Recent advances in fetal fMRI present a new opportunity for neuroscience to study functional human brain connectivity at the time of its emergence. Progress in the field, however, has been hampered by the lack of openly available datasets that can be exploited by researchers across disciplines to develop methods that would address the unique challenges associated with imaging and analysing functional brain in utero, such as unconstrained head motion, dynamically evolving geometric distortions, or inherently low signal-to-noise ratio. Here we describe the developing Human Connectome Project's release of the largest open access fetal fMRI dataset to date, containing 275 scans from 255 foetuses and spanning the period of 20.86 to 38.29 post-menstrual weeks. We present a systematic approach to its pre-processing, implementing multi-band soft SENSE reconstruction, dynamic distortion corrections via phase unwrapping method, slice-to-volume reconstruction and a tailored temporal filtering model, with attention to the prominent sources of structured noise in the in utero fMRI. The dataset is accompanied with an advanced registration infrastructure, enabling group-level data fusion, and contains outputs from the main intermediate processing steps. This allows for various levels of data exploration by the imaging and neuroscientific community, starting from the development of robust pipelines for anatomical and temporal corrections to methods for elucidating the development of functional connectivity in utero. By providing a high-quality template for further method development and benchmarking, the release of the dataset will help to advance fetal fMRI to its deserved and timely place at the forefront of the efforts to build a life-long connectome of the human brain.

Neonatal brain dynamic functional connectivity: impact of preterm birth and association with early childhood neurodevelopment

The doublet nature of the 3 ppm peak previously reported in neonates indicates that a lower macromolecular contribution to the GABA+ 3 ppm signal is likely to be present in this population. Detailed characterisation of age‐related MM contribution rates is required to improve the MRS fitting process, and therefore, to further increase the accuracy of metabolic measurements in neonates. As a first step, we here measure the macromolecular baseline using metabolite-nulling MRS in healthy term neonatal participants.

Point-of-care Magnetic Resonance scanning is a novel and potentially transformative technology, utilising Ultra-Low Field permanent magnets to facilitate bedside neuroimaging. Through iterative T2w sequence optimisation, we demonstrate the feasibility of using ultra-low field portable 64mT MR scanning in neonates. This pilot data demonstrates that images can provide sufficient contrast for tissue differentiation and identify pathological lesions. Scanning with ongoing intensive care was both feasible and safe.

Objectives We assessed cortical changes in children with drug-resistant focal epilepsy using surface-based T1 and T2 relaxometry (qT1 and qT2), to probe alterations in tissue-microstructure, and their relationship to clinical parameters. Methods Data Acquisition 89 children were scanned unsedated on a 3T Achieva-TX scanner (Philips Healthcare) – 43 with drug-resistant focal epilepsy [mean age=12yrs] and 46 healthy controls [mean age=11.5yrs] (See table 1 for acquisition parameters). All images were motion-corrected.¹ Analysis Surface-reconstruction: FLAIR and T1w images were analysed to reconstruct white/grey matter (WM/GM) and pial surfaces.² These surfaces were used to compute equi-volume cortical surface depths by sampling the surface vertices in steps of 20% of cortical volume (0%: WM/GM, 100%: pial surface). qT1 and qT2 surface-mapping: qT1 and qT2 images³ were rigidly co-registered to their corresponding MPRAGE volume, smoothed, and projected to each depth. Group differences in qT1 and qT2: Surface outputs from the HCP structural pipeline are left-right symmetrical, therefore we flipped qT1 and qT2 surface maps of patients with right hemispheric focus and analysed them with left focus patients. Group-wise alterations at each cortical depth were tested.4 Additionally, vertex-wise qT1 and qT2 values at 20% depth were subtracted from those at 80% depth, and group-differences in cortical gradients were tested as an index of intracortical organisation. Associations between qT1 and qT2 changes in patients and disease duration/number of seizures per year were assessed. Age, sex, cortical thickness and curvature were included as covariates.5 6 TFCE was employed as test statistic, and FWE-correction was applied across modalities and contrasts. Results Figure 2A displays depth-wise group differences in qT1 and qT2. Bilateral qT2 increases and ipsilateral qT1 increases were detected in patients in the outermost cortical depths. The detected changes were not associated with clinical variables. Figure 2B displays group differences in qT1 and qT2 cortical gradients. We detected steeper gradients in patients, with increasingly high qT1 and qT2 in the outermost cortical depths bilaterally. The detected changes were not associated with clinical variables. Conclusions We report the presence of widespread, depth-mediated qT1 and qT2 increases in children with focal epilepsy. Changes appear unrelated to focus laterality, and likely represent gliosis, myelin and iron changes, oedema-associated free-water increases, or a combination of these.7 Based on the typically shorter disease duration in children, and on the lack of associations with disease-severity measures, such changes may represent antecedent neurobiological alterations, rather than the cumulative effect of seizure-activity or medication side-effects. References Cordero-Grande L, et al. Motion-corrected MRI with DISORDER: Distributed and incoherent sample orders for reconstruction deblurring using encoding redundancy. Magnetic Resonance in Medicine 2020;84:713–726. Glasser MF, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 2013;80:105–124. Teixeira RPAG, Malik SJ, Hajnal JV. Joint system relaxometry (JSR) and Crámer-Rao lower bound optimization of sequence parameters: A framework for enhanced precision of DESPOT T1 and T2 estimation. Magn Reson Med 2018;79:234–245. Winkler AM, Webster MA, Brooks JC, Tracey I, Smith SM, Nichols TE. Non-parametric combination and related permutation tests for neuroimaging. Human Brain Mapping. 2016;37(4):1486–1511. doi:10.1002/hbm.23115 Galovic M, et al. Resective surgery prevents progressive cortical thinning in temporal lobe epilepsy. Brain 2020;143:3262–3272. Annese J, Pitiot A, Dinov ID, Toga AW. A myelo-architectonic method for the structural classification of cortical areas. NeuroImage 2004;21:15–26. Cercignani M, Dowell NG, Tofts PS. Quantitative MRI of the Brain: Principles of Physical Measurement, Second edition. (CRC Press, 2018).

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization 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 characterized 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 a decrease within the 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.

The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.

Cardiovascular magnetic resonance (CMR) is increasingly used in newborns with congenital heart disease. However, reporting on ventricular volumes and mass is hindered by an absence of normative data in this population. Healthy term (37–41 weeks gestation) newborns underwent non-sedated, free-breathing CMR within the first week of life using the ‘feed and wrap’ technique. End-diastolic volume (EDV), end-systolic volume (ESV) stroke volume (SV) and ejection fraction (EF) were calculated for both left ventricle (LV) and right ventricle (RV). Papillary muscles were separately contoured and included in the myocardial volume. Myocardial mass was calculated by multiplying myocardial volume by 1.05 g/ml. All data were indexed to weight and body surface area (BSA). Inter-observer variability (IOV) was performed on data from 10 randomly chosen infants. Twenty healthy newborns (65

Highlights • Examined effects of prenatal exposure to air pollution on neonatal brain structure.• Large study in healthy term born neonates.• Prenatal air pollution exposure was associated with altered brain morphology.

Objective Tissue abnormalities in focal epilepsy may extend beyond the presumed focus. The underlying pathophysiology of these broader changes is unclear, and it is not known whether they result from ongoing disease processes, treatment-related side-effects, or whether they emerge earlier. Few studies have focused on the period of onset for most focal epilepsies, childhood. Fewer still have utilised quantitative MRI, which may provide a more sensitive and interpretable measure of tissue microstructural change. Here, we aimed to determine common spatial modes of changes in cortical architecture in children with heterogeneous drug-resistant focal epilepsy and, secondarily, whether changes were related to disease severity. Methods To assess cortical microstructure, quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in 43 children with drug-resistant focal epilepsy [age-range=4-18 years] and 46 typically-developing children [age-range=2-18 years]. We assessed depth-dependent qT1 and qT2 values across the neocortex, as well as their gradient of change across cortical depths. We also determined whether global changes seen in group analyses were driven by focal pathologies in individual patients. Finally, as a proof-of-concept, we trained a classifier using qT1 and qT2 gradient maps from patients with radiologically-defined abnormalities (MRI-positive) and healthy controls, and tested if this could classify patients without reported radiological abnormalities (MRI-negative). Results We uncovered depth-dependent qT1 and qT2 increases in widespread cortical areas in patients, likely representing microstructural alterations in myelin or gliosis. Changes did not correlate with disease severity measures, suggesting they may represent antecedent neurobiological alterations. Using a classifier trained with MRI-positive patients and controls, sensitivity was 62% at 100% specificity on held-out MRI-negative patients. Significance These findings suggest the presence of a potential imaging endophenotype of focal epilepsy, detectable irrespective of radiologically-identified abnormalities, and potentially evident pre-symptomatically. Key Points We assessed cortical microstructure in children with focal epilepsy Quantitative T1 and T2 relaxometry (qT1 and qT2) was measured in the neocortex Patients showed extensive qT1/qT2 increases and intracortical organization changes Alterations may appear during cerebral development, prior to disease onset

Abstract 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.

The aim of this study was to gather data from children on their subjective experiences when undergoing high field MRI and compare this to adult data collected with similar questionnaires. Seventeen children and twenty-six healthy adults had brain imaging at 7T. Their experiences which included: (a) acoustic noise, (b) anxiety, (c) metallic taste (d) vertigo (dizziness) and e) involuntary eye movement (nystagmus) or flashing lights were evaluated. We found that children scanned at 7T reported similar experiences to adults.

Background Infants with congenital heart disease (CHD) are at risk of neurodevelopmental impairments, which may be associated with impaired brain growth. We characterized how perioperative brain growth in infants with CHD deviates from typical trajectories and assessed the relationship between individualized perioperative brain growth and clinical risk factors. Methods and Results A total of 36 infants with CHD underwent preoperative and postoperative brain magnetic resonance imaging. Regional brain volumes were extracted. Normative volumetric development curves were generated using data from 219 healthy infants. Z‐scores, representing the degree of positive or negative deviation from the normative mean for age and sex, were calculated for regional brain volumes from each infant with CHD before and after surgery. The degree of Z‐score change was correlated with clinical risk factors. Perioperative growth was impaired across the brain, and it was associated with longer postoperative intensive care stay (false discovery rate P<0.05). Higher preoperative creatinine levels were associated with impaired brainstem, caudate nuclei, and right thalamus growth (all false discovery rate P=0.033). Older postnatal age at surgery was associated with impaired brainstem and right lentiform growth (both false discovery rate P=0.042). Longer cardiopulmonary bypass duration was associated with impaired brainstem and right caudate growth (false discovery rate P<0.027). Conclusions Infants with CHD can have impaired brain growth in the immediate postoperative period, the degree of which associates with postoperative intensive care duration. Brainstem growth appears particularly vulnerable to perioperative clinical course, whereas impaired deep gray matter growth was associated with multiple clinical risk factors, possibly reflecting vulnerability of these regions to short‐ and long‐term hypoxic injury.

In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) 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 of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: 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.

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.

PurposeUltralow‐field (ULF) point‐of‐care MRI systems allow image acquisition without interrupting medical provision, with neonatal clinical care being an important potential application. The ability to measure neonatal brain tissue T1 is a key enabling technology for subsequent structural image contrast optimization, as well as being a potential biomarker for brain development. Here we describe an optimized strategy for neonatal T1 mapping at ULF.MethodsExaminations were performed on a 64‐mT portable MRI system. A phantom validation experiment was performed, and a total of 33 in vivo exams were acquired from 28 neonates with postmenstrual age ranging from 31+4 to 49+0 weeks. Multiple inversion‐recovery turbo spin‐echo sequences were acquired with differing inversion and repetition times. An analysis pipeline incorporating inter‐sequence motion correction generated proton density and T1 maps. Regions of interest were placed in the cerebral deep gray matter, frontal white matter, and cerebellum. Weighted linear regression was used to predict T1 as a function of postmenstrual age.ResultsReduction of T1 with postmenstrual age is observed in all measured brain tissue; the change in T1 per week and 95% confidence intervals is given by dT1 = −21 ms/week [−25, −16] (cerebellum), dT1 = −14 ms/week [−18, −10] (deep gray matter), and dT1 = −35 ms/week [−45, −25] (white matter).ConclusionNeonatal T1 values at ULF are shorter than those previously described at standard clinical field strengths, but longer than those of adults at ULF. T1 reduces with postmenstrual age and is therefore a candidate biomarker for perinatal brain development.

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, though individual brain connectivity maps (connectomes) are by nature high in dimensionality and 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 postmenstrual 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 predominantly involve frontal and temporal regions, thalami, and basal ganglia. 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 of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.

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.

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

BackgroundMaternal obesity may increase offspring risk of cardiovascular disease. We assessed the impact of maternal obesity on cardiac structure and function in newborns as a marker of fetal cardiac growth.MethodsNeonates born to mothers of healthy weight (body mass index (BMI) 20–25 kg/m2, n=56) and to mothers who were obese (BMI ≥30 kg/m2, n=31) underwent 25-minute continuous ECG recording and non-sedated, free-breathing cardiac MRI within 72 hours of birth.ResultsMean (SD) heart rate during sleep was higher in infants born to mothers who were versus were not obese (123 (12.6) vs 114 (9.8) beats/min, p=0.002). Heart rate variability during sleep was lower in infants born to mothers who were versus were not obese (SD of normal-to-normal R-R interval 34.6 (16.8) vs 43.9 (16.5) ms, p=0.05). Similar heart rate changes were seen during wakefulness. Left ventricular end-diastolic volume (2.35 (0.14) vs 2.54 (0.29) mL/kg, p=0.03) and stroke volume (1.50 (0.09) vs 1.60 (0.14), p=0.04) were decreased in infants born to mothers who were versus were not obese. There were no differences in left ventricular end-systolic volume, ejection fraction, output or myocardial mass between the groups.ConclusionMaternal obesity was associated with increased heart rate, decreased heart rate variability and decreased left ventricular volumes in newborns. If persistent, these changes may provide a causal mechanism for the increased cardiovascular risk in adult offspring of mothers with obesity. In turn, modifying antenatal and perinatal maternal health may have the potential to optimise long-term cardiovascular health in offspring.