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Comparison of mitochondrial DNA for species identification
Labounek, René ; Provazník, Ivo (referee) ; Maděránková, Denisa (advisor)
The work deals with the method of recognizing species on the analysis of mitochondrial DNA segment. This analysis and classification using segment gene called CO1 in literatures such as barcode of life. In the beginning of work is analyzed the mitochondrial theory of heredity and conditions of formation of barcode. Practical use is based on this theory in creating database of barcodes generated to different animal species. Data used for creating the library are drawn from public databases NCBI and BOLD Systems. The next part of this work concerns about methods of comparison of the individual barcodes to the others and especially to the barcode of human. Three main computing methods were used tore these analyses: Needleman-Wunsch algorithm, Smith- Waterman algorithm and comparison of similarities using distance matrix. This work also concerns about transformation of DNA molecule sequences from symbols to numeric formats, which is required for the distance matrix comparison method. Algorithms for searching for a barcode of a species and vice versa were created to ease the work with data.
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Simulations of synthetic diffusion MRI data based on Brownian motion
Valla, Radek ; Mikl, Michal (referee) ; Labounek, René (advisor)
This master thesis focuses on dMRI (diffusion magnetic resonance imaging) and its dependance on diffusion in human brain tissue. It is described how to retrieve an image from gained data and its properties, advantages and disadvantages. It mentions problem in detecting kissing fibres due to its similarity with crossing fibres. Design of mathematical models of axons is decribed and suggested measurement to detect difference in signals for kissing and crossing fibres. It describes new simulator of diffusion-weighted MRI (dMRI) data which is able to generate it based on random walk algorithm with geometrical constraints not only for crossing fiber geometry, but also as o novelty for bending and kissing fiber geometries. This study contains results of simulations and disscusion about their usefulness with suggestions for simulator improvement. Simulated dMRI data shows significant difference in certain gradients. Data reconstruction shows, that these reults cannot be reconstructed into the same geometry as it was simulated for.
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Joint EEG-fMRI analysis based on heuristic model
Janeček, David ; Kremláček, Jan (referee) ; Labounek, René (advisor)
The master thesis deals with the joint EEG-fMRI analysis based on a heuristic model that describes the relationship between changes in blood flow in active brain areas and in the electrical activity of neurons. This work also discusses various methods of extracting of useful information from the EEG and their influence on the final result of joined analysis. There were tested averaging methods of electrodes interest, decomposition by principal components analysis and decomposition by independent component analysis. Methods of averaging and decomposition by PCA give similar results, but information about a stimulus vector can not be extracted. Using ICA decomposition, we are able to obtain information relating to the certain stimulation, but there is the problem in the final interpretation and selection of the right components in a blind search for variability coupled with the experiment. It was found out that although components calculated from the time sequence EEG are independent for each to other, their spectrum shifts are correlated. This spectral dependence was eliminated by PCA / ICA decomposition from vectors of spectrum shifts. For this method, each component brings new information about brain activity. The results of the heuristic approach were compared with the results of the joined analysis based on the relative and absolute power approach from frequency bands of interest. And the similarity between activation maps was founded, especially for the heuristic model and the relative power from the gamma band (20-40Hz).
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Analysis of connections between simultaneous EEG and fMRI data
Labounek, René ; Kremláček,, Jan (referee) ; Lamoš, Martin (advisor)
Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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