Functional brain networks emerge prenatally, grow interactively during the first years of life, and optimize both within-network topology and between-network interactions as individuals age. This review summarizes research that has characterized this process over the past two decades, and aims to link functional network growth with emerging behaviors, thereby developing a more holistic understanding of the developing brain and behavior from a functional network perspective. This synthesis suggests that the development of the brain's functional networks follows an overlapping hierarchy, progressing from primary sensory/motor to socioemotional-centered development and finally to higher-order cognitive/executive control networks. Risk-related alterations, resilience factors, treatment effects, and novel therapeutic opportunities are also discussed to encourage the consideration of future imaging-assisted methods for identifying risks and interventions.

Magnetic resonance imaging (MRI) evaluation of the fetal central nervous system (CNS) is a well-established imaging modality that improves detection of fetal neuro anomalies. The utilization of rapid imaging acquisition sequences allows for the high-resolution evaluation of the developing fetus with decreased impact from fetal motion. MRI is often performed after a prenatal screening ultrasound (US) demonstrates an abnormality or if there are known risk factors. The most common neurological indications for fetal MRI include ventriculomegaly, absent cavum septum pellucidum, and enlarged cisterna magna followed by hemorrhagic lesions and intracranial cysts. Knowledge of normal fetal anatomy and development of CNS structures is key for accurate interpretation of fetal brain MRI.

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.

How the lateralized language network and its functions emerge with early auditory experiences remains largely unknown. Here, early auditory development is examined using repeated optical imaging for cochlear implanted (CI) toddlers with congenital deafness from onset of restored hearing to around one year of CI hearing experiences. Machine learning models are constructed to resolve how functional organization of the bilateral language network and its sound processing support the CI children's post-implantation development of auditory and verbal communication skills. Behavioral improvement is predictable by cortical processing as well as by network organization changes, with the highest classification accuracy of 81.57%. For cortical processing, behavioral prediction is better for the left than the right hemisphere and for speech than non-speech processing. For network organization, the best prediction is obtained for resting state, with greater contribution from inter-hemisphere connections between non-homologous regions than from within-hemisphere connections. Most interestingly, systematic connectivity-to-activity models reveal that speech processing of the left language network is developmentally supported largely by global network organization, particularly asymmetric inter-hemisphere communication, rather than functional segregation of local network. These findings collectively confirm the importance of asymmetric inter-hemisphere communication in formation of the lateralized language network and its functional development with early auditory experiences.

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios.

The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized into functional networks. These networks show stronger internal and weaker external functional connectivity (FC), with FC profiles more similar within the same network. Previous studies have shown these networks develop from nascent forms before birth to their mature, adult-like structures in childhood. However, these analyses often rely on adult functional network definitions. This study assesses the potential misidentification of infant functional networks when using adult models and explores the consequences and possible solutions to this problem. Our findings suggest that although adult networks only marginally describe infant FC organization better than chance, misidentification is primarily driven by specific areas. Restricting functional networks to areas with adult-like network clustering revealed consistent within-network FC across scans and throughout development. These areas are also near locations with low network identity variability. Our results highlight the implications of using adult networks for infants and offer guidance for selecting and utilizing functional network models based on research questions and scenarios.

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We use resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predict PMA for term and preterm infants. Predicted ages from each modality are correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generate accurate PMA prediction. Additionally, BAGs are associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

Premature birth affects brain maturation, illustrated by altered brain functional connectivity at term equivalent age (TEA) and alters neurobehavioral outcome. To correct early developmental differences and improve neurological outcome, music during the neonatal intensive care unit (NICU) stay has been proposed as an auditory enrichment with modulatory effects on functional and structural brain development, but longitudinal effects of such interventions have not been studied so far. We longitudinally investigated resting-state functional connectivity (RS-FC) maturation in preterm infants (n = 43). Data-driven Independent Component Analyses (ICA) were performed on scans obtained at 33- and 40-week gestational age (GA), determining the presence of distinct resting-state networks (RSNs). Connectome analysis "accordance measure" quantitively examined the RS-FC both at 33- and 40-week GA. Further comparing the internetwork RS-FC at 33- and 40-week GA provided a circuitry of interest (COI) for significant maturational changes in which the effects on the RS-FC of a music intervention were tested. The connectome analyses resulted in a COI of RS-FC connections significantly maturing from 33 to 40 weeks GA, namely between the thalamic/brainstem and prefrontal-limbic, salience, sensorimotor, auditory, and prefrontal cortical networks; between the prefrontal-limbic and cerebellar, visual and left hemispheric precuneus networks; between the salience and visual, and cerebellar networks; and between the sensorimotor and auditory, and posterior cingulate/precuneus networks. The infants exposed to music exhibited significantly increased maturation in RS-FC between the thalamic/brainstem and salience networks, compared with controls. This study exemplifies that preterm infant RS-FC maturation is modulated through NICU music exposure, highlighting the importance of environmental enrichment for neurodevelopment in premature newborns.

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.

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.

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.

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.

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.

Many psychiatric conditions have their roots in early development. Individual differences in prenatal brain function (which is influenced by a combination of genetic risk and the prenatal environment) likely interact with individual differences in postnatal experience, resulting in substantial variation in brain functional organization and development in infancy. Neuroimaging has been a powerful tool for understanding typical and atypical brain function and holds promise for uncovering the neurodevelopmental basis of psychiatric illness; however, its clinical utility has been relatively limited thus far. A substantial challenge in this endeavor is the traditional approach of averaging brain data across groups despite individuals varying in their brain organization, which likely obscures important clinically relevant individual variation. Precision functional mapping (PFM) is a neuroimaging technique that allows the capture of individual-specific and highly reliable functional brain properties. Here, we discuss how PFM, through its focus on individuals, has provided novel insights for understanding brain organization across the life span and its promise in elucidating the neural basis of psychiatric disorders. We first summarize the extant literature on PFM in normative populations, followed by its limited utilization in studying psychiatric conditions in adults. We conclude by discussing the potential for infant PFM in advancing developmental precision psychiatry applications, given that many psychiatric disorders start during early infancy and are associated with changes in individual-specific functional neuroanatomy. By exploring the intersection of PFM, development, and psychiatric research, this article underscores the importance of individualized approaches in unraveling the complexities of brain function and improving clinical outcomes across development.

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.

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.

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.

The concept of fetal pain results from procedures conducted without anesthesia in preterm newborns and fetuses, which indicate that it is possible to examine fetal pain based on stress hormone, metabolic, and behavioral changes. Anatomical and physiological data suggest that fetuses become capable of processing nociceptive stimuli around midgestation, although the associated changes in fetal brain development remain unclear. What constitutes fetal pain remains controversial in the light of the definition of pain adopted by the International Association for the Study of Pain (IASP), which posits pain as an "unpleasant sensory and emotional experience."

Here, we examine the notion that human fetuses cannot "experience" pain and potential implications of this claim. We highlight the key scientific evidence related to fetal pain, including clinical studies on pain in fetuses and preterm newborns. We argue that consistent patterns of stress hormones, metabolic changes, body movements, hemodynamic changes, and pain-related facial expressions in fetuses exposed to invasive procedures overcome the need for subjective proof of pain as articulated in the IASP definition. No study to date has conclusively proven the absence of fetal pain beyond the age of viability.

Based on the current evidence, we propose that all fetuses receive anesthesia regardless of the invasive procedures being performed to guarantee the least possible pain and physiological, behavioral, or hormonal responses without exposing the mother or her baby to unnecessary complications.

The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brain's maturity relative to normative data. The difference between the individual's predicted and chronological age-or brain age gap (BAG)-represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks-even putatively later developing ones-generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

The fetal period is a critical stage in brain development, and understanding the characteristics of the fetal brain is crucial. Although some studies have explored aspects of fetal brain functional networks, few have specifically focused on sex differences in brain network characteristics. We adopted the graph theory method to calculate brain network functional connectivity and topology properties (including global and nodal properties), and further compared the differences in these parameters between male and female fetuses. We found that male fetuses showed an increased clustering coefficient and local efficiency than female fetuses, but no significant group differences concerning other graph parameters and the functional connectivity matrix. Our study suggests the existence of sex-related distinctions in the topological properties of the brain network at the fetal stage of development and demonstrates an increase in brain network separation in male fetuses compared with female fetuses.

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.

Epidemiological studies have shown associations between prenatal exposure to lead (Pb) and neurodevelopmental effects in young children. Prenatal exposure is generally characterized by measuring the concentration in the umbilical cord at delivery or in the maternal blood during pregnancy. To assess internal Pb exposure during prenatal life, we developed a pregnancy physiologically based pharmacokinetic (p-PBPK) model that to simulates Pb levels in blood and target tissues in the fetus, especially during critical periods for brain development. An existing Pb PBPK model was adapted to pregnant women and fetuses. Using data from literature, both the additional maternal bone remodeling, that causes Pb release into the blood, and the Pb placental transfers were estimated by Bayesian inference. Additional maternal bone remodeling was estimated to start at 21.6 weeks. Placental transfers were estimated between 4.6 and 283 L.day-1 at delivery with high interindividual variability. Once calibrated, the p-PBPK model was used to simulate fetal exposure to Pb. Internal fetal exposure greatly varies over the pregnancy with two peaks of Pb levels in blood and brain at the end of the 1st and 3rd trimesters. Sensitivity analysis shows that the fetal blood lead levels are affected by the maternal burden of bone Pb via maternal bone remodeling and by fetal bone formation at different pregnancy stages. Coupling the p-PBPK model with an effect model such as an adverse outcome pathway could help to predict the effects on children's neurodevelopment.

The deleterious effects of adversity are likely intergenerational, such that one generation's adverse experiences can affect the next. Epidemiological studies link maternal adversity to offspring depression and anxiety, possibly via transmission mechanisms that influence offspring fronto-limbic connectivity. However, studies have not thoroughly disassociated postnatal exposure effects nor considered the role of offspring sex. We utilized infant neuroimaging to test the hypothesis that maternal childhood maltreatment (CM) would be associated with increased fronto-limbic connectivity in infancy and tested brain-behavior associations in childhood. Ninety-two dyads participated (32 mothers with CM, 60 without; 52 infant females, 40 infant males). Women reported on their experiences of CM and non-sedated sleeping infants underwent MRIs at 2.44 ± 2.74 weeks. Brain volumes were estimated via structural MRI and white matter structural connectivity (fiber counts) via diffusion MRI with probabilistic tractography. A subset of parents (n = 36) reported on children's behaviors at age 5.17 ± 1.73 years. Males in the maltreatment group demonstrated greater intra-hemispheric fronto-limbic connectivity (b = 0.96, p= 0.008, [95%CI 0.25, 1.66]), no differences emerged for females. Fronto-limbic connectivity was related to somatic complaints in childhood only for males (r = 0.673, p = 0.006). Our findings suggest that CM could have intergenerational associations to offspring brain development, yet mechanistic studies are needed.

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.

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.

Little is known empirically about connectivity and communication between the two hemispheres of the brain in the first year of life, and what theoretical opinion exists appears to be at variance with the meager extant anatomical evidence. To shed initial light on the question of interhemispheric connectivity and communication, this study investigated brain correlates of interhemispheric transmission of information in young human infants. We analyzed EEG data from 12 4-month-olds undergoing a face-related oddball ERP protocol. The activity in the contralateral hemisphere differed between odd-same and odd-difference trials, with the odd-different response being weaker than the response during odd-same trials. The infants' contralateral hemisphere "recognized" the odd familiar stimulus and "discriminated" the odd-different one. These findings demonstrate connectivity and communication between the two hemispheres of the brain in the first year of life and lead to a better understanding of the functional integrity of the developing human infant brain.

Because fine particulate matter [PM, with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] is a ubiquitous environmental exposure, small changes in cognition associated with PM 2.5 exposure could have great societal costs. Prior studies have demonstrated a relationship between in utero PM 2.5 exposure and cognitive development in urban populations, but it is not known whether these effects are similar in rural populations and whether they persist into late childhood.

In this study, we tested for associations between prenatal PM 2.5 exposure and both full-scale and subscale measures of IQ among a longitudinal cohort at age 10.5 y.

This analysis used data from 568 children enrolled in the Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS), a birth cohort study in California's agricultural Salinas Valley. Exposures were estimated at residential addresses during pregnancy using state of the art, modeled PM 2.5 surfaces. IQ testing was performed by bilingual psychometricians in the dominant language of the child.

A 3 - μ g / m 3 higher average PM 2.5 over pregnancy was associated with - 1.79 full-scale IQ points [95% confidence interval (CI): - 2.98 , - 0.58 ], with decrements specifically in Working Memory IQ (WMIQ) and Processing Speed IQ (PSIQ) subscales [WMIQ - 1.72 (95% CI: - 2.98 , - 0.45 ) and PSIQ - 1.19 (95% CI: - 2.54 , 0.16)]. Flexible modeling over the course of pregnancy illustrated mid-to-late pregnancy (months 5-7) as particularly susceptible times, with sex differences in the timing of susceptible windows and in which subscales were most affected [Verbal Comprehension IQ (VCIQ) and WMIQ in males; and PSIQ in females].

We found that small increases in outdoor PM 2.5 exposure in utero were associated with slightly lower IQ in late childhood, robust to many sensitivity analyses. In this cohort there was a larger effect of PM 2.5 on childhood IQ than has previously been observed, perhaps due to differences in PM composition or because developmental disruption could alter the cognitive trajectory and thus appear more pronounced as children get older. https://doi.org/10.1289/EHP10812.

The human brain begins to develop in the third gestational week and rapidly grows and matures over the course of pregnancy. Compared to fetal structural neurodevelopment, less is known about emerging functional connectivity in utero. Here, we investigated gestational age (GA)-associated in vivo changes in functional brain connectivity during the second and third trimesters in a large dataset of 110 resting-state functional magnetic resonance imaging scans from a cohort of 95 healthy fetuses. Using representational similarity analysis, a multivariate analytical technique that reveals pair-wise similarity in high-order space, we showed that intersubject similarity of fetal functional connectome patterns was strongly related to between-subject GA differences (r = 0.28, P < 0.01) and that GA sensitivity of functional connectome was lateralized, especially at the frontal area. Our analysis also revealed a subnetwork of connections that were critical for predicting age (mean absolute error = 2.72 weeks); functional connectome patterns of individual fetuses reliably predicted their GA (r = 0.51, P < 0.001). Lastly, we identified the primary principal brain network that tracked fetal brain maturity. The main network showed a global synchronization pattern resembling global signal in the adult brain.

Projections between the thalamus and sensory cortices are established early in development and play an important role in regulating sleep as well as in relaying sensory information to the cortex. Atypical thalamocortical functional connectivity frequently observed in children with autism spectrum disorder (ASD) might therefore be linked to sensory and sleep problems common in ASD.

Here, we investigated the relationship between auditory-thalamic functional connectivity measured during natural sleep functional magnetic resonance imaging, sleep problems, and sound sensitivities in 70 toddlers and preschoolers (1.5-5 years old) with ASD compared with a matched group of 46 typically developing children.

In children with ASD, sleep problems and sensory sensitivities were positively correlated, and increased sleep latency was associated with overconnectivity between the thalamus and auditory cortex in a subsample with high-quality magnetic resonance imaging data (n = 29). In addition, auditory cortex blood oxygen level-dependent signal amplitude was elevated in children with ASD, potentially reflecting reduced sensory gating or a lack of auditory habituation during natural sleep.

These findings indicate that atypical thalamocortical functional connectivity can be detected early in development and may play a crucial role in sleep problems and sensory sensitivities in ASD.

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.

Touch represents one of our most important senses throughout life and particularly in the context of our social and emotional experiences. In this review, we draw on research on touch processing from both animal models and humans. Firstly, we briefly describe the cutaneous touch receptors and neural processing of both affective and discriminative touch. We then outline how our sense of touch develops and summarize increasing evidence demonstrating how essential early tactile stimulation is for the development of brain and behavior, with a particular focus on effects of tactile stimulation in infant animals and pediatric massage and Kangaroo care in human infants. Next, the potential mechanisms whereby early tactile stimulation influences both brain and behavioral development are discussed, focusing on its ability to promote neural plasticity changes and brain interhemispheric communication, development of social behavior and bonding, and reward sensitivity through modulation of growth factor, oxytocin, and opioid signaling. Finally, we consider the implications of evidence for atypical responses to touch in neurodevelopmental disorders such as autism spectrum disorder and discuss existing evidence and future priorities for establishing potential beneficial effects of interventions using massage or pharmacological treatments targeting oxytocin or other neurochemical systems.

A major pathological hallmark of neurodegenerative diseases, including Alzheimer's, is a significant reduction in the white matter connecting the two cerebral hemispheres, as well as in the correlated activity between anatomically corresponding bilateral brain areas. However, the underlying circuit mechanisms and the cognitive relevance of cross-hemispheric (CH) communication remain poorly understood. Here, we show that novelty discrimination behavior activates CH neurons and enhances homotopic synchronized neural oscillations in the visual cortex. CH neurons provide excitatory drive required for synchronous neural oscillations between hemispheres, and unilateral inhibition of the CH circuit is sufficient to impair synchronous oscillations and novelty discrimination behavior. In the 5XFAD and Tau P301S mouse models, CH communication is altered, and novelty discrimination is impaired. These data reveal a hitherto uncharacterized CH circuit in the visual cortex, establishing a causal link between this circuit and novelty discrimination behavior and highlighting its impairment in mouse models of neurodegeneration.

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.

Although the neural scaffolding for language is putatively present before birth, the maturation of functional connections among the key nodes of the language network, Broca's and Wernicke's areas, is less known. We leveraged longitudinal and cross-sectional data from three sites collected through six studies to track the development of functional circuits between Broca's and Wernicke's areas from 30 weeks of gestation through 30 months of age in 127 unique participants. Using resting-state fMRI data, functional connectivity was calculated as the correlation between fMRI time courses from pairs of regions, defined as Broca's and Wernicke's in both hemispheres. The primary analysis evaluated 23 individuals longitudinally imaged from 30 weeks postmenstrual age (fetal) through the first postnatal month (neonatal). A secondary analysis in 127 individuals extended these curves into older infants and toddlers. These data demonstrated significant growth of interhemispheric connections including left Broca's and its homolog and left Wernicke's and its homolog from 30 weeks of gestation through the first postnatal month. In contrast, intrahemispheric connections did not show significant increases across this period. These data represent an important baseline for language systems in the developing brain against which to compare those neurobehavioral disorders with the potential fetal onset of disease.

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.

Altered functional connectivity has been reported in infants with prenatal exposure to opioids, which significantly interrupts and influences endogenous neurotransmitter/receptor signaling during fetal programming. Better birth outcomes and long-term developmental outcomes are associated with medication for opioid use disorder (MOUD) during pregnancy, but the neural mechanisms underlying these benefits are largely unknown. We aimed to characterize effects of prenatal opioid/other drug exposure (PODE) and the neural basis for the reported beneficial effects of MOUD by examining neonatal brain functional organization. A cohort of 109 human newborns, 42 PODE, 39 with prenatal exposure to drugs excluding opioids (PDE), 28 drug-free controls (males and females) underwent resting-state fMRI at 2 weeks of age. To examine neural effects of MOUD, PODE infants were separated into subgroups based on whether mothers received MOUD (n = 31) or no treatment (n = 11). A novel heatmap analysis was designed to characterize PODE-associated functional connectivity alterations and MOUD-related effects, and permutation testing identified regions of interest with significant effects. PODE neonates showed alterations beyond those associated with PDE, particularly in reward-related frontal-sensory connectivity. MOUD was associated with a significant reduction of PODE-related alterations in key regions of endogenous opioid pathways including limbic and frontal connections. However, significant residual effects in limbic and subcortical circuitry were observed. These findings confirm altered brain functional organization associated with PODE. Importantly, widespread normalization effects associated with MOUD reveal, for the first time, the potential brain basis of the beneficial effects of MOUD on the developing brain and underscore the importance of this treatment intervention for better developmental outcomes.SIGNIFICANCE STATEMENT This is the first study to reveal the potential neural mechanisms underlying the beneficial effects on the neonate brain associated with MOUD during pregnancy. We identified both normalization and residual effects of MOUD on brain functional architecture by directly comparing neonates prenatally exposed to opioids with MOUD and those exposed to opioids but without MOUD. Our findings confirm altered brain functional organization associated with prenatal opioid exposure and demonstrate that although significant residual effects remain in reward circuitry, MOUD confers significant normalization effects on functional connectivity of regions associated with socioemotional development and reward processing. Together, our results highlight the importance of MOUD intervention for better neurodevelopmental outcomes.

The cerebellum's anatomical and functional organization and network interactions between the cerebellum and the cerebral cortex and subcortical structures are dynamic across the lifespan. Executive, emotional and social (EES) functions have likewise evolved during human development from contributing to primitive behaviors during infancy and childhood to being able to modulate complex actions in adults. In this review, we address how the importance of the cerebellum in the processing of EES functions might change across development. This evolution is driven by the macroscopic and microscopic modifications of the cerebellum that are occurring during development including its increasing connectivity with distant supra-tentorial cortical and sub-cortical regions. As a result of anatomical and functional changes, neuroimaging and clinical data indicate that the importance of the role of the cerebellum in human EES-related networks shifts from being crucial in newborns and young children to being only supportive later in life. In early life, given the immaturity of cortically mediated EES functions, EES functions and motor control and perception are more closely interrelated. At that time, the cerebellum due to its important role in motor control and sequencing makes EES functions more reliant on these computational properties that compute spatial distance, motor intent, and assist in the execution of sequences of behavior related to their developing EES expression. As the cortical brain matures, EES functions and decisions become less dependent upon these aspects of motor behavior and more dependent upon high-order cognitive and social conceptual processes. At that time, the cerebellum assumes a supportive role in these EES-related behaviors by computing their motor and sequential features. We suspect that this evolving role of the cerebellum has complicated the interpretation of its contribution to EES computational demands.

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.

Epidemiological studies are highlighting the negative effects of the exposure to air pollution on children's neurodevelopment. However, most studies assessed children's neurodevelopment using neuropsychological tests or questionnaires. Using magnetic resonance imaging (MRI) to precisely measure global and region-specific brain development would provide details of brain morphology and connectivity. This would help us understand the observed cognitive and behavioral changes related to air pollution exposure. Moreover, most studies assessed only a few air pollutants. This project investigates whether air pollution exposure to many pollutants during pregnancy and childhood is associated with the morphology and connectivity of the brain in school-age children and pre-adolescents.

We used data from the Generation R Study, a population-based birth cohort set up in Rotterdam, the Netherlands in 2002-2006 (n = 9,610). We used land-use regression (LUR) models to estimate the levels of 14 air pollutants at participant's homes during pregnancy and childhood: nitrogen oxides (NOx), nitrogen dioxide (NO2), particulate matter with aerodynamic diameter ≤10 μm (PM10) or ≤2.5 μm (PM2.5), PM between 10 μm and 2.5 μm (PMCOARSE), absorbance of the PM2.5 fraction - a measure of soot (PM2.5absorbance), the composition of PM2.5 such as polycyclic aromatic hydrocarbons (PAHs), organic carbon (OC), copper (Cu), iron (Fe), silicon (Si), zinc (Zn), and the oxidative potential of PM2.5 evaluated using two acellular methods: dithiothreitol (OPDTT) and electron spin resonance (OPESR). We performed MRI measurements of structural morphology (i.e., brain volumes, cortical thickness, and cortical surface area) using T1-weighted images in 6- to 10-year-old school-age children and 9- to 12-year-old pre-adolescents, structural connectivity (i.e., white matter microstructure) using diffusion tensor imaging (DTI) in pre-adolescents, and functional connectivity (i.e., connectivity score between brain areas) using resting-state functional MRI (rs-fMRI) in pre-adolescents. We assessed cognitive function using the Developmental Neuropsychological Assessment test (NEPSY-II) in school-age children. For each outcome, we ran regression analysis adjusted for several socioeconomic and lifestyle characteristics. We performed single-pollutant analyses followed by multipollutant analyses using the deletion/substitution/addition (DSA) approach.

The project has air pollution and brain MRI data for 783 school-age children and 3,857 pre-adolescents. First, exposure to air pollution during pregnancy or childhood was not associated with global brain volumes (e.g., total brain, cortical gray matter, and cortical white matter) in school-age children or pre-adolescents. However, higher pregnancy or childhood exposure to several air pollutants was associated with a smaller corpus callosum and hippocampus, and a larger amygdala, nucleus accumbens, and cerebellum in pre-adolescents, but not in school-age children. Second, higher exposure to several air pollutants during pregnancy was associated with a thinner cortex in various regions of the brain in both school-age children and pre-adolescents. Higher exposure to air pollution during childhood was also associated with a thinner cortex in a single region in pre-adolescents. A thinner cortex in two regions mediated the association between higher exposure to air pollution during pregnancy and an impaired inhibitory control in school-age children. Third, higher exposure to air pollution during childhood was associated with smaller cortical surface areas in various regions of the brain except in a region where we observed a larger cortical surface area in pre-adolescents. In relation to brain structural connectivity, higher exposure to air pollution during pregnancy and childhood was associated with an alteration in white matter microstructure in pre-adolescents. In relation to brain functional connectivity, a higher exposure to air pollution, mainly during pregnancy and early childhood, was associated with a higher brain functional connectivity among several brain regions in pre-adolescents. Overall, we identified several air pollutants associated with brain structural morphology, structural connectivity, and functional connectivity, such as NOx, NO2, PM of various size fractions (i.e., PM10, PMCOARSE, and PM2.5), PM2.5absorbance, PAHs, OC, three elemental components of PM2.5 (i.e., Cu, Si, Zn), and the oxidative potential of PM2.5.

The results of this project suggest that exposure to air pollution during pregnancy and childhood play an adverse role in brain development. We observed this relationship even at levels of exposure that were below the European Union legislations. We acknowledge that identifying the independent effects of specific pollutants was particularly challenging. Most of our conclusions generally refer to traffic-related air pollutants. However, we did identify pollutants specifically originating from brake linings, tire wear, and tailpipe emissions from diesel combustion. The current direction toward innovative solutions for cleaner energy vehicles is a step in the right direction. However, our findings indicate that these measures might not be completely adequate to mitigate health problems attributable to traffic-related air pollution, as we also observed associations with markers of brake linings and tire wear.

Among preterm infants, higher morbidities of neurological disturbances and developmental delays are critical issues. Resting-state networks (RSNs) in the brain are suitable measures for assessing higher-level neurocognition. Since investigating task-related brain activity is difficult in neonates, assessment of RSNs provides invaluable insight into their neurocognitive development.

The participants, 32 term and 71 preterm neonates, were divided into three groups based on gestational age (GA) at birth. Cerebral hemodynamic activity of RSNs was measured using functional near-infrared spectroscopy in the temporal, frontal, and parietal regions.

High-GA preterm infants (GA ≥ 30 weeks) had a significantly stronger RSN than low-GA preterm infants and term infants. Regression analyses of RSNs as a function of postnatal age (PNA) revealed a steeper regression line in the high-GA preterm and term infants than in the low-GA infants, particularly for inter-area brain connectivity between the frontal and left temporal areas.

Slower PNA-dependent development of the frontal-temporal network found only in the low-GA group suggests that significant brain growth optimal in the intrauterine environment takes place before 30 weeks of gestation. The present study suggests a likely reason for the high incidence of neurodevelopmental impairment in early preterm infants.

Resting-state fNIRS measurements in three neonate groups differing in gestational age (GA) showed stronger networks in the high-GA preterm infants than in the term and low-GA infants, which was partly explained by postnatal age (PNA). Regression analyses revealed a similar PNA-dependence in the development of the inter-area networks in the frontal and temporal lobes in the high-GA and term infants, and significantly slower development in the low-GA infants. These results suggest that optimal intrauterine brain growth takes place before 30 weeks of gestation. This explains one of the reasons for the high incidence of neurodevelopmental impairment in early preterm infants.

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.

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.