Functional connectivity in the developing brain: a longitudinal study from 4 to 9months of age

Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism

Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

Neonatal brain dynamic functional connectivity in term and preterm infants and its association with early childhood neurodevelopment

Brain dynamic functional connectivity characterises transient connections between brain regions. Features of brain dynamics have been linked to emotion and cognition in adult individuals, and atypical patterns have been associated with neurodevelopmental conditions such as autism. Although reliable functional brain networks have been consistently identified in neonates, little is known about the early development of dynamic functional connectivity. In this study we characterise dynamic functional connectivity with functional magnetic resonance imaging (fMRI) in the first few weeks of postnatal life in term-born (n = 324) and preterm-born (n = 66) individuals. We show that a dynamic landscape of brain connectivity is already established by the time of birth in the human brain, characterised by six transient states of neonatal functional connectivity with changing dynamics through the neonatal period. The pattern of dynamic connectivity is atypical in preterm-born infants, and associated with atypical social, sensory, and repetitive behaviours measured by the Quantitative Checklist for Autism in Toddlers (Q-CHAT) scores at 18 months of age.

Positive or negative environmental modulations on human brain development: the morpho-functional outcomes of music training or stress

In the last couple of decades, the study of human living brain has benefitted of neuroimaging and non-invasive electrophysiological techniques, which are particularly valuable during development. A number of studies allowed to trace the usual stages leading from pregnancy to adult age, and relate them to functional and behavioral measurements. It was also possible to explore the effects of some interventions, behavioral or not, showing that the commonly followed pathway to adulthood may be steered by external interventions. These events may result in behavioral modifications but also in structural changes, in some cases limiting plasticity or extending/modifying critical periods. In this review, we outline the healthy human brain development in the absence of major issues or diseases. Then, the effects of negative (different stressors) and positive (music training) environmental stimuli on brain and behavioral development is depicted. Hence, it may be concluded that the typical development follows a course strictly dependent from environmental inputs, and that external intervention can be designed to positively counteract negative influences, particularly at young ages. We also focus on the social aspect of development, which starts in utero and continues after birth by building social relationships. This poses a great responsibility in handling children education and healthcare politics, pointing to social accountability for the responsible development of each child.

Functional neural network connectivity at 3 months predicts infant-mother dyadic flexibility during play at 6 months

Early functioning of neural networks likely underlies the flexible switching between internal and external orientation and may be key to the infant's ability to effectively engage in social interactions. To test this hypothesis, we examined the association between infants' neural networks at 3 months and infant-mother dyadic flexibility (denoting the structural variability of their interaction dynamics) at 3, 6, and 9 months. Participants included thirty-five infants (37% girls) and their mothers (87% White). At 3 months, infants participated in a resting-state functional magnetic resonance imaging session, and functional connectivity (FC) within the default mode (DMN) and salience (SN) networks, as well as DMN-SN internetwork FC, were derived using a seed-based approach. When infants were 3, 6, and 9 months, infant-mother dyads completed the Still-Face Paradigm where their individual engagement behaviors were observed and used to quantify dyadic flexibility using state space analysis. Results revealed that greater within-DMN FC, within-SN FC, and DMN-SN anticorrelation at 3 months predicted greater dyadic flexibility at 6 months, but not at 3 and 9 months. Findings suggest that early synchronization and interaction between neural networks underlying introspection and salience detection may support infants' flexible social interactions as they become increasingly active and engaged social partners.

A 1-Tesla MRI system for dedicated brain imaging in the neonatal intensive care unit

Objective

To assess the feasibility of a point-of-care 1-Tesla MRI for identification of intracranial pathologies within neonatal intensive care units (NICUs).

Methods

Clinical findings and point-of-care 1-Tesla MRI imaging findings of NICU patients (1/2021 to 6/2022) were evaluated and compared with other imaging modalities when available.

Results

A total of 60 infants had point-of-care 1-Tesla MRI; one scan was incompletely terminated due to motion. The average gestational age at scan time was 38.5 ± 2.3 weeks. Transcranial ultrasound (n = 46), 3-Tesla MRI (n = 3), or both (n = 4) were available for comparison in 53 (88%) infants. The most common indications for point-of-care 1-Tesla MRI were term corrected age scan for extremely preterm neonates (born at greater than 28 weeks gestation age, 42%), intraventricular hemorrhage (IVH) follow-up (33%), and suspected hypoxic injury (18%). The point-of-care 1-Tesla scan could identify ischemic lesions in two infants with suspected hypoxic injury, confirmed by follow-up 3-Tesla MRI. Using 3-Tesla MRI, two lesions were identified that were not visualized on point-of-care 1-Tesla scan: (1) punctate parenchymal injury versus microhemorrhage; and (2) small layering IVH in an incomplete point-of-care 1-Tesla MRI with only DWI/ADC series, but detectable on the follow-up 3-Tesla ADC series. However, point-of-care 1-Tesla MRI could identify parenchymal microhemorrhages, which were not visualized on ultrasound.

Conclusion

Although limited by field strength, pulse sequences, and patient weight (4.5 kg)/head circumference (38 cm) restrictions, the Embrace^® point-of-care 1-Tesla MRI can identify clinically relevant intracranial pathologies in infants within a NICU setting.

Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10 months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life.

The developing brain structural and functional connectome fingerprint

In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.

Functional Connectivity of the Developing Mouse Cortex

Cross-sectional studies have established a variety of structural, synaptic, and cell physiological changes corresponding to critical periods in cortical development. However, the emergence of functional connectivity (FC) in development has not been fully characterized, and hemodynamic-based measures are vulnerable to any neurovascular coupling changes occurring in parallel. We therefore used optical fluorescence imaging to trace longitudinal calcium FC in the awake, resting-state mouse cortex at 5 developmental timepoints beginning at postnatal day 15 (P15) and ending in early adulthood at P60. Calcium FC displayed coherent functional maps as early as P15, and FC significantly varied in connections between many regions across development, with the developmental trajectory's shape specific to the functional region. Evaluating 325 seed-seed connections, we found that there was a significant increase in FC between P15 and P22 over the majority of the cortex as well as bilateral connectivity and node degree differences in frontal, motor, and retrosplenial cortices after P22. A rebalancing of inter- and intrahemispheric FC and local-distal FC dominance was also observed during development. This longitudinal developmental calcium FC study therefore provides a resource dataset to the field and identifies periods of dynamic change which cross-sectional studies may target for examination of disease states.

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL

BACKGROUND

A trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions.

NEW METHOD

In this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 - 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain.

RESULTS

Our results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI.

COMPARISON WITH EXISTING METHODS

IVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA.

CONCLUSIONS

This study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.

Sleep and brain evolution across the human lifespan: A mutual embrace

Sleep can be considered a window to ascertain brain wellness: it dynamically changes with brain maturation and can even indicate the occurrence of concealed pathological processes. Starting from prenatal life, brain and sleep undergo an impressive developmental journey that accompanies human life throughout all its steps. A complex mutual influence rules this fascinating course and cannot be ignored while analysing its evolution. Basic knowledge on the significance and evolution of brain and sleep ontogenesis can improve the clinical understanding of patient's wellbeing in a more holistic perspective. In this review we summarized the main notions on the intermingled relationship between sleep and brain evolutionary processes across human lifespan, with a focus on sleep microstructure dynamics.

A size-adaptive 32-channel array coil for awake infant neuroimaging at 3 Tesla MRI

PURPOSE

Functional magnetic resonance imaging (fMRI) during infancy poses challenges due to practical, methodological, and analytical considerations. The aim of this study was to implement a hardware-related approach to increase subject compliance for fMRI involving awake infants. To accomplish this, we designed, constructed, and evaluated an adaptive 32-channel array coil.

METHODS

To allow imaging with a close-fitting head array coil for infants aged 1-18 months, an adjustable head coil concept was developed. The coil setup facilitates a half-seated scanning position to improve the infant's overall scan compliance. Earmuff compartments are integrated directly into the coil housing to enable the usage of sound protection without losing a snug fit of the coil around the infant's head. The constructed array coil was evaluated from phantom data using bench-level metrics, signal-to-noise ratio (SNR) performances, and accelerated imaging capabilities for both in-plane and simultaneous multislice (SMS) reconstruction methodologies. Furthermore, preliminary fMRI data were acquired to evaluate the in vivo coil performance.

RESULTS

Phantom data showed a 2.7-fold SNR increase on average when compared with a commercially available 32-channel head coil. At the center and periphery regions of the infant head phantom, the SNR gains were measured to be 1.25-fold and 3-fold, respectively. The infant coil further showed favorable encoding capabilities for undersampled k-space reconstruction methods and SMS techniques.

CONCLUSIONS

An infant-friendly head coil array was developed to improve sensitivity, spatial resolution, accelerated encoding, motion insensitivity, and subject tolerance in pediatric MRI. The adaptive 32-channel array coil is well-suited for fMRI acquisitions in awake infants.

Altered resting-state functional network connectivity in profound sensorineural hearing loss infants within an early sensitive period: A group ICA study

Data from both animal models and deaf children provide evidence for that the maturation of auditory cortex has a sensitive period during the first 2-4 years of life. During this period, the auditory stimulation can affect the development of cortical function to the greatest extent. Thus far, little is known about the brain development trajectory after early auditory deprivation within this period. In this study, independent component analysis (ICA) technique was used to detect the characteristics of brain network development in children with bilateral profound sensorineural hearing loss (SNHL) before 3 years old. Seven resting-state networks (RSN) were identified in 50 SNHL and 36 healthy controls using ICA method, and further their intra-and inter-network functional connectivity (FC) were compared between two groups. Compared with the control group, SNHL group showed decreased FC within default mode network, while enhanced FC within auditory network (AUN) and salience network. No significant changes in FC were found in the visual network (VN) and sensorimotor network (SMN). Furthermore, the inter-network FC between SMN and AUN, frontal network and AUN, SMN and VN, frontal network and VN were significantly increased in SNHL group. The results implicate that the loss and the compensatory reorganization of brain network FC coexist in SNHL infants. It provides a network basis for understanding the brain development trajectory after hearing loss within early sensitive period.

MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances

In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.

Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

Significance: Early monolingual versus bilingual experience induces adaptations in the development of linguistic and cognitive processes, and it modulates functional activation patterns during the first months of life. Resting-state functional connectivity (RSFC) is a convenient approach to study the functional organization of the infant brain. RSFC can be measured in infants during natural sleep, and it allows to simultaneously investigate various functional systems. Adaptations have been observed in RSFC due to a lifelong bilingual experience. Investigating whether bilingualism-induced adaptations in RSFC begin to emerge early in development has important implications for our understanding of how the infant brain's organization can be shaped by early environmental factors. Aims: We attempt to describe RSFC using functional near-infrared spectroscopy (fNIRS) and to examine whether it adapts to early monolingual versus bilingual environments. We also present an fNIRS data preprocessing and analysis pipeline that can be used to reliably characterize RSFC in development and to reduce false positives and flawed results interpretations. Methods: We measured spontaneous hemodynamic brain activity in a large cohort ( N = 99 ) of 4-month-old monolingual and bilingual infants using fNIRS. We implemented group-level approaches based on independent component analysis to examine RSFC, while providing proper control for physiological confounds and multiple comparisons. Results: At the group level, we describe the functional organization of the 4-month-old infant brain in large-scale cortical networks. Unbiased group-level comparisons revealed no differences in RSFC between monolingual and bilingual infants at this age. Conclusions: High-quality fNIRS data provide a means to reliably describe RSFC patterns in the infant brain. The proposed group-level RSFC analyses allow to assess differences in RSFC across experimental conditions. An effect of early bilingual experience in RSFC was not observed, suggesting that adaptations might only emerge during explicit linguistic tasks, or at a later point in development.

Naturalistic Language Input is Associated with Resting-State Functional Connectivity in Infancy

The quantity and quality of the language input that infants receive from their caregivers affects their future language abilities; however, it is unclear how variation in this input relates to preverbal brain circuitry. The current study investigated the relation between naturalistic language input and the functional connectivity (FC) of language networks in human infancy using resting-state functional magnetic resonance imaging (rsfMRI). We recorded the naturalistic language environments of five- to eight-month-old male and female infants using the Linguistic ENvironment Analysis (LENA) system and measured the quantity and consistency of their exposure to adult words (AWs) and adult-infant conversational turns (CTs). Infants completed an rsfMRI scan during natural sleep, and we examined FC among regions of interest (ROIs) previously implicated in language comprehension, including the auditory cortex, the left inferior frontal gyrus (IFG), and the bilateral superior temporal gyrus (STG). Consistent with theory of the ontogeny of the cortical language network (Skeide and Friederici, 2016), we identified two subnetworks posited to have distinct developmental trajectories: a posterior temporal network involving connections of the auditory cortex and bilateral STG and a frontotemporal network involving connections of the left IFG. Independent of socioeconomic status (SES), the quantity of CTs was uniquely associated with FC of these networks. Infants who engaged in a larger number of CTs in daily life had lower connectivity in the posterior temporal language network. These results provide evidence for the role of vocal interactions with caregivers, compared with overheard adult speech, in the function of language networks in infancy.

Functional connectivity patterns predict naturalistic viewing versus rest across development

Cognitive states, such as rest and task engagement, share an 'intrinsic' functional network organization that is subject to minimal variation over time and yields stable signatures within an individual. Importantly, there are also transient state-specific functional connectivity (FC) patterns that vary across neural states. Here, we examine functional brain organization differences that underlie distinct states in a cross-sectional developmental sample. We compare FC fMRI data acquired during naturalistic viewing (i.e., movie-watching) and resting-state paradigms in a large cohort of 157 children and young adults aged 6-20. Naturalistic paradigms are commonly implemented in pediatric research because they maintain the child's attention and contribute to reduced head motion. It remains unknown, however, to what extent the brain-wide functional network organization is comparable during movie-watching and rest across development. Here, we identify a widespread FC pattern that predicts whether individuals are watching a movie or resting. Specifically, we develop a model for prediction of multilevel neural effects (termed PrimeNet), which can with high reliability distinguish between movie-watching and rest irrespective of age and that generalizes across movies. In turn, we characterize FC patterns in the most predictive functional networks for movie-watching versus rest and show that these patterns can indeed vary as a function of development. Collectively, these effects highlight a 'core' FC pattern that is robustly associated with naturalistic viewing, which also exhibits change across age. These results, focused here on naturalistic viewing, provide a roadmap for quantifying state-specific functional neural organization across development, which may reveal key variation in neurodevelopmental trajectories associated with behavioral phenotypes.

Delayed maturation of the structural brain connectome in neonates with congenital heart disease

There is emerging evidence for delayed brain development in neonates with congenital heart disease. We hypothesize that the perioperative development of the structural brain connectome is a proxy to such delays. Therefore, we set out to quantify the alterations and longitudinal pre- to post-operative changes in the connectome in congenital heart disease neonates relative to healthy term newborns and assess factors contributing to disturbed perioperative network development. In this prospective cohort study, 114 term neonates with congenital heart disease underwent cardiac surgery at the University Children's Hospital Zurich. Forty-six healthy term newborns were included as controls. Pre- and post-operative structural connectomes were derived from mean fractional anisotropy values of fibre pathways traced using diffusion MR tractography. Graph theory parameters calculated across a proportional cost threshold range were compared between groups by multi-threshold permutation correction adjusting for confounders. Network-based statistic was calculated for edgewise network comparison. White-matter injury volume was quantified on 3D T1-weighted images. Random coefficient mixed models with interaction terms of (i) cardiac subtype and (ii) injury volume with post-menstrual age at MRI, respectively, were built to assess modifying effects on network development. Pre- and post-operatively, at the global level, efficiency, indicative of network integration, was lower in heart disease neonates than controls. In contrast, local efficiency and transitivity, indicative of network segregation, were higher compared to controls (all P < 0.025 for one-sided t-tests). Pre-operatively, these group differences were also found across multiple widespread nodes (all P < 0.025, accounting for multiple comparison), whereas post-operatively nodal differences were not evident. At the edge-level, the majority of weaker connections in heart disease neonates compared to controls involved inter-hemispheric connections (66.7% pre-operatively; 54.5% post-operatively). A trend showing a more rapid pre- to post-operative decrease in local efficiency was found in class I cardiac sub-type (biventricular defect without aortic arch obstruction) compared to controls. In congenital heart disease neonates, larger white-matter injury volume was associated with lower strength (P = 0.0026) and global efficiency (P = 0.0097). The maturation of the structural connectome is delayed in congenital heart disease neonates, with a pattern of lower structural integration and higher segregation compared to controls. Trend-level evidence indicated that normalized post-operative cardiac physiology in class I sub-types might improve structural network topology. In contrast, the burden of white-matter injury negatively impacts network strength and integration. Further research is needed to elucidate how aberrant structural network development in congenital heart disease represents neural correlates of later neurodevelopmental impairments.

Predictive modeling of neurobehavioral state and trait variation across development

A key goal of human neurodevelopmental research is to map neural and behavioral trajectories across both health and disease. A growing number of developmental consortia have begun to address this gap by providing open access to cross-sectional and longitudinal 'big data' repositories. However, it remains challenging to develop models that enable prediction of both within-subject and between-subject neurodevelopmental variation. Here, we present a conceptual and analytical perspective of two essential ingredients for mapping neurodevelopmental trajectories: state and trait components of variance. We focus on mapping variation across a range of neural and behavioral measurements and consider concurrent alterations of state and trait variation across development. We present a quantitative framework for combining both state- and trait-specific sources of neurobehavioral variation across development. Specifically, we argue that non-linear mixed growth models that leverage state and trait components of variance and consider environmental factors are necessary to comprehensively map brain-behavior relationships. We discuss this framework in the context of mapping language neurodevelopmental changes in early childhood, with an emphasis on measures of functional connectivity and their reliability for establishing robust neurobehavioral relationships. The ultimate goal is to statistically unravel developmental trajectories of neurobehavioral relationships that involve a combination of individual differences and age-related changes.

Development of Dynamic Functional Architecture during Early Infancy

Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, for the first time, thoroughly investigated how the temporal variability of the FC architecture develops at the "global" (entire brain), "mesoscale" (functional system), and "local" (brain region) levels in the first 2 years of age. Our results revealed that, in such a pivotal stage, 1) the whole-brain FC dynamic is linearly increased; 2) the high-order functional systems tend to display increased FC dynamics for both within- and between-network connections, while the primary systems show the opposite trajectories; and 3) many frontal regions have increasing FC dynamics despite large heterogeneity in developmental trajectories and velocities. All these findings indicate that the brain is gradually reconfigured toward a more flexible, dynamic, and adaptive system with globally increasing but locally heterogeneous trajectories in the first 2 postnatal years, explaining why infants have rapidly developing high-order cognitive functions and complex behaviors.

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants

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An automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data.

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Includes integrated dynamic distortion and slice-to-volume motion correction.

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A robust multimodal registration approach which includes custom neonatal templates.

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Incorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection.

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Data analysis of 538 infants imaged at 26–45 weeks post-menstrual age.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20–45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.

Applications of Resting-State fNIRS in the Developing Brain: A Review From the Connectome Perspective

Early brain development from infancy through childhood is closely related to the development of cognition and behavior in later life. Human brain connectome is a novel framework for describing topological organization of the developing brain. Resting-state functional near-infrared spectroscopy (fNIRS), with a natural scanning environment, low cost, and high portability, is considered as an emerging imaging technique and has shown valuable potential in exploring brain network architecture and its changes during the development. Here, we review the recent advances involving typical and atypical development of the brain connectome from neonates to children using resting-state fNIRS imaging. This review highlights that the combination of brain connectome and resting-state fNIRS imaging offers a promising framework for understanding human brain development.

Non-pharmacological strategies to obtain usable magnetic resonance images in non-sedated infants: Systematic review and meta-analysis

BACKGROUND

Although the use of sedation is commonly practiced to keep infants still while receiving magnetic resonance imaging, non-pharmacological strategies are a potential alternative.

OBJECTIVES

The purpose of this study was to determine the success rate of obtaining usable magnetic resonance images in infants with the sole use of non-pharmacological strategies.

DESIGN

Systematic literature review and meta-analysis SETTING: A search was conducted in PubMed, CINAHL and Cochrane Library.

PARTICIPANTS

Human infants from birth to 24 months of age who did not receive any sedation or anesthesia during magnetic resonance imaging METHOD: Articles that reported the success rate of obtaining usable images were included.

RESULTS

Of the 521 non-duplicate articles found, 58 articles were included in the systematic review with sample sizes ranging from 2-457, an average success rate of 87.8%, and an average scan time of 30 min. The most common non-pharmacological technique included feeding and swaddling infants before imaging to encourage infants to sleep during the scan. Meta-analysis performed on 53 articles comprising 3,410 infants found a success rate of 87%, but significant heterogeneity was found (I² = 98.30%). It was more difficult to obtain usable images solely with non-pharmacological techniques if infants were critically ill or a structural magnetic resonance imaging of the brain was required.

CONCLUSION

Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most but not all infants. Tweetable abstract: Non-pharmacological techniques are effective for obtaining usable magnetic resonance imaging scans in most infants.

Emerging functional connectivity differences in newborn infants vulnerable to autism spectrum disorders

Studies in animal models of autism spectrum disorders (ASD) suggest atypical early neural activity is a core vulnerability mechanism which alters functional connectivity and predisposes to dysmaturation of neural circuits. However, underlying biological changes associated to ASD in humans remain unclear. Results from functional connectivity studies of individuals diagnosed with ASD are highly heterogeneous, in part because of complex life-long secondary and/or compensatory events. To minimize these confounds and examine primary vulnerability mechanisms, we need to investigate very early brain development. Here, we tested the hypothesis that brain functional connectivity is altered in neonates who are vulnerable to this condition due to a family history of ASD. We acquired high temporal resolution multiband resting state functional magnetic resonance imaging (fMRI) in newborn infants with and without a first-degree relative with ASD. Differences in local functional connectivity were quantified using regional homogeneity (ReHo) analysis and long-range connectivity was assessed using distance correlation analysis. Neonates who have a first-degree relative with ASD had significantly higher ReHo within multiple resting state networks in comparison to age matched controls; there were no differences in long range connectivity. Atypical local functional activity may constitute a biomarker of vulnerability, that might precede disruptions in long range connectivity reported in older individuals diagnosed with ASD.

Sleep State Modulates Resting-State Functional Connectivity in Neonates

The spontaneous cerebral activity that gives rise to resting-state networks (RSNs) has been extensively studied in infants in recent years. However, the influence of sleep state on the presence of observable RSNs has yet to be formally investigated in the infant population, despite evidence that sleep modulates resting-state functional connectivity in adults. This effect could be extremely important, as most infant neuroimaging studies rely on the neonate to remain asleep throughout data acquisition. In this study, we combine functional near-infrared spectroscopy with electroencephalography to simultaneously monitor sleep state and investigate RSNs in a cohort of healthy term born neonates. During active sleep (AS) and quiet sleep (QS) our newborn neonates show functional connectivity patterns spatially consistent with previously reported RSN structures. Our three independent functional connectivity analyses revealed stronger interhemispheric connectivity during AS than during QS. In turn, within hemisphere short-range functional connectivity seems to be enhanced during QS. These findings underline the importance of sleep state monitoring in the investigation of RSNs.

The developmental trajectory of fronto-temporoparietal connectivity as a proxy of the default mode network: a longitudinal fNIRS investigation

The default mode network (DMN) is a network of brain regions that is activated while we are not engaged in any particular task. While there is a large volume of research documenting functional connectivity within the DMN in adults, knowledge of the development of this network is still limited. There is some evidence for a gradual increase in the functional connections within the DMN during the first 2 years of life, in contrast to other functional resting‐state networks that support primary sensorimotor functions, which are online from very early in life. Previous studies that investigated the development of the DMN acquired data from sleeping infants using fMRI. However, sleep stages are known to affect functional connectivity. In the current longitudinal study, fNIRS was used to measure spontaneous fluctuations in connectivity within fronto‐temporoparietal areas—as a proxy for the DMN—in awake participants every 6 months from 11 months till 36 months. This study validates a method for recording resting‐state data from awake infants, and presents a data analysis pipeline for the investigation of functional connections with infant fNIRS data, which will be beneficial for researchers in this field. A gradual development of fronto‐temporoparietal connectivity was found, supporting the idea that the DMN develops over the first years of life. Functional connectivity reached its maximum peak at about 24 months, which is consistent with previous findings showing that, by 2 years of age, DMN connectivity is similar to that observed in adults.

Cerebral hemodynamic responses in preterm-born neonates to visual stimulation: classification according to subgroups and analysis of frontotemporal-occipital functional connectivity

How neurovascular coupling develops in preterm-born neonates has been largely neglected in scientific research. We measured visually evoked (flicker light) hemodynamic responses (HRs) in preterm-born neonates ( n = 25 , gestational age: 31.71 ± 3.37 weeks, postnatal age: 25.48 ± 23.94 days) at the visual cortex (VC) and left frontotemporal lobe (FTL) using functional near-infrared spectroscopy (fNIRS) neuroimaging. We found that the HR characteristics show a large intersubject variability but could be classified into three groups according to the changes of oxyhemoglobin concentration at the VC [(A) increase, (B) decrease, or (C) inconclusive]. In groups A and B, the HRs at the left FTL were correlated with those at the VC, indicating the presence of a frontotemporal-occipital functional connectivity. Neonates in group A had a higher weight at measurement compared to those in group B, and had the lowest baseline total hemoglobin concentration and hematocrit compared to group C. To the best of our knowledge, this is the first fNIRS study showing (1) that the HRs of preterm-born neonates need to be classified into subgroups, (2) that the subgroups differed in terms of weight at measurement, and (3) that HRs can be observed also at the FTL during visual stimulation. These findings add insights into how neurovascular coupling develops in preterm-born neonates.

The Protracted Maturation of Associative Layer IIIC Pyramidal Neurons in the Human Prefrontal Cortex During Childhood: A Major Role in Cognitive Development and Selective Alteration in Autism

The human specific cognitive shift starts around the age of 2 years with the onset of self-awareness, and continues with extraordinary increase in cognitive capacities during early childhood. Diffuse changes in functional connectivity in children aged 2-6 years indicate an increase in the capacity of cortical network. Interestingly, structural network complexity does not increase during this time and, thus, it is likely to be induced by selective maturation of a specific neuronal subclass. Here, we provide an overview of a subclass of cortico-cortical neurons, the associative layer IIIC pyramids of the human prefrontal cortex. Their local axonal collaterals are in control of the prefrontal cortico-cortical output, while their long projections modulate inter-areal processing. In this way, layer IIIC pyramids are the major integrative element of cortical processing, and changes in their connectivity patterns will affect global cortical functioning. Layer IIIC neurons have a unique pattern of dendritic maturation. In contrast to other classes of principal neurons, they undergo an additional phase of extensive dendritic growth during early childhood, and show characteristic molecular changes. Taken together, circuits associated with layer IIIC neurons have the most protracted period of developmental plasticity. This unique feature is advanced but also provides a window of opportunity for pathological events to disrupt normal formation of cognitive circuits involving layer IIIC neurons. In this manuscript, we discuss how disrupted dendritic and axonal maturation of layer IIIC neurons may lead into global cortical disconnectivity, affecting development of complex communication and social abilities. We also propose a model that developmentally dictated incorporation of layer IIIC neurons into maturing cortico-cortical circuits between 2 to 6 years will reveal a previous (perinatal) lesion affecting other classes of principal neurons. This "disclosure" of pre-existing functionally silent lesions of other neuronal classes induced by development of layer IIIC associative neurons, or their direct alteration, could be found in different forms of autism spectrum disorders. Understanding the gene-environment interaction in shaping cognitive microcircuitries may be fundamental for developing rehabilitation and prevention strategies in autism spectrum and other cognitive disorders.

Longitudinally consistent estimates of intrinsic functional networks

Increasing numbers of neuroimaging studies are acquiring data to examine changes in brain architecture by investigating intrinsic functional networks (IFN) from longitudinal resting-state functional MRI (rs-fMRI). At the subject level, these IFNs are determined by cross-sectional procedures, which neglect intra-subject dependencies and result in suboptimal estimates of the networks. Here, a novel longitudinal approach simultaneously extracts subject-specific IFNs across multiple visits by explicitly modeling functional brain development as an essential context for seeking change. On data generated by an innovative simulation based on real rs-fMRI, the method was more accurate in estimating subject-specific IFNs than cross-sectional approaches. Furthermore, only group-analysis based on longitudinally consistent estimates identified significant developmental effects within IFNs of 246 adolescents from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study. The findings were confirmed by the cross-sectional estimates when the corresponding group analysis was confined to the developmental effects. Those effects also converged with current concepts of neurodevelopment.

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.

First-year development of modules and hubs in infant brain functional networks

The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such studies focusing on the first year of postnatal life are still very limited. Leveraging normally developing longitudinal infant rs-fMRI scans from neonate to one year of age, we investigated how brain functional networks develop at a fine temporal scale (every 3 months). Considering challenges in the infant fMRI-based network analysis, we developed a novel algorithm to construct the robust, temporally consistent and modular structure augmented group-level network based on which functional modules were detected at each age. Our study reveals that the brain functional network is gradually subdivided into an increasing number of functional modules accompanied by the strengthened intra- and inter-modular connectivities. Based on the developing modules, we found connector hubs (the high-centrality regions connecting different modules) emerging and increasing, while provincial hubs (the high-centrality regions connecting regions in the same module) diminishing. Further region-wise longitudinal analysis validates that different hubs have distinct developmental trajectories of the intra- and inter-modular connections suggesting different types of role transitions in network, such as non-hubs to hubs or provincial hubs to connector hubs et al. All findings indicate that functional segregation and integration are both increased in the first year of postnatal life. The module reorganization and hub transition lead to more efficient brain networks, featuring increasingly segregated modular structure and more connector hubs. This study provides the first comprehensive report of the development of functional brain networks at a 3-month interval throughout the first postnatal year of life, which provides essential information to the future neurodevelopmental and developmental disorder studies.

Neonatal brain injury and aberrant connectivity

Brain injury sustained during the neonatal period may disrupt development of critical structural and functional connectivity networks leading to subsequent neurodevelopmental impairment in affected children. These networks can be characterized using structural (via diffusion MRI) and functional (via resting state-functional MRI) neuroimaging techniques. Advances in neuroimaging have led to expanded application of these approaches to study term- and prematurely-born infants, providing improved understanding of cerebral development and the deleterious effects of early brain injury. Across both modalities, neuroimaging data are conducive to analyses ranging from characterization of individual white matter tracts and/or resting state networks through advanced 'connectome-style' approaches capable of identifying highly connected network hubs and investigating metrics of network topology such as modularity and small-worldness. We begin this review by summarizing the literature detailing structural and functional connectivity findings in healthy term and preterm infants without brain injury during the postnatal period, including discussion of early connectome development. We then detail common forms of brain injury in term- and prematurely-born infants. In this context, we next review the emerging body of literature detailing studies employing diffusion MRI, resting state-functional MRI and other complementary neuroimaging modalities to characterize structural and functional connectivity development in infants with brain injury. We conclude by reviewing technical challenges associated with neonatal neuroimaging, highlighting those most relevant to studying infants with brain injury and emphasizing the need for further targeted study in this high-risk population.

The development of brain functional connectivity networks revealed by resting-state functional magnetic resonance imaging

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep conditions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the “three-brain region theory” of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circuit and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, nodes and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB-131115-H0075 on November 15, 2013.

Control networks of the frontal lobes

The human brain is organized into specialized functional brain networks. Some networks are dedicated to early sensory processing, and others to generating motor outputs. Yet, the bulk of the human brain's functional networks is actually dedicated to control processes. The two control networks most important for the impressive repertoire of control-related behaviors that humans are able to instantiate and maintain are the frontoparietal and cinguloopercular networks. We provide evidence that these two control networks largely contribute to nonoverlapping domains of control. These networks largely have been studied using fMRI, which is sensitive only to infraslow activity. Complementary electrophysiological techniques have provided evidence that these networks manifest at substantially faster frequencies (delta-alpha band), supporting their role in coordination of whole-brain functional network activity. Both the frontoparietal and cinguloopercular networks demonstrate protracted development, supporting increases in control-related performance. Recent studies from our lab indicate these control networks exhibit measurable individual specificity, highlighting the importance of individualized paradigms in neuroimaging studies to advance our understanding of typical and atypical control network function throughout the life span.

Effect of prewhitening in resting-state functional near-infrared spectroscopy data

Near-infrared spectroscopy (NIRS) offers the potential to characterize resting-state functional connectivity (RSFC) in populations that are not easily assessed otherwise, such as young infants. In addition to the advantages of NIRS, one should also consider that the RS-NIRS signal requires specific data preprocessing and analysis. In particular, the RS-NIRS signal shows a colored frequency spectrum, which can be observed as temporal autocorrelation, thereby introducing spurious correlations. To address this issue, prewhitening of the RS-NIRS signal has been recently proposed as a necessary step to remove the signal temporal autocorrelation and therefore reduce false-discovery rates. However, the impact of this step on the analysis of experimental RS-NIRS data has not been thoroughly assessed prior to the present study. Here, the results of a standard preprocessing pipeline in a RS-NIRS dataset acquired in infants are compared with the results after incorporating two different prewhitening algorithms. Our results with a standard preprocessing replicated previous studies. Prewhitening altered RSFC patterns and disrupted the antiphase relationship between oxyhemoglobin and deoxyhemoglobin. We conclude that a better understanding of the effect of prewhitening on RS-NIRS data is still needed before directly considering its incorporation to the standard preprocessing pipeline.

Methodological challenges in the comparison of infant fMRI across age groups

Functional MRI (fMRI) in infants is rapidly growing and providing fundamental insights into the origins of brain functions. Comparing brain development at different ages is particularly powerful, but there are a number of methodological challenges that must be addressed if confounds are to be avoided. With development, brains change in composition in a way that alters their tissue contrast, and in size, shape, and gyrification, requiring careful image processing strategies and age-specific standard templates. The hemodynamic response and other aspects of physiology change with age, requiring careful paradigm design and analysis methods. Infants move more, particularly around the second year of age, and move in a different way to adults. This movement can lead to distortion in fMRI images, and requires tailored techniques during acquisition and post-processing. Infants have different sleep patterns, and their sensory periphery is changing macroscopically and in its neural pathways. Finally, once data have been acquired and analyzed, there are important considerations during mapping of brain processes and cognitive functions across age groups. In summary, new methods are critical to the comparison across age groups, and key to maximizing the rate at which infant fMRI can provide insight into the fascinating questions about the origin of cognition.

Resting-State Networks of Adolescents Experiencing Depersonalization-Like Illusions: Cross-sectional and Longitudinal Findings

The mirror-gazing task (MGT) experimentally induces illusions, ranging from simple color changes in the specular image of oneself, to depersonalization-like anomalous self-experiences (ASE) as in experiencing one's specular image as someone else. The objective was to characterize how connectivity in resting-state networks (RSNs) differed in adolescents reporting such depersonalization-like ASEs during the MGT, in a cross-sectional (Y1) and in a longitudinal manner (a year after). 75 adolescents were recruited; for the cross-sectional analysis, participants were split into 2 groups: those who reported depersonalization-like ASEs on the MGT (ASE), and those who did not (NoASE). For the longitudinal analysis, participants were split into 3 groups whether they experienced MGT depersonalization-like ASEs: only at Y1 (Remitters), both times (Persisters), or never (Controls). Participants also filled out self-reports assessing schizotypal personality (Schizotypal Personality Questionnaire [SPQ]), and underwent resting-state functional MRI procedure (rs-fMRI). A group level Independent Component Analysis (ICA) was conducted and voxel-wise inter-group differences within RSNs were examined. The rs-fMRI analysis revealed lower connectivity of specific visual areas within the primary visual network (PVN), and higher connectivity of regions within the Default Mode Network (DMN) when contrasting the ASE and NoASE groups. The areas that were atypically connected within the PVN further presented differential pattern of connectivity in the longitudinal analysis. Atypical connectivity of visual area within the DMN at Y1 was associated with higher scores on the disorganized dimension of schizotypy at the second evaluation. The present study uncovers a subtle signature in the RSNs of non-clinical adolescents who experienced task-induced ASEs.

Development of brain functional connectivity and its relation to infant sustained attention in the first year of life

The study of brain functional connectivity is crucial to understanding the neural mechanisms underlying the improved behavioral performance and amplified ERP responses observed during infant sustained attention. Previous investigations on the development of functional brain connectivity during infancy are primarily confined to the use of functional and structural MRI techniques. The current study examined the relation between infant sustained attention and brain functional connectivity and their development during infancy with high-density EEG recordings. Fifty-nine infants were tested at 6 (N = 15), 8 (N =14), 10 (N = 17), and 12 (N = 13) months. Infant sustained attention was defined by measuring infant heart rate changes during infants' looking. Functional connectivity was estimated from the electrodes on the scalp and with reconstructed cortical source activities in brain regions. It was found that infant sustained attention was accompanied by attenuated functional connectivity in the dorsal attention and default mode networks in the alpha band. Graph theory analyses showed that there was an increase in path length and a decrease in clustering coefficient during infant sustained attention. The functional connectivity within the visual, somatosensory, dorsal attention, and ventral attention networks and graph theory measures of path length and clustering coefficient were found to increase with age. These findings suggest that infant sustained attention is accompanied by distinct patterns of brain functional connectivity. The current findings also suggest the rapid development of functional connectivity in brain networks during infancy.

Early human brain development: Starring the subplate

This review summarizes early human brain development on the basis of neuroanatomical data and functional connectomics. It indicates that the most significant changes in the brain occur during the second half of gestation and the first three months post-term, in particular in the cortical subplate and cerebellum. As the transient subplate pairs a high rate of intricate developmental changes and interactions with clear functional activity, two phases of development are distinguished: a) the transient cortical subplate phase, ending at 3 months post-term when the permanent circuitries in the primary motor, somatosensory and visual cortices have replaced the subplate; and subsequently, b) the phase in which the permanent circuitries dominate. In the association areas the subplate dissolves in the remainder of the first postnatal year. During both phases developmental changes are paralleled by continuous reconfigurations in network activity. The reviewed literature also suggests that disruption of subplate development may play a pivotal role in developmental disorders, such as cerebral palsy, autism spectrum disorders, attention deficit hyperactivity disorder and schizophrenia.

Dynamic causal modelling on infant fNIRS data: A validation study on a simultaneously recorded fNIRS-fMRI dataset

Tracking the connectivity of the developing brain from infancy through childhood is an area of increasing research interest, and fNIRS provides an ideal method for studying the infant brain as it is compact, safe and robust to motion. However, data analysis methods for fNIRS are still underdeveloped compared to those available for fMRI. Dynamic causal modelling (DCM) is an advanced connectivity technique developed for fMRI data, that aims to estimate the coupling between brain regions and how this might be modulated by changes in experimental conditions. DCM has recently been applied to adult fNIRS, but not to infants. The present paper provides a proof-of-principle for the application of this method to infant fNIRS data and a demonstration of the robustness of this method using a simultaneously recorded fMRI-fNIRS single case study, thereby allowing the use of this technique in future infant studies.

fMRI and fNIRS were simultaneously recorded from a 6-month-old sleeping infant, who was presented with auditory stimuli in a block design. Both fMRI and fNIRS data were preprocessed using SPM, and analysed using a general linear model approach. The main challenges that adapting DCM for fNIRS infant data posed included: (i) the import of the structural image of the participant for spatial pre-processing, (ii) the spatial registration of the optodes on the structural image of the infant, (iii) calculation of an accurate 3-layer segmentation of the structural image, (iv) creation of a high-density mesh as well as (v) the estimation of the NIRS optical sensitivity functions. To assess our results, we compared the values obtained for variational Free Energy (F), Bayesian Model Selection (BMS) and Bayesian Model Average (BMA) with the same set of possible models applied to both the fMRI and fNIRS datasets. We found high correspondence in F, BMS, and BMA between fMRI and fNIRS data, therefore showing for the first time high reliability of DCM applied to infant fNIRS data. This work opens new avenues for future research on effective connectivity in infancy by contributing a data analysis pipeline and guidance for applying DCM to infant fNIRS data.

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Connectivity studies give important insights into infant brain development.

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fNIRS is a valuable method for infancy studies, but can we analyse connectivity?

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On fMRI-fNIRS acquired simultaneously, we estimate effective connectivity with DCM.

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We showed high correspondence of DCM values between fMRI and fNIRS data.

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We validated DCM on fNIRS infant data, providing guidance for future projects.

The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development

The human brain undergoes extensive and dynamic growth during the first years of life. The UNC/UMN Baby Connectome Project (BCP), one of the Lifespan Connectome Projects funded by NIH, is an ongoing study jointly conducted by investigators at the University of North Carolina at Chapel Hill and the University of Minnesota. The primary objective of the BCP is to characterize brain and behavioral development in typically developing infants across the first 5 years of life. The ultimate goals are to chart emerging patterns of structural and functional connectivity during this period, map brain-behavior associations, and establish a foundation from which to further explore trajectories of health and disease. To accomplish these goals, we are combining state of the art MRI acquisition and analysis techniques, including high-resolution structural MRI (T1-and T2-weighted images), diffusion imaging (dMRI), and resting state functional connectivity MRI (rfMRI). While the overall design of the BCP largely is built on the protocol developed by the Lifespan Human Connectome Project (HCP), given the unique age range of the BCP cohort, additional optimization of imaging parameters and consideration of an age appropriate battery of behavioral assessments were needed. Here we provide the overall study protocol, including approaches for subject recruitment, strategies for imaging typically developing children 0-5 years of age without sedation, imaging protocol and optimization, a description of the battery of behavioral assessments, and QA/QC procedures. Combining HCP inspired neuroimaging data with well-established behavioral assessments during this time period will yield an invaluable resource for the scientific community.

Infant fMRI: A Model System for Cognitive Neuroscience

Our understanding of the typical human brain has benefitted greatly from studying different kinds of brains and their associated behavioral repertoires, including animal models and neuropsychological patients. This same comparative perspective can be applied to early development - the environment, behavior, and brains of infants provide a model system for understanding how the mature brain works. This approach requires noninvasive methods for measuring brain function in awake, behaving infants. fMRI is becoming increasingly viable for this purpose, with the unique ability to precisely measure the entire brain, including both cortical and subcortical structures. Here we discuss potential lessons from infant fMRI for several domains of adult cognition and consider the challenges of conducting such research and how they might be mitigated.

Functional Connectivity Alterations between Networks and Associations with Infant Immune Health within Networks in HIV Infected Children on Early Treatment: A Study at 7 Years

Although HIV has been shown to impact brain connectivity in adults and youth, it is not yet known to what extent long-term early antiretroviral therapy (ART) may alter these effects, especially during rapid brain development in early childhood. Using both independent component analysis (ICA) and seed-based correlation analysis (SCA), we examine the effects of HIV infection in conjunction with early ART on resting state functional connectivity (FC) in 7 year old children. HIV infected (HIV+) children were from the Children with HIV Early Antiretroviral Therapy (CHER) trial and all initiated ART before 18 months; uninfected children were recruited from an interlinking vaccine trial. To better understand the effects of current and early immune health on the developing brain, we also investigated among HIV+ children the association of FC at 7 years with CD4 count and CD4%, both in infancy (6–8 weeks) and at scan. Although we found no differences within any ICA-generated resting state networks (RSNs) between HIV+ and uninfected children (27 HIV+, 18 uninfected), whole brain connectivity to seeds located at RSN connectivity peaks revealed several loci of FC differences, predominantly from seeds in midline regions (posterior cingulate cortex, paracentral lobule, cuneus, and anterior cingulate). Reduced long-range connectivity and increased short-range connectivity suggest developmental delay. Within the HIV+ children, clinical measures at age 7 years were not associated with FC values in any of the RSNs; however, poor immune health during infancy was associated with localized FC increases in the somatosensory, salience and basal ganglia networks. Together these findings suggest that HIV may affect brain development from its earliest stages and persist into childhood, despite early ART.

Developmental implications of children's brain networks and learning

The human brain works as a synergistic system where information exchanges between functional neuronal networks. Rudimentary networks are observed in the brain during infancy. In recent years, the question of how functional networks develop and mature in children has been a hotly discussed topic. In this review, we examined the developmental characteristics of functional networks and the impacts of skill training on children's brains. We first focused on the general rules of brain network development and on the typical and atypical development of children's brain networks. After that, we highlighted the essentials of neural plasticity and the effects of learning on brain network development. We also discussed two important theoretical and practical concerns in brain network training. Finally, we concluded by presenting the significance of network training in typically and atypically developed brains.

Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature

The development of human cognition results from the emergence of coordinated activity between distant brain areas. Network science, combined with non-invasive functional imaging, has generated unprecedented insights regarding the adult brain's functional organization, and promises to help elucidate the development of functional architectures supporting complex behavior. Here we review what is known about functional network development from birth until adulthood, particularly as understood through the use of resting-state functional connectivity MRI (rs-fcMRI). We attempt to synthesize rs-fcMRI findings with other functional imaging techniques, with macro-scale structural connectivity, and with knowledge regarding the development of micro-scale structure. We highlight a number of outstanding conceptual and technical barriers that need to be addressed, as well as previous developmental findings that may need to be revisited. Finally, we discuss key areas ripe for future research in order to (1) better characterize normative developmental trajectories, (2) link these trajectories to biologic mechanistic events, as well as component behaviors and (3) better understand the clinical implications and pathophysiological basis of aberrant network development.

Developmental Connectomics from Infancy through Early Childhood

The human brain undergoes rapid growth in both structure and function from infancy through early childhood, and this significantly influences cognitive and behavioral development in later life. A newly emerging research framework, developmental connectomics, provides unprecedented opportunities for exploring the developing brain through non-invasive mapping of structural and functional connectivity patterns. Within this framework, we review recent neuroimaging and neurophysiological studies investigating connectome development from 20 postmenstrual weeks to 5 years of age. Specifically, we highlight five fundamental principles of brain network development during the critical first years of life, emphasizing strengthened segregation/integration balance, a remarkable hierarchical order from primary to higher-order regions, unparalleled structural and functional maturations, substantial individual variability, and high vulnerability to risk factors and developmental disorders.

Functional network connectivity predicts treatment outcome during treatment of nicotine use disorder

Altered resting state functional connectivity (rsFC) and functional network connectivity (FNC), which is a measure of coherence between brain networks, may be associated with nicotine use disorder (NUD). We hypothesized that higher connectivity between insula and 1) dorsal anterior cingulate cortex (dACC) and 2) dorsolateral prefrontal cortex (dlPFC) would predict better treatment outcomes. We also performed an exploratory analysis of the associations between FNC values between additional key frontal and striatal regions and treatment outcomes. One hundred and forty four individuals with NUD underwent a resting state session during functional MRI prior to randomization to treatment with varenicline (n=82) or placebo. Group independent component analysis (ICA) was utilized to extract individual subject components and time series from intrinsic connectivity networks in aforementioned regions, and FNC between all possible pairs were calculated. Higher FNC between insula and dACC (rho=0.21) was significantly correlated with lower levels of baseline smoking quantity but did not predict treatment outcome upon controlling for baseline smoking. Higher FNC between putamen and dACC, caudate and dACC, and caudate and dlPFC significantly predicted worse treatment outcome in participants reporting high subjective withdrawal before the scan. FNC between key regions hold promise as biomarkers to predict outcome in NUD.

The Default Mode Network in Healthy Individuals: A Systematic Review and Meta-Analysis

When the brain is not engaged in goal-directed activities and at rest, there are still measureable patterns of activity. One resting-state network, the default mode network (DMN) is responsible for a self-referential introspective state. There are many factors that influence normal changes in brain activity. The purpose of this review is to summarize differences in DMN functional connectivity in healthy individuals by age, sex, cognitive function, and analysis type to characterize what is "normal." Studies were systematically selected up to August 2016. Two reviewers independently used predetermined inclusion and exclusion criteria to identify relevant studies. Studies that provided sufficient information were included in a subsequent voxel-wise meta-analysis. Strength of DMN functional connectivity follows an inverse U-shape, where it is strongest in adulthood and lowest in children and elderly. Cognitive function is positively correlated with DMN functional connectivity. Females exhibit stronger intranetwork connectivity compared with males. Effects of analysis type were inconclusive and more studies need to incorporate complementing techniques. The voxel-wise meta-analysis was only conducted for the age factor. Findings supported an immature network in children compared with adults and a stronger network in adults compared with elderly. This is the first study to review differences of DMN functional connectivity in healthy individuals by age, sex, cognitive function, and analysis type. Findings add to the understanding of normal variance. Furthermore, defining a normal comparative base may allow for the identification of DMN change into pathology. This is important since it may allow for the detection of an intermediate risk phenotype and could serve as a biomarker for treatment response.