View Item 

Show simple item record

Time series analysis of fMRI data: Spatial modelling and Bayesian computation
dc.contributor.authorTeng, Ming
dc.contributor.authorJohnson, Timothy D.
dc.contributor.authorNathoo, Farouk S.
dc.date.accessioned2018-07-13T15:47:41Z
dc.date.available2019-10-01T16:02:11Zen
dc.date.issued2018-08-15
dc.identifier.citationTeng, Ming; Johnson, Timothy D.; Nathoo, Farouk S. (2018). "Time series analysis of fMRI data: Spatial modelling and Bayesian computation." Statistics in Medicine 37(18): 2753-2770.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/144653
dc.publisherWiley Periodicals, Inc.
dc.publisherSpringer Science & Business Media
dc.subject.otherfMRI
dc.subject.othertime series
dc.subject.otherspatial model
dc.subject.otherSPM
dc.subject.otherHamiltonian Monte Carlo
dc.subject.othervariational Bayes
dc.titleTime series analysis of fMRI data: Spatial modelling and Bayesian computation
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144653/1/sim7680.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144653/2/sim7680-sup-0001-supplementary.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/144653/3/sim7680_am.pdf
dc.identifier.doi10.1002/sim.7680
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferencePascual‐Marqui RD, Michel CM, Lehmann D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiology. 1994; 18 ( 1 ): 49 ‐ 65.
dc.identifier.citedreferenceWoolrich MW, Jenkinson M, Brady JM, Smith SM. Fully Bayesian spatio‐temporal modeling of fMRI data. IEEE Trans Med Imaging. 2004; 23 ( 2 ): 213 ‐ 231.
dc.identifier.citedreferenceDuane S, Kennedy AD, Pendleton BJ, Roweth D. Hybrid monte carlo. Phys Letters B. 1987; 195 ( 2 ): 216 ‐ 222.
dc.identifier.citedreferenceNeal RM. Bayesian learning for neural networks. PhD Thesis: University of Toronto; 1995.
dc.identifier.citedreferenceSengupta B, Friston KJ, Penny WD. Gradient‐based MCMC samplers for dynamic causal modelling. NeuroImage. 2016; 125: 1107 ‐ 1118.
dc.identifier.citedreferenceHenson R, Shallice T, Gorno‐Tempini M, Dolan R. Face repetition effects in implicit and explicit memory tests as measured by fMRI. Cerebral Cortex. 2002; 12 ( 2 ): 178 ‐ 186.
dc.identifier.citedreferenceLindquist MA, et al. The statistical analysis of fMRI data. Stat Sci. 2008; 23 ( 4 ): 439 ‐ 464.
dc.identifier.citedreferencePenny W, Kiebel S, Friston K. Variational Bayesian inference for fMRI time series. NeuroImage. 2003; 19 ( 3 ): 727 ‐ 741.
dc.identifier.citedreferenceAlder BJ, Wainwright T. Studies in molecular dynamics. I. General method. J Chem Phys. 1959; 31 ( 2 ): 459 ‐ 466.
dc.identifier.citedreferenceHoffman MD, Gelman A. The No‐U‐turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res. 2014; 15 ( 1 ): 1593 ‐ 1623.
dc.identifier.citedreferenceSidén P, Eklund A, Bolin D, Villani M. Fast Bayesian whole‐brain fMRI analysis with spatial 3D priors. NeuroImage. 2017; 146: 211 ‐ 225.
dc.identifier.citedreferenceAshburner J, Barnes G, Chen CC, et al. SPM12 Manual; 2014.
dc.identifier.citedreferenceHarrison LM, Penny W, Flandin G, Ruff CC, Weiskopf N, Friston KJ. Graph‐partitioned spatial priors for functional magnetic resonance images. NeuroImage. 2008; 43 ( 4 ): 694 ‐ 707.
dc.identifier.citedreferenceBishop CM. Pattern Recognition and Machine Learning. New York: Springer‐Verlag; 2006.
dc.identifier.citedreferenceMoran PA. Notes on continuous stochastic phenomena. Biometrika. 1950; 37 ( 1/2 ): 17 ‐ 23.
dc.identifier.citedreferenceFishman GS, Yarberry LS. An implementation of the batch means method. INFORMS J Comput. 1997; 9 ( 3 ): 296 ‐ 310.
dc.identifier.citedreferenceBeskos A, Pillai N, Roberts G, Sanz‐Serna JM, Stuart A. Optimal tuning of the hybrid Monte Carlo algorithm. Bernoulli. 2013; 19 ( 5A ): 1501 ‐ 1534.
dc.identifier.citedreferenceNeal R. MCMC using Hamiltonian dynamics. In: Brooks S, Gelman A, Jones GL, Meng XL, eds. Handbook of Markov Chain Monte Carlo, Boca Raton, FL, USA: CRC Press; 2011: 113 ‐ 162.
dc.identifier.citedreferenceJordan MI, Ghahramani Z, Jaakkola TS, Saul LK. An introduction to variational methods for graphical models. Machine learn. 1999; 37 ( 2 ): 183 ‐ 233.
dc.identifier.citedreferencePenny WD, Trujillo‐Barreto NJ, Friston KJ. Bayesian fMRI time series analysis with spatial priors. NeuroImage. 2005; 24 ( 2 ): 350 ‐ 362.
dc.identifier.citedreferencePenny W, Flandin G, Trujillo‐Barreto N. Bayesian comparison of spatially regularised general linear models. Human Brain Mapping. 2007; 28 ( 4 ): 275 ‐ 293.
dc.identifier.citedreferenceNathoo FS, Ghosh P. Skew‐elliptical spatial random effect modeling for areal data with application to mapping health utilization rates. Stat Med. 2013; 32 ( 2 ): 290 ‐ 306.
dc.identifier.citedreferenceNathoo F, Lesperance M, Lawson A, Dean C. Comparing variational Bayes with Markov chain Monte Carlo for Bayesian computation in neuroimaging. Stat Methods Med Res. 2013; 22 ( 4 ): 398 ‐ 423.
dc.identifier.citedreferenceYu Z, Prado R, Quinlan EB, Cramer SC, Ombao H. Understanding the impact of stroke on brain motor function: a hierarchical bayesian approach. J Am Stat Assoc. 2016; 111 ( 514 ): 549 ‐ 563. 2, 17.
dc.identifier.citedreferenceRobert C, Casella G. Monte Carlo Statistical Methods. New York: Springer Science & Business Media; 2013.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim7680.pdf
Size:
1.870MB
Format:
PDF
View/Open
Name:
sim7680-sup-0001-supplementary.pdf
Size:
1.439MB
Format:
PDF
View/Open
Name:
sim7680_am.pdf
Size:
4.765MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.