Dissertations / Theses on the topic 'FMRI signal'
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Leach, Sean. "Physiological noise characterisation and signal analysis for fMRI." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437066.
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Kim, Junmo 1976. "Spatio-temporal fMRI signal analysis using information theory." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8982.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 111-112).
Functional MRI is a fast brain imaging technique which measures the spatio-temporal neuronal activity. The development of automatic statistical analysis techniques which calculate brain activation maps from JMRI data has been a challenging problem due to the limitation of current understanding of human brain physiology. In previous work a novel information-theoretic approach was introduced for calculating the activation map for JMRI analysis [Tsai et al , 1999]. In that work the use of mutual information as a measure of activation resulted in a nonparametric calculation of the activation map. Nonparametric approaches are attractive as the implicit assumptions are milder than the strong assumptions of popular approaches based on the general linear model popularized by Friston et al [19941. Here we show that, in addition to the intuitive information-theoretic appeal, such an application of mutual information is equivalent to a hypothesis test when the underlying densities are unknown. Furthermore we incorporate local spatial priors using the well-known Ising model thereby dropping the implicit assumption that neighboring voxel time-series are independent. As a consequence of the hypothesis testing equivalence, calculation of the activation map with local spatial priors can be formulated as mincut/maxflow graph-cutting problem. Such problems can be solved in polynomial time by the Ford and Fulkerson method. Empirical results are presented on three JMRI datasets measuring motor, auditory, and visual cortex activation. Comparisons are made illustrating the differences between the proposed technique and one based on the general linear model.
by Junmo Kim.
S.M.
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Ambrose, Joseph Paul. "Dynamic field theory applied to fMRI signal analysis." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2035.
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In the field of cognitive neuroscience, there is a need for theory-based approaches to fMRI data analysis. The dynamic neural field model-based approach has been developing to meet this demand. This dissertation describes my contributions to this approach. The methods and tools were demonstrated through a case study experiment on response selection and inhibition. The experiment was analyzed via both the standard behavioral approach and the new model-based method, and the two methods were compared head to head. The methods were quantitatively comparable at the individual-level of the analysis. At the group level, the model-based method reveals distinct functional networks localized in the brain. This validates the dynamic neural field model-based approach in general as well as my recent contributions.
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Salloum, Jasmin B. "Behavioral modification of fMRI signal in studies of emotion." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962689300.
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Riedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214196.
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Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.
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Riedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Frontiers Research Foundation, 2016. https://tud.qucosa.de/id/qucosa%3A29972.
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Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing.
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Thomas, Christopher G. "Signal optimization techniques and noise characterization in BOLD-based fMRI." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58241.pdf.
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Purdon, Patrick L. (Patrick Lee) 1974. "Signal processing in functional magnetic resonance imaging (fMRI) of the brain." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50032.
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Maczka, Melissa May. "Investigations into the effects of neuromodulations on the BOLD-fMRI signal." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:96d46d4d-480b-48d7-9f2d-060e76c5f8aa.
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The blood oxygen level dependent functional MRI (BOLD-fMRI) signal is an indirect measure of the neuronal activity that most BOLD studies are interested in. This thesis uses generative embedding algorithms to investigate some of the challenges and opportunities that this presents for BOLD imaging. It is standard practice to analyse BOLD signals using general linear models (GLMs) that assume fixed neurovascular coupling. However, this assumption may cause false positive or negative neural activations to be detected if the biological manifestations of brain diseases, disorders and pharmaceutical drugs (termed "neuromodulations") alter this coupling. Generative embedding can help overcome this problem by identifying when a neuromodulation confounds the standard GLM. When applied to anaesthetic neuromodulations found in preclinical imaging data, Fentanyl has the smallest confounding effect and Pentobarbital has the largest, causing extremely significant neural activations to go undetected. Half of the anaesthetics tested caused overestimation of the neuronal activity but the other half caused underestimation. The variability in biological action between anaesthetic modulations in identical brain regions of genetically similar animals highlights the complexity required to comprehensively account for factors confounding neurovascular coupling in GLMs generally. Generative embedding has the potential to augment established algorithms used to compensate for these variations in GLMs without complicating the standard (ANOVA) way of reporting BOLD results. Neuromodulation of neurovascular coupling can also present opportunities, such as improved diagnosis, monitoring and understanding of brain diseases accompanied by neurovascular uncoupling. Information theory is used to show that the discriminabilities of neurodegenerative-diseased and healthy generative posterior parameter spaces make generative embedding a viable tool for these commercial applications, boasting sensitivity to neurovascular coupling nonlinearities and biological interpretability. The value of hybrid neuroimaging systems over separate neuroimaging technologies is found to be greatest for early-stage neurodegenerative disease.
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Fisher, Julia Marie. "Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/594646.
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Classification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional magnetic resonance imaging data. We utilize a novel construction of the classification data. Additionally, we vary the spatial distribution of signal, the design matrix of the linear model used to construct the classification data, and the feature set available to the classifier. Results indicate that the k-Nearest Neighbors classifier is not sufficient under the current paradigm to adequately classify neural data and localize signal. Further exploration of the data using k-means clustering indicates that this is likely due in part to the amount of noise present in each data point. Suggestions are made for further research.
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Labounek, René. "Analýza souvislostí mezi simultánně měřenými EEG a fMRI daty." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219743.
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Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.
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Eklund, Anders. "Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces." Licentiate thesis, Linköping : Department of Biomedical Engineering, Linköping University, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54040.
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Kojan, Martin. "Potlačení nežádoucí variability ve fMRI datech při analýze pomocí psychofyziologických interakcí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219513.
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The objective of the thesis is to get familiar with the method of psychophysiological interactions and its common inplementation. It is explaining the usual methods of removing disruptive signals from the data processed in correlation analysis and presents the possibility of their implementation. In the practical part it is focused on cerating suggested program and its testing on the real data sets.
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Pastorello, Bruno Fraccini. "Em busca da região epileptiforme em pacientes com epilepsia do lobo temporal: métodos alternativos baseados em fMRI e EEG-fMRI." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-26102011-135335/.
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A epilepsia do lobo temporal (ELT) é a forma mais comum de epilepsia e a mais resistente ao tratamento medicamentoso. Existem diversos tipos de drogas anti-epilépticas usadas no controle das crises. Entretanto, em alguns casos, esse tipo de tratamento não é eficaz e a cirurgia para remoção da zona epileptogênica (ZE) pode ser uma alternativa recomendada. A ZE é definida como aquela onde as crises são originadas. Trata-se de um conceito teórico e, atualmente, não existem técnicas capazes de delimitá-la precisamente. Na prática, exames de EEG, vídeo-EEG, MEG, SPECT, PET e diversas técnicas de MRI, em especial as funcionais, têm sido usados para mapear zonas relacionadas à ZE. Contudo, em alguns casos, os resultados permanecem não convergentes e a determinação da ZE inconclusiva. Desse modo, é evidente a importância do surgimento de novas metodologias para auxiliar a localização da ZE. Assim, pois, o objetivo deste trabalho foi desenvolver dois métodos para a avaliação da ZE, ambos baseados na imagem funcional por ressonância magnética. No primeiro, investigamos possíveis alterações da resposta hemodinâmica (HRF) quando da modulação da pressão parcial de CO2. Para tanto, fizemos um estudo sobre 22 pacientes com ELT e 10 voluntários assintomáticos modulando a pressão parcial de CO2 sanguíneo cerebral por um protocolo de manobra de pausa respiratória e outro de inalação passiva de CO2/ar. Os resultados mostram que o tempo de onset da HRF tende a ser maior e a amplitude da HRF tende a ser menor em áreas do lobo temporal de pacientes com ELT quando comparados com os dados de voluntários assintomáticos. Além disso, os resultados mostram mapas de onset individuais coincidentes com exames de SPECT ictal. O segundo estudo foi baseado em medidas de EEG-fMRI simultâneo. Neste, avaliamos a relação entres as potências dos ritmos cerebrais alfa e teta (EEG) e o contraste BOLD (fMRI) de 41 pacientes com ELT e 7 voluntários assintomáticos em estado de repouso. A análise da banda alfa mostrou correlações negativas nos lobos occipital, parietal e frontal tanto nos voluntários quanto nos pacientes com ELT. As correlações positivas nos voluntários foram dispersas e variáveis em ambos hemisférios cerebrais. Por outro lado, encontramos forte correlação positiva no tálamo e ínsula dos pacientes com ELT. Na análise da banda teta observamos correlações positivas bilaterais nos giros pré e pós central de voluntários. Ainda, foram observados clusters no cíngulo anterior, tálamo, ínsula, putamen, em regiões parietais superior, frontais e giros temporais. Também, utilizamos um cálculo de índice de lateralização (IL) no lobo temporal em confrontos entre pacientes com ELT à direita, pacientes com ELT à esquerda e voluntários assintomáticos. Verificamos que os ILs, utilizando os clusters obtidos nas análises em teta, foram coincidentes com o diagnóstico clínico prévio da localização da ZE em todas as análises dos grupos de pacientes com ELT à direita, e na maioria do grupo de pacientes com ELT à esquerda. De forma geral, verificamos que o método de hipercapnia se mostrou ferramenta interessante na localização da ZE comprovada pelos coincidentes achados pela avaliação de SPECT. Inferimos que o maior tempo de onset e menor amplitude da HRF observadas nos pacientes em relação a voluntários possam estar relacionados a um stress vascular devido à recorrência de crises. Já o método de ritmicidade alfa e teta proposto parece promissor para ser usado na determinação da lateralização da ZE em pacientes com ELT.Temporal lobe epilepsy (TLE) is the most common and resistant form of epilepsy to anti-epileptic drug. There are several types of anti-epileptic drugs used in seizure control. However, in some cases drug treatment is not effective and surgery to remove the epileptogenic zone (EZ) is a recommended alternative. EZ is a theoretical concept and there are many techniques that have been applied to enclose it precisely. In practice, EEG, video-EEG, MEG, SPECT, PET and various MRI techniques, especially functional MRI (fMRI), have been used to map areas related to EZ. However, in some cases, the results remain non-convergent and the EZ, undefined. Therefore, the use of new methodologies to assist the location of EZ have been proposed. Herein, our goal was to develop two methods for assessing the EZ. The first one was designed to access changes in the hemodynamic response (HRF) of the EZ in response to hypercapnia. 22 patients with TLE and 10 normal volunteers were evaluated by modulating the partial pressure of CO2 during the acquisition of fMRI in a breathing holding and a passive inhalation CO2/air protocols. The results show increased onset times and decreased amplitude of the HRF in the temporal lobe of TLE patients compared with asymptomatic volunteers. Moreover, most patients had onset maps coincident with ictal SPECT localizations. The second proposed study was based on simultaneous EEG-fMRI acquisitions. The relationship between powers of alpha and theta bands (EEG) and BOLD contrast (fMRI) was investigated in 41 TLE patients and 7 healthy controls. Alpha band results show a consistent negative correlation in the occipital, parietal and frontal lobes both in controls and TLE patients. In addition, controls show disperse positive correlations in both hemispheres. On the other hand, TLE patients presented strong positive correlations in the thalamus and insula. Theta band analysis, in controls, primarily show positive correlations in bilateral pre-and post-central gyri. In patients, robust positive correlations were observed in the anterior cingulate gyrus, thalamus, insula, putamen, superior parietal, frontal and temporal gyri. Moreover, the lateralization index (LI) indicates a coincidence between the side of the EZ evaluated by clinical diagnosis and clusters detected in the theta band. In conclusion, the hipercapnia study showed to be an interesting tool in locating EZ and the results are similar to SPECT findings. The longer onset and lower amplitude of the HRF observed in patients may be related to a vascular stress due to the recurrence of seizures. Furthermore, alpha and theta rhythms may be a promising tool to be used in determining the lateralization of EZ in patients with TLE.
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Ohlsson, Henrik. "Regularization for Sparseness and Smoothness : Applications in System Identification and Signal Processing." Doctoral thesis, Linköpings universitet, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60531.
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In system identification, the Akaike Information Criterion (AIC) is a well known method to balance the model fit against model complexity. Regularization here acts as a price on model complexity. In statistics and machine learning, regularization has gained popularity due to modeling methods such as Support Vector Machines (SVM), ridge regression and lasso. But also when using a Bayesian approach to modeling, regularization often implicitly shows up and can be associated with the prior knowledge. Regularization has also had a great impact on many applications, and very much so in clinical imaging. In e.g., breast cancer imaging, the number of sensors is physically restricted which leads to long scantimes. Regularization and sparsity can be used to reduce that. In Magnetic Resonance Imaging (MRI), the number of scans is physically limited and to obtain high resolution images, regularization plays an important role. Regularization shows-up in a variety of different situations and is a well known technique to handle ill-posed problems and to control for overfit. We focus on the use of regularization to obtain sparseness and smoothness and discuss novel developments relevant to system identification and signal processing. In regularization for sparsity a quantity is forced to contain elements equal to zero, or to be sparse. The quantity could e.g., be the regression parameter vectorof a linear regression model and regularization would then result in a tool for variable selection. Sparsity has had a huge impact on neighboring disciplines, such as machine learning and signal processing, but rather limited effect on system identification. One of the major contributions of this thesis is therefore the new developments in system identification using sparsity. In particular, a novel method for the estimation of segmented ARX models using regularization for sparsity is presented. A technique for piecewise-affine system identification is also elaborated on as well as several novel applications in signal processing. Another property that regularization can be used to impose is smoothness. To require the relation between regressors and predictions to be a smooth function is a way to control for overfit. We are here particularly interested in regression problems with regressors constrained to limited regions in the regressor-space e.g., a manifold. For this type of systems we develop a new regression technique, Weight Determination by Manifold Regularization (WDMR). WDMR is inspired byapplications in biology and developments in manifold learning and uses regularization for smoothness to obtain smooth estimates. The use of regularization for smoothness in linear system identification is also discussed. The thesis also presents a real-time functional Magnetic Resonance Imaging (fMRI) bio-feedback setup. The setup has served as proof of concept and been the foundation for several real-time fMRI studies.
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Kovářová, Anežka. "Porovnání a optimalizace měření single-echo a multi-echo BOLD fMRI dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377659.
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This master’s thesis deals with functional magnetic resonance and monitoring of the effect of acquisition acceleration methods on the quality of functional images and observed BOLD signal. The basic principles of magnetic resonance imaging, the explanation of the specifics of functional magnetic resonance and the formation and scanning of BOLD signal are described here. Subsequently, there is the definition of fMRI experiment and description of sequences used for fMRI, focusing on aquisition acceleration techniques. The influence of sequence parameters on image quality and the data processing methods are explained aftewards. The practical part describes the parameters of used sequences, the acquisition procedure and the task for the subject during aquisition. Data from 26 healthy volunteers were obtained and analyzed afterwards. Based on this, the differencesbetween the different sequence variants were evaluated and the initial assumption that the multi-echo acquisition yields better results with faster measurements than single-echo was confirmed.
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Chambers, Micah Christopher. "Full Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filters." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/35143.
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Traditional methods of analyzing functional Magnetic Resonance Images use a linear combination of just a few static regressors. This work demonstrates an alternative approach using a physiologically inspired nonlinear model. By using a particle filter to optimize the model parameters, the computation time is kept below a minute per voxel without requiring a linearization of the noise in the state variables. The activation results show regions similar to those found in Statistical Parametric Mapping; however, there are some notable regions not detected by that technique. Though the parameters selected by the particle filter based approach are more than sufficient to predict the Blood-Oxygen-Level-Dependent signal response, more model constraints are needed to uniquely identify a single set of parameters. This illposed nature explains the large discrepancies found in other research that attempted to characterize the model parameters. For this reason the final distribution of parameters is more medically relevant than a single estimate. Because the output of the particle filter is a full posterior probability, the reliance on the mean to estimate parameters is unnecessary. This work presents not just a viable alternative to the traditional method of detecting activation, but an extensible technique of estimating the joint probability distribution function of the Blood-Oxygen-Level-Dependent Signal parameters.Master of Science
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Janeček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.
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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).
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Sturzbecher, Marcio Junior. "Métodos clássicos e alternativos para a análise de dados de fMRI e EEG-fMRI simultâneo em indivíduos assintomáticos, pacientes com epilepsia e com estenose carotídea." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-08072011-174951/.
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O mapeamento das respostas BOLD (Blood Oxygenation Level Dependent) constitui etapa importante nos experimentos de imagem funcional por ressonância magnética (Functional Magnetic Resonance Imaging fMRI) e de EEG-fMRI simultâneo. Em sua grande maioria, a análise de dados de fMRI e de EEG-fMRI está baseada no modelo linear geral (General Linear Model GLM), que procura localizar as respostas BOLD por meio de modelos definidos a priori. Porém, em muitos casos, como em pacientes, variações na forma e/ou atraso podem reduzir a confiabilidade dos resultados. Desse modo, o primeiro objetivo deste trabalho foi explorar métodos clássicos e propor novos métodos para análise de dados de fMRI e de EEG-fMRI simultâneo. Neste trabalho, um método modificado baseado na distância de Kullback-Leibler generalizada (dKLg) foi desenvolvido. Diferentemente do GLM, essa abordagem não requer um modelo para a resposta. Dados simulados foram utilizados para otimizá-lo e compará-lo ao GLM sob diferentes condições de resposta como a relação sinal ruído e a latência. Em seguida, o dKLg foi testado em dados reais, adquiridos em 14 voluntários assintomáticos, submetidos a tarefas motoras e auditivas padrões. Os resultados mostram a equivalência entre o dKLg e o GLM. Em seguida, essa estratégia foi testada em 02 pacientes com com estenose carotídea unilateral. Neste caso, o dKLg foi capaz de detectar regiões significativas ipsilaterais à estenose, não detectadas pelo GLM, em virtude do atraso do sinal BOLD. Em seguida, esses métodos foram aplicados sobre exames de EEG-fMRI realizados em 45 pacientes com epilepsia. Para esse conjunto de dados, mais uma abordagem foi elaborada, que utiliza a Análise de Componentes Independentes (Independent Component Analysis ICA). Denominado ICA-GLM, ele permite extrair de modo semi-automático a amplitude, duração e topografia das descargas epileptiformes interictais (Interictal Epileptiform Discharges IED), favorecendo a inclusão de sinais do EEG de menor destaque. Além dessa vantagem, ele ainda permite incluir modelos do sinal BOLD com diferentes latências, aumentando a abrangência da variabilidade das respostas encontradas em pacientes com epilepsia. A eficiência do ICA-GLM também foi comparado à do GLM e dKLg nos exames de EEG-fMRI. Embora os resultados tenham demonstrado a robustez do GLM, em alguns pacientes o dKLg foi mais eficiente para localizar regiões concordantes que não foram detectadas pelo GLM. Ainda, em boa parte dos casos o ICA-GLM detectou regiões mais extensas e com maior valor estatístico, quando comparado ao GLM. De forma geral, nota-se que o dKLg e ICA-GLM podem ser ferramentas complementares importantes ao GLM, aumentando a sensibilidade dos exames de EEG-fMRI como um todo. Outra etapa importante nas avaliações de EEG-fMRI em pacientes com epilepsia tem sido a utilização de imagens de fontes elétricas (Electrical Source Imaging ESI). Neste trabalho, os mapas de ESI foram obtidos por dois métodos de solução inversa distribuída nunca usados no cenários da EEG-fMRI: Bayesian Model Averaging (BMA) e constrained Low Resolution Electromagnetic Tomography (cLORETA). Além da construção dos mapas de ESI, avaliamos a utilidade de combinar as técnicas de ESI e de EEG-fMRI para promover a diferenciação entre fontes primárias e de propagação temporal. Essa análise permitiu avaliar a concordância entre as regiões detectadas pelo ESI e EEG-fMRI e diferenciar as respostas BOLD relacionadas aos componentes iniciais e posteriores da IED. Embora os resultados ainda sejam preliminares para eleger qual método seria mais eficiente (cLORETA ou BMA), a distância encontrada entre o máximo ESI e o cluster de EEG-fMRI mais próximo foi consistentemente similar, em ambos, com os dados recentes da literatura.Functional magnetic resonance imaging (fMRI) and combined EEG-fMRI usually rely on the successful detection of Blood Oxygenation Level Dependent (BOLD) signal. Typically, the analysis of both fMRI and EEG-fMRI are based on the General Linear Model (GLM) that aims at localizing the BOLD responses associated to an a priori model. However, the responses are not always canonical, as is the case of those from patients, which may reduce the reliability of the results. Therefore, the first objective of the present study was to explore the usage of classical methods, such as the GLM, and to propose alternative strategies to the analysis of fMRI and combined EEG-fMRI. A first method developed was based on the computation of the generalized Kullback-Leibler distance (gKLd), which does not require the use of an a priori model. Simulated data was used to allow quantitative comparison between the gKLd and GLM under different response conditions such as the signal to noise ratio and delay. The gKLd was then tested on real data, first from 14 asymptomatic subjects, submitted to classical motor and auditory fMRI protocols. The results demonstrate that under these conditions the GLM and gKLd are equivalent. The same strategy was applied to 02 patients with unilateral carotid stenosis. Now the dKLg was capable of detecting the expected bilateral BOLD responses that were not detected by the GLM, as a consequence of the response delay imposed by the stenosis. Those comparisons were now extended to the evaluation of EEG-fMRI exams from 45 patients with epilepsy. For this data set, an additional method was used, based on the use of Independent Component Analysis (ICA), which was called ICA-GLM. It allows extracting semi-automatically the amplitude, duration and topography of EEG interictal Epileptiform Discharges (IED), favoring the use of less prominent signals. Moreover, it also allows the use of BOLD response models with different delays, expanding the variability of the responses to be detected in patients with epilepsy. ICA-GLM was also compared to GLM and dKLg in these EEG-fMRI evaluations. Although in general the results have demonstrated the robustness of the GLM, dKLg was more efficient in detecting the responses from some pacients, while the ICA-GLM mostly detected broader regions with more significant results when compared to GLM. In general, dKLg and ICA-GLM seem to offer an important complementary aspect to the GLM, increasing its sensibility in EEG-fMRI as a whole. Another important aspect of EEG-fMRI applied to patients with epilepsy has been the inspection of Electrical Source Imaging (ESI) to evaluate some dynamical aspects of the IED. Herein, ESI maps were obtained from two inverse distributed solutions that were not applied so far to EEG-fMRI: Bayesian Model Averaging (BMA) and constrained Low Resolution Electromagnetic Tomography (cLORETA). Besides, we also evaluated the combined information from ESI and EEG-fMRI in order to differentiate from primary sources to temporal propagation of the signal. Such analysis allowed us to inspect for the correspondence between regions detected by ESI e EEG-fMRI and to separate BOLD signals whose sources are related to the initial and later components of the IED. Although the results are preliminary to determine which ESI method (cLORETA or BMA) is more efficient, the distance between the maximum ESI and the closest EEG-fMRI cluster was consistently similar with those reported in the literature.
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Fajkus, Jiří. "Porovnání pokročilých přístupů pro analýzu fMRI dat u oddball experimentu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219734.
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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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Craddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.
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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2010.Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Montesco, Carlos Alberto Estombelo. "Método de análise de componentes dependentes para o processamento, caracterização e extração de componentes de sinais biomédicos." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-25092008-165152/.
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Na área de processamento de sinais biomédicos a extração de informação, baseada em um conjunto de medidas adquiridas no tempo, é considerada de suma importância. A qualidade desta informação extraída permite avaliar o funcionamento dos diversos órgãos. Objetivos: (1) propor o método de análise de componentes dependentes para auxiliar a extração de componentes de interesse, a partir de medidas multivariadas; (2) caraterizar as componentes extraídas através de representações em termos de tempo e freqüência, e espectro de potência; e, (3) aplicar o método e avaliar as componentes de interesse extraídas no contexto real MCGf, MGG e fMRI. A proposta para a extração fundamenta-se no método chamado de Análise de Componentes Dependentes ACD. As medidas a serem processadas são multivariadas a partir de sensores distribuídos, espacialmente, no corpo humano dando origem a um conjunto de dados correlacionados no tempo e/ou no espaço. Observa-se que os sinais de interesse raramente são registrados de forma isolada, e sim misturados com outros sinais superpostos, ruído e artefatos fisiológicos ou ambientais, onde a relação sinal-ruído é geralmente baixa. Nesse contexto, a estratégia a ser utilizada baseia-se na ACD, que permitirá extrair um pequeno número de fontes, de potencial interesse, com informações úteis. A estratégia ACD para extração de informação é aplicada em três importantes problemas, na área de processamento de sinais biomédicos: (1) detecção do sinal do feto em magnetocardiografia fetal (MCGf); (2) detecção da atividade de resposta elétrica do estômago em magnetogastrografia (MGG); e, (3) detecção de regiões ativas do cérebro em experimentos de imagens por ressonância magnética funcional (Functional Magnetic Resonance Imaging, fMRI). Os resultados, nos três casos estudados, mostraram que o método utilizado, como estratégia, é efetivo e computacionalmente eficiente para extração de sinais de interesse. Concluímos, baseados nas aplicações, que o método proposto é eficaz, mostrando seu potencial para futuras pesquisas clínicas.An important goal in biomedical signal processing is the extraction of information based on a set of physiological measurements made along time. Generally, biomedical signals are electromagnetic measurements. Those measurements (usually made with multichannel equipment) are registered using spatially distributed sensors around some areas of the human body, originating a set of time and/or space correlated data. The signals of interest are rarely registered alone, being usually observed as a mixture of other spurious, noisy signals (sometimes superimposed) and environmental or physiological artifacts. More over, the signal-to-noise ratio is generally low. In many applications, a big number of sensors are available, but just a few sources are of interest and the remainder can be considered noise. For such kind of applications, it is necessary to develop trustful, robust and effective learning algorithms that allow the extraction of only a few sources potentially of interest and that hold useful information. The strategy used here for extraction of sources is applied in three important problems in biomedical signal processing: (1) detection of the fetal magnetocardiogram signal (fMCG); (2) detection of the electrical activity of the stomach in magnetogastrograms (MGG); and (3) detection of active regions of the brain in experiments in functional Magnetic Resonance Imaging (fMRI). The results, within the three cases of study, showed that the DCA method used as strategy is effective and computationally efficient on extraction of desired signals.
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Klímová, Jana. "Vliv výběru souřadnic regionů na výsledky dynamického kauzálního modelování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220028.
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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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Samadi, Samareh. "EEG-fMRI integration for identification of active brain regions using sparse source decomposition." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT021/document.
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L'électroencéphalographie (EEG) est une technique d'imagerie cérébrale non invasive importante, capable d'enregistrer l'activité neuronale avec une grande résolution temporelle (ms), mais avec une résolution spatiale faible. Le problème inverse en EEG est un problème difficile, fortement sous-déterminé : des contraintes ou des a priori sont nécessaires pour aboutir à une solution unique. Récemment, l'intégration de signaux EEG et d'imagerie par résonance magnétique fonctionnelle (fMRI) a été largement considérée. Les données EEG et fMRI relatives à une tâche donnée, reflètent les activités neuronales des mêmes régions. Nous pouvons donc supposer qu'il existe des cartes spatiales communes entre données EEG et fMRI. En conséquence, résoudre le problème inverse en EEG afin de trouver les cartes spatiales des sources EEG congruentes avec celles obtenues par l'analyse de signaux fMRI semble être une démarche réaliste. Le grand défi reste la relation entre l'activité neuronale électrique (EEG) et l'activité hémodynamique (fMRI), qui n'est pas parfaitement connue à ce jour. La plupart des études actuelles reposent sur un modèle neurovasculaire simpliste par rapport à la réalité. Dans ce travail, nous utilisons des a priori et des faits simples et généraux, qui ne dépendent pas des données ou de l'expérience et sont toujours valides, comme contraintes pour résoudre le problème inverse en EEG. Ainsi, nous résolvons le problème inverse en EEG en estimant les sources spatiales parcimonieuses, qui présentent la plus forte corrélation avec les cartes spatiales obtenues par fMRI sur la même tâche. Pour trouver la représentation parcimonieuse du signal EEG, relative à une tâche donnée, on utilise une méthode (semi-aveugle) de séparation de sources avec référence (RSS), qui extrait les sources dont la puissance est la plus corrélée à la tâche. Cette méthode a été validée sur des simulations réalistes et sur des données réelles d'EEG intracrânienne (iEEG) de patients épileptiques. Cette représentation du signal EEG dans l'espace des sources liées à la tâche est parcimonieuse. En recherchant les fonctions d'activation de fMRI similaires à ces sources, on déduit les cartes spatiales de fMRI très précises de la tâche. Ces cartes fournissent une matrice de poids, qui impose que les voxels activés en fMRI doivent être plus importants que les autres voxels dans la résolution du problème inverse en EEG. Nous avons d'abord validé cette méthode sur des données simulées, puis sur des données réelles relatives à une expérience de reconnaissance de visages. Les résultats montrent en particulier que cette méthode est très robuste par rapport au bruit et à la variabilité inter-sujetsElectroencephalography (EEG) is an important non-invasive imaging technique as it records the neural activity with high temporal resolution (ms), but it lacks high spatial resolution. The inverse problem of EEG is underdetermined and a constraint or prior information is needed to find a unique solution. Recently, EEG-fMRI integration is widely considered. These methods can be categoraized in three groups. First group uses the EEG temporal sources as the regressors in the generalized linear method (GLM) which is used to analyze the fMRI data. The second group analyzes EEG and fMRI simultaneously which is known as fusion technique. The last one, which we are interested in, uses the fMRI results as prior information in the EEG inverse problem. The EEG and fMRI data of a specific task, eventually reflect the neurological events of the same activation regions. Therefore, we expect that there exist common spatial patterns in the EEG and the fMRI data. Therefore, solving the EEG inverse problem to find the spatial pattern of the EEG sources which is congruent with the fMRI result seems to be close to the reality. The great challenge is the relationship between neural activity (EEG) and hemodynamic changes (fMRI), which is not discovered by now. Most of the previous studies have used simple neurovascular model because using the realistic model is very complicated. Here, we use general and simple facts as constraints to solve the EEG inverse problem which do not rely on the experiment or data and are true for all cases. Therefore, we solve the EEG inverse problem to estimate sparse connected spatial sources with the highest correlation with the fMRI spatial map of the same task. For this purpose, we have used sparse decomposition method. For finding sparse representation of the EEG signal, we have projected the data on the uncorrelated temporal sources of the activity. We have proposed a semi-blind source separation method which is called reference-based source separation (R-SS) and extracts discriminative sources between the activity and the background. R-SS method has been verified on a realistic simulation data and the intracranial EEG (iEEG) signal of five epileptic patients. We show that the representation of EEG signal in its task related source space is sparse and then a weighted sparse decomposition method is proposed and used to find the spatial map of the activity. In the weighted sparse decomposition method we put fMRI spatial map in the weighting matrix, such that the activated voxels in fMRI are considered more important than the other voxels in the EEG inverse problem. We validated the proposed method on the simulation data and also we applied the method on the real data of the face perception experiment. The results show that the proposed method is stable against the noise and subject variability
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Tornador, Antolin Cristian 1979. "Prognosis and risk models of depression are built from analytical components of the rs-fMRI activity in patients." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/383067.
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Depression is the most common type of emotional disorder among the world's population. It is characterized by negative sentiments, the feeling of guilt, low self-esteem, a loss of interest, a high-level process of reflection, and in general by a decrease of the individual's psychic functions. The new non-invasive neuroimaging techniques have increased the ability of studying possible variations in patients' brain activity. In concrete, functional magnetic resonance imaging (fMRI) has become the most important method to study human brain functions in the past two decades, being non-invasive and with no risk for human health. Biswal and others in 1995, and later Lowe and his colleagues in 1998, showed the existence of continous spontaneous activity in the brain's activity at rest. These fluctuations have also been verified in other species like macaques (Vicent JL et l, 2007). Studying the brain's activity at rest (rs-fMRI) by means of neuroimaging techniques has become a powerful tool for the investigation of diseases, since it has demonstrated a better signal to noise ratio concerning task-based approaches on one hand, and since certain patients could have difficulties to perform cognitive, language or motor tasks on the other hand. However, it seems that because of certain inconsistencies found among studies, rs-fMRI techniques would not reach a practical clinical use of a personalised monitoring, prognosis or pre-diagnosis in individuals with depression. In this respect, even if Grecius MD exposed in 2008 the benefits of rs-fMRI techniques, he also commented that the signal to noise ratio remains to be improved to be used in a clinical routine. Grecius suggested to lenghthen the time of the temporal series at rest, and to improve analysis procedures. The aim of this thesis is to elucidate if the existence of certain factors or components in the functional signal at rest could be used at the clinical health level. In order to achieve this, we use rs-fMRI data on two sets of samples. In the first set of samples, composed by 27 patients with major depression (MDD) and 27 individuals as controls, we design descriptors that describe both static and dynamic aspects of the resting-state signal for the construction of prediction models. Conversely, with the second type of samples (48 twins), we analyse the relation between possible genetic and environmental factors which could explain certain depressive components in the activity in resting condition.
On the one hand, the results show that depression could simultaneously affect different brain networks located in the prefrontal-limbic area, in the DMN, and between the frontoparietal lobes. Besides, it seems that the alterations in these networks could be explained by both static and dynamic aspects existing in the rest signal. Finally, we achieve the creation of models that would partially explain certain clinical phenomenons present in depressive patients by means of global descriptors in these networks. These network descriptors could be used for personalised monitoring in patients with major depression. On the other hand, using the twin sample, we achieve the construction of a risk model from the amygdalar activity which evaluates the risk or predisposition of an individual from analytical components in the activity at rest. The cerebellum of this sample was also analysed, and the environment was found to be possibly modifying the activity in these regionsLa depresión es el tipo de trastorno emocional más común en la población mundial. Se caracteriza por sentimientos de culpa o negativos, baja autoestima, pérdida de interés, alto nivel de reflexión y en general una disminución de las funciones psíquicas del individuo. Las nuevas técnicas de neuroimagen no invasivas han incrementado la habilidad para estudiar posibles variaciones de la actividad cerebral en pacientes. En concreto, las imágenes por resonancia funcional magnética (fRMI) se han convertido en las dos últimas décadas el método más importante, no-invasivo sin riesgo para la salud humana, para el estudio de las funciones cerebrales humanas. Biswal y otros en 1995, y posteriormente Lowe y compañía en 1998, demostraron la existencia de actividad espontanea continua en la actividad cerebral en estado de reposo. Estas fluctuaciones también han sido confirmadas en otras especies como en macacos (Vincent JL y compañía, 2007). El estudio mediante técnicas de neuroimagen sobre la actividad cerebral en reposo (rs-fMRI) se ha convertido en una potente herramienta para el estudio de enfermedades, puesto que, por un lado, se ha demostrado tener una mejor relación señal-ruido respecto a enfoques basados en tareas, y por otro lado, ciertos pacientes podrían tener dificultades para realizar algún tipo de tareas cognitivas, de lenguaje o motoras. Sin embargo, parece ser que debido a ciertas inconsistencias encontradas entre estudios, las técnicas de rs-fMRI no estarían llegando a un uso clínico-práctico para el seguimiento, pronóstico o pre-diagnostico personalizado en individuos con depresión. En línea a esto, aunque Grecius MD en 2008 expuso los beneficios de la técnica rs-fMRI también comentó que para poder ser utilizada en la rutina clínica aún se debería mejorar la relación señal-ruido. Propuso alargar los tiempos de las series temporales en estado de reposo y mejorar los procedimientos de análisis. En esta tesis se trabaja para dilucidar si existen ciertos factores o componentes en la señal funcional en estado de reposo que pudieran ser utilizados para su uso en la salud clínica. Por ello, utilizamos datos de rs-fMRI sobre dos conjunto de muestras. En el primer conjunto, 27 pacientes con depresión mayor (MDD) y 27 individuos como control, diseñamos descriptores que describan aspectos estáticos y dinámicos de la señal de reposo para la construcción de modelos de prónostico. En cambio, con el segundo tipo de muestras, 48 gemelos, analizamos la relación de posibles factores genéticos y de entorno que pudieran explicar ciertos componentes depresivos en la actividad en estado de reposo. Por un lado, los resultados muestran que la depresión pudiera estar afectando diferentes redes cerebrales al mismo tiempo localizadas en la parte prefrontal-limbica, en la red DMN, y entre los lóbulos frontoparietales. Además, parece ser que las alteraciones sobre estas redes pudieran ser explicadas tanto por aspectos estáticos y dinámicos existentes en la señal de reposo. Finalmente, conseguimos crear modelos que explicarían parcialmente ciertos fenómenos clínicos presentes en los pacientes depresivos, mediante descriptores globales de estas redes. Estos descriptores de red pudieran ser utilizados para el seguimiento personalizado en pacientes con depresión mayor. Por otro, utilizando la muestra de gemelos, conseguimos construir un modelo de riesgo a partir de la actividad amigdalar que evalúa el riesgo o propensión de un individuo a partir de componentes analíticas en la actividad de reposo. También sobre esta muestra, se analizó el cerebelo encontrando que el entorno pudiera estar modificando la actividad en estas regiones
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Ries, Anja Kaja [Verfasser], Claus [Akademischer Betreuer] Zimmer, Gil [Gutachter] Westmeyer, and Christine [Gutachter] Preibisch. "Spectral analysis of resting-state fMRI BOLD signal in healthy subjects and patients suffering from major depressive disorder / Anja Kaja Ries ; Gutachter: Gil Westmeyer, Christine Preibisch ; Betreuer: Claus Zimmer." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1185069860/34.
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De, Luca Marilena. "Low frequency signals in FMRI." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418562.
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Reyt, Sébastien. "IRM fonctionnelle chez le rat : défis méthodologiques." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00861856.
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L'imagerie par résonance magnétique fonctionnelle (IRMf ) permet de détecter sur le cerveau entier des activations neuronales en réponse à un stimulus, par le biais de l'observation des modifications hémodynamiques occasionnées. En particulier, l'IRMf est un outil de choix pour l'étude des mécanismes de la stimulation cérébrale profonde et de la stimulation du nerf vague qui sont encore mal connus. Cependant, cette technique n'est pas facilement utilisable chez l'homme en raison des problèmes de sécurité vis-à-vis de l'action des champs magnétiques intenses utilisés en IRM au niveau des électrodes implantées. Les développements méthodologiques chez l'animal sont donc nécessaires. L'objectif principal de cette thèse est l'étude des mécanismes à distance de la stimulation du système nerveux central et périphérique par IRMf chez le rat. Nous présentons dans un premier temps les séquences IRM rapides utilisées en IRMf, comme l'Echo-Planar Imaging multishot, permettant d'imager le cerveau entier en 1 à 2 secondes seulement, ainsi que les différents problèmes posés par l'utilisation de ces séquences, comme les artefacts de susceptibilité magnétique. Le couplage des séquences développées durant cette thèse avec des mesures électrophysiologiques a notamment permis l'imagerie des réseaux épileptiques chez le rat. Dans un second temps, nous développons les problèmes posés par la préparation animale, particulière en IRMf de par le fait que le couplage neurovasculaire doit être préservé sous anesthésie afin de préserver les activations neuronales. Après comparaison avec les anesthésies à l'isoflurane et la kétamine, nous avons déduit que la médétomidine constituait un anesthésique de choix pour l'IRMf du rongeur, et précisons le protocole de préparation animale utilisé pour l'imagerie. De plus, les électrodes utilisées en stimulation intracérébrale induisent des artefacts importants en imagerie, et des électrodes constituées de matériaux amagnétiques sont nécessaires. Nous expliquons pourquoi nous avons choisi des électrodes en carbone, et présentons le protocole de fabrication utilisé. Nous avons ensuite validé ces développements méthodologiques par des expériences d'IRMf de challenges hypercapniques et de stimulation de la patte chez le rat. Puis nous avons conduit une étude IRMf approfondie des mécanismes d'action de la stimulation du nerf vague, en s'intéressant à la distinction entre activations neuronales et effets cardiovasculaires confondants par modélisation causale dynamique. Nous présentons aussi des résultats en IRMf de la stimulation électrique intracérébrale chez le rat. Plusieurs cibles ont été stimulées (noyau géniculé dorso-latéral, gyrus dentelé, striatum et thalamus), et des activations ont été obtenues à distance de l'électrode, conformément aux connaissances actuelles sur les connexions neuroanatomiques de ces noyaux. Ainsi, nous avons mis au point et validé l'IRMf du rat et son application à la stimulation électrique du système nerveux périphérique et central.
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Hebart, Martin. "On the neuronal systems underlying perceptual decision-making and confidence in humans." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2014. http://dx.doi.org/10.18452/16924.
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Die Fähigkeit, Zustände in der Außenwelt zu beurteilen und zu kategorisieren, wird unter dem Oberbegriff „perzeptuelles Entscheiden“ zusammengefasst. In der vorliegenden Arbeit wurde funktionelle Magnetresonanztomografie mit multivariater Musteranalyse verbunden, um offene Fragen zur perzeptuellen Entscheidungsfindung zu beantworten. In der ersten Studie (Hebart et al., 2012) wurde gezeigt, dass der visuelle und parietale Kortex eine Repräsentation abstrakter perzeptueller Entscheidungen aufweisen. Im frühen visuellen Kortex steigt die Menge entscheidungsspezifischer Information mit der Menge an verfügbarer visueller Bewegungsinformation, doch der linke posteriore parietale Kortex zeigt einen negativen Zusammenhang. Diese Ergebnisse zeigen, wo im Gehirn abstrakte Entscheidungen repräsentiert werden und deuten darauf hin, dass die gefundenen Hirnregionen unterschiedlich in den Entscheidungsprozess involviert sind, je nach Menge an verfügbarer sensorischer Information. In der zweiten Studie (Hebart et al., submitted) wurde gezeigt, dass sich eine Repräsentation der Entscheidungsvariable (EV) im fronto-parietalen Assoziationskortex finden lässt. Ferner weist die EV im rechten ventrolateralen präfrontalen Kortex (vlPFC) einen spezifischen Zusammenhang mit konfidenzbezogenen Hirnsignalen im ventralen Striatum auf. Die Ergebnisse deuten darauf hin, dass Konfidenz aus der EV im vlPFC berechnet wird. In der dritten Studie (Christophel et al., 2012) wurde gezeigt, dass der Kurzzeitgedächtnisinhalt im visuellen und posterioren parietalen Kortex, nicht jedoch im präfrontalen Kortex repräsentiert wird. Diese Ergebnisse lassen vermuten, dass der Gedächtnisinhalt in denselben Regionen enkodiert wird, die auch perzeptuelle Entscheidungen repräsentieren können. Zusammenfassend geben die hier errungenen Erkenntnisse Aufschluss über den neuronalen Code des perzeptuellen Entscheidens von Menschen und stellen ein vollständigeres Verständnis der zugrundeliegenden Prozesse in Aussicht.Perceptual decision-making refers to the ability to arrive at categorical judgments about states of the outside world. Here we use functional magnetic resonance imaging and multivariate pattern analysis to identify decision-related brain regions and address a number of open issues in the field of perceptual decision-making. In the first study (Hebart et al., 2012), we demonstrated that perceptual decisions about motion direction are represented in both visual and parietal cortex, even when decoupled from motor plans. While in early visual cortex the amount of information about perceptual choices follows the amount of sensory evidence presented on the screen, the reverse pattern is observed in left posterior parietal cortex. These results reveal the brain regions involved when choices are encoded in an abstract format and suggest that these two brain regions are recruited differently depending on the amount of sensory evidence available. In the second study (Hebart et al., submitted), we show that the perceptual decision variable (DV) is represented throughout fronto-parietal association cortices. The DV in right ventrolateral prefrontal cortex covaries specifically with brain signals in the ventral striatum representing confidence, demonstrating a close link between the two variables. This suggests that confidence is calculated from the perceptual DV encoded in ventrolateral prefrontal cortex. In the third study (Christophel et al., 2012), using a visual short-term memory (VSTM) task, we demonstrate that the content of VSTM is represented in visual cortex and posterior parietal cortex, but not prefrontal cortex. These results constrain theories of VSTM and suggest that the memorized content is stored in regions shown to represent perceptual decisions. Together, these results shed light on the neuronal code underlying perceptual decision-making in humans and offer the prospect for a more complete understanding of these processes.
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Reynaud, Olivier. "Development of FENSI (Flow Enhanced Signal Intensity) perfusion sequence and application to the characterization of microvascular flow dynamics using MRI." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00740639.
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The discoveries, implementations and developments of NMR and MRI have had a major impact in medical imaging. Compared to other imaging modalities (PET, SPECT, CT), current MRI research helps to further and better understand the inner mechanisms of the human body in a less invasive manner. In clinical neuroimaging, perfusion MRI is of spectacular importance to study cerebrovascular diseases and cancer. However, at the moment, there is no perfusion MRI sequence that allows for a complete, non-invasive and precise quantification of microvascular flow dynamics. This work focuses on the use of the recently introduced Flow Enhanced Signal Intensity method (FENSI) to characterize and quantify vasculature at capillary level, at high and ultra high magnetic field (7 and 17.2 tesla). For that purpose, the possible quantification of blood flux with FENSI is explored in vivo. The combination of flux quantification and flow-enhanced signal (compared to Arterial Spin Labeling) can make of FENSI an ideal method to characterize in a complete non-invasive way the brain microvasculature. After removal of magnetization transfer (MT) effects, the blood flow dynamics are studied with FENSI in a very aggressive and propagative rat brain tumor model: the 9L gliosarcoma. The objective is to assess whether FENSI is suitable for a longitudinal non-invasive characterization of microvascular changes associated with tumor growth. The results obtained with FENSI are compared with literature on 9L perfusion and immuno-histochemistry. In the first paper published on FENSI, a first glance was also casted on the potential of the flow enhanced technique when applied to fMRI. The results obtained at the time were contaminated by MT effects. With the implementation of a new MT-free FENSI technique, the possibility to map the brain cerebral functioning based on a quantitative physiological parameter (CBFlux) more directly related to neuronal activity than the usual BOLD signal is within reach. At ultra high field, the influence of different anesthetics on the rat brain microvascular network and BOLD contrast is also considered. After many developments around the FENSI technique, the method is compared to classical ASL and DSC perfusion MRI sequences. The strengths and weaknesses of the FENSI method, its characteristics, 'precautions for use', and potential main applications are detailed and discussed.
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Šejnoha, Radim. "Nástroj pro analýzu pohybu subjektů při měření funkční magnetickou rezonancí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242165.
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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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Liu, Aiping. "Brain connectivity network modeling using fMRI signals." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58126.
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Functional magnetic resonance imaging (fMRI) is one of the most popular non-invasive neuroimaging technologies, which examines human brain at relatively good spatial resolution in both normal and disease states. In addition to the investigation of local neural activity in isolated brain regions, brain connectivity estimated from fMRI has provided a system-level view of brain functions. Despite recent progress on brain connectivity inference, there are still several challenges. Specifically, this thesis focuses on developing novel brain connectivity modeling approaches that can deal with particular challenges of real biomedical applications, including group pattern extraction from a population, false discovery rate control, incorporation of prior knowledge and time-varying brain connectivity network modeling. First, we propose a multi-subject, exploratory brain connectivity modeling approach that allows incorporation of prior knowledge of connectivity and determination of the dominant brain connectivity patterns among a group of subjects. Furthermore, to integrate the genetic information at the population level, a framework for genetically-informed group brain connectivity modeling is developed. We then focus on estimating the time-varying brain connectivity networks. The temporal dynamics of brain connectivity assess the brain in the additional temporal dimension and provide a new perspective to the understanding of brain functions. In this thesis, we develop a sticky weighted time-varying model to investigate the time-dependent brain connectivity networks. As the brain must strike a balance between stability and flexibility, purely assuming that brain connectivity is static or dynamic may be unrealistic. We therefore further propose making joint inference of time-invariant connections and time-varying coupling patterns by employing a multitask learning model. The above proposed methods have been applied to real fMRI data sets, and the disease induced changes on the brain connectivity networks have been observed. The brain connectivity study is able to provide deeper insights into neurological diseases, complementing the traditional symptom-based diagnostic methods. Results reported in this thesis suggest that brain connectivity patterns may serve as potential disease biomarkers in Parkinson's Disease.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Estombelo, Montesco Carlos Alberto. "Método de análise de componentes dependentes para o processamento, caracterização e extração de componentes de sinais biomédicos." reponame:Repositório Institucional da UFS, 2007. https://ri.ufs.br/handle/riufs/1777.
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Na área de processamento de sinais biomédicos a extração de informação, baseada em um conjunto de medidas adquiridas no tempo, é considerada de suma importância. A qualidade desta informação extraída permite avaliar o funcionamento dos diversos órgãos. Objetivos: (1) propor o método de análise de componentes dependentes para auxiliar a extração de componentes de interesse, a partir de medidas multivariadas; (2) caraterizar as componentes extraídas através de representações em termos de tempo e freqüência, e espectro de potência; e, (3) aplicar o método e avaliar as componentes de interesse extraídas no contexto real MCGf, MGG e fMRI. A proposta para a extração fundamenta-se no método chamado de Análise de Componentes Dependentes ACD. As medidas a serem processadas são multivariadas a partir de sensores distribuídos, espacialmente, no corpo humano dando origem a um conjunto de dados correlacionados no tempo e/ou no espaço. Observa-se que os sinais de interesse raramente são registrados de forma isolada, e sim misturados com outros sinais superpostos, ruído e artefatos fisiológicos ou ambientais, onde a relação sinal-ruído é geralmente baixa. Nesse contexto, a estratégia a ser utilizada baseia-se na ACD, que permitirá extrair um pequeno número de fontes, de potencial interesse, com informações úteis. A estratégia ACD para extração de informação é aplicada em três importantes problemas, na área de processamento de sinais biomédicos: (1) detecção do sinal do feto em magnetocardiografia fetal (MCGf); (2) detecção da atividade de resposta elétrica do estômago em magnetogastrografia (MGG); e, (3) detecção de regiões ativas do cérebro em experimentos de imagens por ressonância magnética funcional (Functional Magnetic Resonance Imaging, fMRI). Os resultados, nos três casos estudados, mostraram que o método utilizado, como estratégia, é efetivo e computacionalmente eficiente para extração de sinais de interesse. Concluímos, baseados nas aplicações, que o método proposto é eficaz, mostrando seu potencial para futuras pesquisas clínicas._________________________________________________________________________________________ ABSTRACT: An important goal in biomedical signal processing is the extraction of information based on a set of physiological measurements made along time. Generally, biomedical signals are electromagnetic measurements. Those measurements (usually made with multichannel equipment) are registered using spatially distributed sensors around some areas of the human body, originating a set of time and/or space correlated data. The signals of interest are rarely registered alone, being usually observed as a mixture of other spurious, noisy signals (sometimes superimposed) and environmental or physiological artifacts. More over, the signal-to-noise ratio is generally low. In many applications, a big number of sensors are available, but just a few sources are of interest and the remainder can be considered noise. For such kind of applications, it is necessary to develop trustful, robust and effective learning algorithms that allow the extraction of only a few sources potentially of interest and that hold useful information. The strategy used here for extraction of sources is applied in three important problems in biomedical signal processing: (1) detection of the fetal magnetocardiogram signal (fMCG); (2) detection of the electrical activity of the stomach in magnetogastrograms (MGG); and (3) detection of active regions of the brain in experiments in functional Magnetic Resonance Imaging (fMRI). The results, within the three cases of study, showed that the DCA method used as strategy is effective and computationally efficient on extraction of desired signals.
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Remes, J. (Jukka). "Method evaluations in spatial exploratory analyses of resting-state functional magnetic resonance imaging data." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202228.
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Abstract Resting-state (RS) measurements during functional magnetic resonance imaging (fMRI) have become an established approach for studying spontaneous brain activity. RS-fMRI results are often obtained using explorative approaches like spatial independent component analysis (sICA). These approaches and their software implementations are rarely evaluated extensively or specifically concerning RS-fMRI. Trust is placed in the software that they will work according to the published method descriptions. Many methods and parameters are used despite the lack of test data, and the validity of the underlying models remains an open question. A substantially greater number of evaluations would be needed to ensure the quality of exploratory RS-fMRI analyses. This thesis investigates the applicability of sICA methodology and software in the RS-fMRI context. The experiences were used to formulate general guidelines to facilitate future method evaluations. Additionally, a novel multiple comparison correction (MCC) method, Maxmad, was devised for adjusting evaluation results statistically. With regard to software considerations, the source code of FSL Melodic, popular sICA software, was analyzed against its published method descriptions. Unreported and unevaluated details were found, which implies that one should not automatically assume a correspondence between the literature and the software implementations. The method implementations should rather be subjected to independent reviews. An experimental contribution of this thesis is that the credibility of the emerging sliding window sICAs has been improved by the validation of sICA related preprocessing procedures. In addition to that, the estimation accuracy regarding the results in existing RS-fMRI sICA literature was also shown not to suffer even though repeatability tools like Icasso have not been used in their computation. Furthermore, the evidence against conventional sICA model suggests the consideration of different approaches to analysis of RS-fMRI. The guidelines developed for facilitation of evaluations include adoption of 1) open software development (improved error detection), 2) modular software designs (easier evaluations), 3) data specific evaluations (increased validity), and 4) extensive coverage of parameter space (improved credibility). The proposed Maxmad MCC addresses a statistical problem arising from broad evaluations. Large scale cooperation efforts are proposed concerning evaluations in order to improve the credibility of exploratory RS-fMRI methodsTiivistelmä Aivoista toiminnallisella magneettikuvantamisella (engl. functional magnetic resonance imaging, fMRI) lepotilassa tehdyt mittaukset ovat saaneet vakiintuneen aseman spontaanin aivotoiminnan tutkimuksessa. Lepotilan fMRI:n tulokset saadaan usein käyttämällä exploratiivisia menetelmiä, kuten spatiaalista itsenäisten komponenttien analyysia (engl. spatial independent component analysis, sICA). Näitä menetelmiä ja niiden ohjelmistototeutuksia evaluoidaan harvoin kattavasti tai erityisesti lepotilan fMRI:n kannalta. Ohjelmistojen luotetaan toimivan menetelmäkuvausten mukaisesti. Monia menetelmiä ja parametreja käytetään testidatan puuttumisesta huolimatta, ja myös menetelmien taustalla olevien mallien pätevyys on edelleen epäselvä asia. Eksploratiivisten lepotilan fMRI-datan analyysien laadun varmistamiseksi tarvittaisiin huomattavasti nykyistä suurempi määrä evaluaatioita. Tämä väitöskirja tutki sICA-menetelmien ja -ohjelmistojen soveltuvuutta lepotilan fMRI-tutkimuksiin. Kokemuksien perusteella luotiin yleisiä ohjenuoria helpottamaan tulevaisuuden menetelmäevaluaatioita. Lisäksi väitöskirjassa kehitettiin uusi monivertailukorjausmenetelmä, Maxmad, evaluaatiotulosten tilastolliseen korjaukseen. Tunnetun sICA-ohjelmiston, FSL Melodicin, lähdekoodi analysoitiin suhteessa julkaistuihin menetelmäkuvauksiin. Analyysissa ilmeni aiemmin raportoimattomia ja evaluoimattomia menetelmäyksityiskohtia, mikä tarkoittaa, ettei kirjallisuudessa olevien menetelmäkuvausten ja niiden ohjelmistototeutusten välille pitäisi automaattisesti olettaa vastaavuutta. Menetelmätoteutukset pitäisi katselmoida riippumattomasti. Väitöskirjan kokeellisena panoksena parannettiin liukuvassa ikkunassa suoritettavan sICA:n uskottavuutta varmistamalla sICA:n esikäsittelyjen oikeellisuus. Lisäksi väitöskirjassa näytettiin, että aiempien sICA-tulosten tarkkuus ei ole kärsinyt, vaikka niiden estimoinnissa ei ole käytetty toistettavuustyökaluja, kuten Icasso-ohjelmistoa. Väitöskirjan tulokset kyseenalaistavat myös perinteisen sICA-mallin, minkä vuoksi tulisi harkita siitä poikkeavia lähtökohtia lepotilan fMRI-datan analyysiin. Evaluaatioiden helpottamiseksi kehitetyt ohjeet sisältävät seuraavat periaatteet: 1) avoin ohjelmistokehitys (parantunut virheiden havaitseminen), 2) modulaarinen ohjelmistosuunnittelu (nykyistä helpommin toteutettavat evaluaatiot), 3) datatyyppikohtaiset evaluaatiot (parantunut validiteetti) ja 4) parametriavaruuden laaja kattavuus evaluaatioissa (parantunut uskottavuus). Ehdotettu Maxmad-monivertailukorjaus tarjoaa ratkaisuvaihtoehdon laajojen evaluaatioiden tilastollisiin haasteisiin. Jotta lepotilan fMRI:ssä käytettävien exploratiivisten menetelmien uskottavuus paranisi, väitöskirjassa ehdotetaan laaja-alaista yhteistyötä menetelmien evaluoimiseksi
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Liu, Aiping. "FDR-controlled network modeling and analysis of fMRI and sEMG signals." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37217.
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Neural recording technologies such as functional magnetic resonance imaging (fMRI) and surface electroencephalography (sEMG) provide great potential to studying the underlying neural systems and the related diseases. A broad range of statistical methods have been developed to model interactions between neural components. In this thesis, a false discovery rate (FDR)-controlled exploratory group modeling approach is introduced to model interaction/cooperation between neural components.
Group network modeling for comparison between populations is of great common interest in biomedical signal processing, particularly when there might be considerable heterogeneity within one or more groups, such as disease populations.
A group-level network modeling process, the group PCfdr algorithm with taking into account inter-subject variances, is proposed. The group PCfdr algorithm combines group inference with a graphical modeling approach for discovering statistically significant structure connectivity. Simulation results demonstrate that the group PCfdr algorithm can accurately recover the underlying group network structures and robustly control the FDR at user-specified levels.
To further extract informative features and compare the connectivity patterns across groups at the network level, network analysis methods including graph theoretical analysis, lesion and perturbation analysis are applied to examine the inferred networks. It can provide great potential to investigate the connectivity patterns as well as the particular changes associated with certain disease states.
The proposed network modeling and analysis approach is applied to fMRI data collected from control and Parkinson's Disease (PD) groups. The network analysis results of the PD groups before and after L-dopa medication support the hypothesis that PD subjects could be ameliorated by the medication. In addition, based on the comparison between PD subtypes, we observe that the learned brain effective networks across PD subtypes display different connectivity patterns.
In another sEMG study in low back pain, significant findings of muscle coordination networks are found to be associated with low back pain. The results indicate that the networks representing the normal group clearly exhibit globally symmetrical patterns between the left and right sEMG channels, while the connections between sEMG channels for the patient group are more likely to cluster locally and the learned group networks show the loss of global symmetrical patterns.
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Kundu, Prantik. "Physical analysis of BOLD fMRI signals for functional brain mapping and connectomics." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648842.
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Blanchard, Caroline. "Multisensorialité et kinesthésie : règles et substrats cérébraux de l'intégration multimodale." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4788.
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La kinesthésie chez l’Homme repose sur le traitement des multiples informations sensorielles générées au cours de l’action. Notre démarche expérimentale vise à évaluer l’apport respectif des sensibilités proprioceptive musculaire, tactile et visuelle à la perception des mouvements du corps. Utilisant des leurres sensoriels capables de créer des illusions kinesthésiques, nous recherchons, lors d'approches psychophysiques et en neuroimagerie, les règles et aires cérébrales impliquées dans la fusion multisensorielle. Ce travail confirme que chacune des trois modalités véhicule des informations pertinentes pour le système nerveux central (SNC), qui se combinent différemment selon la vitesse du mouvement et les modalités en présence. Tact et vision véhiculent des informations cinématiques redondantes, complémentaires de la proprioception, relatives aux mouvements du corps dans son environnement. Elles semblent plus fiables pour coder des mouvements lents (Blanchard et al., 2011, 2013). L'IRMf met en lumière un réseau de convergence hétéromodal de perception d’un mouvement de la main et confirme que les régions pariéto-cérebello-insulaires sont le siège de processus d'intégration supramodaux. En combinant trois signaux sensoriels différents, ce travail fournit la preuve originale de leur intégration à différents niveaux du SNC, des aires primaires, jusqu’aux aires supérieures du traitement de l’information. Rappelant le modèle de "Modality Appropriateness" de Welch & Warren (1986), nos résultats soutiennent l'idée d'une pondération des entrées, optimisant la kinesthésie, en fonction de leur relative pertinence à coder un évènement donnéHuman kinesthesia is based on the processing of a great amount of sensory information available during action. To assess the respective contribution of muscle proprioception, vision and touch of self-movement perception, our experimental approach relies on sensory lures likely to generate illusory sensations of movement. In psychophysics and functional neuroimaging (fMRI) experimental setting, we try to better understand how and where this "multisensory fusion" occurs. This work confirms that each modality conveys kinesthetic information relevant for the central nervous system (CNS), is combined in a non-equivalent way according to the velocity of encoded movement and sensory modalities involved. Tact and vision seem to provide redundant cinematic information about body movements relatively to environment, complementary to muscle proprioceptive signals and seem to be more reliable for coding small velocities (Blanchard et al., 2011, 2013). Our neuroimaging results highlight a heteromodal network involved during kinesthesia and confirms that a supramodal insulo-cerebello-parietal region is the substrate for trisensory integration processing. By combining three kinesthetic signals from different sensory origins, this work provides evidence of integration at different levels of the CNS. Recalling the "Modality Appropriateness" model of Welch & Warren (1986), our results also support the idea of a weighted integration of sensory inputs, which would optimize kinesthesia, based on their relative relevance to encode a given event
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Jukuri, T. (Tuomas). "Resting state brain networks in young people with familial risk for psychosis." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526211107.
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Abstract Neuropsychiatric illnesses usually become overtly manifest in adolescence and early adulthood. A critical long-term aim is to be able to prevent the development of such illnesses, which requires instruments to identify subjects at high risk of illness and to offer them effective interventions. There is an indisputable need for more sophisticated methods to enable more precise detection of adolescents and young adults who are at high risk of developing psychosis. Abnormal function in brain networks has been reported in people with schizophrenia and other psychotic disorders. Similar abnormalities have been found also in people at risk for developing psychosis, but it is not known whether this applies also to spontaneous resting state activity in young people with a familial risk for psychosis. We conducted resting-state functional MRI (R-fMRI) in 72 (29 male) young adults with a history of psychosis in one or both parents (FR) but without psychosis themselves, and 72 (29 male) similarly healthy control subjects without familial risk for psychosis. Both groups in the Oulu Brain and Mind study were drawn from the Northern Finland Birth Cohort 1986. All volunteers were 20–25 years old. Parental psychosis was established using the Care Register for Health Care. R-fMRI data was pre-processed using independent component analysis (ICA). A dual regression technique was used to detect between-group differences with p < 0.05 threshold corrected for multiple comparisons at voxel level. FR subjects demonstrated significantly decreased activity compared to control subjects in the default mode network and in the central executive network and increased activity in the cerebellum. The findings clarify previously controversial literature on the subject. The finding suggests that abnormal activity in these brain networks in rest may be associated with increased vulnerability to psychosis. The findings maybe helpful in developing more precise methods for detecting young people at highest risk for developing psychosisTiivistelmä Psykoottisiin häiriöihin sairastutaan yleensä nuoruudessa tai varhaisaikuisuudessa. Psykoositutkimuksen tavoitteena on löytää uusia menetelmiä, joiden avulla kyettäisiin tunnistamaan suurimmassa psykoosiriskissä olevat nuoret, jotta heille voitaisiin tarjota sairautta ennaltaehkäiseviä hoitokeinoja. Skitsofreniaan ja muihin psykoottisiin häiriöihin sairastuneilla on havaittu aivotoiminnan poikkeavuuksia. Samankaltaisia aivotoiminnan poikkeavuuksia on havaittu myös nuorilla, jotka ovat vaarassa sairastua psykoosiin. Toistaiseksi on ollut epäselvää, onko psykoosiin sairastuneiden henkilöiden lapsilla aivohermoverkkojen toiminnan poikkeavuuksia lepotilassa. Suoritimme aivojen lepotilan MRI-tutkimuksen (R-fMRI) 72:lle (29 miestä) nuorelle aikuiselle, joiden jompikumpi vanhempi oli sairastunut psykoosin sekä 72:lle (29 miestä) nuorelle aikuiselle, joiden vanhemmat eivät olleet sairastaneet psykoosia. Molemmat tutkimusryhmät tässä Oulu Brain and Mind -tutkimuksessa olivat Pohjois-Suomen 1986 syntymäkohortin jäseniä. Tutkittavat olivat 20–25 vuoden iässä. Lepotilan toiminnallinen magneettikuvaus suoritettiin 1.5 Teslan Siemensin magneettikuvantamislaitteella. Tutkimuskohteiksi valittiin lepotilan toiminnallinen aivohermoverkko, toiminnan ohjauksesta vastaava aivohermoverkko ja pikkuaivot. Kuvantamisdataan sovellettiin itsenäisten komponenttien analyysia aivohermoverkkojen määrittämistä varten. Ryhmien välisen eron havaitsemiseen käytettiin ei-parametristä permutaatiotestiä, joka kynnystettiin tilastollisesti merkitsevään tasoon (p < 0.05). Lepotilan oletushermoverkossa ja toiminnanohjauksesta vastaavassa aivohermoverkoissa havaittiin vähäisempää aktiivisuutta ja pikkuaivoissa kohonnutta aktiivisuutta perinnöllisessä psykoosiriskissä olevilla nuorilla aikuisilla verrattuna verrokkeihin. Tutkimustulokset selkeyttivät aiempaa ristiriitaista kirjallisuutta tutkimusaiheesta. Tutkimuksessa havaittujen aivoalueiden poikkeava toiminta lepotilassa voi liittyä kohonneeseen psykoosin puhkeamisriskiin. Tutkimuslöydösten avulla voidaan todennäköisesti edesauttaa parempien kuvantamismenetelmien kehittämistä suurimmassa psykoosiriskissä olevien nuorten tunnistamiseen
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Farouj, Younes. "Structured anisotropic sparsity priors for non-parametric function estimation." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI123/document.
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Le problème d'estimer une fonction de plusieurs variables à partir d'une observation corrompue surgit dans de nombreux domaines d'ingénierie. Par exemple, en imagerie médicale cette tâche a attiré une attention particulière et a, même, motivé l'introduction de nouveaux concepts qui ont trouvé des applications dans de nombreux autres domaines. Cet intérêt est principalement du au fait que l'analyse des données médicales est souvent effectuée dans des conditions difficiles car on doit faire face au bruit, au faible contraste et aux transformations indésirables inhérents aux systèmes d'acquisition. D'autre part , le concept de parcimonie a eu un fort impact sur la reconstruction et la restauration d'images au cours des deux dernières décennies. La parcimonie stipule que certains signaux et images ont des représentations impliquant seulement quelques coefficients non nuls. Cela est avéré être vérifiable dans de nombreux problèmes pratiques. La thèse introduit de nouvelles constructions d'a priori de parcimonie dans le cas des ondelettes et de la variation totale. Ces constructions utilisent une notion d'anisotopie généralisée qui permet de regrouper des variables ayant des comportements similaires : ces comportement peuvent peut être liée à la régularité de la fonction, au sens physique des variables ou bien au modèle d'observation. Nous utilisons ces constructions pour l'estimation non-paramétriques de fonctions. Dans le cas des ondelettes, nous montrons l'optimalité de l'approche sur les espaces fonctionnelles habituels avant de présenter quelques exemples d’applications en débruitage de séquences d'images, de données spectrales et hyper-spectrales, écoulements incompressibles ou encore des images ultrasonores. En suite, nous modélisons un problème déconvolution de données d'imagerie par résonance magnétique fonctionnelle comme un problème de minimisation faisant apparaître un a priori de variation totale structuré en espace-temps. Nous adaptons une généralisation de l'éclatement explicite-implicite pour trouver une solution au problème de minimisationThe problem of estimating a multivariate function from corrupted observations arises throughout many areas of engineering. For instance, in the particular field of medical signal and image processing, this task has attracted special attention and even triggered new concepts and notions that have found applications in many other fields. This interest is mainly due to the fact that the medical data analysis pipeline is often carried out in challenging conditions, since one has to deal with noise, low contrast and undesirable transformations operated by acquisition systems. On the other hand, the concept of sparsity had a tremendous impact on data reconstruction and restoration in the last two decades. Sparsity stipulates that some signals and images have representations involving only a few non-zero coefficients. The present PhD dissertation introduces new constructions of sparsity priors for wavelets and total variation. These construction harness notions of generalized anisotropy that enables grouping variables into sub-sets having similar behaviour; this behaviour can be related to the regularity of the unknown function, the physical meaning of the variables or the observation model. We use these constructions for non-parametric estimation of multivariate functions. In the case of wavelet thresholding, we show the optimality of the procedure over usual functional spaces before presenting some applications on denoising of image sequence, spectral and hyperspectral data, incompressible flows and ultrasound images. Afterwards, we study the problem of retrieving activity patterns from functional Magnetic Resonance Imaging data without incorporating priors on the timing, durations and atlas-based spatial structure of the activation. We model this challenge as a spatio-temporal deconvolution problem. We propose the corresponding variational formulation and we adapt the generalized forward-backward splitting algorithm to solve it
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Grooms, Joshua Koehler. "Examining the relationship between BOLD fMRI and infraslow EEG signals in the resting human brain." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53957.
Full textAbstract:
Resting state functional magnetic resonance imaging (fMRI) is currently at the forefront of research on cognition and the brain’s large-scale organization. Patterns of hemodynamic activity that it records have been strongly linked to certain behaviors and cognitive pathologies. These signals are widely assumed to reflect local neuronal activity but our understanding of the exact relationship between them remains incomplete. Researchers often address this using multimodal approaches, pairing fMRI signals with known measures of neuronal activity such as electroencephalography (EEG). It has long been thought that infraslow (< 0.1 Hz) fMRI signals, which have become so important to the study of brain function, might have a direct electrophysiological counterpart. If true, EEG could be positioned as a low-cost alternative to fMRI when fMRI is impractical and therefore could also become much more influential in the study of functional brain networks. Previous works have produced indirect support for the fMRI-EEG relationship, but until recently the hypothesized link between them had not been tested in resting humans. The objective of this study was to investigate and characterize their relationship by simultaneously recording infraslow fMRI and EEG signals in resting human adults. We present evidence strongly supporting their link by demonstrating significant stationary and dynamic correlations between the two signal types. Moreover, functional brain networks appear to be a fundamental unit of this coupling. We conclude that infraslow electrophysiology is likely playing an important role in the dynamic configuration of the resting state brain networks that are well-known to fMRI research. Our results provide new insights into the neuronal underpinnings of hemodynamic activity and a foundational point on which the use of infraslow EEG in functional connectivity studies can be based.
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Galindo, Guarin Liliana. "Neurological soft signs, temperament and schizotypy in patients with schizophrenia and unaffected relatives: an FMRI study." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/403815.
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La esquizofrenia es un trastorno psiquiátrico grave, un síndrome complejo y heterogéneo originado por la alteración del desarrollo del cerebro por factor genéticos o ambientales. Las bases genéticas pueden estar presentes en individuos sin enfermedad como en hermanos de pacientes y pueden ser identificadas a través de marcadores biológicos. Los signos neurológicos menores son discretas alteraciones sensitivo-motoras asociadas con un desarrollo cerebral alterado que se han propuesto como un endofenotipo de la esquizofrenia. Un perfil específico de temperamento y carácter así como la presencia de rasgos de personalidad esquizotípicos también se han relacionado con rasgos de personalidad esquizotípicos se han propuesto como un marcador de vulnerabilidad a la esquizofrenia. La etiopatogénesis de la esquizofrenia sugiere que puede haber una “alteración progresiva del neurodesarrollo”. Esta visión aboga por una alteración en los circuitos funcionales que implican áreas de asociación heteromodal más que alteraciones específicas en un área concreta del cerebro.
El objetivo de este estudio es explorar las anomalías de conectividad funcional en el Default Mode Network relacionados con la asociación entre signos neurológicos menores y rasgos de personalidad en la esquizofrenia.
Para investigar esta asociación se plantea un estudio transversal comparando un grupo de pacientes con esquizofrenia, un grupo de parientes no afectos de pacientes y un grupo de sujetos sanos, para explorar la asociación de estos posibles marcadores biológicos de esquizofrenia se estudio:
a) Asociación entre signos neurológicos menores y rasgos de personalidad : Inventario de temperamento y carácter TCI, Cuestionario de personalidad esquizotípica SPQ) y una evaluación de los signos neurológicos menores.
b) Asociación entre cambios en la conectividad cerebral en el default mode network con la presencia de signos neurológicos: fMRI en estado de reposo..
El principal hallazgo de este estudio es que los pacientes con esquizofrenia y los parientes no afectos presentan un perfil específico de temperamento y carácter con más rasgos de personalidad esquizotípicos que se correlacionan con una mayor presencia se signos neurológicos menores.
Los resultados revelan que la asociación entre estos posibles biomarcadores a nivel teórico como el temperamento (especialmente la evitación del daño, la búsqueda de recompensa y la persistencia) y el carácter (especialmente la autodirección y la cooperación) que se correlaciona con la presencia de signos neurológicos menores en toda la muestra. También los rasgos esquizotípicos de personalidad mostraron una fuerte correlación con la presencia de signos neurológicos menores en toda la muestra.
Los resultados muestran la susceptibilidad a los signos neurológicos menores y a la esquizofrenia está relacionada en ambos casos con diferencias individuales en aspectos de personalidad en parientes no psicóticos de pacientes con esquizofrenia. Estos hallazgos subrayan el valor de usar ambos parámetros para el estudio de poblaciones de riesgo.
Los resultados de neuroimagen mostraron cambios en la conectividad de las redes neuronales por defecto posiblemente asociados a la presencia de signos neurológicos menores. Estos hallazgos apoyan la teoría de la dismetría cognitiva como una posible disfunción en las conexiones córtico-tálamo-cerebelares. Este modelo también podría explicar la diversidad de síntomas de la esquizofrenia y sus asociaciones (como en este estudio que incluye personalidad y funciones sensitivo-motoras).Schizophrenia is a severe psychiatric disorder that has a profound effect on both the individuals affected and society. This common mental illness is a complex, heterogeneous behavioural and cognitive syndrome that seems to originate from disruption of brain development caused by genetic or environmental factors, or both. The genetic basis may be present in individuals without disease, as in the case of relatives of patients, being detectable through biological markers. Neurological soft signs (NSS) are discrete sensorimotor impairments associated with deviant brain development that were postulate as an endophenotype of schizophrenic spectrum disorder. Also the personality traits have been proposed as a vulnerability marker in schizophrenia. A specific profile of temperament and character and the schizotypal personality traits have also been correlated with schizotypal personality traits. These traits and some neurological abnormalities have been shown to aggregate in the relatives of schizophrenia patients. The etiopathogenesis of schizophrenia suggests it may be a "progressive neurodevelopmental disorder". This view postulates a disruption in functional circuits involving hetero modal association areas rather than a specific abnormality in a single brain region. The aim of this study is to explore the abnormalities in the functional connectivity of the default mode network related to the association between neurological soft signs and personality in schizophrenia. To investigate this a cross-sectional study is proposed, comparing a group of patients with schizophrenia, a group of unaffected relatives and a group of healthy controls. In order to explore the association of these potential biomarkers of schizophrenia the study was composed of two parts: a) To explore the association between neurological soft signs and personality traits in schizophrenia, two personality examinations (Temperament and Character Inventory and the Schizotypal Personality Questionnaire) and an evaluation of Neurological Soft Signs were performed. b) To explore the association between cerebral connectivity changes in the default mode network with the presence of neurological soft signs in schizophrenia a functional magnetic resonance scan was performed on participants in a resting state. The major finding in this study was that patients with schizophrenia and non-psychotic relatives display a unique profile of temperament and character and more schizotypal traits that correlate with higher presence of NSS. Our results reveal an association between these hypothesized vulnerability markers, as temperament (especially harm avoidance, reward dependence and persistence) and character (especially self-directedness and cooperativeness) correlated with the presence of NSS in the entire sample. Also the schizotypal traits (total scores and subscores) showed a very strong correlation with the presence of NSS in the entire sample. The results showed that susceptibility to NSS and to schizophrenia are both related to individual differences in personality features in non-psychotic relatives of patients with schizophrenia. These findings highlight the value of using both assessments to study high risk populations. The neuroimaging results showed connectivity changes in the default mode network with a possible association with the presence of neurological soft signs. These findings support the theory of cognitive dysmetria as a possible dysfunction in cortical-thalamic-cerebellar connectivity. This model also could explain the diversity of symptoms in schizophrenia and their associations (like this study that includes personality and sensory and motor functions). One strength of the study is that the relatives of patients with schizophrenia had no familial ties to the patients used, thus decreasing the possibility that similar upbringing would confound the results.
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Demirtaş, Murat. "Exploring functional connectivity dynamics in brain disorders: a whole-brain computational framework for resting state fMRI signals." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/350799.
Full textAbstract:
Brain activity, on every scale, spontaneously fluctuates, thereby exhibiting complex, dynamic interactions that manifest rich synchronization patterns. The past ten years have been dominated by studies intended to further our understanding of the mecha-nisms behind the dynamic interactions within the brain through the basis of its structural and functional connectivity structures. Moreover, there is a tremendous effort to unveil the role that these interactions play in psychiatric disorders. This thesis addresses these questions from novel perspectives. The first pillar of this thesis is the time-varying na-ture of the dynamic interactions between brain regions. The second pillar is the role that FC dynamics play in clinical populations. The third pillar uncovers the connectivity structure that links the observed anatomical and functional connectivity patterns through computational modeling. The final pillar of the thesis proposes a mechanistic explana-tion for brain disorders.L'activitat del cervell fluctua espontàniament a diferents escales i per tant exhibeix in-teraccions dinàmiques i complexes que manifesten patrons de sincronització rics. Du-rant els darrers deu anys han abundat els estudis orientats a comprendre els mecanismes que hi ha darrere les interaccions cerebrals basant-se en les seves estructures funcionals i estructurals. A més, existeix un esforç ingent per desvetllar el paper que aquestes in-teraccions juguen en els trastorns psiquiàtrics. Aquesta tesi aborda les qüestions esmen-tades des de noves perspectives. El primer pilar d'aquesta tesi és la naturalesa variable en el temps de la interacció dinàmica entre diferents regions del cervell. El segon pilar és el paper que aquesta dinàmica de connectivitat funcional juga en diferents poblacions clíniques. El tercer pilar es centra en l'ús de models computacionals per determinar l'es-tructura de connectivitat que relaciona els patrons de connectivitat funcional i anatòmics observats. El quart pilar de la tesi proposa una explicació del mecanisme dels trastorns cerebrals.
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Del, Castello Mariangela. "Analysis of electroencephalography signals collected in a magnetic resonance environment: characterisation of the ballistocardiographic artefact." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13214/.
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L’acquisizione simultanea di segnali elettroencefalografici (EEG) e immagini di risonanza magnetica funzionale (fMRI) permette di investigare attivazioni cerebrali in modo non invasivo. La presenza del campo magnetico altera però in modo non trascurabile la qualità dei segnali EEG acquisiti. In particolare due artefatti sono stati individuati: l’artefatto da gradiente e l’artefatto da ballistocardiogramma (BCG). L’artefatto da BCG è legato all’attività cardiaca del soggetto, ed è caratterizzato da elevata variabilità tra un’occorrenza e l’altra in termini di ampiezza, forma d’onda e durata dell’artefatto. Differenti algoritmi sono stati implementati al fine di rimuoverlo, ma la rimozione completa rimane ancora un difficile obiettivo da raggiungere a causa della sua complessa natura. L’argomento della tesi riguarda l’analisi di segnali EEG acquisiti in ambiente di risonanza magnetica e la caratterizzazione dell’artefatto BCG. L’obiettivo è individuare ulteriori caratteristiche dell’artefatto che possano condurre al miglioramento dei precedenti metodi, o all’implementazione di nuovi. Con questa tesi abbiamo mostrato quali sono i motivi che causano la presenza di residui artefattuali nei segnali EEG processati con i metodi presenti in letteratura. Attraverso analisi statistica abbiamo riscontrato che occorrenze dell’artefatto BCG sono caratterizzate da un ritardo variabile rispetto al picco R sull’ECG, che nella nostra analisi rappresenta l’evento di riferimento nell’attività cardiaca. Abbiamo inoltre trovato che il ritardo R-BCG varia con la frequenza cardiaca. Le successive valutazioni riguardano i maggiori contributi all’artefatto BCG. Attraverso l’analisi alle componenti principali, sono stati individuati due contributi legati al fluire del sangue dal cuore verso il cervello e alla sua pulsatilità nei vasi principali dello scalpo.
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Gonçalves, Murta T. I. "Study of the relationship between the EEG and BOLD signals using intracranial EEG-fMRI data simultaneously acquired in humans." Thesis, University College London (University of London), 2016. http://discovery.ucl.ac.uk/1503776/.
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The principal aim of this work was to further characterise the relationship between the electrophysiological and BOLD fMRI signals at the local level, exploiting the unique opportunity to analyse intracranial EEG (icEEG) and fMRI data recorded simultaneously in humans, during a finger tapping task and at rest. The MR-environment (gradient switch and mechanical vibration) related artefacts corrupting the icEEG data were the first problem tackled; they were characterised and removed using techniques developed by me. The two parts that followed aimed to shed further light on the neurophysiological basis of the BOLD effect. Firstly, the influence of the phase of the low frequency EEG activities (< 30 Hz) on capability of an EEG power - based model to predict the amplitude of finger tapping related BOLD changes was investigated; the strength of the coupling between the phase of and the amplitude of (>70 Hz) (phase-amplitude coupling: PAC) was found to explain variance in addition to a combination of , , and band powers, suggesting that PAC strength and power fluctuations result from complementary neuronal processes. Secondly, five interictal epileptiform discharge (IED) morphology and field extent related features were tested in their individual capability to predict the amplitude of the co-localised BOLD signal; these were the amplitude and rising phase slope, thought to reflect the degree of neuronal activity synchrony; width and energy, thought to reflect the duration of the excitatory post-synaptic potentials; and spatial field extent, thought to reflect the spatial extent of the surrounding, synchronised sources of neuronal activity. Among these features, the IED width was the only one found to explain BOLD signal variance in addition to the IED onsets, suggesting that the amplitude of the BOLD signal is comparatively better predicted by the duration of the underlying field potential, than by the degree of neuronal activity synchrony.
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Domanski, Aleksander Peter Frederick. "Functional dissection of abnormal signal processing performed by the somatosensory cortex of young Fmr1-KO mice." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/18017.
Full textAbstract:
Every second throughout life, cortical circuitry efficiently compresses and interprets huge volumes of incoming sensory information. This high fidelity sensory processing guides normal brain development and is essential for animals’ successful interaction with the environment. Low-level sensory perceptual disturbance is nearly ubiquitous in Autism Spectrum Disorder (ASD), but despite the potential to offer crucial insight into the abnormal development of higher brain function is poorly understood. Fragile X Syndrome (FXS) is the most common heritable cause of ASD. Previous studies in the Fmr1-KO mouse model of FXS report cell-intrinsic, synaptic and local connectivity abnormalities in the neuronal physiology of primary sensory cortices. This suggests that sensory perceptual dysfunction could emerge from interacting circuit-wide pathophysiology to impair neural adaptations that support high fidelity sensory information processing. However, there is little mechanistic consensus about how this might occur. To address this, in this thesis I use brain slice electrophysiology and computer modelling to provide a bottom-up description of how thalamocortical (TC) responses, the principal cortical input for ascending sensory information, are mis-interpreted in the somatosensory Layer 4 (L4) circuit in Fmr1-KOs at a crucial developmental transition to active sensory processing. Recruitment of intracortical L4 network activity could be atypically evoked by lower frequency thalamic stimulation in Fmr1-KO slices. Furthermore, profound alterations to single-cell and network response dynamics were observed, in particular loss of spike timing precision considered critical for sensory circuit performance. These network phenomena were supported by interacting single-cell and local circuitry pathophysiology, including hyperexcitable cortical neurons and temporally distorted feed forward and feedback inhibition. Together, these data demonstrate cortical hypersensitivity to TC inputs and abnormal recruitment of network activity in critical period Fmr1-KO somatosensory cortical circuits. The hyperresponsiveness of intracortical circuitry may underlie tactile hyperexcitability and distorted sensory perception in FXS patients. Interestingly, modelling suggests that many of the alterations of synaptic and neuronal function are compensatory, thus minimizing the impact of the genetic lesion. Thus, this study shows for the first time that circuit level dysfunction emerges in the Fmr1-KO mouse from an accumulation of effects at the synaptic and cellular level; however, it also highlights the challenge of understanding which of these changes are pathological and which are compensatory.
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Wang, Maosen [Verfasser], and Xin [Akademischer Betreuer] Yu. "The Correlation between Astrocytic Calcium and fMRI Signals is Related to the Thalamic Regulation of Cortical States / Maosen Wang ; Betreuer: Xin Yu." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/121048420X/34.
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Kalberlah, Christian [Verfasser]. "Multivariate „Searchlight“-Musterklassifizierung humaner fMRT-Signale in multiplen kognitiven Modalitäten : visuelle Aufmerksamkeit, Syntax und Emotion / Christian Kalberlah." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1035182289/34.
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Thompson, Garth John. "Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patterns." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49083.
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While task-based functional magnetic resonance imaging (fMRI) has helped us understand the functional role of many regions in the human brain, many diseases and complex behaviors defy explanation. Alternatively, if no task is performed, the fMRI signal between distant, anatomically connected, brain regions is similar over time. These correlations in “resting state” fMRI have been strongly linked to behavior and disease. Previous work primarily calculated correlation in entire fMRI runs of six minutes or more, making understanding the neural underpinnings of these fluctuations difficult. Recently, coordinated dynamic activity on shorter time scales has been observed in resting state fMRI: correlation calculated in comparatively short sliding windows and quasi-periodic (periodic but not constantly active) spatiotemporal patterns. However, little relevance to behavior or underlying neural activity has been demonstrated. This dissertation addresses this problem, first by using 12.3 second windows to demonstrate a behavior-fMRI relationship previously only observed in entire fMRI runs. Second, simultaneous recording of fMRI and electrical signals from the brains of anesthetized rats is used to demonstrate that both types of dynamic activity have strong correlates in electrophysiology. Very slow neural signals correspond to the quasi-periodic patterns, supporting the idea that low-frequency activity organizes large scale information transfer in the brain. This work both validates the use of dynamic analysis of resting state fMRI, and provides a starting point for the investigation of the systemic basis of many neuropsychiatric diseases.
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Chuang, Kai-Hsiang, and 莊凱翔. "Identification of fMRI Signal Using Fuzzy Neural Network." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/33318825486856816899.
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碩士國立臺灣大學
電機工程學系
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Functional MRI (fMRI), with its high spatial, temporal resolution, non-radioactive, and non-invasive properties, is potential in the fields ranging from physiology, psychology, pharmacology to clinical applications, etc. However, due to its very low signal-to-noise ratio, post processing is required to identify the active regions.Most contemporary post processing strategies require assumed functional response waveform based on the prior knowledge about experimental paradigm to produce effective results. While most studies are performed in steady state, these methods are not very suitable for complex functional experiments and will produce biased result even when brain*s response is simple. This will limit the applications of functional MRI studies. To fully utilize fMRI to cognition applications, one needs to develop a flexible analysis tool for the complicated signal characteristics of brain responses. Utilizing unsupervised clustering network and fuzzy set theory, we have successfully developed a cascade fuzzy neural network, which combines Kohonen clustering network and Fuzzy C-Means algorithm, to analyze fMRI time signal [30]. By comparing the receiver operating characteristic (ROC) analysis results of our proposed method with other two kinds of conventional post processing methods- correlation coefficient analysis and t- statistical parametric mapping - on a series of testing phantoms, we have proved that our method can identify the actual functional response even when the activation area is very small, under noisy conditions, or even when the actual response is deviated from traditionally assumed box-car type.Human studies involving motor cortex activation also show that our method successfully identifies the functional responses waveform and the active regions as well. Furthermore, it can also discriminate responses in gray matter from those possibly coming from venous vessels. And most of all, even when the experimental paradigm is unknown, such that conventional post processing methods are inapplicable, our method is still effective.With this flexible fMRI tool, psycho-physiologist now can go on and proceed complicated one shot human cognition studies. Linguistic studies including Chinese language and Taiwanese dialect study will be performed in the near future.
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Wibral, Michael. "The BOLD fMRI Signal under Anaesthesia and Hyperoxia." Phd thesis, 2007. http://tuprints.ulb.tu-darmstadt.de/844/1/Wibral2007_EPDA.pdf.
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This study investigated modulations of stimulus induced hemodynamic responses in the macaque monkey brain under anesthesia and hyperoxia. Hyperoxia is present in many anaesthesia protocols used in animal blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies. However, little data exist on the influence of hyperoxia on the magnitude of stimulus-induced relative changes in BOLD fMRI signal (ΔBOLD%). No study to date has investigated these effects in a time-resolved manner, although cerebral vasoregulation offers sites for a time-dependent interaction of hyperoxia and ΔBOLD%. Here we investigated time-dependent effects of an inspiratory oxygen fraction of 90%. We tightly clamped end tidal CO2 and body temperature and recorded physiological parameters relevant to the regional cerebral blood flow (rCBF) in (fentanyl/isoflurane) anaesthetized monkeys while using visual stimulation to elicit ΔBOLD%. The stimulus induced hemodynamic responses were assessed using a general linear model with the visual stimulation time course as a predictor. To clarify whether changes in ΔBOLD% arose from changes in baseline blood oxygenation or rather altered neuronal or vascular reactivity, we directly measured changes in regional cerebral blood volume (rCBV) using monocrystalline ion oxide nanoparticles (MION) as contrast agent. In visual cortex we found a biphasic modulation of stimulus-induced ΔBOLD% under hyperoxia: We observed first a significant decrease in ΔBOLD% by −24% for data averaged over the time interval of 0 to 180min post onset of hyperoxia followed by a subsequent recovery to baseline. rCBVresponse amplitudes were decreased by 21% in the same time interval (0 to 180 min). In the LGN, we neither found a significant modulation of ΔBOLD% nor of MION response amplitude. The cerebrovascular effects of hyperoxia may, therefore, be regionally specific and cannot be explained by a deoxyhemoglobin dilution model accounting for plasma oxygenation without assuming altered neuronal activity or altered neurovascular coupling. In addition we supply quantitative data on the significant influence of the following anesthesia parameters: body temperature, isofulorane/fentanyl doses and the time since administration of the initial anaesthetic methohexital. Complementing the above univariate and model driven analysis of stimulus induced hemodynamic responses we also analyzed our data in a model free multivariate way using independent component analysis (ICA). Results of ICA decompositions were clustered using a hierarchical clustering approach to pick out reliable components for further analysis. Model free data analysis techniques like ICA suffer from the difficulty to assign a physical/physiological meaning to the decomposed signals. To solve this problem, we propose a new analysis technique that exploits the within-cluster variance with respect to certain anaesthesia variables to assign a physiological meaning to a given cluster. We were able to demonstrate that results from the model driven analysis could be replicated when analysing the cluster of components that contained the stimulus driven hemodynamic responses. In addition, our analysis helped to assign a meaning to several other components typically observed in ICA decompositions of fMRI data.