# The Wondrous World of fmri statistics

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1 Outline The Wondrous World of fmri statistics FMRI data and Statistics course, Leiden, The General Linear Model Overview of fmri data analysis steps fmri timeseries Modeling effects of interest Modeling effects of no interest Hypothesis testing T contrasts F contrasts T/F contrasts & significance Dr. Erno J. Hermans F.C. Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands Statistical Inference The multiple comparison problem Bonferroni correction Random field theory based correction Regions of interest / small volume corrections False discovery rate The General Linear Model: Overview of data analysis steps The General Linear Model: fmri timeseries fmri time-series kernel Design matrix Statistical Parametric Map fmri time series Scans consist of voxels Motion correction Smoothing General Linear Model Spatial normalisation Parameter Estimates time Highres anatomical Group analysis Template Voxel = 3D pixel 1

2 The General Linear Model: fmri timeseries The General Linear Model: Overview Signal intensity (a.u.) S Time series of selected voxel fmri signal = task related signal changes + known artefacts + random noise These are known Unknown Explained variance Unexplained variance fmri signal = task related signal changes + noise Absolute signal is of no interest Variation in signal values over time = fmri signal Make a model that best describes the data so that ratio explained variance unexplained variance is maximized! known artefacts + random noise The General Linear Model: Modeling effects of interest The General Linear Model: Modeling effects of interest The simplest possible fmri design: On/Off block design, visual input The simplest possible fmri design: On/Off block design, visual input time time - = activation expressed as correlation 2

3 The General Linear Model: Modeling effects of interest The General Linear Model: Modeling effects of interest The simplest possible fmri design: On/Off block design Model usually represented in gray levels (FSL shows both) Designs with more than one condition: multiple regression Time Rest Left thumb Right thumb The General Linear Model: Modeling effects of no interest fmri signal = task related signal changes + known artefacts + random noise Effects of no-interest Movement parameters Effects of interest Effects of NO interest? Effects of no interest = Predictable variations in the signal caused by effects other than the task: Low-frequency drifts (high-pass filter) and scanner instabilities (model mean intensity of images) Movement (obtain from realignment stage) Heart beat Respiration These effects can be removed from the data by modeling the signal they generate Data (Y) Effects of interest (task-related) 3

4 Effects of no-interest Mean image intensity Effects of no-interest Intercept Data (Y) Effects of interest (task-related) Data (Y) Effects of interest (task-related) Effects of no-interest DCT High pass filter Data (Y) Effects of interest (task-related) 4

5 5

6 B=.37 B=.74 B=1.1 6

7 B=.24 B=1.48 B=.48 B=.73 7

8 B=.25 B=.97 B=.97 B=.49 B=.74 B=.97 B=.97 8

9 B=.14 B=.98 B=.98 B=.97 B=.97 B=.27 B=.41 B=.98 B=.98 B=.97 B=.97 9

10 The General Linear Model: Obtaining parameter estimates B=.54 B=.98 B=.97 = * Y = X * B + error The General Linear Model: Obtaining parameter estimates Hypothesis testing: T contrasts How are parameter estimates calculated? Minimize the amount of residual error error = Y XB Amount of error summarized as sum of squared errors: This is solved by: Error variance is then given by calculated using: (where N = # of observations and H = # of regressors) Left Right thumb thumb Contrast: weighted sum of parameter estimates Is this voxel active during Left thumb movement relative to rest: { etc} Right thumb movement relative to rest: { etc} Right more than left thumb movement: { etc} 10

11 Hypothesis testing: T contrasts Hypothesis testing: F contrasts Does my contrast of parameter estimates explain variance? Contrast of parameter estimates -> mean of the effect t-value -> significance of that contrast (does that factor explain a significant amount of variance?) Test significance with a t-statistic: Null hypothesis: contrast of parameter estimates (c) = 0 (i.e., c B = 0) The t-value is given by: explained variance t = unexplained variance t = c' B MS c'( X ' X ) e 1 c Left thumb Right thumb Is this voxel active during any of the conditions? { etc; etc} Is any variance explained by movement? { etc; etc} etc} etc} etc} etc} Hypothesis testing: T/F contrasts & significance Hypothesis testing: T/F contrasts & significance What is the chance of observing this effect under H0? Chance (P) depends on t/f statistic and degrees of freedom (DF) What is the chance of observing this effect under H0? Should we reject H0? Set an acceptable chance of type 1 error (alpha) Use the null distribution: For fmri timeseries data DF is smaller than # of scans. Autoregression correction is applied to account for this. (e.g., AR(1) model, pre-colouring, pre-whitening) DF = 5 11

12 Hypothesis testing: T/F contrasts & significance Hypothesis testing: T/F contrasts & significance What is the chance of observing this effect under H0? Should we reject H0? Set an acceptable chance of type 1 error (alpha) Use the null distribution: What is the chance of observing this effect under H0? Should we reject H0? Set an acceptable chance of type 1 error (alpha) Use the null distribution: DF = 10 DF = 15 Hypothesis testing: T/F contrasts & significance What is the chance of observing this effect under H0? Should we reject H0? Set an acceptable chance of type 1 error (alpha) Use the null distribution: Hypothesis testing: T/F contrasts & significance t Statistic can be converted to a Z statistic: DF = 25 At DF = T=Z If significant, give our voxel a beautiful bright color! 12

13 Statistical Inference: The multiple comparison problem Statistical Inference: The multiple comparison problem if α =.05, then: P type I error =.05 With a family of two tests: trouble P family wise error = 1-(1-α)^2 =.095 Alpha=.05 (Z>1.65) With a family of > tests: big trouble! P family wise error = 1-(1-α)^20000 =~ Statistical Inference: The multiple comparison problem Statistical Inference: The multiple comparison problem Alpha=.01 (Z>2.33) Alpha=.001 (Z>3.09) 13

14 Statistical Inference: The multiple comparison problem Statistical Inference: The multiple comparison problem Alpha=.0001 (Z>3.72) Alpha= (Z>4.26) Statistical Inference: Bonferroni correction Statistical Inference: Bonferroni correction If P family wise error = 1-(1-α)^n And we want:.05 chance of a single false positive Or: P family wise error =.05 α corrected = α / n α =.05 / = (pretty small!) (or Z>4.60) Alpha= (Z>4.60) BUT: Bonferroni assumes that tests are independent fmri images are spatially correlated 14

15 Statistical Inference: Bonferroni correction Sources of spatial correlation: The spatial resolution of the underlying signal Blurring due to resampling during preprocessing Smoothing that is often deliberately applied. Alternative approach to Bonferroni: 1. Control type I error rate by choosing the threshold at which the expected number of CLUSTERS is Calculate the expected number of clusters based on the smoothness of the image. So: correct for estimated number of true independent tests instead of number of voxels! EULER characteristic: number of clusters in an image as a function of threshold and smoothness. <image+ clusters Blur using Gaussian kernel Defined by its Full Width at Half Maximum (FWHM) <Euler char. 15

16 <image+ clusters 2D Gaussian kernel e.g., single dot <Euler char. From (n=1) simulation to: expected (n= ) Euler characteristic Expected Euler characteristic depends on: 1. Z threshold 2. Smoothness of image How to define smoothness? FWHM of smoothing kernel is the unit of smoothness In our example: - 100*100 pixels - Smoothed with a 2D FWHM of 10*10 pixels - 10*10 = 100 Resolution elements (ResEls) 16

17 Expected Euler Characteristic is a function of: 1. R: Number of ResEls (resolution elements) 2. Z t : Z threshold Formula: E[EC] = R(4log e 2)(2π) -3/2 Z t e -½Zt² Solve the following equation for alpha=.05:.05= R(4log e 2)(2π) -3/2 Z t e -½Zt² So, for 809 ResEls, corrected alpha of.05: Z threshold:

18 Back to example fmri data: Smoothness FWHM: 3*3*2.9 voxels voxels > ResEls After smoothing more (15 mm FWHM): Smoothness FWHM: 5.7*5.9*5.2 voxels voxels > ResEls T threshold: 4.68; 335 degrees of freedom P threshold: Z threshold: 4.60 T threshold: 4.24; 335 degrees of freedom P threshold: Z threshold: 4.18 Statistical Inference: Regions of interest / small volume corrections Regions of interest: - A priori hypotheses about the search area. - Correct only for number of independent tests in this area. Small volume correction : number of resels may vary with the shape of a small volume: Worsley (2003) 18

19 Statistical Inference: False discovery rate Instead of voxel level inference: P family wise error =.05 Now control number of false discoveries: Proportion of false positives =.05 Thank you Order all P values in the volume: P1<=P2 <=P3 <= <=Pn Cutoff = largest value with: Pk < α k / n Note: this changes the inferences you can make. Acknowledgements: Matthijs Vink, Bas Neggers, Matthew Brett (slides/examples/etc) 19

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