On connectivity in the central nervous systeem : a magnetic resonance imaging study

Brain function has long been the realm of philosophy, psychology and psychiatry and since the mid 1800s, of histopathology. Through the advent of magnetic imaging in the end of the last century, an in vivo visualization of the human brain became available. This thesis describes the development of two unique techniques, imaging of diffusion of water protons and manganese enhanced imaging, that both allow for the depiction of white matter tracts. The reported studies show, that these techniques can be used for a three-dimensional depiction of fiber bundles and that quantitative measures reflecting fiber integrity and neuronal function can be extracted from such data. In clinical applications, the potential use of the developed methods is illustrated in human gliomas, as measure for fiber infiltration, and in spinal cord injury, to monitor potential neuroprotective and __regenerative medication.UBL - phd migration 201

MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING

Diffusion tensor imaging (DTI) is a structural magnetic resonance imaging (MRI) technique to record incoherent motion of water molecules and has been used to detect micro structural white matter alterations in clinical studies to explore certain brain disorders. A variety of DTI based techniques for detecting brain disorders and facilitating clinical group analysis have been developed in the past few years. However, there are two crucial issues that have great impacts on the performance of those algorithms. One is that brain neural pathways appear in complicated 3D structures which are inappropriate and inaccurate to be approximated by simple 2D structures, while the other involves the computational efficiency in classifying white matter tracts. The first key area that this dissertation focuses on is to implement a novel computing scheme for estimating regional white matter alterations along neural pathways in 3D space. The mechanism of the proposed method relies on white matter tractography and geodesic distance mapping. We propose a mask scheme to overcome the difficulty to reconstruct thin tract bundles. Real DTI data are employed to demonstrate the performance of the pro- posed technique. Experimental results show that the proposed method bears great potential to provide a sensitive approach for determining the white matter integrity in human brain. Another core objective of this work is to develop a class of new modeling and clustering techniques with improved performance and noise resistance for separating reconstructed white matter tracts to facilitate clinical group analysis. Different strategies are presented to handle different scenarios. For whole brain tractography reconstructed white matter tracts, a Fourier descriptor model and a clustering algorithm based on multivariate Gaussian mixture model and expectation maximization are proposed. Outliers are easily handled in this framework. Real DTI data experimental results show that the proposed algorithm is relatively effective and may offer an alternative for existing white matter fiber clustering methods. For a small amount of white matter fibers, a modeling and clustering algorithm with the capability of handling white matter fibers with unequal length and sharing no common starting region is also proposed and evaluated with real DTI data

Magnetic Resonance Imaging of Gliomas

Open Access.This work was supported in part by grants CTQ2010-20960-C02-02 to P.L.L. and grant SAF2008-01327 to S.C. A.M.M. held an Erasmus Fellowship from Coimbra University and E.C.C. a predoctoral CSIC contract.Peer Reviewe

Development of Diffusion MRI Methodology to Quantify White Matter Integrity Underlying Post-Stroke Anomia

In 1909 German neurologist Korbinian Brodmann wrote “functional localization of the cerebral cortex without the lead of anatomy is impossible... In all domains, physiology has its firmest foundations in anatomy [1”. While histology is the current gold standard for studying brain microstructure, it is primarily a post-mortem technique that has an average resolution of one micrometer making it impractical for studying the entire brain. Diffusion Magnetic Resonance Imaging (dMRI) is ideally suited to study whole-brain tissue microstructure by sensitizing the MRI contrast to water diffusion, which has a length scale on the order of micrometers. Even though dMRI is applied clinically for the detection of acute ischemia, the relation between tissue microstructure and the dMRI signal is complex and not fully understood. The focus of this dissertation was the validation and development of a new biophysical model of the dMRI signal. Notwithstanding, it is important to keep in mind the potential clinical applications of these models, so in parallel we studied the relationship between white matter integrity and language impairments in post-stroke anomia. This application is of interest since response to language treatment is variable and it is currently difficult to predict which patients will benefit. A better understanding of the underlying brain damage could help inform on functionality and recovery potential. Our work resulted in 9 peer-reviewed papers in international journals and 13 abstracts in proceedings at national and international conferences. Using data collected from 32 chronic stroke patients with language impairments, we studied the relation between baseline naming impairments and microstructural integrity of the residual white matter. An existing dMRI technique, Diffusional Kurtosis Imaging (DKI), was used to assess the tissue microstructure along the length of two major white matter bundles: the Inferior Longitudinal Fasciculus (ILF) and the Superior Longitudinal Fasciculus (SLF). The frequency of semantic paraphasias was strongly associated with ILF axonal loss, whereas phonemic paraphasias were strongly associated with SLF axonal loss. This double dissociation between semantic and phonological processing is in agreement with the dual stream model of language processing and corroborates the concept that, during speech production, knowledge association (semantics) depends on the integrity of ventral pathways (ILF), whereas form encoding (phonological encoding) is more localized to dorsal pathways (SLF). Using a smaller dataset of 8 chronic stroke subjects whom underwent speech entrainment therapy, we assessed if naming improvements were supported by underlying changes in microstructure. Remarkably, we saw that a decrease in semantic errors during confrontational naming was related to a renormalization of the microstructure of the ILF. Together, these two studies support the idea that white matter integrity (in addition to regional gray matter damage) impacts baseline stroke impairments and disease progression. Acquiring accurate information about a patient’s linguistic disorder and the underlying neuropathology is often an integral part to developing an appropriate intervention strategy. However, DKI metrics describe the general physical process of diffusion, which can be difficult to interpret biologically. Different pathological processes could lead to similar DKI changes further complicating interpretation and possibly decreasing its specificity to disease. A multitude of biophysical models have been developed to improve the specificity of dMRI. Due to the complexity of biological tissue, assumptions are necessary, which can differ in stringency depending on the dMRI data at hand. One such assumption is that axons can be approximated by water confined to impermeable thin cylinders. In this dissertation, we provide evidence for this “stick model”. Using data from 2 healthy controls we show that the dMRI signal decay behaves as predicted from theory, particularly at strong diffusion weightings. This work validated the foundation of a biophysical model known as Fiber Ball Imaging (FBI), which allows for the calculation of the angular dependence of fiber bundles. Here, we extend FBI by introducing the technique Fiber Ball White Matter (FBWM) modeling that in addition provides estimations for the Axonal Water Fraction (AWF) and compartmental diffusivities. The ability to accurately estimate compartment specific diffusion dynamics could provide the opportunity to distinguish between different disease processes that affect axons differently than the extra-axonal environment (e.g. gliosis). Lastly, we were able to show that FBI data can also be used to calculate compartmental transverse relaxation times (T2). These metrics can be used as biomarkers, aid in the calculation of the myelin content, or be used to reduce bias in diffusion modeling metrics. Future work should focus on the application of FBI and FBWM to the study of white matter in post-stroke anomia. Since FBWM offers the advantage of isolating the diffusion dynamics of the intra- and extra- axonal environments, it could be used to distinguish between pathological processes such as glial cell infiltration and axonal degeneration. A more specific assessment of the structural integrity underlying anomia could provide information on an individual’s recovery potential and could pave the way for more targeted treatment strategies. The isolation of intra-axonal water is also beneficial for a technique known as dMRI tractography, which delineates the pathway of fiber bundles in the brain. dMRI tractography is a popular research tool for studying brain networks but it is notoriously challenging to do in post-stroke brains. In damaged brain tissue, the high extra-cellular water content masks the directionality of fibers; however, since FBI provides the orientational dependence of solely intra-axonal water, it is not affected by this phenomenon. It is important to understand that caution should be taken when applying biophysical models (FBWM/FBI vs. DKI) to the diseased brain as the validation we provided in this work was only for healthy white matter and these experiments should be repeated in pathological white matter

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.The work was supported by SwitchBox-FP7-HEALTH-2010-grant 259772-2. The authors acknowledge Nadine Santos for her help in editing the manuscript

Structural and cognitive correlates of body mass index in healthy older adults

Obesity, commonly measured with body mass index (BMI), is associated with numerous deleterious health conditions and may be a modifier of age-related alterations in brain integrity. Research suggests that white matter integrity observed using diffusion tensor imaging (DTI) is altered with high BMI, but the integrity of specific tracts remains poorly understood. Additionally, no studies have examined fractional anisotropy (FA) of tract integrity in conjunction with neuropsychological evaluation with elevated BMI among older adults. It was hypothesized that elevated BMI would be independently associated with lower white matter integrity and cognitive performance. It was also hypothesized that age and BMI would interact in relation to measures of white matter integrity and cognitive performance. Sixty two healthy older adults aged 51 to 81 were evaluated using DTI and neuropsychological evaluation. Associations were examined between BMI, FA in tracts connecting frontal and temporal lobes, and cognitive ability in domains of executive function, processing speed, and memory. Hierarchical linear regressions were utilized to determine the impact of BMI on FA and cognitive function after accounting for demographics, followed by a test for a BMI by age interaction on FA and cognitive indices. Secondary analyses assessed the sensitivity of DTI diffusivity metrics to elevated BMI, and related tract FA to cognitive performance. After controlling for initial demographic relationships, elevated BMI was associated with lower FA in the uncinate fasciculus, though there was no evidence of an age by BMI interaction relating to FA in this tract. No relationships between BMI and cognition were observed. Secondary analyses did not suggest that DTI diffusivity metrics provide unique information about tract integrity related to high BMI. Overall, results suggest elevated BMI is associated with altered integrity of the uncinate fasciculus. This white matter tract connects frontal and temporal lobes and is involved in memory function. There was no evidence of an age by BMI interaction on FA of the uncinate fasciculus, and BMI did not relate to cognitive performance. Future studies should examine measures of cardiovascular health and systemic inflammation to identify factors influencing relationships between BMI and white matter integrity

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018

Anisotropy Across Fields and Scales

This open access book focuses on processing, modeling, and visualization of anisotropy information, which are often addressed by employing sophisticated mathematical constructs such as tensors and other higher-order descriptors. It also discusses adaptations of such constructs to problems encountered in seemingly dissimilar areas of medical imaging, physical sciences, and engineering. Featuring original research contributions as well as insightful reviews for scientists interested in handling anisotropy information, it covers topics such as pertinent geometric and algebraic properties of tensors and tensor fields, challenges faced in processing and visualizing different types of data, statistical techniques for data processing, and specific applications like mapping white-matter fiber tracts in the brain. The book helps readers grasp the current challenges in the field and provides information on the techniques devised to address them. Further, it facilitates the transfer of knowledge between different disciplines in order to advance the research frontiers in these areas. This multidisciplinary book presents, in part, the outcomes of the seventh in a series of Dagstuhl seminars devoted to visualization and processing of tensor fields and higher-order descriptors, which was held in Dagstuhl, Germany, on October 28–November 2, 2018