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Multi-tensor model based tractography of axonal bundles
Piskořová, Zuzana ; Jiřík, Radovan (referee) ; Labounek, René (advisor)
Cílem semestrální práce je návrh trasovacího algoritmu, který zohledňuje mikrostrukturní vlastnosti nervové tkáně. K této problematice je sepsána rešerše obsahující úvod do problematiky. Je zde popsán jev difuze, princip difuzně váženého MRI a odhad profilu anizotropní difuze. K podrobnější analýze byl vybrán algoritmus COMMIT, u kterého byla navržena alternativní optimalizační metoda.
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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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Simulation of synthetic diffusion tensor data
Labudová, Kristýna ; Mézl, Martin (referee) ; Labounek, René (advisor)
This work deals with different approaches to imaging of diffusion intensity with magnetic resonance. Individual approaches are described and compared. Gaussian model for approximation of diffusion profile is analysed and mathematically determined in details. The next part of this work concerns about process of simulation synthetic diffusion tensor data, adding noise to data and estimation of diffusion tensor from noisy data. Estimation’s accuracy is rated according to deviation of fractional anisotropy of estimated and original tensor and also according to deviation of the main eigenvectors of both tensors. Accuracy of the estimation is evaluated automatically with the programme. There is realization of graphical interface for simulation as well as for automatical evaluation of results described in details. At the end of this work all results are processed and commented and there is also recommendation for optimal adjustment of the data acquisition. 120 gradient directions are the most optimal of all analysed direction. It provides sufficient accuracy of results with optimal time of data acquisition which is suitable for clinical praxis.
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Multi-tensor imaging of spinal cord detail from high anglular resolution dMRI data
Zimolka, Jakub ; Starčuk, Zenon (referee) ; Labounek, René (advisor)
The aim of this work was to establish a comprehensive processing pipeline of cervical spinal cord HARDI dMRI data and T2-weighted anatomical MRI images in high-resolution. In the research part we provide description of anatomical data processing, theoretical background of dMRI, description of current approaches to 3D anisotropic diffusion estimation as well as current imaging methods of spinal cord axonal bundles. As one of the first in the world, we are investigating multiple-direction diffusion models for human in-vivo spinal cord white matter minority bundles imaging. We designed our own processing pipeline utilizing Spinal Cord Toolbox (SCT), FSL, in-house developer scripts and TORQUE-based batch system for grid computation, tested on real data from cervical spinal cord area between segments C4-C6 from 26 healthy volunteers. Designed processing pipeline with one non-automatic step, works from pre-processing to parcelation of selected spinal cord structures based on co-registration with anatomical spinal cord template for 25 subjects. One person data includes motion artifacts for which the proces failed. There are visible waves in sagittal images of some subjects caused probably by blood-vessel pulsing. Local quantification metrics of spinal cord anatomy (fractional anisotropy – FA, fractional volumes of first – f1 and second – f2 direction of anisotropic diffusion) from different parts (white matter, gray matter, cortico-spinal tract) and from different population groups (men vs. women), were extracted from dMRI data. As we expected, FA maps show visible decreases in areas of gray matter. We also detected second diffusion dirrection in slices, where the spinal roots come out. In some areas, fractional volume of second diffusion direction reaches up to 40% of the total component of the dMRI signal. All mentioned parameters probability density functions for all mentioned groups are non-normal distributions. Between male and female groups there were no significant distribution differences for f1 and f2 volumes. The distribution of FA values between men and women is statistically different. Unfortunatelly, there is a significant inter-subject variability in results, which has much higher dispersion than differences between different group distributions. Despite the inter-subject variability, this work significantly extends the knowledge about data acquisiton capabilities and MRI and dMRI data from cervical spinal cord image processing. This work also lays down foundations for utilization of the imaging method in future and planned clinical research, where it will be possible to test the alteration of the spinal cord anatomy on the minor secondary bundles separately.
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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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Tracking of axonal bundles in diffusion MRI brain images
Piskořová, Zuzana ; Vojtíšek, Lubomír (referee) ; Labounek, René (advisor)
The aim of this thesis is to design tracking algorithm which will be able to track white matter bundles in diffusion MRI data, this problem is called tractography. Tractography is feasible because specific profile of diffusion appears in white matter. The introduction to the topic includes summary of methods for estimation of diffusion profile and basic tracking algorithms. In this work diffusion tensor model (DTI) was used for estimation of diffusion profile. Based on the DTI, vector field characterizing direction of diffusion for every voxel was created. Combining vector field with seedpoint, we achieved task solvable by Euler or Runge-Kutta method. Termination criteria were established for maximum curvature of trajectory and minimum value of fractional anisotropy (FA). Algorithm was tested on mathematical and tractographical phantom before it was used on real biological data. The results of tracking on phantoms proved the funcionality of the algorithm. Expected error appeared in areas of crossing fibers, it is related to DTI model limitations. To solve problematic fibers characterized by seedpoint near the border of the fiber, FA-weighted trilinear interpolation was designed. Implementation of this algorithm, however, did not cause better results. The results of tracking on the real data were controversial. Tracking was performed on 5 healthy subjects and 4 anatomicaly specific tracts. The results were compared with tractographic atlas.
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Dectection of brain wakefulness from scalp EEG data with higher order statistics
Semeráková, Nikola ; Ronzhina, Marina (referee) ; Labounek, René (advisor)
Presented master's thesis deals with detection of brain wakefulness from scalp EEG data with higher order statistics. Part of the thesis is a description of electroencephalography, from the method of signal generation, sensing, electroencephraphy, EEG signal artifacts, frequency bands of EEG signal to its possible processing. Furthermore, the concept of mental fatigue and the possibility of its detection in the EEG signal is described. Subsequently, the principles of higher statistical methods of PCA and ICA and the specific possibilities of decomposition of EEG signal are described using these methods, from which the method of group spatial-frequency ICA was chosen as a suitable method for selection of partial oscillatory sources in EEG signal. In the next part there is described a method of acquisition of data, a the suggestion of solution with selected method and a description of the implemented algorithm, that was applied to real 256-lead scalp EEG data captured during a block task focused on subject allertnes. The absolute and relative power of the EEG signal was decomposed. From the achieved results, we observe that the fluctuations of the spatial frequency patterns of relative power (especially for theta and alpha bands) significantly more closely correspond with the change of reaction time and the error of the subjects performing the task. These observations appear to be relatively consistent with previously published literature, and the current study shows that spatial frequency ICA is able to blindly isolate space-frequency patterns whose fluctuations are statistically significantly correlated with parameters (reaction time, error rate) directly flowing from the given task.
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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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Evaluation of eye-blinking artifact effect on fusion result of simultaneous EEG-fMRI data
Dobiš, Lukáš ; Jakubíček, Roman (referee) ; Labounek, René (advisor)
This thesis sets a theoretical framework about simultaneous EEG-fMRI fusion. The work contains a description of basic principles of acquisition, their individual artifact types and preprocessing techniques for each type of data. Thesis mainly deals with suppression of eye blink artifacts in EEG data, by the method of independent component analysis. The following part explains the technique of simultaneous EEG-fMRI fusion in a general linear model and the creation of activation maps of statistically important correlations. This chapter is concluded with a description of methodology needed for result analysis. Finally, the used data are described, and a solution is proposed and applied in process of EEG preprocessing with artifact suppression, data fusion and result evaluation in MATLAB environment. Evaulation results showed that eye blink artifact influences the fusion result computed from relative power values more then that constructed via absolute power values. Tested method didnt supress eye blink artifact completely.
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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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