Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Quantification of cortical folding using MR image data

The cerebral cortex is a thin layer of tissue lining the brain where neural circuits perform important high level functions including sensory perception, motor control and language processing. In the third trimester the fetal cortex folds rapidly from a smooth sheet into a highly convoluted arrangement of gyri and sulci. Premature birth is a high risk factor for poor neurodevelopmental outcome and has been associated with abnormal cortical development, however the nature of the disruption to developmental processes is not fully understood. Recent developments in magnetic resonance imaging have allowed the acquisition of high quality brain images of preterms and also fetuses in-utero. The aim of this thesis is to develop techniques which quantify folding from these images in order to better understand cortical development in these two populations. A framework is presented that quantifies global and regional folding using curvature-based measures. This methodology was applied to fetuses over a wide gestational age range (21.7 to 38.9 weeks) for a large number of subjects (N = 80) extending our understanding of how the cortex folds through this critical developmental period. The changing relationship between the folding measures and gestational age was modelled with a Gompertz function which allowed an accurate prediction of physiological age. A spectral-based method is outlined for constructing a spatio-temporal surface atlas (a sequence of mean cortical surface meshes for weekly intervals). A key advantage of this method is the ability to do group-wise atlasing without bias to the anatomy of an initial reference subject. Mean surface templates were constructed for both fetuses and preterms allowing a preliminary comparison of mean cortical shape over the postmenstrual age range 28-36 weeks. Displacement patterns were revealed which intensified with increasing prematurity, however more work is needed to evaluate the reliability of these findings.Open Acces

Challenges in Brain Magnetic Resonance Image Segmentation

Over the past several decades, the application of magnetic resonance imaging (MRI) has been rapidly expanding in the areas of brain research studies and clinical diagnosis. One of the most important steps in brain MRI data preparation is the removal of unwanted brain regions, which is followed by segmentation of the brain into three main regions – white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) – or into subregions. In brain MRI analysis, image segmentation is commonly used for measuring and visualizing the brain’s anatomical structures, analyzing brain changes, delineating pathological regions, and surgical planning and image-guided interventions. Brain segmentation allows clinicians and researchers to concentrate on a specific region in the brain in their analyses. However, brain segmentation is a difficult task due to high similarities and correlations of image intensity among brain regions. In this review, image segmentation algorithms used for dividing the brain into different sectors were discussed in detail. The potential for using the fuzzy c-means (FCM) unsupervised clustering algorithm and certain hybrid (combined) methods to segment brain MR images was demonstrated. Additionally, certain validation techniques that are required to demonstrate the performance of segmentation methods in terms of accuracy rates were described.