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The understanding of spoken language is mediated by bottom-up and top-down processing in the brain. In this Opinion article, Skeide and Friederici propose how changes in the structure and function of children's brains are associated with the development of language-processing skills. Language-processing functions follow heterogeneous developmental trajectories. The human embryo can already distinguish vowels in utero , but grammatical complexity is usually not fully mastered until at least 7 years of age. Examining the current literature, we propose that the ontogeny of the cortical language network can be roughly subdivided into two main developmental stages. In the first stage extending over the first 3 years of life, the infant rapidly acquires bottom-up processing capacities, which are primarily implemented bilaterally in the temporal cortices. In the second stage continuing into adolescence, top-down processes emerge gradually with the increasing functional selectivity and structural connectivity of the left inferior frontal cortex.

The neural representation of language comprehension has been examined in several meta-analyses of fMRI studies with human adults. To complement this work from a developmental perspective, we conducted a meta-analysis of fMRI studies of auditory language comprehension in human children. Our analysis included 27 independent experiments involving n ​= ​625 children (49% girls) with a mean age of 8.9 years. Activation likelihood estimation and seed-based effect size mapping revealed activation peaks in the pars triangularis of the left inferior frontal gyrus and bilateral superior and middle temporal gyri. In contrast to this distribution of activation in children, previous work in adults found activation peaks in the pars opercularis of the left inferior frontal gyrus and more left-lateralized temporal activation peaks. Accordingly, brain responses during language comprehension may shift from bilateral temporal and left pars triangularis peaks in childhood to left temporal and pars opercularis peaks in adulthood. This shift could be related to the gradually increasing sensitivity of the developing brain to syntactic information.

Our capacity to derive meaning from things that we see and words that we hear is unparalleled in other animal species and current AI systems. Despite a wealth of functional magnetic resonance imaging (fMRI) studies on where different semantic features are processed in the adult brain, the development of these systems in children is poorly understood. Here we conducted an extensive database search and identified 50 fMRI experiments investigating semantic world knowledge, semantic relatedness judgments, and the differentiation of visual semantic object categories in children (total N = 1,018, mean age = 10.1 years, range 4–15 years). Synthesizing the results of these experiments, we found consistent activation in the bilateral inferior frontal gyri (IFG), fusiform gyri (FG), and supplementary motor areas (SMA), as well as in the left middle and superior temporal gyri (MTG/STG). Within this system, we found little evidence for age-related changes across childhood and high overlap with the adult semantic system. In sum, the identification of these cortical areas provides the starting point for further research on the mechanisms by which the developing brain learns to make sense of its environment.

Mathematical ability is heritable and related to several genes expressing proteins in the brain. It is unknown, however, which intermediate neural phenotypes could explain how these genes relate to mathematical ability. Here, we examined genetic effects on cerebral cortical volume of 3-6-year-old children without mathematical training to predict mathematical ability in school at 7-9 years of age. To this end, we followed an exploration sample (n = 101) and an independent replication sample (n = 77). We found that ROBO1, a gene known to regulate prenatal growth of cerebral cortical layers, is associated with the volume of the right parietal cortex, a key region for quantity representation. Individual volume differences in this region predicted up to a fifth of the behavioral variance in mathematical ability. Our findings indicate that a fundamental genetic component of the quantity processing system is rooted in the early development of the parietal cortex.

Developmental dyslexia, a severe deficit in literacy learning, is a neurodevelopmental learning disorder. Yet, it is not clear whether existing neurobiological accounts of dyslexia capture potential predispositions of the deficit or consequences of reduced reading experience. Here, we longitudinally followed 32 children from preliterate to school age using functional and structural magnetic resonance imaging techniques. Based on standardised and age-normed reading and spelling tests administered at school age, children were classified as 16 dyslexic participants and 16 controls. This longitudinal design allowed us to disentangle possible neurobiological predispositions for developing dyslexia from effects of individual differences in literacy experience. In our sample, the disorder can be predicted already before literacy learning from auditory cortex gyrification and aberrant downstream connectivity within the speech processing system. These results provide evidence for the notion that dyslexia may originate from an atypical maturation of the speech network that precedes literacy instruction. •Longitudinal MRI study following preliterate children developing dyslexia.•Auditory cortex folding was more variable in dyslexic children.•Altered speech network connectivity in dyslexia predates literacy instruction.•Combination of neural and behavioural data reliably predicted dyslexia before school.

Mathematical learning deficits are defined as a neurodevelopmental disorder (dyscalculia) in the International Classification of Diseases. It is not known, however, how such deficits emerge in the course of early brain development. Here, we conducted functional and structural magnetic resonance imaging (MRI) experiments in 3- to 6-year-old children without formal mathematical learning experience. We followed this sample until the age of 7 to 9 years, identified individuals who developed deficits, and matched them to a typically developing control group using comprehensive behavioral assessments. Multivariate pattern classification distinguished future cases from controls with up to 87% accuracy based on the regional functional activity of the right posterior parietal cortex (PPC), the network-level functional activity of the right dorsolateral prefrontal cortex (DLPFC), and the effective functional and structural connectivity of these regions. Our results indicate that mathematical learning deficits originate from atypical development of a frontoparietal network that is already detectable in early childhood.

Electroencephalography (EEG) preprocessing varies widely between studies, but its impact on classification performance remains poorly understood. To address this gap, we analyzed seven experiments with 40 participants drawn from the public ERP CORE dataset. We systematically varied key preprocessing steps, such as filtering, referencing, baseline interval, detrending, and multiple artifact correction steps, all of which were implemented in MNE-Python. Then we performed trial-wise binary classification (i.e., decoding) using neural networks ( EEGNet ), or time-resolved logistic regressions. Our findings demonstrate that preprocessing choices influenced decoding performance considerably. All artifact correction steps reduced decoding performance across experiments and models, while higher high-pass filter cutoffs consistently increased decoding performance. For EEGNet , baseline correction further increased decoding performance, and for time-resolved classifiers, linear detrending, and lower low-pass filter cutoffs increased decoding performance. The influence of other preprocessing choices was specific for each experiment or event-related potential component. The current results underline the importance of carefully selecting preprocessing steps for EEG-based decoding. While uncorrected artifacts may increase decoding performance, this comes at the expense of interpretability and model validity, as the model may exploit structured noise rather than the neural signal. Systematic evaluation of EEG preprocessing reveals how filtering, artifact handling, and other steps influence decoding performance, highlighting trade-offs between classification accuracy and neural interpretability.

Children lay the foundation for later academic achievement by acquiring core mathematical abilities in the first school years. Neural reorganization processes associated with individual differences in early mathematical learning, however, are still poorly understood. To fill this research gap, we followed a sample of 5-6-year-old children longitudinally to the end of second grade in school (age 7–8 years) combining magnetic resonance imaging (MRI) with comprehensive behavioral assessments. We report significant links between the rate of neuroplastic change of cortical surface anatomy, and children's early mathematical skills. In particular, most of the behavioral variance (about 73%) of children's visuospatial abilities was explained by the change in cortical thickness in the right superior parietal cortex. Moreover, half of the behavioral variance (about 55%) of children's arithmetic abilities was explained by the change in cortical folding in the right intraparietal sulcus. Additional associations for arithmetic abilities were found for cortical thickness change of the right temporal lobe, and the left middle occipital gyrus. Visuospatial abilities were related to right precentral and supramarginal thickness, as well as right medial frontal gyrus folding plasticity. These effects were independent of other individual differences in IQ, literacy and maternal education. Our findings highlight the critical role of cortical plasticity during the acquisition of fundamental mathematical abilities. •MRI study of cortical plasticity during first years of formal math instruction.•Right superior parietal thickness change was related to visuospatial processing.•Right intraparietal sulcus folding plasticity was related to early arithmetic.•Left occipital, and right fronto-temporal regions showed further associations.•Results link cortical plasticity to basic math learning.

Recent studies suggest that neurobiological anomalies are already detectable in pre-school children with a family history of developmental dyslexia (DD). However, there is a lack of longitudinal studies showing a direct link between those differences at a preliterate age and the subsequent literacy difficulties seen in school. It is also not clear whether the prediction of DD in pre-school children can be significantly improved when considering neurobiological predictors, compared to models based on behavioral literacy precursors only. We recruited 53 pre-reading children either with (N=25) or without a family risk of DD (N=28). Quantitative T1 MNI data and literacy precursor abilities were assessed at kindergarten age. A subsample of 35 children was tested for literacy skills either one or two years later, that is, either in first or second grade. The group comparison of quantitative T1 measures revealed significantly higher T1 intensities in the left anterior arcuate fascicle (AF), suggesting reduced myelin concentration in preliterate children at risk of DD. A logistic regression showed that DD can be predicted significantly better (p=.024) when neuroanatomical differences between groups are used as predictors (80%) compared to a model based on behavioral predictors only (63%). The Wald statistic confirmed that the T1 intensity of the left AF is a statistically significant predictor of DD (p<.05). Our longitudinal results provide evidence for the hypothesis that neuroanatomical anomalies in children with a family risk of DD are related to subsequent problems in acquiring literacy. Particularly, solid white matter organization in the left anterior arcuate fascicle seems to play a pivotal role.

Phonological awareness is the best-validated predictor of reading and spelling skill and therefore highly relevant for developmental dyslexia. Prior imaging genetics studies link several dyslexia risk genes to either brain-functional or brain-structural factors of phonological deficits. However, coherent evidence for genetic associations with both functional and structural neural phenotypes underlying variation in phonological awareness has not yet been provided. Here we demonstrate that rs11100040, a reported modifier of SLC2A3, is related to the functional connectivity of left fronto-temporal phonological processing areas at resting state in a sample of 9- to 12-year-old children. Furthermore, we provide evidence that rs11100040 is related to the fractional anisotropy of the arcuate fasciculus, which forms the structural connection between these areas. This structural connectivity phenotype is associated with phonological awareness, which is in turn associated with the individual retrospective risk scores in an early dyslexia screening as well as to spelling. These results suggest a link between a dyslexia risk genotype and a functional as well as a structural neural phenotype, which is associated with a phonological awareness phenotype. The present study goes beyond previous work by integrating genetic, brain-functional and brain-structural aspects of phonological awareness within a single approach. These combined findings might be another step towards a multimodal biomarker for developmental dyslexia. •rs11100040 is related to fronto-temporal functional connectivity at resting state.•rs11100040 is related to the fractional anisotropy of the arcuate fasciculus.•FA of the arcuate fasciculus is related to phonological awareness.