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Spatial independent component analysis of functional magnetic resonance imaging time-series: characterization of the cortical components

E. Formisano, F. Esposito, N. Kriegeskorte, G. Tedeschi, F. Di Salle, R. Goebel
2002 Neurocomputing  
Spatial independent component analysis (sICA) can be applied to human brain functional magnetic resonance imaging (fMRI) data.  ...  While this problem ultimately requires interpretation, we propose kurtosis of the component histogram, spatial clustering of the component's layout in the brain and one-lag autocorrelation of the time  ...  of cortical voxels in the 4D matrix obtained by functional magnetic resonance imaging (b).  ... 
doi:10.1016/s0925-2312(02)00517-9 fatcat:ichq2dsn7jc6zfq72yhnno66we

Phase Variations in fMRI Time Series Analysis: Friend or Foe? [chapter]

Gisela Hagberg, Elisa Tuzzi
2014 Advanced Brain Neuroimaging Topics in Health and Disease - Methods and Applications  
Acknowledgements The authors would like to thank Valentina Brainovich, who helped us with the fMRI study, and a grant from the Italian Ministry of Health (RF05.103).  ...  in fMRI Time Series Analysis: Friend or Foe?  ...  Phase Variations in fMRI Time Series Analysis: Friend or Foe?  ... 
doi:10.5772/58275 fatcat:z4ce7vh5qzcvvkmhqlwdy4jx7e

A multivariate approach for processing magnetization effects in triggered event-related functional magnetic resonance imaging time series

Fabrizio Esposito, Francesco Di Salle, Franciszek Hennel, Ornella Santopaolo, Marcus Herdener, Klaus Scheffler, Rainer Goebel, Erich Seifritz
2006 NeuroImage  
Here, conventional echo-planar imaging and a post-processing solution based on principal component analysis were employed to remove the dominant eigenimages of the time series, to filter out the global  ...  Triggered event-related functional magnetic resonance imaging requires sparse intervals of temporally resolved functional data acquisitions, whose initiation corresponds to the occurrence of an event,  ...  Acknowledgment Study was supported by the Swiss National Science Foundation grant no. PP00B-103012.  ... 
doi:10.1016/j.neuroimage.2005.09.012 pmid:16242348 fatcat:had7u4wjf5fi3h4d4ctxibzaue

Real-time independent component analysis of fMRI time-series

F Esposito
2003 NeuroImage  
Real-time functional magnetic resonance imaging (fMRI) enables one to monitor a subject's brain activity during an ongoing session.  ...  Off-line data analysis, conversely, may be usefully complemented by data-driven approaches, such as independent component analysis (ICA), which can identify brain activity without a priori temporal assumptions  ...  Introduction Real-time functional magnetic resonance imaging (fMRI) is a promising tool for the noninvasive monitoring of brain activity during an ongoing imaging session.  ... 
doi:10.1016/j.neuroimage.2003.08.012 pmid:14683723 fatcat:eq3mztt2cnagtf42f53i5d65u4

Analysis of functional MRI time-series

K. J. Friston, P. Jezzard, R. Turner
1994 Human Brain Mapping  
A method for detecting significant and regionally specific correlations between sensory input and the brain's physiological response, as measured with functional magnetic resonance imaging (MRI), is presented  ...  The method involves testing for correlations between sensory input and the hemodynamic response after convolving the sensory input with an estimate of the hernodynamic response function.  ...  The work by K.J.F. was performed as part of the Institute Fellows in Theoretical Neurobiology research program at The Neurosciences Institute, which is supported by the Neurosciences Research Foundation  ... 
doi:10.1002/hbm.460010207 fatcat:teh7zvu4jrd2lim7rwjwyap7l4

Activation Detection on fMRI Time Series Using Hidden Markov Model

Rong Duan, Hong Man
2012 Advances in Artificial Neural Systems  
This paper introduces two unsupervised learning methods for analyzing functional magnetic resonance imaging (fMRI) data based on hidden Markov model (HMM).  ...  HMM approach is focused on capturing the first-order statistical evolution among the samples of a voxel time series, and it can provide a complimentary perspective of the BOLD signals.  ...  INTRODUCTION Functional Magnetic Resonance Imaging (fMRI) is a well established technique to monitor brain activities in the field of cognitive neuroscience.  ... 
doi:10.1155/2012/190359 fatcat:u2txdcpexvhy3j5vwr5jd2cfki

Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series

Xiao-Su Hu, Keum-Shik Hong, Shuzhi Sam Ge
2011 Neuroscience Letters  
Another modality, which includes functional magnetic resonance imaging (fMRI), measures the oxygen level variation in the cerebral blood (the hemodynamic response), providing outstanding spatial resolution  ...  Several research groups have utilized independent component analysis (ICA) to separate FOR signals and noises from raw NIRS time series, concluding that the ICA is a promising approach to detect fast neuronal  ... 
doi:10.1016/j.neulet.2011.09.011 pmid:21945547 fatcat:tusfi73nubb67kkxwqcgban5ba

Coil-to-coil physiological noise correlations and their impact on functional MRI time-series signal-to-noise ratio

Christina Triantafyllou, Jonathan R. Polimeni, Boris Keil, Lawrence L. Wald
2016 Magnetic Resonance in Medicine  
Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process.  ...  Purpose-Physiological nuisance fluctuations ("physiological noise") are a major contribution to the time-series Signal to Noise Ratio (tSNR) of functional imaging.  ...  Acknowledgments The authors would like to thank the McGovern Institute for Brain Research at MIT for funding, and Steve Shannon and Sheeba Arnold for the technical support.  ... 
doi:10.1002/mrm.26041 pmid:26756964 pmcid:PMC5565210 fatcat:wdaqfrmrnjhwvp4j6fm6hk2z5e

Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI time-series

Behnaz Jarrahi, Dante Mantini, Joshua Henk Balsters, Lars Michels, Thomas M. Kessler, Ulrich Mehnert, Spyros S. Kollias
2015 Human Brain Mapping  
Differential functional brain network connectivity during visceral interoception as revealed by independent component analysis of fMRI time-series. Human Brain Mapping, 36(11):4438-4468.  ...  In this study, we used spatial independent component analysis (ICA) and functional network connectivity (FNC) approaches to investigate time course correlations across the brain regions during visceral  ...  FUNCTIONAL NETWORK CONNECTIVITY Temporal correlations among the time series of the task-related ICs were assessed using the FNC toolbox (Medical Image Analysis Lab, University of New Mexico, Albuquerque  ... 
doi:10.1002/hbm.22929 pmid:26249369 fatcat:susyfkzumfbzbasj4x6kxxvgja

Analysis of fMRI time series with mutual information

Vanessa Gómez-Verdejo, Manel Martínez-Ramón, José Florensa-Vila, Antonio Oliviero
2012 Medical Image Analysis  
Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain.  ...  Neuroimaging plays a fundamental role in the study of human cognitive neuroscience.  ...  Stefan Possee (School of Medicine, The University of New Mexico) for his useful comments and suggestions and for his permission to use his sensorimotor and cognitive fMRI data in this work.  ... 
doi:10.1016/j.media.2011.11.002 pmid:22155195 fatcat:zkvg7dfhwrds7jo76x2nj3yqsa

Multivariate autoregressive modeling of fMRI time series

L Harrison, W.D Penny, K Friston
2003 NeuroImage  
We propose the use of multivariate autoregressive (MAR) models of functional magnetic resonance imaging time series to make inferences about functional integration within the human brain.  ...  MAR models are time series models and thereby model temporal order within measured brain activity.  ...  Appendix A Bayesian estimation Following the algorithm developed in the study by Penny and Roberts (2002) , the parameters of the posterior distributions are updated iteratively as follows:  ... 
doi:10.1016/s1053-8119(03)00160-5 pmid:12948704 fatcat:2k5gvuafgnhovav64slamvquai

Particle filtering, beamforming and multiple signal classification for the analysis of magnetoencephalography time series: a comparison of algorithms

Annalisa Pascarella, Alberto Sorrentino, Cristina Campi, Michele Piana
2010 Inverse Problems and Imaging  
We present a comparison of three methods for the solution of the magnetoencephalography inverse problem.  ...  Synthetic data with neurophysiological significance are analyzed by the three methods to recover position, orientation and amplitude of the active sources.  ...  Lauri Parkkonen of the Brain Research Unit, Low Temperature Laboratory, Helsinki University of Technology, is kindly acknowledged for useful discussions and for providing us the auditory data.  ... 
doi:10.3934/ipi.2010.4.169 fatcat:ialh2ntt7jbd5o2du2zkqyrvhm

Independence Testing for Multivariate Time Series [article]

Ronak Mehta, Jaewon Chung, Cencheng Shen, Ting Xu, Joshua T. Vogelstein
2020 arXiv   pre-print
This work juxtaposes distance correlation (Dcorr) and multiscale graph correlation (MGC) from independence testing literature and block permutation from time series analysis to address these challenges  ...  The proposed nonparametric procedure is valid and consistent, building upon prior work by characterizing the geometry of the relationship, estimating the time lag at which dependence is maximized, avoiding  ...  Acknowledgements The authors would like to thank Sambit Panda, Hayden Helm, Benjamin Pedigo, and Bijan Varjavand for their help and discussions in preparation of the paper.  ... 
arXiv:1908.06486v3 fatcat:fjuoatnmnng6pddzlysil3ntbi

Measuring the complexity of time series: An application to neurophysiological signals

S.L. Gonzalez Andino, R. Grave de Peralta Menendez, G. Thut, L. Spinelli, O. Blanke, C.M. Michel, T. Landis
2000 Human Brain Mapping  
time series in the time frequency plane.  ...  The most remarkable properties of this measure are twofold: 1) It does not rely on assumptions about the time series such as stationarity or gaussianity and 2) No model of the neural process under study  ...  ACKNOWLEDGMENTS Work supported by the Swiss National Foundation  ... 
doi:10.1002/1097-0193(200009)11:1<46::aid-hbm40>3.0.co;2-5 pmid:10997852 fatcat:eirnysaczjce5fp2aruij24xri

Measuring the complexity of time series: An application to neurophysiological signals

S.L. Gonzalez Andino, R. Grave de Peralta Menendez, G. Thut, L. Spinelli, O. Blanke, C.M. Michel, T. Landis
2000 Human Brain Mapping  
time series in the time frequency plane.  ...  The most remarkable properties of this measure are twofold: 1) It does not rely on assumptions about the time series such as stationarity or gaussianity and 2) No model of the neural process under study  ...  ACKNOWLEDGMENTS Work supported by the Swiss National Foundation  ... 
doi:10.1002/1097-0193(200009)11:1<46::aid-hbm40>3.3.co;2-x pmid:10997852 pmcid:PMC6872020 fatcat:rhu3rpmzt5cj5dpzuaeut6dwyy
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