Biology subjects

Arichi, T.

Publications and source records attributed to Arichi, T..

27 records · Page 2Linked to original sources

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↗

bioRxiv2022

Early structural connectivity within the sensorimotor network: deviations related to prematurity and association to neurodevelopmental outcome

The sensorimotor (SM) network is crucial for optimal neurodevelopment. However, undergoing rapid maturation during the perinatal period, it is particularly vulnerable to preterm birth. Our work explores the prematurity impact on the microstructure and maturation of primary SM white matter (WM) tracts at term-equivalent age (TEA) and evaluates the relationships between these alterations and neurodevelopmental outcome. We analyzed diffusion MRI data from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA, compared to a control group of full-term (FT) neonates paired for age at MRI and sex. We dissected pairwise connections between primary SM cortices and subcortical structures using probabilistic tractography and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. In addition to tract- specific univariate analyses of diffusion metrics, we computed a maturational distance related to prematurity based on a multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Finally, we evaluated the relationships between this distance and Bayley Scales of Infant and Toddler Development (BSID-III) scaled scores at 18 months corrected age. Our results confirm important microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Additionally, comparisons of maturational distances highlight that prematurity has a differential effect on SM tracts which follows the established WM caudo-rostral developmental pattern. Our results suggest a particular vulnerability of projections involving the primary sensorimotor cortices (S1) and of the most rostral tracts, with cortico-cortical and S1-Lenticular tracts presenting the highest alterations at TEA. Finally, NODDI-derived maturational distances of specific tracts seem related to fine motor and cognitive scores. This study expands the understanding of the impact of early WM alterations in the emerging SM network on long-term neurodevelopment. In the future, related approaches have potential to lead to the development of neuroimaging markers for neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

neuroscience↗

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↗

bioRxiv2020

Predicting age and clinical risk from the neonatal connectome

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

neuroscience↗

bioRxiv2020

The Developing Human Connectome Project: typical and disrupted functional connectivity across the perinatal period

The Developing Human Connectome Project (dHCP) is an Open Science project which provides the first large sample of neonatal functional MRI (fMRI) data with high temporal and spatial resolution. This data enables mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organisation in humans. Here, we characterise in unprecedented detail the maturation and integrity of resting-state networks (RSNs) at normal term age in 337 infants (including 65 born preterm). First, we applied group independent component analysis (ICA) to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder which occur frequently in preterm children. Overall, we find a robust, modular, symmetrical functional brain organisation at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher-order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

neuroscience↗

bioRxiv2019

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20 to 45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates. HighlightsO_LIAn automated and robust pipeline to minimally pre-process highly confounded neonatal fMRI data C_LIO_LIIncludes integrated dynamic distortion and slice-to-volume motion correction C_LIO_LIA robust multimodal registration approach which includes custom neonatal templates C_LIO_LIIncorporates an automated and self-reporting QC framework to quantify data quality and identify issues for further inspection C_LIO_LIData analysis of 538 infants imaged at 26-45 weeks post-menstrual age C_LI

neuroscience↗

bioRxiv2018

The network properties of the brain at the time of normal birth support the acquisition of language processing

Language acquisition appears to rely at least in part on recruiting pre-existing brain structures. We hypothesized that the neural substrate for language can be characterized by distinct, non-trivial network properties of the brain, that modulate language acquisition early in development. We tested whether these brain network properties present at the normal age of birth predicted later language abilities, and whether these were robust against perturbation by studying infants exposed to the extreme environmental stress of preterm birth.\n\nWe found that brain network controllability and integration predicted respectively phonological, bottom-up and syntactical, top-down language skills at 20 months, and that syntactical but not phonological functions were modulated by premature extrauterine life. These data show that the neural substrate for language acquisition is a network property present at term corrected age. These distinct developmental trajectories may be relevant to the emergence of social interaction after birth.

neuroscience↗