|
|
Analysis of brain tracks using advanced diffusion methods
Daňková, Martina ; Gajdoš, Martin (referee) ; Vojtíšek, Lubomír (advisor)
The aim of this bachelor’s thesis is to analyze brain pathways using advanced diffusion magnetic resonance methods. The literature review describes the principles of diffusion-weighted imaging, methods of data collection and processing, and available software for analyzing diffusion-weighted data. The practical part of the thesis focuses on designing a functional solution for the analysis of diffusion-weighted data, which is tested on a reduced dataset containing healthy controls and patients with multiple sclerosis. A complete preprocessing, tractographic analysis, and connectome construction are performed on a reduced sample of healthy and ill patients. Additionally, an analysis of the differences between the connectomes of the healthy and the ill is conducted.
|
|
|
Aperiodic component of EEG power spectrum in Parkinson’s disease patients treated by deep brain stimulation
Chrásková, Sofie Hedvika ; Gajdoš, Martin (referee) ; Lamoš, Martin (advisor)
Parkinson's disease (PD) is one of the most common neurodegenerative diseases. The number of diagnosed patients has doubled in the last 30 years. Symptomatic treatment primarily includes pharmacological therapy, as well as modulation of brain activity using deep brain stimulation (DBS). This work focuses on the electrophysiological changes in patients treated with DBS, which may aid the development of this highly successful therapy. As part of the practical section of the work, the effect of DBS on the so-called aperiodic component of the power spectrum of the EEG signal was examined. The results of the work demonstrate that the long-term effects of DBS have an impact on the aperiodic component. Likewise, the work proves that changes in the aperiodic component can be observed when comparing stimulation on and off. These statements support the conclusions of the latest research, which highlight the potential of the aperiodic component as an input signal for individual therapy of Parkinson's disease using adaptive DBS.
|
|
|
Quantitative fMRI as a biomarker in prodromal dementia with Lewy bodies
Venhudová, Aneta ; Mikl, Michal (referee) ; Gajdoš, Martin (advisor)
Dementia with Lewy bodies (DLB) is one of the most common neurodegenerative diseases. With the aging population trend observed in today's society, an increasing prevalence of such diseases is expected. Since the symptoms of various neurodegenerative diseases can be similar but their causes are different, their treatments also vary. It is therefore important to accurately diagnose patients and apply the appropriate therapeutic approach. Early detection of Dementia with Lewy bodies (DLB) symptoms allows an earlier diagnosis, which enables pacients to start with therapy sooner, improving their quality of life with the disease. This bachelor's thesis focuses on use of quantitative fMRI in detection of the prodromal stage of DLB. The theoretical research shortly introduces the issue of DLB, dynamic functional connectivity, the principles of MR Imaging and discusses discriminant analysis methods. In the practical section data of subjects in the prodromal stage of DLB as well as healthy controls are visualized and an algorithm for comparing a new experimental approach to fMRI data processing with the metod, which is being currently used, is proposed, implemented and described. The results of the comparison are discussed.
|
|
|
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
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
Tool for analysis of subject's movements in functional magnetic resonance measurements.
Šejnoha, Radim ; Lamoš, Martin (referee) ; Gajdoš, Martin (advisor)
This diploma thesis deals with an analysis of subject’s movement during measurements with funcional magnetic resonance imaging (fMRI). It focuses on methods of a movement artifacts detection and their removal in fMRI images. Thesis deals with metrics which are used for the movement rate of measured subjects evaluation. Metrics and a correction of movement are implemented into the programme in MATLAB. Comparison of subjects suffering from Parkinson’s disease with a group of healthy control was carried out. Tresholds of individual metrics were suggested and a criterion for the removal of subjects with high movement rate was determined.
|
|
|
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.
|
|
|
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.
|
guest ::
RSS feed.