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The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging

In: Proceedings of COMPSTAT'2010

Author

Listed:
  • Stephen Strother

    (Rotman Research Institute, Baycrest
    University of Toronto, Department of Medical Biophysics)

  • Anita Oder

    (Rotman Research Institute, Baycrest)

  • Robyn Spring

    (Rotman Research Institute, Baycrest
    University of Toronto, Department of Medical Biophysics)

  • Cheryl Grady

    (Rotman Research Institute, Baycrest)

Abstract

We introduce the role of resampling and prediction (p) metrics for flexible discriminant modeling in neuroimaging, and highlight the importance of combining these with measurements of the reproducibility (r) of extracted brain activation patterns. Using the NPAIRS resampling framework we illustrate the use of (p, r) plots as a function of the size of the principal component subspace (Q) for a penalized discriminant analysis (PDA) to: optimize processing pipelines in functional magnetic resonance imaging (fMRI), and measure the global SNR (gSNR) and dimensionality of fMRI data sets. We show that the gSNRs of typical fMRI data sets cause the optimal Q for a PDA to often lie in a phase transition region between gSNR ≃ 1 with large optimal Q versus SNR ≫ 1 with small optimal Q.

Suggested Citation

  • Stephen Strother & Anita Oder & Robyn Spring & Cheryl Grady, 2010. "The NPAIRS Computational Statistics Framework for Data Analysis in Neuroimaging," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 111-120, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_10
    DOI: 10.1007/978-3-7908-2604-3_10
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