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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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Software for automatic data extraction in analysis of brain connectivity
Bujnošková, Eva ; Schwarz, Daniel (referee) ; Jan, Jiří (advisor)
The brain; complex system people want to know about but still they are at the beginning of understanding it. There has been a lot of neuroimaging systems since developement of modern technologies and magnetic resonance imaging is one of them. In last days it isn't enough to examine only structural character of brain, the scientists are dealing with functional states more and more; the functional magnetic resonance imaging is perfectly good tool for this. There is a big amount of researches concerning individual brain regions but also a lot of them dealing with communication across the brain to clear up the causes of human behavior and functional failures. This thesis introduces the brain connectivity exploration, it uses the parcellation by anatomical atlases and it tries to use the knowledge of graph theory as one of the options to determine relations between brain centres and regions. The thesis introduces the software created for extraction of connectivity matrix resulting in graph processing and visualization.
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Altered morphology of white and grey matter in patients with Alzheimer disease and Schizophrenia on MRI
Lahutsina, Anastasiya ; Zach, Petr (advisor) ; Horáček, Jiří (referee) ; Němcová, Veronika (referee)
Cortical folding of the anterior cingulate cortex (ACC), particularly the cingulate (CS) and the paracingulate (PCS) sulci, represents a neurodevelopmental marker. Deviations in in utero development in schizophrenia can be traced using CS and PCS morphometry. In the present study, we measured the length of CS, PCS, and their segments on T1 MRI scans in 93 patients with first episode schizophrenia and 42 healthy controls. Besides the length, the frequency and the left-right asymmetry of CS/PCS were compared in patients and controls. Distribution of the CS and PCS morphotypes in patients was different from controls. Parcellated sulcal pattern CS3a in the left hemisphere was longer in patients (53.8 ± 25.7 mm vs. 32.7 ± 19.4 mm in controls, p < 0.05), while in CS3c it was reversed-longer in controls (52.5 ± 22.5 mm as opposed to 36.2 ± 12.9 mm, n.s. in patients). Non parcellated PCS in the right hemisphere were longer in patients compared to controls (19.4 ± 10.2 mm vs. 12.1 ± 12.4 mm, p < 0.001). Therefore, concurrent presence of PCS1 and CS1 in the left hemisphere and to some extent in the right hemisphere may be suggestive of a higher probability of schizophrenia. Measurement of an hippocampal area or volume is useful in clinical practice as a supportive aid for diagnosis of Alzheimer's disease....
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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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Software for automatic data extraction in analysis of brain connectivity
Bujnošková, Eva ; Schwarz, Daniel (referee) ; Jan, Jiří (advisor)
The brain; complex system people want to know about but still they are at the beginning of understanding it. There has been a lot of neuroimaging systems since developement of modern technologies and magnetic resonance imaging is one of them. In last days it isn't enough to examine only structural character of brain, the scientists are dealing with functional states more and more; the functional magnetic resonance imaging is perfectly good tool for this. There is a big amount of researches concerning individual brain regions but also a lot of them dealing with communication across the brain to clear up the causes of human behavior and functional failures. This thesis introduces the brain connectivity exploration, it uses the parcellation by anatomical atlases and it tries to use the knowledge of graph theory as one of the options to determine relations between brain centres and regions. The thesis introduces the software created for extraction of connectivity matrix resulting in graph processing and visualization.
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