Mobilisation of Hematopoietic CD34+ Precursor Cells in Patients with Acute Stroke Is Safe - Results of an Open-Labeled Non Randomized Phase I/II Trial

German Clinical Trial Register DRKS 00000723

A generalizable deep voxel-guided morphometry algorithm for the detection of subtle lesion dynamics in multiple sclerosis

IntroductionMultiple sclerosis (MS) is a chronic neurological disorder characterized by the progressive loss of myelin and axonal structures in the central nervous system. Accurate detection and monitoring of MS-related changes in brain structures are crucial for disease management and treatment evaluation. We propose a deep learning algorithm for creating Voxel-Guided Morphometry (VGM) maps from longitudinal MRI brain volumes for analyzing MS disease activity. Our approach focuses on developing a generalizable model that can effectively be applied to unseen datasets.MethodsLongitudinal MS patient high-resolution 3D T1-weighted follow-up imaging from three different MRI systems were analyzed. We employed a 3D residual U-Net architecture with attention mechanisms. The U-Net serves as the backbone, enabling spatial feature extraction from MRI volumes. Attention mechanisms are integrated to enhance the model's ability to capture relevant information and highlight salient regions. Furthermore, we incorporate image normalization by histogram matching and resampling techniques to improve the networks' ability to generalize to unseen datasets from different MRI systems across imaging centers. This ensures robust performance across diverse data sources.ResultsNumerous experiments were conducted using a dataset of 71 longitudinal MRI brain volumes of MS patients. Our approach demonstrated a significant improvement of 4.3% in mean absolute error (MAE) against the state-of-the-art (SOTA) method. Furthermore, the algorithm's generalizability was evaluated on two unseen datasets (n = 116) with an average improvement of 4.2% in MAE over the SOTA approach.DiscussionResults confirm that the proposed approach is fast and robust and has the potential for broader clinical applicability

Multi-scale, multi-compartment coupled flow-oxygen models of ischaemic stroke

Cerebrovascular accident, also known as stroke, constitutes a major cause of death and disability worldwide. Ischaemic stroke, accounting for 85% of all stroke, occurs when a cerebral artery is occluded. The consequence is a significant reduction of blood flow in the affected region leading to an insufficient supply of oxygen which is vital for the survival of cerebral tissue. The development of novel therapies for ischaemic stroke has been dependent on experiments in animals, mostly in rats and mice, followed by clinical trials. However, the fundamental differences between the human brain and the rodent brain restrict the translation of therapies from animal to human. Computational physiological models provide a promising alternative for therapy development and improvement, as well as quantitative assessment of individual treatment plans and predicting patient outcome. The in silico clinical trials for treatment of acute ischaemic stroke (INSIST) consortium aims to build a computational framework to simulate acute ischaemic stroke and to evaluate its treatments. In parallel with the INSIST project, this thesis presents a multi-scale, multi-compartment oxygen transport and tissue metabolism model of the human brain, which sets the foundation for estimating treatment outcomes. The proposed model is coupled to an established circulation model and both take a porous continuum approach. The finite element method is employed for numerical implementation which is verified using the method of manufactured solutions. Effective geometric parameters capturing the micro-scale heterogeneities are obtained using statistically accurate microvascular networks; the maximum consumption rate of oxygen is optimised to uniquely define the oxygen distribution. Simulations of healthy state and right middle cerebral artery (R-MCA) occlusion are then carried out on a patient-specific brain mesh. The oxygen predicted lesion appears to be more homogenous and has a larger volume than the one estimated by perfusion alone, possibly due to the effect of oxygen diffusion and metabolism in the tissue. The simulated infarct volume is in qualitative agreement with computed tomography images of a patient who suffered from a R-MCA stroke and compares well to quantitative data reported in the literature

Non-invasive approaches to identify the cause of premature fatigue in Inflammatory Bowel Disease patients

Inflammatory bowel disease (IBD) fatigue is a pervasive secondary disease symptom. The aetiology is poorly understood, meaning that treatment options are sparse. This is of particular concern for the relatively large proportion of patients with quiescent disease, who continue to report an increased perception of fatigue and demonstrate premature exercise fatigue, relative to healthy individuals. Fatigue is multidimensional and can manifest as a disproportionate perception of tiredness, perturbed cognitive functioning and an inability to sustain a required work output during exercise. In contrast to other chronic disease, to date there has been no mechanistic assessment of IBD fatigue reported in the literature. This is congruent with the essential absence of any effective treatment strategies convincingly shown to reduce IBD fatigue burden, independent of targeting known clinical causes. The application of Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) techniques during exercise represents a unique opportunity to non-invasively probe in-vivo metabolism across multiple organs. This thesis seeks to characterise IBD fatigue aetiology by combining laboratory-based assessment of peripheral muscle function and cardiorespiratory fitness, with proton (1H) MRI and phosphorus (31P) MRS during within-bore exercise. This thesis represents the first attempt to comprehensively interrogate IBD physiology with the aim of identifying potential treatment targets for fatigue. Following an introduction to IBD in Chapter one, a detailed review of IBD fatigue aetiology follows in Chapter 2. Chapters 3 and 4 outline the methodology and developmental experiments undertaken to facilitate the MRI and 31P MRS experiments. Chapter 5 details the assessment of peripheral muscle function and body composition in quiescent Crohn’s disease patients relative to a healthy age and BMI matched control group. This is followed by the assessment of cardiovascular, brain and peripheral muscle deconditioning in Chapter 6 and 7. A final discussion chapter is dedicated to a review of the collective findings of this thesis in the context of existing data within the literature base. Suggestions are then made for future research priorities in the field of IBD fatigue

Probing the brain’s white matter with diffusion MRI and a tissue dependent diffusion model

While diffusion MRI promises an insight into white matter microstructure in vivo, the axonal pathways that connect different brain regions together can only partially be segmented using current methods. Here we present a novel method for estimating the tissue composition of each voxel in the brain from diffusion MRI data, thereby providing a foundation for computing the volume of different pathways in both health and disease. With the tissue dependent diffusion model described in this thesis, white matter is segmented by removing the ambiguity caused by the isotropic partial volumes: both grey matter and cerebrospinal fluid. Apart from the volume fractions of all three tissue types, we also obtain estimates of fibre orientations for tractography as well as diffusivity and anisotropy parameters which serve as proxy indices of pathway coherence. We assume Gaussian diffusion of water molecules for each tissue type. The resulting three-tensor model comprises one anisotropic (white matter) compartment modelled by a cylindrical tensor and two isotropic compartments (grey matter and cerebrospinal fluid). We model the measurement noise using a Rice distribution. Markov chain Monte Carlo sampling techniques are used to estimate posterior distributions over the model’s parameters. In particular, we employ a Metropolis Hastings sampler with a custom burn-in and proposal adaptation to ensure good mixing and efficient exploration of the high-probability region. This way we obtain not only point estimates of quantities of interest, but also a measure of their uncertainty (posterior variance). The model is evaluated on synthetic data and brain images: we observe that the volume maps produced with our method show plausible and well delineated structures for all three tissue types. Estimated white matter fibre orientations also agree with known anatomy and align well with those obtained using current methods. Importantly, we are able to disambiguate the volume and anisotropy information thus alleviating partial volume effects and providing measures superior to the currently ubiquitous fractional anisotropy. These improved measures are then applied to study brain differences in a cohort of healthy volunteers aged 25-65 years. Lastly, we explore the possibility of using prior knowledge of the spatial variability of our parameters in the brain to further improve the estimation by pooling information among neighbouring voxels

Non-invasive approaches to identify the cause of premature fatigue in Inflammatory Bowel Disease patients

Inflammatory bowel disease (IBD) fatigue is a pervasive secondary disease symptom. The aetiology is poorly understood, meaning that treatment options are sparse. This is of particular concern for the relatively large proportion of patients with quiescent disease, who continue to report an increased perception of fatigue and demonstrate premature exercise fatigue, relative to healthy individuals. Fatigue is multidimensional and can manifest as a disproportionate perception of tiredness, perturbed cognitive functioning and an inability to sustain a required work output during exercise. In contrast to other chronic disease, to date there has been no mechanistic assessment of IBD fatigue reported in the literature. This is congruent with the essential absence of any effective treatment strategies convincingly shown to reduce IBD fatigue burden, independent of targeting known clinical causes. The application of Magnetic Resonance Imaging (MRI) and Spectroscopy (MRS) techniques during exercise represents a unique opportunity to non-invasively probe in-vivo metabolism across multiple organs. This thesis seeks to characterise IBD fatigue aetiology by combining laboratory-based assessment of peripheral muscle function and cardiorespiratory fitness, with proton (1H) MRI and phosphorus (31P) MRS during within-bore exercise. This thesis represents the first attempt to comprehensively interrogate IBD physiology with the aim of identifying potential treatment targets for fatigue. Following an introduction to IBD in Chapter one, a detailed review of IBD fatigue aetiology follows in Chapter 2. Chapters 3 and 4 outline the methodology and developmental experiments undertaken to facilitate the MRI and 31P MRS experiments. Chapter 5 details the assessment of peripheral muscle function and body composition in quiescent Crohn’s disease patients relative to a healthy age and BMI matched control group. This is followed by the assessment of cardiovascular, brain and peripheral muscle deconditioning in Chapter 6 and 7. A final discussion chapter is dedicated to a review of the collective findings of this thesis in the context of existing data within the literature base. Suggestions are then made for future research priorities in the field of IBD fatigue

Investigating Brain Functional Networks in a Riemannian Framework

The brain is a complex system of several interconnected components which can be categorized at different Spatio-temporal levels, evaluate the physical connections and the corresponding functionalities. To study brain connectivity at the macroscale, Magnetic Resonance Imaging (MRI) technique in all the different modalities has been exemplified to be an important tool. In particular, functional MRI (fMRI) enables to record the brain activity either at rest or in different conditions of cognitive task and assist in mapping the functional connectivity of the brain. The information of brain functional connectivity extracted from fMRI images can be defined using a graph representation, i.e. a mathematical object consisting of nodes, the brain regions, and edges, the link between regions. With this representation, novel insights have emerged about understanding brain connectivity and providing evidence that the brain networks are not randomly linked. Indeed, the brain network represents a small-world structure, with several different properties of segregation and integration that are accountable for specific functions and mental conditions. Moreover, network analysis enables to recognize and analyze patterns of brain functional connectivity characterizing a group of subjects. In recent decades, many developments have been made to understand the functioning of the human brain and many issues, related to the biological and the methodological perspective, are still need to be addressed. For example, sub-modular brain organization is still under debate, since it is necessary to understand how the brain is functionally organized. At the same time a comprehensive organization of functional connectivity is mostly unknown and also the dynamical reorganization of functional connectivity is appearing as a new frontier for analyzing brain dynamics. Moreover, the recognition of functional connectivity patterns in patients affected by mental disorders is still a challenging task, making plausible the development of new tools to solve them. Indeed, in this dissertation, we proposed novel methodological approaches to answer some of these biological and neuroscientific questions. We have investigated methods for analyzing and detecting heritability in twin's task-induced functional connectivity profiles. in this approach we are proposing a geodesic metric-based method for the estimation of similarity between functional connectivity, taking into account the manifold related properties of symmetric and positive definite matrices. Moreover, we also proposed a computational framework for classification and discrimination of brain connectivity graphs between healthy and pathological subjects affected by mental disorder, using geodesic metric-based clustering of brain graphs on manifold space. Within the same framework, we also propose an approach based on the dictionary learning method to encode the high dimensional connectivity data into a vectorial representation which is useful for classification and determining regions of brain graphs responsible for this segregation. We also propose an effective way to analyze the dynamical functional connectivity, building a similarity representation of fMRI dynamic functional connectivity states, exploiting modular properties of graph laplacians, geodesic clustering, and manifold learning

Preprocessing methods for morphometric brain analysis and quality assurance of structural magnetic resonance images

Gegenstand der Dissertation ist die Neuentwicklung und Validierung von Verfahren zur Aufbereitung von anatomischen Daten, die mittels Magnetresonanztomographie gewonnen wurden. Ziel ist dabei die Erfassung von morphometrischen Kennwerten zur Beschreibung der Struktur und Form des Gehirns, wie beispielsweise Volumen, Fläche, Dicke oder Faltung der Großhirnrinde. Die Kennwerte erlauben sowohl die Erforschung individueller gesunder und pathologischer Entwicklung als auch der evolutionären Anpassung des Gehirns. Die zur Datenanalyse notwendige Vorverarbeitung beinhaltet dabei die Angleichung von Bildeigenschaften und individueller Anatomie. Die fortlaufende Weiterentwicklung der Scanner- und Rechentechnik ermöglicht eine zunehmend genauere Bildgebung, erfordert aber die kontinuierliche Anpassung existierender Verfahren. Die Schwerpunkte dieser Dissertation lagen in der Entwicklung neuer Verfahren zur (i) Klassifikation der Hirngewebe (Segmentierung), (ii) räumlichen Abbildung des individuellen Gehirns auf ein Durchschnittsgehirn (Registrierung), (iii) Bestimmung der Dicke der Großhirnrinde und Rekonstruktion einer repräsentativen Oberfläche und (iv) Qualitätssicherung der Eingangsdaten. Die Segmentierung gleicht die Bildeigenschaften unterschiedlicher Protokolle an, während die Registrierung anatomische Merkmale normalisiert und so den Vergleich verschiedener Gehirne ermöglicht. Die Rekonstruktion von Oberflächen erlaubt wiederum die Gewinnung einer Vielzahl weiterer morphometrischer Maße zur spezifischen Charakterisierung des Gehirns und seiner Entwicklung. Anhand von simulierten und realen Daten wird die Validität der neuen Methoden belegt und mit anderen Ansätzen verglichen. Die Verfahren sind Bestandteil der Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), deren Schwerpunkt die Vorverarbeitung von strukturellen Daten ist und die Teil des Statistical Parametric Mapping (SPM) Softwarepaketes in MATLAB ist.This Ph.D. thesis focuses on the development, optimization and validation of preprocessing methods of structural magnetic resonance images of the brain. The preprocessing describes the creation of morphometric data that support a statistical analysis of brain anatomy. Image interferences have to be removed to allow a tissue classification (segmentation). In order to compare different subjects a spatial normalization to an average-shaped brain (template) is required, where atlas maps allow identification of specific brain structures and regions of interest. Beside the analysis in a voxel-grid, the cortex can be represented by surfaces that allow further measures such as the cortical thickness or folding. The derived brain features (such as volume, area, and thickness) permit the individual study of normal and pathological development during the lifespan but also of the evolutionary adaption of the brain. The ongoing progress of imaging and computing technology demands continous enhancement of preprocessing tools but also facilitates the exploration of novel approaches and models. The basis of this thesis is the development of a method that uses a tissue segmentation to estimate the cortical thickness and the central surface in one integrated step. Further essential improvements of surface reconstruction algorithms were achieved by specific refinement of processing steps such as (i) the classification of brain tissue (segmentation), (ii) the spatial mapping of the individual brain to an average brain (registration), (iii) determining the thickness of the cerebral cortex and reconstructing a representative surface and (iv) the quality assurance of input data. The validity of the new methods is proven and compared with other approaches by simulated and real data. The procedures are part of the Computational Anatomy Toolbox (CAT; http://dbm.neuro.uni-jena.de/cat), which focuses on the preprocessing of structural data and is part of the Statistical Parametric Mapping (SPM) software package in MATLAB