Eliza Orasanu

Longitudinal measurement of the developing grey matter in preterm subjects using multi-modal MRI.

Preterm birth is a major public health concern, with the severity and occurrence of adverse outcome increasing with earlier delivery. Being born preterm disrupts a time of rapid brain development: in addition to volumetric growth, the cortex folds, myelination is occurring and there are changes on the cellular level. These neurological events have been imaged non-invasively using diffusion-weighted (DW) MRI. In this population, there has been a focus on examining diffusion in the white matter, but the grey matter is also critically important for neurological health. We acquired multi-shell high-resolution diffusion data on 12 infants born at ≤ 28 weeks of gestational age at two time-points: once when stable after birth, and again at term-equivalent age. We used the Neurite Orientation Dispersion and Density Imaging model (NODDI) (Zhang et al., 2012) to analyse the changes in the cerebral cortex and the thalamus, both grey matter regions. We showed region-dependent changes in NODDI parameters over the preterm period, highlighting underlying changes specific to the microstructure. This work is the first time that NODDI parameters have been evaluated in both the cortical and the thalamic grey matter as a function of age in preterm infants, offering a unique insight into neuro-development in this at-risk population.

Brain volume estimation from post-mortem newborn and fetal MRI.

Objective Minimally invasive autopsy using post-mortem magnetic resonance imaging (MRI) is a valid alternative to conventional autopsy in fetuses and infants. Estimation of brain weight is an integral part of autopsy, but manual segmentation of organ volumes on MRI is labor intensive and prone to errors, therefore unsuitable for routine clinical practice. In this paper we aim to show that volumetric measurements of the post-mortem fetal and neonatal brain can be accurately estimated using semi-automatic techniques and a high correlation can be found with the weights measured from conventional autopsy results. Methods The brains of 17 newborn subjects, part of Magnetic Resonance Imaging Autopsy Study (MaRIAS), were segmented from post-mortem MR images into cerebrum, cerebellum and brainstem using a publicly available neonate brain atlas and semi-automatic segmentation algorithm. The results of the segmentation were averaged to create a new atlas, which was then used for the automated atlas-based segmentation of 17 MaRIAS fetus subjects. As validation, we manually segmented the MR images from 8 subjects of each cohort and compared them with the automatic ones. The semi-automatic estimation of cerebrum weight was compared with the results of the conventional autopsy. Results The Dice overlaps between the manual and automatic segmentations are 0.991 and 0.992 for cerebrum, 0.873 and 0.888 for cerebellum and 0.819 and 0.815 for brainstem, for newborns and fetuses, respectively. Excellent agreement was obtained between the estimated MR weights and autopsy gold standard ones: mean absolute difference of 5 g and 2% maximum error for the fetus cohort and mean absolute difference of 20 g and 11% maximum error for the newborn one. Conclusions The high correlation between the obtained segmentation and autopsy weights strengthens the idea of using post-mortem MRI as an alternative for conventional autopsy of the brain.

Longitudinal development in the preterm thalamus and posterior white matter: MRI correlations between diffusion weighted imaging and T2 relaxometry

Infants born prematurely are at increased risk of adverse neurodevelopmental outcome. The measurement of white matter tissue composition and structure can help predict functional performance. Specifically, measurements of myelination and indicators of myelination status in the preterm brain could be predictive of later neurological outcome. Quantitative imaging of myelin could thus serve to develop biomarkers for prognosis or therapeutic intervention; however, accurate estimation of myelin content is difficult. This work combines diffusion MRI and multi‐component T2 relaxation measurements in a group of 37 infants born very preterm and scanned between 27 and 58 weeks equivalent gestational age. Seven infants have longitudinal data at two time points that we analyze in detail. Our aim is to show that measurement of the myelin water fraction is achievable using widely available pulse sequences and state‐of‐the‐art algorithmic modeling of the MR imaging procedure and that a multi‐component fitting routine to multi‐shell diffusion weighted data can show differences in neurite density and local spatial arrangement in grey and white matter. Inference on the myelin water fraction allows us to demonstrate that the change in diffusion properties of the preterm thalamus is not solely due to myelination (that increase in myelin content accounts for about a third of the observed changes) whilst the decrease in the posterior white matter T2 has no significant component that is due to myelin water content. This work applies multi‐modal advanced quantitative neuroimaging to investigate changing tissue properties in the longitudinal setting. Hum Brain Mapp 37:2479–2492, 2016.

Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching

Infants born extremely preterm (<28 weeks of gestation) are at risk of significant neurodevelopmental sequelae. In these infants birth coincides with a period of rapid brain growth and development, when the brain is also vulnerable to a range of insults. Mapping these changes is crucial for identifying potential biomarkers to predict early impairment.

Longitudinal Measurement of the Developing Thalamus in the Preterm Brain Using Multi-modal MRI

Preterm birth is a significant public health concern. For infants born very preterm (≤ 32 weeks completed gestation), there is a high instance of developmental disability. Due to the heterogeneity of patient outcomes, it is important to investigate early markers of future ability to provide effective and targeted intervention. As a neuronal relay centre, the thalamus is critical for effective cognitive function and, thus, development of white matter connections between the thalamus and cortex is vital. By non-invasively examining the state of the thalamus we can monitor development in the preterm period. To track the development we develop a novel registration technique to combine data from multiple modalities, in order to derive the transformation from a preterm scan, to a scan of the same infant at term-equivalent age. By measuring the changes in diffusion parameters over this period on a per-voxel basis, we hope to provide unique insight into neurodevelopment.

T2 relaxometry in the extremely-preterm brain at adolescence

Survival following very preterm birth is associated with cognitive and behavioral sequelae, which may have identifiable neural correlates. Many survivors of modern neonatal care in the 1990s are now young adults and the evolution of MRI findings into adult life has rarely been evaluated. We have investigated a cohort of 19-year-old adolescents without severe impairments born between 22 and 26 weeks of gestation in 1995 (extremely preterm: EP). Using T2 data derived from magnetic resonance imaging we investigate differences between the brains of 46 EP participants (n = 46) and the brains of a group of term-born controls (n = 20). Despite EP adolescents having significantly reduced gray and white matter volumes, the composition of these tissues, assessed by both single and multi-component relaxometry, appears to be unrelated to either preterm status or gender. This may represent either insensitivity of the imaging technique or reflect that there are only subtle differences between EP subjects and their term-born peers.

Erratum to: Cortical folding of the preterm brain: a longitudinal analysis of extremely preterm born neonates using spectral matching (Brain and Behavior, (2016), 6, (1-17), 10.1002/brb3.488)

[This corrects the article DOI: 10.1002/brb3.488.].

Prefrontal Cortical Folding of the Preterm Brain: A Longitudinal Analysis of Preterm-Born Neonates

Very preterm birth (less than 32 weeks completed gestation) coincides with a rapid period of brain growth and development. Investigating the changes of certain brain regions may allow the development of biomarkers for predicting neurological outcome. The prefrontal cortex, associated with the executive function, undergoes major changes during the last 10 weeks of pregnancy, and therefore its development may be altered by very-preterm birth. In this paper we use surface-based spectral matching techniques to analyse how the prefrontal cortex develops between 30 weeks and 40 weeks equivalent gestational age in 5 infants born preterm. Using this method, we can accurately map the regions where the secondary and tertiary sulci and gyri of the prefrontal cortex will form. Additionally, measurements of cortical curvature can be used to estimate the local bending energy required to generate the observed pattern of cortical folding. Longitudinal measurement of the cortical folding change can provide information about the mechanical properties of the underlying tissue and may be useful in discriminating mechanical changes during growth in this vulnerable period of development.

Longitudinal Analysis of the Preterm Cortex Using Multi-modal Spectral Matching

Extremely preterm birth (less than 32 weeks completed gestation) overlaps with a period of rapid brain growth and development. Investigating longitudinal brain changes over the preterm period in these infants may allow the development of biomarkers for predicting neurological outcome. In this paper we investigate longitudinal changes in cortical thickness, cortical fractional anisotropy and cortical mean diffusivity in a groupwise space obtained using a novel multi-modal spectral matching technique. The novelty of this method consists in its ability to register surfaces with very little shape complexity, like in the case of the early developmental stages of preterm infants, by also taking into account their underlying biology. A multi-modal method also allows us to investigate interdependencies between the parameters. Such tools have great potential in investigating in depth the regions affected by preterm birth and how they relate to each other.

Fitting parametric models of diffusion MRI in regions of partial volume

Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and ‘Neurite Orientation Dispersion and Density Imaging’ (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.