Structural network analysis of brain development in young preterm neonates

Characterizing normal perinatal development of the human brain structural connectivity

Early brain development is characterized by the formation of a highly organized structural connectome, which underlies brain's cognitive abilities and influences its response to diseases and environmental factors. Hence, quantitative assessment of structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the connectome from diffusion MRI data involves complex computations. For the perinatal period, these computations are further challenged by the rapid brain development, inherently low signal quality, imaging difficulties, and high inter-subject variability. These factors make it difficult to chart the normal development of the structural connectome. As a result, there is a lack of reliable normative baselines of structural connectivity metrics at this critical stage in brain development. In this study, we developed a computational method based on spatio-temporal averaging in the image space for determining such baselines. We used this method to analyze the structural connectivity between 33 and 44 postmenstrual weeks using data from 166 subjects. Our results unveiled clear and strong trends in the development of structural connectivity in the perinatal stage. We observed increases in measures of network integration and segregation, and widespread strengthening of the connections within and across brain lobes and hemispheres. We also observed asymmetry patterns that were consistent between different connection weighting approaches. Connection weighting based on fractional anisotropy and neurite density produced the most consistent results. Our proposed method also showed considerable agreement with an alternative technique based on connectome averaging. The new computational method and results of this study can be useful for assessing normal and abnormal development of the structural connectome early in life.

Functional connectivity development in the prenatal and neonatal stages measured by functional magnetic resonance imaging: A systematic review

The prenatal and neonatal periods are two of the most important developmental stages of the human brain. It is therefore crucial to understand normal brain development and how early connections are established during these periods, in order to advance the state of knowledge on altered brain development and eventually identify early brain markers of neurodevelopmental disorders and diseases. In this systematic review (Prospero ID: CRD42024511365), we compiled resting state functional magnetic resonance imaging (fMRI) studies in healthy fetuses and neonates, in order to outline the main characteristics of typical development of the functional brain connectivity during the prenatal and neonatal periods. A systematic search of five databases identified a total of 12 573 articles. Of those, 28 articles met pre-established selection criteria based determined by the authors after surveying and compiling the major limitations reported within the literature. Inclusion criteria were: (1) resting state studies; (2) presentation of original results; (3) use of fMRI with minimum one Tesla; (4) a population ranging from 20 weeks of GA to term birth (around 37-42 weeks of PMA); (5) singleton pregnancy with normal development (absence of any complications known to alter brain development). Exclusion criteria were: (1) preterm studies; (2) post-mortem studies; (3) clinical or pathological studies; (4) twin studies; (5) papers with a sole focus on methodology (i.e. focused on tool and analysis development); (6) volumetric studies; (7) activation map studies; (8) cortical analysis studies; (9) conference papers. A risk of bias assessment was also done to evaluate each article's methodological rigor. 1877 participants were included across all the reviewed articles. Results consistently revealed a developmental gradient of increasing functional brain connectivity from posterior to anterior regions and from proximal-to-distal regions. A decrease in local small-world organization shortly after birth was also observed; small-world characteristics were present in fetuses and newborns, but appeared weaker in the latter group. Also, the posterior-to-anterior gradient could be associated with earlier development of the sensorimotor networks in the posterior regions while more complex higher-order networks (e.g. attention-related) mature later in the anterior regions. The main limitations of this systematic review stem from the inherent limitations of functional imaging in fetuses, mainly: unevenly distributed populations and limited sample sizes; fetal movements in the womb and other imaging obstacles; and a large voxel resolution when imaging a small brain. Another limitation specific to this review is the relatively small number of included articles compared to very a large search result, which may have led to relevant articles having been overlooked.

Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.

Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation

The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.

Agreement of synchronous remote and in-person application of the Alberta Infant Motor Scale: Cohort study

INTRODUCTION

Using standardized scales to assess motor development via telemedicine can increase access for low-income populations. Our aim was to verify the agreement and feasibility between remotely and synchronously applying the Alberta Infant Motor Scale (AIMS) and the in-person format.

METHODS

This was a concordance study, with 77 typical infants aged 4-18 months (mean = 13 months). The AIMS was applied remote via video calls and face-to-face. We applied a questionnaire to caregivers to verify feasibility.

RESULTS

There was a high level of agreement between the remote and in-person assessments, with intraclass correlation coefficients above 0.98 and low standard error measure values (<1 item for each posture, <2 items for the total raw score, and =5% for the normative score). The smallest detectable change was between 1.67 and 2.45 for each posture, 3 for the total raw score, and 6% for the normative score. The Bland-Altman analysis showed low bias with the mean difference close to zero (<0.80) and low error with little dispersion of the difference points around the mean. Caregivers' perspectives on the synchronous remote assessment were positive, with good quality, clear information during the assessment, and comfort with the method.

DISCUSSION

The synchronous remote application of the AIMS may be an alternative for families without access to in-person services that assess motor development.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Pain Exposure and Brain Connectivity in Preterm Infants

Importance

Early-life exposure to painful procedures has been associated with altered brain maturation and neurodevelopmental outcomes in preterm infants, although sex-specific differences are largely unknown.

Objective

To examine sex-specific associations among early-life pain exposure, alterations in neonatal structural connectivity, and 18-month neurodevelopment in preterm infants.

Design, Setting, and Participants

This prospective cohort study recruited 193 very preterm infants from April 1, 2015, to April 1, 2019, across 2 tertiary neonatal intensive care units in Toronto, Canada. Structural connectivity data were available for 150 infants; neurodevelopmental outcomes were available for 123 infants. Data were analyzed from January 1, 2022, to December 31, 2023.

Exposure

Pain was quantified in the initial weeks after birth as the total number of invasive procedures.

Main Outcome and Measure

Infants underwent early-life and/or term-equivalent-age magnetic resonance imaging with diffusion tensor imaging to quantify structural connectivity using graph theory measures and regional connection strength. Eighteen-month neurodevelopmental outcomes were assessed with the Bayley Scales of Infant and Toddler Development, Third Edition. Stratifying by sex, generalized estimating equations were used to assess whether pain exposure modified the maturation of structural connectivity using an interaction term (early-life pain exposure × postmenstrual age [PMA] at scan). Generalized estimating equations were used to assess associations between structural connectivity and neurodevelopmental outcomes, adjusting for extreme prematurity and maternal education.

Results

A total of 150 infants (80 [53%] male; median [IQR] gestational age at birth, 27.1 [25.4-29.0] weeks) with structural connectivity data were analyzed. Sex-specific associations were found between early-life pain and neonatal brain connectivity in female infants only, with greater early-life pain exposure associated with slower maturation in global efficiency (pain × PMA at scan interaction P = .002) and local efficiency (pain × PMA at scan interaction P = .005). In the full cohort, greater pain exposure was associated with lower global efficiency (coefficient, -0.46; 95% CI, -0.78, to -0.15; P = .004) and local efficiency (coefficient, -0.57; 95% CI, -1.04 to -0.10; P = .02) and regional connection strength. Local efficiency (coefficient, 0.003; 95% CI, 0.001-0.004; P = .005) and regional connection strength in the striatum were associated with cognitive outcomes.

Conclusions and Relevance

In this cohort study of very preterm infants, greater exposure to early-life pain was associated with altered maturation of neonatal structural connectivity, particularly in female infants. Alterations in structural connectivity were associated with neurodevelopmental outcomes, with potential regional specificities.

The psychometric properties and feasibility of the Alberta infant motor scale used in telehealth: A scoping review

BACKGROUND

Telehealth can be a viable option for improving equitable access to early motor assessment for financially vulnerable individuals.

AIM

This review aimed to identify the psychometric properties of the Alberta Infant Motor Scale (AIMS) when applied via telehealth and assess its feasibility.

METHODS

Papers were systematically retrieved from electronic databases until October 4, 2023. We included studies that assessed the motor development of infants using the AIMS through telehealth.

RESULTS

Thirteen studies, encompassing 897 infants (449 male and 396 female), were included in this review. Among them, 805 were typical infants and 92 were at risk of motor delay. The age range of participants spanned from 4.9 weeks to 20 months. The studies found good concurrent validity between telehealth and in-person assessments (ICC > 0.98, SEM < 1.6, mean difference = 0.5) and good to excellent reliability (ICC > 0.80) using home videos recorded by parents or video calls with researchers. Telehealth was perceived as feasible by parents, who expressed high satisfaction, and the quality of images and assessments met commendable standards.

CONCLUSION

Considering the limited number of studies that have assessed infants with neuromotor disorders, this scale has the potential for telehealth application in evaluating typical infants.

Preterm-birth alters the development of nodal clustering and neural connection pattern in brain structural network at term-equivalent age

Preterm-born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole-brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term-equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi-shell diffusion-weighted MRI of 28 preterm-born scanned a short time after birth (PB-AB) and at TEA (PB-TEA), and 28 matched term-born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more "connector" hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within-module degree, and participation coefficient) in the limbic/paralimbic, default-mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth-related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.

Network controllability of structural connectomes in the neonatal brain

White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.

Early development of the functional brain network in newborns

During the prenatal period and the first postnatal years, the human brain undergoes rapid growth, which establishes a preliminary infrastructure for the subsequent development of cognition and behavior. To understand the underlying processes of brain functioning and identify potential sources of developmental disorders, it is essential to uncover the developmental rules that govern this critical period. In this study, graph theory modeling and network science analysis were employed to investigate the impact of age, gender, weight, and typical and atypical development on brain development. Local and global topologies of functional connectomes obtained from rs-fMRI data were collected from 421 neonates aged between 31 and 45 postmenstrual weeks who were in natural sleep without any sedation. The results showed that global efficiency, local efficiency, clustering coefficient, and small-worldness increased with age, while modularity and characteristic path length decreased with age. The normalized rich-club coefficient displayed a U-shaped pattern during development. The study also examined the global and local impacts of gender, weight, and group differences between typical and atypical cases. The findings presented some new insights into the maturation of functional brain networks and their relationship with cognitive development and neurodevelopmental disorders.

Correlation of abnormal brain changes with perinatal factors in very preterm infants based on diffusion tensor imaging

Background

It remains unclear whether very preterm (VP) infants have the same level of brain structure and function as full-term (FT) infants. In addition, the relationship between potential differences in brain white matter microstructure and network connectivity and specific perinatal factors has not been well characterized.

Objective

This study aimed to investigate the existence of potential differences in brain white matter microstructure and network connectivity between VP and FT infants at term-equivalent age (TEA) and examine the potential association of these differences with perinatal factors.

Methods

A total of 83 infants were prospectively selected for this study: 43 VP infants (gestational age, or GA: 27–32 weeks) and 40 FT infants (GA: 37–44 weeks). All infants at TEA underwent both conventional magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Significant differences in white matter fractional anisotropy (FA) and mean diffusivity (MD) images between the VP and FT groups were observed using tract-based spatial statistics (TBSS). The fibers were tracked between each pair of regions in the individual space, using the automated anatomical labeling (AAL) atlas. Then, a structural brain network was constructed, where the connection between each pair of nodes was defined by the number of fibers. Network-based statistics (NBS) were used to examine differences in brain network connectivity between the VP and FT groups. Additionally, multivariate linear regression was conducted to investigate potential correlations between fiber bundle numbers and network metrics (global efficiency, local efficiency, and small-worldness) and perinatal factors.

Results

Significant differences in FA were observed between the VP and FT groups in several regions. These differences were found to be significantly associated with perinatal factors such as bronchopulmonary dysplasia (BPD), activity, pulse, grimace, appearance, respiratory (APGAR) score, gestational hypertension, and infection. Significant differences in network connectivity were observed between the VP and FT groups. Linear regression results showed significant correlations between maternal years of education, weight, the APGAR score, GA at birth, and network metrics in the VP group.

Conclusions

The findings of this study shed light on the influence of perinatal factors on brain development in VP infants. These results may serve as a basis for clinical intervention and treatment to improve the outcome of preterm infants.

A computational framework for characterizing normative development of structural brain connectivity in the perinatal stage

Quantitative assessment of the brain's structural connectivity in the perinatal stage is useful for studying normal and abnormal neurodevelopment. However, estimation of the structural connectome from diffusion MRI data involves a series of complex and ill-posed computations. For the perinatal period, this analysis is further challenged by the rapid brain development and difficulties of imaging subjects at this stage. These factors, along with high inter-subject variability, have made it difficult to chart the normative development of the structural connectome. Hence, there is a lack of baseline trends in connectivity metrics that can be used as reliable references for assessing normal and abnormal brain development at this critical stage. In this paper we propose a computational framework, based on spatio-temporal atlases, for determining such baselines. We apply the framework on data from 169 subjects between 33 and 45 postmenstrual weeks. We show that this framework can unveil clear and strong trends in the development of structural connectivity in the perinatal stage. Some of our interesting findings include that connection weighting based on neurite density produces more consistent trends and that the trends in global efficiency, local efficiency, and characteristic path length are more consistent than in other metrics.

Altered development of structural MRI connectome hubs at near-term age in very and moderately preterm infants

Preterm infants may exhibit altered developmental patterns of the brain structural network by endogenous and exogenous stimuli, which are quantifiable through hub and modular network topologies that develop in the third trimester. Although preterm brain networks can compensate for white matter microstructural abnormalities of core connections, less is known about how the network developmental characteristics of preterm infants differ from those of full-term infants. We identified 13 hubs and 4 modules and revealed subtle differences in edgewise connectivity and local network properties between 134 preterm and 76 full-term infants, identifying specific developmental patterns of the brain structural network in preterm infants. The modules of preterm infants showed an imbalanced composition. The edgewise connectivity in preterm infants showed significantly decreased long- and short-range connections and local network properties in the dorsal superior frontal gyrus. In contrast, the fusiform gyrus and several nonhub regions showed significantly increased wiring of short-range connections and local network properties. Our results suggested that decreased local network in the frontal lobe and excessive development in the occipital lobe may contribute to the understanding of brain developmental deviances in preterm infants.

Early Development and the Functional Correlation of Brain Structural Connectivity in Preterm-Born Infants

Exuberant axon growth and competitive pruning lead to dramatic and comprehensive changes in white matter pathways of the infant brain during the first few postnatal months, yet the development of structural configuration in early infancy has not been fully characterized. This study aimed to investigate the developmental trajectory of structural connectivity reflecting relative fiber density in 43 preterm-born infants aged 0–3 months of corrected age without any complications utilizing probabilistic tractography based on fiber orientation distribution and to explore the potential function correlation associated with the network properties based on the Chinese Communication Development of Infant at 10 months of corrected age. The findings revealed significant increases in global efficiency, local efficiency, normalized clustering coefficient, and small-worldness (p_adj < 0.001 for each), while the normalized characteristic path length showed a non-significant decrease with age (p_adj = 0.118). Furthermore, those findings were validated by another parcelation strategy. In addition, the early local efficiency was found to be significantly correlated with words understood at 10 months of corrected age. A unique developmental pattern of structural networks with enhancing efficiency and the small-world property was found in early infancy, which was different from those of neonates or toddlers. In addition, this study revealed a significant correlation between local efficiency and late language comprehension, which indicated that enhanced structural connectivity may lay the structural foundation for language specialization.

Predicting age and clinical risk from the neonatal connectome

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.

Neuroscience meets nurture: challenges of prematurity and the critical role of family-centred and developmental care as a key part of the neuroprotection care bundle

Advances in neonatal-perinatal medicine have resulted in increased survival at lower gestations. Although the incidence of germinal matrix haemorrhage-intraventricular haemorrhage and cystic periventricular leucomalacia is reducing, a new phenotype of preterm brain injury has emerged consisting of a combination of destructive and dysmaturational effects. Consequently, severe neurological disability is reported at a lower rate than previously, but the overall morbidity associated with premature birth continues to present a large global burden and contributes significantly to increased financial costs to health systems and families. In this review, we examine the developmental milestones of fetal brain development and how preterm birth can disrupt this trajectory. We review common morbidities associated with premature birth today. Although drug-based and cell-based neuroprotective therapies for the preterm brain are under intense study, we outline basic, sustainable and effective non-medical, family-centred and developmental care strategies which have the potential to improve neurodevelopmental outcomes for this population and need to be considered part of the future neuroprotection care bundle.

Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis

BACKGROUND

Better biomarkers of eventual outcome are needed for neonatal encephalopathy. To identify the most potent neonatal imaging marker associated with 2-year outcomes, we retrospectively performed diffusion-weighted imaging connectome (DWIC) and fixel-based analysis (FBA) on magnetic resonance imaging (MRI) obtained in the first 4 weeks of life in term neonatal encephalopathy newborns.

METHODS

Diffusion tractography was available in 15 out of 24 babies with MRI, five each with normal, abnormal motor outcome, or death. All 15 except one underwent hypothermia as initial treatment. In abnormal motor and death groups, DWIC found 19 white matter pathways with severely disrupted fiber orientation distributions.

RESULTS

Using random forest classification, these disruptions predicted the follow-up outcomes with 89-99% accuracy. These pathways showed reduced integrity in abnormal motor and death vs. normal tone groups (p < 10^-6). Using ranked supervised multi-view canonical correlation and depicting just three of the five dimensions of the analysis, the abnormal motor and death were clearly differentiated from each other and the normal tone group.

CONCLUSIONS

This study suggests that a machine-learning model for prediction using early DWIC and FBA could be a possible way of developing biomarkers in large MRI datasets having clinical outcomes.

IMPACT

Early connectome and FBA of clinically acquired DWI provide a new noninvasive imaging tool to predict the long-term motor outcomes after birth, based on the severity of white matter injury. Disrupted white matter connectivity as a novel neonatal marker achieves high accuracy of 89-99% to predict 2-year motor outcomes using conventional machine-learning classification. The proposed neonatal marker may allow better prognostication that is important to elucidate neural repair mechanisms and evaluate treatment modalities in neonatal encephalopathy.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

Advanced Brain Imaging in Preterm Infants: A Narrative Review of Microstructural and Connectomic Disruption

Preterm birth disrupts the in utero environment, preventing the brain from fully developing, thereby causing later cognitive and behavioral disorders. Such cerebral alteration occurs beneath an anatomical scale, and is therefore undetectable by conventional imagery. Prematurity impairs the microstructure and thus the histological process responsible for the maturation, including the myelination. Cerebral MRI diffusion tensor imaging sequences, based on water’s motion into the brain, allows a representation of this maturation process. Similarly, the brain’s connections become disorganized. The connectome gathers structural and anatomical white matter fibers, as well as functional networks referring to remote brain regions connected one over another. Structural and functional connectivity is illustrated by tractography and functional MRI, respectively. Their organizations consist of core nodes connected by edges. This basic distribution is already established in the fetal brain. It evolves greatly over time but is compromised by prematurity. Finally, cerebral plasticity is nurtured by a lifetime experience at microstructural and macrostructural scales. A preterm birth causes a negative and early disruption, though it can be partly mitigated by positive stimuli based on developmental neonatal care.

Age-associated network controllability changes in first episode drug-naïve schizophrenia

BACKGROUND

Recent neuroimaging studies revealed dysregulated neurodevelopmental, or/and neurodegenerative trajectories of both structural and functional connections in schizophrenia. However, how the alterations in the brain's structural connectivity lead to dynamic function changes in schizophrenia with age remains poorly understood.

METHODS

Combining structural magnetic resonance imaging and a network control theory approach, the white matter network controllability metric (average controllability) was mapped from age 16 to 60 years in 175 drug-naïve schizophrenia patients and 155 matched healthy controls.

RESULTS

Compared with controls, the schizophrenia patients demonstrated the lack of age-related decrease on average controllability of default mode network (DMN), as well as the right precuneus (a hub region of DMN), suggesting abnormal maturational development process in schizophrenia. Interestingly, the schizophrenia patients demonstrated an accelerated age-related decline of average controllability in the subcortical network, supporting the neurodegenerative model. In addition, compared with controls, the lack of age-related increase on average controllability of the left inferior parietal gyrus in schizophrenia patients also suggested a different pathway of brain development.

CONCLUSIONS

By applying the control theory approach, the present study revealed age-related changes in the ability of white matter pathways to control functional activity states in schizophrenia. The findings supported both the developmental and degenerative hypotheses of schizophrenia, and suggested a particularly high vulnerability of the DMN and subcortical network possibly reflecting an illness-related early marker for the disorder.

Charting the human amygdala development across childhood and adolescence: Manual and automatic segmentation

The developmental pattern of the amygdala throughout childhood and adolescence has been inconsistently reported in previous neuroimaging studies. Given the relatively small size of the amygdala on full brain MRI scans, discrepancies may be partly due to methodological differences in amygdalar segmentation. To investigate the impact of volume extraction methods on amygdala volume, we compared FreeSurfer, FSL and volBrain segmentation measurements with those obtained by manual tracing. The manual tracing method, which we used as the 'gold standard', exhibited almost perfect intra- and inter-rater reliability. We observed systematic differences in amygdala volumes between automatic (FreeSurfer and volBrain) and manual methods. Specifically, compared with the manual tracing, FreeSurfer estimated larger amygdalae, and volBrain produced smaller amygdalae while FSL demonstrated a mixed pattern. The tracing bias was not uniform, but higher for smaller amygdalae. We further modeled amygdalar growth curves using accelerated longitudinal cohort data from the Chinese Color Nest Project (http://deepneuro.bnu.edu.cn/?p=163). Trajectory modeling and statistical assessments of the manually traced amygdalae revealed linearly increasing and parallel developmental patterns for both girls and boys, although the amygdalae of boys were larger than those of girls. Compared to these trajectories, the shapes of developmental curves were similar when using the volBrain derived volumes. FreeSurfer derived trajectories had more nonlinearities and appeared flatter. FSL derived trajectories demonstrated an inverted U shape and were significantly different from those derived from manual tracing method. The use of amygdala volumes adjusted for total gray-matter volumes, but not intracranial volumes, resolved the shape discrepancies and led to reproducible growth curves between manual tracing and the automatic methods (except FSL). Our findings revealed steady growth of the human amygdala, mirroring its functional development across the school age. Methodological improvements are warranted for current automatic tools to achieve more accurate amygdala structure at school age, calling for next generation tools.

Structural and functional connectivity in premature neonates

Advances in neuroimaging have increasingly enabled researchers to investigate whether alterations in brain development commonly identified in preterm infants underlie their high risk of long-term neurodevelopmental impairment, including sensory, motor, cognitive, and psychiatric deficits. This review begins by examining the growing body of literature utilizing advanced magnetic resonance imaging (MRI) techniques to probe structural (via diffusion MRI) and functional (via resting state-functional MRI) connectivity development in the preterm brain during the neonatal period, both in the presence and absence of brain injury. It then details the recent work linking neonatal brain connectivity measures to neurodevelopmental and psychiatric outcomes in prematurely-born cohorts. Finally, building upon the recent substantive growth in the utilization of these neuroimaging modalities, it concludes by highlighting areas in which continued optimization of age-specific acquisition and analysis techniques for these data remains necessary, efforts fundamental to advancing the field toward establishing individual-level predictive capabilities in this high-risk population.

Multi-scale structural rich-club organization of the brain in full-term newborns: a combined DWI and fMRI study

Objective.Our understanding of early brain development is limited due to rapid changes in white matter pathways after birth. In this study, we introduced a multi-scale cross-modal approach to investigate the rich club (RC) organization and topology of the structural brain networks in 40 healthy neonates using diffusion-weighted imaging and resting-state fMRI data.Approach.A group independent component analysis was first performed to identify eight resting state networks (RSNs) used as functional modules. A groupwise whole-brain functional parcellation was also performed at five scales comprising 100-900 parcels. The distribution of RC nodes was then investigated within and between the RSNs. We further assessed the distribution of short and long-range RC, feeder and local connections across different parcellation scales.Main results.Sharing the scale-free characteristic of small-worldness, the neonatal structural brain networks exhibited an RC organization at different nodal scales (NSs). The subcortical, sensory-motor and default mode networks were found to be strongly involved in the RC organization of the structural brain networks, especially in the zones where the RSNs overlapped, with an average cross-scale proportion of 45.9%, 28.5% and 10.5%, respectively. A large proportion of the connector hubs were found to be RC members for the coarsest (73%) to finest (92%) NSs. Our results revealed a prominent involvement of cortico-subcortical and cortico-cerebellar white matter pathways in the RC organization of the neonatal brain. Regardless of the NS, the majority (more than 65.2%) of the inter-RSN connections were long distance RC or feeder with an average physical connection of 105.5 and 97.4 mm, respectively. Several key RC regions were identified, including the insula and cingulate gyri, middle and superior temporal gyri, hippocampus and parahippocampus, fusiform gyrus, precuneus, superior frontal and precentral gyri, calcarine fissure and lingual gyrus.Significance.Our results emphasize the importance of the multi-scale connectivity analysis in assessing the cross-scale reproducibility of the connectivity results concerning the global and local topological properties of the brain networks. Our findings may improve our understanding of the early brain development.

Morphological Development Trajectory and Structural Covariance Network of the Human Fetal Cortical Plate during the Early Second Trimester

During the early second trimester, the cortical plate, or "the developing cortex", undergoes immensely complex and rapid development to complete its major complement of neurons. However, morphological development of the cortical plate and the precise patterning of brain structural covariance networks during this period remain unexplored. In this study, we used 7.0 T high-resolution magnetic resonance images of brain specimens ranging from 14 to 22 gestational weeks to manually segment the cortical plate. Thickness, area expansion, and curvature (i.e., folding) across the cortical plate regions were computed, and correlations of thickness values among different cortical plate regions were measured to analyze fetal cortico-cortical structural covariance throughout development of the early second trimester. The cortical plate displayed significant increases in thickness and expansions in area throughout all regions but changes of curvature in only certain major sulci. The topological architecture and network properties of fetal brain covariance presented immature and inefficient organizations with low degree of integration and high degree of segregation. Altogether, our results provide novel insight on the developmental patterning of cortical plate thickness and the developmental origin of brain network architecture throughout the early second trimester.

Neuroimaging and DNA Methylation: An Innovative Approach to Study the Effects of Early Life Stress on Developmental Plasticity

DNA methylation plays a key role in neural cell fate and provides a molecular link between early life stress and later-life behavioral phenotypes. Here, studies that combine neuroimaging methods and DNA methylation analysis in pediatric population with a history of adverse experiences were systematically reviewed focusing on: targeted genes and neural correlates; statistical models used to examine the link between DNA methylation and neuroimaging data also considering early life stress and behavioral outcomes. We identified 8 studies that report associations between DNA methylation and brain structure/functions in infants, school age children and adolescents faced with early life stress condition (e.g., preterm birth, childhood maltreatment, low socioeconomic status, and less-than optimal caregiving). Results showed that several genes were investigated (e.g., OXTR, SLC6A4, FKBP5, and BDNF) and different neuroimaging techniques were performed (MRI and f-NIRS). Statistical model used ranged from correlational to more complex moderated mediation models. Most of the studies (n = 5) considered DNA methylation and neural correlates as mediators in the relationship between early life stress and behavioral phenotypes. Understanding what role DNA methylation and neural correlates play in interaction with early life stress and behavioral outcomes is crucial to promote theory-driven studies as the future direction of this research fields.

White matter analysis of the extremely preterm born adult brain

The preterm brain has been analysed after birth by a large body of neuroimaging studies; however, few studies have focused on white matter alterations in preterm subjects beyond infancy, especially in individuals born at extremely low gestation age - before 28 completed weeks. Neuroimaging data of extremely preterm young adults are now available to investigate the long-term structural alterations of disrupted neurodevelopment. We examined white matter hierarchical organisation and microstructure in extremely preterm young adults. Specifically, we first identified the putative hubs and peripheral regions in 85 extremely preterm young adults and compared them with 53 socio-economically matched and full-term born peers. Moreover, we analysed Fractional Anisotropy (FA), Mean Diffusivity (MD), Neurite Density Index (NDI), and Orientation Dispersion Index (ODI) of white matter in hubs, peripheral regions, and over the whole brain. Our results suggest that the hierarchical organisation of the extremely preterm adult brain remains intact. However, there is evidence of significant alteration of white matter connectivity at both the macro- and microstructural level, with overall diminished connectivity, reduced FA and NDI, increased MD, and comparable ODI; suggesting that, although the spatial configuration of WM fibres is comparable, there are less WM fibres per voxel. These alterations are found throughout the brain and are more prevalent along the pathways between deep grey matter regions, frontal regions and cerebellum. This work provides evidence that white matter abnormalities associated with the premature exposure to the extrauterine environment not only are present at term equivalent age but persist into early adulthood.

Neuroimaging in infants with prenatal opioid exposure: Current evidence, recent developments and targets for future research

Prenatal opioid exposure (POE) has shown to be a risk factor for adverse long-term cognitive and behavioral outcomes in offspring. However, the neural mechanisms of these outcomes remain poorly understood. While preclinical and human studies suggest that these outcomes may be due to opioid-mediated changes in the fetal and early postnatal brain, other maternal, social, and environmental factors are also shown to play a role. Recent neuroimaging studies reveal brain alterations in children with POE. Early neuroimaging and novel methodology could provide an in vivo mechanistic understanding of opioid mediated alterations in developing brain. However, this is an area of ongoing research. In this review we explore recent imaging developments in POE, with emphasis on the neonatal and infant brain, and highlight some of the challenges of imaging the developing brain in this population. We also highlight evidence from animal models and imaging in older children and youth to understand areas where future research may be targeted in infants with POE.

Disrupted brain connectivity in children treated with therapeutic hypothermia for neonatal encephalopathy

Therapeutic hypothermia following neonatal encephalopathy due to birth asphyxia reduces death and cerebral palsy. However, school-age children without cerebral palsy treated with therapeutic hypothermia for neonatal encephalopathy still have reduced performance on cognitive and motor tests, attention difficulties, slower reaction times and reduced visuo-spatial processing abilities compared to typically developing controls. We acquired diffusion-weighted imaging data from school-age children without cerebral palsy treated with therapeutic hypothermia for neonatal encephalopathy at birth, and a matched control group. Voxelwise analysis (33 cases, 36 controls) confirmed reduced fractional anisotropy in widespread areas of white matter in cases, particularly in the fornix, corpus callosum, anterior and posterior limbs of the internal capsule bilaterally and cingulum bilaterally. In structural brain networks constructed using probabilistic tractography (22 cases, 32 controls), graph-theoretic measures of strength, local and global efficiency, clustering coefficient and characteristic path length were found to correlate with IQ in cases but not controls. Network-based statistic analysis implicated brain regions involved in visuo-spatial processing and attention, aligning with previous behavioural findings. These included the precuneus, thalamus, left superior parietal gyrus and left inferior temporal gyrus. Our findings demonstrate that, despite the manifest successes of therapeutic hypothermia, brain development is impaired in these children.

Altered asymmetries of the structural networks comprising the fronto-limbic brain circuitry of preterm infants

This study aimed to elaborate upon prior findings suggestive of the altered lateralization of structural connectivity in the developing preterm brain by using diffusion tensor imaging tractography to explore how network topological asymmetries in fronto-limbic neural circuitry are altered at 36–41 weeks, postmenstrual age in 64 preterm infants without severe brain injury and 33 term-born infants. We compared the pattern of structural connectivity and network lateralization of the betweenness centrality in the medial fronto-orbital gyrus, superior temporal gyrus, amygdala, and hippocampus—the structures comprising the fronto-limbic brain circuit—between preterm and term infants. Global efficiency, local efficiency, and small-world characteristics did not differ significantly between the two hemispheres in term-born infants, suggesting that integration and segregation are balanced between the left and right hemispheres. However, the preterm brain showed significantly greater leftward lateralization of small-worldness (P = 0.033); the lateralization index of the betweenness centrality revealed that the medial fronto-orbital gyrus (P = 0.008), superior temporal gyrus (P = 0.031), and hippocampus (P = 0.028) showed significantly increased leftward asymmetry in preterm infants relative to term-infants independent of sex, age at imaging, and bronchopulmonary dysplasia. The altered lateralization of fronto-limbic brain circuitry might be involved in the early development of social–emotional disorders in preterm infants.

Hierarchical Complexity of the Macro-Scale Neonatal Brain

The human adult structural connectome has a rich nodal hierarchy, with highly diverse connectivity patterns aligned to the diverse range of functional specializations in the brain. The emergence of this hierarchical complexity in human development is unknown. Here, we substantiate the hierarchical tiers and hierarchical complexity of brain networks in the newborn period, assess correspondences with hierarchical complexity in adulthood, and investigate the effect of preterm birth, a leading cause of atypical brain development and later neurocognitive impairment, on hierarchical complexity. We report that neonatal and adult structural connectomes are both composed of distinct hierarchical tiers and that hierarchical complexity is greater in term born neonates than in preterms. This is due to diversity of connectivity patterns of regions within the intermediate tiers, which consist of regions that underlie sensorimotor processing and its integration with cognitive information. For neonates and adults, the highest tier (hub regions) is ordered, rather than complex, with more homogeneous connectivity patterns in structural hubs. This suggests that the brain develops first a more rigid structure in hub regions allowing for the development of greater and more diverse functional specialization in lower level regions, while connectivity underpinning this diversity is dysmature in infants born preterm.

Preterm birth leads to impaired rich-club organization and fronto-paralimbic/limbic structural connectivity in newborns

Prematurity disrupts brain development during a critical period of brain growth and organization and is known to be associated with an increased risk of neurodevelopmental impairments. Investigating whole-brain structural connectivity alterations accompanying preterm birth may provide a better comprehension of the neurobiological mechanisms related to the later neurocognitive deficits observed in this population. Using a connectome approach, we aimed to study the impact of prematurity on neonatal whole-brain structural network organization at term-equivalent age. In this cohort study, twenty-four very preterm infants at term-equivalent age (VPT-TEA) and fourteen full-term (FT) newborns underwent a brain MRI exam at term age, comprising T2-weighted imaging and diffusion MRI, used to reconstruct brain connectomes by applying probabilistic constrained spherical deconvolution whole-brain tractography. The topological properties of brain networks were quantified through a graph-theoretical approach. Furthermore, edge-wise connectivity strength was compared between groups. Overall, VPT-TEA infants' brain networks evidenced increased segregation and decreased integration capacity, revealed by an increased clustering coefficient, increased modularity, increased characteristic path length, decreased global efficiency and diminished rich-club coefficient. Furthermore, in comparison to FT, VPT-TEA infants had decreased connectivity strength in various cortico-cortical, cortico-subcortical and intra-subcortical networks, the majority of them being intra-hemispheric fronto-paralimbic and fronto-limbic. Inter-hemispheric connectivity was also decreased in VPT-TEA infants, namely through connections linking to the left precuneus or left dorsal cingulate gyrus - two regions that were found to be hubs in FT but not in VPT-TEA infants. Moreover, posterior regions from Default-Mode-Network (DMN), namely precuneus and posterior cingulate gyrus, had decreased structural connectivity in VPT-TEA group. Our finding that VPT-TEA infants' brain networks displayed increased modularity, weakened rich-club connectivity and diminished global efficiency compared to FT infants suggests a delayed transition from a local architecture, focused on short-range connections, to a more distributed architecture with efficient long-range connections in those infants. The disruption of connectivity in fronto-paralimbic/limbic and posterior DMN regions might underlie the behavioral and social cognition difficulties previously reported in the preterm population.

Reduced structural connectivity in cortico-striatal-thalamic network in neonates with congenital heart disease

Impaired brain development has been observed in newborns with congenital heart disease (CHD). We performed graph theoretical analyses and network-based statistics (NBS) to assess global brain network topology and identify subnetworks of altered connectivity in infants with CHD prior to cardiac surgery. Fifty-eight infants with critical/serious CHD prior to surgery and 116 matched healthy controls as part of the developing Human Connectome Project (dHCP) underwent MRI on a 3T system and high angular resolution diffusion MRI (HARDI) was obtained. Multi-tissue constrained spherical deconvolution, anatomically constrained probabilistic tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT2) was used to construct weighted structural networks. Network topology was assessed and NBS was used to identify structural connectivity differences between CHD and control groups. Structural networks were partitioned into core and peripheral nodes, and edges classed as core, peripheral, or feeder. NBS identified one subnetwork with reduced structural connectivity in CHD infants involving basal ganglia, amygdala, hippocampus, cerebellum, vermis, and temporal and parieto-occipital lobe, primarily affecting core nodes and edges. However, we did not find significantly different global network characteristics in CHD neonates. This locally affected sub-network with reduced connectivity could explain, at least in part, the neurodevelopmental impairments associated with CHD.

Non-negative data-driven mapping of structural connections with application to the neonatal brain

Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.

Identification of caveolin-1 domain signatures via machine learning and graphlet analysis of single-molecule super-resolution data

MOTIVATION

Network analysis and unsupervised machine learning processing of single-molecule localization microscopy of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc.) effectively distinguishes caveolae and scaffold blobs.

RESULTS

Caveolae formation requires both Cav1 and the adaptor protein CAVIN1 (also called PTRF). As a supervised learning approach, we applied a wide-field CAVIN1/PTRF mask to CAVIN1/PTRF-transfected PC3 prostate cancer cells and used the random forest classifier to classify blobs based on graphlet frequency distribution (GFD). GFD of CAVIN1/PTRF-positive (PTRF+) and -negative Cav1 clusters showed poor classification accuracy that was significantly improved by stratifying the PTRF+ clusters by either number of localizations or volume. Low classification accuracy (<50%) of large PTRF+ clusters and caveolae blobs identified by unsupervised learning suggests that their GFD is specific to caveolae. High classification accuracy for small PTRF+ clusters and caveolae blobs argues that CAVIN1/PTRF associates not only with caveolae but also non-caveolar scaffolds. At low proximity thresholds (50-100 nm), the caveolae groups showed reduced frequency of highly connected graphlets and increased frequency of completely disconnected graphlets. GFD analysis of single-molecule localization microscopy Cav1 clusters defines changes in structural organization in caveolae and scaffolds independent of association with CAVIN1/PTRF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates

BACKGROUND

Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about the accuracy of how skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network.

METHODS

Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a Johns Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, C_p; characteristic path length, L_p; local efficiency, E_local; global efficiency, E_global) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows.

RESULTS

There were significant differences in volumes of 50 brain regions between the three workflows (P < 0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased C_p, increased L_p, decreased E_local, and decreased E_global, in contrast to the two automatic ones.

CONCLUSIONS

Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.

Altered structural brain networks at term-equivalent age in preterm infants with grade 1 intraventricular hemorrhage

BACKGROUND

Preterm infants are at risk for structural disruption of brain connectivity due to perinatal complications encountered during the fetal and neonatal periods. This study aimed to investigate the development of connectivity using diffusion tensor imaging at near-term age and the effect of grade 1 intraventricular hemorrhage on it.

METHODS

A total of 86 infants (55 preterm infants, 24 full-term infants) without apparent brain injury underwent diffusion magnetic resonance imaging (MRI) between 36 and 41 weeks post-menstrual age. The diffusion-MRI based connectomics were constructed from 64-segmented regions by using the Johns Hopkins University neonate atlas and were weighted with fractional anisotropy. The connectomes were quantified in the structural networks and investigated using network metrics, such as the clustering coefficient, local efficiency, characteristic path length, global efficiency, and small-worldness. We compared the differences in the brain networks of preterm infants with or without grade 1 intraventricular hemorrhage in binary and fractional anisotropy-weighted (wFA) connectomes.

RESULTS

The 55 preterm infants had a mean gestational age at birth of 29.3 ± 4.1 weeks and the 24 term-born infants, 38.1 ± 1.1 weeks. A total of 13 of the 55 preterm infants (23.6%) were diagnosed with grade 1 intraventricular hemorrhage. The development of connectivity of the brain network in preterm infants without intraventricular hemorrhage was comparable at near-term age to that in term infants. The preterm infants with germinal matrix hemorrhage exhibited higher clustering (0.093 ± 0.015 vs. 0.088 ± 0.007, p = 0.027) and local efficiency (0.151 ± 0.022 vs. 0.141 ± 0.010, p = 0.025), implying the potential for segregation. However, the preterm infants with intraventricular hemorrhage revealed a longer path length (0.291 ± 0.035 vs. 0.275 ± 0.019, p = 0.020) and lower global efficiency (3.998 ± 0.473 vs. 4.212 ± 0.281, p = 0.048), indicating a decreased integration in the wFA connectivity matrix than those without germinal matrix hemorrhage, after correcting for gestational age, sex, bronchopulmonary dysplasia, and age at scan.

CONCLUSION

Grade 1 intraventricular hemorrhage in preterm infants may enhance the capacity for local information transfer and the relative reinforcement of the segregation of networks at the expense of global integration capacity.

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model

BACKGROUND AND AIMS

Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model.

METHODS

Seventy-seven very preterm infants (born <31 weeks gestational age (GA)) in a prospective longitudinal cohort underwent diffusion MR imaging (3T Siemens Trio; 64 directions, b ​= ​2000 ​s/mm²). Motor outcome at 2 years corrected age (CA) was measured by Neuro-Sensory Motor Developmental Assessment (NSMDA). Scores were dichotomised into normal (functional score: 0, normal; n ​= ​48) and abnormal scores (functional score: 1-5, mild-profound; n ​= ​29). MRIs were pre-processed to reduce artefacts, upsampled to 1.25 ​mm isotropic resolution and maps of fractional anisotropy (FA) were estimated. Patches extracted from each image were used as inputs to train a CNN, wherein each image patch predicted either normal or abnormal outcome. In a postprocessing step, an image was classified as predicting abnormal outcome if at least 27% (determined by a grid search to maximise the model performance) of its patches predicted abnormal outcome. Otherwise, it was considered as normal. Ten-fold cross-validation was used to estimate performance. Finally, heatmaps of model predictions for patches in abnormal scans were generated to explore the locations associated with abnormal outcome.

RESULTS

For the identification of infants with abnormal motor outcome based on the FA data from early MRI, we achieved mean sensitivity 70% (standard deviation SD 19%), mean specificity 74% (SD 39%), mean AUC (area under the receiver operating characteristic curve) 72% (SD 14%), mean F1 score of 68% (SD 13%) and mean accuracy 73% (SD 19%) on an unseen test data set. Patch-based prediction heatmaps showed that the patches around the motor cortex and somatosensory regions were most frequently identified by the model with high precision (74%) as a location associated with abnormal outcome. Part of the cerebellum, and occipital and frontal lobes were also highly associated with abnormal NSMDA/motor outcome.

DISCUSSION/CONCLUSION

This study established the potential of an early brain MRI-based deep learning CNN model to identify preterm infants at risk of a later motor impairment and to identify brain regions predictive of adverse outcome. Results suggest that predictions can be made from FA maps of diffusion MRIs well before term equivalent age (TEA) without any prior knowledge of which MRI features to extract and associated feature extraction steps. This method, therefore, is suitable for any case of brain condition/abnormality. Future studies should be conducted on a larger cohort to re-validate the robustness and effectiveness of these models.

Diffusion tensor imaging is associated with motor outcomes of very preterm born children at 11 years of age

AIM

Very preterm children born <32 weeks of gestation are at risk for motor difficulties such as cerebral palsy and developmental coordination disorder. This study explores the association between diffusion tensor imaging metrics at term and motor outcomes at 11 years of age.

METHODS

A cohort of 37 very preterm infants (mean gestational age 29 4/7, SD 2 0/7) born in 2004-2006 in Turku University Hospital underwent diffusion tensor imaging at term. A region of interest analysis of fractional anisotropy and mean diffusivity was performed. Motor outcomes at 11 years of age were measured with the Movement Assessment Battery for Children - Second Edition.

RESULTS

The diffusion metrics of the corpus callosum (genu P = .005, splenium P = .049), the left corona radiata (P = .035) and the right optic radiation (P = .017) were related to later motor performance. Mean diffusivity decreased and fractional anisotropy increased in proportion to the improving performance.

CONCLUSION

The diffusion metrics of the genu and splenium of the corpus callosum, the left corona radiata and the right optic radiation at term were associated with motor skills at 11 years of age. Diffusion tensor imaging should be further studied as a potential tool in recognising children at risk for motor impairment.

Automatic, Age Consistent Reconstruction of the Corpus Callosum Guided by Coherency From In Utero Diffusion-Weighted MRI

Reconstruction of white matter connectivity in the fetal brain from in utero diffusion-weighted magnetic resonance imaging (MRI) faces many challenges, including subject motion, small anatomical scale, and limited image resolution and signal. These issues are compounded by the need to track significant changes in structural connectivity throughout development. We present an automated method for improved reliability and completeness of tract extraction across a wide range of gestational ages, based on the geometry of coherent patterns in streamline tractography, and apply it to the reconstruction of the corpus callosum. This method, focused specifically at addressing the challenges of fetal brain imaging, avoids depending on a tractography atlas, and handles variations in size, shape, and tissue properties of developing brains, both between subjects and across ages. Although tractography from in utero MRI generally suffers from a significant number of misleading and missing pathways, we demonstrate the feasibility of extracting the coherent bundle of the corpus callosum while avoiding inappropriate diversions into other tracts.

Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth

Multi-contrast MRI captures information about brain macro- and micro-structure which can be combined in an integrated model to obtain a detailed "fingerprint" of the anatomical properties of an individual's brain. Inter-regional similarities between features derived from structural and diffusion MRI, including regional volumes, diffusion tensor metrics, neurite orientation dispersion and density imaging measures, can be modelled as morphometric similarity networks (MSNs). Here, individual MSNs were derived from 105 neonates (59 preterm and 46 term) who were scanned between 38 and 45 weeks postmenstrual age (PMA). Inter-regional similarities were used as predictors in a regression model of age at the time of scanning and in a classification model to discriminate between preterm and term infant brains. When tested on unseen data, the regression model predicted PMA at scan with a mean absolute error of 0.70  ±  0.56 weeks, and the classification model achieved 92% accuracy. We conclude that MSNs predict chronological brain age accurately; and they provide a data-driven approach to identify networks that characterise typical maturation and those that contribute most to neuroanatomic variation associated with preterm birth.

Network based statistics reveals trophic and neuroprotective effect of early high dose erythropoetin on brain connectivity in very preterm infants

Periventricular white matter injury is common in very preterm infants and it is associated with long term neurodevelopmental impairments. While evidence supports the protective effects of erythropoetin (EPO) in preventing injury, we currently lack the complete understanding of how EPO affects the emergence and maturation of anatomical brain connectivity and function. In this case-control study, connectomic analysis based on diffusion MRI tractography was applied to evaluate the effect of early high-dose EPO in preterm infants. A whole brain, network-level analysis revealed a sub-network of anatomical brain connections in which connectivity strengths were significantly stronger in the EPO group. This distributed network comprised connections predominantly in the frontal and temporal lobe bilaterally, and the effect of EPO was focused on peripheral and feeder connections of the core structural connectivity network. EPO resulted in a globally increased clustering coefficient, higher global and average local efficiency, while higher strength and increased clustering was found for regions in the frontal lobe and cingulate gyrus. The connectivity network most affected by the EPO treatment showed a steeper increase graph theoretical measures with age compared to the placebo group. Our results demonstrate a weak but widespread effect of EPO on the structural connectivity network and a possible trophic effect of EPO reflected by increasing network segregation, predominantly in local connections.

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Erythropoietin (EPO) is a potential neuroprotective agent in very preterm infants.

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EPO leads to increased structural brain connectivity in fronto-temporal regions.

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Clustering coefficient, local and global efficiency increases after EPO treatment.

Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images

Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero.

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.

Accelerated Small-World Property of Structural Brain Networks in Preterm Infants at Term-Equivalent Age

BACKGROUND

The prediction of neurodevelopmental outcomes in preterm infants is one of the clinical challenges of pediatrics. Despite the recent interest in brain development and white matter connectivity using a network-based analysis, very little is known about the brain network of at term-equivalent age in preterm infants.

OBJECTIVE

We aimed to investigate the structural brain network using diffusion MRI following preterm delivery at term-equivalent age compared with term infants and explored the influence of gestational age (GA) and clinical factors.

METHOD

Diffusion tensor imaging data were acquired prospectively from 55 preterm neonates without apparent brain abnormalities (mean gestational age: 29.43 weeks) and 21 full-term infants at term-equivalent age. The global structural brain networks were produced by probabilistic white matter tractography in combination with the Johns Hopkins University neonate atlas to quantify connectivity between different cortical regions.

RESULTS

Compared with full-term infants, preterm infants had significantly lower global efficiency (p = 0.048) and increased small worldness (p = 0.012) after correcting for sex and age at MRI scan. The increased small worldness in the brain network at term-equivalent age was significantly linearly correlated with lower GA after adjusting for sex and the effects of postmenstrual age at MRI scan on the data in preterm infants (β = -0.020, p = 0.037). In multivariate analysis, infants with chronic lung disease had significantly decreased changes in clustering (p = 0.014) and local efficiency (p = 0.027).

CONCLUSION

The accelerated small worldness in preterm infants suggests that the structural brain network after preterm birth is reorganized in maximizing integrated and segregated processing, implying resilience against prematurity-associated pathology.

Neural histology and neurogenesis of the human fetal and infant brain

The human brain develops slowly and over a long period of time which lasts for almost three decades. This enables good spatio-temporal resolution of histogenetic and neurogenetic events as well as an appropriate and clinically relevant timing of these events. In order to successfully apply in vivo neuroimaging data, in analyzing both the normal brain development and the neurodevelopmental origin of major neurological and mental disorders, it is important to correlate these neuroimaging data with the existing data on morphogenetic, histogenetic and neurogenetic events. Furthermore, when performing such correlation, the genetic, genomic, and molecular biology data on phenotypic specification of developing brain regions, areas and neurons should also be included. In this review, we focus on early developmental periods (form 8 postconceptional weeks to the second postnatal year) and describe the microstructural organization and neural circuitry elements of the fetal and early postnatal human cerebrum.

Early breast milk exposure modifies brain connectivity in preterm infants

Preterm infants are at increased risk of alterations in brain structure and connectivity, and subsequent neurocognitive impairment. Breast milk may be more advantageous than formula feed for promoting brain development in infants born at term, but uncertainties remain about its effect on preterm brain development and the optimal nutritional regimen for preterm infants. We test the hypothesis that breast milk exposure is associated with improved markers of brain development and connectivity in preterm infants at term equivalent age. We collected information about neonatal breast milk exposure and brain MRI at term equivalent age from 47 preterm infants (mean postmenstrual age [PMA] 29.43 weeks, range 23.28-33.0). Network-Based Statistics (NBS), Tract-based Spatial Statistics (TBSS) and volumetric analysis were used to investigate the effect of breast milk exposure on white matter water diffusion parameters, tissue volumes, and the structural connectome. Twenty-seven infants received exclusive breast milk feeds for ≥75% of days of in-patient care and this was associated with higher connectivity in the fractional anisotropy (FA)-weighted connectome compared with the group who had < 75% of days receiving exclusive breast milk feeds (NBS, p = 0.04). Within the TBSS white matter skeleton, the group that received ≥75% exclusive breast milk days exhibited higher FA within the corpus callosum, cingulum cingulate gyri, centrum semiovale, corticospinal tracts, arcuate fasciculi and posterior limbs of the internal capsule compared with the low exposure group after adjustment for PMA at birth, PMA at image acquisition, bronchopulmonary dysplasia, and chorioamnionitis (p < 0.05). The effect on structural connectivity and tract water diffusion parameters was greater with ≥90% exposure, suggesting a dose effect. There were no significant groupwise differences in brain volumes. Breast milk feeding in the weeks after preterm birth is associated with improved structural connectivity of developing networks and greater FA in major white matter fasciculi.