Abstract
Language acquisition appears to rely at least in part on recruiting pre-existing brain 2 structures. We hypothesized that the neural substrate for language can be characterized by 3 distinct, non-trivial network properties of the brain, that modulate language acquisition early 4 in development. We tested whether these brain network properties present at the normal age 5 of birth predicted later language abilities, and whether these were robust against perturbation 6 by studying infants exposed to the extreme environmental stress of preterm birth. 7
Abstract
We found that brain network controllability and integration predicted respectively 8 phonological, 'bottom-up' and syntactical, 'top-down' language skills at 20 months, and that 9 syntactical but not phonological functions were modulated by premature extrauterine life. 10
Abstract
These data show that the neural substrate for language acquisition is a network property 11 present at term corrected age. These distinct developmental trajectories may be relevant to 12 the emergence of social interaction after birth. 13 14 3
Abstract
Language acquisition appears to rely at least in part on the recruitment of pre-existing brain 2 structures and capacities [1] [2] [3] : during the last trimester of pregnancy temporal brain areas are 3 active during passive language listening 4-6 ; ventral and dorsal white-matter bundles are 4 present during early infancy 7, 8 ; and over the first months of postnatal life, infants progress 5 from a general capacity to discriminate any phonetic contrast to a more specific response to 6 the combination of vowels and consonants of their native language 9-11 . The establishment of 7 a phonetic repertoire allows infants progressively to infer statistical patterns in speech and 8 language structure 12, 13 and later progress to the first production of words at 9-12 months of 9 age and multiword utterances and grammar by 18-24 months of age 2 . Before children start to 10 read, the pre-existing connectivity profile of the visual word form area predicts its future 11 location 3 , thus also suggesting that cognitive brain development also takes advantage of pre-12 existing cortical circuits 14 . 13
Abstract
However, the evolution of a neural substrate for language during the perinatal period remains 14 incompletely understood. Brain development is extremely rapid at this time and normal 15 maturation is significantly disrupted by environmental pressures such as preterm birth which 16 lead to widespread abnormalities in structure and connectivity, including substantial changes 17 within regions involved in language processing 15,16 , which significantly predict later language 18 function. Nevertheless despite these significant abnormalities, the effect of early adversity on 19 language function is complex. Language acquisition is frequently disrupted by preterm birth. 20
Abstract
However the effect is complex in preterm infants: as whilst earlier exposure to speech does 21 not alter language perception in early childhood 17 , they show increasing risk of impairments 22 later in life [18] [19] [20] [21] . This suggests that some aspects of language acquisition are more susceptible 23 to environmental pressures during the preterm period than others. 24 4 From 30 to 44 weeks gestational age brain network architecture matures rapidly, and at term 1 corrected age the brain shows scale-free macroscopic structural connectivity largely similar 2 to the adult 22, 23 . Although widespread abnormalities are induced by preterm birth, this is less 3 evident on the connections between the hub nodes 22 rather than the feeder nodes of the 4 network 22,24 , which therefore predicts robust network functionality is retained despite 5 widespread damage. There is also increasing evidence that multisensory input and 6 presumably processing across different brain systems are integral to language acquisition in 7 the perinatal period 25 . This combination of factors led us to hypothesize that in preterm 8 infants without focal destructive brain lesions, the neural substrate for language is in part a 9 network property of the network. 10
Abstract
Here, we examine language development in the perinatal period as properties of the large-11 scale structural connectivity network 26 , testing the prediction that network controllability and 12 integration are critical features in effective language acquisition early in development. 13
Abstract
Studying a group of infants born preterm allowed us to investigate the effect of adverse 14 environmental stimuli on these language network properties. We found that language abilities 15 in early childhood are predicted by network metrics at the normal time of birth. We show that 16 'bottom-up' phonological processing is unaffected by preterm delivery while 'top-down' 17 syntactic processing is altered by premature extra-uterine exposure. These results show that 18 the neural substrate for language is at least in part a network property present at term 19 corrected age and suggest a mechanism whereby language acquisition may support the 20 development of social behaviors. 21 22 5
Abstract
Results
Abstract
We acquired high b value, high angular resolution diffusion weighted-magnetic resonance 2 imaging (DW-MRI) from 43 preterm born infants (median gestational age (GA) at birth of 30 3 weeks) at term equivalent age (median postmenstrual age (PMA) at scan of 42 weeks) (Table  4 
Main text 1
Language acquisition appears to rely at least in part on the recruitment of pre-existing brain 2 structures and capacities [1] [2] [3] : during the last trimester of pregnancy temporal brain areas are 3 active during passive language listening [4] [5] [6] ; ventral and dorsal white-matter bundles are 4 present during early infancy 7, 8 ; and over the first months of postnatal life, infants progress 5 from a general capacity to discriminate any phonetic contrast to a more specific response to 6 the combination of vowels and consonants of their native language [9] [10] [11] . The establishment of 7 a phonetic repertoire allows infants progressively to infer statistical patterns in speech and 8 language structure 12, 13 and later progress to the first production of words at 9-12 months of 9 age and multiword utterances and grammar by 18-24 months of age 2 . Before children start to 10 read, the pre-existing connectivity profile of the visual word form area predicts its future 11 location 3 , thus also suggesting that cognitive brain development also takes advantage of pre-12 existing cortical circuits 14 . 13 However, the evolution of a neural substrate for language during the perinatal period remains 14 incompletely understood. Brain development is extremely rapid at this time and normal 15 maturation is significantly disrupted by environmental pressures such as preterm birth which 16 lead to widespread abnormalities in structure and connectivity, including substantial changes 17 within regions involved in language processing 15, 16 , which significantly predict later language 18 function. Nevertheless despite these significant abnormalities, the effect of early adversity on 19 language function is complex. Language acquisition is frequently disrupted by preterm birth. 20
Main text 1
However the effect is complex in preterm infants: as whilst earlier exposure to speech does 21 not alter language perception in early childhood 17 , they show increasing risk of impairments 22 later in life [18] [19] [20] [21] . This suggests that some aspects of language acquisition are more susceptible 23 to environmental pressures during the preterm period than others. 24 found that there was not a significant relationship between GA at birth and the regional 1 measures of network integration (either one-tail or two-tails T-test; FWE-corrected p-values 2 > 0.7005, tested against 10,000 permutations). We also found that behavioral measures could 3 not explain the modulation language brain integration by GA at birth (univariate GLM 4 testing; FWE-corrected p-values > 0.1730). Finally, we also tested whether multivariate 5 patterns of brain or behavior measures could significantly discriminate between preterm 6 infants with low and high olivermadison@example.net. Using the LDA, we also found no evidence for this 7 relationship (brain measures: accuracy = 0.58; p-value = 0.30; behavioral measures: accuracy 8 = 0.56; p-value = 0.40; significance of classification was assessed against 10,000 9 permutations brain or behavior measures). 10 These additional analyses suggest that the modulation of language brain integration by the 11 degree of prematurity is not due to the effect of premature birth on a single brain region or 12 cognitive skill. Instead, premature birth appears to affect the complex brain network which 13 underlies higher-order language abilities. 14 Discussion 1
Main text 1
These results demonstrate that distinct childhood language abilities can be predicted by 2 specific network metrics in the perinatal period. This suggests that the brain's structural 3 network is intrinsically organized to predispose itself for language acquisition. We further 4 found that premature environmental exposure significantly affected the relationship between 5 network features and syntactic but not phonological abilities, suggesting distinct 6 neurodevelopment trajectories for top-down and bottom-up human language acquisition. 7
Main text 1
A structural brain network underpins efficient language learning. We show that even at 8 the time of normal human birth, the brain's structural network already possesses specific 9 patterns of controllability and integration within regions known to play a fundamental role in 10 language function throughout the lifespan. 11
Main text 1
We first studied structural network controllability which predicts the ability of individual 12 nodes (or drivers) to theoretically steer the network's activity output. We found that the left 13 temporal and supramarginal cortices were key drivers of activity across the network which 14 predict inter-subject differences in childhood language skills. In keeping with this finding, the 15 temporal regions have been shown to be activated in an adult-like fashion during passive 16 listening of phoneme deviants and human speech even before and around the time of normal 17 birth 5, 6 . Here, we additionally show that language brain network controllability represents an 18 early form of structural specialization which supports the bottom-up, phonological learning 19 of auditory-verbal material, and may represent an early substrate for auditory-verbal working 20 memory 40 . 21
Main text 1
We then studied network integration, to measure the ability of individual nodes (or hubs) to 22 facilitate global intermodular connectivity. In this context, we found that the left prefrontal 23 cortex, the temporal poles, and the right occipital cortex, are key parts of a brain hub system 24 responsible for the development of receptive and expressive linguistic skills and problem 1 solving. Previous studies have shown that the left prefrontal region responds to speech 2 presentation by three months of age 4 , and that some degree of lateralization in response to 3 phoneme deviance is already present in this area in the preterm brain even before term 4 equivalent age 6 . Although it was previously thought that the left inferior frontal gyrus does 5 not show selectivity for syntax until 9-10 years of age 1,46 , our findings provide evidence that 6 structural connectivity for top-down, syntactic and semantic abilities is established as a 7 network property as early as at the normal time of birth, preceding the emergence of 8 functional specialization and its behavioral correlate. It is also possible that these network 9
Main text 1
properties may aid the learning of reading skills, through mediating the neuroarchitectural 10 integration of the vision and language processing areas 14 . 11
Main text 1
Together, these findings demonstrate the presence of a structural brain network that is already 12 organized to support environmental interaction and language learning. Of particular 13 significance, these results are in agreement with brain network models of mature language 14 processing and development. The left inferior and middle-frontal regions, and the superior 15 and middle-temporal cortices are known to alter their activity during auditory-verbal working 16 memory tasks 47 and are hypothesized to represent the brain basis underlying the learning of 17 new words and unfamiliar phoneme combinations 40 . This left language network is also 18 thought to support core syntactic language processing in sentence-level argument structure 19 48, 49 . The left inferior frontal gyrus has also been postulated to specifically modulate top-20 down processing of sentence-level syntax 50,51 by regulating the conversion of lexical and 21 phonological information received from the superior temporal cortex via the arcuate 22 fasciculus into an articulation code 48, 52, 53 . 23
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Structural controllability in this context may reflect a network property that is complementary 1 to network integration. It is possible that direct long-distance connections already present at 2 this age 8 enable higher-order prefrontal areas to perform top-down modulation on structural 3 controllers such as the temporal regions, in order to ensure efficient language learning. The 4 presence of these two network properties at the time of normal birth may represent a required 5 computational pathway before specific refinement through experience dependent plasticity 6 during critical windows for language acquisition in early childhood 54 . 7
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Premature environmental exposure alters brain networks that are key for syntactic 8 language acquisition. Although the posterior temporal areas have already started to develop 9 the neural organization that underlies language processing before term equivalent age 6 , 10 earlier exposure to speech in preterm infants does not lead to accelerated development of 11 phonological skills 17 . Our results expand on this further by confirming that the neonatal brain 12 network features which predict bottom-up language abilities in later childhood are minimally 13 influenced by early environmental exposure. In contrast, we found that the prefrontal brain 14 network architecture which predicts childhood top-down language skills such as semantic and 15 syntactic language learning are significantly altered by premature environmental exposure. 16
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This may be because such processes are computationally more expensive than phonology and 17 rely on connections that are not as well developed in the perinatal period as those of the 18 temporal cortices. These findings are consistent with those of our previous study which 19 showed that preterm birth affects the structural connectivity of sparsely-connected areas, 20 whilst minimally impacting those related to hub regions 22 . 21
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Preterm-born children are known to have difficulties in complex language attainment and 22 function which increase with age 55, 56 . These may result from general cognitive difficulties 23 rather than a specific phonological impairment 20, 57 , as higher-order semantic and syntactic 24 14 knowledge is thought to involve hierarchical integration across language areas and a 1 significant working memory component 1 . The results of this study show that premature 2 environmental exposure alters the prefrontal neonatal brain network structure underlying 3 future, top-down language learning. 4
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Although structural network controllability represents an important step towards a novel 5 mechanistic understanding of the human brain's structure-function relationship 26, 29 , it is 6 nevertheless important to acknowledge the limitations of this framework 36, 37 . Although 7 system brain dynamics may be non-linear in nature to some degree, the framework we used 8 relied on linear modelling as such methods have been successfully applied previously to 9 predict dynamics in the adult human brain 35 . The assumption of linear dynamics is 10 nevertheless true when close to operating points and over short time horizons 36, 37 . 11
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Conclusions. In this work, we provide evidence that at the normal time of birth, in addition 12 to known local and areal mechanisms, the neural basis for language acquisition includes 13
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properties of the overall structural brain network, and that network controllability and 14 integration respectively underlie bottom-up and top-down language skills in childhood. We 15 further show that preterm birth leads to specific impairment these properties that affect future 16 semantic and syntactic learning. This work provides insight into the fundamental relationship 17 between the early establishment of human brain network architecture and complex behavior, 18 and has implications for fostering healthy long-term outcomes and social interaction.
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Infants. Preterm infants were recruited as part of the Evaluation of Preterm Imaging study 2 (Eprime), and were imaged at term equivalent age over a 3 year period (2010-2013). The 3 study was reviewed and approved by the National Research Ethics Service, and all infants 4 were studied following written parental consent. A cohort of 43 preterm born infants (median 5 age at birth of 30.14 gestational (GA) weeks; range 24 -32; 18 females) with no evidence of 6 focal abnormality on MRI were imaged using high-angular resolution diffusion-weighted 7 olivermadison@example.net postmenstrual (PMA) weeks (range 39 -46). 8
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Standardized neurodevelopmental assessment at a median age of 22 months (range: 21 -24 9 months; median of 20 months corrected for prematurity) was carried out by experienced 10 pediatricians with the Bayley III scales of infant and toddler development (BSID-III) 58 and of 11 the Parent Report of Children's Abilities Revised (PARCAR) 59 . None of these behavioral 12 scales were significantly correlated with GA at birth or PMA at time of MRI scan. As 13 previously described in the literature, there was a significant effect of socioeconomic score 60 . 14 The BSID-II measures the actual cognitive and linguistic abilities that the child is able to 15 demonstrate to the examiner during the testing session. Specifically, the receptive 16 communication subscale aims to measure receptive language development, with items 17 covering vocabulary and semantics (i.e. picture and object series identification; 18 understanding of inhibitory words), and morphology and syntax (i.e. object categories 19 identification; understanding of plurals; understanding of pronouns; understanding of past 20 tense). Whilst the expressive communication subscale aims to test expressive language 21 development, with its items measuring semantics (i.e. the use of one-word approximations; 22 naming action picture series), morphology, and syntax (i.e. production of present progressive 23 form; production of prepositions; explain how an object is used; production of multiple-word 24 questions). The cognitive scale aims to measure i.e. attention, problem solving, reasoning, 1 memory, object novelty, two-step actions, size discrimination, number sequences repetition, 2 and representational play. The PARCAR measures the level of behavioral capacities reported 3 by the parents or guardians. 34 items are summed to give a nonverbal cognitive subscale 4 score. Whilst linguistic development is assessed with 2 subscales that are part of the 5
1
MacArthur Communicative Development Index Words and Sentences 61 : a 100-word 6 vocabulary checklist and 18 items to assess grammatical competence. 7
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Acquisition of MRI imaging data. All MRI studies were supervised by an experienced 8 pediatrician or nurse trained in neonatal resuscitation. Pulse oximetry, temperature, and heart 9 rate were monitored throughout the period of image acquisition; hearing protection in the 10 form of silicone-based putty placed in the external ear (President Putty, Coltene; Whaledent) 11
1
and Mini-muffs (Natus Medical Inc.) was used for each infant. Sedation (25-50 mg/kg oral 12 chloral hydrate) was administered to 33 infants. Imaging was acquired using an eight-channel 13 MRI data pre-processing. Structural MRI volumes were processed in order to extract 24 tissues segmentation 62 . Diffusion MRI volumes were visually inspected and processed. B0 25 field inhomogeneities, eddy currents, and inter-volume motion were corrected and outlier 1 slices were replaced using topup and eddy tools in FSL5 [63] [64] [65] . B1 field bias was corrected 2 using ITK-N4
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66 . All rigid registrations in native subject space were estimated using FSL 3 boundary-based registration optimized for neonatal tissue contrasts [67] [68] [69] ; and nonlinear 4 registrations to the T2-weighted template were estimated using Advanced Normalization 5 Tools (ANTs) 70 . All transformation pairs were calculated independently and combined into a 6 single transform in order to reduce interpolation error. This preprocessing approach was 7 previously published 8 . 8
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Neonatal connectome. Estimation of fiber orientation distribution was computed through 9 constrained spherical deconvolution with maximum spherical harmonic order of 8 71 . Whole-10 brain fiber tracking was performed using anatomically constrained probabilistic tractography 11 from the MRtrix3 package with 10 million streamlines 27,72 and SIFT2 regularization 73 . 12
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Subject-specific weighted adjacency matrices of the brain connectome were computed based 13 on a 90-region neonatal atlas 28 and edge weights were defined by the number of streamlines 14 connecting each pair of nodes 36 . 15
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Dynamic model of neural function. Drawing from previous work predicting nonlinear brain 16 dynamic processes using simplified linear models 34,35 , we employed a noise-free linear 17 discrete-time and time-invariant network model: 18
1
based on the underlying structural connectome, here : ℝ / 0 → ℝ 2 describes the state of 20 activity of brain regions over time, and ∈ ℝ 2×2 is the structural white-matter connectome 21 reconstructed with diffusion neuroimaging tractography streamlines. The input matrix ) 22 identifies the control points in the brain where = 7 , … , : and ) = <7 … <: , and 23 = denotes the ?@ canonical vector of dimension . The input < : ℝ / 0 → ℝ : denotes the 1 control strategy. 2 Network controllability diagnostics. We studied the controllability of this dynamical 3 system, the capacity of steering the state of the system to a specific target state by means of 4 an external control input 74 . In order words, the capacity of manipulating the dynamic 5 trajectories of a system. 6
1
Classic results in control theory ensure that controllability of the dynamical network model 7 previously explicated from a set of network nodes is equivalent to the controllability 8
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Gramian < being invertible, where 9
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In accordance with 36 , we applied this framework to choose controls nodes one at a time, and 11 thus the input matrix in fact reduces to a one-dimensional vector. 12 We studied two different quantitative, regional control strategies that describe the ability to 13 move the network into different states. 14 Structural average controllability. Structural average controllability describes the ease of 15 transition to many states nearby on an energy landscape (low-energy state transitions) 36 . Analysis of Linear Models (PALM) 91 and each PLS permuted model was fitted with cross-24 validation and Monte-Carlo repetition as specified above. 25
1
For the significant models we characterized PLS components with statistically significant 1 correlation between PLS scores pairs (Family-wise error (FWE) < 0.05, corrected across PLS 2 modes). Where XS are the predictor scores (the PLS components that are linear combinations 3 of the variables in X); and YS the response scores (the linear combinations of the responses 4 with which the PLS components XS have maximum covariance). 5
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For each significant PLS mode, we extracted and normalized the PLS weights (W*), the 6 linear combinations of the original variables that define the PLS component 38, 88 . Given the 7 PLS weight correlation between significant PLS components, we then studied the first 8 principal component of W* across the significant PLS modes in order to derive a summary 9 measure of feature importance for each brain region. The same procedure was repeated to 10 extract the W* from the PLS permuted models in order to calculate the non-parametric 11 statistical significance of each brain region (corrected for FWE across brain regions) 91 . To 12 aid interpretation only brain regions with significant positive W* were considered (one-tail 13 FWE p-value < 0.05, corrected across regions; corresponding to -log 10 (p-value) > 1.3010, 14 corrected across regions) 92 . PLS weights maps not corrected for multiple comparisons are 15 also provided (Fig. S2, S3 ). PLS coefficients encoding the involvement of behavioral 16 measures in the PLS modes were also extracted and ranked. To aid the interpretation of these 17 coefficients, we also calculated the correlation between each behavioral skill and the first 18 principal component of W* (PLS weights of X) across the significant PLS modes 93 , thus 19 scaling PLS weights of Y between 0 and 1 (Fig. 2c, 3c) . 20 In order to deliver a more exhaustive characterization of the identified significant model, we 21 also provide further information regarding the relation between predictors (within model) and 22 the PLS model estimation (Fig. S5, S6, S7 ). All statistical analyses were performed using 23 MATLAB (R2015b, The MathWorks, Inc., Natick, MA, USA). 24
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Testing the effect of degree of prematurity at birth on brain-behavior links. We removed 1 the effect of all confounds from the dependent and independent variables: PMA and sex from 2 brain metrics; SES and sex from behavior metrics. We then used the same pipeline 3 highlighted before to estimate PLS regression models for both language brain controllability 4 and language brain integration. We tested the effect of premature environmental exposure on 5 the identified brain models of language acquisition by testing whether the PLS scores pairs 6 could discriminate between high vs low olivermadison@example.net. We split the sample onto those preterm 7 infants with GA at birth less or equal to 29 weeks (referred as "low" GA group); and those 8 with GA at birth greater then 29 weeks (referred as "high" GA group). A post-hoc data-9 driven test performed using k-means clustering confirmed the cut-off of 29 GA weeks as the 10 optimal point to divide our sample in two groups. We performed multivariate cross-subject 11 classification based on all estimated PLS scores pairs (or PLS individual subject weights) 12 using cross-validated linear discriminant analysis (LDA) implemented in the FSLNets 13 toolbox for MATLAB. Statistical significance of LDA classification accuracy was calculated 14 with 10,000 permutation of PLS scores pairs. 15
1
The effect of postnatal age (or days of ex-utero life) was not considered a variable of interest 16 as it is a linear combination of PMA minus GA and therefore is highly correlated (Fig. S8) . 17
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Testing significance of brain-behavior models against permuted null models and against 18 additional covariates of interest. Aware of the caveats of structural controllability and 19 network neuroscience 33 , we also assessed the statistical significance of our brain-language 20 results using random null models. For 1,000 times, for each participant, we randomized the 21 individual structural brain network while simultaneously preserving the degree-, weight-and 22 strength-distributions using the function null_model_und_sign from the Brain Connectivity 23
1
Toolbox 41 . We then fed these matrices to the same pipeline and quantified PLS model 24 goodness of fitting for random networks. This allowed us to compare the "genuine" models 25 of language brain network controllability and integration against the respective random null 1 models' distributions and calculate the non-parametric random null models-based statistical 2 significance. 3 4
Supplementary information 1

Supplementary results 2
Alternative models of language brain acquisition. We investigated distinct brain network 3 metrics as alternative mechanisms for explaining language brain acquisition. These 4 alternative models were assessed with the same pipeline as above. We tested the following 5 atlas-based brain metrics: streamlines count; weighted degree (ranked; calculated as nodal 6 strength); structural (average) controllability (ranked); network segregation (calculated as 7 within-module degree z-score); atlas-based cortical and sub-cortical volume. We found that 8 none of these alternative models significantly linked neonatal brain architecture and language 9 abilities in childhood (Table S2) . Furthermore, the two models on interest (language brain 10 (modal) controllability and network integration), remained significant even after Bonferroni 11 correction across models. 12 Quantification of PLS model significance against random null models. Aware of the 13 limitations and caveats of structural network controllability 33 , and more in general of graph 14 theoretical measures, we also tested the identified significant models against non-parametric 15 permutation-based null network models. For 1,000 times, for each participant, we 16 randomized the individual structural brain network while simultaneously preserving the 17 degree-, weight-and strength-distributions. We then fed these matrices to the same pipeline 18 as above and quantified PLS model goodness of fitting for random networks. This allowed us 19 to compare the "genuine" models of language brain network controllability and integration 20 against the respective random null models' distributions. Crucially both models resulted to 21 significantly relate brain-behavior pairs even against random null models (Table S3) . 22
Supplementary results 2
Testing the effect of additional covariates of interest in the identified brain network 23 models of language acquisition. We performed additional tests to understand whether in-24 scanner head-motion and cortical brain volume could have driven the estimation of brain 1 network models of language acquisition. After regressing these variables into network 2 connectivity, we show that models' cross-validation remained significant thus demonstrating 3 that neither in-scanner head motion or cortical and sub-cortical volume was driving the 4 estimation of brain network models of language acquisition (Table S4) . 5 node across the network eigenvectors was characterized. This was then used to study: (d) 7
Figure captions
Structural controllability, which predicts individual nodes (or drivers) that are theoretically 8 able to change a network's activity state. In this example network, the node highlighted in 9 green is a network driver which can change network nodes activity and steer the system to a 10 different state. (Table 2a) . 11
Figure captions
Together these results suggest the left temporal lobe acts as a network driver of the brain 12 activity underlying the acquisition of phonological abilities. E s s e n t i a l t i a l c S e n s o r y n e u r o n I n t e r n e u r o n M o t o r n e u r o n I n p u t ( t o u c h ) G e n t l e t o u c h , S c h e m a t i c . e l e g a n s i o n I I I A ) . o r k . A c c o r d i n g n d i s r u p t s o m o t i o n ; t m a p p e d w o r k ) c a n l y u n k n o w n i n v o l v e m e n t o f P D B i n C . e l e g a n s l o c o m o t i o n , a n d f u d i f f e r e n c e s b e t w e e n i n d i v i d u a l n e u r o n s w i t h i n t h e D D n e u r o n a l c , T h e C . e l e g a n s c o n n e c t o m e u s e d i n o u r s t u d y , c o n s i s t i n g o f 2 7 9 ( t h e 2 8 2 n o n -p h a r y n g e a l n e u r o n s , e x c l u d i n g C A N L / R a n d V C 0 6 w n o t m a k e c o n n e c t i o n s w i t h t h e r e s t o f t h e n e t w o r k ) a n d 9 5 m u s c l e s s i z e i s p r o p o r t i o n a l t o t h e s u m o f i t s i n -a n d o u t -d e g r e e s . F i l l e d n o r e p r e s e n t t h e n e u r o n s t r a d i t i o n a l l y a s s i g n e d t o t h e c i r c u i t s g e n t l e t o u c h r e s p o n s e , h i n t i n g a t t h e c o m p l e x i t y o f u n c t i o n f r o m t h e w i r i n g d i a g r a m a l o n e . (Table 2b) . These results provide the first quantitative 11 evidence that the left prefrontal cortex at the time of normal birth has organized structural 12 connectivity which aids the later development of top-down linguistic abilities. (lower or equal to 29 weeks). In red: sample with high GA at birth (higher than 29 weeks). 8
Figure captions
This cut-off choice was confirmed by a post-hoc, data-driven k-means clustering analyses as 9 the optimal value to divide the sample into two groups. 10 (FWE-corrected p-value < 0.05 across all brain regions), respectively for language brain 12 controllability (a) and language brain integration (b). These regions are also shown in Figure  13 2 and 3. 14 (a) 15 Table 3 : Language brain integration discriminates between infants with high and low 4 gestational olivermadison@example.net. To understand whether the degree of prematurity has an effect on 5 language brain controllability and/or integration, we trained and tested PLS individual subject 6 weights (or PLS score pairs: XS, YS) to discriminate between neonates with low vs high GA olivermadison@example.net. We found that language brain integration (but not language brain controllability) 8 carries information which significantly discriminates between infants with GA at birth lower 9 or greater than 29 weeks, suggesting an effect of degree of prematurity specifically in this 10 brain mechanisms for language acquisition. 11 Table S2 : Comparison between alternative models of language brain acquisition. We 2 report Adj-R 2 , MSE and MPSE values for all models tested. All models were tested using 3 PLS regression. They were all based on the same cross-validated pipeline, with Monte-Carlo 4 repetition, and response variable permutations; based on the same set of response variables 5 and on an equal number of predictors. Only the models of language brain controllability and 6 integration resulted to be significant (*) (even after Bonferroni correction across models) and 7 had lowest MPSE and Akaike information criterion values. 8
XS,YS

Streamlines count
Weighted degree (ranked) (nodal strenght) Table S3 : Statistical significance against random null networks. Significant models of 3 language brain controllability and integration were also compared against random null 4 models. For 1,000 times, for each participant, the individual structural brain network was 5 randomized while simultaneously preserving the degree-, weight-and strength-distributions. 6
Streamlines count
These individual random null networks were then linked to linguistic and cognitive measures 7 in childhood using PLS regression. Genuine language brain controllability and integration 8
Streamlines count
were then compared against the estimated fitting distributions of random null networks in 9 order to calculate statistical significance. 

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