Дисертації з теми "FMRI signal"
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Leach, Sean. "Physiological noise characterisation and signal analysis for fMRI." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437066.
Повний текст джерелаKim, Junmo 1976. "Spatio-temporal fMRI signal analysis using information theory." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8982.
Повний текст джерелаIncludes bibliographical references (p. 111-112).
Functional MRI is a fast brain imaging technique which measures the spatio-temporal neuronal activity. The development of automatic statistical analysis techniques which calculate brain activation maps from JMRI data has been a challenging problem due to the limitation of current understanding of human brain physiology. In previous work a novel information-theoretic approach was introduced for calculating the activation map for JMRI analysis [Tsai et al , 1999]. In that work the use of mutual information as a measure of activation resulted in a nonparametric calculation of the activation map. Nonparametric approaches are attractive as the implicit assumptions are milder than the strong assumptions of popular approaches based on the general linear model popularized by Friston et al [19941. Here we show that, in addition to the intuitive information-theoretic appeal, such an application of mutual information is equivalent to a hypothesis test when the underlying densities are unknown. Furthermore we incorporate local spatial priors using the well-known Ising model thereby dropping the implicit assumption that neighboring voxel time-series are independent. As a consequence of the hypothesis testing equivalence, calculation of the activation map with local spatial priors can be formulated as mincut/maxflow graph-cutting problem. Such problems can be solved in polynomial time by the Ford and Fulkerson method. Empirical results are presented on three JMRI datasets measuring motor, auditory, and visual cortex activation. Comparisons are made illustrating the differences between the proposed technique and one based on the general linear model.
by Junmo Kim.
S.M.
Ambrose, Joseph Paul. "Dynamic field theory applied to fMRI signal analysis." Diss., University of Iowa, 2016. https://ir.uiowa.edu/etd/2035.
Повний текст джерелаSalloum, Jasmin B. "Behavioral modification of fMRI signal in studies of emotion." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=962689300.
Повний текст джерелаRiedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214196.
Повний текст джерелаRiedel, Philipp, Mark J. Jacob, Dirk K. Müller, Nora C. Vetter, Michael N. Smolka, and Michael Marxen. "Amygdala fMRI Signal as a Predictor of Reaction Time." Frontiers Research Foundation, 2016. https://tud.qucosa.de/id/qucosa%3A29972.
Повний текст джерелаThomas, Christopher G. "Signal optimization techniques and noise characterization in BOLD-based fMRI." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ58241.pdf.
Повний текст джерелаPurdon, Patrick L. (Patrick Lee) 1974. "Signal processing in functional magnetic resonance imaging (fMRI) of the brain." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50032.
Повний текст джерелаMaczka, Melissa May. "Investigations into the effects of neuromodulations on the BOLD-fMRI signal." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:96d46d4d-480b-48d7-9f2d-060e76c5f8aa.
Повний текст джерелаFisher, Julia Marie. "Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/594646.
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 джерелаEklund, Anders. "Signal Processing for Robust and Real-Time fMRI With Application to Brain Computer Interfaces." Licentiate thesis, Linköping : Department of Biomedical Engineering, Linköping University, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54040.
Повний текст джерелаKojan, Martin. "Potlačení nežádoucí variability ve fMRI datech při analýze pomocí psychofyziologických interakcí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219513.
Повний текст джерелаPastorello, Bruno Fraccini. "Em busca da região epileptiforme em pacientes com epilepsia do lobo temporal: métodos alternativos baseados em fMRI e EEG-fMRI." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-26102011-135335/.
Повний текст джерелаTemporal lobe epilepsy (TLE) is the most common and resistant form of epilepsy to anti-epileptic drug. There are several types of anti-epileptic drugs used in seizure control. However, in some cases drug treatment is not effective and surgery to remove the epileptogenic zone (EZ) is a recommended alternative. EZ is a theoretical concept and there are many techniques that have been applied to enclose it precisely. In practice, EEG, video-EEG, MEG, SPECT, PET and various MRI techniques, especially functional MRI (fMRI), have been used to map areas related to EZ. However, in some cases, the results remain non-convergent and the EZ, undefined. Therefore, the use of new methodologies to assist the location of EZ have been proposed. Herein, our goal was to develop two methods for assessing the EZ. The first one was designed to access changes in the hemodynamic response (HRF) of the EZ in response to hypercapnia. 22 patients with TLE and 10 normal volunteers were evaluated by modulating the partial pressure of CO2 during the acquisition of fMRI in a breathing holding and a passive inhalation CO2/air protocols. The results show increased onset times and decreased amplitude of the HRF in the temporal lobe of TLE patients compared with asymptomatic volunteers. Moreover, most patients had onset maps coincident with ictal SPECT localizations. The second proposed study was based on simultaneous EEG-fMRI acquisitions. The relationship between powers of alpha and theta bands (EEG) and BOLD contrast (fMRI) was investigated in 41 TLE patients and 7 healthy controls. Alpha band results show a consistent negative correlation in the occipital, parietal and frontal lobes both in controls and TLE patients. In addition, controls show disperse positive correlations in both hemispheres. On the other hand, TLE patients presented strong positive correlations in the thalamus and insula. Theta band analysis, in controls, primarily show positive correlations in bilateral pre-and post-central gyri. In patients, robust positive correlations were observed in the anterior cingulate gyrus, thalamus, insula, putamen, superior parietal, frontal and temporal gyri. Moreover, the lateralization index (LI) indicates a coincidence between the side of the EZ evaluated by clinical diagnosis and clusters detected in the theta band. In conclusion, the hipercapnia study showed to be an interesting tool in locating EZ and the results are similar to SPECT findings. The longer onset and lower amplitude of the HRF observed in patients may be related to a vascular stress due to the recurrence of seizures. Furthermore, alpha and theta rhythms may be a promising tool to be used in determining the lateralization of EZ in patients with TLE.
Ohlsson, Henrik. "Regularization for Sparseness and Smoothness : Applications in System Identification and Signal Processing." Doctoral thesis, Linköpings universitet, Reglerteknik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60531.
Повний текст джерелаKovářová, Anežka. "Porovnání a optimalizace měření single-echo a multi-echo BOLD fMRI dat." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377659.
Повний текст джерелаChambers, Micah Christopher. "Full Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filters." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/35143.
Повний текст джерелаMaster of Science
Janeček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.
Повний текст джерелаSturzbecher, Marcio Junior. "Métodos clássicos e alternativos para a análise de dados de fMRI e EEG-fMRI simultâneo em indivíduos assintomáticos, pacientes com epilepsia e com estenose carotídea." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-08072011-174951/.
Повний текст джерелаFunctional magnetic resonance imaging (fMRI) and combined EEG-fMRI usually rely on the successful detection of Blood Oxygenation Level Dependent (BOLD) signal. Typically, the analysis of both fMRI and EEG-fMRI are based on the General Linear Model (GLM) that aims at localizing the BOLD responses associated to an a priori model. However, the responses are not always canonical, as is the case of those from patients, which may reduce the reliability of the results. Therefore, the first objective of the present study was to explore the usage of classical methods, such as the GLM, and to propose alternative strategies to the analysis of fMRI and combined EEG-fMRI. A first method developed was based on the computation of the generalized Kullback-Leibler distance (gKLd), which does not require the use of an a priori model. Simulated data was used to allow quantitative comparison between the gKLd and GLM under different response conditions such as the signal to noise ratio and delay. The gKLd was then tested on real data, first from 14 asymptomatic subjects, submitted to classical motor and auditory fMRI protocols. The results demonstrate that under these conditions the GLM and gKLd are equivalent. The same strategy was applied to 02 patients with unilateral carotid stenosis. Now the dKLg was capable of detecting the expected bilateral BOLD responses that were not detected by the GLM, as a consequence of the response delay imposed by the stenosis. Those comparisons were now extended to the evaluation of EEG-fMRI exams from 45 patients with epilepsy. For this data set, an additional method was used, based on the use of Independent Component Analysis (ICA), which was called ICA-GLM. It allows extracting semi-automatically the amplitude, duration and topography of EEG interictal Epileptiform Discharges (IED), favoring the use of less prominent signals. Moreover, it also allows the use of BOLD response models with different delays, expanding the variability of the responses to be detected in patients with epilepsy. ICA-GLM was also compared to GLM and dKLg in these EEG-fMRI evaluations. Although in general the results have demonstrated the robustness of the GLM, dKLg was more efficient in detecting the responses from some pacients, while the ICA-GLM mostly detected broader regions with more significant results when compared to GLM. In general, dKLg and ICA-GLM seem to offer an important complementary aspect to the GLM, increasing its sensibility in EEG-fMRI as a whole. Another important aspect of EEG-fMRI applied to patients with epilepsy has been the inspection of Electrical Source Imaging (ESI) to evaluate some dynamical aspects of the IED. Herein, ESI maps were obtained from two inverse distributed solutions that were not applied so far to EEG-fMRI: Bayesian Model Averaging (BMA) and constrained Low Resolution Electromagnetic Tomography (cLORETA). Besides, we also evaluated the combined information from ESI and EEG-fMRI in order to differentiate from primary sources to temporal propagation of the signal. Such analysis allowed us to inspect for the correspondence between regions detected by ESI e EEG-fMRI and to separate BOLD signals whose sources are related to the initial and later components of the IED. Although the results are preliminary to determine which ESI method (cLORETA or BMA) is more efficient, the distance between the maximum ESI and the closest EEG-fMRI cluster was consistently similar with those reported in the literature.
Fajkus, Jiří. "Porovnání pokročilých přístupů pro analýzu fMRI dat u oddball experimentu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219734.
Повний текст джерелаCraddock, Richard Cameron. "Support vector classification analysis of resting state functional connectivity fMRI." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/31774.
Повний текст джерелаCommittee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthony. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Montesco, Carlos Alberto Estombelo. "Método de análise de componentes dependentes para o processamento, caracterização e extração de componentes de sinais biomédicos." Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-25092008-165152/.
Повний текст джерелаAn important goal in biomedical signal processing is the extraction of information based on a set of physiological measurements made along time. Generally, biomedical signals are electromagnetic measurements. Those measurements (usually made with multichannel equipment) are registered using spatially distributed sensors around some areas of the human body, originating a set of time and/or space correlated data. The signals of interest are rarely registered alone, being usually observed as a mixture of other spurious, noisy signals (sometimes superimposed) and environmental or physiological artifacts. More over, the signal-to-noise ratio is generally low. In many applications, a big number of sensors are available, but just a few sources are of interest and the remainder can be considered noise. For such kind of applications, it is necessary to develop trustful, robust and effective learning algorithms that allow the extraction of only a few sources potentially of interest and that hold useful information. The strategy used here for extraction of sources is applied in three important problems in biomedical signal processing: (1) detection of the fetal magnetocardiogram signal (fMCG); (2) detection of the electrical activity of the stomach in magnetogastrograms (MGG); and (3) detection of active regions of the brain in experiments in functional Magnetic Resonance Imaging (fMRI). The results, within the three cases of study, showed that the DCA method used as strategy is effective and computationally efficient on extraction of desired signals.
Klímová, Jana. "Vliv výběru souřadnic regionů na výsledky dynamického kauzálního modelování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220028.
Повний текст джерелаSamadi, Samareh. "EEG-fMRI integration for identification of active brain regions using sparse source decomposition." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT021/document.
Повний текст джерелаElectroencephalography (EEG) is an important non-invasive imaging technique as it records the neural activity with high temporal resolution (ms), but it lacks high spatial resolution. The inverse problem of EEG is underdetermined and a constraint or prior information is needed to find a unique solution. Recently, EEG-fMRI integration is widely considered. These methods can be categoraized in three groups. First group uses the EEG temporal sources as the regressors in the generalized linear method (GLM) which is used to analyze the fMRI data. The second group analyzes EEG and fMRI simultaneously which is known as fusion technique. The last one, which we are interested in, uses the fMRI results as prior information in the EEG inverse problem. The EEG and fMRI data of a specific task, eventually reflect the neurological events of the same activation regions. Therefore, we expect that there exist common spatial patterns in the EEG and the fMRI data. Therefore, solving the EEG inverse problem to find the spatial pattern of the EEG sources which is congruent with the fMRI result seems to be close to the reality. The great challenge is the relationship between neural activity (EEG) and hemodynamic changes (fMRI), which is not discovered by now. Most of the previous studies have used simple neurovascular model because using the realistic model is very complicated. Here, we use general and simple facts as constraints to solve the EEG inverse problem which do not rely on the experiment or data and are true for all cases. Therefore, we solve the EEG inverse problem to estimate sparse connected spatial sources with the highest correlation with the fMRI spatial map of the same task. For this purpose, we have used sparse decomposition method. For finding sparse representation of the EEG signal, we have projected the data on the uncorrelated temporal sources of the activity. We have proposed a semi-blind source separation method which is called reference-based source separation (R-SS) and extracts discriminative sources between the activity and the background. R-SS method has been verified on a realistic simulation data and the intracranial EEG (iEEG) signal of five epileptic patients. We show that the representation of EEG signal in its task related source space is sparse and then a weighted sparse decomposition method is proposed and used to find the spatial map of the activity. In the weighted sparse decomposition method we put fMRI spatial map in the weighting matrix, such that the activated voxels in fMRI are considered more important than the other voxels in the EEG inverse problem. We validated the proposed method on the simulation data and also we applied the method on the real data of the face perception experiment. The results show that the proposed method is stable against the noise and subject variability
Tornador, Antolin Cristian 1979. "Prognosis and risk models of depression are built from analytical components of the rs-fMRI activity in patients." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/383067.
Повний текст джерелаLa depresión es el tipo de trastorno emocional más común en la población mundial. Se caracteriza por sentimientos de culpa o negativos, baja autoestima, pérdida de interés, alto nivel de reflexión y en general una disminución de las funciones psíquicas del individuo. Las nuevas técnicas de neuroimagen no invasivas han incrementado la habilidad para estudiar posibles variaciones de la actividad cerebral en pacientes. En concreto, las imágenes por resonancia funcional magnética (fRMI) se han convertido en las dos últimas décadas el método más importante, no-invasivo sin riesgo para la salud humana, para el estudio de las funciones cerebrales humanas. Biswal y otros en 1995, y posteriormente Lowe y compañía en 1998, demostraron la existencia de actividad espontanea continua en la actividad cerebral en estado de reposo. Estas fluctuaciones también han sido confirmadas en otras especies como en macacos (Vincent JL y compañía, 2007). El estudio mediante técnicas de neuroimagen sobre la actividad cerebral en reposo (rs-fMRI) se ha convertido en una potente herramienta para el estudio de enfermedades, puesto que, por un lado, se ha demostrado tener una mejor relación señal-ruido respecto a enfoques basados en tareas, y por otro lado, ciertos pacientes podrían tener dificultades para realizar algún tipo de tareas cognitivas, de lenguaje o motoras. Sin embargo, parece ser que debido a ciertas inconsistencias encontradas entre estudios, las técnicas de rs-fMRI no estarían llegando a un uso clínico-práctico para el seguimiento, pronóstico o pre-diagnostico personalizado en individuos con depresión. En línea a esto, aunque Grecius MD en 2008 expuso los beneficios de la técnica rs-fMRI también comentó que para poder ser utilizada en la rutina clínica aún se debería mejorar la relación señal-ruido. Propuso alargar los tiempos de las series temporales en estado de reposo y mejorar los procedimientos de análisis. En esta tesis se trabaja para dilucidar si existen ciertos factores o componentes en la señal funcional en estado de reposo que pudieran ser utilizados para su uso en la salud clínica. Por ello, utilizamos datos de rs-fMRI sobre dos conjunto de muestras. En el primer conjunto, 27 pacientes con depresión mayor (MDD) y 27 individuos como control, diseñamos descriptores que describan aspectos estáticos y dinámicos de la señal de reposo para la construcción de modelos de prónostico. En cambio, con el segundo tipo de muestras, 48 gemelos, analizamos la relación de posibles factores genéticos y de entorno que pudieran explicar ciertos componentes depresivos en la actividad en estado de reposo. Por un lado, los resultados muestran que la depresión pudiera estar afectando diferentes redes cerebrales al mismo tiempo localizadas en la parte prefrontal-limbica, en la red DMN, y entre los lóbulos frontoparietales. Además, parece ser que las alteraciones sobre estas redes pudieran ser explicadas tanto por aspectos estáticos y dinámicos existentes en la señal de reposo. Finalmente, conseguimos crear modelos que explicarían parcialmente ciertos fenómenos clínicos presentes en los pacientes depresivos, mediante descriptores globales de estas redes. Estos descriptores de red pudieran ser utilizados para el seguimiento personalizado en pacientes con depresión mayor. Por otro, utilizando la muestra de gemelos, conseguimos construir un modelo de riesgo a partir de la actividad amigdalar que evalúa el riesgo o propensión de un individuo a partir de componentes analíticas en la actividad de reposo. También sobre esta muestra, se analizó el cerebelo encontrando que el entorno pudiera estar modificando la actividad en estas regiones
Ries, Anja Kaja [Verfasser], Claus [Akademischer Betreuer] Zimmer, Gil [Gutachter] Westmeyer, and Christine [Gutachter] Preibisch. "Spectral analysis of resting-state fMRI BOLD signal in healthy subjects and patients suffering from major depressive disorder / Anja Kaja Ries ; Gutachter: Gil Westmeyer, Christine Preibisch ; Betreuer: Claus Zimmer." München : Universitätsbibliothek der TU München, 2019. http://d-nb.info/1185069860/34.
Повний текст джерелаDe, Luca Marilena. "Low frequency signals in FMRI." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418562.
Повний текст джерелаReyt, Sébastien. "IRM fonctionnelle chez le rat : défis méthodologiques." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00861856.
Повний текст джерелаHebart, Martin. "On the neuronal systems underlying perceptual decision-making and confidence in humans." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2014. http://dx.doi.org/10.18452/16924.
Повний текст джерелаPerceptual decision-making refers to the ability to arrive at categorical judgments about states of the outside world. Here we use functional magnetic resonance imaging and multivariate pattern analysis to identify decision-related brain regions and address a number of open issues in the field of perceptual decision-making. In the first study (Hebart et al., 2012), we demonstrated that perceptual decisions about motion direction are represented in both visual and parietal cortex, even when decoupled from motor plans. While in early visual cortex the amount of information about perceptual choices follows the amount of sensory evidence presented on the screen, the reverse pattern is observed in left posterior parietal cortex. These results reveal the brain regions involved when choices are encoded in an abstract format and suggest that these two brain regions are recruited differently depending on the amount of sensory evidence available. In the second study (Hebart et al., submitted), we show that the perceptual decision variable (DV) is represented throughout fronto-parietal association cortices. The DV in right ventrolateral prefrontal cortex covaries specifically with brain signals in the ventral striatum representing confidence, demonstrating a close link between the two variables. This suggests that confidence is calculated from the perceptual DV encoded in ventrolateral prefrontal cortex. In the third study (Christophel et al., 2012), using a visual short-term memory (VSTM) task, we demonstrate that the content of VSTM is represented in visual cortex and posterior parietal cortex, but not prefrontal cortex. These results constrain theories of VSTM and suggest that the memorized content is stored in regions shown to represent perceptual decisions. Together, these results shed light on the neuronal code underlying perceptual decision-making in humans and offer the prospect for a more complete understanding of these processes.
Reynaud, Olivier. "Development of FENSI (Flow Enhanced Signal Intensity) perfusion sequence and application to the characterization of microvascular flow dynamics using MRI." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00740639.
Повний текст джерелаŠejnoha, Radim. "Nástroj pro analýzu pohybu subjektů při měření funkční magnetickou rezonancí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242165.
Повний текст джерелаLiu, Aiping. "Brain connectivity network modeling using fMRI signals." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58126.
Повний текст джерелаApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Estombelo, Montesco Carlos Alberto. "Método de análise de componentes dependentes para o processamento, caracterização e extração de componentes de sinais biomédicos." reponame:Repositório Institucional da UFS, 2007. https://ri.ufs.br/handle/riufs/1777.
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
Liu, Aiping. "FDR-controlled network modeling and analysis of fMRI and sEMG signals." Thesis, University of British Columbia, 2011. http://hdl.handle.net/2429/37217.
Повний текст джерелаKundu, Prantik. "Physical analysis of BOLD fMRI signals for functional brain mapping and connectomics." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648842.
Повний текст джерелаBlanchard, Caroline. "Multisensorialité et kinesthésie : règles et substrats cérébraux de l'intégration multimodale." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4788.
Повний текст джерелаHuman kinesthesia is based on the processing of a great amount of sensory information available during action. To assess the respective contribution of muscle proprioception, vision and touch of self-movement perception, our experimental approach relies on sensory lures likely to generate illusory sensations of movement. In psychophysics and functional neuroimaging (fMRI) experimental setting, we try to better understand how and where this "multisensory fusion" occurs. This work confirms that each modality conveys kinesthetic information relevant for the central nervous system (CNS), is combined in a non-equivalent way according to the velocity of encoded movement and sensory modalities involved. Tact and vision seem to provide redundant cinematic information about body movements relatively to environment, complementary to muscle proprioceptive signals and seem to be more reliable for coding small velocities (Blanchard et al., 2011, 2013). Our neuroimaging results highlight a heteromodal network involved during kinesthesia and confirms that a supramodal insulo-cerebello-parietal region is the substrate for trisensory integration processing. By combining three kinesthetic signals from different sensory origins, this work provides evidence of integration at different levels of the CNS. Recalling the "Modality Appropriateness" model of Welch & Warren (1986), our results also support the idea of a weighted integration of sensory inputs, which would optimize kinesthesia, based on their relative relevance to encode a given event
Jukuri, T. (Tuomas). "Resting state brain networks in young people with familial risk for psychosis." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526211107.
Повний текст джерелаTiivistelmä Psykoottisiin häiriöihin sairastutaan yleensä nuoruudessa tai varhaisaikuisuudessa. Psykoositutkimuksen tavoitteena on löytää uusia menetelmiä, joiden avulla kyettäisiin tunnistamaan suurimmassa psykoosiriskissä olevat nuoret, jotta heille voitaisiin tarjota sairautta ennaltaehkäiseviä hoitokeinoja. Skitsofreniaan ja muihin psykoottisiin häiriöihin sairastuneilla on havaittu aivotoiminnan poikkeavuuksia. Samankaltaisia aivotoiminnan poikkeavuuksia on havaittu myös nuorilla, jotka ovat vaarassa sairastua psykoosiin. Toistaiseksi on ollut epäselvää, onko psykoosiin sairastuneiden henkilöiden lapsilla aivohermoverkkojen toiminnan poikkeavuuksia lepotilassa. Suoritimme aivojen lepotilan MRI-tutkimuksen (R-fMRI) 72:lle (29 miestä) nuorelle aikuiselle, joiden jompikumpi vanhempi oli sairastunut psykoosin sekä 72:lle (29 miestä) nuorelle aikuiselle, joiden vanhemmat eivät olleet sairastaneet psykoosia. Molemmat tutkimusryhmät tässä Oulu Brain and Mind -tutkimuksessa olivat Pohjois-Suomen 1986 syntymäkohortin jäseniä. Tutkittavat olivat 20–25 vuoden iässä. Lepotilan toiminnallinen magneettikuvaus suoritettiin 1.5 Teslan Siemensin magneettikuvantamislaitteella. Tutkimuskohteiksi valittiin lepotilan toiminnallinen aivohermoverkko, toiminnan ohjauksesta vastaava aivohermoverkko ja pikkuaivot. Kuvantamisdataan sovellettiin itsenäisten komponenttien analyysia aivohermoverkkojen määrittämistä varten. Ryhmien välisen eron havaitsemiseen käytettiin ei-parametristä permutaatiotestiä, joka kynnystettiin tilastollisesti merkitsevään tasoon (p < 0.05). Lepotilan oletushermoverkossa ja toiminnanohjauksesta vastaavassa aivohermoverkoissa havaittiin vähäisempää aktiivisuutta ja pikkuaivoissa kohonnutta aktiivisuutta perinnöllisessä psykoosiriskissä olevilla nuorilla aikuisilla verrattuna verrokkeihin. Tutkimustulokset selkeyttivät aiempaa ristiriitaista kirjallisuutta tutkimusaiheesta. Tutkimuksessa havaittujen aivoalueiden poikkeava toiminta lepotilassa voi liittyä kohonneeseen psykoosin puhkeamisriskiin. Tutkimuslöydösten avulla voidaan todennäköisesti edesauttaa parempien kuvantamismenetelmien kehittämistä suurimmassa psykoosiriskissä olevien nuorten tunnistamiseen
Farouj, Younes. "Structured anisotropic sparsity priors for non-parametric function estimation." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI123/document.
Повний текст джерелаThe problem of estimating a multivariate function from corrupted observations arises throughout many areas of engineering. For instance, in the particular field of medical signal and image processing, this task has attracted special attention and even triggered new concepts and notions that have found applications in many other fields. This interest is mainly due to the fact that the medical data analysis pipeline is often carried out in challenging conditions, since one has to deal with noise, low contrast and undesirable transformations operated by acquisition systems. On the other hand, the concept of sparsity had a tremendous impact on data reconstruction and restoration in the last two decades. Sparsity stipulates that some signals and images have representations involving only a few non-zero coefficients. The present PhD dissertation introduces new constructions of sparsity priors for wavelets and total variation. These construction harness notions of generalized anisotropy that enables grouping variables into sub-sets having similar behaviour; this behaviour can be related to the regularity of the unknown function, the physical meaning of the variables or the observation model. We use these constructions for non-parametric estimation of multivariate functions. In the case of wavelet thresholding, we show the optimality of the procedure over usual functional spaces before presenting some applications on denoising of image sequence, spectral and hyperspectral data, incompressible flows and ultrasound images. Afterwards, we study the problem of retrieving activity patterns from functional Magnetic Resonance Imaging data without incorporating priors on the timing, durations and atlas-based spatial structure of the activation. We model this challenge as a spatio-temporal deconvolution problem. We propose the corresponding variational formulation and we adapt the generalized forward-backward splitting algorithm to solve it
Grooms, Joshua Koehler. "Examining the relationship between BOLD fMRI and infraslow EEG signals in the resting human brain." Thesis, Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53957.
Повний текст джерелаGalindo, Guarin Liliana. "Neurological soft signs, temperament and schizotypy in patients with schizophrenia and unaffected relatives: an FMRI study." Doctoral thesis, Universitat Autònoma de Barcelona, 2017. http://hdl.handle.net/10803/403815.
Повний текст джерелаSchizophrenia is a severe psychiatric disorder that has a profound effect on both the individuals affected and society. This common mental illness is a complex, heterogeneous behavioural and cognitive syndrome that seems to originate from disruption of brain development caused by genetic or environmental factors, or both. The genetic basis may be present in individuals without disease, as in the case of relatives of patients, being detectable through biological markers. Neurological soft signs (NSS) are discrete sensorimotor impairments associated with deviant brain development that were postulate as an endophenotype of schizophrenic spectrum disorder. Also the personality traits have been proposed as a vulnerability marker in schizophrenia. A specific profile of temperament and character and the schizotypal personality traits have also been correlated with schizotypal personality traits. These traits and some neurological abnormalities have been shown to aggregate in the relatives of schizophrenia patients. The etiopathogenesis of schizophrenia suggests it may be a "progressive neurodevelopmental disorder". This view postulates a disruption in functional circuits involving hetero modal association areas rather than a specific abnormality in a single brain region. The aim of this study is to explore the abnormalities in the functional connectivity of the default mode network related to the association between neurological soft signs and personality in schizophrenia. To investigate this a cross-sectional study is proposed, comparing a group of patients with schizophrenia, a group of unaffected relatives and a group of healthy controls. In order to explore the association of these potential biomarkers of schizophrenia the study was composed of two parts: a) To explore the association between neurological soft signs and personality traits in schizophrenia, two personality examinations (Temperament and Character Inventory and the Schizotypal Personality Questionnaire) and an evaluation of Neurological Soft Signs were performed. b) To explore the association between cerebral connectivity changes in the default mode network with the presence of neurological soft signs in schizophrenia a functional magnetic resonance scan was performed on participants in a resting state. The major finding in this study was that patients with schizophrenia and non-psychotic relatives display a unique profile of temperament and character and more schizotypal traits that correlate with higher presence of NSS. Our results reveal an association between these hypothesized vulnerability markers, as temperament (especially harm avoidance, reward dependence and persistence) and character (especially self-directedness and cooperativeness) correlated with the presence of NSS in the entire sample. Also the schizotypal traits (total scores and subscores) showed a very strong correlation with the presence of NSS in the entire sample. The results showed that susceptibility to NSS and to schizophrenia are both related to individual differences in personality features in non-psychotic relatives of patients with schizophrenia. These findings highlight the value of using both assessments to study high risk populations. The neuroimaging results showed connectivity changes in the default mode network with a possible association with the presence of neurological soft signs. These findings support the theory of cognitive dysmetria as a possible dysfunction in cortical-thalamic-cerebellar connectivity. This model also could explain the diversity of symptoms in schizophrenia and their associations (like this study that includes personality and sensory and motor functions). One strength of the study is that the relatives of patients with schizophrenia had no familial ties to the patients used, thus decreasing the possibility that similar upbringing would confound the results.
Demirtaş, Murat. "Exploring functional connectivity dynamics in brain disorders: a whole-brain computational framework for resting state fMRI signals." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/350799.
Повний текст джерелаL'activitat del cervell fluctua espontàniament a diferents escales i per tant exhibeix in-teraccions dinàmiques i complexes que manifesten patrons de sincronització rics. Du-rant els darrers deu anys han abundat els estudis orientats a comprendre els mecanismes que hi ha darrere les interaccions cerebrals basant-se en les seves estructures funcionals i estructurals. A més, existeix un esforç ingent per desvetllar el paper que aquestes in-teraccions juguen en els trastorns psiquiàtrics. Aquesta tesi aborda les qüestions esmen-tades des de noves perspectives. El primer pilar d'aquesta tesi és la naturalesa variable en el temps de la interacció dinàmica entre diferents regions del cervell. El segon pilar és el paper que aquesta dinàmica de connectivitat funcional juga en diferents poblacions clíniques. El tercer pilar es centra en l'ús de models computacionals per determinar l'es-tructura de connectivitat que relaciona els patrons de connectivitat funcional i anatòmics observats. El quart pilar de la tesi proposa una explicació del mecanisme dels trastorns cerebrals.
Del, Castello Mariangela. "Analysis of electroencephalography signals collected in a magnetic resonance environment: characterisation of the ballistocardiographic artefact." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13214/.
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 джерелаDomanski, Aleksander Peter Frederick. "Functional dissection of abnormal signal processing performed by the somatosensory cortex of young Fmr1-KO mice." Thesis, University of Edinburgh, 2014. http://hdl.handle.net/1842/18017.
Повний текст джерелаWang, Maosen [Verfasser], and Xin [Akademischer Betreuer] Yu. "The Correlation between Astrocytic Calcium and fMRI Signals is Related to the Thalamic Regulation of Cortical States / Maosen Wang ; Betreuer: Xin Yu." Tübingen : Universitätsbibliothek Tübingen, 2020. http://d-nb.info/121048420X/34.
Повний текст джерелаKalberlah, Christian [Verfasser]. "Multivariate „Searchlight“-Musterklassifizierung humaner fMRT-Signale in multiplen kognitiven Modalitäten : visuelle Aufmerksamkeit, Syntax und Emotion / Christian Kalberlah." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2013. http://d-nb.info/1035182289/34.
Повний текст джерелаThompson, Garth John. "Neural basis and behavioral effects of dynamic resting state functional magnetic resonance imaging as defined by sliding window correlation and quasi-periodic patterns." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49083.
Повний текст джерелаChuang, Kai-Hsiang, and 莊凱翔. "Identification of fMRI Signal Using Fuzzy Neural Network." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/33318825486856816899.
Повний текст джерела國立臺灣大學
電機工程學系
85
Functional MRI (fMRI), with its high spatial, temporal resolution, non-radioactive, and non-invasive properties, is potential in the fields ranging from physiology, psychology, pharmacology to clinical applications, etc. However, due to its very low signal-to-noise ratio, post processing is required to identify the active regions.Most contemporary post processing strategies require assumed functional response waveform based on the prior knowledge about experimental paradigm to produce effective results. While most studies are performed in steady state, these methods are not very suitable for complex functional experiments and will produce biased result even when brain*s response is simple. This will limit the applications of functional MRI studies. To fully utilize fMRI to cognition applications, one needs to develop a flexible analysis tool for the complicated signal characteristics of brain responses. Utilizing unsupervised clustering network and fuzzy set theory, we have successfully developed a cascade fuzzy neural network, which combines Kohonen clustering network and Fuzzy C-Means algorithm, to analyze fMRI time signal [30]. By comparing the receiver operating characteristic (ROC) analysis results of our proposed method with other two kinds of conventional post processing methods- correlation coefficient analysis and t- statistical parametric mapping - on a series of testing phantoms, we have proved that our method can identify the actual functional response even when the activation area is very small, under noisy conditions, or even when the actual response is deviated from traditionally assumed box-car type.Human studies involving motor cortex activation also show that our method successfully identifies the functional responses waveform and the active regions as well. Furthermore, it can also discriminate responses in gray matter from those possibly coming from venous vessels. And most of all, even when the experimental paradigm is unknown, such that conventional post processing methods are inapplicable, our method is still effective.With this flexible fMRI tool, psycho-physiologist now can go on and proceed complicated one shot human cognition studies. Linguistic studies including Chinese language and Taiwanese dialect study will be performed in the near future.
Wibral, Michael. "The BOLD fMRI Signal under Anaesthesia and Hyperoxia." Phd thesis, 2007. http://tuprints.ulb.tu-darmstadt.de/844/1/Wibral2007_EPDA.pdf.
Повний текст джерела