Network modelling methods for FMRI

NeuroImage - Tập 54 - Trang 875-891 - 2011
Stephen M. Smith1, Karla L. Miller1, Gholamreza Salimi-Khorshidi1, Matthew Webster1, Christian F. Beckmann1,2, Thomas E. Nichols1,3, Joseph D. Ramsey4, Mark W. Woolrich1,5
1FMRIB (Oxford University Centre for Functional MRI of the Brain), Dept. Clinical Neurology, University of Oxford, UK
2Department of Clinical Neuroscience, Imperial College London, UK
3Departments of Statistics and Manufacturing, Warwick University, UK
4Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA
5OHBA (Oxford University Centre for Human Brain Activity), Dept. Psychiatry, University of Oxford, UK

Tài liệu tham khảo

Baccalá, 2001, Partial directed coherence: a new concept in neural structure determination, Biol. Cybern., 84, 463, 10.1007/PL00007990 Banerjee, 2006, Convex optimization techniques for fitting sparse Gaussian graphical models, 96 Buxton, 1998, Dynamics of blood flow and oxygenation changes during brain activation: the balloon model, Magn. Reson. Med., 39, 855, 10.1002/mrm.1910390602 Chang, 2010, Time-frequency dynamics of resting-state brain connectivity measured with fMRI, Neuroimage, 50, 81, 10.1016/j.neuroimage.2009.12.011 Chang, 2008, Mapping and correction of vascular hemodynamic latency in the BOLD signal, Neuroimage, 43, 90, 10.1016/j.neuroimage.2008.06.030 Chickering, 2003, Optimal structure identification with greedy search, J. Mach. Learn. Res., 3, 507 Daunizeau, 2009, Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models, Phys. D Nonlinear Phenom., 238, 2089, 10.1016/j.physd.2009.08.002 Dauwels, 2010, A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG, Neuroimage, 49, 668, 10.1016/j.neuroimage.2009.06.056 David, O., in press. fMRI connectivity, meaning and empiricism: Comments on: Roebroeck et al. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage, Corrected Proof:–. David, 2008, Identifying neural drivers with functional MRI: an electrophysiological validation, PLoS Biol., 6, e315, 10.1371/journal.pbio.0060315 de Pasquale, 2010, Temporal dynamics of spontaneous MEG activity in brain networks, Proc. Natl Acad. Sci., 107, 6040, 10.1073/pnas.0913863107 Deshpande, 2010, Assessing and compensating for zero-lag correlation effects in time-lagged Granger causality analysis of fMRI, IEEE Trans. Biomed. Eng., 57, 1446, 10.1109/TBME.2009.2037808 Deshpande, 2010, Effect of hemodynamic variability on Granger causality analysis of fMRI, Neuroimage, 52, 884, 10.1016/j.neuroimage.2009.11.060 Fox, 2009, The global signal and observed anticorrelated resting state brain networks, J. Neurophysiol., 101, 3270, 10.1152/jn.90777.2008 Freenor, M. and Glymour, C., 2010. Searching the DCM model space, and some generalizations. NeuroImage. In submission. Friedman, 2008, Sparse inverse covariance estimation with the Graphical Lasso, Biostat, 9, 432, 10.1093/biostatistics/kxm045 Friston, 1994, Functional and effective connectivity in neuroimaging: a synthesis, Hum. Brain Mapp., 2, 56, 10.1002/hbm.460020107 Friston, 2009, Causal modelling and brain connectivity in functional magnetic resonance imaging, PLoS Biol., 7, e1000033, 10.1371/journal.pbio.1000033 Friston, K., in press. Dynamic causal modeling and Granger causality. Comments on: The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage, Corrected Proof:–. Friston, 2003, Dynamic causal modelling, Neuroimage, 19, 1273, 10.1016/S1053-8119(03)00202-7 Geweke, 1984, Measures of conditional linear dependence and feedback between time series, J. Am. Stat. Assoc., 79, 907, 10.1080/01621459.1984.10477110 Goebel, 2003, Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping, Magn. Reson. Imaging, 21, 1251, 10.1016/j.mri.2003.08.026 Granger, 1969, Investigating causal relations by econometric models and cross-spectral methods, Econometrica, 37, 424, 10.2307/1912791 Grinsted, 2004, Application of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlinear Processes Geophys., 11, 561, 10.5194/npg-11-561-2004 Guo, 2008, Partial Granger causality—eliminating exogenous inputs and latent variables, J. Neurosci. Meth., 172, 79, 10.1016/j.jneumeth.2008.04.011 Handwerker, 2004, Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses, Neuroimage, 21, 1639, 10.1016/j.neuroimage.2003.11.029 Havlicek, 2010, Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data, NeuroImage, 53, 65, 10.1016/j.neuroimage.2010.05.063 Kamiński, 1997, Topographic analysis of coherence and propagation of EEG activity during sleep and wakefulness, Electroencephalogr. Clin. Neurophysiol., 102, 216, 10.1016/S0013-4694(96)95721-5 Kiviniemi, 2003, Independent component analysis of nondeterministic fMRI signal sources, Neuroimage, 19, 253, 10.1016/S1053-8119(03)00097-1 Kiviniemi, 2009, Functional segmentation of the brain cortex using high model order group PICA, Hum. Brain Mapp., 30, 3865, 10.1002/hbm.20813 Larkin, 1971 Marrelec, 2009, Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI, Hum. Brain Mapp., 30, 941, 10.1002/hbm.20555 Marrelec, 2006, Partial correlation for functional brain interactivity investigation in functional MRI, Neuroimage, 32, 228, 10.1016/j.neuroimage.2005.12.057 McIntosh, 1994, Structural equation modeling and its application to network analysis in functional brain imaging, Hum. Brain Mapp., 2, 2, 10.1002/hbm.460020104 Meek, 1995, Causal inference and causal explanation with background knowledge, 403 Nalatore, 2007, Mitigating the effects of measurement noise on Granger causality, Phys. Rev. E, 75, 31123.1, 10.1103/PhysRevE.75.031123 Nolte, 2008, Robustly estimating the flow direction of information in complex physical systems, Phys. Rev. Lett., 100, 234101.1, 10.1103/PhysRevLett.100.234101 Patel, 2006, A Bayesian approach to determining connectivity of the human brain, Hum. Brain Mapp., 27, 267, 10.1002/hbm.20182 Pereda, 2005, Nonlinear multivariate analysis of neurophysiological signals, Prog. Neurobiol., 77, 1, 10.1016/j.pneurobio.2005.10.003 Popa, 2009, Contrasting activity profile of two distributed cortical networks as a function of attentional demands, J. Neurosci., 29, 1191, 10.1523/JNEUROSCI.4867-08.2009 Quian Quiroga, 2002, Performance of different synchronization measures in real data: a case study on electroencephalographic signals, Phys. Rev. E, 65, 41903, 10.1103/PhysRevE.65.041903 Ramsey, 2010, Six problems for causal inference from fMRI, Neuroimage, 49, 1545, 10.1016/j.neuroimage.2009.08.065 Ramsey, 2006, Adjacency-faithfulness and conservative causal inference Richardson, 2001, Automated discovery of linear feedback models Ringo, 1994, Time is of the essence: a conjecture that hemispheric specialization arises from interhemispheric conduction delay, Cereb. Cortex, 4, 331, 10.1093/cercor/4.4.331 Roebroeck, 2005, Mapping directed influence over the brain using Granger causality and fMRI, Neuroimage, 25, 230, 10.1016/j.neuroimage.2004.11.017 Roebroeck, A., Formisano, E., and Goebel, R., in press. Reply to Friston and David: After comments on: The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage, Corrected Proof:–. Roebroeck, A., Formisano, E., and Goebel, R., in press. The identification of interacting networks in the brain using fMRI: model selection, causality and deconvolution. NeuroImage, Corrected Proof:–. Rogers, 2010, Functional MRI and multivariate autoregressive models, Magnetic Resonance Imaging, 28, 1058, 10.1016/j.mri.2010.03.002 Seth, 2010, A MATLAB toolbox for Granger causal connectivity analysis, J. Neurosci. Meth., 186, 262, 10.1016/j.jneumeth.2009.11.020 Shannon, 1948, A mathematical theory of communication, Bell Syst. Tech. J., 27, 379, 10.1002/j.1538-7305.1948.tb01338.x Shimizu, 2006, A linear non-Gaussian acyclic model for causal discovery, J. Mach. Learn. Res., 7, 2003 Tiao, 1976, Effect of temporal aggregation on the dynamic relationship of two time series variables, Biometrika, 63, 513, 10.1093/biomet/63.3.513 Wei, 1978, The effect of temporal aggregation on parameter estimation in distributed lag model, J. Econometrics, 8, 237, 10.1016/0304-4076(78)90032-5 Weiss, 1984, Systematic sampling and temporal aggregation in time series models, J. Econometrics, 26, 271, 10.1016/0304-4076(84)90022-8 Witt, 2009, The effects of computational method, data modeling, and TR on effective connectivity results, Brain Imaging Behav., 3, 220, 10.1007/s11682-009-9064-5 Wright, 1920, Correlation and causation, J. Agric. Res., 20, 557 Zhang, 2008, On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias, Artif. Intell., 172, 1873, 10.1016/j.artint.2008.08.001 Zhou, 2009, MATLAB toolbox for functional connectivity, Neuroimage, 47, 1590, 10.1016/j.neuroimage.2009.05.089