|
|
Exploring Brain Network Connectivity through Hemodynamic Modeling
Havlíček, Martin ; Hluštík, Petr (referee) ; Šmídl,, Václav (referee) ; Jan, Jiří (advisor)
Zobrazení funkční magnetickou rezonancí (fMRI) využívající "blood-oxygen-level-dependent" efekt jako indikátor lokální aktivity je velmi užitečnou technikou k identifikaci oblastí mozku, které jsou aktivní během percepce, kognice, akce, ale také během klidového stavu. V poslední době také roste zájem o studium konektivity mezi těmito oblastmi, zejména v klidovém stavu. Tato práce předkládá nový a originální přístup k problému nepřímého vztahu mezi měřenou hemodynamickou odezvou a její příčinou, tj. neuronálním signálem. Zmíněný nepřímý vztah komplikuje odhad efektivní konektivity (kauzálního ovlivnění) mezi různými oblastmi mozku z dat fMRI. Novost prezentovaného přístupu spočívá v použití (zobecněné nelineární) techniky slepé dekonvoluce, což dovoluje odhad endogenních neuronálních signálů (tj. vstupů systému) z naměřených hemodynamických odezev (tj. výstupů systému). To znamená, že metoda umožňuje "data-driven" hodnocení efektivní konektivity na neuronální úrovni i v případě, že jsou měřeny pouze zašumělé hemodynamické odezvy. Řešení tohoto obtížného dekonvolučního (inverzního) problému je dosaženo za použití techniky nelineárního rekurzivního Bayesovského odhadu, který poskytuje společný odhad neznámých stavů a parametrů modelu. Práce je rozdělena do tří hlavních částí. První část navrhuje metodu k řešení výše uvedeného problému. Metoda využívá odmocninové formy nelineárního kubaturního Kalmanova filtru a kubaturního Rauch-Tung-Striebelova vyhlazovače, ovšem rozšířených pro účely řešení tzv. problému společného odhadu, který je definován jako simultánní odhad stavů a parametrů sekvenčním přístupem. Metoda je navržena především pro spojitě-diskrétní systémy a dosahuje přesného a stabilního řešení diskretizace modelu kombinací nelineárního (kubaturního) filtru s metodou lokální linearizace. Tato inverzní metoda je navíc doplněna adaptivním odhadem statistiky šumu měření a šumů procesu (tj. šumů neznámých stavů a parametrů). První část práce je zaměřena na inverzi modelu pouze jednoho časového průběhu; tj. na odhad neuronální aktivity z fMRI signálu. Druhá část generalizuje navrhovaný přístup a aplikuje jej na více časových průběhů za účelem umožnění odhadu parametrů propojení neuronálního modelu interakce; tj. odhadu efektivní konektivity. Tato metoda představuje inovační stochastické pojetí dynamického kauzálního modelování, což ji činí odlišnou od dříve představených přístupů. Druhá část se rovněž zabývá metodami Bayesovského výběru modelu a navrhuje techniku pro detekci irelevantních parametrů propojení za účelem dosažení zlepšeného odhadu parametrů. Konečně třetí část se věnuje ověření navrhovaného přístupu s využitím jak simulovaných tak empirických fMRI dat, a je významných důkazem o velmi uspokojivých výsledcích navrhovaného přístupu.
|
|
| |
|
|
Visualization and export outputs from functional magnetic resonance imaging
Přibyl, Jakub ; Gajdoš, Martin (referee) ; Slavíček, Tomáš (advisor)
Thesis discusses the principles and methodology for measuring functional magnetic resonance imaging (fMRI), basically the origin and use of BOLD signal types used experiments. Further attention is paid fMRI data processing and statistical analysis. Subsequent chapters are devoted to a brief description of the most common software tools used to analyze data from fMRI. The main section was to create a program in MATLAB with a detailed graphic user interface for easy visualization and export output from analyzes of fMRI data. The second half is devoted to describing the program developer and graphic user interface, including key functionality. The final section describes the application program with real data from clinical studies of dynamic connectivity and use in an international project APGem.
|
|
|
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).
|
|
|
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.
|
|
|
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.
|
|
|
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.
|
|
|
Deconvolution of hemodynamic response from fMRI data
Bartoň, Marek ; Kolář, Radim (referee) ; Havlíček, Martin (advisor)
This paper deals with the variability of HRF, which may have crucial impact on outcomes of fMRI neuronal activation detection in some cases. There are three methods described - averaging, regression deconvolution and biconjugate gradient method - which provide HRF shape estimation. In frame of simulations regression method, which uses B-spline curves of 4-th order for window length of 30 s, was chosen as the most robust method. Deconvolution estimates was used as HRF models for classic analyse of fMRI data, concretely visual oddball paradigm, via general linear model. Enlargement of localizated areas was observed and after expert consultation with scientific employees from neurology clinic, outcomes was evaluated as relevant. Furthermore Matlab application, which provides confortable observation of HRF variability among brain areas, was made.
|
|
|
Neuromarketing and it´s application when working with Mary Kay.
Šetková, Lenka ; Chylíková, Hana (advisor) ; Steinerová, Karolína (referee)
This thesis introduces neuromarketing as a new form of highly effective marketing. Theoretical part describes the selected tools for measuring brain's activity, fundamental terms in neuromarketing, mirrors neurons, soma markers. Mentioned are also subliminal messages. Further, attention is paid to describe some of the differences between male and female brain. Practical part of this thesis introduces the company and brand Mary Kay, including global marketing activities. Then, it contains analysis of brand positioning on the Czech market, evaluating of the current marketing activities of Independent Beauty Consultant and a proposal for improvement (of marketing activities) and increase in sales volume.
|
|
| |
guest ::
