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Statistical Issues in fMRI for Brain Imaging

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  • Nicole A. Lazar
  • William F. Eddy
  • Christopher R. Genovese
  • Joel Welling

Abstract

Functional magnetic resonance imaging is a technique developed in the last decade and used in the fields of cognitive psychology and neuroscience, among others, to study the processes underlying the working of the human brain. In this paper we examine some of the statistical issues in functional magnetic resonance imaging for brain research. We start by giving a brief introduction to the physics of magnetic resonance imaging. Using a psychological experiment as a case study, we then describe questions of design and statistical analysis. The data obtained from functional magnetic resonance imaging studies are of a highly complex nature, displaying both spatial and temporal correlation, as well as high levels of noise from different sources. Given this, the scope for statistics is vast, and is not limited to simple analysis of the data, once collected.

Suggested Citation

  • Nicole A. Lazar & William F. Eddy & Christopher R. Genovese & Joel Welling, 2001. "Statistical Issues in fMRI for Brain Imaging," International Statistical Review, International Statistical Institute, vol. 69(1), pages 105-127, April.
    • Handle: RePEc:bla:istatr:v:69:y:2001:i:1:p:105-127
    DOI: 10.1111/j.1751-5823.2001.tb00482.x
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    Cited by:

    1. Ani Eloyan & Shanshan Li & John Muschelli & Jim J Pekar & Stewart H Mostofsky & Brian S Caffo, 2014. "Analytic Programming with fMRI Data: A Quick-Start Guide for Statisticians Using R," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-13, February.

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