Cognitive, Affective, & Behavioral Neuroscience

, Volume 13, Issue 3, pp 587–597 | Cite as

Harnessing graphics processing units for improved neuroimaging statistics

  • Anders EklundEmail author
  • Mattias Villani
  • Stephen M. LaConte


Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.


Non-parametric statistics Neuroimaging Bayesian statistics Graphics processing units Spatial normalization fMRI VBM DTI 



Anders Eklund owns the company Wanderine Consulting, which has done consulting work for the company Accelereyes (the creators of the MATLAB GPU inferface Jacket). The authors would like to thank Gerdien van Eersel for making us aware of the special issue on improved reliability and validity of neuroimaging findings.


  1. Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry – The methods. NeuroImage, 11(6), 805–821. doi: 10.1006/nimg.2000.0582 PubMedCrossRefGoogle Scholar
  2. Aufferman, W. F., Ngan, S-C., & Hu, X. (2002). Cluster significance testing using the bootstrap. NeuroImage, 17(2), 583–591. doi: 10.1006/nimg.2002.1223
  3. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51(3), 1126–1139. doi: 10.1016/j.neuroimage.2010.02.082 PubMedCrossRefGoogle Scholar
  4. Bergfield, K. L., Hanson, K. D., Chen, K., Teipel, S. J., Hampel, H., Rapoport, S. I., & Alexander, G. E. (2010). Age-related networks of regional covariance in MRI gray matter: Reproducible multivariate patterns in healthy aging. NeuroImage, 49(2), 1750–1759. doi: 10.1016/j.neuroimage.2009.09.051 PubMedCrossRefGoogle Scholar
  5. Biswal, B. B., Taylor, P. A., & Ulmer, J. L. (2001). Use of jackknife resampling techniques to estimate the confidence intervals of fMRI parameters. Journal of Computer Assisted Tomography, 25(1), 113–120.PubMedCrossRefGoogle Scholar
  6. Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., & Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4734–4739. doi: 10.1073/pnas.0911855107 PubMedCrossRefGoogle Scholar
  7. Björnsdotter, M., Rylander, K., & Wessberg, J. (2011). A Monte Carlo method for locally multivariate brain mapping. NeuroImage, 56(2), 508–516. doi: 10.1016/j.neuroimage.2010.07.044 PubMedCrossRefGoogle Scholar
  8. Bookstein, F. L. (2001). “Voxel-based morphometry” should not be used with imperfectly registered images. NeuroImage, 14(6), 1454–1462. doi: 10.1006/nimg.2001.0770 PubMedCrossRefGoogle Scholar
  9. Boubela, R. N., Huf, W., Kalcher, K., Sladky, R., Filzmoser, P., Pezawas, L., & Moser, E. (2012). A highly parallelized framework for computationally intensive MR data analysis. Magnetic Resonance Materials in Physics, Biology and Medicine, 25(4), 313–320. doi: 10.1007/s10334-011-0290-7 CrossRefGoogle Scholar
  10. Brammer, M. J., Bullmore, E. T., Simmons, A., Williams, S. C. R., Grasby, P. M., Howard, R. J., & Rabe-Hesketh, S. (1997). Generic brain activation mapping in functional magnetic resonance imaging: A nonparametric approach. Magnetic Resonance Imaging, 15(7), 763–770. doi: 10.1016/S0730-725X(97)00135-5 PubMedCrossRefGoogle Scholar
  11. Brett, M., Johnsrude, I. S., & Owen, A. M. (2002). The problem of functional localization in the human brain. Nature Reviews Neuroscience, 3, 243–249. doi: 10.1038/nrn756 PubMedCrossRefGoogle Scholar
  12. Bullmore, E. T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., & Brammer, M. J. (1999). Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Transactions on Medical Imaging, 18(1), 32–42. doi: 10.1109/42.750253 PubMedCrossRefGoogle Scholar
  13. Bullmore, E., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., & Brammer, M. (2001). Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains. Human Brain Mapping, 12(2), 61–78. doi: 10.1002/1097-0193(200102)12:2<61::AID-HBM1004>3.0.CO;2-W
  14. Che, S., Boyer, M., Meng, J., Tarjan, D., Sheaffer, J. W., & Skadron, K. (2008). A performance study of general-purpose applications on graphics processors using CUDA. Journal of Parallel and Distributed Computing, 68(10), 1370–1380. doi: 10.1016/j.jpdc.2008.05.014 CrossRefGoogle Scholar
  15. Chib, S., & Jeliazkov, J. (2001). Marginal likelihood from the Metropolis-Hastings output. Journal of the American Statistical Association, 96(453), 270–281. doi: 10.1198/016214501750332848 CrossRefGoogle Scholar
  16. Chung, S., Pelletier, D., Sdika, M., Lu, Y., Berman, J. I., & Henry, R. G. (2008). Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing. NeuroImage, 39(4), 1693–1705. doi: 10.1016/j.neuroimage.2007.10.039 PubMedCrossRefGoogle Scholar
  17. Cox, R. W., Jesmanowicz, A., & Hyde, J. S. (1995). Real-time functional magnetic resonance imaging. Magnetic resonance in Medicine, 33(2), 230–236. doi: 10.1002/mrm.1910330213 PubMedCrossRefGoogle Scholar
  18. Cubon, V. A., Putukian, M., Boyer, C., & Dettwiler, A. (2011). A diffusion tensor imaging study on the white matter skeleton in individuals with sports-related concussion. Journal of Neurotrauma, 28(2), 189–201. doi: 10.1089/neu.2010.1430 PubMedCrossRefGoogle Scholar
  19. deCharms, R. C. (2008). Applications of real-time fMRI. Nature Reviews Neuroscience, 9, 720–729. doi: 10.1038/nrn2414 PubMedCrossRefGoogle Scholar
  20. Dolbeau, R., Bihan S., & Bodin, F. (2007). HMPP: A hybrid multi-core parallel programming environment. Proceedings of the Workshop on general-purpose processing on graphics processing units Google Scholar
  21. Dwass, M. (1957). Modified randomization tests for nonparametric hypotheses. Annals of Mathematical Statistics, 28(1), 181–187. doi: 10.1214/aoms/1177707045 CrossRefGoogle Scholar
  22. Eklund, A., Andersson, M., & Knutsson, H. (2010). Phase based volume registration using CUDA. International conference on acoustics, speech and signal processing (ICASSP), 658–651. doi: 10.1109/ICASSP.2010.5495134
  23. Eklund, A., Andersson, M., & Knutsson, H. (2011a). Fast random permutation tests enable objective evaluation of methods for single-subject fMRI analysis. International Journal of Biomedical Imaging. doi: 10.1155/2011/627947. Article ID 627947.Google Scholar
  24. Eklund, A., Andersson, M., & Knutsson, H. (2012a). fMRI analysis on the GPU – possibilities and challenges. Computer Methods and Programs in Biomedicine, 105(2), 145–161. doi: 10.1016/j.cmpb.2011.07.007 PubMedCrossRefGoogle Scholar
  25. Eklund, A., Dufort, P., Forsberg, D., & LaConte, S. M. (2012b). Medical image processing on the GPUPast, present and future. Manuscript submitted for publication.Google Scholar
  26. Eklund, A, Björnsdotter, M., Stelzer, J., & LaConte, S.M. (2013). Searchlight goes GPU – Fast multi-voxel pattern analysis of fMRI data. International society for magnetic resonance in medicine (ISMRM)Google Scholar
  27. Eklund, A., Forsberg, D., Andersson, M., & Knutsson, H. (2011b). Using the local phase of the magnitude of the local structure tensor for image registration. Lecture notes in computer science, Scandinavian conference on image analysis (SCIA), 6688, 414–423. doi: 10.1007/978-3-642-21227-7_39 CrossRefGoogle Scholar
  28. Eklund, A., Andersson, M., Josephson, C., Johannesson, M., & Knutsson, H. (2012c). Does parametric fMRI analysis with SPM yield valid results? - An empirical study of 1484 rest datasets. NeuroImage, 61(3), 565–578. doi: 10.1016/j.neuroimage.2012.03.093 PubMedCrossRefGoogle Scholar
  29. Feinberg, D. A., & Yacoub, E. (2012). The rapid development of high speed, resolution and precision in fMRI. NeuroImage, 62(2), 720–725. doi: 10.1016/j.neuroimage.2012.01.049 PubMedCrossRefGoogle Scholar
  30. Ferreira da Silva, A. R. (2011a). A Bayesian multilevel model for fMRI data analysis. Computer Methods and Programs in Biomedicine, 102(3), 238–252. doi: 10.1016/j.cmpb.2010.05.003 PubMedCrossRefGoogle Scholar
  31. Ferreira da Silva, A. R. (2011b). cudaBayesreg: Parallel implementation of a Bayesian multilevel model for fMRI data analysis. Journal of Statistical Software, 44(4), 1–24.Google Scholar
  32. Fluck, O., Vetter, C., Wein, W., Kamen, A., Preim, B., & Westermann, R. (2011). A survey of medical image registration on graphics hardware. Computer Methods and Programs in Biomedicine, 104(3), e45–e57. doi: 10.1016/j.cmpb.2010.10.009 PubMedCrossRefGoogle Scholar
  33. Friman, O., Borga, M., Lundberg, P., & Knutsson, H. (2003). Adaptive analysis of fMRI data. NeuroImage, 19(3), 837–845. doi: 10.1016/S1053-8119(03)00077-6 PubMedCrossRefGoogle Scholar
  34. Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic casual modelling. NeuroImage, 19(4), 1273–1302. doi: 10.1016/S1053-8119(03)00202-7 PubMedCrossRefGoogle Scholar
  35. Friston, K. J., Holmes, A. P., & Worsley, K. J. (1999). How many subjects constitute a study? NeuroImage, 10(1), 1–5. doi: 10.1006/nimg.1999.0439 PubMedCrossRefGoogle Scholar
  36. Friston, K. J., Ashburner, J., Frith, C. D., Poline, J. B., Heather, J. D., & Frackowiak, R. S. J. (1995a). Spatial registration and normalization of images. Human Brain Mapping, 3(3), 165–189. doi: 10.1002/hbm.460030303 CrossRefGoogle Scholar
  37. Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D., & Frackowiak, R. S. J. (1995b). Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping, 2(4), 189–210. doi: 10.1002/hbm.460020402 CrossRefGoogle Scholar
  38. Friston, K. J., Penny, W., Phillips, C., Kiebel, S., Hinton, G., & Ashburner, J. (2002). Classical and Bayesian inference in neuroimaging: Theory. NeuroImage, 16(2), 465–483. doi: 10.1006/nimg.2002.1090 PubMedCrossRefGoogle Scholar
  39. Garland, M., Le Grand, S., Nickolls, J., Anderson, J., Hardwick, J., Morton, S., ... Volkov, V. (2008). Parallel computing experiences with CUDA. IEEE Micro, 28(4), 13–27. doi: 10.1109/MM.2008.57
  40. Genovese, C. R. (2000). A Bayesian time-course model for functional magnetic resonance imaging data. Journal of the American Statistical Association, 95(451), 691–703. doi: 10.1080/01621459.2000.10474253 CrossRefGoogle Scholar
  41. Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image segmentation using boundary-based registration. NeuroImage, 48(1), 63–72. doi: 10.1016/j.neuroimage.2009.06.060 PubMedCrossRefGoogle Scholar
  42. Grigis, A., Noblet, V., Heitz, F., Blanc, F., de Seze, F., Kremer, S., & Armspach, J.-P. (2012). Longitudinal change detection in diffusion MRI using multivariate statistical testing on tensors. NeuroImage, 60(4), 2206–2221. doi: 10.1016/j.neuroimage.2012.02.049 PubMedCrossRefGoogle Scholar
  43. Gudbjartsson, H., & Patz, S. (1995). The Rician distribution of noisy MRI data. Magnetic Resonance in Medicine, 34(6), 910–914. doi: 10.1002/mrm.1910340618 PubMedCrossRefGoogle Scholar
  44. Guo, G. (2012). Parallel statistical computing for statistical inference. Journal of Statistical Theory and Practice, 6, 536–565. doi: 10.1080/15598608.2012.695705 CrossRefGoogle Scholar
  45. Gössi, C., Fahrmeir, L., & Auer, D. P. (2001). Bayesian modeling of the hemodynamic response function in BOLD fMRI. NeuroImage, 14(1), 140–148. doi: 10.1006/nimg.2001.0795 CrossRefGoogle Scholar
  46. Habeck, C., & Stern, Y. (2010). Multivariate data analysis for neuroimaging data: Overview and application to Alzheimer’s disease. Cell Biochemistry and Biophysics, 58(2), 53–67. doi: 10.1007/s12013-010-9093-0 PubMedCrossRefGoogle Scholar
  47. Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, M., & Schnabel, J. A. (2012). MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration. Medical Image Analysis, 16(7), 1423–1435. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  48. Hemmendorff, M., Andersson, M. T., Kronander, T., & Knutsson, H. (2002). Phase-based multidimensional volume registration. IEEE Transactions on Medical Imaging, 21(12), 1536–1543. doi: 10.1109/TMI.2002.806581 PubMedCrossRefGoogle Scholar
  49. Hernandez, M., Guerrero, G.D., Cecilia, J.M., Garcia, J.M., Inuggi, A., & Sotiropoulos, S.N. (2012). Accelerating fibre orientation estimation from diffusion weighted resonance imaging using GPUs. Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 622–626. doi: 10.1109/PDP.2012.46
  50. Holmes, A. P., Blair, R. C., Watson, J. D. G., & Ford, I. (1996). Nonparametric Analysis of Statistic Images from Functional Mapping Experiments. Journal of Cerebral Blood Flow & Metabolism, 16, 7–22. doi: 10.1097/00004647-199601000-00002 CrossRefGoogle Scholar
  51. Huang, T., Tang, Y., & Ju, S. (2011). Accelerating image registration of MRI by GPU-based parallel computation. Magnetic Resonance Imaging, 29(5), 712–716. doi: 10.1016/j.mri.2011.02.027 PubMedCrossRefGoogle Scholar
  52. Jones, D. K., & Cercignani, M. (2010). Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine, 23(7), 803–820. doi: 10.1002/nbm.1543 PubMedCrossRefGoogle Scholar
  53. Jones, D. K., & Pierpaoli, C. (2005). Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magnetic resonance in medicine, 53(5), 1143–1149. doi: 10.1002/mrm.20466 PubMedCrossRefGoogle Scholar
  54. Jones, D. K., Griffin, L. D., Alexander, D. C., Catani, M., Horsfield, M. A., Howard, R., & Williams, S. C. R. (2002). Spatial normalization and averaging of diffusion tensor MRI data sets. NeuroImage, 17(2), 592–617. doi: 10.1006/nimg.2002.1148 PubMedCrossRefGoogle Scholar
  55. Kawasaki, Y., Suzuki, M., Kherif, F., Takahashi, T., Zhou, S.-Y., Nakamura, K., & Kurachi, M. (2007). Multivariate voxel-based morphometry successfully differentiates schizophrenia patients from healthy controls. NeuroImage, 34(1), 235–242. doi: 10.1016/j.neuroimage.2006.08.018 PubMedCrossRefGoogle Scholar
  56. Kimberg, D. Y., Coslett, H. B., & Schwartz, M. F. (2007). Power in voxel-based lesion-symptom mapping. Journal of Cognitive Neuroscience, 19(7), 1067–1080. doi: 10.1162/jocn.2007.19.7.1067 PubMedCrossRefGoogle Scholar
  57. Kirk, D. B., & Hwu, W. W. (2010). Programming massively parallel processors: A hands-on approach. Morgan KauffmannGoogle Scholar
  58. Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information based functional brain mapping. Proceedings of the National Academy of Sciences, 103(10), 3863–3868. doi: 10.1073/pnas.0600244103 CrossRefGoogle Scholar
  59. LaConte, S. M., Strother, S., Cherkassky, V., Anderson, J., & Hu, X. (2005). Support vector machines for temporal classification of block design fMRI data. NeuroImage, 26(2), 317–329. doi: 10.1016/j.neuroimage.2005.01.048 PubMedCrossRefGoogle Scholar
  60. LaConte, S. M. (2011). Decoding fMRI brain states in real-time. NeuroImage, 56(2), 440–454. doi: 10.1016/j.neuroimage.2010.06.052 PubMedCrossRefGoogle Scholar
  61. Lazar, M., & Alexander, A. L. (2005). Bootstrap white matter tractography (BOOT-TRAC). NeuroImage, 24(2), 524–532. doi: 10.1016/j.neuroimage.2004.08.050 PubMedCrossRefGoogle Scholar
  62. Lee, A., Yau, C., Giles, M. B., Doucet, A., & Holmes, C. C. (2010). On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods. Journal of computational and graphical statistics, 19(4), 769–789. doi: 10.1198/jcgs.2010.10039 PubMedCrossRefGoogle Scholar
  63. McGraw, T., & Nadar, M. (2007). Stochastic DT-MRI connectivity mapping on the GPU. IEEE Transactions on visualization and computer graphics, 13(6), 1504–1511. doi: 10.1109/TVCG.2007.70597 PubMedCrossRefGoogle Scholar
  64. McIntosh, A. R., Chau, W. K., & Protzner, A. B. (2004). Spatiotemporal analysis of event-related fMRI data using partial least squares. NeuroImage, 23(2), 764–775. doi: 10.1016/j.neuroimage.2004.05.018 PubMedCrossRefGoogle Scholar
  65. Mellor, M., & Brady, M. (2004). Non-rigid multimodal image registration using local phase, Lecture Notes in Computer Science: Vol 3216, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 789–796. doi: 10.1007/978-3-540-30135-6_96
  66. Mellor, M., & Brady, M. (2005). Phase mutual information as similarity measure for registration. Medical Image Analysis, 9(4), 330–343. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  67. Membarth, R., Hannig, F., Teich, J., Korner, M., & Eckert, W. (2011). Frameworks for GPU accelerators: A comprehensive evaluation using 2D/3D image registration. IEEE Symposium on Application specific processors (SASP), 7871. doi: 10.1109/SASP.2011.5941083
  68. Mitchell, T. M., Hutchinson, R., Niculescu, R. S., Pereira, F., Wang, X., Just, M., & Newman, S. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57(1–2), 145–175. doi: 10.1023/B:MACH.0000035475.85309.1b CrossRefGoogle Scholar
  69. Nandy, R. R., & Cordes, D. (2003). Novel nonparametric approach to canonical correlation analysis with applications to low CNR functional MRI data. Magnetic Resonance in Medicine, 50(2), 354–365. doi: 10.1002/mrm.10537 PubMedCrossRefGoogle Scholar
  70. Nandy, R., & Cordes, D. (2007). A semi-parametric approach to estimate the family-wise error rate in fMRI using resting-state data. NeuroImage, 34(4), 1562–1576. doi: 10.1016/j.neuroimage.2006.10.025 PubMedCrossRefGoogle Scholar
  71. Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25. doi: 10.1002/hbm.1058 PubMedCrossRefGoogle Scholar
  72. Nichols, T. E., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12(5), 419–446. doi: 10.1191/0962280203sm341ra PubMedCrossRefGoogle Scholar
  73. Nieto-Castanon, A., Ghosh, S. S., Tourville, J. A., & Guenther, F. H. (2003). Region of interest based analysis of functional imaging data. NeuroImage, 19(4), 1303–1316. doi: 10.1016/S1053-8119(03)00188-5 PubMedCrossRefGoogle Scholar
  74. Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: Multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. doi: 10.1016/j.tics.2006.07.005 PubMedCrossRefGoogle Scholar
  75. Owens, J. D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A. E., & Purcell, T. J. (2007). A survey of general-purpose computation on graphics hardware. Computer Graphics Forum, 26(1), 80–113. doi: 10.1111/j.1467-8659.2007.01012.x CrossRefGoogle Scholar
  76. Park, H.-J., Kubicki, M., Shenton, M. E., Guimond, A., McCarley, R. W., Maier, S. E., & Westin, C.-F. (2003). Spatial normalization of diffusion tensor MRI using multiple channels. NeuroImage, 20(4), 1995–2009. doi: 10.1016/j.neuroimage.2003.08.008 PubMedCrossRefGoogle Scholar
  77. Penny, W., Kiebel, S., & Friston, K. J. (2003). Variational Bayesian inference for fMRI time series. NeuroImage, 19(3), 727–741. doi: 10.1016/S1053-8119(03)00071-5 PubMedCrossRefGoogle Scholar
  78. Penny, W., Trujillo-Barreto, N. J., & Friston, K. J. (2005). Bayesian fMRI time series analysis with spatial priors. NeuroImage, 24(2), 350–362. doi: 10.1016/j.neuroimage.2004.08.034 PubMedCrossRefGoogle Scholar
  79. Roland, P. E., Geyer, S., Amunts, K., Schormann, T., Schleicher, A., Malikovic, A., & Zilles, K. (1997). Cytoarchitectural maps of the human brain in standard anatomical space. Human Brain Mapping, 5(4), 222–227. doi: 10.1002/(SICI)1097-0193(1997)5:4<222::AID-HBM3>3.0.CO;2-5
  80. Rugg-Gunn, F. J., Eriksson, S. H., Symms, M. R., Barker, G. J., & Duncan, J. S. (2001). Diffusion tensor imaging of cryptogenic and acquired partial epilepsies. Brain, 124(3), 627–636. doi: 10.1093/brain/124.3.627 PubMedCrossRefGoogle Scholar
  81. Sanders, J., & Kandrot, E. (2010). CUDA by example: An introduction to General-Purpose GPU Programming. Addison-Wesley ProfessionalGoogle Scholar
  82. Scarpazza, C., Sartori, G., De Simone, M. S., & Mechelli, A. (2013). When the single subject matters more than the group: Very high false positive rates in single case voxel based morphometry. NeuroImage, 70, 175–188. doi: 10.1016/j.neuroimage.2012.12.045 PubMedCrossRefGoogle Scholar
  83. Shams, R., Sadeghi, P., Kennedy, R., & Hartley, R. (2010). A survey of medical image registration on multicore and the GPU. IEEE Signal Processing Magazine, 27(2), 50–60. doi: 10.1109/MSP.2009.935387 CrossRefGoogle Scholar
  84. Shterev, I. D., Jung, S.-H., George, S. L., & Owzar, K. (2010). permGPU: Using graphics processing units in RNA microarray association studies. BMC Bioinformatics, 11, 329. doi: 10.1186/1471-2105-11-329 PubMedCrossRefGoogle Scholar
  85. Silver, M., Montana, G., & Nichols, T. E. (2011). False positives in neuroimaging genetics using voxel-based morphometry data. NeuroImage, 54(2), 992–1000. doi: 10.1016/j.neuroimage.2010.08.049 PubMedCrossRefGoogle Scholar
  86. Smith, A. M., Lewis, B. K., Ruttimann, U. E., Ye, F. Q., Sinnwell, T. M., Yang, Y., & Frank, J. A. (1999). Investigation of low frequency drift in fMRI signal. NeuroImage, 9(5), 526–533. doi: 10.1006/nimg.1999.0435 PubMedCrossRefGoogle Scholar
  87. Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., & Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505. doi: 10.1016/j.neuroimage.2006.02.024 PubMedCrossRefGoogle Scholar
  88. Stelzer, J., Chen, Y., & Turner, R. (2013). Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control. NeuroImage, 65, 69–82. doi: 10.1016/j.neuroimage.2012.09.063 PubMedCrossRefGoogle Scholar
  89. Suchard, M. A., Wang, Q., Chan, C., Frelinger, J., Cron, A., & West, M. (2010). Understanding GPU programming for statistical computation: Studies in massively parallel massive mixtures. Journal of Computational and Graphical Statistics, 19(2), 419–438. doi: 10.1198/jcgs.2010.10016 PubMedCrossRefGoogle Scholar
  90. Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., & Poline, J.-B. (2006). Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Human brain mapping, 27(8), 678–693. doi: 10.1002/hbm.20210 PubMedCrossRefGoogle Scholar
  91. Thomas, A. G., Marrett, S., Saad, Z. S., Ruff, D. A., Martin, A., & Bandettini, P. A. (2009). Functional but not structural changes associated with learning: An exploration of longitudinal Voxel-Based Morphometry (VBM). NeuroImage, 48(1), 117–125. doi: 10.1016/j.neuroimage.2009.05.097 PubMedCrossRefGoogle Scholar
  92. van Hemert, J. L., & Dickerson, J. A. (2011). Monte Carlo randomization tests for large-scale abundance datasets on the GPU. Computer Methods and Programs in Biomedicine, 101(1), 80–86. doi: 10.1016/j.cmpb.2010.04.010 PubMedCrossRefGoogle Scholar
  93. Wachinger, C., & Navab, N. (2012). Entropy and Laplacian images: Structural representations for multi-modal registration. Medical Image Analysis, 16(1), 1–17. doi: 10.1016/ PubMedCrossRefGoogle Scholar
  94. Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., & Birbaumer, N. (2003). Physiological self regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. NeuroImage, 19(3), 577–586. doi: 10.1016/S1053-8119(03)00145-9 PubMedCrossRefGoogle Scholar
  95. Wilke, M. (2012). An iterative jackknife approach for assessing reliability and power of fMRI group analyses. PLoS ONE, 7(4), e35578. doi: 10.1371/journal.pone.0035578 PubMedCrossRefGoogle Scholar
  96. Wolfe, M. (2010). Implementing the PGI accelerator model. Proceedings of the workshop on general-purpose computation on graphics processing units, 4350. doi: 10.1145/1735688.1735697
  97. Woolrich, M. W. (2012). Bayesian inference in fMRI. NeuroImage, 62(2), 801–810. doi: 10.1016/j.neuroimage.2011.10.047 PubMedCrossRefGoogle Scholar
  98. Woolrich, M. W., Jenkinson, M., Brady, J. M., & Smith, S. M. (2004). Fully Bayesian spatio-temporal modeling of FMRI data. IEEE Transactions on Medical Imaging, 23(2), 213–231. doi: 10.1109/TMI.2003.823065 PubMedCrossRefGoogle Scholar
  99. Worsley, K. J., Marrett, S., Neelin, P., & Evans, A. C. (1992). A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12(6), 900–918. doi: 10.1038/jcbfm.1992.127 PubMedCrossRefGoogle Scholar
  100. Zhu, H., Ibrahim, J. G., Tang, N., Rowe, D. B., Hao, X., Bansal, R., & Peterson, B. S. (2007). A statistical analysis of brain morphology using wild bootstrapping. IEEE Transactions on Medical Imaging, 26(7), 954–966. doi: 10.1109/TMI.2007.897396 PubMedCrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Anders Eklund
    • 1
    Email author
  • Mattias Villani
    • 2
  • Stephen M. LaConte
    • 1
    • 3
  1. 1.Virginia Tech Carilion Research Institute, Virginia TechRoanokeUSA
  2. 2.Division of Statistics, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden
  3. 3.School of Biomedical Engineering & SciencesVirginia Tech-Wake Forest UniversityBlacksburgUSA

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