Zach Eaton-Rosen

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.

NiftyNet: a deep-learning platform for medical imaging

Highlights • An open-source platform is implemented based on TensorFlow APIs for deep learning in medical imaging domain.• A modular implementation of the typical medical imaging machine learning pipeline facilitates (1) warm starts with established pre-trained networks, (2) adapting existing neural network architectures to new problems, and (3) rapid prototyping of new solutions.• Three deep-learning applications, including segmentation, regression, image generation and representation learning, are presented as concrete examples illustrating the platform’s key features.

Measurement of myelin in the preterm brain: multi-compartment diffusion imaging and multi-component T2 relaxometry.

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 predictive biomarkers; however, accurate estimation of myelin content is difficult. In this work we show that measurement of the myelin water fraction (MWF) is achievable using widely available pulse sequences and state-of-the-art algorithmic modelling of the MR imaging. We show results of myelin water fraction measurement at both 30 (4 infants) and 40 (2 infants) weeks equivalent gestational age (EGA) and show that the spatial pattern of myelin is different between these ages. Furthermore we apply a multi-component fitting routine to multi-shell diffusion weighted data to show differences in neurite density and local spatial arrangement in grey and white matter. Finally we combine these results to investigate the relationships between the diffusion and myelin measurements to show that MWF in the preterm brain may be measured alongside multi-component diffusion characteristics using clinically feasible MR sequences.

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.

Multi-modal Measurement of the Myelin-to-Axon Diameter g-ratio in Preterm-born Neonates and Adult Controls

Infants born prematurely are at increased risk of adverse functional outcome. The measurement of white matter tissue composition and structure can help predict functional performance and this motivates the search for new multi-modal imaging biomarkers. In this work we develop a novel combined biomarker from diffusion MRI and multi-component T2 relaxation measurements in a group of infants born very preterm and scanned between 30 and 40 weeks equivalent gestational age. We also investigate this biomarker on a group of seven adult controls, using a multi-modal joint model-fitting strategy. The proposed emergent biomarker is tentatively related to axonal energetic efficiency (in terms of axonal membrane charge storage) and conduction velocity and is thus linked to the tissue electrical properties, giving it a good theoretical justification as a predictive measurement of functional outcome.

Investigating the maturation of microstructure and radial orientation in the preterm human cortex with diffusion MRI

&NA; Preterm birth disrupts and alters the complex developmental processes in the cerebral cortex. This disruption may be a contributing factor to widespread delay and cognitive difficulties in the preterm population. Diffusion‐weighted magnetic resonance imaging (DW MRI) is a noninvasive imaging technique that makes inferences about cellular structures, at scales smaller than the imaging resolution. One established finding is that DW MRI shows a transient radial alignment in the preterm cortex. In this study, we quantify this maturational process with the “radiality index”, a parameter that measures directional coherence, which we expect to change rapidly in the perinatal period. To measure this index, we used structural T2‐weighted MRI to segment the cortex and generate cortical meshes. We obtained normal vectors for each face of the mesh and compared them to the principal diffusion direction, calculated by both the DTI and DIAMOND models, to generate the radiality index. The subjects included in this study were 89 infants born at fewer than 34 weeks completed gestation, each imaged at up to four timepoints between 27 and 42 weeks gestational age. In this manuscript, we quantify the longitudinal trajectory of radiality, fractional anisotropy and mean diffusivity from the DTI and DIAMOND models. For the radiality index and fractional anisotropy, the DIAMOND model offers improved sensitivity over the DTI model. The radiality index has a consistent progression across time, with the rate of change depending on the cortical lobe. The occipital lobe changes most rapidly, and the frontal and temporal least: this is commensurate with known developmental anatomy. Analysing the radiality index offers information complementary to other diffusion parameters. HighlightsAcquired DWMRI at up to four perinatal timepoints from 89 infants born at <34 weeks gestation.Fitted the DIAMOND and DTI models within the cortical tissue.Evaluated the radiality index, a local measure how ‘radial’ diffusion is to the cortex, and analysed this by region.This index shows rates of maturation that depend on the lobe: the occipital lobe matures fastest.The DIAMOND model is more sensitive to these changes than the DTI model.

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.

Anatomy-Driven Modelling of Spatial Correlation for Regularisation of Arterial Spin Labelling Images

Arterial spin labelling (ASL) allows blood flow to be measured in the brain and other organs of the body, which is valuable for both research and clinical use. Unfortunately, ASL suffers from an inherently low signal to noise ratio, necessitating methodological advances in ASL acquisition and processing. Spatial regularisation improves the effective signal to noise ratio, and is a common step in ASL processing. However, the standard spatial regularisation technique requires a manually-specified smoothing kernel of an arbitrary size, and can lead to loss of fine detail. Here, we present a Bayesian model of spatial correlation, which uses anatomical information from structural images to perform principled spatial regularisation, modelling the underlying signal and removing the need to set arbitrary smoothing parameters. Using data from a large cohort (N = 130) of preterm-born adolescents and age-matched controls, we show our method yields significant improvements in test-retest reproducibility, increasing the correlation coefficient by 14% relative to Gaussian smoothing and giving a corresponding improvement in statistical power. This novel technique has the potential to significantly improve single inversion time ASL studies, allowing more reliable detection of perfusion differences with a smaller number of subjects.

Beyond the Resolution Limit: Diffusion Parameter Estimation in Partial Volume

Diffusion MRI is a frequently-used imaging modality that can infer microstructural properties of tissue, down to the scale of microns. For single-compartment models, such as the diffusion tensor (DT), the model interpretation depends on voxels having homogeneous composition. This limitation makes it difficult to measure diffusion parameters for small structures such as the fornix in the brain, because of partial volume. In this work, we use a segmentation from a structural scan to calculate the tissue composition for each diffusion voxel. We model the measured diffusion signal as a linear combination of signals from each of the tissues present in the voxel, and fit parameters on a per-region basis by optimising over all diffusion data simultaneously. We test the proposed method by using diffusion data from the Human Connectome Project (HCP). We downsample the HCP data, and show that our method returns parameter estimates that are closer to the high-resolution ground truths than for classical methods. We show that our method allows accurate estimation of diffusion parameters for regions with partial volume. Finally, we apply the method to compare diffusion in the fornix for adults born extremely preterm and matched controls.

Differential rates of perinatal maturation of human primary and nonprimary auditory cortex

Abstract Primary and nonprimary cerebral cortex mature along different timescales; however, the differences between the rates of maturation of primary and nonprimary cortex are unclear. Cortical maturation can be measured through changes in tissue microstructure detectable by diffusion magnetic resonance imaging (MRI). In this study, diffusion tensor imaging (DTI) was used to characterize the maturation of Heschl’s gyrus (HG), which contains both primary auditory cortex (pAC) and nonprimary auditory cortex (nAC), in 90 preterm infants between 26 and 42 weeks postmenstrual age (PMA). The preterm infants were in different acoustical environments during their hospitalization: 46 in open ward beds and 44 in single rooms. A control group consisted of 15 term-born infants. Diffusion parameters revealed that (1) changes in cortical microstructure that accompany cortical maturation had largely already occurred in pAC by 28 weeks PMA, and (2) rapid changes were taking place in nAC between 26 and 42 weeks PMA. At term equivalent PMA, diffusion parameters for auditory cortex were different between preterm infants and term control infants, reflecting either delayed maturation or injury. No effect of room type was observed. For the preterm group, disturbed maturation of nonprimary (but not primary) auditory cortex was associated with poorer language performance at age two years.