False Discovery Rate for Functional Neuroimaging Thomas Nichols Department of Biostatistics University of Michigan - PowerPoint PPT Presentation

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False Discovery Rate for Functional Neuroimaging Thomas Nichols Department of Biostatistics University of Michigan PowerPoint Presentation
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False Discovery Rate for Functional Neuroimaging Thomas Nichols Department of Biostatistics University of Michigan
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False Discovery Rate for Functional Neuroimaging Thomas Nichols Department of Biostatistics University of Michigan

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  1. False Discovery RateforFunctional NeuroimagingThomas NicholsDepartment of BiostatisticsUniversity of Michigan With thanks to... Christopher Genovese & Nicole Lazar Carnegie Mellon University Keith Worlsey McGill University

  2. Outline • Introduction to Functional Neuroimaging • Multiple Comparison Problem • A Multiple Comparison Solution: False Discovery Rate (FDR) • FDR Properties • FDR Example

  3. Tap fingers Rest Introduction:Functional Neuroimaging •  Neuronal Activity  Blood Flow • Many functional neuroimaging methods measurecorrelates of blood flow • Functional Magnetic Resonance Imaging (fMRI) • Based on intrinsic properties of tissue • Blood Oxygenation Level Dependent effect (BOLD) •  Blood flow  fMRI Signal

  4. fMRI Multiple Comparisons Problem 1,000 • 4-Dimensional Data • 1,000 multivariate observations,each with 100,000 elements • 100,000 time series, each with 1,000 observations • Massively UnivariateApproach • 100,000 hypothesistests • Massive MCP! . . . 3 2 1

  5. Solutions forMultiple Comparison Problem • A MCP Solution Must Control False Positives • How to measure multiple false positives? • Familywise Error Rate (FWER) • Chance of any false positives • Controlled by Bonferroni & Random Field Methods • False Discovery Rate (FDR) • Proportion of false positives among rejected tests

  6. Signal False Discovery RateIllustration: Noise Signal+Noise

  7. 11.3% 11.3% 12.5% 10.8% 11.5% 10.0% 10.7% 11.2% 10.2% 9.5% 6.7% 10.5% 12.2% 8.7% 10.4% 14.9% 9.3% 16.2% 13.8% 14.0% Control of Per Comparison Rate at 10% Percentage of Null Pixels that are False Positives Control of Familywise Error Rate at 10% FWE Occurrence of Familywise Error Control of False Discovery Rate at 10% Percentage of Activated Pixels that are False Positives

  8. p(i) i/V q/c(V) Benjamini & Hochberg Procedure • Select desired limit q on E(FDR) • Order p-values, p(1)p(2) ...  p(V) • Let r be largest i such that • Reject all hypotheses corresponding top(1), ... , p(r). 1 p(i) p-value i/V q/c(V) 0 0 1 i/V JRSS-B (1995) 57:289-300

  9. Benjamini & Hochberg Procedure • c(V) = 1 • Positive Regression Dependency on Subsets • Technical condition, special cases include • Independent data • Multivariate Normal with all positive correlations • Result by Benjamini & Yekutieli, Annals of Statistics, in press. • c(V) = i=1,...,V 1/i log(V)+0.5772 • Arbitrary covariance structure

  10. Signal Intensity 3.0 Signal Extent 1.0 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = z = 1

  11. Signal Intensity 3.0 Signal Extent 2.0 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = z = 2

  12. Signal Intensity 3.0 Signal Extent 3.0 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = z = 3

  13. Signal Intensity 3.0 Signal Extent 5.0 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = 0.000252 z = 3.48 4

  14. Signal Intensity 3.0 Signal Extent 9.5 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = 0.001628 z = 2.94 5

  15. Signal Intensity 3.0 Signal Extent 16.5 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = 0.007157 z = 2.45 6

  16. Signal Intensity 3.0 Signal Extent 25.0 Noise Smoothness 3.0 Benjamini & Hochberg:Varying Signal Extent p = 0.019274 z = 2.07 7

  17. Benjamini & Hochberg: Properties • Adaptive • Larger the signal, the lower the threshold • Larger the signal, the more false positives • False positives constant as fraction of rejected tests • Not a problem with imaging’s sparse signals • Smoothness OK • Smoothing introduces positive correlations

  18. FDR: Example • Verbal fluency data • 14 42-second blocks • ABABAB... • A: Two syllable words presented aurally • B: Silence • Imaging parameters • 2Tesla scanner, TR = 7 sec • 84 64x64x64 images of 3 x 3 x 3 mm voxels

  19. FDR Example:Plot of FDR Inequality p(i) ( i/V ) ( q/c(V) )

  20. FDR: Example FDR  0.05Indep/PRDSt0 = 3.8119 FDR  0.05Arbitrary Cov.t0 = 5.0747 FWER  0.05Bonferronit0 = 5.485

  21. FDR Software for SPM http://www.sph.umich.edu/~nichols/FDR

  22. FDR: Conclusions • False Discovery Rate • A new false positive metric • Benjamini & Hochberg FDR Method • Straightforward solution to fNI MCP • Just one way of controlling FDR • New methods under developmente.g. C. Genovese or J. Storey • Limitations • Arbitrary dependence result less sensitive Start Ill http://www.sph.umich.edu/~nichols/FDR Prop

  23. Positive Regression Dependency • Does fMRI data exhibit total positive correlation? • Example • 160 scan experiment • Spatialautocorrelationof residuals • Single voxelwith all others • Negative correlationexists!