View Item 

Show simple item record

Bayesian log‐Gaussian Cox process regression: applications to meta‐analysis of neuroimaging working memory studies
dc.contributor.authorSamartsidis, Pantelis
dc.contributor.authorEickhoff, Claudia R.
dc.contributor.authorEickhoff, Simon B.
dc.contributor.authorWager, Tor D.
dc.contributor.authorBarrett, Lisa Feldman
dc.contributor.authorAtzil, Shir
dc.contributor.authorJohnson, Timothy D.
dc.contributor.authorNichols, Thomas E.
dc.date.accessioned2019-01-15T20:25:07Z
dc.date.available2020-03-03T21:29:35Zen
dc.date.issued2019-01
dc.identifier.citationSamartsidis, Pantelis; Eickhoff, Claudia R.; Eickhoff, Simon B.; Wager, Tor D.; Barrett, Lisa Feldman; Atzil, Shir; Johnson, Timothy D.; Nichols, Thomas E. (2019). "Bayesian log‐Gaussian Cox process regression: applications to meta‐analysis of neuroimaging working memory studies." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68(1): 217-234.
dc.identifier.issn0035-9254
dc.identifier.issn1467-9876
dc.identifier.urihttps://hdl.handle.net/2027.42/146885
dc.publisherWiley
dc.subject.otherWorking memory
dc.subject.otherRandom‐effects meta‐analysis
dc.subject.otherMetaregression
dc.subject.otherFunctional magnetic resonance imaging
dc.titleBayesian log‐Gaussian Cox process regression: applications to meta‐analysis of neuroimaging working memory studies
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146885/1/rssc12295_am.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146885/2/rssc12295-sup-0001-SupInfo.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146885/3/rssc12295.pdf
dc.identifier.doi10.1111/rssc.12295
dc.identifier.sourceJournal of the Royal Statistical Society: Series C (Applied Statistics)
dc.identifier.citedreferenceRasmussen, C. E. and Williams, C. K. I. ( 2005 ) Gaussian Processes for Machine Learning. Cambridge: MIT Press.
dc.identifier.citedreferenceMøller, J. and Waagepetersen, R. P. ( 2004 ) Statistical Inference and Simulation for Spatial Point Processes. Boca Raton: Chapman and Hall–CRC.
dc.identifier.citedreferenceMøller, J. and Waagepetersen, R. P. ( 2007 ) Modern statistics for spatial point processes. Scand. J. Statist., 34, 643 – 684.
dc.identifier.citedreferenceMontagna, S., Wager, T., Feldman Barrett, L., Johnson, T. D. and Nichols, T. E. ( 2018 ) Spatial Bayesian latent factor regression modeling of coordinate‐based meta‐analysis data. Biometrics, 74, 342 – 353.
dc.identifier.citedreferenceNeal, R. M. ( 1996 ) Bayesian Learning for Neural Networks. New York: Springer.
dc.identifier.citedreferenceNeal, R. M. ( 2011 ) MCMC using Hamiltonian dynamics. In Handbook of Markov Chain Monte Carlo (eds S. Brooks, A. Gelman, G. L. Jones and X. Meng ), ch. 5, pp. 113 – 162. Boca Raton: Chapman and Hall–CRC.
dc.identifier.citedreferenceOwen, A. M., McMillan, K. M., Laird, A. R. and Bullmore, E. ( 2005 ) N‐back working memory paradigm: a meta‐analysis of normative functional neuroimaging studies. Hum. Brain Mappng, 25, 46 – 59.
dc.identifier.citedreferencePark, T. and van Dyk, D. A. ( 2009 ) Partially collapsed Gibbs samplers: illustrations and applications. J. Computnl Graph. Statist., 18, 283 – 305.
dc.identifier.citedreferenceRadua, J. and Mataix‐Cols, D. ( 2009 ) Voxel‐wise meta‐analysis of grey matter changes in obsessive‐compulsive disorder. Br. J. Psychiatr., 195, 393 – 402.
dc.identifier.citedreferenceRadua, J., Mataix‐Cols, D., Phillips, M. L., El‐Hage, W., Kronhaus, D. M., Cardoner, N. and Surguladze, S. ( 2012 ) A new meta‐analytic method for neuroimaging studies that combines reported peak coordinates and statistical parametric maps. Eur. Psychiatr., 27, 605 – 611.
dc.identifier.citedreferenceRottschy, C., Langner, R., Dogan, I., Reetz, K., Laird, A. R., Schulz, J. B., Fox, P. T. and Eickhoff, S. B. ( 2012 ) Modelling neural correlates of working memory: a coordinate‐based meta‐analysis. NeuroImage, 60, 830 – 846.
dc.identifier.citedreferenceRue, H. and Held, L. ( 2005 ) Gaussian Markov Random Fields: Theory and Applications. Boca Raton: Chapman and Hall–CRC.
dc.identifier.citedreferenceSalimi‐Khorshidi, G., Smith, S. M., Keltner, J. R., Wager, T. D. and Nichols, T. E. ( 2009 ) Meta‐analysis of neuroimaging data: a comparison of image‐based and coordinate‐based pooling of studies. NeuroImage, 45, 810 – 823.
dc.identifier.citedreferenceSamartsidis, P., Montagna, S., Laird, A. R., Fox, P. T., Johnson, T. D. and Nichols, T. E. ( 2017 ) Estimating the number of missing experiments in a neuroimaging meta‐analysis. Preprint bioRxiv 225425.
dc.identifier.citedreferenceSimpson, D., Illian, J., Lindgren, F., Sørbye, S. and Rue, H. ( 2016 ) Going off grid: computationally efficient inference for log‐Gaussian Cox processes. Biometrika, 103, 49 – 70.
dc.identifier.citedreferenceTaylor, B. M. and Diggle, P. J. ( 2014 ) INLA or MCMC?: A tutorial and comparative evaluation for spatial prediction in log‐Gaussian Cox processes. J. Statist. Computn Simuln, 84, 2266 – 2284.
dc.identifier.citedreferenceTurkeltaub, P. E., Eden, G. F., Jones, K. M. and Zeffiro, T. A. ( 2002 ) Metaanalysis of the functional neuroanatomy of single‐word reading: method and validation. NeuroImage, 16, 765 – 780.
dc.identifier.citedreferenceWaagepetersen, R. P. ( 2004 ) Convergence of posteriors for discretized log Gaussian Cox processes. Statist. Probab. Lett., 66, 229 – 235.
dc.identifier.citedreferenceWager, T. D., Jonides, J. and Reading, S. ( 2004 ) Neuroimaging studies of shifting attention: a meta‐analysis. NeuroImage, 22, 1679 – 1693.
dc.identifier.citedreferenceWager, T. D., Lindquist, M. and Kaplan, L. ( 2007 ) Meta‐analysis of functional neuroimaging data: current and future directions. Socl Cogn. Affect. Neursci., 2, 150 – 158.
dc.identifier.citedreferenceWager, T. D. and Smith, E. E. ( 2003 ) Neuroimaging studies of working memory. Cogn. Affect. Behav. Neursci., 3, 255 – 274.
dc.identifier.citedreferenceWood, A. T. A. and Chan, G. ( 1994 ) Simulation of stationary Gaussian processes in [0, 1] d. J. Computnl Graph. Statist., 3, 409 – 432.
dc.identifier.citedreferenceYue, Y. R., Lindquist, M. A. and Loh, J. M. ( 2012 ) Meta‐analysis of functional neuroimaging data using Bayesian nonparametric binary regression. Ann. Appl. Statist., 6, 697 – 718.
dc.identifier.citedreferenceZhang, H. ( 2004 ) Inconsistent estimation and asymptotically equal interpolations in model‐based geostatistics. J. Am. Statist. Ass., 99, 250 – 261.
dc.identifier.citedreferenceMurray, I., Adams, R. P. and MacKay, D. J. ( 2010 ) Elliptical slice sampling. J. Mach. Learn. Res. Wrkshp Conf. Proc., 9, 541 – 548.
dc.identifier.citedreferenceBenes, V., Bodlák, K., Møller, J. and Waagepetersen, R. P. ( 2002 ) Bayesian analysis of log Gaussian Cox processes for disease mapping. Technical Report. Department of Mathematical Sciences, Aalborg University, Aalborg.
dc.identifier.citedreferenceChristensen, O. F., Roberts, G. O. and Sköld, M. ( 2006 ) Robust Markov chain Monte Carlo methods for spatial generalized linear mixed models. J. Computnl Graph. Statist., 15, 1 – 17.
dc.identifier.citedreferenceChristensen, O. F. and Waagepetersen, R. P. ( 2002 ) Bayesian prediction of spatial count data using generalized linear mixed models. Biometrics, 58, 280 – 286.
dc.identifier.citedreferenceDesikan, R. S., Sgonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S. and Killiany, R. J. ( 2006 ) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968 – 980.
dc.identifier.citedreferenceDietrich, C. R. and Newsam, G. N. ( 1993 ) A fast and exact method for multidimensional Gaussian stochastic simulations. Wat. Resour. Res., 29, 2861 – 2869.
dc.identifier.citedreferenceDiggle, P. J., Moraga, P., Rowlingson, B. and Taylor, B. M. ( 2013 ) Spatial and spatio‐temporal log‐Gaussian Cox processes: extending the geostatistical paradigm. Statist. Sci., 28, 542 – 563.
dc.identifier.citedreferenceDuane, S., Kennedy, A. D., Pendleton, B. J. and Roweth, D. ( 1987 ) Hybrid Monte Carlo. Phys. Lett. B, 195, 216 – 222.
dc.identifier.citedreferenceEickhoff, S. B., Bzdok, D., Laird, A. R., Kurth, F. and Fox, P. T. ( 2012 ) Activation likelihood estimation meta‐analysis revisited. NeuroImage, 59, 2349 – 2361.
dc.identifier.citedreferenceGelman, A., Meng, X.‐L. and Stern, H. ( 1996 ) Posterior predictive assessment of model fitness via realized discrepancies. Statist. Sin., 6, 733 – 807.
dc.identifier.citedreferenceGirolami, M. and Calderhead, B. ( 2011 ) Riemann manifold Langevin and Hamiltonian Monte Carlo methods (with discussion). J. R. Statist. Soc. B, 73, 123 – 214.
dc.identifier.citedreferenceGneiting, T. and Raftery, A. E. ( 2007 ) Strictly proper scoring rules, prediction, and estimation. J. Am. Statist. Ass., 102, 359 – 378.
dc.identifier.citedreferenceGreenland, S. ( 1994 ) Invited commentary: a critical look at some popular metaanalytic methods. Am. J. Epidem., 140, 290 – 296.
dc.identifier.citedreferenceHartung, J., Knapp, G. and Sinha, B. K. ( 2008 ) Statistical Meta‐analysis with Applications. Hoboken: Wiley.
dc.identifier.citedreferenceHoffman, M. and Gelman, A. ( 2014 ) The No‐U‐turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15, 1593 – 1623.
dc.identifier.citedreferenceIllian, J. B., Sørbye, S. H. and Rue, H. ( 2012a ) A toolbox for fitting complex spatial point process models using integrated nested Laplace approximation (INLA). Ann. Appl. Statist., 6, 1499 – 1530.
dc.identifier.citedreferenceIllian, J. B., Sørbye, S. H., Rue, H. and Hendrichsen, D. K. ( 2012b ) Using INLA to fit a complex point process model with temporally varying effects—a case study. J. Environ. Statist., 3, 1 – 25.
dc.identifier.citedreferenceJaakkola, T. and Jordan, M. ( 2000 ) Bayesian parameter estimation via variational methods. Statist. Comput., 10, 25 – 37.
dc.identifier.citedreferenceKang, J., Johnson, T. D., Nichols, T. E. and Wager, T. D. ( 2011 ) Meta analysis of functional neuroimaging data via Bayesian spatial point processes. J. Am. Statist. Ass., 106, 124 – 134.
dc.identifier.citedreferenceKang, J., Nichols, T. E., Wager, T. D. and Johnson, T. D. ( 2014 ) A Bayesian hierarchical spatial point process model for multi‐type neuroimaging metaanalysis. Ann. Appl. Statist., 8, 1561 – 1582.
dc.identifier.citedreferenceLaird, A. R., Lancaster, J. J. and Fox, P. T. ( 2005 ) Brainmap: the social evolution of a human brain mapping database. Neuroinformatics, 3, 65 – 77.
dc.identifier.citedreferenceLeininger, T. J. and Gelfand, A. E. ( 2017 ) Bayesian inference and model assessment for spatial point patterns using posterior predictive samples. Baysn Anal., 12, 1 – 30.
dc.identifier.citedreferenceLiang, S., Carlin, B. P. and Gelfand, A. E. ( 2009 ) Analysis of Minnesota colon and rectum cancer point patterns with spatial and nonspatial covariate information. Ann. Appl. Statist., 3, 943 – 962.
dc.identifier.citedreferenceMarshall, T. and Roberts, G. ( 2012 ) An adaptive approach to Langevin MCMC. Statist. Comput., 22, 1041 – 1057.
dc.identifier.citedreferenceMøller, J., Syversveen, A. R. and Waagepetersen, R. P. ( 1998 ) Log Gaussian Cox processes. Scand. J. Statist., 25, 451 – 482.
dc.identifier.citedreferenceMøller, J. and Waagepetersen, R. P. ( 2003 ) An introduction to simulation‐based inference for spatial point processes. In Spatial Statistics and Computational Methods (ed. J. Møller ), ch. 4, pp. 143 – 198. Berlin: Springer.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
rssc12295_am.pdf
Size:
886.9KB
Format:
PDF
View/Open
Name:
rssc12295-sup-0001-SupInfo.pdf
Size:
1.452MB
Format:
PDF
View/Open
Name:
rssc12295.pdf
Size:
3.178MB
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.