Network state transitions during cortical development.

Mammalian cortical networks are active before synaptogenesis begins in earnest, before neuronal migration is complete, and well before an animal opens its eyes and begins to actively explore its surroundings. This early activity undergoes several transformations during development. The most important of these is a transition from episodic synchronous network events, which are necessary for patterning the neocortex into functionally related modules, to desynchronized activity that is computationally more powerful and efficient. Network desynchronization is perhaps the most dramatic and abrupt developmental event in an otherwise slow and gradual process of brain maturation. In this Review, we summarize what is known about the phenomenology of developmental synchronous activity in the rodent neocortex and speculate on the mechanisms that drive its eventual desynchronization. We argue that desynchronization of network activity is a fundamental step through which the cortex transitions from passive, bottom-up detection of sensory stimuli to active sensory processing with top-down modulation.

Functional network modules overlap and are linked to interindividual connectome differences during human brain development.

The modular structure of functional connectomes in the human brain undergoes substantial reorganization during development. However, previous studies have implicitly assumed that each region participates in one single module, ignoring the potential spatial overlap between modules. How the overlapping functional modules develop and whether this development is related to gray and white matter features remain unknown. Using longitudinal multimodal structural, functional, and diffusion MRI data from 305 children (aged 6 to 14 years), we investigated the maturation of overlapping modules of functional networks and further revealed their structural associations. An edge-centric network model was used to identify the overlapping modules, and the nodal overlap in module affiliations was quantified using the entropy measure. We showed a regionally heterogeneous spatial topography of the overlapping extent of brain nodes in module affiliations in children, with higher entropy (i.e., more module involvement) in the ventral attention, somatomotor, and subcortical regions and lower entropy (i.e., less module involvement) in the visual and default-mode regions. The overlapping modules developed in a linear, spatially dissociable manner, with decreased entropy (i.e., decreased module involvement) in the dorsomedial prefrontal cortex, ventral prefrontal cortex, and putamen and increased entropy (i.e., increased module involvement) in the parietal lobules and lateral prefrontal cortex. The overlapping modular patterns captured individual brain maturity as characterized by chronological age and were predicted by integrating gray matter morphology and white matter microstructural properties. Our findings highlight the maturation of overlapping functional modules and their structural substrates, thereby advancing our understanding of the principles of connectome development.

Human adolescent brain similarity development is different for paralimbic versus neocortical zones.

Adolescent development of human brain structural and functional networks is increasingly recognized as fundamental to emergence of typical and atypical adult cognitive and emotional proodal magnetic resonance imaging (MRI) data collected from N [Formula: see text] 300 healthy adolescents (51%; female; 14 to 26 y) each scanned repeatedly in an accelerated longitudinal design, to provide an analyzable dataset of 469 structural scans and 448 functional MRI scans. We estimated the morphometric similarity between each possible pair of 358 cortical areas on a feature vector comprising six macro- and microstructural MRI metrics, resulting in a morphometric similarity network (MSN) for each scan. Over the course of adolescence, we found that morphometric similarity increased in paralimbic cortical areas, e.g., insula and cingulate cortex, but generally decreased in neocortical areas, and these results were replicated in an independent developmental MRI cohort (N [Formula: see text] 304). Increasing hubness of paralimbic nodes in MSNs was associated with increased strength of coupling between their morphometric similarity and functional connectivity. Decreasing hubness of neocortical nodes in MSNs was associated with reduced strength of structure-function coupling and increasingly diverse functional connections in the corresponding fMRI networks. Neocortical areas became more structurally differentiated and more functionally integrative in a metabolically expensive process linked to cortical thinning and myelination, whereas paralimbic areas specialized for affective and interoceptive functions became less differentiated, as hypothetically predicted by a developmental transition from periallocortical to proisocortical organization of the cortex. Cytoarchitectonically distinct zones of the human cortex undergo distinct neurodevelopmental programs during typical adolescence.

Molecular and Cellular Mechanisms of Motor Circuit Development.

Motor circuits represent the main output of the central nervous system and produce dynamic behaviors ranging from relatively simple rhythmic activities like swimming in fish and breathing in mammals to highly sophisticated dexterous movements in humans. Despite decades of research, the development and function of motor circuits remain poorly understood. Breakthroughs in the field recently provided new tools and tractable model systems that set the stage to discover the molecular mechanisms and circuit logic underlying motor control. Here, we describe recent advances from both vertebrate (mouse, frog) and invertebrate (nematode, fruit fly) systems on cellular and molecular mechanisms that enable motor circuits to develop and function and highlight conserved and divergent mechanisms necessary for motor circuit development.

Glial Control of Cortical Neuronal Circuit Maturation and Plasticity.

The brain is a highly adaptable organ that is molded by experience throughout life. Although the field of neuroscience has historically focused on intrinsic neuronal mechanisms of plasticity, there is growing evidence that multiple glial populations regulate the timing and extent of neuronal plasticity, particularly over the course of development. This review highlights recent discoveries on the role of glial cells in the establishment of cortical circuits and the regulation of experience-dependent neuronal plasticity during critical periods of neurodevelopment. These studies provide strong evidence that neuronal circuit maturation and plasticity are non-cell autonomous processes that require both glial-neuronal and glial-glial cross talk to proceed. We conclude by discussing open questions that will continue to guide research in this nascent field.

Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns.

The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase-phase (PPCs), amplitude-amplitude (AACs), and phase-amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.

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

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

Strength and resilience of developing brain circuits predict adolescent emotional and stress responses during the COVID-19 pandemic.

The COVID-19 pandemic has had profound but incompletely understood adverse effects on youth. To elucidate the role of brain circuits in how adolescents responded to the pandemic's stressors, we investigated their prepandemic organization as a predictor of mental/emotional health in the first ~15 months of the pandemic. We analyzed resting-state networks from n = 2,641 adolescents [median age (interquartile range) = 144.0 (13.0) months, 47.7% females] in the Adolescent Brain Cognitive Development study, and longitudinal assessments of mental health, stress, sadness, and positive affect, collected every 2 to 3 months from May 2020 to May 2021. Topological resilience and/or network strength predicted overall mental health, stress and sadness (but not positive affect), at multiple time points, but primarily in December 2020 and May 2021. Higher resilience of the salience network predicted better mental health in December 2020 (ß = 0.19, 95% CI = [0.06, 0.31], P = 0.01). Lower connectivity of left salience, reward, limbic, and prefrontal cortex and its thalamic, striatal, amygdala connections, predicted higher stress (ß = -0.46 to -0.20, CI = [-0.72, -0.07], P < 0.03). Lower bilateral robustness (higher fragility) and/or connectivity of these networks predicted higher sadness in December 2020 and May 2021 (ß = -0.514 to -0.19, CI = [-0.81, -0.05], P < 0.04). These findings suggest that the organization of brain circuits may have played a critical role in adolescent stress and mental/emotional health during the pandemic.

Role of leucine-rich repeat proteins in the development and function of neural circuits.

The nervous system consists of an ensemble of billions of neurons interconnected in a highly specific pattern that allows proper propagation and integration of neural activities. The organization of these specific connections emerges from sequential developmental events including axon guidance, target selection, and synapse formation. These events critically rely on cell-cell recognition and communication mediated by cell-surface ligands and receptors. Recent studies have uncovered central roles for leucine-rich repeat (LRR) domain-containing proteins, not only in organizing neural connectivity from axon guidance to target selection to synapse formation, but also in various nervous system disorders. Their versatile LRR domains, in particular, serve as key sites for interactions with a wide diversity of binding partners. Here, we focus on a few exquisite examples of secreted or membrane-associated LRR proteins in Drosophila and mammals and review the mechanisms by which they regulate diverse aspects of nervous system development and function.

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development.

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.

Functional structure of local connections and differentiation of cerebral cortex areas in the neonate.

Neuroimaging research on functional connectivity can provide valuable information on the developmental differentiation of the infant cerebral cortex into its functional areas. We examined healthy neonates to comprehensively map brain functional connectivity using a combination of local measures that uniquely capture the rich spatial structure of cerebral cortex functional connections. Optimal functional MRI scans were obtained in 61 neonates. Local functional connectivity maps were based on Iso-Distance Average Correlation (IDAC) measures. Single distance maps and maps combining three distinct IDAC measures were used to assess different levels of cortical area functional differentiation. A set of brain areas showed higher connectivity than the rest of the brain parenchyma in each local distance map. These areas were consistent with those supporting basic aspects of the neonatal repertoire of adaptive behaviors and included the sensorimotor, auditory and visual cortices, the frontal operculum/anterior insula (relevant for sucking, swallowing and the sense of taste), paracentral lobule (processing anal and urethral sphincter activity), default mode network (relevant for self-awareness), and limbic-emotional structures such as the anterior cingulate cortex, amygdala and hippocampus. However, the results also indicate that brain areas presumed to be actively developing may not necessarily be mature. In fact, combined distance, second-level maps confirmed that the functional differentiation of the cerebral cortex into functional areas in neonates is far from complete. Our results provide a more comprehensive understanding of the developing brain systems, while also highlighting the substantial developmental journey that the neonatal brain must undergo to reach adulthood.

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

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

FMRP regulation of aggrecan mRNA translation controls perineuronal net development.

Perineuronal nets (PNNs) are mesh-like structures on the surfaces of parvalbumin-expressing inhibitory and other neurons, and consist of proteoglycans such as aggrecan, brevican, and neurocan. PNNs regulate the Excitatory/Inhibitory (E/I) balance in the brain and are formed at the closure of critical periods of plasticity during development. PNN formation is disrupted in Fragile X Syndrome, which is caused by silencing of the fragile X messenger ribonucleoprotein 1 (Fmr1) gene and loss of its protein product FMRP. FXS is characterized by impaired synaptic plasticity resulting in neuronal hyperexcitability and E/I imbalance. Here, we investigate how PNN formation is altered in FXS. PNNs are reduced in Fmr1 KO mouse brain when examined by staining for the lectin Wisteria floribunda agglutin (WFA) and aggrecan. Examination of PNNs by WFA staining at P14 and P42 in the hippocampus, somatosensory cortex, and retrosplenial cortex shows that they were reduced in these brain regions at P14 but mostly less so at P42 in Fmr1 KO mice. However, some differential FMRP regulation of PNN development in these brain regions persists, perhaps caused by asynchrony in PNN development between brain regions in wild-type animals. During development, aggrecan PNN levels in the brain were reduced in all brain regions in Fmr1 KO mice. Aggrecan mRNA levels were unchanged at these times, suggesting that FMRP is normally an activator of aggrecan mRNA translation. This hypothesis is buttressed by the observations that FMRP binds aggrecan mRNA and that ribosome profiling data show that aggrecan mRNA is associated with reduced numbers of ribosomes in Fmr1 KO mouse brain, indicating reduced translational efficiency. Moreover, aggrecan mRNA poly(A) tail length is also reduced in Fmr1 KO mouse brain, suggesting a relationship between polyadenylation and translational control. We propose a model where FMRP modulates PNN formation through translational up-regulation of aggrecan mRNA polyadenylation and translation.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

Characterizing normal perinatal development of the human brain structural connectivity.

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

Functional dissociation of the language network and other cognition in early childhood.

Is language distinct from other cognition during development? Does neural machinery for language emerge from general-purpose neural mechanisms, becoming tuned for language after years of experience and maturation? Answering these questions will shed light on the origins of domain-specificity in the brain. We address these questions using precision fMRI, scanning young children (35 months to 9 years of age) on an auditory language localizer, spatial working memory localizer (engaging the domain-general multiple demand [MD] network), and a resting-state scan. We create subject-specific functional regions of interest for each network and examine their selectivity, specificity, and functional connectivity. We find young children show domain-specific, left-lateralized language activation, and that the language network is not responsive to domain-general cognitive load. Additionally, the cortically adjacent MD network is selective to cognitive load, but not to language. These networks show higher within versus between-network functional connectivity. This connectivity is stable across ages (examined cross-sectionally and longitudinally), whereas language responses increase with age and across time within subject, reflecting a domain-specific developmental change. Overall, we provide evidence for a double dissociation of the language and MD network throughout development, in both their function and connectivity. These findings suggest that domain-specificity, even for uniquely human cognition like language, develops early and distinctly from mechanisms that presumably support other human cognition.

Frontoparietal and salience network synchronizations during nonsymbolic magnitude processing predict brain age and mathematical performance in youth.

The development and refinement of functional brain circuits crucial to human cognition is a continuous process that spans from childhood to adulthood. Research increasingly focuses on mapping these evolving configurations, with the aim to identify markers for functional impairments and atypical development. Among human cognitive systems, nonsymbolic magnitude representations serve as a foundational building block for future success in mathematical learning and achievement for individuals. Using task-based frontoparietal (FPN) and salience network (SN) features during nonsymbolic magnitude processing alongside machine learning algorithms, we developed a framework to construct brain age prediction models for participants aged 7-30. Our study revealed differential developmental profiles in the synchronization within and between FPN and SN networks. Specifically, we observed a linear increase in FPN connectivity, concomitant with a decline in SN connectivity across the age span. A nonlinear U-shaped trajectory in the connectivity between the FPN and SN was discerned, revealing reduced FPN-SN synchronization among adolescents compared to both pediatric and adult cohorts. Leveraging the Gradient Boosting machine learning algorithm and nested fivefold stratified cross-validation with independent training datasets, we demonstrated that functional connectivity measures of the FPN and SN nodes predict chronological age, with a correlation coefficient of .727 and a mean absolute error of 2.944 between actual and predicted ages. Notably, connectivity within the FPN emerged as the most contributing feature for age prediction. Critically, a more matured brain age estimate is associated with better arithmetic performance. Our findings shed light on the intricate developmental changes occurring in the neural networks supporting magnitude representations. We emphasize brain age estimation as a potent tool for understanding cognitive development and its relationship to mathematical abilities across the critical developmental period of youth. PRACTITIONER POINTS: This study investigated the prolonged changes in the brain's architecture across childhood, adolescence, and adulthood, with a focus on task-state frontoparietal and salience networks. Distinct developmental pathways were identified: frontoparietal synchronization strengthens consistently throughout development, while salience network connectivity diminishes with age. Furthermore, adolescents show a unique dip in connectivity between these networks. Leveraging advanced machine learning methods, we accurately predicted individuals' ages based on these brain circuits, with a more mature estimated brain age correlating with better math skills.

Roles of axon guidance molecules in neuronal wiring in the developing spinal cord.

The spinal cord receives, relays and processes sensory information from the periphery and integrates this information with descending inputs from supraspinal centres to elicit precise and appropriate behavioural responses and orchestrate body movements. Understanding how the spinal cord circuits that achieve this integration are wired during development is the focus of much research interest. Several families of proteins have well-established roles in guiding developing spinal cord axons, and recent findings have identified new axon guidance molecules. Nevertheless, an integrated view of spinal cord network development is lacking, and many current models have neglected the cellular and functional diversity of spinal cord circuits. Recent advances challenge the existing spinal cord axon guidance dogmas and have provided a more complex, but more faithful, picture of the ontogenesis of vertebrate spinal cord circuits.

EEG Functional Connectivity Analysis for the Study of the Brain Maturation in the First Year of Life.

Brain networks are hypothesized to undergo significant changes over development, particularly during infancy. Thus, the aim of this study is to evaluate brain maturation in the first year of life in terms of electrophysiological (EEG) functional connectivity (FC). Whole-brain FC metrics (i.e., magnitude-squared coherence, phase lag index, and parameters derived from graph theory) were extracted, for multiple frequency bands, from baseline EEG data recorded from 146 typically developing infants at 6 (T6) and 12 (T12) months of age. Generalized linear mixed models were used to test for significant differences in the computed metrics considering time point and sex as fixed effects. Correlational analyses were performed to ascertain the potential relationship between FC and subjects' cognitive and language level, assessed with the Bayley-III scale at 24 (T24) months of age. The results obtained highlighted an increased FC, for all the analyzed frequency bands, at T12 with respect to T6. Correlational analyses yielded evidence of the relationship between FC metrics at T12 and cognition. Despite some limitations, our study represents one of the first attempts to evaluate brain network evolution during the first year of life while accounting for correspondence between functional maturation and cognitive improvement.

Sparser and Less Efficient Hippocampal-Prefrontal Projections account for Developmental Network Dysfunction in a Model of Psychiatric Risk Mediated by Gene-Environment Interaction.

Precise information flow from the hippocampus (HP) to prefrontal cortex (PFC) emerges during early development and accounts for cognitive processing throughout life. On flip side, this flow is selectively impaired in mental illness. In mouse models of psychiatric risk mediated by gene-environment interaction (GE), the prefrontal-hippocampal coupling is disrupted already shortly after birth. While this impairment relates to local miswiring in PFC and HP, it might be also because of abnormal connectivity between the two brain areas. Here, we test this hypothesis by combining in vivo electrophysiology and optogenetics with in-depth tracing of projections and monitor the morphology and function of hippocampal afferents in the PFC of control and GE mice of either sex throughout development. We show that projections from the hippocampal CA1 area preferentially target layer 5/6 pyramidal neurons and interneurons, and to a lesser extent layer 2/3 neurons of prelimbic cortex (PL), a subdivision of PFC. In neonatal GE mice, sparser axonal projections from CA1 pyramidal neurons with decreased release probability reach the PL. Their ability to entrain layer 5/6 oscillatory activity and firing is decreased. These structural and functional deficits of hippocampal-prelimbic connectivity persist, yet are less prominent in prejuvenile GE mice. Thus, besides local dysfunction of HP and PL, weaker connectivity between the two brain areas is present in GE mice throughout development.SIGNIFICANCE STATEMENT Poor cognitive performance in mental disorders comes along with prefrontal-hippocampal dysfunction. Recent data from mice that model the psychiatric risk mediated by gene-environment (GE) interaction identified the origin of deficits during early development, when the local circuits in both areas are compromised. Here, we show that sparser and less efficient connectivity as well as cellular dysfunction are the substrate of the weaker excitatory drive from hippocampus (HP) to prefrontal cortex (PFC) as well as of poorer oscillatory coupling between the two brain areas in these mice. While the structural and functional connectivity deficits persist during the entire development, their magnitude decreases with age. The results add experimental evidence for the developmental miswiring hypothesis of psychiatric disorders.