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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.
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Automatic separation of signal and noise components in fMRI data
Ježek, David ; Lamoš, Martin (referee) ; Mikl,, Michal (advisor)
This work focuses on functional magnetic resonance imaging methods with an emphasis on the decomposition of fMRI data using principal and independent component analysis and subsequent analysis of these components. The aim of this work is to propose and apply appropriate metrics to distinguish between signal and noise components of fMRI data. Subsequently, develop an algorithm for automatic classification of fMRI components using machine learning methods. The last step will be testing this algorithm on a dataset provided by the Multimodal and Functional Imaging Laboratory at CEITEC Masaryk University.
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Influence of region coordinates selection on dynamic causal modelling results
Klímová, Jana ; Mikl, Michal (referee) ; Lamoš, Martin (advisor)
This thesis deals with functional magnetic resonance imaging (fMRI), in particular with dynamic causal modelling (DCM) as one of the methods for effective brain connectivity analysis. It has been studied the effect of signal coordinates selection, which was used as an input of DCM analysis, on its results based on simulated data testing. For this purpose, a data simulator has been created and described in this thesis. Furthermore, the methodology of testing the influence of the coordinates selection on DCM results has been reported. The coordinates shift rate has been simulated by adding appropriate levels of various types of noise signals to the BOLD signal. Consequently, the data has been analyzed by DCM. The program has been supplemented by a graphical user interface. To determine behaviour of the model, Monte Carlo simulations have been applied. Results in the form of dependence of incorrectly estimated connections between brain areas on the level of the noise signals have been processed and discussed.
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Event Fixation Related Potential During Visual Emotion Stimulation
Mičánková, Veronika ; Lamoš, Martin (referee) ; Potočňák, Tomáš (advisor)
Cílem této diplomové práce je najít a popsat souvislost mezi fixací očí v emočně zabarveném stimulu, kterým je obrázek či video, a EEG signálu. K tomuto studiu je třeba vyvinout softwarové nástroje v prostředí Matlab k úpravě a zpracování dat získaných z eye trackeru a propojení s EEG signály pomocí nově vytvořených markerů. Na základě získaných znalostí o fixacích, jsou v prostředí BrainVision Analyzeru EEG data zpracovány a následně jsou segmentovány a průměrovány jako evokované potenciály pro jednotlivé stimuly (ERP a EfRP). Tato práce je vypracována ve spolupráci s Gipsa-lab v rámci výzkumného projektu.
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Cortical-subcortical interactions in EEG data of patients with pharmacoresistant epilepsy
Šíma, Jan ; Králík, Martin (referee) ; Lamoš, Martin (advisor)
This bachelor's thesis deals with the elaboration of a literature search on epilepsy and electroencephalography signals with a focus on patients with drug-resistant epilepsy and the analysis of cortico-subcortical relationships. The theoretical part describes the chapters of epilepsy, electroencephalography, the possibility of pre-processing EEG data and analytical methods, which describe the cortico-subcortical interactions. The practical part contains pre-processing of EEG data, analysis of methods used, data analysis, results, discussion, and conclusion. The data analysis itself is performed by the Phase-amplitude coupling method. The discussion discusses the results, limitations, and other possible connections. The conclusion summarizes the whole bachelor thesis.
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Microstates analysis in EEG data of sleep-deprived subjects
Křápková, Monika ; Koudelka, Vlastimil (referee) ; Lamoš, Martin (advisor)
This bachelor’s thesis deals with the processing and analysis of EEG data in sleep deprived subjects. In the theoretical part, the electroencephalography method is presented first. Further, there are possibilities of preprocessing and analysis of EEG data, introduction to statistics, and the last one is a research on the influence of sleep deprivation on human electrophysiology. The practical part consists of the preprocessing of EEG data, EEG microstates analysis and statistical evaluation of the results from the study of sleep deprivation. Finally, the results of this part are discussed in a separate chapter.
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Data processing in real-time fMRI neurofeedback
Bečička, Martin ; Slavíček, Tomáš (referee) ; Lamoš, Martin (advisor)
Tato práce se zabývá digitálním filtrováním dat získaných z fMRI neurofeedbacku v reálném čase. Práce analyzuje dosavadní řešení používané v CEITEC MU, se zaměřením na zkrácení prodlení na začátku každého neurofeedback bloku, které je způsobeno digitálním filtrováním. Dosavadní řešení používá, hlavně pro jeho online a vyhlazovací vlastnosti, nelineární Kalmánův filtr. Analýzou 150 průběhu fMRI neurofeedback sezení byla zjištěna dolní hranice, kterou nelineární Kalmánův filtr potřebuje k naučení. Počet potřebných vzorků je významně menši než je nastaveno v dosavadním řešení. Další možnosti zkrácení prodlení byly prozkoumány a klouzavý průměrovací filtr byl vybrán jako optimální kompromis mezi dobou prodlení, zpoždění filtru a jeho vyhlazovacími vlastnostmi.
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Linear and Adaptive Filters for ECG Signals
Kubát, Milan ; Lamoš, Martin (referee) ; Kozumplík, Jiří (advisor)
In this work, I deal with ECG signal interference suppression using linear and adaptive filters. This falls in field of signal preprocessing. The aim is to properly filter the signal, while maintaining its diagnostic value. I designed filters based on spectral lines resetting, Lynn’s filters and two types of adaptive filters. In the next part, results of different filtering ways are compared.
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Relationship between Electrophysiological Activity and Dynamic Functional Connectivity of Large-scale Brain Networks in fMRI Data
Lamoš, Martin ; Hlinka, Jaroslav (referee) ; Kremláček, Jan (referee) ; Jan, Jiří (advisor)
Functional brain connectivity is a marker of the brain state. Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during EEG data analysis may leave part of the neural activity unrecognized. A proposed approach blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. The blind decomposition of EEG spectrogram by Parallel Factor Analysis has been shown to be a useful technique for uncovering patterns of neural activity where each pattern contains three signatures (spatial, temporal, and spectral). The decomposition takes into account the trilinear structure of EEG data, as compared to the standard approaches of electrode averaging, electrode subset selection or using standard frequency bands. The simultaneously acquired BOLD fMRI data were decomposed by Independent Component Analysis. Dynamic functional connectivity was computed on the component’s time series using a sliding window correlation, and functional connectivity network states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of functional connectivity network states and the fluctuations of EEG spectral patterns. Three patterns related to the dynamics of functional connectivity network states were found. Previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. This work suggests that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.
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