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

Abstract

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.

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

“…We investigated the rates of change per week in cortical thickness, cortical fractional anisotropy and cortical mean diffusivity in the cortex. The cortical FA is decreasing in most regions of the brain, and the cortical MD is increasing, results which match previous studies at the global level [6]. The cortical thickness is increasing in most regions, except the temporal lobe, where it is slightly decreasing.…”

“…Briefly, images were bias-corrected then segmented using the help of neonate brain atlases and an adaptive EM algorithm. The preprocessing of the diffusion images was done as described in [6]. We obtained FA and MD maps by fitting a diffusion tensor model to the data.…”

Early development of structural networks and the impact of prematurity on brain connectivity

“…For example, neurite orientation dispersion and density imaging (NODDI) (Zhang et al, 2012) is a multi-compartment model which provides quantitative measures that correlate to tissue microstructure with greater specificity than established imaging techniques, such as diffusion tensor imaging (DTI). NODDI has been used previously to characterise WM (Kunz et al, 2014) and GM (Eaton-Rosen et al, 2015) tissue characteristics during early development, and is a promising biologically interpretable (Colgan et al, 2016) alternative to anisotropy measures to weight brain connectivity (Lemkaddem et al, 2014). …”

Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging

“…NODDI has been used to examine both normal brain development and disease conditions (Winston, et al, 2014; Lemkaddem, et al, 2014; Timmers, et al, 2015; Grussu, et al, 2015; Wen, et al, 2015; Figini, et al, 2014; Eaton-Rosen, et al, 2015; Kunz, et al, 2014; Jelescu, et al, 2015; Magnollay, et al, 2014; Adluru, et al, 2014; Billiet, et al, 2014; Stikov, et al, 2015). Such studies provide a more detailed analysis of the microstructural subtleties and underlying mechanisms associated with these illnesses and processes.…”