Plasticity of neonatal neuronal networks in very premature infants: Source localization of temporal theta activity, the first endogenous neural biomarker, in temporoparietal areas

Does Electrophysiological Maturation Shape Language Acquisition?

Infants master temporal patterns of their native language at a developmental trajectory from slow to fast: Shortly after birth, they recognize the slow acoustic modulations specific to their native language before tuning into faster language-specific patterns between 6 and 12 months of age. We propose here that this trajectory is constrained by neuronal maturation-in particular, the gradual emergence of high-frequency neural oscillations in the infant electroencephalogram. Infants' initial focus on slow prosodic modulations is consistent with the prenatal availability of slow electrophysiological activity (i.e., theta- and delta-band oscillations). Our proposal is consistent with the temporal patterns of infant-directed speech, which initially amplifies slow modulations, approaching the faster modulation range of adult-directed speech only as infants' language has advanced sufficiently. Moreover, our proposal agrees with evidence from premature infants showing maturational age is a stronger predictor of language development than ex utero exposure to speech, indicating that premature infants cannot exploit their earlier availability of speech because of electrophysiological constraints. In sum, we provide a new perspective on language acquisition emphasizing neuronal development as a critical driving force of infants' language development.

Assessing the effects of head modelling errors and measurement noise on EEG source localization accuracy in preterm newborns: A single-subject study

The accuracy of electroencephalogram (EEG) source localization is compromised because of head modelling errors. In this study, we investigated the effect of inaccuracy in the conductivity of head tissues and head model structural deficiencies on the accuracy of EEG source analysis in premature neonates. A series of EEG forward and inverse simulations was performed by introducing structural deficiencies into the reference head models to generate test models, which were then used to investigate head modelling errors caused by cerebrospinal fluid (CSF) exclusion, lack of grey matter (GM)-white matter (WM) distinction, fontanel exclusion and inaccuracy in skull conductivity. The modelling errors were computed between forward and inverse solutions obtained using the reference and test models generated for each deficiency. Our results showed that the exclusion of CSF from the head model had a strong widespread effect on the accuracy of the EEG source localization with position errors lower than 4.17 mm. The GM and WM distinction also caused strong localization errors (up to 3.5 mm). The exclusion of fontanels from the head model also strongly affected the accuracy of the EEG source localization for sources located beneath the fontanels with a maximum localization error of 4.37 mm. Similarly, inaccuracies in the skull conductivity caused errors in EEG forward and inverse modelling in sources beneath cranial bones. Our results indicate that the accuracy of EEG source imaging in premature neonates can be largely improved by using head models, which include not only the brain, skull and scalp but also the CSF, GM, WM and fontanels.

Evolution of cross-frequency coupling between endogenous oscillations over the temporal cortex in very premature neonates

Temporal theta activity in coalescence with slow-wave (TTA-SW) is one of the first neurobiomarkers of the neurodevelopment of perisylvian networks in the electroencephalography (EEG). Dynamic changes in the microstructure and activity within neural networks are reflected in the EEG. Slow oscillation slope can reflect synaptic strength, and cross-frequency coupling (CFC), associated with several putative functions in adults, can reflect neural communication. Here, we investigated the evolution of CFC, in terms of SW theta phase-amplitude coupling (PAC), during the course of very early development between 25 and 32 weeks of gestational age in 23 premature neonates. We used high-resolution EEG and dipole models as spatial filters to extract the source waveforms corresponding to TTA-SW. We also carried out nonlinear phase-dependent correlation measurements to examine whether the characteristics of the SW slopes are associated with TTA-SW coupling. We show that neurodevelopment leads to temporal accumulation of the SW theta PAC toward the trough of SW. Steepness of the negative going slope of SW determined the degree of this coupling. Systematic modulation of SW-TTA CFC during development is a signature of the complex development of local cortico-cortical perisylvian networks and distant thalamo-cortical neural circuits driving this nested activity over the perisylvian networks.

Identifying Emergent Mesoscopic-Macroscopic Functional Brain Network Dynamics in Infants at Term-Equivalent Age with Electric Source Neuroimaging

Aim: To identify and characterize the functional brain networks at the time when the brain is yet to develop higher order functions in term-born and preterm infants at term-equivalent age. Introduction: Although functional magnetic resonance imaging (fMRI) data have revealed the existence of spatially structured resting-state brain activity in infants, the temporal information of fMRI data limits the characterization of fast timescale brain oscillations. In this study, we use infants' high-density electroencephalography (EEG) to characterize spatiotemporal and spectral functional organizations of brain network dynamics. Methods: We used source-reconstructed EEG and graph theoretical analyses in 100 infants (84 preterm, 16 term born) to identify the rich-club topological organization, temporal dynamics, and spectral fingerprints of dynamic functional brain networks. Results: Five dynamic functional brain networks are identified, which have rich-club topological organizations, distinctive spectral fingerprints (in the delta and low-alpha frequency), and scale-invariant temporal dynamics (<0.1 Hz): The default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor, and visual-limbic system. The temporal dynamics of these networks are correlated in a hierarchically leading-following organization, showing that infant brain networks arise from long-range synchronization of band-limited cortical oscillation based on interacting fast- and slow-coherent cortical oscillations. Conclusion: Dynamic functional brain networks do not solely depend on the maturation of cognitive networks; instead, the brain network dynamics exist in infants at term age well before the childhood and adulthood, and hence, it offers a quantitative measurement of neurotypical development in infants. Clinical Trial Registration Number: ACTRN12615000591550. Impact statement Our work offers novel functional insights into the brain network characterization in infants, providing a new functional basis for future deployable prognostication approaches.

Normal EEG during the neonatal period: maturational aspects from premature to full-term newborns

Electroencephalography (EEG) is the reference tool for the analysis of brain function, reflecting normal and pathological neuronal network activity. During the neonatal period, EEG patterns evolve weekly, according to gestational age. The first analytical criteria for the various maturational stages and standardized neonatal EEG terminology were published by a group of French neurophysiologists training in Paris (France) in 1999. These criteria, defined from analog EEG, were completed in 2010 with digital EEG analysis. Since then, this work has continued, aided by the technical progress in EEG acquisition, the improvement of knowledge on the maturating processes of neuronal networks, and the evolution of critical care. In this review, we present an exhaustive and didactic overview of EEG characteristics from extremely premature to full-term infants. This update is based on the scientific literature, enhanced by the study of normal EEGs of extremely premature infants by our group of neurophysiologists. For educational purposes, particular attention has been paid to illustrations using new digital tools.

Back to basics: the neuronal substrates and mechanisms that underlie the electroencephalogram in premature neonates

Electroencephalography is the only clinically available technique that can address the premature neonate normal and pathological functional development week after week. The changes in the electroencephalogram (EEG) result from gradual structural and functional modifications that arise during the last trimester of pregnancy. Here, we review the structural changes over time that underlie the establishment of functional immature neural networks, the impact of certain anatomical specificities (fontanelles, connectivity, etc.) on the EEG, limitations in EEG interpretation, and the utility of high-resolution EEG (HR-EEG) in premature newborns (a promising technique with a high degree of spatiotemporal resolution). In particular, we classify EEG features according to whether they are manifestations of endogenous generators (i.e. theta activities that coalesce with a slow wave or delta brushes) or come from a broader network. Furthermore, we review publications on EEG in premature animals because the data provide a better understanding of what is happening in premature newborns. We then discuss the results and limitations of functional connectivity analyses in premature newborns. Lastly, we report on the magnetoelectroencephalographic studies of brain activity in the fetus. A better understanding of complex interactions at various structural and functional levels during normal neurodevelopment (as assessed using electroencephalography as a benchmark method) might lead to better clinical care and monitoring for premature neonates.

The intimate relationship between coalescent generators in very premature human newborn brains: Quantifying the coupling of nested endogenous oscillations

Temporal theta slow-wave activity (TTA-SW) in premature infants is a specific neurobiomarker of the early neurodevelopment of perisylvian networks observed as early as 24 weeks of gestational age (wGA). It is present at the turning point between non-sensory driven spontaneous networks and cortical network functioning. Despite its clinical importance, the underlying mechanisms responsible for this spontaneous nested activity and its functional role have not yet been determined. The coupling between neural oscillations at different timescales is a key feature of ongoing neural activity, the characteristics of which are determined by the network structure and dynamics. The underlying mechanisms of cross-frequency coupling (CFC) are associated with several putative functions in adults. In order to show that this generic mechanism is already in place early in the course of development, we analyzed electroencephalography recordings from sleeping preterm newborns (24-27 wGA). Employing cross-frequency phase-amplitude coupling analyses, we found that TTAs were orchestrated by the SWs defined by a precise temporal relationship. Notably, TTAs were synchronized to the SW trough, and were suppressed during the SW peak. Spontaneous endogenous TTA-SWs constitute one of the very early signatures of the developing temporal neural networks with key functions, such as language and communication. The presence of a fine-tuned relationship between the slow activity and the TTA in premature neonates emphasizes the complexity and relative maturity of the intimate mechanisms that shape the CFC, the disruption of which can have severe neurodevelopmental consequences.

Construction and validation of a database of head models for functional imaging of the neonatal brain

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub.

Effect of structural complexities in head modeling on the accuracy of EEG source localization in neonates

OBJECTIVE

Neonatal electroencephalography (EEG) source localization is highly prone to errors due to head modeling deficiencies. In this study, we investigated the effect of head model complexities on the accuracy of EEG source localization in full term neonates using a realistic volume conductor head model.

APPROACH

We performed numerical simulations to investigate source localization errors caused by cerebrospinal fluid (CSF) and fontanel exclusion and gray matter (GM)/white matter (WM) distinction using the finite element method.

MAIN RESULTS

Our results showed that the exclusion of CSF from the head model could cause significant localization errors mostly for sources closer to the inner surface of the skull. With a less pronounced effect compared to the CSF exclusion, the discrimination between GM and WM also widely affected all sources, especially those located in deeper structures. The exclusion of the fontanels from the head model led to source localization errors for sources located in areas beneath the fontanels. Our finding clearly shows that the CSF inclusion and GM/WM distinction in EEG inverse modeling can substantially reduce EEG source localization errors. Moreover, fontanels should be included in neonatal head models, particularly in source localization applications, in which sources of interest are located beneath or in vicinity of fontanels.

SIGNIFICANCE

Our findings have practical implications for a better understanding of the impact of head model complexities on the accuracy of EEG source localization in neonates.

The Emergence of Hierarchical Somatosensory Processing in Late Prematurity

The somatosensory system has a hierarchical organization. Information processing increases in complexity from the contralateral primary sensory cortex to bilateral association cortices and this is represented by a sequence of somatosensory-evoked potentials recorded with scalp electroencephalographies. The mammalian somatosensory system matures over the early postnatal period in a rostro-caudal progression, but little is known about the development of hierarchical information processing in the human infant brain. To investigate the normal human development of the somatosensory hierarchy, we recorded potentials evoked by mechanical stimulation of hands and feet in 34 infants between 34 and 42 weeks corrected gestational age, with median postnatal age of 3 days. We show that the shortest latency potential was evoked for both hands and feet at all ages with a contralateral somatotopic source in the primary somatosensory cortex (SI). However, the longer latency responses, localized in SI and beyond, matured with age. They gradually emerged for the foot and, although always present for the hand, showed a shift from purely contralateral to bilateral hemispheric activation. These results demonstrate the rostro-caudal development of human somatosensory hierarchy and suggest that the development of its higher tiers is complete only just before the time of normal birth.

Impact of prematurity on neurodevelopment

The consequences of prematurity on brain functional development are numerous and diverse, and impact all brain functions at different levels. Prematurity occurs between 22 and 36 weeks of gestation. This period is marked by extreme dynamics in the physiologic maturation, structural, and functional processes. These different processes appear sequentially or simultaneously. They are dependent on genetic and/or environmental factors. Disturbance of these processes or of the fine-tuning between them, when caring for premature children, is likely to induce disturbances in the structural and functional development of the immature neural networks. These will appear as impairments in learning skills progress and are likely to have a lasting impact on the development of children born prematurely. The level of severity depends on the initial alteration, whether structural or functional. In this chapter, after having briefly reviewed the neurodevelopmental, structural, and functional processes, we describe, in a nonexhaustive manner, the impact of prematurity on the different brain, motor, sensory, and cognitive functions.

Neurovascular coupling in the developing neonatal brain at rest

The neonatal brain is an extremely dynamic organization undergoing essential development in terms of connectivity and function. Several functional imaging investigations of the developing brain have found neurovascular coupling (NVC) patterns that contrast with those observed in adults. These discrepancies are partly due to that NVC is still developing in the neonatal brain. To characterize the vascular response to spontaneous neuronal activations, a multiscale multimodal noninvasive approach combining simultaneous electrical, hemodynamic, and metabolic recordings has been developed for preterm infants. Our results demonstrate that the immature vascular network does not adopt a unique strategy to respond to spontaneous cortical activations. NVC takes on different forms in the same preterm infant during the same recording session in response to very similar types of neural activation. This includes (a) positive stereotyped hemodynamic responses (increases in HbO, decreases in HbR together with increases in rCBF and rCMRO2), (b) negative hemodynamic responses (increases in HbR, decreases in HbO together with decreases in rCBF and rCMRO2), and (c) Increases and decreases in both HbO‐HbR and rCMRO2 together with no changes in rCBF. Age‐related NVC maturation is demonstrated in preterm infants, which can contribute to a better understanding/prevention of cerebral hemodynamic risks in these infants.

Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts

Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.