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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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A comparison of effective and functional connectivity methods in fMRI
Gajdoš, Martin ; Schwarz, Daniel (referee) ; Jan, Jiří (advisor)
Functional magnetic resonance imaging (fMRI) is recent important method, used in neuroimaging. The aim of this thesis is to develop software tool for comparison of two methods for functional and effective connectivity estimation. In this thesis are described the basics of magnetic resonance imaging, fMRI, basic terms of fMRI experiments and generally are described methods of functional and effective connectivity. Then are more detailed mentioned methods of dynamic causal modeling (DCM), Granger causal modeling (GCM) and independent component analysis (ICA). Practical implementation of DCM in toolbox SMP and ICA in toolbox GIFT is also mentioned. In purpose to describe behavior of DCM and GCM in dependence on several parameters are performed Monte Carlo simulations. Then the concept and realization of software tool for simulating connectivity and comparison of DCM and GCM are described. Finally results of DCM and GCM comparison and results of Monte Carlo simulations are discussed.
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Localization of EEG scalp electrodes in structural MRI data
Koutek, Petr ; Gajdoš, Martin (referee) ; Harabiš, Vratislav (advisor)
The objective of this thesis is to design an algorithm used for localization of scalp electrodes in MRI structural data. The algorithm is based on fact that electrodes are visible on visualized head surface. The surface of a head is subdivided into smaller fragments, which are transformed from 3D space into 2D. The electrodes are then located in 2D space by use of registration techniques. The proposed algorithm is able to correctly locate up to 73% EEG electrodes, assuming that the subject has short hair. In case when a subject has long hair, the portion of correctly detected electrodes is 49%. The probability of false detection is 22% when the object is short-haired and 35% when long-haired. The algorithm should facilitate the process of EEG electrodes localization during examinations combining imaging modalities of type EEG and MRI.
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Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with designing a tool for automatically evaluating the quality of electroencephalographic data. In the theoretical part of the thesis, there is a theoretical basis in the anatomy of the central nervous system and the brain, followed by a description of the origin and propagation of the action potential through the nervous system. Furthermore, the theoretical part of the work is devoted to electroencephalography (EEG) and the description of the EEG recording, including typical artefacts in it. The following describes the methods used to detect and remove artefacts. These are primary methods for extracting data quality features. The practical part of the thesis contains a description of the design of a tool for automatic EEG quality assessment and its testing on artificial and real data. The last part of the work is devoted to the discussion of the results of the success of the detection of channels or sections with artefacts and the possible further extension of the tool.
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Toolbox for automatic EEG data quality assessment
Meloun, Jan ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
This thesis deals with the design of a tool for the automatic evaluation of EEG data quality. The theoretical part of the thesis contains a description of the formation and propagation of the action potential through the nervous system. Furthermore, a theoretical description of the EEG recording and its artifacts. The following is a description of the methods used to detect artifacts. In the practical part of the thesis, there is a description of the design of the tool for automatic EEG quality assessment, including a discussion of the results based on the provided data.
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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Processing of MREG MRI data
Lampert, Frederik ; Mikl, Michal (referee) ; Gajdoš, Martin (advisor)
MR-encephalography (MREG) is an innovative method of ultrafast magnetic resonance imaging. Most of the publications about this method are concerning about acquisition and reconstruction of raw data. Studies dedicated to standardization of preprocessing MREG data have not been published yet, which led to motivation of creating this bachelor thesis. The main goal of this thesis is to set an optimal way of preprocessing MREG data, which could be advised for future studies utilizing this method. The main goal of this work was divided into several subgoals, consisting of making a literary review, implementation of general method for data preprocessing and suggesting an alternative ways of data preprocessing and their implementation into MATLAB programming language. Suggested ways of data preprocessing were evaluated by created criteria, described in this work. Results of the evaluation were discussed and interpreted by graphs. Based on the results of the evaluation, an optimal way for preprocessing data was set. It consists of movement and geometric distortion correction accomplished by SPM Realign & UNWARP function, spatial normalisation to EPI MNI template and spatial smoothing by Gaussian kernel.
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Coregistration of DKI MRI data with high b-values
Krejčí, Ladislav ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
Magnetic resonance (MRI), DKI data, DTI data, CHARMED b-value, coregistration, voxels, image processing, registration methods, registration software
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Noise and artifact suppression in fMRI data based on multi-echo data and independent component analysis
Pospíšil, Jan ; Gajdoš, Martin (referee) ; Mikl, Michal (advisor)
The main task of this work is to design an algorithm for suppressing unwanted noise and artifacts in fMRI data using the analysis of independent components and multi-echo data. The theoretical part deals with the basic principles of magnetic resonance, including construction and image data processing. The practical part presents a pilot design of a method inspired by a professional publication in the Matlab software environment, where this design is subsequently tested on real fMRI data provided by the Laboratory of Multimodal and Functional Imaging, CEITEC MU.
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Coregistration of DKI MRI data with high b-values
Krejčí, Ladislav ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
This semestral thesis deals with several options of medical images registration. Basic properties of DKI data are presented in the begining. In the following chapters it is focused on methods, which are removing motion distortion and geometrical inaccuracies. These methods are oversampling, transformation smoothing and optimalization. Software doing registration itself is also covered in this thesis. Methods of comparing registration success rates are described in the end.
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