Frequency of spontaneous BOLD signal shifts during infancy and correlates with cognitive performance

Frequency-dependent effective connections between local signals and the global brain signal during resting-state

The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09831-0.

Natural frequencies of neural activities and cognitions may serve as precise targets of rhythmic interventions to the aging brain

Rhythmic neural activities are critical to the efficiency of regulatory procedures in brain functions. However, brain functions usually decline in aging as accompanied by frequency shift and temporal dedifferentiation of neural activities. Considering the strong oscillations and long-lasting after-effects induced by rhythmic brain stimulations, we suggest that non-invasive rhythmic brain stimulation technique may help restore the natural frequencies of neural activities in aging to that in younger and healthy brains. Although with tremendous work to do, this technique offers great opportunities for the restoration of normal brain functions in aging, or even in those suffering from neurodegenerative diseases and neuropsychiatric disorders.

Functional brain activity is highly associated with cortical myelination in neonates

Functional organization of the human cerebral cortex is highly constrained by underlying brain structures, but how functional activity is associated with different brain structures during development is not clear, especially at the neonatal stage. Since long-range functional connectivity is far from mature in the dynamically developing neonatal brain, it is of great scientific significance to investigate the relationship between different structural and functional features at the local level. To this end, for the first time, correlation and regression analyses were performed to examine the relationship between cortical morphology, cortical myelination, age, and local brain functional activity, as well as functional connectivity strength using high-resolution structural and resting-state functional MRI data of 177 neonates (29-44 postmenopausal weeks, 98 male and 79 female) from both static and dynamic perspectives. We found that cortical myelination was most strongly associated with local brain functional activity across the cerebral cortex than other cortical structural features while controlling the age effect. These findings suggest the crucial role of cortical myelination in local brain functional development at birth, providing valuable insights into the fundamental biological basis of functional activity at this early developmental stage.

fMRI spectral signatures of sleep

The conventional wisdom that sleep is a global state, affecting the whole brain uniformly and simultaneously, was overturned by the discovery of local sleep, where individual neuronal populations were found to be asleep and the rest of the brain awake. However, due to the difficulty of monitoring local neuronal states in humans, our understanding of local sleep remains limited. Using simultaneous functional MRI (fMRI) and electroencephalography, we find that the oscillations of brain hemodynamic activity provide signatures of sleep at a local neuronal population level. We show that the fMRI signatures of sleep can be employed to monitor local neuronal states and investigate which brain regions are the first to fall asleep or wake up at wake–sleep transitions.

Sleep can be distinguished from wake by changes in brain electrical activity, typically assessed using electroencephalography (EEG). The hallmark of nonrapid-eye-movement (NREM) sleep is the shift from high-frequency, low-amplitude wake EEG to low-frequency, high-amplitude sleep EEG dominated by spindles and slow waves. Here we identified signatures of sleep in brain hemodynamic activity, using simultaneous functional MRI (fMRI) and EEG. We found that, at the transition from wake to sleep, fMRI blood oxygen level–dependent (BOLD) activity evolved from a mixed-frequency pattern to one dominated by two distinct oscillations: a low-frequency (<0.1 Hz) oscillation prominent in light sleep and correlated with the occurrence of spindles, and a high-frequency oscillation (>0.1 Hz) prominent in deep sleep and correlated with the occurrence of slow waves. The two oscillations were both detectable across the brain but exhibited distinct spatiotemporal patterns. During the falling-asleep process, the low-frequency oscillation first appeared in the thalamus, then the posterior cortex, and lastly the frontal cortex, while the high-frequency oscillation first appeared in the midbrain, then the frontal cortex, and lastly the posterior cortex. During the waking-up process, both oscillations disappeared first from the thalamus, then the frontal cortex, and lastly the posterior cortex. The BOLD oscillations provide local signatures of spindle and slow wave activity. They may be employed to monitor the regional occurrence of sleep or wakefulness, track which regions are the first to fall asleep or wake up at the wake–sleep transitions, and investigate local homeostatic sleep processes.

The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What's more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Infant and Child MRI: A Review of Scanning Procedures

Magnetic resonance imaging (MRI) is a safe method to examine human brain. However, a typical MR scan is very sensitive to motion, and it requires the subject to lie still during the acquisition, which is a major challenge for pediatric scans. Consequently, in a clinical setting, sedation or general anesthesia is often used. In the research setting including healthy subjects anesthetics are not recommended for ethical reasons and potential longer-term harm. Here we review the methods used to prepare a child for an MRI scan, but also on the techniques and tools used during the scanning to enable a successful scan. Additionally, we critically evaluate how studies have reported the scanning procedure and success of scanning. We searched articles based on special subject headings from PubMed and identified 86 studies using brain MRI in healthy subjects between 0 and 6 years of age. Scan preparations expectedly depended on subject's age; infants and young children were scanned asleep after feeding and swaddling and older children were scanned awake. Comparing the efficiency of different procedures was difficult because of the heterogeneous reporting of the used methods and the success rates. Based on this review, we recommend more detailed reporting of scanning procedure to help find out which are the factors affecting the success of scanning. In the long term, this could help the research field to get high quality data, but also the clinical field to reduce the use of anesthetics. Finally, we introduce the protocol used in scanning 2 to 5-week-old infants in the FinnBrain Birth Cohort Study, and tips for calming neonates during the scans.

Abnormal intrinsic functional network hubs in diabetic retinopathy patients

BACKGROUND

However, whether the whole-brain functional network hub changes occur in diabetic retinopathy patients remains unknown.

PURPOSE

The purpose of the study was to investigate the function network centrality and connectivity changes in diabetic retinopathy patients using the voxel-wise degree centrality method.

MATERIALS AND METHODS

Thirty-four diabetic retinopathy patients (18 male and 16 female) and 38 healthy controls (18 male and 20 female) closely matched in age, sex, and education were enrolled in the study. Graph theory-based network analysis was performed to investigate the degree centrality between two groups.

RESULTS

Compared with healthy controls, diabetic retinopathy patients had significantly higher degree centrality values in the pons and bilateral caudate and had significantly lower degree centrality values in the left lingual and right lingual, and right angular/middle occipital gyrus (MOG). Moreover, diabetic retinopathy patients exhibited increased functional connectivity between the bilateral lingual and right cerebellum lobe and right fusiform/bilateral caudate and increased functional connectivity between the right angular/MOG and bilateral anterior cingulum and right cuneus/bilateral precuneus and increased functional connectivity between the bilateral caudate and right lingual and right superior occipital gyrus. In contrast, diabetic retinopathy patients showed decreased functional connectivity between bilateral lingual and left lingual and right lingual and left superior occipital gyrus and decreased functional connectivity between the angular/MOG and right inferior occipital gyrus/right fusiform and left MOG/inferior occipital gyrus and decreased functional connectivity between the bilateral caudate and bilateral cerebellum crus1.

CONCLUSION

Our results highlight that reorganization of the hierarchy of the cortical connectivity network related to visual network.

Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life

Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.

Neonatal brain connectivity outliers identify over forty percent of IQ outliers at 4 years of age

BACKGROUND

Defining reliable brain markers for the prediction of abnormal behavioral outcomes remains an urgent but extremely challenging task in neuroscience research. This is particularly important for infant studies given the most dramatic brain and behavioral growth during infancy.

METHODS

In this study, we proposed a novel prediction scheme through abstracting individual newborn's whole-brain functional connectivity pattern to three outlier measures (Triple O) and tested the hypothesis that neonates identified as "brain outliers" based on Triple O were more likely to develop as IQ outliers at 4 years of age. Without need for training with behavioral data, Triple O represents a novel proof-of-concept approach to predict later IQ outcomes based on neonatal brain data.

RESULTS

Triple O correctly identified 42.1% true IQ outliers among a mixed cohort of 175 newborns with different term, twin, and maternal disorder statuses. Triple O also reached a high level of specificity (96.2%) and overall accuracy (90.3%). Further incorporating a demographic information indicator, the enhanced Triple O+ could further differentiate between high and low 4YR IQ outliers. Validation tests against seven independent reference samples revealed highly consistent results and a minimum sample size of ~50 for robust performance.

CONCLUSIONS

Considering that postnatal brain growth and various environmental factors likely also contribute to 4YR IQ, the fact that Triple O, based purely on neonatal functional connectivity data, could identify >40% of 4YR IQ outliers is striking. Together with the very high level of specificity, each outlier predicted by Triple O represents a meaningful risk but future efforts are needed to explore ways to identify the rest of outliers. Overall, with no need for training, a high level of robustness, and a minimal requirement on sample size, the proposed Triple O approach demonstrates great potential to predict later outlying IQ performances using neonatal functional connectivity data.

The Subgrouping Structure of Newborns with Heterogenous Brain-Behavior Relationships

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain-behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain-behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain-behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain-behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain-behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain-behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Intrinsic excitation-inhibition imbalance affects medial prefrontal cortex differently in autistic men versus women

Excitation-inhibition (E:I) imbalance is theorized as an important pathophysiological mechanism in autism. Autism affects males more frequently than females and sex-related mechanisms (e.g., X-linked genes, androgen hormones) can influence E:I balance. This suggests that E:I imbalance may affect autism differently in males versus females. With a combination of in-silico modeling and in-vivo chemogenetic manipulations in mice, we first show that a time-series metric estimated from fMRI BOLD signal, the Hurst exponent (H), can be an index for underlying change in the synaptic E:I ratio. In autism we find that H is reduced, indicating increased excitation, in the medial prefrontal cortex (MPFC) of autistic males but not females. Increasingly intact MPFC H is also associated with heightened ability to behaviorally camouflage social-communicative difficulties, but only in autistic females. This work suggests that H in BOLD can index synaptic E:I ratio and that E:I imbalance affects autistic males and females differently.

The functional brain networks that underlie visual working memory in the first two years of life

Visual working memory (VWM) is a central cognitive system used to compare views of the world and detect changes in the local environment. This system undergoes dramatic development in the first two years; however, we know relatively little about the functional organization of VWM at the level of the brain. Here, we used image-based functional near-infrared spectroscopy (fNIRS) to test four hypotheses about the spatial organization of the VWM network in early development. Four-month-olds, 1-year-olds, and 2-year-olds completed a VWM task while we recorded neural activity from 19 cortical regions-of-interest identified from a meta-analysis of the adult fMRI literature on VWM. Results showed significant task-specific functional activation near 6 of 19 ROIs, revealing spatial consistency in the brain regions activated in our study and brain regions identified to be part of the VWM network in adult fMRI studies. Working memory related activation was centered on bilateral anterior intraparietal sulcus (aIPS), left temporoparietal junction (TPJ), and left ventral occipital complex (VOC), while visual exploratory measures were associated with activation in right dorsolateral prefrontal cortex, left TPJ, and bilateral IPS. Results show that a distributed brain network underlies functional changes in VWM in infancy, revealing new insights into the neural mechanisms that support infants’ improved ability to remember visual information and to detect changes in an on-going visual stream.

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A distributed brain network underlies functional changes in VWM in infancy and toddlerhood.

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This network shows robust engagement of similar brain regions identified in fMRI studies with adults as early as four months.

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Working memory related activation was centered on bilateral anterior intraparietal sulcus, left temporoparietal junction, and left ventral occipital complex

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Visual exploratory measures were associated with activation in right dorsolateral prefrontal cortex, bilateral anterior intraparietal sulcus, and left temporoparietal junction.

Resting-state networks of the neonate brain identified using independent component analysis

Resting-state functional magnetic resonance imaging (rs-fMRI) has been successfully used to probe the intrinsic functional organization of the brain and to study brain development. Here, we implemented a combination of individual and group independent component analysis (ICA) of FSL on a 6-min resting-state data set acquired from 21 naturally sleeping term-born (age 26 ± 6.7 d), healthy neonates to investigate the emerging functional resting-state networks (RSNs). In line with the previous literature, we found evidence of sensorimotor, auditory/language, visual, cerebellar, thalmic, parietal, prefrontal, anterior cingulate as well as dorsal and ventral aspects of the default-mode-network. Additionally, we identified RSNs in frontal, parietal, and temporal regions that have not been previously described in this age group and correspond to the canonical RSNs established in adults. Importantly, we found that careful ICA-based denoising of fMRI data increased the number of networks identified with group-ICA, whereas the degree of spatial smoothing did not change the number of identified networks. Our results show that the infant brain has an established set of RSNs soon after birth.

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.

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.

Growing a social brain

It has long been assumed that social animals, such as humans, are born with a brain system that has evolved to support social affiliation. However, the evidence does not necessarily support this assumption. Alternatively, social animals can be defined as those who cannot survive alone and rely on members from their group to regulate their ongoing physiology (or allostasis). The rather simple evolutionary constraint of social dependency for survival can be sufficient to make the social environment vitally salient, and to provide the ultimate driving force for socially crafted brain development and learning. In this Perspective, we propose a framework for sociality and specify a set of hypotheses on the mechanisms of social development and underlying neural systems. The theoretical shift proposed here implies that profound human characteristics, including but not limited to sociality, are acquired at an early age, while social interactions provide key wiring instructions that determine brain development.

Recent advances in the use of MRI to assess early human cortical development

Over the past decade, a number of advanced magnetic resonance-based methods have been brought to bear on questions related to early development of the human cerebral cortex. Herein, we describe studies employing analysis of cortical surface folding (cortical cartography), cortical microstructure (diffusion anisotropy), and cortically-based functional networks (resting state-functional connectivity MRI). The fundamentals of each MR method are described, followed by a discussion of application of the method to developing cortex and potential clinical uses. We use premature birth as an exemplar of how these modalities can be used to investigate the effects of medical and environmental variables on early cortical development.

Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months

Objective

Functional connectivity magnetic resonance imaging (fcMRI) of neonates with perinatal brain injury could improve prediction of motor impairment before symptoms manifest, and establish how early brain organization relates to subsequent development. This cohort study is the first to describe and quantitatively assess functional brain networks and their relation to later motor skills in neonates with a diverse range of perinatal brain injuries.

Methods

Infants (n = 65, included in final analyses: n = 53) were recruited from the neonatal intensive care unit (NICU) and were stratified based on their age at birth (premature vs. term), and on whether neuropathology was diagnosed from structural MRI. Functional brain networks and a measure of disruption to functional connectivity were obtained from 14 min of fcMRI acquired during natural sleep at term-equivalent age.

Results

Disruption to connectivity of the somatomotor and frontoparietal executive networks predicted motor impairment at 4 and 8 months. This disruption in functional connectivity was not found to be driven by differences between clinical groups, or by any of the specific measures we captured to describe the clinical course.

Conclusion

fcMRI was predictive over and above other clinical measures available at discharge from the NICU, including structural MRI. Motor learning was affected by disruption to somatomotor networks, but also frontoparietal executive networks, which supports the functional importance of these networks in early development. Disruption to these two networks might be best addressed by distinct intervention strategies.

Resting state functional connectivity of the anterior striatum and prefrontal cortex predicts reading performance in school-age children

The current study investigated the neural basis of reading performance in 60 school-age Spanish-speaking children, aged 6 to 9years. By using a data-driven approach and an automated matching procedure, we identified a left-lateralized resting state network that included typical language regions (Wernicke's and Broca's regions), prefrontal cortex, pre- and post-central gyri, superior and middle temporal gyri, cerebellum, and subcortical regions, and explored its relevance for reading performance (accuracy, comprehension and speed). Functional connectivity of the left frontal and temporal cortices and subcortical regions predicted reading speed. These results extend previous findings on the relationship between functional connectivity and reading competence in children, providing new evidence about such relationships in previously unexplored regions in the resting brain, including the left caudate, putamen and thalamus. This work highlights the relevance of a broad network, functionally synchronized in the resting state, for the acquisition and perfecting of reading abilities in young children.

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 Connectivity of the Infant Human Brain: Plastic and Modifiable

Infancy is a critical and immensely important period in human brain development. Subtle changes during this stage may be greatly amplified with the unfolding of different developmental processes, exerting far-reaching consequences. Studies of the structure and behavioral manifestations of the infant brain are fruitful. However, the specific functional brain mechanisms that enable the execution of different behaviors remained elusive until the advent of functional connectivity fMRI (fcMRI), which provides an unprecedented opportunity to probe the infant functional brain development in vivo. Since its inception, a burgeoning field of infant brain functional connectivity study has emerged and thrived during the past decade. In this review, we describe (1) findings of normal development of functional connectivity networks and their relationships to behaviors and (2) disruptions of the normative functional connectivity development due to identifiable genetic and/or environmental risk factors during the first 2 years of human life. Technical considerations of infant fcMRI are also provided. It is our hope to consolidate previous findings so that the field can move forward with a clearer picture toward the ultimate goal of fcMRI-based objective methods for early diagnosis/identification of risks and evaluation of early interventions to optimize developing functional connectivity networks in this critical developmental window.

Mapping an index of the myelin g-ratio in infants using magnetic resonance imaging

Optimal myelination of neuronal axons is essential for effective brain and cognitive function. The ratio of the axon diameter to the outer fiber diameter, known as the g-ratio, is a reliable measure to assess axonal myelination and is an important index reflecting the efficiency and maximal conduction velocity of white matter pathways. Although advanced neuroimaging techniques including multicomponent relaxometry (MCR) and diffusion tensor imaging afford insight into the microstructural characteristics of brain tissue, by themselves they do not allow direct analysis of the myelin g-ratio. Here, we show that by combining myelin content information (obtained with mcDESPOT MCR) with neurite density information (obtained through NODDI diffusion imaging) an index of the myelin g-ratio may be estimated. Using this framework, we present the first quantitative study of myelin g-ratio index changes across childhood, examining 18 typically developing children 3months to 7.5years of age. We report a spatio-temporal pattern of maturation that is consistent with histological and developmental MRI studies, as well as theoretical studies of the myelin g-ratio. This work represents the first ever in vivo visualization of the evolution of white matter g-ratio indices throughout early childhood.

A neural window on the emergence of cognition

Can babies think? A fundamental challenge for cognitive neuroscience is to answer when brain functions begin and in what form they first emerge. This is challenging with behavioral tasks, as it is difficult to communicate to an infant what a task requires, and motor function is impoverished, making execution of the appropriate response difficult. To circumvent these requirements, neuroimaging provides a complementary route for assessing the emergence of cognition. Starting from the prerequisites of cognitive function and building stepwise, we review when the cortex forms and when it becomes gyrated and regionally differentiated. We then discuss when white matter tracts mature and when functional brain networks arise. Finally, we assess the responsiveness of these brain systems to external events. We find that many cognitive systems are observed surprisingly early. Some emerge before birth, with activations in the frontal lobe even in the first months of gestation. These discoveries are changing our understanding of the nature of cognitive networks and their early function, transforming cognitive neuroscience, and opening new windows for education and investigation. Infant neuroimaging also has tremendous clinical potential, for both detecting atypical development and facilitating earlier intervention. Finally, we discuss the key technical developments that are enabling this nascent field.