Biology subjects

Dehaene-Lambertz, G.

Publications and source records attributed to Dehaene-Lambertz, G..

At least 19 recordsLinked to original sources

bioRxiv2026

APICE-Py: An Open-Source MNE-Python Pipeline for Scalable EEG Preprocessing

Electroencephalography (EEG) is fundamental to cognitive neuroscience as it provides a direct measure of human neural activities with millisecond precision. Its noninvasive nature allows for the study of brain function across diverse age groups and experimental contexts--from newborns to adults, and from tightly controlled laboratory environments to more naturalistic real-world settings. However, EEG signals--especially those recorded from infants--are highly prone to noise and arti-facts, posing significant challenges for data analysis. To address these issues, we present APICE-Py (Automated Preprocessing for Infants Continuous EEG), an open-source preprocessing pipeline originally designed as a matlab toolbox for infant EEG and now re-implemented in Python to support scalable and flexible analysis across developmental and adult datasets. APICE-Py is built upon three core principles: (i) adaptive artifact detection on continuous data using data-driven thresholds rather than fixed cutoffs; (ii) hierarchical artifact correction, combining short-segment correction via Principal Component Analysis (PCA) with broader segment and continuous data correction using Spherical Spline Interpolation (SSI); and (iii)transparent reporting, providing comprehensive quality logs and decision-tracking to ensure reproducibility and informed analysis. We summarize the underlying algorithms, release an implementation compatible with common EEG formats, and demonstrate its use on three datasets spanning neonates, 5-month-old infants, and child-parent hyperscanned data, acquired using high-density wet electrodes and mobile gel-based EEG systems. When benchmarked against the original MATLAB implementation, APICE-Py achieved comparable levels of data quality and trial retention. While the original pipeline was developed for early developmental EEG (e.g., infants), we show that the pipeline further extends its applicability to both children and adult datasets, enabling robust preprocessing across a broad age range. Moreover, it supports data acquired using a variety of EEG configurations and experimental settings, highlighting its flexibility across age groups, hardware systems, and paradigms. The APICE-Py source code and documentation are freely available at https://github.com/neurokidslab/apice-py.

neuroscience↗

bioRxiv2026

A supervised digital game intervention supports language and communication in young children.

The role of digital media in early childhood development remains highly debated, particularly regarding its impact on language acquisition. While excessive or unsupervised screen exposure has been linked to poorer outcomes, less is known about whether structured and interactive uses of technology can support learning. Building on previous research, we evaluated a brief, educator-supervised tablet-based intervention in 246 children aged 2-5 years from low- to middle-socioeconomic backgrounds attending public early education centers. Using a pre-post design with matched study and control groups, children completed 4-8 short training sessions (15 minutes each) involving interactive word-image associations spanning multiple linguistic categories. Preschoolers additionally engaged in prompted vocalization. Across age groups (2-3, 3-4, and 4-5 years), children in the intervention showed greater gains in language comprehension than controls, including receptive language in toddlers ({beta} = 0.49, p = 0.009), vocabulary and morphology in younger preschoolers ({beta} = 0.59-0.68, all p < 0.05), and grammar comprehension in older preschoolers ({beta} = 0.30, p = 0.038). These effects were consistent after accounting for child and parental characteristics. Together, these findings suggest that the developmental impact of digital media depends less on exposure itself than on how it is used. When embedded in structured, socially guided interactions, even brief tablet-based activities may support early language development

developmental biology↗

bioRxiv2026

Brain mechanisms of mathematical development: A longitudinal fMRI study from preschool to second grade

How does the developing brain, initially equipped only with elementary mathematical intuitions, acquire higher mathematical concepts? Through a longitudinal functional MRI study of children from preschool through first and second grade, we tracked how neural responses to mathematical and non-mathematical statements change in the first two years of formal schooling and use the data to evaluate several theories of developmental change. Before school, when listening to math statements, children already engage an adult-like cortical network, with partial specialization for geometry. Over the first two years of school, we observe an overall increase in math-related activation, a small recruitment of additional neural territory, reduced activation for facts that get better known, and a small overall increase in the dimensionality of representational space. fMRI responses to individual sentences suggest that these mechanisms, particularly in left inferior frontal gyrus and bilateral intraparietal sulcus, all contribute to childrens growing mastery of mathematical concepts.

neuroscience↗

bioRxiv2025

Languages of the brain: fMRI dissection of the amodal networks for language, mathematics, and social knowledge

The ability to compose complex mental representations by recombining simpler primitives is a characteristic of the human brain that manifests itself in a variety of domains such as spoken or written language, mathematics, or social reasoning. While it is known that these domains rest on partially distinct brain networks, they are rarely investigated together in the same paradigm. Here, we present a systematic functional MRI study of written and spoken word and sentence processing in each of these domains. In a true-false judgment task, adult participants were presented with a hierarchy of meaningless and meaningful stimuli bearing on various domains of general semantic knowledge and mathematics. The results show that distinct brain regions are activated by (1) syntax and semantics; (2) math versus non-math knowledge; (3) different domains of mathematics, such as geometry versus arithmetic; (4) social knowledge versus other knowledge. All of these regions are activated essentially identically in the written and spoken modalities. The results were replicated in a group of adolescents. Our experiments show that the human brain comprises distinct amodal networks for various domains of linguistic and semantic knowledge, and provide a simple paradigm to dissect them within a short fMRI session.

neuroscience↗

bioRxiv2025

From Syllables to Words: EEG Evidence of Different Age Trajectories in Speech Tracking and Statistical Learning in Infants at High and Low Likelihood for Autism

Delayed onset of canonical babbling and first words is often reported in infants later diagnosed with autism spectrum disorder. Identifying the neural mechanisms underlying language acquisition in autism is therefore critical to inform early diagnosis, prognosis, and intervention strategies. In this study, we investigated two speech processing mechanisms previously identified as atypical in children and adults with autism: the neural ability to track syllables; and statistical learning, the capacity to detect speech regularities beneath surface variability. We recorded 83 longitudinal high-density EEGs from 44 infants (2.5-22.6 months) at high (HL) and low (LL) likelihood for autism and assessed their verbal outcomes at 20 months. Neural entrainment was measured at syllable and word frequencies during exposure to a multi-speaker stream of concatenated tri-syllabic words, followed by a word recognition test using ERP recording. Our findings revealed reduced tracking abilities at the syllabic level in HL infants, a measure that correlated with verbal outcomes. While HL infants did not exhibit deficits in statistical learning itself, they displayed reduced novelty orientation during the word recognition test, indicated by a reduced late ERP. By contrast, multi-talker variability temporarily disrupted word segmentation around 12 months in LL infants, but not in HL infants, potentially reflecting decreased sensitivity to human voices variability in the HL group. These results emphasize the importance of longitudinal protocols employing online, implicit measures to track the hierarchical stages of speech processing in both HL and LL infants.

neuroscience↗

bioRxiv2025

Impact of education and music training on the development of early abstraction abilities

Platos Republic, Einsteins Theory of relativity, Vilvadis Four Seasons are all remarkable examples of humans unique ability to create and manipulate complex abstract structures, whether in language, mathematics or music. Yet the mechanisms by which children develop such abstract thinking, and the role of education and structured experiences such as musical practice in shaping these abilities remain unclear. To explore these questions, we conducted cross-sectional behavioral experiments with 566 children aged 4 to 8, spanning four educational grades, half of whom participated in a violin training program since the age of four. Two experiments examined how children encode, process and compress auditory sequences and visual patterns, while a third examined their sensitivity to geometric regularities. Our results reveal the emergence of symbolic reasoning as early as the start of formal schooling, yet with deeper abstraction as a function of grade. By first grade, children encoded complex auditory sequences within a Language of Thought (LoT) similar to adults. Additionally, when confronted with quadrilaterals, children showed increasing sensitivity to geometric regularities, suggesting a developmental transition from perceptual to symbolic reasoning. However, we did not observe significant impact of musical practice on abstraction abilities across any of the domains tested. We discuss whether and how the impact of education and extracurricular activities such as music could be enhanced.

neuroscience↗

bioRxiv2025

A proposal for unifying statistical learning at different scales: Long-Horizon Associative Learning

Sensory inputs are rich with temporal patterns that unfold across multiple timescales. Discovering these regularities is essential for predicting future events and navigating the environment efficiently. Numerous models have been proposed to account for learning at specific temporal scales; however, they are often designed in isolation and rely on narrowly tuned statistical measures, limiting their generalizability to other paradigms. In contrast, humans typically learn without prior knowledge of the underlying structure or the relevant timescale at which regularities occur. Here, we present a unifying account of statistical learning that spans a wide range of temporal dependencies, from adjacent and non-adjacent transitions to complex network structures. This model, Long-Horizon Associative Learning (L-HAL), offers a biologically grounded implementation of the successor representation, or equivalently, the Free Energy Minimization Model. Reanalyzing data from 11 previously published studies, we show that a single neural mechanism, based on graded temporal overlap of associative traces and governed by a single free parameter ({beta}), captures both local statistical regularities and higher-order structural properties. This initial domain-general associative learning process, emerging from the graded structure of associations, may later scaffold to higher-level operations such as grouping, categorization, rule abstraction, and memory formation. Overall, this framework offers a conceptual synthesis that bridges disparate strands of the statistical learning literature and reframes apparent paradigm-specific effects as different expressions of a common underlying computation

neuroscience↗

bioRxiv2024

Statistical learning beyond words in human neonates

Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infants toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech -speaker identity and phonemes- to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks. HIGHLIGHTSO_LIHuman neonates are sensitive to regularities in speech, encompassing both phonetic content and voice dimension. C_LIO_LIThere is no observed advantage for statistical computations of linguistic content over voice content. C_LIO_LIBoth speech dimensions are processed in parallel by distinct networks. C_LIO_LIPhonetic regularities evoked a specific ERP component in the post-learning phase, suggesting activations along the pathway to the lexicon. C_LI

neuroscience↗

bioRxiv2024

Influence of environmental and biological factors on functional connectivity of the superior temporal sulcus in neonates scanned at term age

The superior temporal sulcus (STS), one of the first sulci visible during brain development, is a key region for human communication, notably hosting linguistic functions in the left hemisphere. Fetuses and premature newborns already process external sound, but the auditory environment is vastly different in-utero and ex-utero. Does this have an impact on the development of the auditory and linguistic networks? To answer this question, we studied the functional connectivity of regions bordering the STS, delimited in each individual including full-term and preterm male and female neonates born at different gestational ages but all scanned at term. We found that in addition to the expected contralateral connectivity, various STS parts were more strongly connected to specific distant regions, revealing the typical auditory/linguistic division across the two banks of the STS reported in adults. Furthermore, the right posterior STS was more connected to the contralateral hemisphere than the left. Finally, sex and premature birth had effects on both STS volume and connectivity. Female neonates displayed a lesser left-right asymmetry in STS depth, and heightened connectivity from the left posterior STS compared to males. Most importantly, despite equivalent scan age, full-term newborns had deeper sulci compared with preterms, and local connectivity in the right temporal region increased linearly with gestation length. These results emphasize the impact of both sex and early auditory environment on the setting up of the cerebral networks that might contribute to explain the milder impact of premature birth on language in females.

neuroscience↗

bioRxiv2024

Long-horizon associative learning explains human sensitivity to statistical and network structures in auditory sequences

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and non-adjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (~150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

neuroscience↗

bioRxiv2024

The role of conscious attention in statistical learning: evidence from patients with impaired consciousness

The debate over whether conscious attention is necessary for statistical learning has produced mixed and conflicting results. Testing individuals with impaired consciousness may provide some insight, but very few studies have been conducted due to the difficulties associated with testing such patients. In this study, we examined the ability of patients with varying levels of consciousness disorders (DOC), including coma, unresponsive wakefulness syndrome, minimally conscious patients, and emergence from minimally conscious state patients, to extract statistical regularities from an artificial language composed of four randomly concatenated pseudowords. We used a methodology based on frequency tagging in EEG, which was developed in our previous studies on speech segmentation in sleeping neonates. Our study had two main objectives: firstly, to assess the automaticity of the segmentation process and explore correlations between the level of covert consciousness and the abilities to extract statistical regularities, second, to explore a potential new diagnostic indicator to aid in patient management by examining the correlation between successful statistical learning markers and consciousness level. We observed that segmentation abilities were preserved in some minimally conscious patients, suggesting that statistical learning is an inherently automatic low-level process. Due to significant inter-individual variability, word segmentation may not be a sufficiently robust candidate for clinical use, unlike temporal accuracy of auditory syllable responses, which correlates strongly with coma severity. Therefore, we propose that frequency tagging of an auditory stimulus train, a simple and robust measure, should be further investigated as a possible metric candidate for DOC diagnosis.

neuroscience↗

bioRxiv2023

Detection of regularities in auditory sequences before and at term-age in human neonates

During the last trimester of gestation, fetuses and preterm neonates begin to respond to sensory stimulation and to discover the structure of their environment. Yet, neuronal migration is still ongoing. This late migration notably concerns the supra-granular layers neurons, which are believed to play a critical role in encoding predictions and detecting regularities. In order to gain a deeper understanding of how the brain processes and perceives regularities during this stage of development, we conducted a study in which we recorded event-related potentials (ERP) in 31-wGA preterm and full-term neonates exposed to alternating auditory sequences (e.g. "ba ga ba ga ba"), when the regularity of these sequences was violated by a repetition (e.g., "ba ga ba ga ga"). We compared the ERPs in this case to those obtained when violating a simple repetition pattern ("ga ga ga ga ga" vs "ga ga ga ga ba"). Our results indicated that both preterm and full-term neonates were able to detect violations of regularity in both types of sequences, indicating that as early as 31 weeks gestational age, human neonates are sensitive to the conditional statistics between successive auditory elements. Full-term neonates showed an early and similar mismatch response (MMR) in the repetition and alternating sequences. In contrast, 31-wGA neonates exhibited a two-component MMR. The first component which was only observed for simple sequences with repetition, corresponded to sensory adaptation. It was followed much later by a deviance-detection component that was observed for both alternation and repetition sequences. This pattern confirms that MMRs detected at the scalp may correspond to a dual cortical process and shows that deviance detection computed by higher-level regions accelerates dramatically with brain maturation during the last weeks of gestation to become indistinguishable from bottom-up sensory adaptation at term. HighlightsO_LIStarting at 31 wGA, neonates are sensitive to conditional statistics between successive events. C_LIO_LIThe MisMatch Response detected at the scalp may correspond to a dual cortical process C_LIO_LIThe prediction error signal accelerates during the third trimester of gestation C_LIO_LIIt overlaps with the phenomenon of sensory adaptation at term age C_LI

neuroscience↗

bioRxiv2023

Brain mechanisms of reversible symbolic reference:a potential singularity of the human brain

The emergence of symbolic thinking has been proposed as a dominant cognitive criterion to distinguish humans from other primates during hominization. Although the proper definition of a symbol has been the subject of much debate, one of its simplest features is bidirectional attachment: the content is accessible from the symbol, and vice versa. Behavioural observations scattered over the past four decades suggest that this criterion might not be met in non-human primates, as they fail to generalise an association learned in one temporal order (A to B) to the reverse order (B to A). Here, we designed an implicit fMRI test to investigate the neural mechanisms of arbitrary audio-visual and visual-visual pairing in monkeys and humans and probe their spontaneous reversibility. After learning a unidirectional association, humans showed surprise signals when this learned association was violated. Crucially, this effect occurred spontaneously in both learned and reversed directions, within an extended network of high-level brain areas, including, but also going beyond, the language network. In monkeys, by contrast, violations of association effects occurred solely in the learned direction and were largely confined to sensory areas. We propose that a human-specific brain network may have evolved the capacity for reversible symbolic reference.

neuroscience↗

bioRxiv2022

Humans parsimoniously represent auditory sequences by pruning and completing the underlying network structure

Successive auditory inputs are rarely independent, their relationships ranging from local transitions between elements to hierarchical and nested representations. In many situations, humans retrieve these dependencies even from limited datasets. However, this learning at multiple scale levels is poorly understood. Here We used the formalism proposed by network science to study the representation of local and higher order structures, and their interaction, in auditory sequences. We show that human adults exhibited biases in their perception of local transitions between elements, which made them sensitive to high-order structures such as network communities. This behavior is consistent with the creation of a parsimonious simplified model from the evidence they receive, achieved by pruning and completing relationships between network elements. This observation suggests that the brain does not rely on exact memories but compressed representations of the world. Moreover, this bias can be analytically modeled by a memory/efficiency trade-off. This model correctly account for previous findings, including local transition probabilities as well as high order network structures, unifying statistical learning across scales. We finally propose putative brain implementations of such bias.

neuroscience↗

bioRxiv2022

Evolution of reading and face circuits during the first three years of reading acquisition

Although words and faces activate neighboring regions in the fusiform gyrus, we lack an understanding of how such category selectivity emerges during development. To investigate the organization of reading and face circuits at the earliest stage of reading acquisition, we measured the fMRI responses to words, faces, houses, and checkerboards in three groups of 60 French children: 6-year-old pre-readers, 6-year-old beginning readers and 9-year-old advanced readers. The results showed that specific responses to written words were absent prior to reading, but emerged in beginning readers, irrespective of age. Likewise, specific responses to faces were barely visible in pre-readers and continued to evolve in the 9-year-olds, yet primarily driven by age rather than by schooling. Crucially, the sectors of ventral visual cortex that become specialized for words and faces harbored their own functional connectivity prior to reading acquisition: the VWFA with left-hemispheric spoken language areas, and the FFA with the contralateral region and the amygdalae. The results support the view that reading acquisition occurs through the recycling of a pre-existing but plastic circuit which, in pre-readers, already connects the VWFA site to other distant language areas. We argue that reading acquisition does not compete with the face system directly, through a pruning of preexisting face responses, but indirectly, by hindering the slow growth of face responses in the left hemisphere, thus increasing a pre-existing right hemispheric bias. HighlightsO_LIWritten words and faces activate neighboring areas of the fusiform gyri, but their developmental trajectory is different. C_LIO_LIThe growth of word-induced activation in VWFA is primarily due to schooling. C_LIO_LIThe growth of face responses is primarily affected by age rather than by schooling. C_LIO_LIWord and face-related areas exhibit distinct functional connectivity even prior to reading acquisition C_LIO_LIVWFA is initially functionally connected with left-hemisphere spoken language areas, and FFA with amygdala and contralateral FFA. C_LI

neuroscience↗

bioRxiv2021

Tracking transitional probabilities and segmenting auditory sequences are dissociable processes in adults and neonates

Since speech is a continuous stream with no systematic boundaries between words, how do pre-verbal infants manage to discover words? A proposed solution is that they might use the transitional probability between adjacent syllables, which drops at word boundaries. Here, we tested the limits of this mechanism by increasing the size of the word-unit to 4 syllables, and its automaticity by testing asleep neonates. Using markers of statistical learning in neonates EEG, compared to adult behavioral performances in the same task, we confirmed that statistical learning is automatic enough to be efficient even in sleeping neonates. But we also revealed that: 1) Successfully tracking transition probabilities in a sequence is not sufficient to segment it 2) Prosodic cues, as subtle as subliminal pauses, enable to recover segmenting capacities 3) Adults and neonates capacities are remarkably similar despite the difference of maturation and expertise. Finally, we observed that learning increased the similarity of neural responses across infants, providing a new neural marker to monitor learning. Thus, from birth, infants are equipped with adult-like tools, allowing to extract small coherent word-like units within auditory streams, based on the combination of statistical analyses and prosodic cues.

neuroscience↗

bioRxiv2021

From computing transition probabilities to word recognition in sleeping neonates, a two-step neural tale

Extracting statistical regularities from the environment is a primary learning mechanism, which might support language acquisition. While it is known that infants are sensitive to transition probabilities between syllables in continuous speech, the format of the encoded representation remains unknown. Here we used electrophysiology to investigate how 31 full-term neonates process an artificial language build by the random concatenation of four pseudo-words and which information they retain. We used neural entrainment as a marker of the regularities the brain is tracking in the stream during learning. Then, we compared the evoked-related potentials (ERP) to different triplets to further explore the format of the information kept in memory. After only two minutes of familiarization with the artificial language, we observed significant neural entrainment at the word rate over left temporal electrodes compared to a random stream, demonstrating that sleeping neonates automatically and rapidly extracted the word pattern. ERPs significantly differed between triplets starting or not with the correct first syllable in the test phase, but no difference was associated with later violations in transition probabilities, revealing a change in the representation format between segmentation and memory processes. If the transition probabilities were used to segment the stream, the retained representation relied on syllables ordinal position, but still without a complete representation of the words at this age. Our results revealed a two-step learning strategy, probably involving different brain regions.

neuroscience↗

bioRxiv2021

Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies

Infant electroencephalography (EEG) presents several challenges compared with adult data. Recordings are typically short. Motion artifacts heavily contaminate the data. The EEG neural signal and the artifacts change throughout development. Traditional data preprocessing pipelines have been developed mainly for event-related potentials analyses, and they required manual steps, or use fixed thresholds for rejecting epochs. However, larger datasets make the use of manual steps infeasible, and new analytical approaches may have different preprocessing requirements. Here we propose an Automated Pipeline for Infants Continuous EEG (APICE). APICE is fully automated, flexible, and modular. Artifacts are detected using multiple algorithms and adaptive thresholds, making it suitable to different age groups and testing procedures without redefining parameters. Artifacts detection and correction of transient artifacts is performed on continuous data, allowing for better data recovery and providing flexibility (i.e., the same preprocessing is usable for different analyses). Here we describe APICE and validate it using two infant datasets of different ages tested in different experimental paradigms. We also tested the combination of APICE with common data cleaning methods such as Independent Component Analysis and Denoising Source Separation. APICE uses EEGLAB and compatible custom functions. It is freely available at https://github.com/neurokidslab/eeg_preprocessing, together with example scripts.

neuroscience↗