Thesis discusses the principles and methodology for measuring functional magnetic resonance imaging (fMRI), basically the origin and use of BOLD signal types used experiments. Further attention is paid fMRI data processing and statistical analysis. Subsequent chapters are devoted to a brief description of the most common software tools used to analyze data from fMRI. The main section was to create a program in MATLAB with a detailed graphic user interface for easy visualization and export output from analyzes of fMRI data. The second half is devoted to describing the program developer and graphic user interface, including key functionality. The final section describes the application program with real data from clinical studies of dynamic connectivity and use in an international project APGem.

This master’s thesis deals with problematic of correlates finding of heart rate changes in fMRI data. The first part describes principle of fMRI, creation of BOLD signal, data acquisition, their pre-processing and analysis. The next part describes heart rate variability and its impact on fMRI data. The following section is dedicated to pre-processing of heart rate time series to the form, which can be used in correlates finding of heart rate variability and fMRI data with generalized linear model. The process of statistical testing and its result with discussion can be found in the last part of this thesis.

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Jusqu'à l'avènement de méthodes de neuroimagerie non invasives les connaissances du cerveau sont acquis par l'étude de ses lésions, des analyses post-mortem et expérimentations invasives. De nos jours, les techniques modernes d'imagerie telles que l'IRMf sont capables de révéler plusieurs aspects du cerveau humain à une résolution spatio-temporelle progressivement élevé. Cependant, afin de pouvoir répondre à des questions neuroscientifiques de plus en plus complexes, les améliorations techniques dans l'acquisition doivent être jumelés à de nouvelles méthodes d'analyse des données. Dans cette thèse, je propose différentes applications de l'apprentissage statistique au traitement des données d'IRMf. Souvent, les données acquises par le scanner IRMf suivent une étape de sélection de variables dans lequel les cartes d'activation sont extraites du signal IRMf. La première contribution de cette thèse est l'introduction d'un modèle nommé Rank-1 GLM (R1-GLM) pour l'estimation jointe des cartes d'activation et de la fonction de réponse hémodynamique (HRF). Nous quantifions l'amélioration de cette approche par rapport aux procédures existantes sur différents jeux de données IRMf. La deuxième partie de cette thèse est consacrée au problème de décodage en IRMf, ce est à dire, la tâche de prédire quelques informations sur les stimuli à partir des cartes d'activation du cerveau. D'un point de vue statistique, ce problème est difficile due à la haute dimensionnalité des données, souvent des milliers de variables, tandis que le nombre d'images disponibles pour la formation est faible, typiquement quelques centaines. Nous examinons le cas où la variable cible est composé à partir de valeurs discrets et ordonnées. La deuxième contribution de cette thèse est de proposer les deux mesures suivantes pour évaluer la performance d'un modèle de décodage: l'erreur absolue et de désaccord par paires. Nous présentons plusieurs modèles qui optimisent une approximation convexe de ces fonctions de perte et examinent leur performance sur des ensembles de données IRMf. Motivé par le succès de certains modèles de régression ordinales pour la tâche du décodage basé IRMf, nous nous tournons vers l'étude de certaines propriétés théoriques de ces méthodes. La propriété que nous étudions est connu comme la cohérence de Fisher. La troisième, et la plus théorique, la contribution de cette thèse est d'examiner les propriétés de cohérence d'une riche famille de fonctions de perte qui sont utilisés dans les modèles de régression ordinales. / Until the advent of non-invasive neuroimaging modalities the knowledge of the human brain came from the study of its lesions, post-mortem analyses and invasive experimentations. Nowadays, modern imaging techniques such as fMRI are revealing several aspects of the human brain with progressively high spatio-temporal resolution. However, in order to answer increasingly complex neuroscientific questions the technical improvements in acquisition must be matched with novel data analysis methods. In this thesis we examine different applications of machine learning to the processing of fMRI data. We propose novel extensions and investigate the theoretical properties of different models. % The goal of an fMRI experiments is to answer a neuroscientific question. However, it is usually not possible to perform hypothesis testing directly on the data output by the fMRI scanner. Instead, fMRI data enters a processing pipeline in which it suffers several transformations before conclusions are drawn. Often the data acquired through the fMRI scanner follows a feature extraction step in which time-independent activation coefficients are extracted from the fMRI signal. The first contribution of this thesis is the introduction a model named Rank-1 GLM (R1-GLM) for the joint estimation of time-independent activation coefficients and the hemodynamic response function (HRF). We quantify the improvement of this approach with respect to existing procedures on different fMRI datasets. The second part of this thesis is devoted to the problem of fMRI-based decoding, i.e., the task of predicting some information about the stimuli from brain activation maps. From a statistical standpoint, this problem is challenging due to the high dimensionality of the data, often thousands of variables, while the number of images available for training is small, typically a few hundreds. We examine the case in which the target variable consist of discretely ordered values. The second contribution of this thesis is to propose the following two metrics to assess the performance of a decoding model: the absolute error and pairwise disagreement. We describe several models that optimize a convex surrogate of these loss functions and examine their performance on different fMRI datasets. Motivated by the success of some ordinal regression models for the task of fMRI-based decoding, we turn to study some theoretical properties of these methods. The property that we investigate is known as consistency or Fisher consistency and relates the minimization of a loss to the minimization of its surrogate. The third, and most theoretical, contribution of this thesis is to examine the consistency properties of a rich family of surrogate loss functions that are used in the context of ordinal regression. We give sufficient conditions for the consistency of the surrogate loss functions considered. This allows us to give theoretical reasons for some empirically observed differences in performance between surrogates.

This dissertation proposes a set of methods for improving spatial localization of cerebral metabolic changes using functional magnetic resonance imaging (fMRI). Blood oxygen level dependent (BOLD) fMRI estabilished itself as the most frequently used technique for mapping brain activity in humans. It is non-invasive and allows to obtain information about brain oxygenation changes in a few minutes. It was discovered in 1990 and, since then, it contributed enormously to the developments in neuroscientific research. Nevertheless, the BOLD contrast suffers from inherent limitations. This comes from the fact that the observed response is the result of a complex interplay between cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral metabolic rate of oxygen consumption (CMRO2) and has a strong dependency on baseline blood volume and oxygenation. Therefore, the observed response is mislocalized from the site where the metabolic activity takes place and it is subject to high variability across experiments due to normal brain physiology. Since the peak of BOLD changes can be as much as 4 mm apart from the site of metabolic changes, the problem of spatial mislocalization is particularly constraining at submillimeter resolution. Three methods are proposed in this work in order to overcome this limitation and make data more comparable. The first method involves a modification of an estabilished model for calibration of BOLD responses (the dilution model), in order to render it applicable at higher resolutions. The second method proposes a model-free scaling of the BOLD response, based on spatial normalization by a purely vascular response pattern. The third method takes into account the hypothesis that the cortical vasculature could act as a low-pass filter for BOLD fluctuations as the blood is carried downstream, and investigates differences in frequency composition of cortical laminae. All methods are described and tested on a depth-dependent scale in the human motor cortex.

Formal argumentation aims to provide a structured framework for cognitively compatible automated reasoning in the context of artificial intelligence; however, due to its roots in mathematical logic, formal argumentation research is typically focused on formal, ‘object-level’ aspects. It has yet to be studied how environmental, ‘meta-level’ structures, can affect human intuitions regarding the formalized model. To bridge this gap, we examined whether bold text used in visualization of argumentation frameworks, the core structures of formal argumentation,affects human assessment of the acceptability of the arguments and the confidence in the assessment. 48 participants divided into four condition groups evaluated the acceptability of four sets of arguments with simple and simplified floating reinstatement. We put different arguments(or none) into bold print to nudge a decision (and to form a control group, respectively). The results show limited evidence in favor of bold text having an increasing effect on the acceptability of the topic argument but no significant changes in the confidence in the answer. A replication study with a larger sample size is warranted to increase confidence in the results.

<p>Chronic kidney disease (CKD) has an annual mortality rate of 22% and can cause secondary complications including hypertension, anemia, secondary hyperparathyroidism, and malnutrition. Currently, clinical diagnosis and evaluation of CKD involves blood and urine testing and biopsy. MRI is not currently used to image CKD, but there is an interest in developing MRI techniques to test kidney function. Usually, renal functional MRI refers to single images reflecting tissue oxygenation. Using time series information may offer additional information about changes in kidneys as a result of disease. In this thesis, blood oxygen level-dependent (BOLD) MRI and diffusion weighted imaging (DWI) were used to investigate the effects of breath holding and water loading on kidneys. First, BOLD MRI was used to measure effects of breath holding on BOLD signal intensity. DWI and fractal analysis were used to measure changes in diffusion, perfusion and microcirculation shortly after water loading. Breath holding results showed no effect on temporal BOLD signal intensity in young, healthy subjects. A significant decrease in signal intensity was measured in the kidney of a single subject with impaired renal function. Although the renal BOLD signal was found to have fractal characteristics, no changes were measured using this technique between pre- and post-water loading scans during the time period examined. Because the signal appears to behave fractally, this technique may be a good candidate for similar kidney function studies in the future. DWI also remained unchanged as a result of water loading during the post-water loading time period examined.</p> / Master of Applied Science (MASc)

Assessment of diffuse brain disorders, where the brain may appear normal, has proven difficult to translate into personalized treatments. Previous methods based on brain magnetic resonance imaging (MRI) resting state blood oxygen level dependent (rs-BOLD) signal routinely rely on group analysis where large data sets are assessed using region-of interest (ROI) or probabilistic independent component analysis (PICA) to identify temporal synchrony or desynchrony among regions of the brain. Brain connectivity occurs in a complex, multilevel and multi-temporal manner, driving the fluctuations observed in local oxygen demand. These fluctuations have previously been characterized as fractal, as they auto-correlate at different time scales. In this study we propose a model-free complexity analysis based on the fractal dimension of the rs-BOLD signal, acquired with MRI. The fractal dimension can be interpreted as a measure of signal complexity and connectivity. Previous studies have suggested that reduction in signal complexity can be associated with disease. Therefore, we hypothesized that a detectable differences in rs-BOLD signal complexity could be observed between patients with diffuse or heterogeneous brain disorders and healthy controls. In this study, we obtained anatomical and functional data from patients with brain disorders where traditional methods have been insufficient to fully assess the condition. More specifically, we tested our method on mild traumatic brain injury, autism spectrum disorder, chemotherapy-induced cognitive impairment and chronic fatigue syndrome patients. Three major databases from the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) project were used to acquire large numbers of age matched healthy controls. Healthy control data was downloaded from the the Autism Brain Imaging Data Exchange (ABIDE), the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Human Connectome Project specifically matching our experimental design. In all of our studies, the voxel-wise rs-BOLD signal fractal dimension was calculated following a procedure described by Eke and Herman et al. 2000. This method was previously used to assess brain rs-BOLD signal in small mammals and humans. The method consists of estimating the Hurst exponent in the frequency domain using a power spectral density approach and refining the estimation in the time domain with de-trended fluctuation analysis and signal summation conversion methods. Voxel-wise fractal dimension (FD) was then calculated for every subject in the control and patient groups to create ROI-based Z-scores for each individual patient. Voxel-wise validation of FD normality across controls was studied and non-Gaussian voxels, determined using kurtosis and skewness calculations, were eliminated from subsequent analysis. To maintain a 95 % confidence level, only regions where Z-score values were at least 2 standard deviations away from the mean were included in the analysis. In the case of chronic fatigue patients and chemotherapy induced cognitive impairment, DTI analysis was added to also determine whether white matter abnormalities were also relevent. Similar Z-score analysis on DTI metrics was also performed. Brain microscopic networks, modeled as complex systems, become affected in diffuse brain disorders. Z-scoring of the fractal rs-BOLD frequency domain delineated patient-specific regional brain anomalies which correlated with patient-specific symptoms. This technique can be used alone, or in combination with DTI Z-scoring, to characterize a single patient without any need for group analysis, making it ideal for personalized diagnostics. / Thesis / Doctor of Philosophy (PhD)

Analysis of biological samples plays a crucial role in disease diagnostics and monitoring, as well as in research. For the analysis, detection and quantification is of the essence, and can be performed with immunoassays. These immunoassays exploit antibodies, and their characteristic specificity, to target analytes. Furthermore, immunoassays can be either singleplex or multiplex, meaning they assess one single, or multiple analytes in parallel. As of today, singleplex methods such as ELISA dominate the market, favoured for their precision and reliability. However, multiplexing exhibit reductions in time, costs, and materials. Nevertheless, one common challenge is the detection of small amounts of analyte, possibly discovering and monitoring earlier stages of disease. For this purpose, Cavidi AB has developed a signal amplification technology, namely the binding oligo ladder detection (BOLD). Currently, this technique is applicable to the singleplex market, while the implementation on the multiplexing remains. Here,  we present a literature review of a selection of multiplexing techniques, and a thorough investigation of their possible compatibility with Cavidi AB’s signal enhancement technology BOLD. After undergoing a comprehensive analysis to evaluate various multiplexing technologies and theircompatibility with BOLD, a spectrum of compatibility levels were revealed. Some of the multiplexing technologies, exemplified by Luminex’s xMAP technology, demonstrated inherent compatibility. Contrarily, other platforms, including Olink’s PEA technology, exhibited incompatibility. Moreover, certain technologies, such as Standard Biotools’ CyTOF technology, were found to possess potential compatibility if modification to either BOLD or to the respective multiplexing technology were to be implemented. Furthermore, the need for signal amplification was identified to vary amongst the versatile technologies and the different analytes. This stems from the fact that the lowest detectable concentration level fluctuates between different analytes and technologies. Therefore, Cavidi AB should target companies with a generally high limit of detection (LOD). In addition, because of the fluctuations in LOD between analytes, specifying to which analytes an integration of BOLD would be beneficial, is recommended.

Immunoassays are widely used for detection of antigens in a wide range of applications including assays in pharmaceutical development. Immunoassays are continuously improved in many aspects including automatization, miniaturization and extending the dynamic range. The need to measure low abundance molecules are challenging and the need to improve the sensitivity is desired. The Gyrolab technology is a miniaturized immunoassay performed in an automated system covering a broad concentration range. In order to  extend the sensitivity, the technology is combined with Binding Oligo Ladder Detection (BOLD) amplification. The technology behind BOLD or Exazym ® utilizes a DNA primer, a polymerase, and a template (RNA) to generate a ladder-like modified DNA strand. Antibodies with affinity for the polymerized DNA:RNA hybrid strand (duplex) conjugated with reporter molecules are introduced to the system, resulting in an increased number of signal-generating molecules associated with each bound analyte molecule. In this thesis, the development of an ultra-sensitive immunoassay is pursued by applying Exazym ® add-on reagents to the Gyrolab platform, comparing performance with the standard Gyrolab sandwich assay and other commercially available high-performing TNF-α assays. The work includes characterization of a wide range of reaction variables involved in the BOLD signal amplification process including hybridization, polymerization, and detection of a synthetic oligonucleotide. The breakthrough involves the introduction of Allophycocyanin (APC) as a fluorescent conjugate, significantly improving sensitivity and signal-to-noise ratios. The BOLD amplified sensitivity for the TNF-α assay approaches levels seen in ultra-sensitive biomarker assays like Erenna ® and Simoa®. Exazym® technology on the Gyrolab platform allows highly sensitive biomarker assays with minimal sample volume and a 1–2-hour run-time. The study marks substantial progress in achieving ultra-sensitive biomarker assays on the Gyrolab platform through BOLD signal amplification. The use of APC-conjugated detection reagents holds promise for future optimization studies.