Hubs in the human fetal brain network

Evaluating the effects of volume censoring on fetal functional connectivity

Advances in neuroimaging have enabled non-invasive investigation of fetal brain development in vivo. Resting-state functional magnetic resonance imaging (rs-fMRI) has provided critical insights into emerging brain networks in fetuses. However, acquiring high-quality fetal rs-fMRI remains challenging due to the unpredictable and unconstrained motion of the fetal head. Nuisance regression, where the brain signal is regressed onto translational and rotational head motion parameters, has been widely and effectively used in adults to reduce the influence of motion. However, subsequent studies have revealed that associations between head motion and large-scale brain functional connectivity (FC) persisted even after regression. In ex utero groups (e.g., newborns, toddlers, and adults), censoring high-motion volumes has shown effectiveness in mitigating such lingering impacts of head motion. While censoring high motion volumes has been utilized in fetal rs-fMRI, a systematic assessment of the effectiveness of regression and censoring high motion volumes in fetuses has not been done. Establishing the effectiveness of censoring in fetal rs-fMRI is critical to avoid possible bias in findings resulting from head motion. To address this knowledge gap, we investigated the associations between head motion and fetal rs-fMRI at different analysis scales: blood oxygenation level dependent (BOLD) time series and whole-brain FC. We used a dataset of 120 fetal scans collected from 104 healthy fetuses. We found that nuisance regression reduced the association between head motion, defined by frame-by-frame displacement (FD) of head position, and BOLD time series data in all regions of interest (ROI) encompassing the whole brain. Nuisance regression, however, was not effective in reducing the impact of head motion on FC. Fetuses' FC profiles significantly predicted average FD (r = 0.09 ± 0.08; p < 10-3) after regression, suggesting a lingering effect of motion on whole-brain patterns. To dissociate head motion and the FC, we used volume censoring and evaluated its efficacy in correcting motion at different thresholds. We demonstrated that censored data improved resting state data's ability to predict neurobiological features, such as gestational age and sex (accuracy = 55.2 ± 2.9% with 1.5 mm vs. 44.6 ± 3.6% with no censoring). Collectively, our results highlight the importance of data censoring in reducing the lingering impact of head motion on fetal rs-fMRI, thus attenuating motion-related bias. Like older age groups such as neonates and adults, combining regression and censoring techniques is recommended for large-scale FC analysis, e.g., network-based analysis, for fetuses.

Deciphering network dysregulations and temporo-spatial dynamics in disorders of consciousness: insights from minimum spanning tree analysis

Objectives

The neural mechanism associated with impaired consciousness is not fully clear. We aim to explore the association between static and dynamic minimum spanning tree (MST) characteristics and neural mechanism underlying impaired consciousness.

Methods

MSTs were constructed based on full-length functional magnetic resonance imaging (fMRI) signals and fMRI signal segments within each time window. Global and local measures of static MSTs, as well as spatio-temporal interaction characteristics of dynamic MSTs were investigated.

Results

A disruption or an alteration in the functional connectivity, the decreased average coupling strength and the reorganization of hub nodes were observed in patients with minimally conscious state (MCS) and patients with vegetative state (VS). The analysis of global and local measures quantitatively supported altered static functional connectivity patterns and revealed a slower information transmission efficiency in both patient groups. From a dynamic perspective, the spatial distribution of hub nodes exhibited relative stability over time in both normal and patient populations. The increased temporal variability in multiple brain regions within resting-state networks associated with consciousness was detected in MCS patients and VS patients, especially thalamus. As well, the increased spatial variability in multiple brain regions within these resting-state networks was detected in MCS patients and VS patients. In addition, local measure and spatio-temporal variability analysis indicated that the differences in network structure between two groups of patients were mainly in frontoparietal network and auditory network.

Conclusion

Our findings suggest that altered static and dynamic MST characteristics may shed some light on neural mechanism underlying impaired consciousness.

Trajectories of human brain functional connectome maturation across the birth transition

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

Functional connectivity of the pediatric brain

PURPOSE

This review highlights the importance of functional connectivity in pediatric neuroscience, focusing on its role in understanding neurodevelopment and potential applications in clinical practice. It discusses various techniques for analyzing brain connectivity and their implications for clinical interventions in neurodevelopmental disorders.

METHODS

The principles and applications of independent component analysis and seed-based connectivity analysis in pediatric brain studies are outlined. Additionally, the use of graph analysis to enhance understanding of network organization and topology is reviewed, providing a comprehensive overview of connectivity methods across developmental stages, from fetuses to adolescents.

RESULTS

Findings from the reviewed studies reveal that functional connectivity research has uncovered significant insights into the early formation of brain circuits in fetuses and neonates, particularly the prenatal origins of cognitive and sensory systems. Longitudinal research across childhood and adolescence demonstrates dynamic changes in brain connectivity, identifying critical periods of development and maturation that are essential for understanding neurodevelopmental trajectories and disorders.

CONCLUSION

Functional connectivity methods are crucial for advancing pediatric neuroscience. Techniques such as independent component analysis, seed-based connectivity analysis, and graph analysis offer valuable perspectives on brain development, creating new opportunities for early diagnosis and targeted interventions in neurodevelopmental disorders, thereby paving the way for personalized therapeutic strategies.

Greater Neighborhood Disadvantage Is Associated with Alterations in Fetal Functional Brain Network Structure

OBJECTIVE

To determine the association between neighborhood disadvantage (ND) and functional brain development of in utero fetuses.

STUDY DESIGN

We conducted an observational study using Social Vulnerability Index (SVI) scores to assess the impact of ND on a prospectively recruited sample of healthy pregnant women from Washington, DC. Using 79 functional magnetic resonance imaging scans from 68 healthy pregnancies at a mean gestational age of 33.12 weeks, we characterized the overall functional brain network structure using a graph metric approach. We used linear mixed effects models to assess the relationship between SVI and gestational age on 5 graph metrics, adjusting for multiple scans.

RESULTS

Exposure to greater ND was associated with less well integrated functional brain networks, as observed by longer characteristic path lengths and diminished global efficiency (GE), as well as diminished small world propensity (SWP). Across gestational ages, however, the association between SVI and network integration diminished to a negligible relationship in the third trimester. Conversely, SWP was significant across pregnancy, but the relationship changed such that there was a negative association with SWP earlier in the second trimester that inverted around the transition to the third trimester to a positive association.

CONCLUSIONS

These data directly connect ND and altered functional brain maturation in fetuses. Our results suggest that, even before birth, proximity to environmental stressors in the wider neighborhood environment are associated with altered brain development.

Advances in Fetal Brain Imaging

Over the last 20 years, there have been remarkable developments in fetal brain MR imaging analysis methods. This article delves into the specifics of structural imaging, diffusion imaging, functional MR imaging, and spectroscopy, highlighting the latest advancements in motion correction, fetal brain development atlases, and the challenges and innovations. Furthermore, this article explores the clinical applications of these advanced imaging techniques in comprehending and diagnosing fetal brain development and abnormalities.

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.

Brain structural and functional outcomes in the offspring of women experiencing psychological distress during pregnancy

In-utero exposure to maternal psychological distress is increasingly linked with disrupted fetal and neonatal brain development and long-term neurobehavioral dysfunction in children and adults. Elevated maternal psychological distress is associated with changes in fetal brain structure and function, including reduced hippocampal and cerebellar volumes, increased cerebral cortical gyrification and sulcal depth, decreased brain metabolites (e.g., choline and creatine levels), and disrupted functional connectivity. After birth, reduced cerebral and cerebellar gray matter volumes, increased cerebral cortical gyrification, altered amygdala and hippocampal volumes, and disturbed brain microstructure and functional connectivity have been reported in the offspring months or even years after exposure to maternal distress during pregnancy. Additionally, adverse child neurodevelopment outcomes such as cognitive, language, learning, memory, social-emotional problems, and neuropsychiatric dysfunction are being increasingly reported after prenatal exposure to maternal distress. The mechanisms by which prenatal maternal psychological distress influences early brain development include but are not limited to impaired placental function, disrupted fetal epigenetic regulation, altered microbiome and inflammation, dysregulated hypothalamic pituitary adrenal axis, altered distribution of the fetal cardiac output to the brain, and disrupted maternal sleep and appetite. This review will appraise the available literature on the brain structural and functional outcomes and neurodevelopmental outcomes in the offspring of pregnant women experiencing elevated psychological distress. In addition, it will also provide an overview of the mechanistic underpinnings of brain development changes in stress response and discuss current treatments for elevated maternal psychological distress, including pharmacotherapy (e.g., selective serotonin reuptake inhibitors) and non-pharmacotherapy (e.g., cognitive-behavior therapy). Finally, it will end with a consideration of future directions in the field.

Characterizing Large-Scale Human Circuit Development with In Vivo Neuroimaging

Large-scale coordinated patterns of neural activity are crucial for the integration of information in the human brain and to enable complex and flexible human behavior across the life span. Through recent advances in noninvasive functional magnetic resonance imaging (fMRI) methods, it is now possible to study this activity and how it emerges in the living fetal brain across the second half of human gestation. This work has demonstrated that functional activity in the fetal brain has several features in keeping with highly organized networks of activity, which are undergoing a highly programmed and rapid sequence of development before birth, in which long-range connections emerge and core features of the mature functional connectome (such as hub regions and a gradient organization) are established. In this review, the findings of these studies are summarized, their relationship to the known changes in developmental neurobiology is considered, and considerations for future work in the context of limitations to the fMRI approach are presented.

Late Neonatal Sepsis in Very-low-birth-weight Premature Newborns Is Associated With Alterations in Neurodevelopment at Twenty-five Months of Age

AIM

To evaluate the impact of late-onset sepsis (LOS) on the neurodevelopment of very-low-birth-weight (VLBW) premature infants.

METHODS

This is a retrospective cohort study of VLBW premature infants. The Mental Development Index (MDI) was determined for a population of 546 VLBW infants, at 14 and 25 months of age, and evaluated using the Bayley test. A history of meningitis or early neonatal sepsis was considered an exclusion criterion. The study parameters analyzed included perinatal variables, the development of neonatal comorbidities and a history of LOS. Multivariate linear regression and multinomial logistic regression analyses were performed.

RESULTS

LOS was observed in 115 newborns, among whom microbiological testing showed that 65.0% presented Gram-positive bacteria, with Staphylococcus epidermidis being responsible for 55.4%. There was a significant association between the 25-month MDI and a history of LOS. This represents a decrease of 7.9 points in the MDI evaluation of newborns with a history of LOS. The latter history is also associated with the following neurodevelopmental alternations: mild motor disorders [odds ratio (OR): 2.75; 95% confidence intervals (CI): 1.07-7.05], moderate cognitive delay (OR: 3.07; 95% CI: 1.17-8.00) and cerebral palsy (OR: 2.41; 95% CI: 1.09-5.35).

CONCLUSIONS

In our study cohort, LOS was associated with alterations in neurodevelopment, including reduced MDI, together with motor and cognitive disorders and cerebral palsy. To improve neurodevelopmental outcomes in this group of newborns, neonatal intensive care unit personnel should focus attention on preventing hospital-acquired infections.

Association between degree centrality and neurocognitive impairments in patients with Schizophrenia: A Longitudinal rs-fMRI Study

BACKGROUND

Evidence indicates that patients with schizophrenia (SZ) experience significant changes in their functional connectivity during antipsychotic treatment. Despite previous reports of changes in brain network degree centrality (DC) in patients with schizophrenia, the relationship between brain DC changes and neurocognitive improvement in patients with SZ after antipsychotic treatment remains elusive.

METHODS

A total of 74 patients with acute episodes of chronic SZ and 53 age- and sex-matched healthy controls were recruited. The Positive and Negative Syndrome Scale (PANSS), Symbol Digit Modalities Test, digital span test (DST), and verbal fluency test were used to evaluate the clinical symptoms and cognitive performance of the patients with SZ. Patients with SZ were treated with antipsychotics for six weeks starting at baseline and underwent MRI and clinical interviews at baseline and after six weeks, respectively. We then divided the patients with SZ into responding (RS) and non-responding (NRS) groups based on the PANSS scores (reduction rate of PANSS ≥50%). DC was calculated and analyzed to determine its correlation with clinical symptoms and cognitive performance.

RESULTS

After antipsychotic treatment, the patients with SZ showed significant improvements in clinical symptoms, semantic fluency performance. Correlation analysis revealed that the degree of DC increase in the left anterior inferior parietal lobe (aIPL) after treatment was negatively correlated with changes in the excitement score (r = -0.256, p = 0.048, adjusted p = 0.080), but this correlation failed the multiple test correction. Patients with SZ showed a significant negative correlation between DC values in the left aIPL and DST scores after treatment, which was not observed at the baseline (r = -0.359, p = 0.005, adjusted p = 0.047). In addition, we did not find a significant difference in DC between the RS and NRS groups, neither at baseline nor after treatment.

CONCLUSIONS

The results suggested that DC changes in patients with SZ after antipsychotic treatment are correlated with neurocognitive performance. Our findings provide new insights into the neuropathological mechanisms underlying antipsychotic treatment of SZ.

Altered Functional Connectivity during Mild Transient Respiratory Impairment Induced by a Resistive Load

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without altering transcutaneous oxygen saturation. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptual area (postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores. These findings may provide new insights into the visual networks that contribute to mild respiratory impairments.

Integrating fractional amplitude of low-frequency fluctuation and functional connectivity to investigate the mechanism and prognosis of severe traumatic brain injury

Objective

Functional magnetic resonance imaging (fMRI) has been used for evaluating residual brain function and predicting the prognosis of patients with severe traumatic brain injury (sTBI). This study aimed to integrate the fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to investigate the mechanism and prognosis of patients with sTBI.

Methods

Sixty-five patients with sTBI were included and underwent fMRI scanning within 14 days after brain injury. The patient's outcome was assessed using the Glasgow Outcome Scale-Extended (GOSE) at 6 months post-injury. Of the 63 patients who met fMRI data analysis standards, the prognosis of 18 patients was good (GOSE scores ≥ 5), and the prognosis of 45 patients was poor (GOSE scores ≤  4). First, we apply fALFF to identify residual brain functional differences in patients who present different prognoses and conjoined it in regions of interest (ROI)-based FC analysis to investigate the residual brain function of sTBI at the acute phase of sTBI. Then, the area under the curve (AUC) was used to evaluate the predictive ability of the brain regions with the difference of fALFF and FC values.

Results

Patients who present good outcomes at 6 months post-injury have increased fALFF values in the Brodmann area (7, 18, 31, 13, 39 40, 42, 19, 23) and decreased FC values in the Brodmann area (28, 34, 35, 36, 20, 28, 34, 35, 36, 38, 1, 2, 3, 4, 6, 13, 40, 41, 43, 44, 20, 28 35, 36, 38) at the acute phase of sTBI. The parameters of these alterations can be used for predicting the long-term outcomes of patients with sTBI, of which the fALFF increase in the temporal lobe, occipital lobe, precuneus, and middle temporal gyrus showed the highest predictive ability (AUC = 0.883).

Conclusion

We provide a compensatory mechanism that several regions of the brain can be spontaneously activated at the acute phase of sTBI in those who present with a good prognosis in the 6-month follow-up, that is, a destructive mode that increases its fALFF in the local regions and weakens its FC to the whole brain. These findings provide a theoretical basis for developing early intervention targets for sTBI patients.

Resting-State Functional MRI and PET Imaging as Noninvasive Tools to Study (Ab)Normal Neurodevelopment in Humans and Rodents

Neurodevelopmental disorders (NDDs) are a group of complex neurologic and psychiatric disorders. Functional and molecular imaging techniques, such as resting-state functional magnetic resonance imaging (rs-fMRI) and positron emission tomography (PET), can be used to measure network activity noninvasively and longitudinally during maturation in both humans and rodent models. Here, we review the current knowledge on rs-fMRI and PET biomarkers in the study of normal and abnormal neurodevelopment, including intellectual disability (ID; with/without epilepsy), autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), in humans and rodent models from birth until adulthood, and evaluate the cross-species translational value of the imaging biomarkers. To date, only a few isolated studies have used rs-fMRI or PET to study (abnormal) neurodevelopment in rodents during infancy, the critical period of neurodevelopment. Further work to explore the feasibility of performing functional imaging studies in infant rodent models is essential, as rs-fMRI and PET imaging in transgenic rodent models of NDDs are powerful techniques for studying disease pathogenesis, developing noninvasive preclinical imaging biomarkers of neurodevelopmental dysfunction, and evaluating treatment-response in disease-specific models.

Experience of early-life pain in premature infants is associated with atypical cerebellar development and later neurodevelopmental deficits

BACKGROUND

Infants born very and extremely premature (V/EPT) are at a significantly elevated risk for neurodevelopmental disorders and delays even in the absence of structural brain injuries. These risks may be due to earlier-than-typical exposure to the extrauterine environment, and its bright lights, loud noises, and exposures to painful procedures. Given the relative underdeveloped pain modulatory responses in these infants, frequent pain exposures may confer risk for later deficits.

METHODS

Resting-state fMRI scans were collected at term equivalent age from 148 (45% male) infants born V/EPT and 99 infants (56% male) born at term age. Functional connectivity analyses were performed between functional regions correlating connectivity to the number of painful skin break procedures in the NICU, including heel lances, venipunctures, and IV placements. Subsequently, preterm infants returned at 18 months, for neurodevelopmental follow-up and completed assessments for autism risk and general neurodevelopment.

RESULTS

We observed that V/EPT infants exhibit pronounced hyperconnectivity within the cerebellum and between the cerebellum and both limbic and paralimbic regions correlating with the number of skin break procedures. Moreover, skin breaks were strongly associated with autism risk, motor, and language scores at 18 months. Subsample analyses revealed that the same cerebellar connections strongly correlating with breaks at term age were associated with language dysfunction at 18 months.

CONCLUSIONS

These results have significant implications for the clinical care of preterm infants undergoing painful exposures during routine NICU care, which typically occurs without anesthesia. Repeated pain exposures appear to have an increasingly detrimental effect on brain development during a critical period, and effects continue to be seen even 18 months later.

[Stress during prenatal and early postnatal period when everything begins]

Early severe stresses are known to affect the biological and psychological development in childhood. Good and adaptable stress during prenatal and early postnatal period can switch to traumatic during these highly susceptible developmental stages. These different stresses modulate genetic/epigenetic processes and the setting up of connectome during these highly plastic and adaptable time periods. The polyvagal processes control the base of the security/well-being perception of the newborn by the onset of synchronized interactions between the mother/parent/nurse and the baby. These positive adjustments in mirror lead to attachment and social links and to implicit learning processes leading to a balanced emotional and cognitive development.

Intergenerational Transmission of Maternal Childhood Maltreatment Prior to Birth: Effects on Human Fetal Amygdala Functional Connectivity

OBJECTIVE

Childhood maltreatment (CM) is a potent risk factor for developing psychopathology later in life. Accumulating research suggests that the influence is not limited to the exposed individual but may also be transmitted across generations. In this study, we examine the effect of CM in pregnant women on fetal amygdala-cortical function, prior to postnatal influences.

METHOD

Healthy pregnant women (N = 89) completed fetal resting-state functional magnetic resonance imaging (rsfMRI) scans between the late second trimester and birth. Women were primarily from low socioeconomic status households with relatively high CM. Mothers completed questionnaires prospectively evaluating prenatal psychosocial health and retrospectively evaluating trauma from their own childhood. Voxelwise functional connectivity was calculated from bilateral amygdala masks.

RESULTS

Connectivity of the amygdala network was relatively higher to left frontal areas (prefrontal cortex and premotor) and relatively lower to right premotor area and brainstem areas in fetuses of mothers exposed to higher CM. These associations persisted after controlling for maternal socioeconomic status, maternal prenatal distress, measures of fetal motion, and gestational age at the time of scan and at birth.

CONCLUSION

Pregnant women's experiences of CM are associated with offspring brain development in utero. The strongest effects were found in the left hemisphere, potentially indicating lateralization of the effects of maternal CM on the fetal brain. This study suggests that the time frame of the Developmental Origins of Health and Disease research should be extended to exposures from mothers' childhood, and indicates that the intergenerational transmission of trauma may occur prior to 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.

Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm

Introduction

The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored.

Methods

From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex.

Results

A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age.

Discussion

This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.

Default mode network functional connectivity strength in utero and the association with fetal subcortical development

The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.

Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data

Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.

Maturational networks of human fetal brain activity reveal emerging connectivity patterns prior to ex-utero exposure

A key feature of the fetal period is the rapid emergence of organised patterns of spontaneous brain activity. However, characterising this process in utero using functional MRI is inherently challenging and requires analytical methods which can capture the constituent developmental transformations. Here, we introduce a novel analytical framework, termed "maturational networks" (matnets), that achieves this by modelling functional networks as an emerging property of the developing brain. Compared to standard network analysis methods that assume consistent patterns of connectivity across development, our method incorporates age-related changes in connectivity directly into network estimation. We test its performance in a large neonatal sample, finding that the matnets approach characterises adult-like features of functional network architecture with a greater specificity than a standard group-ICA approach; for example, our approach is able to identify a nearly complete default mode network. In the in-utero brain, matnets enables us to reveal the richness of emerging functional connections and the hierarchy of their maturational relationships with remarkable anatomical specificity. We show that the associative areas play a central role within prenatal functional architecture, therefore indicating that functional connections of high-level associative areas start emerging prior to exposure to the extra-utero environment.

Resting-state fMRI reveals longitudinal alterations in brain network connectivity in the zQ175DN mouse model of Huntington's disease

Huntington's disease is an autosomal, dominantly inherited neurodegenerative disease caused by an expansion of the CAG repeats in exon 1 of the huntingtin gene. Neuronal degeneration and dysfunction that precedes regional atrophy result in the impairment of striatal and cortical circuits that affect the brain's large-scale network functionality. However, the evolution of these disease-driven, large-scale connectivity alterations is still poorly understood. Here we used resting-state fMRI to investigate functional connectivity changes in a mouse model of Huntington's disease in several relevant brain networks and how they are affected at different ages that follow a disease-like phenotypic progression. Towards this, we used the heterozygous (HET) form of the zQ175DN Huntington's disease mouse model that recapitulates aspects of human disease pathology. Seed- and Region-based analyses were performed at different ages, on 3-, 6-, 10-, and 12-month-old HET and age-matched wild-type mice. Our results demonstrate decreased connectivity starting at 6 months of age, most prominently in regions such as the retrosplenial and cingulate cortices, pertaining to the default mode-like network and auditory and visual cortices, part of the associative cortical network. At 12 months, we observe a shift towards decreased connectivity in regions such as the somatosensory cortices, pertaining to the lateral cortical network, and the caudate putamen, a constituent of the subcortical network. Moreover, we assessed the impact of distinct Huntington's Disease-like pathology of the zQ175DN HET mice on age-dependent connectivity between different brain regions and networks where we demonstrate that connectivity strength follows a nonlinear, inverted U-shape pattern, a well-known phenomenon of development and normal aging. Conversely, the neuropathologically driven alteration of connectivity, especially in the default mode and associative cortical networks, showed diminished age-dependent evolution of functional connectivity. These findings reveal that in this Huntington's disease model, altered connectivity starts with cortical network aberrations which precede striatal connectivity changes, that appear only at a later age. Taken together, these results suggest that the age-dependent cortical network dysfunction seen in rodents could represent a relevant pathological process in Huntington's disease progression.

Robust sex differences in functional brain connectivity are present in utero

Sex-based differences in brain structure and function are observable throughout development and are thought to contribute to differences in behavior, cognition, and the presentation of neurodevelopmental disorders. Using multiple support vector machine (SVM) models as a data-driven approach to assess sex differences, we sought to identify regions exhibiting sex-dependent differences in functional connectivity and determine whether they were robust and sufficiently reliable to classify sex even prior to birth. To accomplish this, we used a sample of 110 human fetal resting state fMRI scans from 95 fetuses, performed between 19 and 40 gestational weeks. Functional brain connectivity patterns classified fetal sex with 73% accuracy. Across SVM models, we identified features (functional connections) that reliably differentiated fetal sex. Highly consistent predictors included connections in the somatomotor and frontal areas alongside the hippocampus, cerebellum, and basal ganglia. Moreover, high consistency features also implicated a greater magnitude of cross-region connections in females, while male weighted features were predominately within anatomically bounded regions. Our findings indicate that these differences, which have been observed later in childhood, are present and reliably detectable even before birth. These results show that sex differences arise before birth in a manner that is consistent and reliable enough to be highly identifiable.

Laminar dynamics of deep projection neurons and mode of subplate formation are hallmarks of histogenetic subdivisions of the human cingulate cortex before onset of arealization

The cingulate gyrus, as a prominent part of the human limbic lobe, is involved in the integration and regulation of complex emotional, executive, motivational, and cognitive functions, attributed to several functional regions along the anteroposterior axis. In contrast to increasing knowledge of cingulate function in the adult brain, our knowledge of cingulate development is based primarily on classical neuroembryological studies. We aimed to reveal the laminar and cellular development of the various cingulate regions during the critical period from 7.5 to 15 postconceptional weeks (PCW) before the formation of Brodmann type arealization, employing diverse molecular markers on serial histological sections of postmortem human fetal brains. The study was performed by analysis of: (1) deep projection neuron (DPN) markers laminar dynamics, (2) all transient laminar compartments, and (3) characteristic subplate (SP) formation-expansion phase. We found that DPN markers labeling an incipient cortical plate (CP) were the first sign of regional differentiation of the dorsal isocortical and ventral mesocortical belt. Remarkably, increased width of the fibrillar marginal zone (MZ) towards the limbus, in parallel with the narrowing of CP containing DPN, as well as the diminishment of subventricular zone (SVZ) were reliable landmarks of early mesocortical differentiation. Finally, the SP formation pattern was shown to be a crucial event in the isocortical cingulate portion, given that the mesocortical belt is characterized by an incomplete CP delamination and absence of SP expansion. In conclusion, laminar DPN markers dynamics, together with the SVZ size and mode of SP formation indicate regional belt-like cingulate cortex differentiation before the corpus callosum expansion and several months before Brodmann type arealization.

Fetal behavior during MRI changes with age and relates to network dynamics

Fetal motor behavior is an important clinical indicator of healthy development. However, our understanding of associations between fetal behavior and fetal brain development is limited. To fill this gap, this study introduced an approach to automatically and objectively classify long durations of fetal movement from a continuous four-dimensional functional magnetic resonance imaging (fMRI) data set, and paired behavior features with brain activity indicated by the fMRI time series. Twelve-minute fMRI scans were conducted in 120 normal fetuses. Postnatal motor function was evaluated at 7 and 36 months age. Fetal motor behavior was quantified by calculating the frame-wise displacement (FD) of fetal brains extracted by a deep-learning model along the whole time series. Analyzing only low motion data, we characterized the recurring coactivation patterns (CAPs) of the supplementary motor area (SMA). Results showed reduced motor activity with advancing gestational age (GA), likely due in part to loss of space (r = -.51, p < .001). Evaluation of individual variation in motor movement revealed a negative association between movement and the occurrence of coactivations within the left parietotemporal network, controlling for age and sex (p = .003). Further, we found that the occurrence of coactivations between the SMA to posterior brain regions, including visual cortex, was prospectively associated with postnatal motor function at 7 months (r = .43, p = .03). This is the first study to pair fetal movement and fMRI, highlighting potential for comparisons of fetal behavior and neural network development to enhance our understanding of fetal brain organization.

Graph Theory Measures and Their Application to Neurosurgical Eloquence

Improving patient safety and preserving eloquent brain are crucial in neurosurgery. Since there is significant clinical variability in post-operative lesions suffered by patients who undergo surgery in the same areas deemed compensable, there is an unknown degree of inter-individual variability in brain 'eloquence'. Advances in connectomic mapping efforts through diffusion tractography allow for utilization of non-invasive imaging and statistical modeling to graphically represent the brain. Extending the definition of brain eloquence to graph theory measures of hubness and centrality may help to improve our understanding of individual variability in brain eloquence and lesion responses. While functional deficits cannot be immediately determined intra-operatively, there has been potential shown by emerging technologies in mapping of hub nodes as an add-on to existing surgical navigation modalities to improve individual surgical outcomes. This review aims to outline and review current research surrounding novel graph theoretical concepts of hubness, centrality, and eloquence and specifically its relevance to brain mapping for pre-operative planning and intra-operative navigation in neurosurgery.

EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task

In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.

The fetal pain paradox

Controversy exists as to when conscious pain perception in the fetus may begin. According to the hypothesis of cortical necessity, thalamocortical connections, which do not form until after 24-28 weeks gestation, are necessary for conscious pain perception. However, anesthesiologists and neonatologists treat age-matched neonates as both conscious and pain-capable due to observable and measurable behavioral, hormonal, and physiologic indicators of pain. In preterm infants, these multimodal indicators of pain are uncontroversial, and their presence, despite occurring prior to functional thalamocortical connections, has guided the use of analgesics in neonatology and fetal surgery for decades. However, some medical groups state that below 24 weeks gestation, there is no pain capacity. Thus, a paradox exists in the disparate acknowledgment of pain capability in overlapping patient populations. Brain networks vary by age. During the first and second trimesters, the cortical subplate, a unique structure that is present only during fetal and early neonatal development, forms the first cortical network. In the third trimester, the cortical plate assumes this function. According to the subplate modulation hypothesis, a network of connections to the subplate and subcortical structures is sufficient to facilitate conscious pain perception in the fetus and the preterm neonate prior to 24 weeks gestation. Therefore, similar to other fetal and neonatal systems that have a transitional phase (i.e., circulatory system), there is now strong evidence for transitional developmental phases of fetal and neonatal pain circuitry.

Harnessing the Power of Advanced Fetal Neuroimaging to Understand In Utero Footprints for Later Neuropsychiatric Disorders

Adverse intrauterine events may profoundly impact fetal risk for future adult diseases. The mechanisms underlying this increased vulnerability are complex and remain poorly understood. Contemporary advances in fetal magnetic resonance imaging (MRI) have provided clinicians and scientists with unprecedented access to in vivo human fetal brain development to begin to identify emerging endophenotypes of neuropsychiatric disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and schizophrenia. In this review, we discuss salient findings of normal fetal neurodevelopment from studies using advanced, multimodal MRI that have provided unparalleled characterization of in utero prenatal brain morphology, metabolism, microstructure, and functional connectivity. We appraise the clinical utility of these normative data in identifying high-risk fetuses before birth. We highlight available studies that have investigated the predictive validity of advanced prenatal brain MRI findings and long-term neurodevelopmental outcomes. We then discuss how ex utero quantitative MRI findings can inform in utero investigations toward the pursuit of early biomarkers of risk. Lastly, we explore future opportunities to advance our understanding of the prenatal origins of neuropsychiatric disorders using precision fetal imaging.

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.

Morphological development of the human fetal striatum during the second trimester

The morphological development of the fetal striatum during the second trimester has remained poorly described. We manually segmented the striatum using 7.0-T MR images of the fetal specimens ranging from 14 to 22 gestational weeks. The global development of the striatum was evaluated by volume measurement. The absolute volume (Vabs) of the caudate nucleus (CN) increased linearly with gestational age, while the relative volume (Vrel) showed a quadratic growth. Both Vabs and Vrel of putamen increased linearly. Through shape analysis, the changes of local structure in developing striatum were specifically demonstrated. Except for the CN tail, the lateral and medial parts of the CN grew faster than the middle regions, with a clear rostral-caudal growth gradient as well as a distinct "outside-in" growth gradient. For putamen, the dorsal and ventral regions grew obviously faster than the other regions, with a dorsal-ventral bidirectional developmental pattern. The right CN was larger than the left, whereas there was no significant hemispheric asymmetry in the putamen. By establishing the developmental trajectories, spatial heterochrony, and hemispheric dimorphism of human fetal striatum, these data bring new insight into the fetal striatum development and provide detailed anatomical references for future striatal studies.

RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data

Resting-state functional magnetic resonance imaging (rs-fMRI) most recently has proved to open a measureless window on functional neurodevelopment in utero. Fetal brain activation and connectivity maps can be heavily influenced by 1) fetal-specific motion effects on the time-series and 2) the accuracy of time-series spatial normalization to a standardized gestational-week (GW) specific fetal template space.Due to the absence of a standardized and generalizable image processing protocol, the objective of the present work was to implement a validated fetal rs-fMRI preprocessing pipeline (RS-FetMRI) divided into 6 inter-dependent preprocessing modules (i.e., M1 to M6) and designed to work entirely as an extension for Statistical Parametric Mapping (SPM).RS-FetMRI pipeline output analyses on rs-fMRI time-series sampled from a cohort of fetuses acquired on both 1.5 T and 3 T MRI scanning systems showed increased efficacy of estimation of the degree of movement coupled with an efficient motion censoring procedure, resulting in increased number of motion-uncorrupted volumes and temporal continuity in fetal rs-fMRI time-series data. Moreover, a "structural-free" SPM-based spatial normalization procedure granted a high degree of spatial overlap with high reproducibility and a significant improvement in whole-brain and parcellation-specific Temporal Signal-to-Noise Ratio (TSNR) mirrored by functional connectivity analysis.To our knowledge, the RS-FetMRI pipeline is the first semi-automatic and easy-to-use standardized fetal rs-fMRI preprocessing pipeline completely integrated in MATLAB-SPM able to remove entry barriers for new research groups into the field of fetal rs-fMRI, for both research or clinical purposes, and ultimately to make future fetal brain connectivity investigations more suitable for comparison and cross-validation.

An integrative perspective on the role of touch in the development of intersubjectivity

Touch concerns a fundamental component of sociality. In this review, we examine the hypothesis that somatomotor development constitutes a crucial psychophysiological element in the ontogeny of intersubjectivity. An interdisciplinary perspective is provided on how the communication channel of touch contributes to the sense of self and extends to the social self. During gestation, the transformation of random movements into organized sequences of actions with sensory consequences parallels the development of the brain's functional architecture. Brain subsystems shaped by the coordinated activity of somatomotor circuits to support these first body-environment interactions are the first brain functional arrangements to develop. We propose that tactile self-referring behaviour during gestation constitutes a prototypic mode of interpersonal exchange that supports the subsequent development of intersubjective exchange. The reviewed research suggests that touch constitutes a pivotal bodily experience that in early stages builds and later filters self-other interactions. This view is corroborated by the fact that aberrant social-affective touch experiences appear fundamentally associated with attachment anomalies, interpersonal trauma, and personality disorders. Given the centrality of touch for the development of intersubjectivity and for psychopathological conditions in the social domain, dedicated research is urged to elucidate the role of touch in the evolution of subjective self-other coding.

The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks

We demonstrate a data-driven approach for calculating a "causal connectome" of directed connectivity from resting-state fMRI data using a greedy adjacency search and pairwise non-Gaussian edge orientations. We used this approach to construct n = 442 causal connectomes. These connectomes were very sparse in comparison to typical Pearson correlation-based graphs (roughly 2.25% edge density) yet were fully connected in nearly all cases. Prominent highly connected hubs of the causal connectome were situated in attentional (dorsal attention) and executive (frontoparietal and cingulo-opercular) networks. These hub networks had distinctly different connectivity profiles: attentional networks shared incoming connections with sensory regions and outgoing connections with higher cognitive networks, while executive networks primarily connected to other higher cognitive networks and had a high degree of bidirected connectivity. Virtual lesion analyses accentuated these findings, demonstrating that attentional and executive hub networks are points of critical vulnerability in the human causal connectome. These data highlight the central role of attention and executive control networks in the human cortical connectome and set the stage for future applications of data-driven causal connectivity analysis in psychiatry.

Empirical evaluation of human fetal fMRI preprocessing steps

Increased study and methodological innovation have led to growth in the field of fetal brain fMRI. An important gap yet to be addressed is optimization of fetal fMRI preprocessing. Rapid developmental changes, imaged within the maternal compartment using an abdominal coil, introduce novel constraints that challenge established methods used in adult fMRI. This study evaluates the impact of (1) normalization to a group mean-age template versus normalization to an age-matched template; (2) independent components analysis (ICA) denoising at two criterion thresholds; and (3) smoothing using three kernel sizes. Data were collected from 121 fetuses (25-39 weeks, 43.8% female). Results indicate that the mean age template is superior in older fetuses, but less optimal in younger fetuses. ICA denoising at a more stringent threshold is superior to less stringent denoising. A larger smoothing kernel can enhance cross-hemisphere functional connectivity. Overall, this study provides improved understanding of the impact of specific steps on fetal image quality. Findings can be used to inform a common set of best practices for fetal fMRI preprocessing.

Motion correction and volumetric reconstruction for fetal functional magnetic resonance imaging data

Motion correction is an essential preprocessing step in functional Magnetic Resonance Imaging (fMRI) of the fetal brain with the aim to remove artifacts caused by fetal movement and maternal breathing and consequently to suppress erroneous signal correlations. Current motion correction approaches for fetal fMRI choose a single 3D volume from a specific acquisition timepoint with least motion artefacts as reference volume, and perform interpolation for the reconstruction of the motion corrected time series. The results can suffer, if no low-motion frame is available, and if reconstruction does not exploit any assumptions about the continuity of the fMRI signal. Here, we propose a novel framework, which estimates a high-resolution reference volume by using outlier-robust motion correction, and by utilizing Huber L2 regularization for intra-stack volumetric reconstruction of the motion-corrected fetal brain fMRI. We performed an extensive parameter study to investigate the effectiveness of motion estimation and present in this work benchmark metrics to quantify the effect of motion correction and regularised volumetric reconstruction approaches on functional connectivity computations. We demonstrate the proposed framework's ability to improve functional connectivity estimates, reproducibility and signal interpretability, which is clinically highly desirable for the establishment of prognostic noninvasive imaging biomarkers. The motion correction and volumetric reconstruction framework is made available as an open-source package of NiftyMIC.

Individual variability in functional organization of the neonatal brain

The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? And what is the individual variability in this organization in neonates? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We quantified individual variability across neonates, and found the most individual variability in the ventral attention networks. Crucially, the variability of these networks was not driven by SNR differences or differences from adult networks (Yeo et al., 2011). Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks and identified potential genes of interest for future developmental and individual variability research. Overall, we found neonatal connectomes (even at the voxel-level) can reveal broad individual-specific information processing units. The presence of individual differences in neonates and the framework for personalized parcellations demonstrated here has the potential to improve prediction of behavior and future outcomes from neonatal and infant brain data.

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

Automated Brain Masking of Fetal Functional MRI with Open Data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network

Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.

Early and late development of hub connectivity in the human brain

Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.

Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

Increasing evidence supports a link between maternal prenatal cannabis use and altered neural and physiological development of the child. However, whether cannabis use relates to altered human brain development prior to birth, and specifically, whether maternal prenatal cannabis use relates to connectivity of fetal functional brain systems, remains an open question. The major objective of this study was to identify whether maternal prenatal cannabis exposure (PCE) is associated with variation in human brain hippocampal functional connectivity prior to birth. Prenatal drug toxicology and fetal fMRI data were available in a sample of 115 fetuses [43 % female; mean age 32.2 weeks (SD = 4.3)]. Voxelwise hippocampal connectivity analysis in a subset of age and sex-matched fetuses revealed that PCE was associated with alterations in fetal dorsolateral, medial and superior frontal, insula, anterior temporal, and posterior cingulate connectivity. Classification of group differences by age 5 outcomes suggest that compared to the non-PCE group, the PCE group is more likely to have increased connectivity to regions associated with less favorable outcomes and to have decreased connectivity to regions associated with more favorable outcomes. This is preliminary evidence that altered fetal neural connectome may contribute to neurobehavioral vulnerability observed in children exposed to cannabis in utero.

Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery

Studies reporting significant associations between maternal prenatal stress and child outcomes are frequently confounded by correlates of prenatal stress that influence the postnatal rearing environment. The major objective of this study is to identify whether maternal prenatal stress is associated with variation in human brain functional connectivity prior to birth. We utilized fetal fMRI in 118 fetuses [48 female; mean age 32.9 weeks (SD = 3.87)] to evaluate this association and further addressed whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Community detection was used to empirically define networks and enrichment was used to isolate differential within- or between-network connectivity effects. Significance for χ2 enrichment was determined by randomly permuting the subject pairing of fetal brain connectivity and maternal stress values 10,000 times. Mixtures modelling was used to test whether fetal neural differences were related to maternal health behaviors, social support, or birth outcomes. Increased maternal prenatal negative affect/stress was associated with alterations in fetal frontoparietal, striatal, and temporoparietal connectivity (β = 0.82, p < 0.001). Follow-up analysis demonstrated that these associations were stronger in women with better health behaviors, more positive interpersonal support, and lower overall stress (β = 0.16, p = 0.02). Additionally, magnitude of stress-related differences in neural connectivity was marginally correlated with younger gestational age at delivery (β = -0.18, p = 0.05). This is the first evidence that negative affect/stress during pregnancy is reflected in functional network differences in the human brain in utero, and also provides information about how positive interpersonal and health behaviors could mitigate prenatal brain programming.

Early magnetic resonance imaging biomarkers of schizophrenia spectrum disorders: Toward a fetal imaging perspective

There is mounting evidence to implicate the intrauterine environment as the initial pathogenic stage for neuropsychiatric disease. Recent developments in magnetic resonance imaging technology are making a multimodal analysis of the fetal central nervous system a reality, allowing analysis of structural and functional parameters. Exposures to a range of pertinent risk factors whether preconception or in utero can now be indexed using imaging techniques within the fetus' physiological environment. This approach may determine the first "hit" required for diseases that do not become clinically manifest until adulthood, and which only have subtle clinical markers during childhood and adolescence. A robust characterization of a "multi-hit" hypothesis may necessitate a longitudinal birth cohort; within this investigative paradigm, the full range of genetic and environmental risk factors can be assessed for their impact on the early developing brain. This will lay the foundation for the identification of novel biomarkers and the ability to devise methods for early risk stratification and disease prevention. However, these early markers must be followed over time: first, to account for neural plasticity, and second, to assess the effects of postnatal exposures that continue to drive the individual toward disease. We explore these issues using the schizophrenia spectrum disorders as an illustrative paradigm. However, given the potential richness of fetal magnetic resonance imaging, and the likely overlap of biomarkers, these concepts may extend to a range of neuropsychiatric conditions.

Evaluating the sensitivity of functional connectivity measures to motion artifact in resting-state fMRI data

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Global Network Organization of the Fetal Functional Connectome

Recent advances in brain imaging have enabled non-invasive in vivo assessment of the fetal brain. Characterizing brain development in healthy fetuses provides baseline measures for identifying deviations in brain function in high-risk clinical groups. We examined 110 resting state MRI data sets from fetuses at 19 to 40 weeks' gestation. Using graph-theoretic techniques, we characterized global organizational features of the fetal functional connectome and their prenatal trajectories. Topological features related to network integration (i.e., global efficiency) and segregation (i.e., clustering) were assessed. Fetal networks exhibited small-world topology, showing high clustering and short average path length relative to reference networks. Likewise, fetal networks' quantitative small world indices met criteria for small-worldness (σ > 1, ω = [-0.5 0.5]). Along with this, fetal networks demonstrated global and local efficiency, economy, and modularity. A right-tailed degree distribution, suggesting the presence of central areas that are more highly connected to other regions, was also observed. Metrics, however, were not static during gestation; measures associated with segregation-local efficiency and modularity-decreased with advancing gestational age. Altogether, these suggest that the neural circuitry underpinning the brain's ability to segregate and integrate information exists as early as the late 2nd trimester of pregnancy and reorganizes during the prenatal period. Significance statement. Mounting evidence for the fetal origins of some neurodevelopmental disorders underscores the importance of identifying features of healthy fetal brain functional development. Alterations in prenatal brain connectomics may serve as early markers for identifying fetal-onset neurodevelopmental disorders, which in turn provide improved surveillance of at-risk fetuses and support the initiation of early interventions.

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas.

Premature Birth and Developmental Programming: Mechanisms of Resilience and Vulnerability

The third trimester of pregnancy represents a sensitive phase for infant brain plasticity when a series of fast-developing cellular events (synaptogenesis, neuronal migration, and myelination) regulates the development of neural circuits. Throughout this dynamic period of growth and development, the human brain is susceptible to stress. Preterm infants are born with an immature brain and are, while admitted to the neonatal intensive care unit, precociously exposed to stressful procedures. Postnatal stress may contribute to altered programming of the brain, including key systems such as the hypothalamic-pituitary-adrenal axis and the autonomic nervous system. These neurobiological systems are promising markers for the etiology of several affective and social psychopathologies. As preterm birth interferes with early development of stress-regulatory systems, early interventions might strengthen resilience factors and might help reduce the detrimental effects of chronic stress exposure. Here we will review the impact of stress following premature birth on the programming of neurobiological systems and discuss possible stress-related neural circuits and pathways involved in resilience and vulnerability. Finally, we discuss opportunities for early intervention and future studies.

Association of Prenatal Maternal Anxiety With Fetal Regional Brain Connectivity

IMPORTANCE

Maternal psychological distress during pregnancy is associated with adverse obstetric outcomes and neuropsychiatric deficits in children. Currently unavailable in vivo interrogation of fetal brain function could provide critical insights into the onset and timing of altered neurodevelopmental trajectories.

OBJECTIVE

To investigate the association between prenatal maternal stress, anxiety, and depression and in vivo fetal brain resting state functional connectivity.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included pregnant women scanned between January 2016 and April 2019. A total of 50 pregnant women with healthy pregnancies were prospectively recruited from low-risk obstetric clinics in the Washington DC area and were scanned at Children's National in Washington DC.

EXPOSURES

Maternal stress, anxiety, and depression.

MAIN OUTCOMES AND MEASURES

The association of prenatal maternal stress, anxiety, and depression with whole-brain connectivity was analyzed using multivariate distance matrix regression. Prenatal maternal stress, anxiety, and depression were assessed using the Perceived Stress Scale, Spielberger State Anxiety Inventory and Spielberger Trait Anxiety Inventory, and the Edinburgh Postnatal Depression Scale, respectively. Whole-brain connectivity was measured from 100 functionally defined regions of interest.

RESULTS

This study analyzed 59 resting-state functional connectivity magnetic resonance image data sets from the fetuses (mean [SD] gestational age, 33.52 [4 weeks]) of 50 healthy pregnant women (mean [SD] age, 33.77 [5.51]). Mean (SD) scores for the questionnaires were as follows: Spielberger State Anxiety Inventory, 26.66 (6.72) (range, 20-48); Spielberger Trait Anxiety Inventory, 28.09 (6.62) (range, 20-50); Perceived Stress Scale, 9.27 (5.13) (range, 1-25); and Edinburgh Postnatal Depression Scale 3.24 (2.84) (range, 0-14). Prenatal maternal anxiety scores measured using the Spielberger Trait and State Anxiety Inventories were associated with differences in fetal connectivity (Spielberger State Anxiety Inventory: pseudo-R2 = 0.019, P = .04; Spielberger Trait Anxiety Inventory: pseudo-R2 = 0.021, P = .007). Interhemispheric connections, such as those involving the parietofrontal and occipital association cortices, were associated with reduced maternal prenatal anxiety, and those between the brainstem and sensorimotor areas were associated with higher anxiety scores.

CONCLUSIONS AND RELEVANCE

In this cohort study, an association was found between prenatal maternal anxiety and disturbances in fetal brain functional connectivity, suggesting altered fetal programming. Early onset of functional deviations suggests the need for more widespread screening of pregnant women for symptoms of anxiety.