Statistical Analysis I. Basic Hypothesis-Driven Analyses

1. Statistical Analysis I. Basic Hypothesis-Driven Analyses fMRI Graduate Course November 13, 2002

2. When do we not need statistical analysis? Inter-ocular Trauma Test (Lockhead, personal communication)

3. Why use statistical analyses? • Replaces simple subtractive methods • Signal highly corrupted by noise • Typical SNRs: 0.2 – 0.5 • Sources of noise • Thermal variation (unstructured) • Physiological variability (structured) • Assesses quality of data • How reliable is an effect? • Allows distinction of weak, true effects from strong, noisy effects

4. Statistical Parametric Maps • 1. Brain maps of statistical quality of measurement • Examples: correlation, regression approaches • Displays likelihood that the effect observed is due to chance factors • Typically expressed in probability (e.g., p < 0.001) • 2. Effect size • Determined by comparing task-related variability and non-task-related variability • Signal change divided by noise (SNR) • Typically expressed as t or z statistics

5. Why use effect size measures? • Dissociate size of signal change from reliability of signal change • Understanding reliability of change allows quantification of error probabilities • Types of Errors • Type I: Rejecting null hypothesis when it is true • Calling “active” voxels that really have no activity • To minimize false positives, adopt a high threshold for significance • Type II: Accepting null hypothesis when it is false • Calling “inactive” voxels that are really associated with the task • To minimize incorrect rejections, adopt a low threshold for significance

6. Simple Statistical Analyses • Common • t-test across conditions • Fourier • t-test at time points • Correlation • General Linear Model • Other tests • Kolmogorov-Smirnov • Iterative Connectivity Mapping

7. 5% T – Tests across Conditions • Compares difference between means to population variability • Uses t distribution • Defined as the likely distribution due to chance between samples drawn from a single population • Commonly used across conditions in blocked designs • Potential problem: Multiple Comparisons

8. Drift Artifact and T-Test

9. Fourier Analysis • Fourier transform: converts information in time domain to frequency domain • Used to change a raw time course to a power spectrum • Hypothesis: any repetitive/blocked task should have power at the task frequency • BIAC function: FFTMR • Calculates frequency and phase plots for time series data. • Equivalent to correlation in frequency domain • At short durations, like a sine wave (single frequency) • At long durations, like a trapezoid (multiple frequencies) • Subset of multiple regression • Same as if used sine and cosine as regressors

10. Power 12s on, 12s off Frequency (Hz)

13. T/Z – Tests across Time Points • Determines whether a single data point in an epoch is significantly different from baseline • BIAC Tool: tstatprofile • Creates: • Avg_V*.img • StdDev_V*.img • ZScore_V*.img

14. Correlation • Special case of General Linear Model • Blocked t-test is equivalent to correlation with square wave function • Allows use of any reference waveform • Correlation coefficient describes match between observation and expectation • Ranges from -1 to 1 • Amplitude of response does not affect correlation directly • BIAC tool: tstatprofile

15. Problems with Correlation Approaches • Limited by choice of HDR • Poorly chosen HDR can significantly impair power • Examples from previous weeks • May require different correlations across subjects • Assume random variation around HDR • Do not model variability contributing to noise (e.g., scanner drift) • Such variability is usually removed in preprocessing steps • Do not model interactions between successive events

16. Kolmogorov – Smirnov (KS) Test • Statistical evaluation of differences in cumulative density function • Cf. t-test evaluates differences in mean p < 10-30 ns p < 10-30

17. Iterative Connectivity Mapping • Acquire two data sets • 1: Defines regions of interest and hypothetical connections • 2: Evaluates connectivity based on low frequency correlations • Use of Continuous Data Sets • Null Data • Task Data • Can see connections between functional areas (e.g., between Broca’s and Wernicke’s Areas) Hampson et al., Hum. Brain. Map., 2002

18. Use of Continuous Tasks to Evaluate Functional Connectivity Hampson et al., Hum. Brain. Map., 2002

19. The General Linear Model

20. Basic Concepts of the GLM • GLM treats the data as a linear combination of model functions plus noise • Model functions have known shapes • Amplitude of functions are unknown • Assumes linearity of HDR; nonlinearities can be modeled explicitly • GLM analysis determines set of amplitude values that best account for data • Usual cost function: least-squares deviance of residual after modeling (noise)

21. Signal, noise, and the General Linear Model Amplitude (solve for) Measured Data Noise Design Model Cf. Boynton et al., 1996

22. Form of the GLM Model Functions Model Functions Model * Amplitudes = + Data Noise N Time Points N Time Points

23. Implementation of GLM in SPM

24. The Problem of Multiple Comparisons

25. The Problem of Multiple Comparisons P < 0.05 (1682 voxels) P < 0.01 (364 voxels) P < 0.001 (32 voxels)

26. Options for Multiple Comparisons • Statistical Correction (e.g., Bonferroni) • Gaussian Field Theory • Cluster Analyses • ROI Approaches

27. Statistical Corrections • If more than one test is made, then the collective alpha value is greater than the single-test alpha • That is, overall Type I error increases • One option is to adjust the alpha value of the individual tests to maintain an overall alpha value at an acceptable level • This procedure controls for overall Type I error • Known as Bonferroni Correction

29. Bonferroni Correction • Very severe correction • Results in very strict significance values for even medium data sets • Typical brain may have about 15,000-20,000 functional voxels • PType1 ~ 1.0 ; Corrected alpha ~ 0.000003 • Greatly increases Type II error rate • Is not appropriate for correlated data • If data set contains correlated data points, then the effective number of statistical tests may be greatly reduced • Most fMRI data has significant correlation

30. Gaussian Field Theory • Approach developed by Worsley and colleagues to account for multiple comparisons • Forms basis for much of SPM • Provides false positive rate for fMRI data based upon the smoothness of the data • If data are very smooth, then the chance of noise points passing threshold is reduced

31. Figures from http://www.irsl.org/~fet/Presentations/wavestatfield/wavestatfield.html

32. Cluster Analyses • Assumptions • Assumption I: Areas of true fMRI activity will typically extend over multiple voxels • Assumption II: The probability of observing an activation of a given voxel extent can be calculated • Cluster size thresholds can be used to reject false positive activity • Forman et al., Mag. Res. Med. (1995) • Xiong et al., Hum. Brain Map. (1995)

33. How many foci of activation? Data from motor/visual event-related task (used in laboratory)

34. How large should clusters be? • At typical alpha values, even small cluster sizes provide good correction • Spatially Uncorrelated Voxels • At alpha = 0.001, cluster size 3 reduces Type 1 rate to << 0.00001 per voxel • Highly correlated Voxels • Smoothing (FW = 0.5 voxels) increases needed cluster size to 7 or more voxels • Efficacy of cluster analysis depends upon shape and size of fMRI activity • Not as effective for non-convex regions • Power drops off rapidly if cluster size > activation size Data from Forman et al., 1995

35. ROI Comparisons • Changes basis of statistical tests • Voxels: ~16,000 • ROIs : ~ 1 – 100 • Each ROI can be thought of as a very large volume element (e.g., voxel) • Anatomically-based ROIs do not introduce bias • Potential problems with using functional ROIs • Functional ROIs result from statistical tests • Therefore, they cannot be used (in themselves) to reduce the number of comparisons

36. Are there differences between voxel-wise and ROI analyses?

37. Summary of Multiple Comparison Correction • Basic statistical corrections are often too severe for fMRI data • What are the relative consequences of different error types? • Correction decreases Type I rate: false positives • Correction increases Type II rate: misses • Alternate approaches may be more appropriate for fMRI • Cluster analyses • Region of interest approaches • Smoothing and Gaussian Field Theory

38. Fixed and Random Effects Comparisons

39. How do we compare across subjects? • Fixed-effects Model • Uses data from all subjects to construct statistical test • Examples • Averaging across subjects before a t-test • Taking all subjects’ data and then doing an ANOVA • Allows inference to subject sample • Random-effects Model • Accounts for inter-subject variance in analyses • Allows inferences to population from which subjects are drawn • Especially important for group comparisons • Beginning to be required by reviewers/journals

40. How are random-effects models run? • Assumes that activation parameters may vary across subjects • Since subjects are randomly chosen, activation parameters may vary within group • Fixed-effects models assume that parameters are constant across individuals • Calculates descriptive statistic for each subject • i.e., t-test for each subject based on correlation • Uses all subjects’ statistics in a one-sample t-test • i.e., another t-test based only on significance maps

41. Summary of Hypothesis Tests • Simple experimental designs • Blocked: t-test, Fourier analysis • Event-related: correlation, t-test at time points • Complex experimental designs • Regression approaches (GLM) • Critical problem: Minimization of Type I Error • Strict Bonferroni correction is too severe • Cluster analyses improve • Accounting for smoothness of data also helps • Use random-effects analyses to allow generalization to the population