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Reduction of movement artifacts in BOLD fMRI data using rejection of motion-corrupted scans
Svatoň, Jan ; Gajdoš, Martin (referee) ; Mikl, Michal (advisor)
Tato bakalářská práce ze zprvu zabývá elementárními principy magnetické rezonance a zdrojů šumu a artefaktů v datech. Dále práce podrobněji pojednává o pohybovém artefaktu a navrhuje dvě vhodné metody pro lokalizaci a odstranění pohybem postižených skenů BOLD fMRI dat. Metody jsou poté implementovány v prostředí MATLAB a otestovány na vhodných datasetech poskytnutých Laboratoří multimodálního a funkčního zobrazování, CEITEC MU. Nakonec jsou prezentovány a vyhodnoceny výsledky zároveň s doporučením pro vhodný způsob eliminace pohybového artefaktu v datech.
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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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Correlates finding of heart rate changes in fMRI data
Jurečková, Kateřina ; Gajdoš, Martin (referee) ; Bartoň, Marek (advisor)
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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Reduction of movement artifacts in BOLD fMRI data using rejection of motion-corrupted scans
Svatoň, Jan ; Gajdoš, Martin (referee) ; Mikl, Michal (advisor)
Tato bakalářská práce ze zprvu zabývá elementárními principy magnetické rezonance a zdrojů šumu a artefaktů v datech. Dále práce podrobněji pojednává o pohybovém artefaktu a navrhuje dvě vhodné metody pro lokalizaci a odstranění pohybem postižených skenů BOLD fMRI dat. Metody jsou poté implementovány v prostředí MATLAB a otestovány na vhodných datasetech poskytnutých Laboratoří multimodálního a funkčního zobrazování, CEITEC MU. Nakonec jsou prezentovány a vyhodnoceny výsledky zároveň s doporučením pro vhodný způsob eliminace pohybového artefaktu v datech.
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|
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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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Correlates finding of heart rate changes in fMRI data
Jurečková, Kateřina ; Gajdoš, Martin (referee) ; Bartoň, Marek (advisor)
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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