A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data

Annals of Applied Statistics

Volumn 10, Issue 2, 2016, Pages 638-666

  Zhang, Linlin ^a   Guindani, Michele ^b   Versace, Francesco ^c   Engelmann, Jeffrey M ^b   Vannucci, Marina ^a  

^a Rice University   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^c UNIVERSITY OF OKLAHOMA COLLEGE OF MEDICINE   (United States)

Author keywords

Multi subject fMRI; Spatiotemporal linear regression; Variable selection priors; Variational Bayes

Indexed keywords

EID: 84979937090     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/16-AOAS926     Document Type: Article

References (63)

1. BANERJEE, S., CARLIN, B. P. and GELFand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data, 2nd ed. Monographs on Statistics and Applied Probability 135. CRC Press, Boca Raton, FL. MR3362184
2. BARRIENTOS, A. F., JARA, A. and QUINTANA, F. A. (2012). On the support of MacEachern’s dependent Dirichlet processes and extensions. Bayesian Anal. 7 277–309. MR2934952
3. BISHOP, C. M. (2006). Pattern Recognition and Machine Learning. Springer, New York. MR2247587
4. BLEI, D. M. and JORDAN, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Anal. 1 121–143 (electronic). MR2227367
5. BOWMAN, F., CAFFO, B., BASSETT, S. and KILTS, C. (2008). A Bayesian hierarchical framework for spatial modeling of fMRI data. NeuroImage 39 146–156.
6. BUXTON, R. and FRANK, L. (1997). A model for the coupling between cerebral blood flow and oxygen metabolism during neural stimulation. J. Cereb. Blood Flow Metab. 17 64–72.
7. CARBONETTO, P. and STEPHENS, M. (2012). Scalable variational inference for Bayesian variable selection in regression, and its accuracy in genetic association studies. Bayesian Anal. 7 73–107. MR2896713
8. DAUBECHIES, I. (1992). Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics 61. SIAM, Philadelphia, PA. MR1162107
9. EFRON, B. (2008). Microarrays, empirical Bayes and the two-groups model. Statist. Sci. 23 1–22. MR2431866
10. FADILI, M. J. and BULLMORE, E. T. (2002). Wavelet-generalised least squares: A new BLU esti-mator of linear regression models with 1/f errors. NeuroImage 15 217–232.
11. FERGUSON, T. S. (1973). A Bayesian analysis of some nonparametric problems. Ann. Statist. 1 209–230. MR0350949
12. FLandIN, G. and PENNY, W. D. (2007). Bayesian fMRI data analysis with sparse spatial basis function priors. NeuroImage 34 1108–1125.
13. FRISTON, K. J. (1994). Functional and effective connectivity in neuroimaging: A synthesis. Hum. Brain Mapp. 2 56–78.
14. FRISTON, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity 1 13–36.
15. FRISTON, K. J., JEZZARD, P. and TURNER, R. (1994). Analysis of functional MRI time-series. Hum. Brain Mapp. 1 153–171.
16. FRISTON, K. J. and PENNY, W. (2003). Posterior probability maps and SPMs. NeuroImage 19 1240–1249.
17. FRISTON, K. J., HOLMES, A. P., POLINE, J. B., GRASBY, P. J., WILLIAMS, S. C. R., FRACK-OWIAK, R. S. J. and TURNER, R. (1995). Analysis of fMRI time-series revisited. NeuroImage 2 45–53.
18. FRISTON, K. J., PENNY, W., PHILLIPS, C., KIEBEL, S., HINTON, G. and ASHBURNER, J. (2002). Classical and Bayesian inference in neuroimaging: Theory. NeuroImage 16 465–483.
19. HARRISON, L. M. and GREEN, G. G. R. (2010). A Bayesian spatiotemporal model for very large data sets. NeuroImage 50 1126–1141.
20. HARTVIG, N. V. and JENSEN, J. L. (2000). Spatial mixture modeling of fMRI data. Hum. Brain Mapp. 11 233–248.
21. HOLMES, A. P. and FRISTON, K. J. (1998). Generalisability, random effects & population inference. Neuroimage 7 S754.
22. ISHWARAN, H. and JAMES, L. F. (2001). Gibbs sampling methods for stick-breaking priors. J. Amer. Statist. Assoc. 96 161–173. MR1952729
23. JBABDI, S., WOOLRICH, M. W. and BEHRENS, T. E. J. (2009). Multiple-subjects connectivity-based parcellation using hierarchical Dirichlet process mixture models. NeuroImage 44 373–384.
24. JEONG, J., VANNUCCI, M. and KO, K. (2013). A wavelet-based Bayesian approach to regres-sion models with long memory errors and its application to fMRI data. Biometrics 69 184–196. MR3058065
25. JOHNSON, T. D., LIU, Z., BARTSCH, A. J. and NICHOLS, T. E. (2013). A Bayesian non-parametric Potts model with application to pre-surgical FMRI data. Stat. Methods Med. Res. 22 364–381. MR3190664
26. JOSET, A. E., GAZZOLA, V. and KEYSERS, C. (2009). An introduction to anatomical ROI-based fMRI classification analysis. Brain Res. 1282 114–125.
27. KALUS, S., SÄMANN, P. G. and FAHRMEIR, L. (2014). Classification of brain activation via spatial Bayesian variable selection in fMRI regression. Adv. Data Anal. Classif. 8 63–83. MR3168680
28. KIM, S., SMYTH, P. and STERN, H. (2006). A nonparametric Bayesian approach to detecting spatial activation patterns in fMRI data. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2006 217–224.
29. LEE, K.-J., JONES, G. L., CAFFO, B. S. and BASSETT, S. S. (2014). Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data. Bayesian Anal. 9 699–731. MR3256061
30. LI, F., ZHANG, T., WANG, Q., GONZALEZ, M. Z., MARESH, E. L. and COAN, J. A. (2015). Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression. Ann. Appl. Stat. 9 687–713. MR3371331
31. LINDQUIST, M. A. (2008). The statistical analysis of fMRI data. Statist. Sci. 23 439–464. MR2530545
32. MEYER, F. G. (2003). Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series. IEEE Trans. Med. Imag. 22 315–322.
33. MÜLLER, P., PARMIGIANI, G. and RICE, K. (2007). FDR and Bayesian multiple comparisons rules. In Bayesian Statistics 8 (J. M. Bernardo, M. J. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith and M. West, eds.). Oxford Sci. Publ. 349–370. Oxford Univ. Press, Oxford. MR2433200
34. NEWTON, M. A., NOUEIRY, A., SARKAR, D. and AHLQUIST, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture method. Biostatistics 5 155–176.
35. PENNY, W., KIEBEL, S. and FRISTON, K. J. (2003). Variational Bayesian inference for fmri time series. NeuroImage 19 727–741.
36. PENNY, W. D., TRUJILLO-BARRETO, N. and FRISTON, K. J. (2005). Bayesian fMRI time series analysis with spatial priors. NeuroImage 24 350–362.
37. PROPP, J. G. and WILSON, D. B. (1996). Exact sampling with coupled Markov chains and applica-tions to statistical mechanics. In Proceedings of the Seventh International Conference on Random Structures and Algorithms (Atlanta, GA, 1995) 9 223–252. Random Structures Algorithms, 1-2. MR1611693
38. QUIRÓS, A., DIEZ, R. M. and GAMERMAN, D. (2010). Bayesian spatiotemporal model of fMRI data. NeuroImage 49 442–456.
39. RAFTERY, A. E. and LEWIS, S. M. (1992). One long run with diagnostics: Implementation strategies for Markov chain Monte Carlo. Statist. Sci. 7 493–497.
40. RODRÍGUEZ, A., DUNSON, D. B. and GELFand, A. E. (2008). The nested Dirichlet process. J. Amer. Statist. Assoc. 103 1131–1144. MR2528831
41. ROSENBLATT, J. D., VINK, M. and BENJAMINI, Y. (2014). Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation. NeuroImage 84 113–121.
42. RUE, H., MARTINO, S. and CHOPIN, N. (2009). Approximate Bayesian inference for latent Gaus-sian models by using integrated nested Laplace approximations. J. R. Stat. Soc. Ser. B Stat. Methodol. 71 319–392. MR2649602
43. SANYAL, N. and FERREIRA, M. A. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. NeuroImage 63 1519–1531.
44. SAVITSKY, T. and VANNUCCI, M. (2010). Spiked Dirichlet process priors for Gaussian process models. J. Probab. Stat. Art. ID 201489, 14. MR2745498
45. SAVITSKY, T., VANNUCCI, M. and SHA, N. (2011). Variable selection for nonparametric Gaussian process priors: Models and computational strategies. Statist. Sci. 26 130–149. MR2849913
46. SETHURAMAN, J. (1994). A constructive definition of Dirichlet priors. Statist. Sinica 4 639–650. MR1309433
47. SMITH, M. and FAHRMEIR, L. (2007). Spatial Bayesian variable selection with application to func-tional magnetic resonance imaging. J. Amer. Statist. Assoc. 102 417–431. MR2370843
48. SU, S., CAFFO, B., GARRETT-MAYER, E. and BASSETT, S. (2009). Modified test statistics by inter-voxel variance shrinkage with an application to fMRI. Biostatistics 10 219–227.
49. SUN, W., REICH, B. J., CAI, T. T., GUINDANI, M. and SCHWARTZMAN, A. (2015). False discov-ery control in large-scale spatial multiple testing. J. R. Stat. Soc. Ser. B. Stat. Methodol. 77 59–83. MR3299399
50. TEH, Y. W., JORDAN, M. I., BEAL, M. J. and BLEI, D. M. (2006). Hierarchical Dirichlet processes. J. Amer. Statist. Assoc. 101 1566–1581. MR2279480
51. TZOURIO-MAZOYER, N., LandEAU, B., PAPATHANASSIOU, D., CRIVELLO, F., ETARD, O., DELCROIX, N., MAZOYER, B. and JOLIOT, M. (2002). Automated anatomical labeling of ac-tivations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15 273–289.
52. VERSACE, F., ENGELMANN, J. M., JACKSON, E. F., SLAPIN, A., CORTESE, K. M., BEVERS, T. B. and SCHOVER, L. R. (2013). Brain responses to erotic and other emotional stimuli in breast cancer survivors with and without distress about low sexual desire: A preliminary fmri study. Brain Imaging Behav. 7 533–542.
53. WANG, C., PAISLEY, J. W. and BLEI, D. M. (2011). Online variational inference for the hierarchical Dirichlet process. In International Conference on Artificial Intelligence and Statistics 752–760.
54. WOOLRICH, M. W., BEHRENS, T. and SMITH, S. (2004). Constrained linear basis sets for HRF modelling using variational Bayes. NeuroImage 21 1748–1761.
55. WOOLRICH, M. W., JENKINSON, M., BRADY, J. M. and SMITH, S. M. (2004). Fully Bayesian spatio-temporal modeling of fMRI data. IEEE Trans. Med. Imag. 23 213–231.
56. WORNELL, G. W. and OPPENHEIM, A. V. (1992). Estimation of fractal signals from noisy mea-surements using wavelets. IEEE Trans. Signal Process. 40 611–623.
57. WORSLEY, K. J. and FRISTON, K. J. (1995). Analysis of fMRI time-series revisited-again. Neu-roImage 2 173–181.
58. XIA, J., LIANG, F. and WANG, Y. (2009). FMRI analysis through Bayesian variable selection with a spatial prior. IEEE Int. Symp. on Biomedical Imaging 714–717.
59. XU, L., JOHNSON, T. D., NICHOLS, T. E. and NEE, D. E. (2009). Modeling inter-subject variability in fMRI activation location: A Bayesian hierarchical spatial model. Biometrics 65 1041–1051. MR2756491
60. YAN, F., XU, N. and QI, Y. (2009). Parallel inference for latent dirichlet allocation on graphics processing units. In Advances in Neural Information Processing Systems 2134–2142.
61. ZHANG, L., GUINDANI, M., VERSACE, F. and VANNUCCI, M. (2014). A spatio-temporal nonpara-metric Bayesian variable selection model of fMRI data for clustering correlated time courses. NeuroImage 95 162–175.
62. ZHANG, L., GUINDANI, M. and VANNUCCI, M. (2015). Bayesian models for fMRI data analysis. Wiley Interdiscip. Rev.: Comput. Stat. 7 21–41.
63. ZHANG, L., GUINDANI, M., VERSACE, F., ENGELMANN, J. and VANNUCCI, M. (2016). Sup-plement to “A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data.” DOI:10.1214/16-AOAS926SUPP.