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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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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.
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Structural and functional connectivity assessment in patients with Parkinson's disease
Klobušiaková, Patrícia ; Keller, Jiří (referee) ; Mekyska, Jiří (advisor)
Early changes in visuospatial functions predict dementia in Parkinson’s disease (PD). The aim of this work is to assess both structural and functional connectivity of the fasciculus longitudinalis inferior (ILF), which is engaged in visuospatial processing, in PD patients in comparison to healthy controls, and to find associations between connectivity changes and cognitive performance in the patient groups with or without mild cognitive impairment (MCI). To achieve our goal we recruited PD patients with normal cognition (PD-NC, n = 23) and PD with MCI (PD-MCI, n = 21) as well as healthy controls (HC, n = 48). Bidirectional iterative parcellation was used to isolate ILF tracts and their respective endpoints (occipital lobe and anterior temporal lobe) in each subject. The endpoints then served as regions of interest for functional connectivity calculation. We found ILF microstructural connectivity impairment in PD-MCI group, as measured by mean diffusivity, fractional anisotropy and radial diffusivity. In addition, the functional connectivity of ILF tracts was decreased already in the PD-NC. Both structural and functional connectivity deterioration was associated with visuospatial dysfunction in PD-MCI. These changes could serve as potential markers of disease progression or treatment effects monitoring.
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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.
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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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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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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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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.
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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.
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