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Comparison of advanced analysis of fMRI data from oddball experiment
Fajkus, Jiří ; Jan, Jiří (referee) ; Provazník, Ivo (advisor)
This master´s thesis deals with processing and analysis of data, acquired from experimental examination performed with functional magnetic resonance imaging technique. It is an oddball type experimental task and its goal is an examination of cognitive functions of the subject. The principles of functional magnetic resonance imaging, possibilities of experimental design, processing of acquired data, modeling of a response of organism and statistical analysis are described in this work. Furthermore, particular parts of preprocessing and analysis are carried out using real data set from experiment. The main goal of this work is suggestion and realization of model, which enables advanced categorization of stimuli, considering the type of previous rare stimulus and the number of frequent stimuli within them. With its in-depth categorization, this model enables detail exploration of cerebral processes, associated mainly with attention, memory, expectancy or cognitive closure. The second point of that work is an evaluation of models of hemodynamic response, which are applied in statistical analysis of data from fMRI experiment. Comparison of basis functions, the models of hemodynamic response to experimental stimulation used for general linear model, is performed in this work. The result of this comparison is an evaluation of detection efficiency of activated voxels, false positivity rate and computational and user difficulty.
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Mapping of motion artefact in fMRI
Nováková, Marie ; Kremláček, Jan (referee) ; Jan, Jiří (advisor)
This thesis summarizes a theory of magnetic resonance and the method of functional magnetic resonance. It is focused on the influence of motion artifacts and image preprocessing methods, especially realign. It deals with the possibility of using movement parameters obtained in the process of alignment of functional scans to create maps that show the expression of motion artifacts. In this thesis, three different methods were designed, implemented a tested. These methods lead to the creation of probability, power and statistical group maps showing areas typically affected by movement artifacts.
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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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Software for patients and brain regions selection suitable for analysis of connectivity in fMRI
Slavíček, Tomáš ; Lamoš, Martin (referee) ; Jan, Jiří (advisor)
The aim of this thesis is to create an exploratory tool for functional magnetic resonance imaging data, which allows quickly and easily making a selection of persons and areas suitable for group analysis of connectivity. In the first chapters of this work is mentioned history of brain research and comparison of methods used in functional imaging. Next they are discussed the theoretical basis of fMRI methods, such as the formation of BOLD signal, acquisition parameters of MRI images and methods for designing experiments. The following chapter describes in detail the analysis of recorded data from the pre-processing to the interpretation of results. The last chapter of the first part describes problems of group analysis in SPM8 software. The second half of this work is dedicated to the description of developed program from data input to saving the results, including detailed descriptions of key features. In conclusion, there is a chapter characterizing the application of developed program on real data from clinical studies, including the results and evaluation of the usability of program. The program will mainly be used in neuroscience research.
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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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Effect of brain regions coordinates selection on dynamic causal modelling results
Veselá, Martina ; Harabiš, Vratislav (referee) ; Lamoš, Martin (advisor)
Master’s thesis is aimed at familiarization with the principles of measurement and data processing functional magnetic resonance, focusing on the analysis of effective connectivity using dynamic causal modelling (DCM). The practical part includes three main thematic areas relating to the description of the processing and evaluation of measured or simulated data. First, there is on sample dataset described the neuroscientific SPM toolbox to analyze measured data. Then follows introduction of the proposed approach with which is investigated the behavior of the model estimation neural interactions with respect to the change of input parameters. This phenomenon is also simulated and on base of achieved results is recommended optimal approach to analyzing effective connectivity using dynamic causal modeling for the group of subjects. The last circuit in the practical part is assessment of shift the coordinates of brain areas on dynamic causal modelling results for the group of subjects from the data obtained from real measurements. Obtained results from simulated data and the results obtained from measured data are evaluated and discussed in the final part.
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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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Hemodynamic model in effective brain connectivity analysis using fMRI
Holeček, Tomáš ; Harabiš, Vratislav (referee) ; Lamoš, Martin (advisor)
Modeling of hemodynamic response based on changes of synaptic activity is used for mapping active areas of the brain or functional organization of the brain using functional magnetic resonance imaging. Hemodynamic model is one of the methods for modeling hemodynamic response. Balloon model is the main part of the hemodynamic model. Hemodynamic model is used in the mapping active areas using general linear model and in the effective brain connectivity analysis using dynamic causal modelling. This bachelor´s thesis is focused on description of the hemodynamic model, its parameters and impact of every single parameter of the hemodynamic model on its response.
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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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Inverse values of EEG signal power in joint EEG-fMRI analysis
Sanetrníková, Dominika ; Kolář, Radim (referee) ; Labounek, René (advisor)
The first part of this thesis summarizes the basic theory of brain activity measurement using the BOLD signal and scalp EEG, the effect of noise phenomena in the data and its suppression, the merger of the fusion of the measured data using the general linear model and the current implementation of computational algorithms in the software library EEG Regressor Builder 1.0. Within the own solution of this thesis, the changes of the software library to version 1.1 were realized according to the requirements of the bachelor thesis. The hypothesis that temporal changes of the EEG relative band power (20 - 40Hz) has the same spatial correlates with the BOLD signal as the inverse power in the frequency range 0-12Hz. The hypothesis was rejected based on the calculation of similarity criterions between 3D activation maps for different parameter settings of the joint analysis calculations. As an appropriate criterions were chosen the correlation coefficient and the cosine criterion. The Euclidean distance was proved to be unfit. Also it was proved the inverse power value of EEG signal in the given frequency band brings to the common EEG-fMRI analysis an anti-correlated signal to the normal absolute power in the same frequency band. Furthermore the influence of regressors describing motion artifacts reduces the number of supra-thresholded voxels.
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