Abnormality detection in white matter in utero, applied to fetuses with Congenital Heart Disease
Poster No:
392
Submission Type:
Abstract Submission
Authors:
Sian Wilson1, Daniel Cromb1, Vyacheslav Karolis2, Daan Christiaens3, Alena Uus4, Russell Macleod1, Anthony Price1, Joseph Hajnal1, A. Edwards1, Kiho Im5, Jonathan O'Muircheartaigh6, Donald Tournier1, Serena Counsell1
Institutions:
1King's College London, London, England, 2King's College London, London, Not in the States, 3KU Leuven, Leuven, Other, 4King's College London, London, Other, 5Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA, 6King's College London, London, London
First Author:
Co-Author(s):
Kiho Im
Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School
Boston, MA
Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School
Boston, MA
Introduction:
Impaired volumetric development of transient fetal compartments underlying white matter (WM) has been observed in fetuses with congenital heart disease (CHD), suggesting impaired WM) development in utero (Rollins et al., 2021). However, microstructural WM development in CHD has not been investigated. We present an approach to identify individual deviations from the normal trajectory of diffusion contrast change, with high spatiotemporal specificity, within specific WM fibre bundles as they emerge from transient fetal compartments. We explore whether volumetric differences in these zones are accompanied by changes at the microstructural level, using multi-shell high angular resolution diffusion imaging (HARDI), comparing healthy control fetuses to a cohort with various types of CHD.
Methods:
The study population included 235 healthy controls (22 – 37 weeks gestational age (GA), 129 male) and 26 fetuses with various forms of CHD (23 – 38 weeks GA, 19 male). T2 and HARDI volumes were acquired with the Developing Human Connectome Project acquisition protocol (Price et al., 2019) on a Philips Achieva 3T system, with a 32-channel cardiac coil. HARDI data was collected with a combined spin echo and field echo (SAFE) sequence at 2 mm isotropic resolution, using a multi-shell diffusion encoding that consists of 15 volumes at b=0 s/mm2, 46 volumes at b=400 s/mm2, and 80 volumes at b=1000 s/mm2 (Christiaens et al., 2019). HARDI datasets were reconstructed to 0.8 mm, using a data driven representation of the spherical harmonics and radial decomposition (SHARD). The SHARD pipeline caters to the motion corrupted fetal data, using dynamic distortion correction and slice-to-volume motion correction framework (Cordero-Grande et al., 2019, Christiaens et al., 2021). Subsequent diffusion processing and tractography to estimate thalamocortical pathways was performed using MRtrix3 (Wilson et al., 2023, Tournier et al., 2019). 30 cross-sections were taken along the tracts (Figure 1A,B), and values of underlying diffusion metrics were sampled and averaged within each slice. We used Gaussian process regression (GPR) implemented in GPy, to predict and characterise the normative range of tissue fraction values within each cross-section (Figure 1C-F). We quantified the deviation from normal using a Z-score, computing the difference between predicted and observed values, normalised by prediction uncertainty (Marquand et al. 2016).
Results:
We identified unique maturational trends within different fetal tissue types across the second to third trimester (Figure 1). We quantified different rates of change in tissue fraction maturation between deep grey matter and cortical plate compartments, and in the intermediate zone/white matter compartment, tissue fraction maturation follows a parabolic curve. We also observe fluctuations in the level of variability between individuals along the tract. When examining the z scores of fetuses with CHD, all fetuses showed a high proportion of normal z-scores along the white matter, with isolated regions deviating from the normal mean. Regions of abnormality appeared to be specific to each subject, and at this stage, we did not find consistent patterns across the cohort or for subjects with a specific diagnosis.
·Figure 1
·Figure 2
Conclusions:
The analysis framework highlights unique maturational trends for different fetal tissue types across the second to third trimester. In fetuses with CHD we observed normal z scores along a large proportion of the WM, but identified specific regions of abnormality. Abnormal regions were unique to individuals, reflecting the highly dynamic development of the fetal brain and the heterogeneity of CHD subtypes within this cohort. Further post-hoc testing and clustering approaches will be required to investigate this further. This approach is tailored to detect differences within individuals, and we were statistically underpowered to draw conclusions about CHD at the group level.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Novel Imaging Acquisition Methods:
Diffusion MRI
Keywords:
Congenital
Design and Analysis
Development
DISORDERS
Pediatric Disorders
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Fetal
1|2Indicates the priority used for review
Provide references using author date format
Christiaens, D. et al. (2021) ‘Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI’, NeuroImage, 225, p. 117437.Available at: https://doi.org/10.1016/j.neuroimage.2020.117437.
Cordero-Grande, L. et al. (2019) ‘Complex diffusion-weighted image estimation via matrix recovery under general noise models’, NeuroImage, 200,pp. 391–404. Available at: https://doi.org/10.1016/j.neuroimage.2019.06.039.
Cromb, D. et al. (2023) ‘Total and Regional Brain Volumes in Fetuses With Congenital Heart Disease’, Journal of Magnetic Resonance Imaging, p.jmri.29078. Available at: https://doi.org/10.1002/jmri.29078.
Limperopoulos, C. et al. (2010) ‘Brain Volume and Metabolism in Fetuses With Congenital Heart Disease: Evaluation With Quantitative MagneticResonance Imaging and Spectroscopy’, Circulation, 121(1), pp. 26–33. Available at: https://doi.org/10.1161/CIRCULATIONAHA.109.865568.
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Rollins, C.K. et al. (2021) ‘Regional Brain Growth Trajectories in Fetuses with Congenital Heart Disease’, Annals of Neurology, 89(1), pp. 143–157.Available at: https://doi.org/10.1002/ana.25940.
Tournier, J.-D. et al. (2019) ‘MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation’, NeuroImage, 202,p. 116137. Available at: https://doi.org/10.1016/j.neuroimage.2019.116137.
Wilson, S. et al. (2023) ‘Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain’, eLife, 12, p. e83727. Available at:https://doi.org/10.7554/eLife.83727.s Christiaens, D. et al. (2019) ‘Fetal diffusion MRI acquisition and analysis in the developing Human ConnectomeProject.’