Biology subjects

Edwards, A. D.

Publications and source records attributed to Edwards, A. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Psychotic-like experiences in children born very preterm: evidence from clinical and population-based cohorts

Background and aimVery preterm birth (VPT; [≤]32 weeks gestation) is associated with an increased risk of later psychiatric disorders, including psychosis. Although psychosis typically emerges in adulthood, subclinical early signs along the psychosis continuum, such as psychotic-like experiences (PLEs), can be observed much earlier. We therefore aimed to study PLEs in childhood in VPT individuals recruited from a clinical cohort compared with full-term (FT) controls. We subsequently investigated whether findings could be replicated in an independent population-based cohort. MethodsPrimary analyses were conducted in the Brain, Immunity and Psychopathology (BIPP) study, including 197 children born VPT recruited through Neonatal Intensive Care Units and 72 FT controls assessed at a mean age of 10.50{+/-}1.77 years. Between-group differences in PLEs were then examined in the Adolescent Brain Cognitive Development (ABCD) study, including 149 children born VPT and 9519 FT controls assessed at a mean age of 9.94 {+/-} 0.63 years. PLEs were assessed using the Prodromal Questionnaire-Brief Child Version (PQ-BC), yielding frequency and distress-related scores for both the total scale and three specific domains (unusual thought content, perceptual abnormalities, disorganised speech). Regression models tested associations between birth status (VPT and control) and PQ-BC scores adjusting for age, sex, and socio-economic status, with secondary models additionally adjusting for cognitive ability and broader psychopathology. Pooled analyses examined cohort effects (BIPP and ABCD) and cohort-by-group status (VPT and control) interactions. ResultsIn BIPP, VPT birth was associated with higher PQ-BC total ({beta}=1.61, p=0.004) and distress scores ({beta}=0.78, p=0.039), with the strongest and most consistent associations observed for perceptual abnormalities across total score (sum of endorsed items), distressing items, and distress severity scores (all p[≤]0.01). These associations were attenuated but largely persisted after adjustment for cognitive ability and broader psychopathology, particularly for perceptual abnormalities. In ABCD, VPT birth was not significantly associated with global or domain-specific PQ-BC outcomes. DiscussionVPT birth is associated with increased vulnerability to PLEs in childhood, particularly in the domain of perceptual abnormalities. The lack of clear replication in the population-based ABCD cohort may reflect differences in the composition of its VPT subgroup, which may not fully represent VPT individuals typically seen in clinical cohorts.

developmental biology↗

bioRxiv2025

Charting structural brain asymmetry across the human lifespan

Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimers disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.

neuroscience↗

bioRxiv2025

Phenotypic and Functional Characterization of Oncohistone Mutations in Breast Cancers

Although mutations in genes encoding histones display a similar prevalence to that of some other somatic mutations in cancer, the underlying mechanisms by which histone mutations drive tumorigenesis have not been fully explored. Herein, we curated missense mutations occurring in core histone genes in breast cancers using data from MSK-IMPACT and cBioPortal to identify high frequency breast cancer-associated histone gene mutations. We characterized 17 high frequency oncohistone mutations in H2A H2B, and H3 that are enriched in breast cancer samples and occurred at glutamate (E), aspartate (D), serine (S), and arginine (R) residues. Many of these mutants co-occur with PIK3CA mutations in breast cancer samples. The oncohistone mutants were expressed in MCF-7 breast cancer cells or MCF-10A mammary epithelial cells and screened for effects on oncogenic phenotypes in a variety of cell- and tumor-based assays (i.e., proliferation, migration, invasion, competitive outgrowth, transformation). In addition, we examined the effects of selected mutants on DNA damage and gene expression. Our results indicate that the collection of oncohistone mutants that we screened have varying phenotypic and functional effects. Some can promote cancer-related phenotypes, with H2B-E76Q, H3-E97K, and H3-E105K eliciting strong oncogenic phenotypes and alterations in gene expression. All three of these mutants showed cooperativity with an activating mutation in PIK3CA (E545K), or a chemical activator of PI3K (UCL-TRO-1938), in assays of MCF-10A proliferation. H3-E105K also strongly promoted transformation of MCF-10A cells in an assay of growth on low attachment substrate independently of PIK3CA. Our results indicate that some high frequency oncohistone mutants can have oncogenic activity in breast cancers, and may act as potential cancer drivers. Collectively, these observations presented here can be used as a resource to connect biological and molecular outcomes to oncohistones in breast cancers. SignificanceIn this study, we identified and characterized 17 high frequency oncohistone mutations in H2A H2B, and H3 that are enriched in breast cancer samples and promote cancer-related phenotypes when expressed in cells. Many of these oncohistone mutations co-occur in breast cancer samples with mutations in PIK3CA, the most frequently mutated gene in breast cancer, and may act as potential cancer drivers.

cancer biology↗

bioRxiv2024

Inhibition of CELA1 Improves Septation in the Mouse Hyperoxia Model of Impaired Alveolar Development

A key feature of bronchopulmonary dysplasia (BPD) is impaired alveolar septation. In later live, BPD survivors are more susceptible to childhood respiratory problems and have reduced respiratory function as adults. Chymotrypsin-like elastase 1 (CELA1) is a serine protease expressed in AT2 cells that mediates emphysema progression in adult mouse models. CELA1 binds and cleaves tropoelastin in response to strain. Its expression is developmentally regulated. Using the mouse hyperoxia model of impaired alveolar development we hypothesized a role for CELA1 in impaired alveolar development (IAD). In C57BL6 mouse pup lungs exposed to 80% oxygen for 14 days Cela1 mRNA increased 1.9-fold (p<0.05) and protein 2.6-fold (p<0.01). Protein levels normalized after 14 days in room air. Analysis of an existing single cell mRNA-seq dataset showed Cela1 mRNA in AT2 cells, alveolar macrophages and interstitial macrophages. The fraction of cells with Cela1 mRNA increased with hyperoxia. By flow cytometry the only Cela1-specific difference in immune cell populations was a 2-fold increase in lung eosinophils in room air (p<0.05). After 14 days of exposure to 80% oxygen Cela1-/- mice had better alveolarization with an average mean linear intercept of 80 m compared to 111m (p<0.001). Treatment of hyperoxia-exposed pups with subcutaneous anti-Cela1 KF4 antibody offered similar protection compared to IgG (59 m vs. 67 m, p<0.001).Human BPD specimens demonstrated CELA1 in AT2 cells and myeloid cells. These data indicate that hyperoxia-induced increases in CELA1 are partially responsible for IAD and suggest a potential role in premature neonates exposed to high FiO2.

developmental biology↗

bioRxiv2024

Molecular signatures of cortical expansion in the human fetal brain

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, Brain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, Brain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using Brain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The Brain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.

neuroscience↗

bioRxiv2023

Dynamic changes in subplate and cortical plate microstructure precede the onset of cortical folding in vivo

Cortical gyrification takes place predominantly during the second to third trimester, alongside other fundamental developmental processes, such as the development of white matter connections, lamination of the cortex and formation of neural circuits. The mechanistic biology that drives the formation cortical folding patterns remains an open question in neuroscience. In our previous work, we modelled the in utero diffusion signal to quantify the maturation of microstructure in transient fetal compartments, identifying patterns of change in diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester. In this work, we apply the same modelling approach to explore whether microstructural maturation of these compartments is correlated with the process of gyrification. We quantify the relationship between sulcal depth and tissue anisotropy within the cortical plate (CP) and underlying subplate (SP), key transient fetal compartments often implicated in mechanistic hypotheses about the onset of gyrification. Using in utero high angular resolution multi-shell diffusion-weighted imaging (HARDI) from the Developing Human Connectome Project (dHCP), our analysis reveals that the anisotropic, tissue component of the diffusion signal in the SP and CP decreases immediately prior to the formation of sulcal pits in the fetal brain. By back-projecting a map of folded brain regions onto the unfolded brain, we find evidence for cytoarchitectural differences between gyral and sulcal areas in the late second trimester, suggesting that regional variation in the microstructure of transient fetal compartments precedes, and thus may have a mechanistic function, in the onset of cortical folding in the developing human brain.

neuroscience↗

bioRxiv2023

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI

Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain bounti tissue).

neuroscience↗

bioRxiv2023

Geneneralising the HCP multimodal cortical parcellation to UK Biobank

The Human Connectome Project Multimodal Parcellation (HCP_MMP1.0) provides a robust in vivo map of the cerebral cortex, which demonstrates variability in structure and function that cannot be captured through diffeomorphic image registration alone. The HCP successfully employed a fully-connected neural network architecture to capture this variation, however it is unclear whether this approach generalises to other datasets with less rich imaging protocols. In this paper we propose and validate a novel geometric deep learning framework for generating individualised HCP_MMP1.0 parcellations in UK Biobank (UKB), an extremely rich resource that has led to numerous breakthroughs in neuroscience. To address substantial differences in image acquisition (for example, 6 minutes of resting-state fMRI per subject for UKB vs. 60 minutes per subject for HCP), we introduce a multi-step learning procedure including pretraining with a convolutional autoencoder. Compared to a fully-connected baseline, our proposed framework improved average detection rate across all areas by 10.4% (99.1% vs 88.7%), and detection of the worst performing area by 51.0% (80.9% vs. 29.9%). Importantly, this was not a result of the framework predicting one consistent parcellation across subjects, as visual inspection indicated that our method was sensitive to atypical cortical topographies. Code and trained models will be made available at https://www.github.com.

neuroscience↗

bioRxiv2022

Exploring cognitive, behavioural and autism trait network topology in very preterm and term-born children

Compared to full-term (FT) born peers, children who were born very preterm (VPT; <32 weeks gestation) are likely to display more cognitive and behavioural difficulties, including inattention, anxiety and socio-communication problems. In the published literature, such difficulties tend to be studied independently, thus failing to account for how different aspects of child development interact. The current study aimed to investigate childrens cognitive and behavioural outcomes as interconnected, dynamically related facets of development that influence one another. Participants were 93 VPT and 55 FT children (median age 8.79 years). IQ was evaluated with the Wechsler Intelligence Scale for Children - 4th edition (WISC-IV), autism spectrum condition (ASC) traits with the Social Responsiveness Scale - 2nd edition (SRS-2), behavioural and emotional problems with the Strengths and Difficulties Questionnaire (SDQ), temperament with the Temperament in Middle Childhood Questionnaire (TMCQ) and executive function with the Behaviour Rating Inventory of Executive Functioning (BRIEF-2). Outcome measures were studied in VPT and FT children using Network Analysis, a method that graphically represents partial correlations between variables and yields information on each variables propensity to form a bridge between other variables. Results showed that VPT and FT children exhibited marked topological differences. Bridges (i.e., the variables most connected to others) in the VPT group network were: SDQ Conduct Problems scale and BRIEF-2 Organisation of Materials scale. In the FT group network, the most important bridges were: the BRIEF-2 Initiate, SDQ Emotional Problems and SDQ Prosocial Behaviours scales. These findings highlight the importance of targeting different aspects of development to support VPT and FT children in person-based interventions.

developmental biology↗

bioRxiv2022

Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks

Very preterm birth (VPT; [≤] 32 weeks gestation) is associated with altered brain development and cognitive and behavioral difficulties across the lifespan. However, heterogeneity in outcomes among individuals born VPT makes it challenging to identify those most vulnerable to neurodevelopmental sequelae. Here, we aimed to stratify VPT children into distinct behavioral subgroups and explore between-subgroup differences in neonatal brain structure and function. 198 VPT children (98 females) previously enrolled in the Evaluation of Preterm Imaging study (EudraCT 2009-011602-42) underwent Magnetic Resonance Imaging at term-equivalent age and neuropsychological assessments at 4-7 years. Using an integrative clustering approach, we combined neonatal socio-demographic, clinical factors and childhood socio-emotional and executive function outcomes, to identify distinct subgroups of children based on their similarity profiles in a multidimensional space. We characterized resultant subgroups using domain-specific outcomes (temperament, psychopathology, IQ and cognitively stimulating home environment) and explored between-subgroup differences in neonatal brain volumes (voxel-wise Tensor-Based-Morphometry), functional connectivity (voxel-wise degree centrality) and structural connectivity (Tract-Based-Spatial-Statistics). Results showed two-and three-cluster data-driven solutions. The two-cluster solution comprised a resilient subgroup (lower psychopathology and higher IQ, executive function and socio-emotional outcomes) and an at-risk subgroup (poorer behavioral and cognitive outcomes). The three-cluster solution showed an additional third intermediate subgroup displaying behavioral and cognitive outcomes intermediate between the resilient and at-risk subgroups. The resilient subgroup had the most cognitively stimulating home environment and the at-risk subgroup showed the highest neonatal clinical risk, while the intermediate subgroup showed the lowest clinical but the highest socio-demographic risk. Compared to the intermediate subgroup, the resilient subgroup displayed larger neonatal insular and orbitofrontal volumes and stronger orbitofrontal functional connectivity, while the at-risk group showed widespread white matter microstructural alterations. These findings suggest that risk stratification following VPT birth is feasible and could be used translationally to guide personalized interventions aimed at promoting childrens resilience.

neuroscience↗

bioRxiv2022

Maturational networks of fetal brain activity

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.

neuroscience↗

bioRxiv2022

Development of neonatal brain functional centrality and alterations associated with preterm birth

Formation of the functional connectome in early life underpins future learning and behaviour. However, our understanding of how the functional organisation of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterised voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and decrease within motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive, and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

neuroscience↗

bioRxiv2021

Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no comprehensive comparison of these approaches to one another, nor to existing Euclidean methods, to date. This paper benchmarks a collection of geometric and traditional deep learning models on phenotype prediction and segmentation of sphericalised neonatal cortical surface data, from the publicly available Developing Human Connectome Project (dHCP). Tasks include prediction of postmenstrual age at scan, gestational age at birth and segmentation of the cortical surface into anatomical regions defined by the M-CRIB-S atlas. Performance was assessed not only in terms of model precision, but also in terms of network dependence on image registration, and model interpretation via occlusion. Networks were trained both on sphericalised and anatomical cortical meshes. Findings suggest that the utility of geometric deep learning over traditional deep learning is highly task-specific, which has implications for the design of future deep learning models on the cortical surface. The code, and instructions for data access, are available from https://github.com/Abdulah-Fawaz/Benchmarking-Surface-DL.

neuroscience↗

bioRxiv2021

Neonatal multi-modal cortical profiles predict 18-month developmental outcomes

Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.

neuroscience↗

bioRxiv2021

High-throughput, multiplex microfluidic test strip for the determination of antibiotic susceptibility in uropathogenic E. coli with smartphone detection

Antibiotic resistance in urinary tract infections is a major global challenge and improved cost-effective and high throughput antibiotic susceptibility tests (AST) are urgently needed to inform correct antibiotic selection. We evaluated a high throughput microfluidic test strip for AST and minimum inhibitory concentration (MIC) determination in 20 urinary pathogenic E. coli (UPEC) isolates using six commonly prescribed or therapeutically beneficial antibiotics. The microfluidic MIC performs broth microdilution in 1 microliter volume capillaries, 100 X smaller than standard broth microdilution. Each test strip contains 10 parallel capillaries which are dipped into a single well of a 96 well plate, significantly increasing throughput over a microtitre plate. When tested with clinical UPEC isolates at standardised inoculum density, these devices gave 100% essential agreement (+/- 1 doubling dilution of antibiotic) to the gold standard microplate broth microdilution method described by CLSI. Although for some antibiotic/isolate combinations an earlier endpoint readout reduced accuracy, MIC test strips read at a 6h endpoint still gave 69 - 100 % essential agreement depending on the antibiotic. Growth could be detected significantly earlier than 6h, but with a trade-off between speed vs accuracy. These high-throughput, multiplexed test strips could be used to increase throughput and give faster results than microplates while retaining the core broth microdilution methodology of gold standard techniques for AST and MIC determination.

microbiology↗

bioRxiv2021

Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies

Multiple studies have demonstrated less favourable childhood outcomes in infants born in early term (37-38 weeks gestation) compared to those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain and subsequent neurodevelopment in term-born babies. Here we characterise structural brain correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome. We used T2 and diffusion weighted Magnetic Resonance Images acquired in the neonatal period from a cohort (n=454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor based morphometry (TBM) and tract based spatial statistics (TBSS). Neurodevelopment was subsequently assessed at age 18 months using the Bayley-III Scales of Infant and Toddler Development, and the effects of gestational age at birth and related neuroimaging findings on outcome were analysed with linear regression. Infants born earlier had areas of higher relative ventricular volume, and lower relative brain volume in the basal ganglia, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Linear regression models predicting outcome from gestational age at birth were significantly improved by adding neuroimaging features associated with gestational age at birth. This work adds to the growing body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.

neuroscience↗

bioRxiv2021

The developing brain structural and functional connectome fingerprint

In the mature brain, structural and functional connectivity fingerprints can be used to identify the uniqueness of an individual. However, whether the characteristics that make a brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes only allowed to identify 12% of the participants. These suggests that structural connectivity is more stable in early life and can represent a potential connectome fingerprint. Thus, a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.

neuroscience↗

bioRxiv2021

Multi-channel 4D parametrized Atlas of Macro- and Microstructural Neonatal Brain Development

Structural and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. The existing models of the developing brain in time include only either structural or diffusion channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work we propose methodology to generate multi-channel (MC) continuous spatio-temporal parametrized atlas of brain development based on MC registration driven by both T2-weighted and orientation distribution functions (ODF) channels along with the Gompertz model (GM) fitting of the signals and spatial transformations in time. We construct a 4D MC atlas of neonatal brain development during 38 to 44 week PMA range from 170 normal term subjects from developing Human Connectomme Project. The resulting atlas consists of fourteen spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. We demonstrate applicability of the atlas for quantitative region-specific comparison of 140 term and 40 preterm subjects scanned at the term-equivalent age. We show multi-parametric microstructural differences in multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.

neuroscience↗