Altered Amygdala Development and Fear Processing in Prematurely Born Infants.

CONTEXT: Prematurely born children have a high risk of developmental and behavioral disabilities. Cerebral abnormalities at term age have been clearly linked with later behavior alterations, but existing studies did not focus on the amygdala. Moreover, studies of early amygdala development after premature birth in humans are scarce. OBJECTIVE: To compare amygdala volumes in very preterm infants at term equivalent age (TEA) and term born infants, and to relate premature infants' amygdala volumes with their performance on the Laboratory Temperament Assessment Battery (Lab-TAB) fear episode at 12 months. PARTICIPANTS: Eighty one infants born between 2008 and 2014 at the University Hospitals of Geneva and Lausanne, taking part in longitudinal and functional imaging studies, who had undergone a magnetic resonance imaging (MRI) scan at TEA enabling manual amygdala delineation. OUTCOMES: Amygdala volumes assessed by manual segmentation of MRI scans; volumes of cortical and subcortical gray matter, white matter and cerebrospinal fluid (CSF) automatically segmented in 66 infants; scores for the Lab-TAB fear episode for 42 premature infants at 12 months. RESULTS: Amygdala volumes were smaller in preterm infants at TEA than term infants (mean difference 138.03 mm(3), p < 0.001), and overall right amygdala volumes were larger than left amygdala volumes (mean difference 36.88 mm(3), p < 0.001). White matter volumes were significantly smaller (p < 0.001) and CSF volumes significantly larger (p < 0.001) in preterm than in term born infants, while cortical and subcortical gray matter volumes were not significantly different between groups. Amygdala volumes showed significant correlation with the intensity of the escape response to a fearsome toy (rs = 0.38, p = 0.013), and were larger in infants showing an escape response compared to the infants showing no escape response (mean difference 120.97 mm(3), p = 0.005). Amygdala volumes were not significantly correlated with the intensity of facial fear, distress vocalizations, bodily fear and positive motor activity in the fear episode. CONCLUSION: Our results indicate that premature birth is associated with a reduction in amygdala volumes and white matter volumes at TEA, suggesting that altered amygdala development might be linked to alterations in white matter connectivity reported in premature infants. Moreover, our data suggests that such alterations might affect infants' fear-processing capabilities

Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition.

Extreme prematurity and pregnancy conditions leading to intrauterine growth restriction (IUGR) affect thousands of newborns every year and increase their risk for poor higher order cognitive and social skills at school age. However, little is known about the brain structural basis of these disabilities. To compare the structural integrity of neural circuits between prematurely born controls and children born extreme preterm (EP) or with IUGR at school age, long-ranging and short-ranging connections were noninvasively mapped across cortical hemispheres by connection matrices derived from diffusion tensor tractography. Brain connectivity was modeled along fiber bundles connecting 83 brain regions by a weighted characterization of structural connectivity (SC). EP and IUGR subjects, when compared with controls, had decreased fractional anisotropy-weighted SC (FAw-SC) of cortico-basal ganglia-thalamo-cortical loop connections while cortico-cortical association connections showed both decreased and increased FAw-SC. FAw-SC strength of these connections was associated with poorer socio-cognitive performance in both EP and IUGR children

Fetal brain tissue annotation and segmentation challenge results.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

Heritability of fractional anisotropy in human white matter: a comparison of Human Connectome Project and ENIGMA-DTI data

The degree to which genetic factors influence brain connectivity is beginning to be understood. Large-scale efforts are underway to map the profile of genetic effects in various brain regions. The NIH-funded Human Connectome Project (HCP) is providing data valuable for analyzing the degree of genetic influence underlying brain connectivity revealed by state-of-the-art neuroimaging methods. We calculated the heritability of the fractional anisotropy (FA) measure derived from diffusion tensor imaging (DTI) reconstruction in 481 HCP subjects (194/287 M/F) consisting of 57/60 pairs of mono- and dizygotic twins, and 246 siblings. FA measurements were derived using (Enhancing NeuroImaging Genetics through Meta-Analysis) ENIGMA DTI protocols and heritability estimates were calculated using the SOLAR-Eclipse imaging genetic analysis package. We compared heritability estimates derived from HCP data to those publicly available through the ENIGMA-DTI consortium, which were pooled together from five-family based studies across the US, Europe, and Australia. FA measurements from the HCP cohort for eleven major white matter tracts were highly heritable (h2 = 0.53–0.90, p < 10− 5), and were significantly correlated with the joint-analytical estimates from the ENIGMA cohort on the tract and voxel-wise levels. The similarity in regional heritability suggests that the additive genetic contribution to white matter microstructure is consistent across populations and imaging acquisition parameters. It also suggests that the overarching genetic influence provides an opportunity to define a common genetic search space for future gene-discovery studies. Uniquely, the measurements of additive genetic contribution performed in this study can be repeated using online genetic analysis tools provided by the HCP ConnectomeDB web application

Interactive histogenesis of axonal strata and proliferative zones in the human fetal cerebral wall

Development of the cerebral wall is characterized by partially overlapping histogenetic events. However, little is known with regards to when, where, and how growing axonal pathways interact with progenitor cell lineages in the proliferative zones of the human fetal cerebrum. We analyzed the developmental continuity and spatial distribution of the axonal sagittal strata (SS) and their relationship with proliferative zones in a series of human brains (8-40 post-conceptional weeks; PCW) by comparing histological, histochemical, and immunocytochemical data with magnetic resonance imaging (MRI). Between 8.5 and 11 PCW, thalamocortical fibers from the intermediate zone (IZ) were initially dispersed throughout the subventricular zone (SVZ), while sizeable axonal "invasion" occurred between 12.5 and 15 PCW followed by callosal fibers which "delaminated" the ventricular zone-inner SVZ from the outer SVZ (OSVZ). During midgestation, the SS extensively invaded the OSVZ, separating cell bands, and a new multilaminar axonal-cellular compartment (MACC) was formed. Preterm period reveals increased complexity of the MACC in terms of glial architecture and the thinning of proliferative bands. The addition of associative fibers and the formation of the centrum semiovale separated the SS from the subplate. In vivo MRI of the occipital SS indicates a "triplet" structure of alternating hypointense and hyperintense bands. Our results highlighted the developmental continuity of sagittally oriented "corridors" of projection, commissural and associative fibers, and histogenetic interaction with progenitors, neurons, and glia. Histogenetical changes in the MACC, and consequently, delineation of the SS on MRI, may serve as a relevant indicator of white matter microstructural integrity in the developing brain

Extreme prematurity and intra uterine growth restriction effects in brain network topology at school age

Higher risk for long-term behavioral and emotional sequelae, with attentional problems (with or without hyperactivity) is now becoming one of the hallmarks of extreme premature (EP) birth and birth after pregancy conditions leading to poor intra uterine growth restriction (IUGR) [1,2]. However, little is know so far about the neurostructural basis of these complexe brain functional abnormalities that seem to have their origins in early critical periods of brain development. The development of cortical axonal pathways happens in a series of sequential events. The preterm phase (24-36 post conecptional weeks PCW) is known for being crucial for growth of the thalamocortical fiber bundles as well as for the development of long projectional, commisural and projectional fibers [3]. Is it logical to expect, thus, that being exposed to altered intrauterine environment (altered nutrition) or to extrauterine environment earlier that expected, lead to alterations in the structural organization and, consequently, alter the underlying white matter (WM) structure. Understanding rate and variability of normal brain development, and detect differences from typical development may offer insight into the neurodevelopmental anomalies that can be imaged at later stages. Due to its unique ability to non-invasively visualize and quantify in vivo white matter tracts in the brain, in this study we used diffusion MRI (dMRI) tractography to derive brain graphs [4,5,6]. This relatively simple way of modeling the brain enable us to use graph theory to study topological properties of brain graphs in order to study the effects of EP and IUGR on childrens brain connectivity at age 6 years old

Lésions cérébrales du prématuré et techniques d'imagerie à visée pronostique du développement neurocognitif [Cerebral brain injury in preterm infants and the role of neuroimaging in predicting neurodevelopmental outcomes].

Due to advances in neonatal intensive care over the last decades, the pattern of brain injury seen in very preterm infants has evolved in more subtle lesions that are still essential to diagnose in regard to neurodevelopmental outcome. While cranial ultrasound is still used at the bedside, magnetic resonance imaging (MRI) is becoming increasingly used in this population for the assessment of brain maturation and white and grey matter lesions. Therefore, MRI provides a better prognostic value for the neurodevelopmental outcome of these preterms. Furthermore, the development of new MRI techniques, such as diffusion tensor imaging, resting state functional connectivity and magnetic resonance spectroscopy, may further increase the prognostic value, helping to counsel parents and allocate early intervention services

Improved statistical evaluation of group differences in connectomes by screening-filtering strategy with application to study maturation of brain connections between childhood and adolescence.

Detecting local differences between groups of connectomes is a great challenge in neuroimaging, because the large number of tests that have to be performed and the impact on multiplicity correction. Any available information should be exploited to increase the power of detecting true between-group effects. We present an adaptive strategy that exploits the data structure and the prior information concerning positive dependence between nodes and connections, without relying on strong assumptions. As a first step, we decompose the brain network, i.e., the connectome, into subnetworks and we apply a screening at the subnetwork level. The subnetworks are defined either according to prior knowledge or by applying a data driven algorithm. Given the results of the screening step, a filtering is performed to seek real differences at the node/connection level. The proposed strategy could be used to strongly control either the family-wise error rate or the false discovery rate. We show by means of different simulations the benefit of the proposed strategy, and we present a real application of comparing connectomes of preschool children and adolescents