Дисертації з теми "FMRI Data"
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Ji, Yongnan. "Data-driven fMRI data analysis based on parcellation." Thesis, University of Nottingham, 2001. http://eprints.nottingham.ac.uk/12645/.
Повний текст джерелаBai, 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.
Повний текст джерелаTitle 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.
Plumpton, Catrin Oliver. "Classifier ensembles for streaming fMRI data." Thesis, Bangor University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540419.
Повний текст джерелаPerez, Carlos Arturo. "Discovering causal relationships from fMRI data." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000189.
Повний текст джерелаSubmitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 90 pages. Includes bibliographical references.
Turkay, Kemal Dogus. "Simulated Fmri Toolbox." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611465/index.pdf.
Повний текст джерелаSoldati, 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.
Повний текст джерелаAlowadi, Nahed. "Population based spatio-temporal probabilistic modelling of fMRI data." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8210/.
Повний текст джерелаChen, Xu. "Accelerated estimation and inference for heritability of fMRI data." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/67103/.
Повний текст джерелаCorte, 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/.
Повний текст джерелаStromberg, 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.
Повний текст джерелаBehjat, 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.
Повний текст джерелаLiao, 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.
Повний текст джерелаLai, 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.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаMojtahedi, 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.
Повний текст джерелаChoudhary, 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.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаDi, 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.
Повний текст джерелаL’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.
KIM, NAMHEE. "A semiparametric statistical approach to Functional MRI data." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1262295445.
Повний текст джерелаBudde, 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.
Повний текст джерелаBränberg, 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.
Повний текст джерелаWilliamitis, 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.
Повний текст джерелаKeller, Merlin. "Selection of a model of cerebral activity for fMRI Group Data analysis." Paris 11, 2010. http://www.theses.fr/2010PA112045.
Повний текст джерелаThis 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
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.
Повний текст джерелаIncludes 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.
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.
Повний текст джерелаGli 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.
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.
Повний текст джерелаThis 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.
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/.
Повний текст джерелаRossi, 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/.
Повний текст джерелаRiemenschneider, 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.
Повний текст джерелаSobotková, 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.
Повний текст джерелаFountalis, 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.
Повний текст джерелаSong, 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.
Повний текст джерелаThis 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.
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.
Повний текст джерелаRichter, 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.
Повний текст джерелаRydell, 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.
Повний текст джерелаGonçalves, 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/.
Повний текст джерелаYoshida, 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.
Повний текст джерелаNIGRI, 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.
Повний текст джерелаBarnathan, Michael. "Mining Complex High-Order Datasets." Diss., Temple University Libraries, 2010. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/82058.
Повний текст джерелаPh.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
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.
Повний текст джерелаWirsich, 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.
Повний текст джерелаThe 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
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.
Повний текст джерелаTucciarelli, 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.
Повний текст джерелаKulkarni, 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.
Повний текст джерелаRemes, 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.
Повний текст джерелаTiivistelmä 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
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.
Повний текст джерелаIn 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.
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.
Повний текст джерелаNegli 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.
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.
Повний текст джерелаYou, Xiaozhen. "Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy." FIU Digital Commons, 2010. http://digitalcommons.fiu.edu/etd/176.
Повний текст джерелаLabounek, 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.
Повний текст джерела