Advertisement

Parallel Hierarchical Agglomerative Clustering for fMRI Data

  • Mélodie AngelettiEmail author
  • Jean-Marie Bonny
  • Franck Durif
  • Jonas Koko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10777)

Abstract

This paper describes three parallel strategies for Ward’s algorithm with OpenMP or/and CUDA. Faced with the difficulty of a priori modelling of elicited brain responses by a complex paradigm in fMRI experiments, data-driven analysis have been extensively applied to fMRI data. A promising approach is clustering data which does not make stringent assumptions such as spatial independence of sources. Thirion et al. have shown that hierarchical agglomerative clustering (HAC) with Ward’s minimum variance criterion is a method of choice. However, HAC is computationally demanding, especially for distance computation. With our strategy, for single subject analysis, a speed-up of up to 7 was achieved on a workstation. For group analysis (concatenation of several subjects), a speed-up of up to 20 was achieved on a workstation.

Keywords

Hierarchical agglomerative clustering OpenMP CUDA fMRI Distance computation 

References

  1. 1.
    Abraham, A., Pedregosa, F., Eickenberg, M., et al.: Machine learning for neuroimaging with scikit-learn. Front. Neuroinformatics 8, 14 (2014)CrossRefGoogle Scholar
  2. 2.
    Calhoun, V.D., et al.: Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Mapp. 13, 43–53 (2001)CrossRefGoogle Scholar
  3. 3.
    Chang, D., et al.: Compute pairwise Euclidean distances of data points with GPUs. In: Proceedings of the IASTED International Symposium Computational Biology and Bioinformatics (CBB 2008) (2008)Google Scholar
  4. 4.
    Cordes, D., Haughton, V.M., Arfanakis, K., et al.: Mapping functionally related regions of brain with functional connectivity MR imaging. Am. J. Neuroradiol. 21, 1636–1644 (2000)Google Scholar
  5. 5.
    Dash, M., Petrutiu, S., Scheuermann, P.: Efficient parallel hierarchical clustering. In: Danelutto, M., Vanneschi, M., Laforenza, D. (eds.) Euro-Par 2004. LNCS, vol. 3149, pp. 363–371. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27866-5_47 CrossRefGoogle Scholar
  6. 6.
    Daubechies, I., et al.: Independent component analysis for brain fMRI does not select for independence. Proc. Nat. Acad. Sci. 106, 10415–10422 (2009)CrossRefGoogle Scholar
  7. 7.
    Gao, X., et al.: Comparison between spatial and temporal independent component analysis for blind source separation in fMRI data. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 2, pp. 690–692. IEEE (2011)Google Scholar
  8. 8.
    Golland, Y., et al.: Data-driven clustering reveals a fundamental subdivision of the human cortex into two global systems. Neuropsychologia 46, 540–553 (2008)CrossRefGoogle Scholar
  9. 9.
    Gonzalez-Castillo, J., et al.: Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis. Proc. Nat. Acad. Sci. 109, 5487–5492 (2012)CrossRefGoogle Scholar
  10. 10.
    Kim, S., Ouyang, M.: Compute distance matrices with GPU. Glob. Sci. Technol. Forum (2012).  https://doi.org/10.5176/2251-1652_ADPC12.07
  11. 11.
    Lance, G.N., Williams, W.T.: A general theory of classificatory sorting strategies 1. Hierarchical systems. Comput. J. 9, 373–380 (1967)CrossRefGoogle Scholar
  12. 12.
    Li, Q., et al.: A chunking method for Euclidean distance matrix calculation on large dataset using multi-GPU, pp. 208–213. IEEE (2010)Google Scholar
  13. 13.
    Matias Rodrigues, J.F., von Mering, C.: HPC-CLUST: distributed hierarchical clustering for large sets of nucleotide sequences. Bioinformatics 30, 287–288 (2014)CrossRefGoogle Scholar
  14. 14.
    McKeown, M.J., Sejnowski, T.J.: Independent component analysis of fMRI data: examining the assumptions. Hum. Brain Mapp. 6, 368–372 (1998)CrossRefGoogle Scholar
  15. 15.
    Olson, C.F.: Parallel algorithms for hierarchical clustering. Parallel Comput. 21, 1313–1325 (1995)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Rasmussen, E.M., Willett, P.: Efficiency of hierarchic agglomerative clustering using the ICL distributed array processor. J. Documentation 45, 1–24 (1989)CrossRefGoogle Scholar
  18. 18.
    Thirion, B., et al.: Which fMRI clustering gives good brain parcellations? Front. Neurosci. 8, 167 (2014)CrossRefGoogle Scholar
  19. 19.
    Zhang, Q., Zhang, Y.: Hierarchical clustering of gene expression profiles with graphics hardware acceleration. Pattern Recogn. Lett. 27, 676–681 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mélodie Angeletti
    • 1
    • 2
    Email author
  • Jean-Marie Bonny
    • 2
  • Franck Durif
    • 3
  • Jonas Koko
    • 1
  1. 1.Université Clermont Auvergne, CNRS, LIMOSClermont-FerrandFrance
  2. 2.INRA, AgroResonance - UR370 QuaPA, Centre Auvergne-Rhône-AlpesSaint Genès ChampanelleFrance
  3. 3.CHU Clermont-Ferrand, Service de Neurologie AClermont-FerrandFrance

Personalised recommendations