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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Influence of parcellation atlas on quality of classification in patients with neurodegenerative dissease
Montilla, Michaela ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
The aim of the thesis is to define the dependency of the classification of patients affected by neurodegenerative diseases on the choice of the parcellation atlas. Part of this thesis is the application of the functional connectivity analysis and the calculation of graph metrics according to the method published by Olaf Sporns and Mikail Rubinov [1] on fMRI data measured at CEITEC MU. The application is preceded by the theoretical research of parcellation atlases for brain segmentation from fMRI frames and the research of mathematical methods for classification as well as classifiers of neurodegenerative diseases. The first chapters of the thesis brings a theoretical basis of knowledge from the field of magnetic and functional magnetic resonance imaging. The physical principles of the method, the conditions and the course of acquisition of image data are defined. The third chapter summarizes the graph metrics used in the diploma thesis for analyzing and classifying graphs. The paper presents a brief overview of the brain segmentation methods, with the focuse on the atlas-based segmentation. After a theoretical research of functional connectivity methods and mathematical classification methods, the findings were used for segmentation, calculation of graph metrics and for classification of fMRI images obtained from 96 subjects into the one of two classes using Binary classifications by support vector machines and linear discriminatory analysis. The data classified in this study was measured on patiens with Parkinson’s disease (PD), Alzheimer’s disease (AD), Mild cognitive impairment (MCI), a combination of PD and MCI and subjects belonging to the control group of healthy individuals. For pre-processing and analysis, the MATLAB environment, the SPM12 toolbox and The Brain Connectivity Toolbox were used.
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