Relevant bibliographies by topics / FMRI

Academic literature on the topic 'FMRI'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "FMRI"

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Alvarez-Mora, Maria Isabel, Ines Agusti, Robin Wijngaard, Estefania Martinez-Barrios, Tamara Barcos, Aina Borras, Sara Peralta, et al. "Evaluation of FMR4, FMR5 and FMR6 Expression Levels as Non-Invasive Biomarkers for the Diagnosis of Fragile X-Associated Primary Ovarian Insufficiency (FXPOI)." Journal of Clinical Medicine 11, no. 8 (April 14, 2022): 2186. http://dx.doi.org/10.3390/jcm11082186.

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Female FMR1 (Fragile X mental retardation 1) premutation carriers are at risk for developing fragile X-associated primary ovarian insufficiency (FXPOI), a condition characterized by amenorrhea before age 40 years. Not all women with a FMR1 premutation suffer from primary ovarian insufficiency and nowadays there are no molecular or other biomarkers that can help predict the occurrence of FXPOI. Long non-coding RNAs (lncRNAs) comprise a group of regulatory transcripts which have versatile molecular functions, making them important regulators in all aspects of gene expression. In recent medical studies, lncRNAs have been described as potential diagnostic biomarkers in many diseases. The present study was designed to determine the expression profile of three lncRNAs derived from the FMR1 locus, FMR4, FMR5 and FMR6, in female FMR1 premutation carriers in order: (i) to determine a possible role in the pathogenesis of FXPOI and (ii) to investigate whether they could serve as a biomarker for the diagnosis of FXPOI. FMR4, FMR5 and FMR6 transcripts levels were evaluated in total RNA extracted from peripheral blood by digital droplet PCR and compared between FMR1 premutation carriers with FXPOI and without FXPOI. The diagnostic value of lncRNAs was evaluated by receiver operating characteristic (ROC) analysis. Results revealed a significant association between FXPOI and high expression levels of FMR4. No association was obtained for FMR5 or FMR6. ROC curve analysis revealed that FMR4 can distinguish FMR1 premutation carrier with FXPOI with a diagnostic power of 0.67. These findings suggest a potential role of FMR4 as a possible biomarker for FXPOI.
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Choi, Uk-Su, Yul-Wan Sung, and Seiji Ogawa. "Effects of Physiological Signal Removal on Resting-State Functional MRI Metrics." Brain Sciences 13, no. 1 (December 20, 2022): 8. http://dx.doi.org/10.3390/brainsci13010008.

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Resting-state fMRIs (rs-fMRIs) have been widely used for investigation of diverse brain functions, including brain cognition. The rs-fMRI has easily elucidated rs-fMRI metrics, such as the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC). To increase the applicability of these metrics, higher reliability is required by reducing confounders that are not related to the functional connectivity signal. Many previous studies already demonstrated the effects of physiological artifact removal from rs-fMRI data, but few have evaluated the effect on rs-fMRI metrics. In this study, we examined the effect of physiological noise correction on the most common rs-fMRI metrics. We calculated the intraclass correlation coefficient of repeated measurements on parcellated brain areas by applying physiological noise correction based on the RETROICOR method. Then, we evaluated the correction effect for five rs-fMRI metrics for the whole brain: FC, fALFF, ReHo, VMHC, and DC. The correction effect depended not only on the brain region, but also on the metric. Among the five metrics, the reliability in terms of the mean value of all ROIs was significantly improved for FC, but it deteriorated for fALFF, with no significant differences for ReHo, VMHC, and DC. Therefore, the decision on whether to perform the physiological correction should be based on the type of metric used.
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Schabdach, Jenna, Rafael Ceschin, Vanessa Schmithorst, M. Dylan Tisdall, Aaron Alexander-Bloch, and Ashok Panigrahy. "A Descriptive Review of the Impact of Patient Motion in Early Childhood Resting-State Functional Magnetic Resonance Imaging." Diagnostics 12, no. 5 (April 20, 2022): 1032. http://dx.doi.org/10.3390/diagnostics12051032.

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Resting-state functional magnetic images (rs-fMRIs) can be used to map and delineate the brain activity occurring while the patient is in a task-free state. These resting-state activity networks can be informative when diagnosing various neurodevelopmental diseases, but only if the images are high quality. The quality of an rs-fMRI rapidly degrades when the patient moves during the scan. Herein, we describe how patient motion impacts an rs-fMRI on multiple levels. We begin with how the electromagnetic field and pulses of an MR scanner interact with a patient’s physiology, how movement affects the net signal acquired by the scanner, and how motion can be quantified from rs-fMRI. We then present methods for preventing motion through educational and behavioral interventions appropriate for different age groups, techniques for prospectively monitoring and correcting motion during the acquisition process, and pipelines for mitigating the effects of motion in existing scans.
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Desai, Virendra R., Aditya Vedantam, Sandi K. Lam, Lucia Mirea, Stephen T. Foldes, Daniel J. Curry, P. David Adelson, Angus A. Wilfong, and Varina L. Boerwinkle. "Language lateralization with resting-state and task-based functional MRI in pediatric epilepsy." Journal of Neurosurgery: Pediatrics 23, no. 2 (February 2019): 171–77. http://dx.doi.org/10.3171/2018.7.peds18162.

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OBJECTIVEDetermining language laterality in patients with intractable epilepsy is important in operative planning. Wada testing is the gold standard, but it has a risk of stroke. Both Wada and task-based functional MRI (tb-fMRI) require patient cooperation. Recently, resting-state fMRI (rs-fMRI) has been explored for language lateralization. In the present study, the correlation between rs-fMRI and tb-fMRI in language lateralization is estimated in a pediatric population with intractable epilepsy.METHODSrs-fMRI and tb-fMRI language lateralization testing performed as part of epilepsy surgery evaluation was retrospectively reviewed.RESULTSTwenty-nine patients underwent rs-fMRI and tb-fMRI; a total of 38 rs-fMRI studies and 30 tb-fMRI studies were obtained. tb-fMRI suggested left dominance in 25 of 30 cases (83%), right in 3 (10%), and in 2 (7%) the studies were nondiagnostic. In rs-fMRI, 26 of 38 studies (68%) suggested left dominance, 3 (8%) right dominance, 6 (16%) bilateral, and 3 (8%) were nondiagnostic. When tb-fMRI lateralized to the left hemisphere (25 cases), rs-fMRI was lateralized to the left in 23 patients (92%) and it was bilateral/equal in 2 (8%). When tb-fMRI lateralized to the right (3 cases), rs-fMRI lateralized to the right in all cases (100%). The overall concordance rate was 0.93 (95% CI 0.76–0.99) when considering cases with tb-fMRI and rs-fMRI performed within 6 months of each other, and tb-fMRI results were not nondiagnostic.CONCLUSIONSrs-fMRI significantly correlated with tb-fMRI in lateralizing language and suggests the potential role for identifying hemispheric dominance via rs-fMRI. Further investigation and validation studies are warranted.
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Wei, Pengxu, Zhi Lan, Zeping Lv, and Yubo Fan. "Brainstem fMRI." Encyclopedia 1, no. 1 (December 22, 2020): 4–11. http://dx.doi.org/10.3390/encyclopedia1010003.

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The human brainstem plays important roles in maintaining basic vital functions. In comparison with brain functional magnetic resonance imaging (fMRI), only a few fMRI studies investigating the brainstem have been reported because of a number of technical challenges. This entry briefly introduces technical difficulties, recent advances, and further directions of brainstem fMRI in humans.
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Nielsen, F. A., M. S. Christ, K. H. Madsen, T. E. Lund, and L. K. Hansen. "fMRI neuroinformatics." IEEE Engineering in Medicine and Biology Magazine 25, no. 2 (March 2006): 112–19. http://dx.doi.org/10.1109/memb.2006.1607675.

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Utting, Jane F. "BLOODLESS fMRI." Neuroreport 12, no. 15 (October 2001): A87. http://dx.doi.org/10.1097/00001756-200110290-00002.

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Jasanoff, Alan. "Bloodless fMRI." Trends in Neurosciences 30, no. 11 (November 2007): 603–10. http://dx.doi.org/10.1016/j.tins.2007.08.002.

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Hoge, Richard D. "Calibrated fMRI." NeuroImage 62, no. 2 (August 2012): 930–37. http://dx.doi.org/10.1016/j.neuroimage.2012.02.022.

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Bartelle, Benjamin B., Ali Barandov, and Alan Jasanoff. "Molecular fMRI." Journal of Neuroscience 36, no. 15 (April 13, 2016): 4139–48. http://dx.doi.org/10.1523/jneurosci.4050-15.2016.

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Dissertations / Theses on the topic "FMRI"

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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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Allan, Thomas. "Novel fMRI analysis." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.659287.

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Functional Magnetic Resonance Imaging using the Blood Oxygenation Level Dependent (BOLD) contrast allows the brain's neural activity to be measured indirectly. This BOLD signal contains a wealth of information including changes in brain activity and functional connectivity (FC). FC is a measure of how correlated spatially separate brain regions are with each other. The work in this thesis is primarily concerned with novel methods of analysing the BOLD signal, in particular to give new measures of FC. A particular problem with typical measures of FC is that they assume that the networks are large scale and distributed, and that they originate from low frequency, static oscillations. It is clear from the way that we interact with the world that these assumptions are wrong, requiring a dynamic approach to investigate FC and the origins of what might be driving this. Here, a method combining short window correlation analysis and paradigm free mapping, a technique to detect spontaneous BOLD events without prior knowledge of their timings, is used to study the dynamic nature of these networks. It is further shown that these networks are at least in part driven by spontaneous activity, and that the rate of this spontaneous activity can be modulated by a task. These spontaneous events are then combined with network masks and temporal Independent Component Analysis to decompose these large scale networks into smaller sub-networks. Finally, the effects of spontaneous BOLD events on attention and task performance during a visual task is evaluated, highlighting how different brain regions that are not associated with a task can distract the subject's attention. It is shown that BOLD events that relate to a specific task use highly focal specific regions of the brain, confirming the spatial specificity of brain regions to a particular function.
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Pavlicova, Martina. "Thresholding FMRI images." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1097769474.

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Pavlicová, Martina. "Thresholding FMRI images." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1097769474.

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Thesis (Ph. D.)--Ohio State University, 2004.
Title from first page of PDF file. Document formatted into pages; contains xvii, 109 p.; also includes graphics (some col.) Includes bibliographical references (p. 107-109). Available online via OhioLINK's ETD Center
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Barreto, Felipe Rodrigues. "O acoplamento neurovascular e metabólico do córtex visual ativado de sujeitos jovens saudáveis durante a disponibilidade reduzida de oxigênio." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-29092016-142720/.

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O tecido cerebral é altamente dependente de uma complexa rede vascular e um suprimento adequado de oxigênio, uma vez que o metabolismo oxidativo é a principal via de produção de ATP. Entretanto, durante o aumento da atividade neuronal existe uma relação não linear entre fluxo sanguíneo cerebral e consumo de oxigênio, verificado por tomografia de emissão de pósitrons e posteriormente por técnicas quantitativas de ressonância magnética nuclear. O aumento mais pronunciado do fluxo sanguíneo em comparação com o consumo de oxigênio levanta questões sobre a possibilidade de o oxigênio atuar como um fator limitante. Apesar dos efeitos devastadores da privação completa de oxigênio ao tecido cerebral dentro de minutos, a redução da disponibilidade de oxigênio por curtos períodos de tempo é comum em pacientes com apneia do sono e está associada como fator de risco à hipertensão e acidentes vasculares. Acreditamos que a obtenção de novas informações sobre o efeito da disponibilidade de oxigênio na regulação da resposta vascular e do metabolismo energético no cérebro humano in vivo é crucial para um melhor entendimento de aspectos básicos do metabolismo energético cerebral e sua relação com o sistema neurovascular. Nesta tese foi avaliado o impacto da redução da disponibilidade de oxigênio no acoplamento neurovascular e metabólico do cérebro humano saudável. Dois estudos foram realizados na presença de hipóxia moderada, com saturação sanguínea entre 80 a 85%, e normóxia como condição de controle. O primeiro utilizou técnicas quantitativas de ressonância magnética funcional (fMRI) em 3T para caracterizar a resposta vascular evocada de 9 sujeitos saudáveis perante a estimulação visual. O segundo visou caracterizar as concentrações metabólicas em repouso e também as alterações induzidas pela estimulação visual em 11 sujeitos, utilizando a técnica de espectroscopia de ressonância magnética funcional (fMRS) em 7T. Os dados de fMRI mostraram reduções significativas das áreas corticais recrutadas durante a hipóxia moderada, embora as áreas comuns às três técnicas que continuaram ativas demonstraram respostas com amplitude de fluxo e volume sanguíneos similares a normoxia. Além disto, a variação de consumo de oxigênio devido à estimulação visual foi menor durante a hipóxia. Tais achados potencialmente poderiam indicar diminuição da extensão do recrutamento neuronal, porém um novo desacoplamento entre atividade neuronal e a resposta vascular, ou seja, aumento da atividade neuronal sem uma mesma resposta vascular durante a hipóxia moderada não poderia ser descartado. O estudo de fMRS demonstrou alterações metabólicas (glutamato e lactato) induzidas pela estimulação similares em ambas as condições gasosas. Entretanto, alterações significativas nas concentrações de aspartato, glutamato e glutamina foram observadas entre as condições no repouso. A combinação dos achados de ambos os estudos aqui apresentados sugere que a hipóxia moderada não resulta na diminuição do recrutamento neuronal, pois variações similares de glutamato e lactato, considerados fortes marcadores do aumento de atividade neuronal, foram observadas durante hipóxia moderada. Entretanto, há evidências de que a disponibilidade reduzida de oxigênio leva a alterações no mecanismo do acoplamento vascular e também no metabolismo basal. Análises futuras serão necessárias para verificar se existe um mecanismo fisiológico que explica as alterações vasculares e metabólicas aqui observadas.
The cerebral tissue is highly dependent on a complex vascular network and a tight regulated supply of oxygen, since oxidative metabolism is the primary source of ATP synthesis. Increased neuronal activity leads to a well-established mismatch between CBF and CMRO2, measured by PET and nuclear magnetic resonance techniques. The much larger CBF evoked response as compared to CMRO2 response raises questions about the role played by oxygen as a potential limiting factor. Despite the devastating effects of intense hypoxia to cerebral tissue, moderate oxygen deprivation through short periods of time is frequent in chronic disorders such as obstructive sleep apnea and has been suggested to be a risk factor for morbidities such as hypertension and stroke. Identifying the impact of mild hypoxia on functional brain metabolism in the healthy human brain is a crucial step for understanding basics aspects of cerebral bioenergetics and its relationship with the neurovascular system. In this thesis we evaluate the impact of reduced oxygen availability in the neurovascular and metabolic coupling of the healthy human brain. Two studies were performed in the presence of mild hypoxia, with 80 to 85% arterial blood oxygen saturation, and normoxia as the control condition. The first study utilized functional Magnetic Resonance Imaging techniques (fMRI) at 3T to characterize the vascular response to visual stimulation in 9 subjects. The second study aimed at characterizing the neurochemical profile of the human brain and quantifying the stimulus-induced metabolic changes as measured by fMRS at 7T in 11 subjects. The fMRI data showed significant reductions in the recruited cortical areas during mild hypoxia, although activated areas in all three imaging modalities showed responses with similar amplitude of blood flow and volume from normoxia. In addition, the variation of oxygen consumption due to stimulation was smaller during mild hypoxia. These findings could potentially suggest decreased neuronal recruitment, although a new decoupling between neuronal activity and vascular response (i.e. similar neuronal recruitment with different vascular response) could not be discarded. The fMRS study showed similar stimulus-induced glutamate and lactate changes during both gas conditions. However, significant concentration differences were observed in aspartate, glutamate and glutamine during rest conditions. Finally, the combination of the data from the two studies herein presented suggests that mild hypoxia does not result in reduced neuronal recruitment despite the altered vascular response, as shown by the similar glutamate and lactate stimulus-induced responses, known to be strong markers of increased neuronal activity. However, there are evidences that support altered neurovascular coupling and metabolic concentrations during reduced oxygen availability at rest. Further analysis will be necessary to elucidate how the new steady state concentrations of aspartate, glutamate and glutamine could be linked to physiological mechanism that potentially alters the neurovascular response.
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Bungert, Andreas. "TMS combined with fMRI." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546548.

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Bischoff, Matthias [Verfasser]. "Neurofunctional correlates of audiovisual binding in fMRI, EEG and EEG-guided fMRI / Matthias Bischoff." Gießen : Universitätsbibliothek, 2014. http://d-nb.info/1068589051/34.

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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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Drobnjak, Ivana. "FMRI simulator : development and applications." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.444904.

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De, Luca Marilena. "Low frequency signals in FMRI." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418562.

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Books on the topic "FMRI"

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Ulmer, Stephan, and Olav Jansen, eds. fMRI. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8.

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Ulmer, Stephan, and Olav Jansen, eds. fMRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68132-8.

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Ulmer, Stephan, and Olav Jansen, eds. fMRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1.

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Faro, Scott H., and Feroze B. Mohamed, eds. BOLD fMRI. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1329-6.

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Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-87919-0.

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Mulert, Christoph, and Louis Lemieux, eds. EEG - fMRI. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07121-8.

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Filippi, Massimo, ed. fMRI Techniques and Protocols. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-5611-1.

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Filippi, Massimo, ed. fMRI Techniques and Protocols. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-60327-919-2.

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FMRI techniques and protocols. New York: Humana, 2009.

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Uludag, Kamil, Kamil Ugurbil, and Lawrence Berliner, eds. fMRI: From Nuclear Spins to Brain Functions. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7591-1.

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Book chapters on the topic "FMRI"

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Ulmer, Stephan. "Introduction." In fMRI, 3–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_1.

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Binder, Jeffrey R. "Use of fMRI Language Lateralization for Quantitative Prediction of Naming and Verbal Memory Outcome in Left Temporal Lobe Epilepsy Surgery." In fMRI, 119–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_10.

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Heiss, Wolf-Dieter. "Mapping of Recovery from Poststroke Aphasia: Comparison of PET and fMRI." In fMRI, 141–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_11.

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Kim, Peter D., Charles L. Truwit, and Walter A. Hall. "Functional Magnetic Resonance-Guided Brain Tumor Resection." In fMRI, 155–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_12.

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Mehdorn, H. Maximillian, Simone Goebel, and Arya Nabavi. "Direct Cortical Stimulation and fMRI." In fMRI, 169–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_13.

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Glynn, Simon M., and John A. Detre. "Imaging Epilepsy and Epileptic Seizures Using fMRI." In fMRI, 177–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_14.

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Staudt, Martin. "Multimodal Brain Mapping in Patients with Early Brain Lesions." In fMRI, 191–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_15.

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Hertz-Pannier, Lucie, and Marion Noulhiane. "Special Issues in fMRI Involving Children." In fMRI, 197–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_16.

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Eickhoff, Simon B., and Christian Grefkes. "Modeling Connectivity in Health and Disease: Examples from the Motor System." In fMRI, 213–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_17.

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Siebner, Hartwig R., and Damian M. Herz. "fMRI in Parkinson’s Disease." In fMRI, 227–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_18.

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Conference papers on the topic "FMRI"

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Chen, Zikuan, and Vince D. Calhoun. "Phase fMRI reveals sparser function connectivity than magnitude fMRI." In Biomedical Applications in Molecular, Structural, and Functional Imaging, edited by Barjor Gimi and Andrzej Krol. SPIE, 2019. http://dx.doi.org/10.1117/12.2511513.

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Mavromihelaki, Evangelia, Jessica Eccles, Neil Harrison, Hugo Critchley, and Katerina Mania. "Cyberball3D+ for fMRI." In ACM SIGGRAPH 2014 Posters. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2614217.2614278.

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Aharony, Nadav, Wei Pan, Cory Ip, Inas Khayal, and Alex Pentland. "The social fMRI." In the 13th international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2030112.2030171.

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de Araújo, Dráulio B. "FMRI in Epilepsy." In MEDICAL PHYSICS: Eighth Mexican Symposium on Medical Physics. AIP, 2004. http://dx.doi.org/10.1063/1.1811811.

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Turner, Aysegul, Sinem B. Erdogan, and Ata Akin. "Translating fMRI to fNIRS." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6695979.

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Seghouane, Abd-Krim. "FMRI: Principles and analysis." In 2013 8th InternationalWorkshop on Systems, Signal Processing and their Applications (WoSSPA). IEEE, 2013. http://dx.doi.org/10.1109/wosspa.2013.6602328.

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Raz, Jonathan, and Bruce I. Turetsky. "Wavelet ANOVA and fMRI." In SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, edited by Michael A. Unser, Akram Aldroubi, and Andrew F. Laine. SPIE, 1999. http://dx.doi.org/10.1117/12.366814.

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Zhang, Nanyin, Xiao-Hong Zhu, Zhongming Liu, Bin He, and Wei Chen. "Quantitatively interpreting fMRI signal." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650190.

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Hollinger, Avrum, Christopher Steele, Virginia Penhune, Robert Zatorre, and Marcelo Wanderley. "fMRI-compatible electronic controllers." In the 7th international conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1279740.1279790.

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Hill, Jason E., Xiangyu Liu, Brian Nutter, and Sunanda Mitra. "A task-related and resting state realistic fMRI simulator for fMRI data validation." In SPIE Medical Imaging, edited by Martin A. Styner and Elsa D. Angelini. SPIE, 2017. http://dx.doi.org/10.1117/12.2254777.

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Reports on the topic "FMRI"

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Carpenter, Patricia A., and Marcel A. Just. Computational and fMRI Studies of Visualization. Fort Belvoir, VA: Defense Technical Information Center, November 2001. http://dx.doi.org/10.21236/ada399977.

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Just, Marcel A. Computational and fMRI Studies of Visualization. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada495982.

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Carr, Walter. An fMRI Study of TBI Associated with Blast Injury. Fort Belvoir, VA: Defense Technical Information Center, March 2009. http://dx.doi.org/10.21236/ada501624.

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Wishart, Heather. Development and Validation of an fMRI Pain Metric for MS. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada561913.

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Whitten, Lori. Functional Magnetic Resonance Imaging (fMRI): An Invaluable Tool in Translational Neuroscience. Research Triangle Park, NC: RTI Press, December 2012. http://dx.doi.org/10.3768/rtipress.2012.op.0010.1212.

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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 18. Hilbert Press, 2018. http://dx.doi.org/10.56441/hilbertpress.1818.9585.

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Abstract:
CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 20. Hilbert Press, 2020. http://dx.doi.org/10.56441/hilbertpress.2048.3738.

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Abstract:
CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Nieto-Castanon, Alfonso. CONN functional connectivity toolbox (RRID:SCR_009550), Version 19. Hilbert Press, 2019. http://dx.doi.org/10.56441/hilbertpress.1927.9364.

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Abstract:
CONN is a Matlab-based cross-platform software for the computation, display, and analysis of functional connectivity in fMRI (fcMRI). Connectivity measures include seed-to-voxel connectivity maps, ROI-to- ROI connectivity matrices, graph properties of connectivity networks, generalized psychophysiological interaction models (gPPI), intrinsic connectivity, local correlation and other voxel-to-voxel measures, independent component analyses (ICA), and dynamic component analyses (dyn-ICA). CONN is available for resting state data (rsfMRI) as well as task-related designs. It covers the entire pipeline from raw fMRI data to hypothesis testing, including spatial coregistration, ART-based scrubbing, aCompCor strategy for control of physiological and movement confounds, first-level connectivity estimation, and second-level random-effect analyses and hypothesis testing.
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Jai, Tun-Min Catherine, Dan Fan, Weidong Cai, and Forrest S. Bao. The Effect of Sensory Visual Presentations on Consumer's Buying Decisions: A fMRI Study. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-45.

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Jensen, Karen. A Turn-Key System for fMRI, Quarterly Report, Period Ending 31 March 1997. Fort Belvoir, VA: Defense Technical Information Center, April 1997. http://dx.doi.org/10.21236/ada324976.

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