Statistical Analysis

1. Statistical Analysis fMRI Graduate Course November 2, 2005

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, task 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

9. What are our statistics for?

11. Which is more important to avoid: Type I or Type II errors?

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

13. 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 • Subset of general linear model

14. Drift Artifact and t-Test

15. 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 • Subset of general linear model • Same as if used sine and cosine as regressors

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

18. 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

19. 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

20. 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 that correlation template is Gaussian • 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

21. Kolmogorov – Smirnov (KS) Test • Statistical evaluation of differences in cumulative density function • Cf. t-test evaluates differences in mean • Useful if distributions have same mean but different shape A B C

22. 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

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

24. The General Linear Model

25. 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)

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

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

28. Implementation of GLM in SPM Model Parameters Images

29. The Problem of Multiple Comparisons

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

31. B C A t = 2.10, p < 0.05 (uncorrected) t = 3.60, p < 0.001 (uncorrected) t = 7.15, p < 0.05, Bonferroni Corrected

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

33. 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

35. 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

36. 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

38. 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)

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

40. 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

41. False Discovery Rate • Controls the expected proportion of false positive values among suprathreshold values • Genovese, Lazar, and Nichols (2002, NeuroImage) • Does not control for chance of any face positives • FDR threshold determined based upon observed distribution of activity • So, sensitivity increases because metric becomes more lenient as voxels become significant • Weak familywise Type I error rate

42. 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

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

45. 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 • False Discovery Rate

46. Fixed and Random Effects Comparisons

47. How do we compare across subjects? • Fixed-effects Model • Assumes that effect is constant (“fixed”) in the population • 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 • Assumes that effect varies across the population • Accounts for inter-subject variance in analyses • Allows inferences to population from which subjects are drawn • Especially important for group comparisons • Required by many reviewers/journals

49. 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

50. 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