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High-resolution volume reconstruction from multiple motion-corrupted stacks of 2D slices plays an increasing role for fetal brain Magnetic Resonance Imaging (MRI) studies. Currently existing reconstruction methods are time-consuming and often require user interactions to localize and extract the brain from several stacks of 2D slices. We propose a fully automatic framework for fetal brain reconstruction that consists of four stages: 1) fetal brain localization based on a coarse segmentation by a Convolutional Neural Network (CNN), 2) fine segmentation by another CNN trained with a multi-scale loss function, 3) novel, single-parameter outlier-robust super-resolution reconstruction, and 4) fast and automatic high-resolution visualization in standard anatomical space suitable for pathological brains. We validated our framework with images from fetuses with normal brains and with variable degrees of ventriculomegaly associated with open spina bifida, a congenital malformation affecting also the brain. Experiments show that each step of our proposed pipeline outperforms state-of-the-art methods in both segmentation and reconstruction comparisons including expert-reader quality assessments. The reconstruction results of our proposed method compare favorably with those obtained by manual, labor-intensive brain segmentation, which unlocks the potential use of automatic fetal brain reconstruction studies in clinical practice.

•We investigated the long-term effect of extreme prematurity on structural and microstructural characteristics of the brain network.•The hierarchical organisation of the extremely preterm adult brain is intact; however, the extremely preterm brain has significantly reduced structural connectivity and neurite density compared to the term brain.•The most significant alterations are observed in the somatosensory and motor cortical areas, and in the deep nuclei. The preterm brain has been analysed after birth by a large body of neuroimaging studies; however, few studies have focused on white matter alterations in preterm subjects beyond infancy, especially in individuals born at extremely low gestation age - before 28 completed weeks. Neuroimaging data of extremely preterm young adults are now available to investigate the long-term structural alterations of disrupted neurodevelopment. We examined white matter hierarchical organisation and microstructure in extremely preterm young adults. Specifically, we first identified the putative hubs and peripheral regions in 85 extremely preterm young adults and compared them with 53 socio-economically matched and full-term born peers. Moreover, we analysed Fractional Anisotropy (FA), Mean Diffusivity (MD), Neurite Density Index (NDI), and Orientation Dispersion Index (ODI) of white matter in hubs, peripheral regions, and over the whole brain. Our results suggest that the hierarchical organisation of the extremely preterm adult brain remains intact. However, there is evidence of significant alteration of white matter connectivity at both the macro- and microstructural level, with overall diminished connectivity, reduced FA and NDI, increased MD, and comparable ODI; suggesting that, although the spatial configuration of WM fibres is comparable, there are less WM fibres per voxel. These alterations are found throughout the brain and are more prevalent along the pathways between deep grey matter regions, frontal regions and cerebellum. This work provides evidence that white matter abnormalities associated with the premature exposure to the extrauterine environment not only are present at term equivalent age but persist into early adulthood.

The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, which decomposes fMRI data into modes. Each mode comprises of a neural activation time course and a spatial mapping. Two versions of HMF are proposed in which the neural activation time course of each mode is convolved with either the canonical HRF or predetermined subject-specific HRFs. Firstly, HMF with the canonical HRF is applied to two open-source cohorts. These cohorts comprise of several task experiments including motor, incidental memory, spatial coherence discrimination, verbal discrimination task and a very short localization task, engaging multiple parts of the eloquent cortex. HMF modes were obtained whose neural activation time course followed original task timings and whose corresponding spatial map matched cortical areas known to be involved in the respective task processing. Secondly, the alignment of these neural activation time courses to task timings were further improved by replacing the canonical HRF with subject-specific HRFs during HMF mode computation. In addition to task-related modes, HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.

Acoustic noise can have profound effects on wellbeing, impacting the health of pregnant women and their fetus. Mounting evidence suggests neural memory traces are formed by auditory learning in utero. A better understanding of the fetal auditory environment is therefore critical to avoid exposure to damaging noise levels. Using anatomical data from MRI scans of pregnant patients ( N = 4 ) from 24 weeks of gestation, we develop a computational model to quantify fetal exposure to acoustic field. We obtain acoustic transfer characteristics across the human audio range and pressure maps in transverse planes passing through the uterus at 5 kHz, 10 kHz and 20 kHz, showcasing multiple scattering and modal patterns. Our calculations show that the sound transmitted in utero is attenuated by as little as 6 dB below 1 kHz, confirming results from animal studies that the maternal abdomen and pelvis do not shelter the fetus from external noise. There are limited in vitro and in vivo models to study human fetal exposure to environmental noise. Here, the authors develop a computational model to quantify fetal exposure to acoustic fields, obtaining acoustic transfer characteristics across the human audio range.

Lower cerebral blood flow (CBF) is associated with cardiovascular disease and vascular risk factors, and is increasingly acknowledged as an important contributor to cognitive decline and dementia. In this cross-sectional study, we examined the association between CBF and cognitive functioning in a community-based, multi-ethnic cohort. From the SABRE (Southall and Brent Revisited) study, we included 214 European, 151 South Asian and 87 African Caribbean participants (71 ± 5 years; 39%F). We used 3T pseudo-continuous arterial spin labeling to estimate whole-brain, hematocrit corrected CBF. We measured global cognition and three cognitive domains (memory, executive functioning/attention and language) with a neuropsychological test battery. Associations were investigated using linear regression analyses, adjusted for demographic variables, vascular risk factors and MRI measures. Across groups, we found an association between higher CBF and better performance on executive functioning/attention (standardized ß [stß] = 0.11, < 0.05). Stratification for ethnicity showed associations between higher CBF and better performance on memory and executive functioning/attention in the white European group (stß = 0.14; < 0.05 and stß = 0.18; < 0.01 respectively), associations were weaker in the South Asian and African Caribbean groups. In a multi-ethnic community-based cohort we showed modest associations between CBF and cognitive functioning. In particular, we found an association between higher CBF and better performance on executive functioning/attention and memory in the white European group. The observations are consistent with the proposed role of cerebral hemodynamics in cognitive decline.

Increasing placental perfusion (PP) could improve outcomes of growth‐restricted fetuses. One way of increasing PP may be by using phosphodiesterase (PDE)‐5 inhibitors, which induce vasodilatation of vascular beds. We used a combination of clinically relevant magnetic resonance imaging (MRI) techniques to characterize the impact that tadalafil infusion has on maternal, placental and fetal circulations. At 116–117 days’ gestational age (dGA; term, 150 days), pregnant ewes (n = 6) underwent fetal catheterization surgery. At 120–123 dGA ewes were anaesthetized and MRI scans were performed during three acquisition windows: a basal state and then ∼15–75 min (TAD 1) and ∼75–135 min (TAD 2) post maternal administration (24 mg; intravenous bolus) of tadalafil. Phase contrast MRI and T2 oximetry were used to measure blood flow and oxygen delivery. Placental diffusion and PP were assessed using the Diffusion‐Relaxation Combined Imaging for Detailed Placental Evaluation—‘DECIDE’ technique. Uterine artery (UtA) blood flow when normalized to maternal left ventricular cardiac output (LVCO) was reduced in both TAD periods. DECIDE imaging found no impact of tadalafil on placental diffusivity or fetoplacental blood volume fraction. Maternal‐placental blood volume fraction was increased in the TAD 2 period. Fetal DO2 ${D_{{{\\mathrm{O}}_2}}}$and V̇O2 ${\\dot V_{{{\\mathrm{O}}_2}}}$were not affected by maternal tadalafil administration. Maternal tadalafil administration did not increase UtA blood flow and thus may not be an effective vasodilator at the level of the UtAs. The increased maternal–placental blood volume fraction may indicate local vasodilatation of the maternal intervillous space, which may have compensated for the reduced proportion of UtA DO2 ${D_{{{\\mathrm{O}}_2}}}$ . What is the central question of this study? Tadalafil is under consideration as an intervention strategy for fetal growth restriction: what is the impact of tadalafil on uterine artery blood flow, placental perfusion and fetal oxygen delivery? What is the main finding and its importance? Tadalafil does not increase uterine artery blood flow, placental perfusion or fetal oxygen delivery. Tadalafil may not be suitable as an intervention strategy for fetal growth restriction. The MRI techniques used herein would aid in the appropriate selection of future intervention strategies.

Magnetic resonance imaging (MRI) assessment of fetal blood oxygen saturation (SO2) can transform the clinical management of high‐risk pregnancies affected by fetal growth restriction (FGR). Here, a novel MRI method assesses the feasibility of identifying normally grown and FGR fetuses in sheep and is then applied to humans. MRI scans are performed in pregnant ewes at 110 and 140 days (term = 150d) gestation and in pregnant women at 28+3 ± 2+5 weeks to measure feto‐placental SO2. Birth weight is collected and, in sheep, fetal blood SO2 is measured with a blood gas analyzer (BGA). Fetal arterial SO2 measured by BGA predicts fetal birth weight in sheep and distinguishes between fetuses that are normally grown, small for gestational age, and FGR. MRI feto‐placental SO2 in late gestation is related to fetal blood SO2 measured by BGA and body weight. In sheep, MRI feto‐placental SO2 in mid‐gestation is related to fetal SO2 later in gestation. MRI feto‐placental SO2 distinguishes between normally grown and FGR fetuses, as well as distinguishing FGR fetuses with and without normal Doppler in humans. Thus, a multi‐compartment placental MRI model detects low placental SO2 and distinguishes between small hypoxemic fetuses and normally grown fetuses. The authors use a sheep model of human pregnancy to validate magnetic resonance imaging (MRI) fetal oxygenation measurements with invasive blood gas‐samples. They present evidence that this method is predictive of fetal growth rate in sheep and human pregnancies. These findings may lead to development of novel MRI‐based diagnostic tool for tracking fetal oxygenation in pregnancies complicated by placental insufficiency.

Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download . We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require.

The number of paediatric patients living with a prolonged Disorder of Consciousness (DoC) is growing in high-income countries, thanks to substantial improvement in intensive care. Life expectancy is extending due to the clinical and nursing management achievements of chronic phase needs, including infections. However, long-known pharmacological therapies such as amantadine and zolpidem, as well as novel instrumental approaches using direct current stimulation and, more recently, stem cell transplantation, are applied in the absence of large paediatric clinical trials and rigorous age-balanced and dose-escalated validations. With evidence building up mainly through case reports and observational studies, there is a need for well-designed paediatric clinical trials and specific research on 0–4-year-old children. At such an early age, assessing residual and recovered abilities is most challenging due to the early developmental stage, incompletely learnt motor and cognitive skills, and unreliable communication; treatment options are also less explored in early age. In middle-income countries, the lack of rehabilitation services and professionals focusing on paediatric age hampers the overall good assistance provision. Young and fast-evolving health insurance systems prevent universal access to chronic care in some countries. In low-income countries, rescue networks are often inadequate, and there is a lack of specialised and intensive care, difficulty in providing specific pharmaceuticals, and lower compliance to intensive care hygiene standards. Despite this, paediatric cases with DoC are reported, albeit in fewer numbers than in countries with better-resourced healthcare systems. For patients with a poor prospect of recovery, withdrawal of care is inhomogeneous across countries and still heavily conditioned by treatment costs as well as ethical and cultural factors, rather than reliant on protocols for assessment and standardised treatments. In summary, there is a strong call for multicentric, international, and global health initiatives on DoC to devote resources to the paediatric age, as there is now scope for funders to invest in themes specific to DoC affecting the early years of the life course.

Neonatal outcomes have improved over the last decade following significant thrust in this area, but stillbirth, preterm birth and neonatal brain injury remain acute global problems with long-lasting parental and family psychological trauma. In 2020, 1 in every 225 pregnancies in UK ended in stillbirth, with 2 million stillbirths reported worldwide. Over 40% of all stillbirths occur during labor—a loss that could be avoided with improved fetal monitoring and timely access to emergency obstetric care when required. Nearly one-fourth of global neonatal mortality relates to intrapartum-related events. Currently, available monitoring tools rely on surrogate markers such as serial fetal size measurement, doppler assessment of fetoplacental perfusion, fetal heart rate variability, fetal movements and maternal circulating placental proteins to identify the vulnerable fetus. Continuous cardiotocography (CTG) is the current standard of monitoring for fetal assessment in labor, but a Cochrane review indicated that it failed to significantly reduce poor outcomes in newborn infants, and resulted in an increase in the number of Caesarean sections. There is an urgent need for the development of a monitoring platform to directly measure acute or chronic changes related to fetoplacental compromise which can be operated with ease both in the hospital and remotely in the home environment in high-risk pregnancies. In recent years, there has been some promising development to identify compromised fetuses using advanced technologies and artificial intelligence-based approaches. We present here the current state of fetoplacental monitoring, focussing primarily on antepartum monitoring and discuss a possible way forward using digital biomarkers in this area to protect babies and mothers in future.