003e\n\u003cli\u003eGhio, M., Cara, C. \u0026amp; Tettamanti, M. The prenatal brain readiness for speech processing: A review on foetal development of auditory and primordial language networks. \u003cem\u003eNeurosci. Biobehav. Rev.\u003c/em\u003e \u003cstrong\u003e128\u003c/strong\u003e, 709\u0026ndash;719 (2021).\u003c/li\u003e\n\u003cli\u003eGranier‐Deferre, C., Ribeiro, A., Jacquet, A.-Y. \u0026amp; Bassereau, S. Near-term fetuses process temporal features of speech. \u003cem\u003eDev. Sci.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 336\u0026ndash;352 (2011).\u003c/li\u003e\n\u003cli\u003eSchleussner, E. \u0026amp; Schneider, U. Developmental changes of auditory-evoked fields in fetuses. \u003cem\u003eExp. Neurol.\u003c/em\u003e \u003cstrong\u003e190\u003c/strong\u003e, 59\u0026ndash;64 (2004).\u003c/li\u003e\n\u003cli\u003eDubois, J. \u003cem\u003eet al.\u003c/em\u003e Mapping the early cortical folding process in the preterm newborn brain. \u003cem\u003eCereb. Cortex N. Y. N 1991\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 1444\u0026ndash;1454 (2008).\u003c/li\u003e\n\u003cli\u003eHabas, P. A. \u003cem\u003eet al.\u003c/em\u003e Early Folding Patterns and Asymmetries of the Normal Human Brain Detected from in Utero MRI. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 13\u0026ndash;25 (2012).\u003c/li\u003e\n\u003cli\u003eKhan, S. \u003cem\u003eet al.\u003c/em\u003e Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e185\u003c/strong\u003e, 593\u0026ndash;608 (2019).\u003c/li\u003e\n\u003cli\u003eMartino, J. \u003cem\u003eet al.\u003c/em\u003e Analysis of the subcomponents and cortical terminations of the perisylvian superior longitudinal fasciculus: a fiber dissection and DTI tractography study. \u003cem\u003eBrain Struct. Funct.\u003c/em\u003e \u003cstrong\u003e218\u003c/strong\u003e, 105\u0026ndash;121 (2013).\u003c/li\u003e\n\u003cli\u003eJakab, A. \u003cem\u003eet al.\u003c/em\u003e Fetal functional imaging portrays heterogeneous development of emerging human brain networks. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, (2014).\u003c/li\u003e\n\u003cli\u003eCanini, M. \u003cem\u003eet al.\u003c/em\u003e Subcortico-Cortical Functional Connectivity in the Fetal Brain: A Cognitive Development Blueprint. \u003cem\u003eCereb. Cortex Commun.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, (2020).\u003c/li\u003e\n\u003cli\u003eThomason, M. E. \u003cem\u003eet al.\u003c/em\u003e Intrinsic Functional Brain Architecture Derived from Graph Theoretical Analysis in the Human Fetus. \u003cem\u003ePLOS ONE\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, e94423 (2014).\u003c/li\u003e\n\u003cli\u003eTurk, E. \u003cem\u003eet al.\u003c/em\u003e Functional Connectome of the Fetal Brain. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 9716\u0026ndash;9724 (2019).\u003c/li\u003e\n\u003cli\u003eBourguignon, N. J., Nadig, A. \u0026amp; Valois, D. The Biolinguistics of Autism: Emergent Perspectives. \u003cem\u003eBiolinguistics\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, 124\u0026ndash;165 (2012).\u003c/li\u003e\n\u003cli\u003eDella Rosa, P. A. \u003cem\u003eet al.\u003c/em\u003e The effects of the functional interplay between the Default Mode and Executive Control Resting State Networks on cognitive outcome in preterm born infants at 6 months of age. \u003cem\u003eBrain Cogn.\u003c/em\u003e \u003cstrong\u003e147\u003c/strong\u003e, 105669 (2021).\u003c/li\u003e\n\u003cli\u003eBayley, N. Bayley Scales of Infant and Toddler Development, Third Edition. https://doi.org/10.1037/t14978-000 (2012).\u003c/li\u003e\n\u003cli\u003eKostović, I., Rado\u0026scaron;, M., Kostović-Srzentić, M. \u0026amp; Krsnik, Ž. Fundamentals of the Development of Connectivity in the Human Fetal Brain in Late Gestation: From 24 Weeks Gestational Age to Term\u003cem\u003e. J Neuropathol Exp Neurol\u003c/em\u003e \u003cstrong\u003e80\u003c/strong\u003e, 393\u0026ndash;414 (2021).\u003c/li\u003e\n\u003cli\u003eMacArthur-Bates Communicative Development Inventories. https://mb-cdi.stanford.edu/.\u003c/li\u003e\n\u003cli\u003eAshburner, J. \u0026amp; Friston, K. J. Unified segmentation. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 839\u0026ndash;851 (2005).\u003c/li\u003e\n\u003cli\u003eWhitfield-Gabrieli, S. \u0026amp; Nieto-Castanon, A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. \u003cem\u003eBrain Connect.\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 125\u0026ndash;141 (2012).\u003c/li\u003e\n\u003cli\u003eBehzadi, Y., Restom, K., Liau, J. \u0026amp; Liu, T. T. A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 90\u0026ndash;101 (2007).\u003c/li\u003e\n\u003cli\u003eGholipour, A. \u003cem\u003eet al.\u003c/em\u003e A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 476 (2017).\u003c/li\u003e\n\u003cli\u003eBornstein, M. H., Hahn, C.-S., Putnick, D. L. \u0026amp; Pearson, R. M. Stability of core language skill from infancy to adolescence in typical and atypical development. \u003cem\u003eSci. Adv.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, eaat7422 (2018).\u003c/li\u003e\n\u003cli\u003ePace, A., Alper, R., Burchinal, M. R., Golinkoff, R. M. \u0026amp; Hirsh-Pasek, K. Measuring success: Within and cross-domain predictors of academic and social trajectories in elementary school. \u003cem\u003eEarly Child. Res. Q.\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 112\u0026ndash;125 (2019).\u003c/li\u003e\n\u003cli\u003eHohm, E., Jennen-Steinmetz, C., Schmidt, M. H. \u0026amp; Laucht, M. Language development at ten months. \u003cem\u003eEur. Child Adolesc. Psychiatry\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 149\u0026ndash;156 (2007).\u003c/li\u003e\n\u003cli\u003eCatts, H. W., Bridges, M. S., Little, T. D. \u0026amp; Tomblin, J. B. Reading Achievement Growth in Children With Language Impairments. \u003cem\u003eJ. Speech Lang. Hear. Res.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1569\u0026ndash;1579 (2008).\u003c/li\u003e\n\u003cli\u003eSch\u0026ouml;pf, V., Kasprian, G., Brugger, P. C. \u0026amp; Prayer, D. Watching the fetal brain at \u0026lsquo;rest\u0026rsquo;. \u003cem\u003eInt. J. Dev. Neurosci.\u003c/em\u003e \u003cstrong\u003e30\u003c/strong\u003e, 11\u0026ndash;17 (2012).\u003c/li\u003e\n\u003cli\u003eScheinost, D. \u003cem\u003eet al.\u003c/em\u003e Functional connectivity for the language network in the developing brain: 30 weeks of gestation to 30 months of age. \u003cem\u003eCereb. Cortex\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 3289\u0026ndash;3301 (2022).\u003c/li\u003e\n\u003cli\u003eCantiani, C. \u003cem\u003eet al.\u003c/em\u003e Reduced left-lateralized pattern of event-related EEG oscillations in infants at familial risk for language and learning impairment. \u003cem\u003eNeuroImage Clin.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 101778 (2019).\u003c/li\u003e\n\u003cli\u003eLanger, N. \u003cem\u003eet al.\u003c/em\u003e White Matter Alterations in Infants at Risk for Developmental Dyslexia. \u003cem\u003eCereb. Cortex N. Y. N 1991\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 1027\u0026ndash;1036 (2017).\u003c/li\u003e\n\u003cli\u003ePackman, A. \u003cem\u003eet al.\u003c/em\u003e White matter connectivity in neonates at risk of stuttering: Preliminary data. \u003cem\u003eNeurosci. Lett.\u003c/em\u003e \u003cstrong\u003e781\u003c/strong\u003e, 136655 (2022).\u003c/li\u003e\n\u003cli\u003eRaschle, N. M., Zuk, J. \u0026amp; Gaab, N. Functional characteristics of developmental dyslexia in left-hemispheric posterior brain regions predate reading onset. \u003cem\u003eProc. Natl. Acad. Sci.\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 2156\u0026ndash;2161 (2012).\u003c/li\u003e\n\u003cli\u003eVandermosten, M., Hoeft, F. \u0026amp; Norton, E. Integrating MRI brain imaging studies of pre-reading children with current theories of developmental dyslexia: A review and quantitative meta-analysis. \u003cem\u003eCurr. Opin. Behav. Sci.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, (2016).\u003c/li\u003e\n\u003cli\u003eYu, X. \u003cem\u003eet al.\u003c/em\u003e Patterns of Neural Functional Connectivity in Infants at Familial Risk of Developmental Dyslexia. \u003cem\u003eJAMA Netw. Open\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, e2236102 (2022).\u003c/li\u003e\n\u003cli\u003eSkeide, M. A. \u003cem\u003eet al.\u003c/em\u003e Genetic dyslexia risk variant is related to neural connectivity patterns underlying phonological awareness in children. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, 414\u0026ndash;421 (2015).\u003c/li\u003e\n\u003cli\u003eHuber, E., Corrigan, N. M., Yarnykh, V. L., Ferjan Ram\u0026iacute;rez, N. \u0026amp; Kuhl, P. K. Language Experience during Infancy Predicts White Matter Myelination at Age 2 Years. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 1590\u0026ndash;1599 (2023).\u003c/li\u003e\n\u003cli\u003eTooley, U. A., Bassett, D. S. \u0026amp; Mackey, A. P. Environmental influences on the pace of brain development. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 372\u0026ndash;384 (2021).\u003c/li\u003e\n\u003cli\u003eLawson, G. M., Duda, J. T., Avants, B. B., Wu, J. \u0026amp; Farah, M. J. Associations between children\u0026rsquo;s socioeconomic status and prefrontal cortical thickness. \u003cem\u003eDev. Sci.\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 641\u0026ndash;652 (2013).\u003c/li\u003e\n\u003cli\u003eMcDermott, C. L. \u003cem\u003eet al.\u003c/em\u003e Longitudinally Mapping Childhood Socioeconomic Status Associations with Cortical and Subcortical Morphology. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 1365\u0026ndash;1373 (2019).\u003c/li\u003e\n\u003cli\u003eNoble, K. G. \u003cem\u003eet al.\u003c/em\u003e Family income, parental education and brain structure in children and adolescents. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 773\u0026ndash;778 (2015).\u003c/li\u003e\n\u003cli\u003eHanson, J. L. \u003cem\u003eet al.\u003c/em\u003e Resting state coupling between the amygdala and ventromedial prefrontal cortex is related to household income in childhood and indexes future psychological vulnerability to stress. \u003cem\u003eDev. Psychopathol.\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 1053\u0026ndash;1066 (2019).\u003c/li\u003e\n\u003cli\u003eEickhoff, S. B., Heim, S., Zilles, K. \u0026amp; Amunts, K. A systems perspective on the effective connectivity of overt speech production. \u003cem\u003ePhilos. Trans. R. Soc. Math. Phys. Eng. Sci.\u003c/em\u003e \u003cstrong\u003e367\u003c/strong\u003e, 2399\u0026ndash;2421 (2009).\u003c/li\u003e\n\u003cli\u003eVeroude, K., Norris, D. G., Shumskaya, E., Gullberg, M. \u0026amp; Indefrey, P. Functional connectivity between brain regions involved in learning words of a new language. \u003cem\u003eBrain Lang.\u003c/em\u003e \u003cstrong\u003e113\u003c/strong\u003e, 21\u0026ndash;27 (2010).\u003c/li\u003e\n\u003cli\u003eKreisler, A. \u003cem\u003eet al.\u003c/em\u003e The anatomy of aphasia revisited. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e54\u003c/strong\u003e, 1117\u0026ndash;1123 (2000).\u003c/li\u003e\n\u003cli\u003eAfif, A., Bouvier, R., Buenerd, A., Trouillas, J. \u0026amp; Mertens, P. Development of the human fetal insular cortex: study of the gyration from 13 to 28 gestational weeks. \u003cem\u003eBrain Struct. Funct.\u003c/em\u003e \u003cstrong\u003e212\u003c/strong\u003e, 335\u0026ndash;346 (2007).\u003c/li\u003e\n\u003cli\u003eGhaziri, J. \u003cem\u003eet al.\u003c/em\u003e Subcortical structural connectivity of insular subregions. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 8596 (2018).\u003c/li\u003e\n\u003cli\u003eUddin, L. Q., Kinnison, J., Pessoa, L. \u0026amp; Anderson, M. L. Beyond the Tripartite Cognition\u0026ndash;Emotion\u0026ndash;Interoception Model of the Human Insular Cortex. \u003cem\u003eJ. Cogn. Neurosci.\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 16\u0026ndash;27 (2014).\u003c/li\u003e\n\u003cli\u003eCanini, M. \u003cem\u003eet al. \u003c/em\u003eFunctional connectivity markers of prematurity at birth predict neurodevelopmental outcomes at 6, 12, 24, and 36 months. \u003cem\u003eInternational Journal of Behavioral Development \u003c/em\u003e(2025)\u003c/li\u003e\n\u003cli\u003eTopping, K., Dekhinet, R. \u0026amp; Zeedyk, S. Parent\u0026ndash;infant interaction and children\u0026rsquo;s language development. \u003cem\u003eEduc Psychol\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 391\u0026ndash;426 (2013).\u003c/li\u003e\n\u003cli\u003eCanini, M. \u003cem\u003eet al.\u003c/em\u003e Maternal anxiety-driven modulation of fetal limbic connectivity designs a backbone linking neonatal brain functional topology to socio-emotional development in early childhood. \u003cem\u003eJ. Neurosci. Res.\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 1484\u0026ndash;1503 (2023).\u003c/li\u003e\n\u003cli\u003eThomason, M. E. \u003cem\u003eet al.\u003c/em\u003e Prenatal lead exposure impacts cross-hemispheric and long-range connectivity in the human fetal brain. \u003cem\u003eNeuroImage\u003c/em\u003e \u003cstrong\u003e191\u003c/strong\u003e, 186\u0026ndash;192 (2019).\u003c/li\u003e\n\u003cli\u003eWu, Y., De Asis-Cruz, J. \u0026amp; Limperopoulos, C. Brain structural and functional outcomes in the offspring of women experiencing psychological distress during pregnancy. \u003cem\u003eMol. Psychiatry\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 2223\u0026ndash;2240 (2024).\u003c/li\u003e\n\u003cli\u003eThomason, M. E. \u003cem\u003eet al.\u003c/em\u003e Miswiring the brain: Human prenatal \u0026Delta;9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity. \u003cem\u003eDev. Cogn. Neurosci.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 101000 (2021).\u003c/li\u003e\n\u003cli\u003eSmyser, C. D. \u003cem\u003eet al.\u003c/em\u003e Resting-state network complexity and magnitude are reduced in prematurely born infants. \u003cem\u003eCereb Cortex\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 322\u0026ndash;333 (2016).\u003c/li\u003e\n\u003cli\u003eArichi, T. \u003cem\u003eet al.\u003c/em\u003e Localization of spontaneous bursting neuronal activity in the preterm human brain with simultaneous EEG-fMRI. \u003cem\u003eeLife\u003c/em\u003e \u003cstrong\u003e6\u003c/strong\u003e, e27814 (2017).\u003c/li\u003e\n\u003cli\u003eKostović, I., Kostović-Srzentić, M., Benjak, V., Jovanov-Milo\u0026scaron;ević, N. \u0026amp; Rado\u0026scaron;, M. Developmental dynamics of radial vulnerability in the cerebral compartments in preterm infants and neonates. (2014).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Correlations between functional connectivity and familial risk.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 469px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional connectivity and aggregate familial risk score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 219px;\"\u003e\n \u003cp\u003e\u003cem\u003eConnection\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cem\u003erho\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Bootstrap CI 95%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 174px;\"\u003e\n \u003cp\u003e\u003cem\u003ePartial correlation model\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 219px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cem\u003erho\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 219px;\"\u003e\n \u003cp\u003eL Insula \u0026ndash; L Caudate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.75 -0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 219px;\"\u003e\n \u003cp\u003eL Insula \u0026ndash; L Rolandic Operculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e-0.75 -0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpearman\u0026apos;s rho coefficient and the associated p-values are reported. Confidence intervals (CI) were computed using bootstrap (n = 10000). The two rightmost columns report rho and p for the partial correlation model with prenatal speech exposure and prenatal noise exposure as nuisance variables. L = left.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eCorrelations between functional connectivity and prenatal speech exposure.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 476px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional connectivity and prenatal speech exposure score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cem\u003eConnection\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cem\u003erho\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;Bootstrap CI 95%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cem\u003ePartial correlation model\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003erho\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\"\u003e\n \u003cp\u003eL Rolandic Operculum \u0026ndash; L Middle Temporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.28 0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\"\u003e\n \u003cp\u003eL Rolandic Operculum \u0026ndash; L Superior Temporal Pole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e0.29 0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 242px;\"\u003e\n \u003cp\u003eR Middle Orbital Frontal - R Heschl Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 125px;\"\u003e\n \u003cp\u003e-0.74 -0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpearman\u0026apos;s rho coefficient and the associated p-values are reported. Confidence intervals (CI) were computed using bootstrap (n = 10000). The two rightmost columns report rho and p for the partial correlation model with famililal risk and prenatal noise exposure as nuisance variables. L = left; R = right.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"foetal neurodevelopment; early language acquisition, functional connectivity, auditory and language networks, familial risk, language disorders","lastPublishedDoi":"10.21203/rs.3.rs-6113185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6113185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe maturation of the auditory-language brain network begins before birth, driven by gene-environment interactions. We investigated the association between familial and environmental factors and the foetal development of this network, as well as the predictive value of this association for postnatal language outcomes. Using prenatal resting-state fMRI, we examined 25 foetuses to identify functional connectivity within the auditory-language network. Postnatal language was assessed longitudinally between 1-3 years using the Bayley-III scale. Familial risk for language disorders and prenatal speech exposure were quantified using a newly developed questionnaire. The analysis in foetuses identified an auditory-language network. In this network, foetuses with higher speech exposure exhibited increased connectivity between left-hemisphere regions and decreased connectivity between homologous right-hemisphere regions. Higher familial risk was linked to reduced connectivity within the left language network. Regression analyses revealed that prenatal functional connectivity between insula, caudate nucleus, and rolandic operculum significantly predicted postnatal language. These findings underscore the critical role of genetic and environmental influences in functionally shaping the foetal auditory-language network, with lasting impacts on early language development. By integrating prenatal brain connectivity, familial risk, and speech exposure, this study provides new insights into prenatal language neurodevelopment, highlighting its importance for future language capabilities.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Marco Tettamanti \u0026amp; Pasquale Anthony Della Rosa contributed equally.\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Prenatal Brain Connectivity and Postnatal Language: How Familial Risk and Prenatal Speech Exposure Shape Early Language Skills","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-21 09:06:16","doi":"10.21203/rs.3.rs-6113185/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-06T07:37:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T15:35:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-24T20:58:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226725918313214729114231493911391752874","date":"2025-03-24T18:09:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115046236426354919140833964974614400834","date":"2025-03-18T16:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288970697236694174037126265379485216414","date":"2025-03-18T15:08:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-18T11:07:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-10T07:07:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-06T13:16:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-06T13:15:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c33d8fe0-0478-41b8-aa44-4e18f3a3d38f","owner":[],"postedDate":"March 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45968205,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Dyslexia"},{"id":45968206,"name":"Biological sciences/Neuroscience/Cognitive neuroscience/Language"},{"id":45968207,"name":"Biological sciences/Neuroscience/Diseases of the nervous system/Developmental disorders"},{"id":45968208,"name":"Biological sciences/Psychology/Human behaviour"}],"tags":[],"updatedAt":"2025-09-29T16:05:59+00:00","versionOfRecord":{"articleIdentity":"rs-6113185","link":"https://doi.org/10.1038/s41598-025-17531-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-26 15:58:02","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2025-03-21 09:06:16","video":"","vorDoi":"10.1038/s41598-025-17531-y","vorDoiUrl":"https://doi.org/10.1038/s41598-025-17531-y","workflowStages":[]},"version":"v1","identity":"rs-6113185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6113185","identity":"rs-6113185","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}