196 research outputs found
Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates
The study of cerebral anatomy in developing neonates is of great importance for
the understanding of brain development during the early period of life. This
dissertation therefore focuses on three challenges in the modelling of cerebral
anatomy in neonates during brain development. The methods that have been
developed all use Magnetic Resonance Images (MRI) as source data.
To facilitate study of vascular development in the neonatal period, a set of image
analysis algorithms are developed to automatically extract and model cerebral
vessel trees. The whole process consists of cerebral vessel tracking from
automatically placed seed points, vessel tree generation, and vasculature
registration and matching. These algorithms have been tested on clinical Time-of-
Flight (TOF) MR angiographic datasets.
To facilitate study of the neonatal cortex a complete cerebral cortex segmentation
and reconstruction pipeline has been developed. Segmentation of the neonatal
cortex is not effectively done by existing algorithms designed for the adult brain
because the contrast between grey and white matter is reversed. This causes pixels
containing tissue mixtures to be incorrectly labelled by conventional methods. The
neonatal cortical segmentation method that has been developed is based on a novel
expectation-maximization (EM) method with explicit correction for mislabelled
partial volume voxels. Based on the resulting cortical segmentation, an implicit
surface evolution technique is adopted for the reconstruction of the cortex in
neonates. The performance of the method is investigated by performing a detailed
landmark study.
To facilitate study of cortical development, a cortical surface registration algorithm
for aligning the cortical surface is developed. The method first inflates extracted
cortical surfaces and then performs a non-rigid surface registration using free-form
deformations (FFDs) to remove residual alignment. Validation experiments using
data labelled by an expert observer demonstrate that the method can capture local
changes and follow the growth of specific sulcus
Analysis of the developing brain using image registration
Imperial Users onl
Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study
Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually.
Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults.
Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications.
Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth
Univariate and multivariate pattern analysis of preterm subjects: a multimodal neuroimaging study
Background: Widespread lasting functional connectivity (FC) and brain volume changes in cortices and subcortices after premature birth have been researched in recent studies. However, the relationship remains unclear between spontaneously slow blood oxygen dependent level (BOLD) fluctuations and gray matter volume (GMV) changes in specific brain areas, such as temporal insular cortices, and whether classification methods based on MRI could be successfully applied to the identification of preterm individuals. In this thesis I hypothesized that in prematurely born adults 1. Ongoing neural excitability and brain activity, as estimated by regional functional connectivity of resting state functional MRI (rs-fMRI) is accompanied with altered low-frequency fluctuations and neonatal complications; 2. Altered regional functional connectivity is connected with superimposed cerebral structural reductions; and 3. multivariate neuroanatomical and functional brain patterns could be treated as features to identify preterm subjects from term subjects individually.
Methods: To investigate these hypotheses, the principal results of structural alterations were measured with voxel-based morphometry (VBM), while rs-fMRI outcomes were estimated with amplitude of low-frequency fluctuations (ALFF) in analysis with ninety-four very preterm/very low birth weight (VP/VLBW) and ninety-two full-term (FT) born young adults.
Results: The results of the thesis support the hypotheses by showing that, in univariate results, first in VP/VLBW grownups, ALFF was decreased in the left lateral temporal cortices no matter with global signal regression, and this reduction was closely associated with neonatal complications and cognitive variables. Second overlapped brain regions were found between reduced ALFF and reduced brain volumes in the left temporal cortices, and positively associated with each other, demonstrating a potential relationship between VBM and ALFF in this brain area. In multimodal multivariate pattern recognition analysis (MVPA), the gray matter volume (GMV) classifier displayed a higher accuracy (80.7%) contrast with the ALFF classifier (77.4%). The late fusion of GMV and ALFF did not outperform single GMV modality classification by reaching 80.4% accuracy. Moderator analysis from both rs-fMRI and structural MRI (sMRI) uncovered that the neuro-prematurity performance was predominantly determined by neonatal complications.
Conclusions: In conclusion, these outcomes exhibit the long term effects of premature labour on lateral temporal cortices, which changed in both ongoing BOLD fluctuations and decreased cerebral structural volumes. This thesis further provided evidence that multivariate pattern analysis such as support vector machine (SVM) may identify imaging-based biomarkers and reliably detect signatures of preterm birth
Early Brain Activity Relates to Subsequent Brain Growth in Premature Infants
Recent experimental studies have shown that early brain activity is crucial for neuronal survival and the development of brain networks; however, it has been challenging to assess its role in the developing human brain. We employed serial quantitative magnetic resonance imaging to measure the rate of growth in circumscribed brain tissues from preterm to term age, and compared it with measures of electroencephalographic (EEG) activity during the first postnatal days by 2 different methods. EEG metrics of functional activity were computed: EEG signal peak-to-peak amplitude and the occurrence of developmentally important spontaneous activity transients (SATs). We found that an increased brain activity in the first postnatal days correlates with a faster growth of brain structures during subsequent months until term age. Total brain volume, and in particular subcortical gray matter volume, grew faster in babies with less cortical electrical quiescence and with more SAT events. The present findings are compatible with the idea that (1) early cortical network activity is important for brain growth, and that (2) objective measures may be devised to follow early human brain activity in a biologically reasoned way in future research as well as during intensive care treatmen
Studying neuroanatomy using MRI
The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works
Spatio-temporal Modeling and Analysis of Brain Development
The incidence of preterm birth is increasing and has emerged as a leading cause of neurodevelopmental
impairment in childhood. In early development, defined here as the
period before and around birth, the brain undergoes significant morphological, functional
and appearance changes. The scope and rate of change is arguably greater than at any
other time in life, but quantitative markers of this period of development are limited. Improved
understanding of cerebral changes during this critical period is important for mapping
normal growth, and for investigating mechanisms of injury associated with risk factors for
maldevelopment such as premature birth. The objective of this thesis is the development
of methods for spatio-temporal modeling and quantitative measures of brain development
that can assist understanding the patterns of normal growth and can guide interventions
designed to reduce the burden of preterm brain injury.
An approach for constructing high-definition spatio-temporal atlases of the developing
brain is introduced. A novelty in the proposed approach is the use of a time-varying kernel
width, to overcome the variations in the distribution of subjects at different ages. This leads
to an atlas that retains a consistent level of detail at every time-point. The resulting 4D
fetal and neonatal average atlases have greater anatomic definition than currently available
4D atlases, an important factor in improving registrations between the atlas and individual
subjects with clear anatomical structures and atlas-based automatic segmentation. The
fetal atlas provides a natural benchmark for assessing preterm born neonates and gives some
insight into differences between the groups.
Also, a novel framework for longitudinal registration which can accommodate large intra-subject
anatomical variations is introduced. The framework exploits previously developed
spatio-temporal atlases, which can aid the longitudinal registration process as it provides
prior information about the missing anatomical evolution between two scans taken over large
time-interval.
Finally, a voxel-wise analysis framework is proposed which complements the analysis of
changes in brain morphology by the study of spatio-temporal signal intensity changes in
multi-modal MRI, which can offer a useful marker of neurodevelopmental changes
Processing of structural neuroimaging data in young children:bridging the gap between current practice and state-of-the-art methods
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies
Effect of perinatal adversity on structural connectivity of the developing brain
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
Non-standard templates for non-standard populations: optimizing template selection for voxel-based morphometry pre-processing
The human brain is a complex and powerful organ, directing every aspect of life from somatosensory and motor function to visceral responses to higher order cognition. Neurological and psychiatric disorders often disrupt normal functioning. While the clinical symptoms of such disorders are known, their biological underpinnings are not as clearly characterized. Structural
neuroimaging is a powerful, non-invasive tool that can play a critical role in finding biomarkers of these illnesses.
Currently, variations in pre-processing techniques yield inconsistent and conflicting results. As neuroimaging is a nascent branch of medical research, gold standards in imaging methodologies have not yet been established. Quantitatively validating and optimizing the way these images are preprocessed is the first step towards standardization.
Voxel-based morphometry (VBM) is one technique that is commonly used to compare whole-brain structural differences between groups. Statistical tests are used to compare intensities of voxels throughout all brain scans in each group. In order to ensure that comparable voxels are being tested, the images must be fitted into a common space, which is done through image preprocessing. Spatial normalization to templates is an early pre-processing step that is executed unreliably as many options for both templates and normalization algorithms exist. To determine the effect variations in template usage may cause, we utilized a VBM approach to detect simulated lesions. Template performance was analyzed by comparing the accuracy with which the lesion was detected
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