Author name cluster

Mary Rutherford

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

YNICL Journal 2021 Journal Article

Brain volumetry in fetuses that deliver very preterm: An MRI pilot study

Lisa Story
Alice Davidson
Prachi Patkee
Bobbi Fleiss
Vanessa Kyriakopoulou
Kathleen Colford
Srividhya Sankaran
Paul Seed

BACKGROUND: Infants born preterm are at increased risk of neurological complications resulting in significant morbidity and mortality. The exact mechanism and the impact of antenatal factors has not been fully elucidated, although antenatal infection/inflammation has been implicated in both the aetiology of preterm birth and subsequent neurological sequelae. It is therefore hypothesized that processes driving preterm birth are affecting brain development in utero. This study aims to compare MRI derived regional brain volumes in fetuses that deliver 37 weeks were included. Median gestation at MRI of the preterm group was 26.8 weeks (range 19.4-31.4) and control group 26.2 weeks (range 21.7-31.9). No difference was found in supra-tentorial brain volume, ventricular volume or cerebellar volume but the eCSF and cerebral cortex volumes were smaller in fetuses that delivered preterm (p < 0.001 in both cases). CONCLUSION: Fetuses that deliver preterm have a reduction in cortical and eCSF volumes. This is a novel finding and needs further investigation. If alterations in brain development are commencing antenatally in fetuses that subsequently deliver preterm, this may present a window for in utero therapy in the future.

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YNIMG Journal 2019 Journal Article

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

Matteo Bastiani
Jesper L.R. Andersson
Lucilio Cordero-Grande
Maria Murgasova
Jana Hutter
Anthony N. Price
Antonios Makropoulos
Sean P. Fitzgibbon

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

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YNICL Journal 2018 Journal Article

Exploring the multiple-hit hypothesis of preterm white matter damage using diffusion MRI

Madeleine L. Barnett
Nora Tusor
Gareth Ball
Andrew Chew
Shona Falconer
Paul Aljabar
Jessica A. Kimpton
Nigel Kennea

BACKGROUND: Preterm infants are at high risk of diffuse white matter injury and adverse neurodevelopmental outcome. The multiple hit hypothesis suggests that the risk of white matter injury increases with cumulative exposure to multiple perinatal risk factors. Our aim was to test this hypothesis in a large cohort of preterm infants using diffusion weighted magnetic resonance imaging (dMRI). METHODS: (38-45) weeks. dMRI data were analyzed using tract based spatial statistics and the relationship between dMRI measures in white matter and individual perinatal risk factors was assessed. We tested the hypothesis that increased exposure to perinatal risk factors was associated with lower fractional anisotropy (FA), and higher radial, axial and mean diffusivity (RD, AD, MD) in white matter. Neurodevelopmental performance was investigated using the Bayley Scales of Infant and Toddler Development, Third Edition (BSITD-III) in a subset of 381 infants at 20 months corrected age. We tested the hypothesis that lower FA and higher RD, AD and MD in white matter were associated with poorer neurodevelopmental performance. RESULTS: Identified risk factors for diffuse white matter injury were lower GA at birth, fetal growth restriction, increased number of days requiring ventilation and parenteral nutrition, necrotizing enterocolitis and male sex. Clinical chorioamnionitis and patent ductus arteriosus were not associated with white matter injury. Multivariate analysis demonstrated that fetal growth restriction, increased number of days requiring ventilation and parenteral nutrition were independently associated with lower FA values. Exposure to cumulative risk factors was associated with reduced white matter FA and FA values at term equivalent age were associated with subsequent neurodevelopmental performance. CONCLUSION: This study suggests multiple perinatal risk factors have an independent association with diffuse white matter injury at term equivalent age and exposure to multiple perinatal risk factors exacerbates dMRI defined, clinically significant white matter injury. Our findings support the multiple hit hypothesis for preterm white matter injury.

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YNIMG Journal 2014 Journal Article

Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction

Giulio Ferrazzi
Maria Kuklisova Murgasova
Tomoki Arichi
Christina Malamateniou
Matthew J. Fox
Antonios Makropoulos
Joanna Allsop
Mary Rutherford

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1. 5T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies.

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YNIMG Journal 2007 Journal Article

Automatic segmentation and reconstruction of the cortex from neonatal MRI

Hui Xue
Latha Srinivasan
Shuzhou Jiang
Mary Rutherford
A. David Edwards
Daniel Rueckert
Joseph V. Hajnal

Segmentation and reconstruction of cortical surfaces from magnetic resonance (MR) images are more challenging for developing neonates than adults. This is mainly due to the dynamic changes in the contrast between gray matter (GM) and white matter (WM) in both T1- and T2-weighted images (T1w and T2w) during brain maturation. In particular in neonatal T2w images WM typically has higher signal intensity than GM. This causes mislabeled voxels during cortical segmentation, especially in the cortical regions of the brain and in particular at the interface between GM and cerebrospinal fluid (CSF). We propose an automatic segmentation algorithm detecting these mislabeled voxels and correcting errors caused by partial volume effects. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme. Quantitative validation against manual segmentation demonstrates good performance (the mean Dice value: 0. 758±0. 037 for GM and 0. 794±0. 078 for WM). The inner, central and outer cortical surfaces are then reconstructed using implicit surface evolution. A landmark study is performed to verify the accuracy of the reconstructed cortex (the mean surface reconstruction error: 0. 73 mm for inner surface and 0. 63 mm for the outer). Both segmentation and reconstruction have been tested on 25 neonates with the gestational ages ranging from ∼27 to 45 weeks. This preliminary analysis confirms previous findings that cortical surface area and curvature increase with age, and that surface area scales to cerebral volume according to a power law, while cortical thickness is not related to age or brain growth.

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