Academic literature on the topic 'FMRI signal'
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Journal articles on the topic "FMRI signal"
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.
Full textKim, Seong-Gi, and Seiji Ogawa. "Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals." Journal of Cerebral Blood Flow & Metabolism 32, no. 7 (March 7, 2012): 1188–206. http://dx.doi.org/10.1038/jcbfm.2012.23.
Full textLogothetis, Nikos K. "The neural basis of the blood–oxygen–level–dependent functional magnetic resonance imaging signal." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1424 (August 29, 2002): 1003–37. http://dx.doi.org/10.1098/rstb.2002.1114.
Full textHayward, Peter. "Ephemeral signal in fMRI." Lancet Neurology 2, no. 4 (April 2003): 204. http://dx.doi.org/10.1016/s1474-4422(03)00369-7.
Full textBednařík, Petr, Ivan Tkáč, Federico Giove, Mauro DiNuzzo, Dinesh K. Deelchand, Uzay E. Emir, Lynn E. Eberly, and Silvia Mangia. "Neurochemical and BOLD Responses during Neuronal Activation Measured in the Human Visual Cortex at 7 Tesla." Journal of Cerebral Blood Flow & Metabolism 35, no. 4 (January 7, 2015): 601–10. http://dx.doi.org/10.1038/jcbfm.2014.233.
Full textGui, Renzhou, Tongjie Chen, and Han Nie. "Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/7691294.
Full textWang, Maosen, Yi He, Terrence J. Sejnowski, and Xin Yu. "Brain-state dependent astrocytic Ca2+ signals are coupled to both positive and negative BOLD-fMRI signals." Proceedings of the National Academy of Sciences 115, no. 7 (January 30, 2018): E1647—E1656. http://dx.doi.org/10.1073/pnas.1711692115.
Full textGrössinger, Doris, Silvia Erika Kober, Stefan M. Spann, Rudolf Stollberger, and Guilherme Wood. "Real-Time Functional Magnetic Resonance Imaging as a Tool for Neurofeedback." Lernen und Lernstörungen 9, no. 3 (July 2020): 151–62. http://dx.doi.org/10.1024/2235-0977/a000300.
Full textTong, Yunjie, Kimberly P. Lindsey, and Blaise deB Frederick. "Partitioning of Physiological Noise Signals in the Brain with Concurrent Near-Infrared Spectroscopy and fMRI." Journal of Cerebral Blood Flow & Metabolism 31, no. 12 (August 3, 2011): 2352–62. http://dx.doi.org/10.1038/jcbfm.2011.100.
Full textShen, Yuji, Risto A. Kauppinen, Rishma Vidyasagar, and Xavier Golay. "A Functional Magnetic Resonance Imaging Technique Based on Nulling Extravascular Gray Matter Signal." Journal of Cerebral Blood Flow & Metabolism 29, no. 1 (August 27, 2008): 144–56. http://dx.doi.org/10.1038/jcbfm.2008.96.
Full textDissertations / Theses on the topic "FMRI signal"
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.
Full textKim, Junmo 1976. "Spatio-temporal fMRI signal analysis using information theory." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8982.
Full textIncludes 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.
Full textSalloum, 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.
Full textRiedel, 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.
Full textRiedel, 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.
Full textThomas, 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.
Full textPurdon, 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.
Full textMaczka, 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.
Full textFisher, Julia Marie. "Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters." Thesis, The University of Arizona, 2015. http://hdl.handle.net/10150/594646.
Full textBooks on the topic "FMRI signal"
Chen, Jean, Garth John Thompson, Shella Keilholz, and Peter Herman, eds. Origins of the Resting-State fMRI Signal. Frontiers Media SA, 2020. http://dx.doi.org/10.3389/978-2-88966-285-2.
Full textRamani, Ramachandran, ed. Functional MRI. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780190297763.001.0001.
Full textSeeck, Margitta, L. Spinelli, Jean Gotman, and Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.
Full textStamatakis, Emmanuel A., Eleni Orfanidou, and Andrew C. Papanicolaou. Functional Magnetic Resonance Imaging. Edited by Andrew C. Papanicolaou. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199764228.013.7.
Full textMeijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.
Full textBook chapters on the topic "FMRI signal"
Kayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 25–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34342-1_4.
Full textKayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 23–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68132-8_4.
Full textKayser, Christoph, and Nikos K. Logothetis. "The Electrophysiological Background of the fMRI Signal." In fMRI, 15–27. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41874-8_3.
Full textLei, Xu. "Simultaneous EEG-fMRI." In EEG Signal Processing and Feature Extraction, 377–405. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9113-2_18.
Full textTurner, Robert. "Signal Sources in Bold Contrast FMRI." In Advances in Experimental Medicine and Biology, 19–25. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4899-0056-2_2.
Full textUludağ, Kâmil, and Kâmil Uğurbil. "Physiology and Physics of the fMRI Signal." In fMRI: From Nuclear Spins to Brain Functions, 163–213. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7591-1_8.
Full textYlipaavalniemi, Jarkko, Seppo Mattila, Antti Tarkiainen, and Ricardo Vigário. "Brains and Phantoms: An ICA Study of fMRI." In Independent Component Analysis and Blind Signal Separation, 503–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_63.
Full textGruber, P., C. Kohler, and F. J. Theis. "A Toolbox for Model-Free Analysis of fMRI Data." In Independent Component Analysis and Signal Separation, 209–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74494-8_27.
Full textBazán, Paulo Rodrigo, and Edson Amaro. "fMRI and fNIRS Methods for Social Brain Studies: Hyperscanning Possibilities." In Social and Affective Neuroscience of Everyday Human Interaction, 231–54. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08651-9_14.
Full textChatzichristos, Christos, Eleftherios Kofidis, Yiannis Kopsinis, Manuel Morante Moreno, and Sergios Theodoridis. "Higher-Order Block Term Decomposition for Spatially Folded fMRI Data." In Latent Variable Analysis and Signal Separation, 3–15. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53547-0_1.
Full textConference papers on the topic "FMRI signal"
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.
Full textParker, David, Raphael T. Gerraty, and Qolamreza R. Razlighi. "Optimal signal recovery from interleaved FMRI data." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7164131.
Full textSoltanian-Zadeh, Hamid, Gholam-Ali Hossein-Zadeh, and Babak A. Ardekani. "fMRI activation detection in wavelet signal subspace." In Medical Imaging 2002, edited by Anne V. Clough and Chin-Tu Chen. SPIE, 2002. http://dx.doi.org/10.1117/12.463602.
Full textSeghouane, 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.
Full textOberlin, Thomas, Christian Barillot, Remi Gribonval, and Pierre Maurel. "Symmetrical EEG-FMRI imaging by sparse regularization." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362708.
Full textXu, Hao, Alexander Lorbert, Peter J. Ramadge, J. Swaroop Guntupalli, and James V. Haxby. "Regularized hyperalignment of multi-set fMRI data." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319668.
Full textWeizman, L., K. L. Miller, Y. C. Eldar, O. Maayan, and M. Chiew. "PEAR: PEriodic and ApeRiodic signal separation for fast FMRI." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8036872.
Full textSeiyama, Akitoshi, Yasuhiro Ooi, and Junji Seki. "Implication of output signal from Optical Topography and fMRI." In 2007 IEEE/ICME International Conference on Complex Medical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/iccme.2007.4381879.
Full textYoshida, Shinichi. "Decoding of emotional visual stimuli using fMRI brain signal." In 2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). IEEE, 2016. http://dx.doi.org/10.1109/icis.2016.7550878.
Full textMogultay, Hazal, Sarper Alkan, and Fatos T. Yarman-Vural. "Classification of fMRI data by using clustering." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7130360.
Full textReports on the topic "FMRI signal"
Pizarro, Rodrigo, Raúl Delgado, Huáscar Eguino, and Carlos Pimenta. Marco conceptual para la clasificación del gasto público en cambio climático en América Latina y el Caribe. Banco Interamericano de Desarrollo, September 2022. http://dx.doi.org/10.18235/0004449.
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