Iterative Dual LDA: A Novel Classification Algorithm for Resting State fMRI

  • Zobair AryaEmail author
  • Ludovica Griffanti
  • Clare E. Mackay
  • Mark Jenkinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)


Resting-state functional MRI (rfMRI) provides valuable information about functional changes in the brain and is a strong candidate for biomarkers in neurodegenerative diseases. However, commonly used analysis techniques for rfMRI have undesirable features when used for classification. In this paper, we propose a novel supervised learning algorithm based on Linear Discriminant Analysis (LDA) that does not require any decomposition or parcellation of the data and does not need the user to apply any prior knowledge of potential discriminatory networks. Our algorithm extends LDA to obtain a pair of discriminatory spatial maps, and we use computationally efficient methods and regularisation to cope with the large data size, high-dimensionality and low-sample-size typical of rfMRI. The algorithm performs well on simulated rfMRI data, and better than an Independent Component Analysis (ICA)-based discrimination method on a real Parkinson’s disease rfMRI dataset.


Functional Connectivity Linear Discriminant Analysis Independent Component Analysis Independent Component Analysis Outgoing Connection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by EPSRC and MRC [grant number EP/L016052/1], and the NIHR Oxford Biomedical Research Centre.


  1. 1.
    Beckmann, C., Mackay, C., Filippini, N., et al.: Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. In: OHBM (2009)Google Scholar
  2. 2.
    Beckmann, C., Smith, S.: Probabilistic ICA for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23, 137–152 (2004)CrossRefGoogle Scholar
  3. 3.
    Castellanos, F., Martino, A.D., Craddock, R., et al.: Clinical applications of the functional connectome. NeuroImage 80, 527–540 (2013)CrossRefGoogle Scholar
  4. 4.
    Fisher, R.: The use of multiple measurements in taxonomic problems. Ann. Eugenics 7, 179–188 (1936)CrossRefGoogle Scholar
  5. 5.
    Griffanti, L., Rolinski, M., Szewczyck-Krolikowski, K., et al.: Challenges in the reproducibility of clinical studies with resting state fMRI: an example in early Parkinson’s disease. NeuroImage 124, 704–713 (2016)CrossRefGoogle Scholar
  6. 6.
    Rosazza, C., Minati, L.: Resting-state brain networks: literature review and clinical applications. Neurol. Sci. 32, 773–785 (2011)CrossRefGoogle Scholar
  7. 7.
    Salimi-Khorshidi, G., Douaud, G., Beckmann, C., et al.: Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 90, 449–468 (2014)CrossRefGoogle Scholar
  8. 8.
    Szewczyk-Krolikowski, K., Menke, R., Rolinski, M., et al.: Functional connectivity in the basal ganglia network differentiates PD patients from controls. Neuorology 83, 208–214 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Zobair Arya
    • 1
    Email author
  • Ludovica Griffanti
    • 1
  • Clare E. Mackay
    • 2
  • Mark Jenkinson
    • 1
  1. 1.FMRIBUniversity of OxfordOxfordUK
  2. 2.Department of PsychiatryUniversity of OxfordOxfordUK

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