Effect of perinatal adversity on structural connectivity of the developing brain
View/ Open
Date
30/11/2018Author
Blesa Cábez, Manuel
Metadata
Abstract
Globally, preterm birth (defined as birth at <37 weeks of gestation) affects
around 11% of deliveries and it is closely associated with cerebral palsy,
cognitive impairments and neuropsychiatric diseases in later life.
Magnetic Resonance Imaging (MRI) has utility for measuring different
properties of the brain during the lifespan. Specially, diffusion MRI has been
used in the neonatal period to quantify the effect of preterm birth on white
matter structure, which enables inference about brain development and
injury.
By combining information from both structural and diffusion MRI, is it possible
to calculate structural connectivity of the brain. This involves calculating a
model of the brain as a network to extract features of interest. The process
starts by defining a series of nodes (anatomical regions) and edges
(connections between two anatomical regions). Once the network is created,
different types of analysis can be performed to find features of interest,
thereby allowing group wise comparisons.
The main frameworks/tools designed to construct the brain connectome have
been developed and tested in the adult human brain. There are several
differences between the adult and the neonatal brain: marked variation in
head size and shape, maturational processes leading to changes in signal
intensity profiles, relatively lower spatial resolution, and lower contrast
between tissue classes in the T1 weighted image. All of these issues make
the standard processes to construct the brain connectome very challenging
to apply in the neonatal population. Several groups have studied the neonatal
structural connectivity proposing several alternatives to overcome these
limitations.
The aim of this thesis was to optimise the different steps involved in
connectome analysis for neonatal data. First, to provide accurate parcellation
of the cortex a new atlas was created based on a control population of term
infants; this was achieved by propagating the atlas from an adult atlas
through intermediate childhood spatio-temporal atlases using image
registration. After this the advanced anatomically-constrained tractography
framework was adapted for the neonatal population, refined using software
tools for skull-stripping, tissue segmentation and parcellation specially
designed and tested for the neonatal brain. Finally, the method was used to
test the effect of early nutrition, specifically breast milk exposure, on
structural connectivity in preterm infants. We found that infants with higher
exposure to breastmilk in the weeks after preterm birth had improved
structural connectivity of developing networks and greater fractional
anisotropy in major white matter fasciculi. These data also show that the
benefits are dose dependent with higher exposure correlating with increased
white matter connectivity.
In conclusion, structural connectivity is a robust method to investigate the
developing human brain. We propose an optimised framework for the
neonatal brain, designed for our data and using tools developed for the
neonatal brain, and apply it to test the effect of breastmilk exposure on
preterm infants.