Improved Assessment of Significant Activation in Functional Magnetic Resonance Imaging (fMRI): Use of a Cluster-Size Threshold

Authors

  • Steven D. Forman MD, Ph.D,

    Corresponding author
    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
    • Department of Psychiatry, University of Pittsburgh, BST W1642, 3811 Ohara Street, Pittsburgh PA 15213
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  • Jonathan D. Cohen,

    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
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  • Mark Fitzgerald,

    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
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  • William F. Eddy,

    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
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  • Mark A. Mintun,

    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
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  • Douglas C. Noll

    1. University of Pittsburgh, Highland Drive Veterans Administration Medical Center and Carnegie-Mellon University, Pittsburgh Pennsylvania.
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Abstract

The typical functional magnetic resonance (fMRI) study presents a formidable problem of multiple statistical comparisons (i.e, > 10,000 in a 128 x 128 image). To protect against false positives, investigators have typically relied on decreasing the per pixel false positive probability. This approach incurs an inevitable loss of power to detect statistically significant activity. An alternative approach, which relies on the assumption that areas of true neural activity will tend to stimulate signal changes over contiguous pixels, is presented. If one knows the probability distribution of such cluster sizes as a function of per pixel false positive probability, one can use cluster-size thresholds independently to reject false positives. Both Monte Carlo simulations and fMRI studies of human subjects have been used to verify that this approach can improve statistical power by as much as fivefold over techniques that rely solely on adjusting per pixel false positive probabilities.

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