Effect of perinatal adversity on structural connectivity of the developing brain

Globally, preterm birth (defined as birth at <37 weeks of gestation) affects around 11% of deliveries and it is closely associated with cerebral palsy, cognitive impairments and neuropsychiatric diseases in later life. Magnetic Resonance Imaging (MRI) has utility for measuring different properties of the brain during the lifespan. Specially, diffusion MRI has been used in the neonatal period to quantify the effect of preterm birth on white matter structure, which enables inference about brain development and injury. By combining information from both structural and diffusion MRI, is it possible to calculate structural connectivity of the brain. This involves calculating a model of the brain as a network to extract features of interest. The process starts by defining a series of nodes (anatomical regions) and edges (connections between two anatomical regions). Once the network is created, different types of analysis can be performed to find features of interest, thereby allowing group wise comparisons. The main frameworks/tools designed to construct the brain connectome have been developed and tested in the adult human brain. There are several differences between the adult and the neonatal brain: marked variation in head size and shape, maturational processes leading to changes in signal intensity profiles, relatively lower spatial resolution, and lower contrast between tissue classes in the T1 weighted image. All of these issues make the standard processes to construct the brain connectome very challenging to apply in the neonatal population. Several groups have studied the neonatal structural connectivity proposing several alternatives to overcome these limitations. The aim of this thesis was to optimise the different steps involved in connectome analysis for neonatal data. First, to provide accurate parcellation of the cortex a new atlas was created based on a control population of term infants; this was achieved by propagating the atlas from an adult atlas through intermediate childhood spatio-temporal atlases using image registration. After this the advanced anatomically-constrained tractography framework was adapted for the neonatal population, refined using software tools for skull-stripping, tissue segmentation and parcellation specially designed and tested for the neonatal brain. Finally, the method was used to test the effect of early nutrition, specifically breast milk exposure, on structural connectivity in preterm infants. We found that infants with higher exposure to breastmilk in the weeks after preterm birth had improved structural connectivity of developing networks and greater fractional anisotropy in major white matter fasciculi. These data also show that the benefits are dose dependent with higher exposure correlating with increased white matter connectivity. In conclusion, structural connectivity is a robust method to investigate the developing human brain. We propose an optimised framework for the neonatal brain, designed for our data and using tools developed for the neonatal brain, and apply it to test the effect of breastmilk exposure on preterm infants

Prosodic Structure as a Parallel to Musical Structure

Funding for Open Access provided by the UMD Libraries Open Access Publishing Fund.What structural properties do language and music share? Although early speculation identified a wide variety of possibilities, the literature has largely focused on the parallels between musical structure and syntactic structure. Here, we argue that parallels between musical structure and prosodic structure deserve more attention. We review the evidence for a link between musical and prosodic structure and find it to be strong. In fact, certain elements of prosodic structure may provide a parsimonious comparison with musical structure without sacrificing empirical findings related to the parallels between language and music. We then develop several predictions related to such a hypothesis

Représentations mentales de séquences chez le nourrisson : une approche en électrophysiologie

A ce jour, seul le cerveau du nourrisson est capable d’appréhender et de maîtriser la complexité du langage humain. La recherche en psychologie du développement a investi beaucoup d’énergie pour tenter de percer le mystère de l’acquisition du langage, révélant d’impressionnantes capacités précoces permettant le traitement et la représentation de la parole. La récente émergence de techniques de neuro-imagerie non-invasives offre aujourd’hui de nouveaux outils et de nouvelles perspectives pour d’étude des mécanismes d’apprentissage du langage. Cette thèse a pour but d’explorer les mécanismes permettant l’acquisition et la représentation de structures linguistiques, grâce à une approche en électrophysiologie. La première partie de ce manuscrit consiste en une contribution méthodologique à la neuro-imagerie du développement. Sur la base de données acquises en imagerie par résonnance magnétique (IRM), nous avons localisé les positions du système international 10/20 pour le placement d’électrodes – utilisé aussi bien en électroencéphalographie (EEG) qu’en spectroscopie proche infra-rouge (NIRS) – par rapport aux structures cérébrales internes. Cette étude a permis de quantifier la variabilité interindividuelle de ces positions, mais également de construire un modèle de cerveau complété d’un atlas anatomique pour le nourrisson. Dans une seconde partie de cette thèse, grâce à l’EEG haute-densité, nous avons pu démontrer que dès 8 mois, les nourrissons étaient capables de mettre en œuvre de puissance mécanismes d’analyse statistique, permettant d’extraire les dépendances entre syllabes non-adjacentes, pour segmenter un flux continu de parole en unités distinctes. L’analyse des réponses neurales a révélé une hiérarchie de processus cérébraux soutenant le traitement des syllabes mais aussi des unités segmentées. Enfin, dans une dernière partie, nous proposons un paradigme expérimental permettant d’étudier non seulement l’extraction mais aussi la représentation de séquences linguistiques sous la forme d’expressions unifiées. Nous avons pu établir grâce à cette étude que dès 5 mois, les nourrissons étaient capables de former de solides représentations de séquences définies par des répétitions, leur permettant de catégoriser et manipuler plusieurs structures. Pour conclure, cette thèse vient compléter les études comportementales sur l’acquisition du langage, grâce à une approche des processus cérébraux soutenant l’apprentissage de séquences. La richesse du signal EEG a permis de mettre en évidence une hiérarchie de traitements complexes.To this day, the infant brain is the only known learning system able to apprehend and master the complexity of the human language. Developmental psychologists have dedicated a lot of efforts to break down the mystery of language acquisition, revealing precocious and impressive abilities for processing and encoding speech sequences. The recent emergence of non-invasive neuroimaging techniques provides a new tool to explore language learning mechanisms from a different perspective. In the present thesis, we aimed at investigating the encoding mechanisms of the structural properties of a speech sequence from an electrophysiological perspective. In the first part of this thesis, we provided the developmental neuroimaging community with a methodological contribution. Based on magnetic resonance imaging (MRI) data, we virtually localized the standardized sensor placement system for both electroencephalography (EEG) and near infrared spectroscopy (NIRS) relative to the internal brain structures, and assessed their variability. We additionally provided an infant brain template with an anatomical atlas which will be valuable for studies in which individual anatomical information cannot be obtained. In the second part of this thesis, using high-density EEG, we demonstrated that 8 month-old infants could deploy powerful learning mechanisms for capturing the statistical dependencies between non-adjacent syllable units, in order to chunk a continuous speech stream. Interestingly, a hierarchy of neural processes tracked both the syllables and the chunked constituents of the sequence. Finally, in a third cognitive EEG study, we proposed an experimental design to assess infants’ ability to not only extract but also encode the structure of speech sequences into unified mental schemas. The results of this study established that 5 month-old infants could form robust mental representations for repetition-based sequences, allowing them to represent, categorize and operate on multiple structures. Inspection of various neural measurements revealed that several stages of the processing hierarchy were affected by the acquired mental representations. Overall, this thesis complements behavioral research on language acquisition with a window onto the early neural mechanisms allowing sequence encoding, revealing a hierarchy of increasingly complex computations in the encoding of linguistic structures

Symbolic labeling in 5-month-old human infants

International audienceHumans' ability to create and manipulate symbolic structures far exceeds that of other animals. We hypothesized that this ability rests on an early capacity to use arbitrary signs to represent any mental representation, even as abstract as an algebraic rule. In three experiments, we collected high-density EEG recordings while 150 5-month-old infants were presented with speech triplets characterized by their abstract syllabic structure-the location of syllable repetition-which predicted a following arbitrary label (e.g., ABA words were followed by a fish picture, AAB words by a lion). After a brief learning phase, EEG responses to novel words revealed that infants built expectations about the upcoming label based on the triplet structure and were surprised when it happened to be incongruent. Preverbal infants were thus able to recode the incoming triplets into abstract mental variables to which arbitrary labels were flexibly assigned. Importantly, infants also generalized to novel trials in which the pairing order was reversed (with the label preceding the auditory structure). Beyond conditioned associations, infants instantly inferred a bidirectional mapping between the abstract structures and the following label, a foundational operation for any symbolic system

The Cerebral Bases of Language Acquisition

International audienceThe development of noninvasive brain-imaging techniques has opened the black box of the infant brain. Instead of postulating theories based on the delayed consequences of, fortunately rare, early lesions, we can now study healthy infant responses to speech. Rather than a brain limited to primary areas or, on the contrary, a poorly specialized brain, brain-imaging studies have revealed a functional architecture in infants that is close to what is described in adults. In particular, a hierarchy of increasingly integrated computations is observed along the superior temporal regions, and the processing of different speech features is already segregated along parallel neural pathways with different hemispheric biases. Yet, although highly structured, the infant brain still differs from the adult brain, with particularly delayed brain responses arising from frontal regions. We can expect that a better understanding of the computational abilities of this early network may provide insight into the mechanisms underlying language acquisition

The power of rhythms: how steady-state evoked responses reveal early neurocognitive development.

Electroencephalography (EEG) is a non-invasive and painless recording of cerebral activity, particularly well-suited for studying young infants, allowing the inspection of cerebral responses in a constellation of different ways. Of particular interest for developmental cognitive neuroscientists is the use of rhythmic stimulation, and the analysis of steady-state evoked potentials (SS-EPs) - an approach also known as frequency tagging. In this paper we rely on the existing SS-EP early developmental literature to illustrate the important advantages of SS-EPs for studying the developing brain. We argue that (1) the technique is both objective and predictive: the response is expected at the stimulation frequency (and/or higher harmonics), (2) its high spectral specificity makes the computed responses particularly robust to artifacts, and (3) the technique allows for short and efficient recordings, compatible with infants' limited attentional spans. We additionally provide an overview of some recent inspiring use of the SS-EP technique in adult research, in order to argue that (4) the SS-EP approach can be implemented creatively to target a wide range of cognitive and neural processes. For all these reasons, we expect SS-EPs to play an increasing role in the understanding of early cognitive processes. Finally, we provide practical guidelines for implementing and analyzing SS-EP studies.info:eu-repo/semantics/publishe

Speech encoding by coupled cortical theta and gamma oscillations

Many environmental stimuli present a quasi-rhythmic structure at different timescales that the brain needs to decompose and integrate. Cortical oscillations have been proposed as instruments of sensory de-multiplexing, i.e., the parallel processing of different frequency streams in sensory signals. Yet their causal role in such a process has never been demonstrated. Here, we used a neural microcircuit model to address whether coupled theta-gamma oscillations, as observed in human auditory cortex, could underpin the multiscale sensory analysis of speech. We show that, in continuous speech, theta oscillations can flexibly track the syllabic rhythm and temporally organize the phoneme-level response of gamma neurons into a code that enables syllable identification. The tracking of slow speech fluctuations by theta oscillations, and its coupling to gamma-spiking activity both appeared as critical features for accurate speech encoding. These results demonstrate that cortical oscillations can be a key instrument of speech de-multiplexing, parsing, and encoding