Dissertations / Theses on the topic 'FMRI Data'
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1
Ji, Yongnan. "Data-driven fMRI data analysis based on parcellation." Thesis, University of Nottingham, 2001. http://eprints.nottingham.ac.uk/12645/.
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Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. As with many other neuroiroaging tools, the group analysis of fMRI data often requires a transformation of the individual datasets to a common stereotaxic space, where the different brains have a similar global shape and size. However, the local inaccuracy of this procedure gives rise to a series of issues including a lack of true anatomical correspondence and a loss of subject specific activations. Inter-subject parcellation of fMRI data has been proposed as a means to alleviate these problems. Within this frame, the inter-subject correspondence is achieved by isolating homologous functional parcels across individuals, rather than by matching voxels coordinates within a stereotaxic space. However, the large majority of parcellation methods still suffer from a number of shortcomings owing to their dependence on a general linear model. Indeed, for all its appeal, a GLM-based parcellation approach introduces its own biases in the form of a priori knowledge about such matters as the shape of the Hemodynamic Response Function (HRF) and taskrelated signal changes. In this thesis, we propose a model-free data-driven parcellation approach to singleand multi-subject parcellation. By modelling brain activation without an relying on an a priori model, parcellation is optimized for each individual subject. In order to establish correspondences of parcels across different subjects, we cast this problem as a multipartite graph partitioning task. Parcels are considered as the vertices of a weighted complete multipartite graph. Cross subject parcel matching becomes equivalent to partitioning this graph into disjoint cliques with one and only one parcel from each subject in each clique. In order to solve this NP-hard problem, we present three methods: the OBSA algorithm, a method with quadratic programming and an intuitive approach. We also introduce two quantitative measures of the quality of parcellation results. We apply our framework to two fMRI data sets and show that both our single- and multi-subject parcellation techniques rival or outperform model-based methods in terms of parcellation accuracy.
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Bai, Ping Truong Young K. Smith Richard L. "Temporal-spatial modeling for fMRI data." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2007. http://dc.lib.unc.edu/u?/etd,1481.
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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2007.Title from electronic title page (viewed Apr. 25, 2008). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
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Plumpton, Catrin Oliver. "Classifier ensembles for streaming fMRI data." Thesis, Bangor University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540419.
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Perez, Carlos Arturo. "Discovering causal relationships from fMRI data." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000189.
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Thesis (M.S.)--University of West Florida, 2009.Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 90 pages. Includes bibliographical references.
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Turkay, Kemal Dogus. "Simulated Fmri Toolbox." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611465/index.pdf.
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In this thesis a simulated fMRI toolbox is developed in order to generate simulated data to compare and benchmark different functional magnetic resonance image analysis methods. This toolbox is capable of loading a high resolution anatomic brain volume, generating 4D fMRI data in the same data space with the anatomic image, and allowing the user to create block and event-related design paradigms. Common fMRI artifacts such as scanner drift, cardiac pulsation, habituation and task related or spontaneous head movement can be incorporated into the 4D fMRI data. Input to the toolbox is possible through MINC 2.0 file format, and output is provided in ANALYZE format. The major contribution of this toolbox is its facilitation of comparison of fMRI analysis methods by generating several different fMRI data under varying noise and experiment parameters.
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Soldati, Nicola. "Novel data-driven analysis methods for real-time fMRI and simultaneous EEG-fMRI neuroimaging." Doctoral thesis, University of Trento, 2012. http://eprints-phd.biblio.unitn.it/842/1/Soldati_PhD_thesis.pdf.
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Real-time neuroscience can be described as the use of neuroimaging techniques to extract and evaluate brain activations during their ongoing development. The possibility to track these activations opens the doors to new research modalities as well as practical applications in both clinical and everyday life. Moreover, the combination of different neuroimaging techniques, i.e. multimodality, may reduce several limitations present in each single technique. Due to the intrinsic difficulties of real-time experiments, in order to fully exploit their potentialities, advanced signal processing algorithms are needed. In particular, since brain activations are free to evolve in
an unpredictable way, data-driven algorithms have the potentials of being more suitable than model-driven ones. In fact, for example, in neurofeedback experiments brain activation tends to change its properties due to training or task eects thus evidencing the need for adaptive algorithms.
Blind Source Separation (BSS) methods, and in particular Independent Component Analysis (ICA) algorithms, are naturally suitable to such kind of conditions. Nonetheless, their applicability in this framework needs further investigations.
The goals of the present thesis are: i) to develop a working real-time set up for performing experiments; ii) to investigate different state of the art ICA algorithms with the aim of identifying the most suitable (along with their optimal parameters), to be adopted in a real-time MRI environment; iii) to investigate novel ICA-based methods for performing real-time MRI neuroimaging; iv) to investigate novel methods to perform data fusion between EEG and fMRI data acquired simultaneously.
The core of this thesis is organized around four "experiments", each one addressing one of these specic aims. The main results can be summarized as follows.
Experiment 1: a data analysis software has been implemented along with the hardware acquisition set-up for performing real-time fMRI. The set-up has been developed with the aim of having a framework into which it would be possible to test and run the novel methods proposed to perform real-time
fMRI.
Experiment 2: to select the more suitable ICA algorithm to be implemented in the system, we investigated theoretically and compared empirically the performance of 14 different ICA algorithms systematically sampling different growing window lengths, model order as well as a priori conditions
(none, spatial or temporal). Performance is evaluated by computing the spatial and temporal correlation to a target component of brain activation as well as computation time. Four algorithms are identied as best performing without prior information (constrained ICA, fastICA, jade-opac
and evd), with their corresponding parameter choices. Both spatial and temporal priors are found to almost double the similarity to the target at not computation costs for the constrained ICA method.
Experiment 3: the results and the suggested parameters choices from experiment 2 were implemented to monitor ongoing activity in a sliding-window approach to investigate different ways in which ICA-derived a priori information could be used to monitor a target independent component: i)
back-projection of constant spatial information derived from a functional localizer, ii) dynamic use of temporal , iii) spatial, or both iv) spatial-temporal ICA constrained data. The methods were evaluated based on spatial and/or temporal correlation with the target IC component monitored, computation time and intrinsic stochastic variability of the algorithms. The results show that the back-projection method offers the highest performance both in terms of time course reconstruction and speed. This method is very fast and effective as far as the monitored IC has a strong and well defined behavior, since it relies on an accurate description of the spatial behavior.
The dynamic methods oer comparable performances at cost of higher computational time. In particular the spatio-temporal method performs comparably in terms of computational time to back-projection, offering more variable performances in terms of reconstruction of spatial maps and time courses.
Experiment 4: finally, Higher Order Partial Least Square based method combined with ICA is proposed and investigated to integrate EEG-fMRI data acquired simultaneously. This method showed to be promising, although more experiments are needed.
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Alowadi, Nahed. "Population based spatio-temporal probabilistic modelling of fMRI data." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8210/.
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High-dimensional functional magnetic resonance imaging (fMRI) data is characterized by complex spatial and temporal patterns related to neural activation. Mixture based Bayesian spatio-temporal modelling is able to extract spatiotemporal components representing distinct haemodyamic response and activation patterns. A recent development of such approach to fMRI data analysis is so-called spatially regularized mixture model of hidden process models (SMM-HPM). SMM-HPM can be used to reduce the four-dimensional fMRI data of a pre-determined region of interest (ROI) to a small number of spatio-temporal prototypes, sufficiently representing the spatio-temporal features of the underlying neural activation. Summary statistics derived from these features can be interpreted as quantification of (1) the spatial extent of sub-ROI activation patterns, (2) how fast the brain respond to external stimuli; and (3) the heterogeneity in single ROIs. This thesis aims to extend the single-subject SMM-HPM to a multi-subject SMM-HPM so that such features can be extracted at group-level, which would enable more robust conclusion to be drawn.
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Chen, Xu. "Accelerated estimation and inference for heritability of fMRI data." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/67103/.
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In this thesis, we develop some novel methods for univariate and multivariate analyses of additive genetic factors including heritability and genetic correlation. For the univariate heritability analysis, we present 3 newly proposed estimation methods—Frequentist ReML, LR-SD and LR-SD ReML. The comparison of these novel and those currently available approaches demonstrates the non-iterative LRSD method is extremely fast and free of any convergence issues. The properties of this LR-SD method motivate the use of the non-parametric permutation and bootstrapping inference approaches. The permutation framework also allows the utilization of spatial statistics, which we find increases the statistical sensitivity of the test. For the bivariate genetic analysis, we generalize the univariate LR-SD method to the bivariate case, where the integration of univariate and bivariate LR-SD provides a new estimation method for genetic correlation. Although simulation studies show that our measure of genetic correlation is not ideal, we propose a closely related test statistic based on the ERV, which we show to be a valid hypothesis test for zero genetic correlation. The rapid implementation of this ERV estimator makes it feasible to use with permutation as well. Finally, we consider a method for high-dimensional multivariate genetic analysis based on pair-wise correlations of different subject pairs. While traditional genetic analysis models the correlation over subjects to produce an estimate of heritability, this approach estimates correlation over a (high-dimensional) phenotype for pairs of subjects, and then estimates heritability based on the difference in MZ-pair and DZ-pair correlations. A significant two-sample t-test comparing MZ and DZ correlations implies the existence of heritable elements. The resulting summary measure of aggregate heritability, defined as twice the difference of MZ and DZ mean correlations, can be treated as a quick screening estimate of whole-phenotype heritability that is closely related to the average of traditional heritability.
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Corte, Coi Claudio. "Network approaches for the analysis of resting state fMRI data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10820/.
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Negli ultimi anni la teoria dei network è stata applicata agli ambiti più diversi, mostrando
proprietà caratterizzanti tutti i network reali. In questo lavoro abbiamo applicato gli strumenti
della teoria dei network a dati cerebrali ottenuti tramite MRI funzionale “resting”,
provenienti da due esperimenti. I dati di fMRI sono particolarmente adatti ad essere studiati
tramite reti complesse, poiché in un esperimento si ottengono tipicamente più di centomila
serie temporali per ogni individuo, da più di 100 valori ciascuna.
I dati cerebrali negli umani sono molto variabili e ogni operazione di acquisizione dati,
così come ogni passo della costruzione del network, richiede particolare attenzione. Per
ottenere un network dai dati grezzi, ogni passo nel preprocessamento è stato effettuato
tramite software appositi, e anche con nuovi metodi da noi implementati.
Il primo set di dati analizzati è stato usato come riferimento per la caratterizzazione
delle proprietà del network, in particolare delle misure di centralità, dal momento che
pochi studi a riguardo sono stati condotti finora. Alcune delle misure usate indicano valori
di centralità significativi, quando confrontati con un modello nullo. Questo comportamento
`e stato investigato anche a istanti di tempo diversi, usando un approccio sliding
window, applicando un test statistico basato su un modello nullo pi`u complesso.
Il secondo set di dati analizzato riguarda individui in quattro diversi stati di riposo, da
un livello di completa coscienza a uno di profonda incoscienza. E' stato quindi investigato
il potere che queste misure di centralità hanno nel discriminare tra diversi stati, risultando
essere dei potenziali bio-marcatori di stati di coscienza. E’ stato riscontrato inoltre che non
tutte le misure hanno lo stesso potere discriminante. Secondo i lavori a noi noti, questo `e il
primo studio che caratterizza differenze tra stati di coscienza nel cervello di individui sani
per mezzo della teoria dei network.
10
Stromberg, David A. "Performance of AIC-Selected Spatial Covariance Structures for fMRI Data." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd981.pdf.
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Behjat, Hamid. "Statistical Parametric Mapping of fMRI data using Spectral Graph Wavelets." Thesis, Linköpings universitet, Medicinsk informatik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81143.
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In typical statistical parametric mapping (SPM) of fMRI data, the functional data are pre-smoothed using a Gaussian kernel to reduce noise at the cost of losing spatial specificity. Wavelet approaches have been incorporated in such analysis by enabling an efficient representation of the underlying brain activity through spatial transformation of the original, un-smoothed data; a successful framework is the wavelet-based statistical parametric mapping (WSPM) which enables integrated wavelet processing and spatial statistical testing. However, in using the conventional wavelets, the functional data are considered to lie on a regular Euclidean space, which is far from reality, since the underlying signal lies within the complex, non rectangular domain of the cerebral cortex. Thus, using wavelets that function on more complex domains such as a graph holds promise. The aim of the current project has been to integrate a recently developed spectral graph wavelet transform as an advanced transformation for fMRI brain data into the WSPM framework. We introduce the design of suitable weighted and un-weighted graphs which are defined based on the convoluted structure of the cerebral cortex. An optimal design of spatially localized spectral graph wavelet frames suitable for the designed large scale graphs is introduced. We have evaluated the proposed graph approach for fMRI analysis on both simulated as well as real data. The results show a superior performance in detecting fine structured, spatially localized activation maps compared to the use of conventional wavelets, as well as normal SPM. The approach is implemented in an SPM compatible manner, and is included as an extension to the WSPM toolbox for SPM.
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Liao, Chuanhong 1964. "Estimating the delay of the hemodynamic response in fMRI data." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31260.
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The technique of functional magnetic resonance imaging (fMRI) is rapidly developing from one of technical interest to wide clinical application. fMRI exploits the fact that brain neural activity produces a change in blood oxygenation level dependent (BOLD) response which is recorded at each point in the brain. In a typical experiment, a subject is given a stimulus or cognitive task, and the statistical question is to relate it to the BOLD response, usually via a linear model. The BOLD response is not instantaneous; it is delayed and smoothed by about 6 seconds. In this thesis we propose a rapid method of estimating and making inference about this delay. Our method is compared to other alternatives, and validated on an fMRI data set from an experiment in pain perception.
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Lai, Ian 1980. "A Web-based tutorial for statistical analysis of fMRI data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29669.
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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (p. 59-63).
A dearth of educational material exists for functional magnetic resonance imaging (fMRI), a relatively new tool used in neuroscience research. A computer demonstration for understanding statistical analysis in fMRI was developed in Matlab, along with an accompanying tutorial for its users. The demo makes use of Dview, an existing software package for viewing 3D brain data, and utilizes precomputed data to improve interactivity. The demo and client were used in an HST graduate course in methods for acquisition and analysis of fMRI data. For wider accessibility, a Web-based version of the demo was designed with a client/server architecture. The Java client has a layered design for flexibility, and the Matlab server interfaces with Dview to take advantage of its functionality. The client and server communicate via a simple protocol through the Matlab Web Server. The Web-based version of the demo was implemented successfully. Future work includes implementation of additional demo features and expansion of the tutorial before dissemination to a wider group of medical and neuroscience researchers.
by Ian Lai.
M.Eng.and S.B.
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Ash, Thomas William John. "Use of statistical classifiers in the analysis of fMRI data." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609710.
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Mojtahedi, Sina. "Analyzing Efective Connectivity Of Brain Using Fmri Data : Dcm And Ppi." Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615496/index.pdf.
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In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is its ability to detect brain disorders in early stages. Some brain disorders are Schizophrenia, MS and Major Depression disease. Comparing the effective connectivity between a healthy and unhealthy brain will help to diagnose brain disorder. In this master study, two methods named Dynamic Causal Modeling (DCM) and Psychophysiological Interaction (PPI) are used to compare effective connectivity and neuronal activity between different regions of brain when there are three different stimulations. Since the neural activity is latent in fMRI data, there is a need to a model which is able to transfer data from neuronal level to a visible data like Blood-Oxygen level dependent (BOLD) signal. DCM uses a haemodynamic balloon model (HD) to represent this data transfer. The hemodynamic model must be so that the parameters of neural and BOLD signal be the same. It should be noted that what is looked for is not the BOLD signal but the neuronal activity. In this study, as the first step, we did preprocessing of MR images and after ROI`s are created using the program MARSBAR. Ten ROIs, which are thought to have connections between them are selected by considering the stimulations used in the experiments in obtaining the data used in this thesis. The data used contains fMRI images of 11 healthy subjects. Stimulations of experiment are applied to images got from group analysis of 11 healthy subjects. These Stimulations are then used in preparing the design matrix and the parameters related to DCM. These parameters are the values related to connection matrices defining bilinear dynamic model on ROI. Bayesian method is used to select best model between all these models. Another method of PPI is also applied to analyze effective connectivity between 10 ROIs. This method considers two issues of physiological and psychological effects. Like DCM, the preprocessing steps and ROI selection is done for PPI and hemodynamic model is designed for this method. Neural and hemodynamic responses of ROIs are compared using this method.
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Choudhary, Vijay Singh 1979. "A client-server software application for statistical analysis of fMRI data." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30141.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.Includes bibliographical references (leaves 63-66).
Statistical analysis methods used for interrogating functional magnetic resonance imaging (fMRI) data are complex and continually evolving. There exist a scarcity of educational material for fMRI. Thus, an instructional based software application was developed for teaching the fundamentals of statistical analysis in fMRI. For wider accessibility, the application was designed with a client/server architecture. The Java client has a layered design for flexibility and a nice Graphical User Interface (GUI) for user interaction. The application client can be deployed to multiple platforms in heterogeneous and distributed network. The future possibility of adding real-time data processing capabilities in the server led us to choose CGI/Perl/C as server side technologies. The client and server communicates via a simple protocol through the Apache Web Server. The application provides students with opportunities for hands-on exploration of the key concepts using phantom data as well as sample human fMRI data. The simulation allows students to control relevant parameters and observe intermediate results for each step in the analysis stream (spatial smoothing, motion correction, statistical model parameter selection etc.). Eventually this software tool and the accompanying tutorial will be disseminated to researchers across the globe via Biomedical Informatics Research Network (BIRN) portal.
by Vijay Singh Choudhary.
S.M.
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Lennartz, Carolin [Verfasser], and Jürgen [Akademischer Betreuer] Hennig. "Inference of sparse cerebral connectivity from high temporal resolution fMRI data." Freiburg : Universität, 2020. http://d-nb.info/1216826684/34.
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Di, Bono Maria Grazia. "Beyond mind reading: advanced machine learning techniques for FMRI data analysis." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426149.
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The advent of functional Magnetic Resonance Imaging (fMRI) has significantly improved the knowledge about the neural correlates of perceptual and cognitive processes. The aim of this thesis is to discuss the characteristics of different approaches for fMRI data analysis, from the conventional mass univariate analysis (General Linear Model - GLM), to the multivariate analysis (i.e., data-driven and pattern based methods), and propose a novel, advanced method (Functional ANOVA Models of Gaussian Kernels - FAM-GK) for the analysis of fMRI data acquired in the context of fast event-related experiments. FAM-GK is an embedded method for voxel selection and is able to capture the nonlinear spatio-temporal dynamics of the BOLD signals by performing nonlinear estimation of the experimental conditions. The impact of crucial aspects concerning the use of pattern recognition methods on the fMRI data analysis, such as voxel selection, the choice of classifier and tuning parameters, the cross-validation techniques, are investigated and discussed by analysing the results obtained in four neuroimaging case studies.
In a first study, we explore the robustness of nonlinear Support Vector regression (SVR), combined with a filter approach for voxel selection, in the case of an extremely complex regression problem, in which we had to predict the subjective experience of participants immersed in a virtual reality environment.
In a second study, we face the problem of voxel selection combined with the choice of the best classifier, and we propose a methodology based on genetic algorithms and nonlinear support vector machine (GA-SVM) efficiently combined in a wrapper approach.
In a third study we compare three pattern recognition techniques (i.e., linear SVM, nonlinear SVM, and FAM-GK) for investigating the neural correlates of the representation of numerical and non-numerical ordered sequences (i.e., numbers and letters) in the horizontal segment of the Intraparietal Sulcus (hIPS). The FAM-GK method significantly outperformed the other two classifiers. The results show a partial overlapping of the two representation systems suggesting the existence of neural substrates in hIPS codifying the cardinal and the ordinal dimensions of numbers and letters in a partially independent way.
Finally, in the last preliminary study, we tested the same three pattern recognition methods on fMRI data acquired in the context of a fast event-related experiment. The FAM-GK method shows a very high performance, whereas the other classifiers fail to achieve an acceptable classification performance.L’avvento della tecnica di Risonanza Magnetica funzionale (fMRI) ha notevolmente migliorato le conoscenze sui correlati neurali sottostanti i processi cognitivi. Obiettivo di questa tesi è stato quello di illustrare e discutere criticamente le caratteristiche dei diversi approcci per l’analisi dei dati fMRI, dai metodi convenzionali di analisi univariata (General Linear Model - GLM) ai metodi di analisi multivariata (metodi data-driven e di pattern recognition), proponendo una nuova tecnica avanzata (Functional ANOVA Models of Gaussian Kernels - FAM-GK) per l’analisi di dati fMRI acquisiti con paradigmi sperimentali fast event-related. FAM-GK è un metodo embedded per la selezione dei voxels, che è in grado di catturare le dinamiche non lineari spazio-temporali del segnale BOLD, effettuando stime non lineari delle condizioni sperimentali. L’impatto degli aspetti critici riguardanti l’uso di tecniche di pattern recognition sull’analisi di dati fMRI, tra cui la selezione dei voxels, la scelta del classificatore e dei suoi parametri di apprendimento, le tecniche di cross-validation, sono valutati e discussi analizzando i risultati ottenuti in quattro casi di studio. In un primo studio, abbiamo indagato la robustezza di Support Vector regression (SVR) non lineare, integrato con un approccio di tipo filter per la selezione dei voxels, in un caso di un problema di regressione estremamente complesso, in cui dovevamo predire l’esperienza soggettiva di alcuni partecipanti immersi in un ambiente di realtà virtuale. In un secondo studio, abbiamo affrontato il problema della selezione dei voxels integrato con la scelta del miglior classificatore, proponendo un metodo basato sugli algoritmi genetici e SVM non lineare (GA-SVM) in un approccio di tipo wrapper. In un terzo studio, abbiamo confrontato tre metodi di pattern recognition (SVM lineare, SVM non lineare e FAM-GK) per indagare i correlati neurali della rappresentazione di sequenze ordinate numeriche e non-numeriche (numeri e lettere) a livello del segmento orizzontale del solco intraparitale (hIPS). Le prestazioni di classificazione di FAM-GK sono risultate essere significativamente superiori rispetto a quelle degli alti due classificatori. I risultati hanno mostrato una parziale sovrapposizione dei due sistemi di rappresentazione, suggerendo l’esistenza di substrati neurali nelle regioni hIPS che codificano le dimensioni cardinale e ordinale dei numeri e delle lettere in modo parzialmente indipendente. Infine, nel quarto studio preliminare, abbiamo testato e confrontato gli stessi tre classificatori su dati fMRI acquisiti durante un esperimento fast event-related. FAM-GK ha mostrato delle prestazioni di classificazione piuttosto elevate, mentre le prestazioni degli altri due classificatori sono risultate essere di poco superiori al caso.
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KIM, NAMHEE. "A semiparametric statistical approach to Functional MRI data." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262295445.
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Budde, Kiran Kumar. "A Matlab Toolbox for fMRI Data Analysis: Detection, Estimation and Brain Connectivity." Thesis, Linköpings universitet, Datorseende, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-81314.
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Functional Magnetic Resonance Imaging (fMRI) is one of the best techniques for neuroimaging and has revolutionized the way to understand the brain functions. It measures the changes in the blood oxygen level-dependent (BOLD) signal which is related to the neuronal activity. Complexity of the data, presence of different types of noises and the massive amount of data makes the fMRI data analysis a challenging one. It demands efficient signal processing and statistical analysis methods. The inference of the analysis is used by the physicians, neurologists and researchers for better understanding of the brain functions. The purpose of this study is to design a toolbox for fMRI data analysis. It includes methods to detect the brain activity maps, estimation of the hemodynamic response (HDR) and the connectivity of the brain structures. This toolbox provides methods for detection of activated brain regions measured with Bayesian estimator. Results are compared with the conventional methods such as t-test, ordinary least squares (OLS) and weighted least squares (WLS). Brain activation and HDR are estimated with linear adaptive model and nonlinear method based on radial basis function (RBF) neural network. Nonlinear autoregressive with exogenous inputs (NARX) neural network is developed to model the dynamics of the fMRI data. This toolbox also provides methods to brain connectivity such as functional connectivity and effective connectivity. These methods are examined on simulated and real fMRI datasets.
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Bränberg, Stefan. "Computing network centrality measures on fMRI data using fully weighted adjacency matrices." Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128177.
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A lot of interesting research is currently being done in the field of neuroscience, a recent subject being the effort to analyse the the human brain connectome and its functional connectivity. One way this is done is by applying graph-theory based network analysis, such as centrality, on data from fMRI measurements. This involves creating a graph representation from a correlation matrix containing the correlations over time between all measured voxels. Since the input data can be very big, this results in computations that are too memory and time consuming for an ordinary computer. Researchers have used different techniques to work around this problem, examples include thresholding correlations when creating the adjacency matrix and using a smaller input data with lower resolution.This thesis proposes three ways to compute two different centrality measures, degree centrality and eigenvector centrality, on fully weighted adjacency matrices that are built from complete correlation matrices computed from high resolution input data. The first is reducing the problem by doing the calculations in optimal order and avoiding the construction of the large correlation matrix. The second solution is to distribute and do the computations in parallel on a large computer cluster using MPI. The third solution is to calculate as large sets as possible on an ordinary laptop using shared-memory parallelism with OpenMP. Algorithms are presented for the different solutions, and the effectiveness of the implementations of them is tested.
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Williamitis, Joseph M. "Using fMRI BOLD Imaging to Motion-Correct Associated, Simultaneously Imaged PET Data." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1620585748146734.
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Keller, Merlin. "Selection of a model of cerebral activity for fMRI Group Data analysis." Paris 11, 2010. http://www.theses.fr/2010PA112045.
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L’imagerie par résonance magnétique fonctionnelle (IRMf ) permet d’acquérir des images tridimensionnelles de l’activité cérébrale d’un sujet soumis à une séquence de stimulations sensorielles. L’analyse statistique des ces données permet de détecter les aires cérébrales actives en réponse aux différentes stimulations. Lorsque plusieurs sujets ont été recrutés pour une expérience, l’analyse de groupe consiste à généraliser les résultats individuels à la population d’intérêt dont sont issus les sujets. La variabilité morphologique du cerveau humain rend cependant la comparaison des images acquises sur les différents sujets problématique. L’approche usuelle pour contrer cette difficulté consiste à recaler les sujets dans un Référentiel commun, puis de comparer les cerveau séparément en chaque point de ce référentiel. Cette étape de recalage n’ étant jamais parfaite, il en résulte une incertitude sur la localisation spatiale de chaque sujet. Nous proposons dans un premier temps d’ étendre le modèle classique d’analyse de groupe afin de prendre en compte cette incertitude spatiale. Dans un deuxième temps, nous développons à partir de ce modèle une nouvelle approche de détection d’aires cérébrales actives, basée sur des régions d’intérêt prédéfinies plutôt que sur les procédures de seuillage couramment utiliséesThis thesis is dedicated to the statistical analysis of multi-sub ject fMRI data, with the purpose of identifying bain structures involved in certain cognitive or sensori-motor tasks, in a reproducible way across sub jects. To overcome certain limitations of standard voxel-based testing methods, as implemented in the Statistical Parametric Mapping (SPM) software, we introduce a Bayesian model selection approach to this problem, meaning that the most probable model of cerebral activity given the data is selected from a pre-defined collection of possible models. Based on a parcellation of the brain volume into functionally homogeneous regions, each model corresponds to a partition of the regions into those involved in the task under study and those inactive. This allows to incorporate prior information, and avoids the dependence of the SPM-like approach on an arbitrary threshold, called the clusterforming threshold, to define active regions. By controlling a Bayesian risk, our approach balances false positive and false negative risk control. Urthermore, it is based on a generative model that accounts for the spatial uncertainty on the localization of individual effects, due to spatial normalization errors. On both simulated and real fMRI datasets, we show that this new paradigm corrects several biases of the SPM-like approach, which either swells or misses the different active regions, depending on the choice of a cluster-forming threshold
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Kim, Yongwook Bryce. "Comparison of data-driven analysis methods for identification of functional connectivity in fMRI." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/43036.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 97-103).
Data-driven analysis methods, such as independent component analysis (ICA) and clustering, have found a fruitful application in the analysis of functional magnetic resonance imaging (fMRI) data for identifying functionally connected brain networks. Unlike the traditional regression-based hypothesis-driven analysis methods, the principal advantage of data-driven methods is their applicability to experimental paradigms in the absence of a priori model of brain activity. Although ICA and clustering rely on very different assumptions on the underlying distributions, they produce surprisingly similar results for signals with large variation. The main goal of this thesis is to understand the factors that contribute to the differences in the identification of functional connectivity based on ICA and a more general version of clustering, Gaussian mixture model (GMM), and their relations. We provide a detailed empirical comparison of ICA and clustering based on GMM. We introduce a component-wise matching and comparison scheme of resulting ICA and GMM components based on their correlations. We apply this scheme to the synthetic fMRI data and investigate the influence of noise and length of time course on the performance of ICA and GMM, comparing with ground truth and with each other. For the real fMRI data, we propose a method of choosing a threshold to determine which of resulting components are meaningful to compare using the cumulative distribution function of their empirical correlations. In addition, we present an alternate method to model selection for selecting the optimal total number of components for ICA and GMM using the task-related and contrast functions. For extracting task-related components, we find that GMM outperforms ICA when the total number of components are less then ten and the performance between ICA and GMM is almost identical for larger numbers of the total components. Furthermore, we observe that about a third of the components of each model are meaningful to be compared to the components of the other.
by Yongwook Bryce Kim.
S.M.
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Mazzonetto, Ilaria. "EEG source reconstruction accuracy and integration of simultaneous EEG-fMRI resting state data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3422668.
Full textAbstract:
The resting state functional magnetic resonance imaging (fMRI) approach has allowed to investigate the large scale organization of processing systems in the human brain, revealing that it can be viewed as an integrative network of functionally interacting regions. However, to date the neuronal basis of the fluctuations of the fMRI signal at rest are not fully understood, preventing the possibility to elucidate their functional role. In this scenario, the integration with information derived from electroencephalography (EEG) is very useful, since conversely from fMRI, EEG represents a direct measure of neuronal activity. EEG-fMRI resting state studies investigating the correlation between fMRI signals and corresponding global EEG spectral characteristics in single spectral bands have provided a certain degree of inconsistency in the results. This may be due to the fact that the distinct functional networks involve more than a single frequency band and therefore analysis of simultaneous EEG/fMRI data should consider the whole frequency spectrum. A couple of studies have been performed in this directions but they either did not investigate how the scalp distribution of the EEG spectral metrics affects the patterns of correlations between EEG spectral dynamics and fMRI-derived resting state network or did not identify the specific scalp regions that specifically determined the pattern of observed results. To overcome this gap, with the aim to identify specific spatio-spectral fingerprints of distinct networks, a first study was conducted using an analytical approach that allows to take into account the interplay between the different EEG frequency bands and the corresponding topographic distribution within each network. Specifically, this approach was applied to four sub-components of the Default Mode Network (DMN). Results revealed for the first time the presence of distinctive subcomponent-specific spatial-frequency patterns of correlation between the fMRI signal and EEG rhythm. It should however be noted that spatial resolution of the EEG signal is too low to reliably infer about the location of the involved EEG sources. Therefore, a further step forward could be to try extending the findings of the first study in this direction by performing a source estimation study. Since it is not clear whether the 64 channels EEG system employed in the first study can provide adequate localization performance as regard our regions of interest, an investigation of the source reconstruction accuracy throughout the brain was performed in a second study. Specifically, the 64-channel montage was compared to 32-channel montage, the standard in the clinical practice, as well as to 128-channel montage and to 256- channel montage, considered as the upper reference point. Unlike previous studies, source performances were evaluated all over the cortical grey matter. Results indicate that the localization of the cortical sources of the spatio-spectral fingerprints revealed by the previous study can be adequately inferred by using 64 channels, but a confirmation study with a 128, or even better 256, channels montage is needed. Moreover, particular attention should be paid to investigate deep regions, where localization performance is worse regardless the number of electrodes used.Gli studi di risonanza magnetica funzionale (fMRI) in resting state hanno permesso di studiare l'organizzazione del cervello umano su ampia scala, rivelando che esso può essere visto come una rete di regioni funzionalmente connesse (networks). Ad oggi, però, le basi neurali delle fluttuazioni del segnale fMRI nelle varie regioni nella condizione di resting non sono pienamente comprese e ciò impedisce di chiarire il loro ruolo funzionale. In questo scenario, l'integrazione con l'informazione derivata dall'elettroencefalografia (EEG) è molto utile poiché questa,contrariamente alla risonanza magnetica funzionale, fornisce una misura diretta dell'attività neuronale. Finora, gli studi EEG-fMRI in condizioni di riposo che valutano le correlazioni fra il segnale fMRI e le caratteristiche spettrali del segnale EEG in una singola banda di interesse hanno portato a risultati tra loro incosistenti. Questo può essere dovuto al fatto che network funzionalmente distinti possono coinvolgere più di una singola banda, e quindi andrebbe analizzato l'intero spettro delle frequenze. Alcuni studi sono stati condotti in questa direzione ma o non hanno studiato come la distribuzione delle frequenze sullo scalpo influenza i pattern di correlazioni, o non hanno individuato quali regioni dello scalpo determinano in maniera specifica il pattern dei risultati osservati. Per superare questo limite, con lo scopo di identificare gli specifici correlati spazio-spettrali dei vari networks, un primo studio è stato condotto usando un approccio analitico che permette di considerare la relazione tre le differenti bande di frequenza EEG e la corrispondente distribuzione topografica all'interno di ciascun network. Specificatamente, questo approccio è stato applicato a quattro sottocomponenti del Default Mode Network. I risultati hanno rilevato per la prima volta la presenza di specifici pattern spazio-spettrali di correlazioni tra il segnale fMRI di un network e i diversi ritmi EEG. Dato che la risoluzione spaziale dell'EEG non permette di fare precise inferenze sulla localizzazione spaziale delle sorgenti neurali corrispondenti, un ulteriore passo in avanti potrebbe essere quello di estendere questi risultati con uno studio di ricostruzione delle sorgenti corticali. Inoltre, visto che non è chiaro se il sistema EEG a 64 canali utilizzato nel primo studio possa fornire performance accettabili, è stato fatto un secondo studio volto a valutare l’adeguatezza di questo sistema allo scopo. Nello specifico, l'accuratezza nel localizzare le sorgenti EEG ottenuta con il montaggio a 64 canali è stata confrontata con quelle ottenute con montaggi a 32 canali, lo standard in clinica, a 128 e a 256 canali. Diversamente da studi precedenti, le performance sono state valutate su tutto lo scalpo. I risultati indicano che le sorgenti corticali dei correlati spazio-spettrali dei network individuati nello studio precedente possono essere localizzate con una risoluzione spaziale adeguata usando 64 canali, sebbene sia necessario uno studio confermativo con 128 o 256 canali. Inoltre, andrebbe prestata particolare attenzione nel caso vengano investigate regioni cerebrali più profonde, nelle queli le performance sono basse a prescindere dal numero di canali utilizzato.
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Lashkari, Danial. "In search of functional specificity in the brain : generative models for group fMRI data." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/65968.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student submitted PDF version of thesis.
Includes bibliographical references (p. 151-174).
In this thesis, we develop an exploratory framework for design and analysis of fMRI studies. In our framework, the experimenter presents subjects with a broad set of stimuli/tasks relevant to the domain under study. The analysis method then automatically searches for likely patterns of functional specificity in the resulting data. This is in contrast to the traditional confirmatory approaches that require the experimenter to specify a narrow hypothesis a priori and aims to localize areas of the brain whose activation pattern agrees with the hypothesized response. To validate the hypothesis, it is usually assumed that detected areas should appear in consistent anatomical locations across subjects. Our approach relaxes the conventional anatomical consistency constraint to discover networks of functionally homogeneous but anatomically variable areas. Our analysis method relies on generative models that explain fMRI data across the group as collections of brain locations with similar profiles of functional specificity. We refer to each such collection as a functional system and model it as a component of a mixture model for the data. The search for patterns of specificity corresponds to inference on the hidden variables of the model based on the observed fMRI data. We also develop a nonparametric hierarchical Bayesian model for group fMRI data that integrates the mixture model prior over activations with a model for fMRI signals. We apply the algorithms in a study of high level vision where we consider a large space of patterns of category selectivity over 69 distinct images. The analysis successfully discovers previously characterized face, scene, and body selective areas, among a few others, as the most dominant patterns in the data. This finding suggests that our approach can be employed to search for novel patterns of functional specificity in high level perception and cognition.
by Danial Lashkari.
Ph.D.
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Mengucci, Carlo. "WISDoM: Wishart Distributed Matrices Multiple Order classification. Definition and application to fMRI resting state data." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15865/.
Full textAbstract:
In this work we introduce the Wishart Distributed Matrices Multiple Order Classification (WISDoM) method.
The WISDoM Classification method consists of a pipeline for single feature analysis, supervised learning,cross validation and classification for any problems whose elements can be tied to a symmetric positive-definite matrix representation.
The general idea is for informations about properties of a certain system contained in a symmetric positive-definite matrix representation (i.e covariance and correlation matrices) to be extracted by modelling an estimated distribution for the expected classes of a given problem.
The application to fMRI data classification and clustering processing follows naturally: the WISDoM classification method has been tested on the ADNI2 (Alzheimer's Disease Neuroimaging Initiative) database.
The goal was to achieve good classification performances between Alzheimer's Disease diagnosed patients (AD) and Normal Control (NC) subjects, while retaining informations on which features were the most informative decision-wise.
In our work, the informations about topological properties contained in ADNI2 functional correlation matrices are extracted by modelling an estimated Wishart distribution for the expected diagnostical groups AD and NC, and allowed a complete separation between the two groups.
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Rossi, Magi Lorenzo. "Graph-based analysis of brain resting-state fMRI data in nocturnal frontal lobe epileptic patients." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8332/.
Full textAbstract:
Il lavoro che ho sviluppato presso l'unità di RM funzionale del Policlinico S.Orsola-Malpighi, DIBINEM, è incentrato sull'analisi dati di resting state - functional Magnetic Resonance Imaging (rs-fMRI) mediante l'utilizzo della graph theory, con lo scopo di valutare eventuali differenze in termini di connettività cerebrale funzionale tra un campione di pazienti affetti da Nocturnal Frontal Lobe Epilepsy (NFLE) ed uno di controlli sani.
L'epilessia frontale notturna è una peculiare forma di epilessia caratterizzata da crisi che si verificano quasi esclusivamente durante il sonno notturno. Queste sono contraddistinte da comportamenti motori, prevalentemente distonici, spesso complessi, e talora a semiologia bizzarra.
L'fMRI è una metodica di neuroimaging avanzata che permette di misurare indirettamente l'attività neuronale. Tutti i soggetti sono stati studiati in condizioni di resting-state, ossia di veglia rilassata.
In particolare mi sono occupato di analizzare i dati fMRI con un approccio innovativo in campo clinico-neurologico, rappresentato dalla graph theory. I grafi sono definiti come strutture matematiche costituite da nodi e links, che trovano applicazione in molti campi di studio per la modellizzazione di strutture di diverso tipo.
La costruzione di un grafo cerebrale per ogni partecipante allo studio ha rappresentato la parte centrale di questo lavoro. L'obiettivo è stato quello di definire le connessioni funzionali tra le diverse aree del cervello mediante l'utilizzo di un network. Il processo di modellizzazione ha permesso di valutare i grafi neurali mediante il calcolo di parametri topologici che ne caratterizzano struttura ed organizzazione.
Le misure calcolate in questa analisi preliminare non hanno evidenziato differenze nelle proprietà globali tra i grafi dei pazienti e quelli dei controlli. Alterazioni locali sono state invece riscontrate nei pazienti, rispetto ai controlli, in aree della sostanza grigia profonda, del sistema limbico e delle regioni frontali, le quali rientrano tra quelle ipotizzate essere coinvolte nella fisiopatologia di questa peculiare forma di epilessia.
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Riemenschneider, Bruno [Verfasser], and Jürgen [Akademischer Betreuer] Hennig. "Highly accelerated fMRI using non-cartesian trajectories: enhanced data acquisition and enabling real-time reconstruction." Freiburg : Universität, 2019. http://d-nb.info/120482617X/34.
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Sobotková, Marika. "Neurofeedback aktivity amygdaly pomocí funkční magnetické rezonance." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-378028.
Full textAbstract:
The aim of this diploma thesis is real-time fMRI neurofeedback. In this case, the activity of amygdala is monitored and controled by an emotional regulatory visual task. A procedure to process measured data online and to incorporate it into the stimulus protocol has been proposed. A pilot study was carried out. Offline analysis of measured data was performed, including evaluation of the results of the analysis. The data is processed in MATLAB using the functions of the SPM library.
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Fountalis, Ilias. "From spatio-temporal data to a weighted and lagged network between functional domains: Applications in climate and neuroscience." Diss., Georgia Institute of Technology, 2016. http://hdl.handle.net/1853/55008.
Full textAbstract:
Spatio-temporal data have become increasingly prevalent and important for both science and enterprises. Such data are typically embedded in a grid with a resolution larger than the true dimensionality of the underlying system. One major task is to identify the distinct semi-autonomous functional components of the spatio-temporal system and to infer their interconnections. In this thesis, we propose two methods that identify the functional components of a spatio-temporal system. Next, an edge inference process identifies the possibly lagged and weighted connections between the system’s components. The weight of an edge accounts for the magnitude of the interaction between two components; the lag associated with each edge accounts for the temporal ordering of these interactions.
The first method, geo-Cluster, infers the spatial components as “areas”; spatially contiguous, non-overlapping, sets of grid cells satisfying a homogeneity constraint in terms of their average pair-wise cross-correlation. However, in real physical systems the underlying physical components might overlap. To this end we also propose
δ-MAPS, a method that first identifies the epicenters of activity of the functional components of the system and then creates domains – spatially contiguous, possibly overlapping, sets of grid cells that satisfy the same homogeneity constraint.
The proposed framework is applied in climate science and neuroscience. We show how these methods can be used to evaluate cutting edge climate models and identify lagged relationships between different climate regions. In the context of neuroscience, the method successfully identifies well-known “resting state networks” as well as a few areas forming the backbone of the functional cortical network. Finally, we contrast the proposed methods to dimensionality reduction techniques (e.g., clustering PCA/ICA) and show their limitations.
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Song, Andrew Hyungsuk. "Closer look at the fMRI data analysis pipeline and its application in anesthesia resting state experiment." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/113158.
Full textAbstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 91-93).
The main focus of the thesis is the resting state fMRI data analysis, with much emphasis on the anesthesia fMRI experiments. Under this central topic, three separate themes are developed: resting state fMRI data analysis overview, improved denoising techniques, and application to the Dexmedetomidine experiment data. In the first part, important and confusing resting state data analysis steps are explored indepth, focusing on how and why the pipeline is different from that of the task-based fMRI. In the second part, the Principal Component Analysis (PCA) based denoising technique is introduced and compared against the conventional fMRI denoising techniques. Finally, with the PCA denoising technique, the functional connectivity of the brainstem with the brain is assessed for the Dexmedetomidine-induced unconscious subjects. We found that the functional connectivity between the Locus Ceruleus (LC) in the brainstem and the Thalamus & Posterior Cingulate Cortex (PCC) is the neural correlates of the Dexmedetomidine-induced unconsciousness.
by Andrew Hyungsuk Song.
M. Eng.
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Dauvermann, Maria Regina. "Investigation into functional large-scale networks in individuals with schizophrenia using fMRI data and Dynamic Causal Modelling." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/10022.
Full textAbstract:
Schizophrenia is a complex and severe psychiatric disorder with positive symptoms, negative symptoms and cognitive deficits. Preclinical neurobiological studies showed that alterations of dopaminergic and glutamatergic neurotransmitter circuits involving the prefrontal cortex resulted in cognitive impairment such as working memory. Functional activation and functional connectivity findings of functional Magnetic Resonance Imaging (fMRI) data provided support for prefrontal dysfunction during fMRI working memory tasks in individuals with schizophrenia. However, these findings do not offer a neurobiological interpretation of the fMRI data. Biophysical modelling of functional large-scale networks has been designed for the analysis of fMRI data, which can be interpreted in a mechanistic way. This approach may enable the interpretation of fMRI data in terms of altered synaptic plasticity processes found in schizophrenia. One such process is gating mechanism, which has been shown to be altered for the thalamo-cortical and meso-cortical connection in schizophrenia. The primary aim of the thesis was to investigate altered synaptic plasticity and gating mechanisms with Dynamic Causal Modelling (DCM) within functional large-scale networks during two fMRI tasks in individuals with schizophrenia. Applying nonlinear DCM to the verbal fluency fMRI task of the Edinburgh High Risk Study, we showed that the connection strengths with nonlinear modulation for the thalamo-cortical connection was reduced in subjects at high familial risk of schizophrenia when compared to healthy controls. These results suggest that nonlinear DCM enables the investigation of altered synaptic plasticity and gating mechanism from fMRI data. For the Scottish Family Mental Health Study, we reported two different optimal linear models for individuals with established schizophrenia (EST) and healthy controls during working memory function. We suggested that this result may indicate that EST and healthy controls used different functional large-scale networks. The results of nonlinear DCM analyses may suggest that gating mechanism was intact in EST and healthy controls. In conclusion, the results presented in this thesis give evidence for the role of synaptic plasticity processes as assessed in functional large-scale networks during cognitive tasks in individuals with schizophrenia.
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Richter, Nils [Verfasser]. "On the use of Single-Trial Response Time in the GLM Analysis of fMRI Data / Nils Richter." Köln : Deutsche Zentralbibliothek für Medizin, 2011. http://d-nb.info/101787090X/34.
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Rydell, Joakim. "Advanced MRI Data Processing." Doctoral thesis, Linköping : Department of Biomedical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10038.
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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/.
Full textAbstract:
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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Yoshida, Kosuke. "Interpretable machine learning approaches to high-dimensional data and their applications to biomedical engineering problems." Kyoto University, 2018. http://hdl.handle.net/2433/232416.
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NIGRI, ANNA. "Quality data assessment and improvement in pre-processing pipeline to minimize impact of spurious signals in functional magnetic imaging (fMRI)." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2911412.
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In the recent years, the field of quality data assessment and signal denoising in functional magnetic resonance imaging (fMRI) is rapidly evolving and the identification and reduction of spurious signal with pre-processing pipeline is one of the most discussed topic. In particular, subject motion or physiological signals, such as respiratory or/and cardiac pulsatility, were showed to introduce false-positive activations in subsequent statistical analyses.
Different measures for the evaluation of the impact of motion related artefacts, such as frame-wise displacement and root mean square of movement parameters, and the reduction of these artefacts with different approaches, such as linear regression of nuisance signals and scrubbing or censoring procedure, were introduced. However, we identify two main drawbacks: i) the different measures used for the evaluation of motion artefacts were based on user-dependent thresholds, and ii) each study described and applied their own pre-processing pipeline. Few studies analysed the effect of these different pipelines on subsequent analyses methods in task-based fMRI.The first aim of the study is to obtain a tool for motion fMRI data assessment, based on auto-calibrated procedures, to detect outlier subjects and outliers volumes, targeted on each investigated sample to ensure homogeneity of data for motion.
The second aim is to compare the impact of different pre-processing pipelines on task-based fMRI using GLM based on recent advances in resting state fMRI preprocessing pipelines. Different output measures based on signal variability and task strength were used for the assessment.
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Barnathan, Michael. "Mining Complex High-Order Datasets." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/82058.
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Computer and Information SciencePh.D.
Selection of an appropriate structure for storage and analysis of complex datasets is a vital but often overlooked decision in the design of data mining and machine learning experiments. Most present techniques impose a matrix structure on the dataset, with rows representing observations and columns representing features. While this assumption is reasonable when features are scalar and do not exhibit co-dependence, the matrix data model becomes inappropriate when dependencies between non-target features must be modeled in parallel, or when features naturally take the form of higher-order multilinear structures. Such datasets particularly abound in functional medical imaging modalities, such as fMRI, where accurate integration of both spatial and temporal information is critical. Although necessary to take full advantage of the high-order structure of these datasets and built on well-studied mathematical tools, tensor analysis methodologies have only recently entered widespread use in the data mining community and remain relatively absent from the literature within the biomedical domain. Furthermore, naive tensor approaches suffer from fundamental efficiency problems which limit their practical use in large-scale high-order mining and do not capture local neighborhoods necessary for accurate spatiotemporal analysis. To address these issues, a comprehensive framework based on wavelet analysis, tensor decomposition, and the WaveCluster algorithm is proposed for addressing the problems of preprocessing, classification, clustering, compression, feature extraction, and latent concept discovery on large-scale high-order datasets, with a particular emphasis on applications in computer-assisted diagnosis. Our framework is evaluated on a 9.3 GB fMRI motor task dataset of both high dimensionality and high order, performing favorably against traditional voxelwise and spectral methods of analysis, discovering latent concepts suggestive of subject handedness, and reducing space and time complexities by up to two orders of magnitude. Novel wavelet and tensor tools are derived in the course of this work, including a novel formulation of an r-dimensional wavelet transform in terms of elementary tensor operations and an enhanced WaveCluster algorithm capable of clustering real-valued as well as binary data. Sparseness-exploiting properties are demonstrated and variations of core algorithms for specialized tasks such as image segmentation are presented.
Temple University--Theses
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Tan, Lirong. "Identification of Disease Biomarkers from Brain fMRI Data using Machine Learning Techniques: Applications in Sensorineural Hearing Loss and Attention Deficit Hyperactivity Disorder." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447689755.
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Wirsich, Jonathan. "EEG-fMRI and dMRI data fusion in healthy subjects and temporal lobe epilepsy : towards a trimodal structure-function network characterization of the human brain." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM5040.
Full textAbstract:
La caractérisation de la structure du cerveau humain et les motifs fonctionnelles qu’il fait apparaitre est un défi central pour une meilleure compréhension des pathologies du réseau cérébral telle que l’épilepsie du lobe temporal. Cette caractérisation pourrait aider à améliorer la prédictibilité clinique des résultats d’une chirurgie visant à traiter l’épilepsie.Le fonctionnement du cerveau peut être étudié par l’électroencéphalographie (EEG) ou par l’imagerie de résonance magnétique fonctionnelle (IRMf), alors que la structure peut être caractérisé par l’IRM de diffusion (IRMd). Nous avons utilisé ces modalités pour mesurer le fonctionnement du cerveau pendant une tache de reconnaissance de visages et pendant le repos dans le but de faire le lien entre les modalités d’une façon optimale en termes de résolution temporale et spatiale. Avec cette approche on a mis en évidence une perturbation des relations structure-fonction chez les patients épileptiques.Ce travail a contribué à améliorer la compréhension de l’épilepsie comme une maladie de réseau qui affecte le cerveau à large échelle et non pas au niveau d’un foyer épileptique local. Dans le futur, ces résultats pourraient être utilisés pour guider des interventions chirurgicales mais ils fournissent également des approches nouvelles pour évaluer des traitements pharmacologiques selon ses implications fonctionnelles à l’échelle du cerveau entierThe understanding human brain structure and the function patterns arising from it is a central challenge to better characterize brain network pathologies such as temporal lobe epilepsies, which could help to improve the clinical predictability of epileptic surgery outcome.Brain functioning can be accessed by both electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), while brain structure can be measured with diffusion MRI (dMRI). We use these modalities to measure brain functioning during a face recognition task and in rest in order to link the different modalities in an optimal temporal and spatial manner. We discovered disruption of the network processing famous faces as well a disruption of the structure-function relation during rest in epileptic patients.This work broadened the understanding of epilepsy as a network disease that changes the brain on a large scale not limited to a local epileptic focus. In the future these results could be used to guide clinical intervention during epilepsy surgery but also they provide new approaches to evaluate pharmacological treatment on its functional implications on a whole brain scale
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Tucciarelli, Raffaele. "Characterizing the spatiotemporal profile and the level of abstractness of action representations: neural decoding of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/368799.
Full textAbstract:
When we observe other people's actions, a network of temporal, parietal and frontal regions is recruited, known as action observation network (AON). This network includes areas that have been reported to be involved when we perform actions ourselves. Such findings support the view that action understanding occurs by simulating actions in our own motor system (motor theories of action understanding). Alternatively, it has been argued that actions are understood based on a perceptual analysis, with access to action knowledge stored in the conceptual system (cognitive theories of action understanding). It has been argued earlier that areas that play a crucial role for action understanding should be able to (a) distinguish between different actions, and (b) generalize across the ways in which the action is performed (e.g. Dinstein, Thomas, Behrmann, & Heeger, 2008; Oosterhof, Tipper, & Downing, 2013; Caramazza, Anzelotti, Strnad, & Lingnau, 2014). Here we argue that one additional criterion needs to be met: an area that plays a crucial role for action understanding should have access to such abstract action information early, around the time when the action is recognized. An area that has access to abstract action information after the action has been recognized is unlikely to contribute to the process of action understanding.
In this thesis, I report three neuroimaging studies in which we used magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to characterize the temporal dynamics of abstract representations of observed actions (Study 1 and 2), meaning that generalize across lower level dimensions, and to characterize the type of information encoded in the regions of the AON (Study 3).
Specifically, in Study 1 we examined where in the brain and at which point in time it is possible to distinguish between pointing and grasping actions irrespective of the way in which they are performed (reach direction, effector) using MEG in combination with multivariate pattern analysis (MVPA) and source analysis. We show that regions in the left lateral occipitotemporal cortex (LOTC) have the earliest access to abstract action representations. By contrast, precentral regions, though recruited relatively early, have access to abstract action representations substantially later than left LOTC. In Study 2, we tested the temporal dynamics of the neural decoding related to the oscillatory activity induced by observation of actions performed with different effectors (hand, foot). We observed that temporal regions are able to discriminate all the presented actions before effector-related decoding within effector-specific motor regions. Finally, in Study 3 we investigated what aspect of an action is encoded within the regions of the AON. Object-directed actions induce a change of states, e.g. opening a bottle means changing its state from closed to open. It is still unclear how and in which brain regions these neural representations are encoded. Using fMRI-based multivoxel pattern decoding, we aimed at dissociating the neural representations of states and action functions. Participants observed stills of objects (e.g., window blinds) that were in either open or closed states, and videos of actions involving the same objects, i.e., open or close window. Action videos could show the object manipulation only (invisible change), or the complete action scene (visible change). This design allowed us to detect neural representations of action scenes, states and action functions independently of each other. We found different sub-regions within LOTC containing information related to object states, action functions, or both. These findings provide important information regarding the organization of action semantics in the brain and the role of LOTC in action understanding.
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Tucciarelli, Raffaele. "Characterizing the spatiotemporal profile and the level of abstractness of action representations: neural decoding of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) data." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1592/1/PhD_thesis-_Raffaele_Tucciarelli.pdf.
Full textAbstract:
When we observe other people's actions, a network of temporal, parietal and frontal regions is recruited, known as action observation network (AON). This network includes areas that have been reported to be involved when we perform actions ourselves. Such findings support the view that action understanding occurs by simulating actions in our own motor system (motor theories of action understanding). Alternatively, it has been argued that actions are understood based on a perceptual analysis, with access to action knowledge stored in the conceptual system (cognitive theories of action understanding). It has been argued earlier that areas that play a crucial role for action understanding should be able to (a) distinguish between different actions, and (b) generalize across the ways in which the action is performed (e.g. Dinstein, Thomas, Behrmann, & Heeger, 2008; Oosterhof, Tipper, & Downing, 2013; Caramazza, Anzelotti, Strnad, & Lingnau, 2014). Here we argue that one additional criterion needs to be met: an area that plays a crucial role for action understanding should have access to such abstract action information early, around the time when the action is recognized. An area that has access to abstract action information after the action has been recognized is unlikely to contribute to the process of action understanding.
In this thesis, I report three neuroimaging studies in which we used magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to characterize the temporal dynamics of abstract representations of observed actions (Study 1 and 2), meaning that generalize across lower level dimensions, and to characterize the type of information encoded in the regions of the AON (Study 3).
Specifically, in Study 1 we examined where in the brain and at which point in time it is possible to distinguish between pointing and grasping actions irrespective of the way in which they are performed (reach direction, effector) using MEG in combination with multivariate pattern analysis (MVPA) and source analysis. We show that regions in the left lateral occipitotemporal cortex (LOTC) have the earliest access to abstract action representations. By contrast, precentral regions, though recruited relatively early, have access to abstract action representations substantially later than left LOTC. In Study 2, we tested the temporal dynamics of the neural decoding related to the oscillatory activity induced by observation of actions performed with different effectors (hand, foot). We observed that temporal regions are able to discriminate all the presented actions before effector-related decoding within effector-specific motor regions. Finally, in Study 3 we investigated what aspect of an action is encoded within the regions of the AON. Object-directed actions induce a change of states, e.g. opening a bottle means changing its state from closed to open. It is still unclear how and in which brain regions these neural representations are encoded. Using fMRI-based multivoxel pattern decoding, we aimed at dissociating the neural representations of states and action functions. Participants observed stills of objects (e.g., window blinds) that were in either open or closed states, and videos of actions involving the same objects, i.e., open or close window. Action videos could show the object manipulation only (invisible change), or the complete action scene (visible change). This design allowed us to detect neural representations of action scenes, states and action functions independently of each other. We found different sub-regions within LOTC containing information related to object states, action functions, or both. These findings provide important information regarding the organization of action semantics in the brain and the role of LOTC in action understanding.
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Kulkarni, Praveen P. "Functional MRI Data Analysis Techniques and Strategies to Map the Olfactory System of a Rat Brain." Digital WPI, 2006. https://digitalcommons.wpi.edu/etd-dissertations/37.
Full textAbstract:
Understanding mysteries of a brain represents one of the great challenges for modern science. Functional magnetic resonance imaging (fMRI) has two features that make it unique amongst other imaging modalities used in behavioral neuroscience. First, it can be entirely non-invasive and second, fMRI has the spatial and temporal resolution to resolve patterns of neuronal activity across the entire brain in less than a minute. fMRI indirectly detects neural activity in different parts of the brain by comparing contrast in MR signal intensity prior to and following stimulation. Areas of the brain with increased synaptic and neuronal activity require increased levels of oxygen to sustain this activity. Enhanced brain activity is accompanied by an increase in metabolism followed by increases in blood flow and blood volume. The enhanced blood flow usually exceeds the metabolic demand exposing the active brain area to high level of oxygenated hemoglobin. Oxygenated hemoglobin increases the MR signal intensity that can be detected in MR scanner. This relatively straight forward scenario is, unfortunately, oversimplified. The fMRI signal change to noise ratio is extremely small. In this work a quantitative analysis strategy to analyze fMRI data was successfully developed, implemented and optimized for the rat brain. Therein, each subject is registered or aligned to a complete volume-segmented rat atlas. The matrices that transformed the subject's anatomy to the atlas space are used to embed each slice within the atlas. All transformed pixel locations of the anatomy images are tagged with the segmented atlas major and minor regions creating a fully segmented representation of each subject. This task required the development of a full 3D surface atlas based upon 2D non-uniformly spaced 2D slices from an existing atlas. A multiple materials marching cube (M3C) algorithm was used to generate these 1277 subvolumes. After this process, they were coalesced into a dozen major zones of the brain (amygdaloid complex, cerebrum, cerebellum, hypothalamus, etc.). Each major brain category was subdivided into approximately 10 sub-major zones. Many scientists are interested in behavior and reactions to pain, pleasure, smell, for example. Consequently, the 3D volume atlas was segmented into functional zones as well as the anatomical regions. A utility (program) called Tree Browser was developed to interactively display and choose different anatomical and/or functional areas. Statistical t-tests are performed to determine activation on each subject within their original coordinate system. Due to the multiple t-test analyses performed, a false-positive detection controlling mechanism was introduced. A statistical composite of five components was created for each group. The individual analyses were summed within groups. The strategy developed in this work is unique as it registers segments and analyzes multiple subjects and presents a composite response of the whole group. This strategy is robust, incredibly fast and statistically powerful. The power of this system was demonstrated by mapping the olfactory system of a rat brain. Synchronized changes in neuronal activity across multiple subjects and brain areas can be viewed as functional neuro-anatomical circuits coordinating the thoughts, memories and emotions for particular behaviors using this fMRI module.
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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.
Full textAbstract:
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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Gavazzeni, Joachim. "Age differences in arousal, perception of affective pictures, and emotional memory enhancement : Appraisal, Electrodermal activity, and Imaging data." Doctoral thesis, Stockholm University, Department of Psychology, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7346.
Full textAbstract:
In contrast to effortful cognitive functions, emotional functioning may remain stable or even be enhanced in older adults. It is unclear how affective functions in aging correspond to subjective experiences and physiological changes. In Study I, ratings of emotional intensity and neural activity to facial expressions using functional magnetic resonance imaging (fMRI) were analyzed in younger and older adults. Negative expressions resulted in increased neural activity in the right amygdala and hippocampus in younger adults, and increased activation in the right insular cortex in older adults. There were no age differences in subjective ratings. In Study II, subjective ratings of, and skin conductance response (SCR) to, neutral and negative pictures were studied. The ratings of negative pictures were higher for older adults compared to younger adults. SCRs increased in both age groups for the negative pictures, but magnitude of SCRs was significantly larger in younger adults. Finally, in Study III, emotional memory after a one-year retention interval was tested. The memory performance of both age groups was higher in response to negative pictures compared to neutral ones, although the performance was generally higher for younger adults. SCR at encoding was the better arousal predictor for memory, but only in younger adults. The results indicate age-related changes in affective processing. Age differences may involve a gradual shift from bottom-up processes, to more top-down processes. The results are discussed in a wider lifespan perspective taking into consideration the accumulated life experience of older adults.
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Zangrossi, Andrea. "Detecting cognitive states from the analysis of structural and functional images of the brain: two applications of Multi-Voxel Pattern Analysis on MRI and fMRI data." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3425706.
Full textAbstract:
In recent years, the efficacy and accuracy of multivariate analysis techniques on neuroimaging data has been tested on different topics. These methods have shown the ability to decode mental states from the analysis of brain scans, for this reason it has been called “brain reading”. The predictions can be applied to general mental states, referring to stable conditions not related to a contingent task (e.g., a neurological diagnosis), or specific mental states, referring to task-related cognitive processes (e.g., the perception of a category of stimuli). According to several neuroscientists, brain reading approach can potentially be useful for applications in both clinical and forensic neuroscience in the future.
In the present dissertation, two applications of the brain reading approach are presented on two relevant topics for clinical and forensic neuroscience that have not been extensively investigated with these methods. In Section A, this approach is tested on decoding different levels of Cognitive Reserve from the pattern of grey matter volume, in two MRI studies. In Section B two fMRI studies investigate the possibility of decoding real autobiographical memories from brain activity.
The aim of this thesis is to contribute to the amount of studies showing the usefulness of multivariate techniques in decoding “mental states” starting from the analysis of structural and functional brain imaging data, as well as the potential uses in clinical and forensic settings.Negli ultimi anni, l’efficacia e l’accuratezza di tecniche di analisi multivariata di analisi dei dati di neuroimmagine sono state testate su diversi argomenti. Questi metodi hanno mostrato la possibilità di decodificare stati mentali a partire dall’analisi di dati di neuroimmagine cerebrale, per questo motivo sono stati indicati con l’espressione “brain reading”. Le inferenze possono essere applicate a “stati mentali” generali, ovvero condizioni stabili e non legate ad uno specifico task (ad esempio una diagnosi neurologica), o a specifici “stati mentali”, ovvero processi cognitivi elicitati da specifici task (ad esempio la percezione di stimoli di una data categoria). Secondo molti autori le caratteristiche di questo approccio lo rendono strumento potenzialmente utile per future applicazioni sia cliniche che forensi. Nel presente lavoro sono state testate due applicazioni dell’approccio di brain reading su temi rilevanti per le neuroscienze in ambito clinico e forense, e poco studiati con questi metodi. Nella Sezione A verranno presentati due studi su dati MRI sulla possibilità di discriminare tra diversi livelli di Riserva Cognitiva a partire dal pattern di volume di materia grigia cerebrale. Nella Sezione B in due studi fMRI abbiamo investigato la possibilità di rilevare ricordi autobiografici sulla base dell’attività cerebrale. L’obiettivo del presente lavoro è quello di contribuire al crescente numero di studi che hanno discusso l’utilità di tecniche multivariate nella decodifica di “stati mentali” a partire dall’analisi di dati di neuroimmagine strutturale o funzionale, e le potenziali applicazioni applicazioni chiniche e forensi.
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Perez, Daniel Antonio. "Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data." Thesis, Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34858.
Full textAbstract:
Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.
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You, Xiaozhen. "Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/176.
Full textAbstract:
This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.
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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.
Full textAbstract:
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.