Group Sta*s*cs in MEG/EEG

Transcription

1 Group Sta*s*cs in MEG/EEG Will Woods NIF Fellow Brain and Psychological Sciences Research Centre Swinburne University of Technology

2 A Cau*onary tale.

3 A Cau*onary tale.

4 A Cau*onary tale.

5 Overview Introduc*on to nonparametric sta*s*cs in neuroimaging Specific issues related to MEG beamforming Some non- standard examples

6 Nonparametric Group Sta*s*cs Overview Single Group A minus B : Ac*ve Baseline or Ac*ve 1 Ac*ve 2 A - B Two- sample T- sta*s*c Map (normalized to Z- sta*s*c) First Level Analysis for a single individual

7 Nonparametric Group Sta*s*cs Overview Single Group Mul*ple individuals in group mul*ple first- level sta*s*cal maps P1 P2 P3 Pn. One- sample T- sta*s*c Map Second Level Analysis for the group

8 Nonparametric Group Sta*s*cs Single Group Overview Significance? Don t have to test against parametric T distribu*on use Non- parametric sta*s*cs - Null hypothesis is that condi*on labels (A,B) are arbitrary. Construct null distribu*on by repeatedly permu*ng (randomising) labels Procedure: Randomly choose some individuals and switch the labels on the data i.e. flip sign on two- sample T- sta*s*c (A- B) - > (B- A) = - (A- B) P1 P2 P3 Pn Permuted One- sample T- sta*s*c Map

9 Nonparametric Group Sta*s*cs Single Group Overview Mul*ple comparisons problem - 10,000 s of voxels test each one separately? Use Maximum Sta*s*cs Omnibus null hypothesis: If the largest voxel is not significant, none of them are. In prac*ce, just record the largest voxel value in the permuta*on T- map. (single threshold) Repeat many *mes (100 s 1000 s) with different random label assignments. Histogram of values gives the null distribu*on against which we can test each of the voxels from the original, unpermuted, sta*s*cal image.

10 Maximum Sta*s*cs Test original data against Null distribu*on (Nichols)

11 Nonparametric Group Sta*s*cs Overview Two Groups First level (individual) analysis remains the same Group 1 Group 2 - Two- sample T- sta*s*c Map Don t switch labels at first level. Instead, shuffle members of groups. (Null hypothesis is that there is no difference between groups)

12 Nonparametric Group Sta*s*cs Cluster based sta*s*cs Overview Choose a primary threshold Record size of largest supra- threshold cluster in each permuta*on T- sta*s*c map Test all clusters in original T- map against cluster null distribu*on Measure size by number of voxels? Primary threshold

13 Nonparametric Group Sta*s*cs Cluster based sta*s*cs Overview Choose a primary threshold Record size of largest supra- threshold cluster in each permuta*on T- sta*s*c map Test all clusters in original T- map against cluster null distribu*on Measure size by number of voxels? Primary threshold

14 Nonparametric Group Sta*s*cs Cluster based sta*s*cs Overview Choose a primary threshold Record size of largest supra- threshold cluster in each permuta*on T- sta*s*c map Test all clusters in original T- map against cluster null distribu*on Measure size by Exceedence Mass Primary threshold

15 MEG Beamforming and Group Sta*s*cs

16 Beamforming Primer Beamformer uses the covariance of data

17 Beamforming Primer Beamformer uses the covariance of data Choose weights to minimise power of source

18 Beamforming Primer Beamformer uses the covariance of data Choose weights to minimise power of source Subject to constraint:

19 Beamforming Primer Beamformer uses the covariance of data Choose weights to minimise power of source Subject to constraint: Solution:

20 Covariance Matrix

21 Permuta*on Sta*s*cs Individual Two sample T sta*s*c Easy to do because each point k corresponds to the same anatomical loca*on for both condi*on A and condi*on B

22 Permuta*on Sta*s*cs Group For a regular grid there is no natural anatomical correspondence between grid points across par*cipants. Could interpolate over the whole brain volume for each par*cipant, then use fmri type volumetric procedures. Or..

23 Permuta*on Sta*s*cs Group MNI Standard Brain Structural Normalisa*on Affine or Nonlinear Transforma*on FSL SPM ANTS etc P1 P2 P3.

24 Permuta*on Sta*s*cs Group MNI Standard Brain Structural Normalisa*on Affine or Nonlinear Transforma*on FSL SPM ANTS etc P1 P2 P3.

25 Permuta*on Sta*s*cs Group MNI Standard Brain Structural Normalisa*on Affine or Nonlinear Transforma*on FSL SPM ANTS etc P1 P2 P3.

26 Permuta*on Sta*s*cs Group MNI Standard Brain Do sta*s*cs on grid point beamformer values Faster Not doing sta*s*cs on interpolated values P1 P2 P3.

27 Spoken Word Synaesthesia Asghar, Woods et al. YNiC Nature Neuroscience 2002 Nunn et al. fmri Study

28 Spoken Word Synaesthesia Asghar, Woods et al. YNiC Nature Neuroscience 2002 Nunn et al. fmri Study R Synaesthetes MEG - BETA Decrease R L V4/V8 (colour) V1/V2 (vision)

29 Spoken Word Synaesthesia Asghar, Woods et al. YNiC Nature Neuroscience 2002 Nunn et al. fmri Study Synaesthetes MEG - BETA Decrease R L R V4/V8 V4/V8 (colour) (colour) V1/V2 (vision) Controls MEG - ALPHA Decrease L V4/V8 (colour)

30 Cluster Sta*s*cs and Beamformers

31 Cluster Sta*s*cs and Beamformers

32 Cluster Sta*s*cs and Beamformers

33 Cluster Sta*s*cs and Beamformers Spa*al resolu*on is signal strength dependent

34 Cluster Sta*s*cs and Beamformers Primary Threshold Spa*al resolu*on is signal strength dependent

35 Cluster Sta*s*cs and Beamformers Cluster Threshold Spa*al resolu*on is signal strength dependent

36 Cluster Sta*s*cs and Beamformers Cluster Threshold Spa*al resolu*on is signal strength dependent

37 Cluster Sta*s*cs and Beamformers Cluster Threshold Spa*al resolu*on is signal strength dependent

38 Cluster Sta*s*cs and Beamformers Cluster Threshold Spa*al resolu*on is signal strength dependent Normalise cluster size with respect to the filter inverse FWHM

39 Mass Univariate Analysis Tutorial review Groppe et al 2011 (Matlab toolbox available) Look for clusters in *me and space (and frequency band)

40 Mass Univariate Analysis Groppe et al 2011

41 Mass Univariate Analysis Groppe et al 2011

42 Mass Univariate Analysis Groppe et al 2011

43 Threshold- Free Cluster Enhancement (TFCE) How to avoid the ambiguity of choosing a primary threshold for cluster sta*s*cs?

44 Threshold- Free Cluster Enhancement (TFCE) How to avoid the ambiguity of choosing a primary threshold for cluster sta*s*cs? Smith & Nichols 2009

45 Threshold- Free Cluster Enhancement (TFCE) How to avoid the ambiguity of choosing a primary threshold for cluster sta*s*cs? Do Single Threshold Maximum Sta*s*cs on TFCE signal Smith & Nichols 2009

46 Group Virtual Electrode Statistics W... W... W... W... W... W... W... W 4 W 3 W... W... W 2 W 248 W 1

47 Stockwell Transform (*me v frequency plot) Average of n epochs Evoked Activity Total Activity

48 Time- Frequency Sta*s*cs Time / Freq power plots group analysis Baseline Ac*ve Difference?

49 Time- Frequency Sta*s*cs Baseline Ac*ve Remove Baseline mean for each frequency from Ac*ve Window

50 Time- Frequency Sta*s*cs Induced Power Evoked Power Jackknife Variance es*mate Construct first level map of T sta*s*cs for each Time/Freq point using mean and variance across epochs (for induced) or jackknifed variance (for evoked)

51 Time- Frequency Sta*s*cs P1 P2 Pn. Two Group analysis usual procedure, shuffle par*cipants between groups Single group exchange Ac*ve, Passive labels Cannot flip sign (P - A) - (A P) Have to repeat baseline adjus*ng procedure at first level

52 Time- Frequency Sta*s*cs P1 P2 Pn. Two Group analysis usual procedure, shuffle par*cipants between groups Single group exchange Ac*ve, Passive labels Cannot flip sign (P - A) - (A P) Have to repeat baseline adjus*ng procedure at first level Use TFCE to generate a Cluster Enhanced Group Time/Freq map, and do Single Threshold analysis.

53 Spoken Word Synaesthesia Asghar, Woods et al. YNiC

54 Source Stability Index Beamformer Virtual electrode for each grid loca*on Single condi*on 1 / # 1 / # 1 / #

55 Source Stability Index Beamformer 1 / # 1 / # 1 / #

56 SSI Beamformer and Sta*s*cs Single condi*on measure of the amount of phase locking in a response Cannot shuffle labels between condi*ons Null distribu*on from surrogate data at first (individual) level Surrogate data generated by adding a single random phase angle to all frequency components of a given epoch Destroy phase locking (evoked) response while retaining all other structure.

57 SSI Beamformer and Sta*s*cs Group level open ques*on. Can generate surrogates at first level when doing second level group analysis (computa*onally intensive). No natural way to take simple first level sta*s*c (one- sided T) through to a group analysis because there are no condi*on labels to permute. Could just do first level procedure at group level with evoked *me series normalised by RMS power in each individual?

58 Detec*ng changes in Nonlinearity in underlying dynamics Con*nuous res*ng data divided into 2 sec epochs Use Tisean Matlab toolbox to test for nonlinearity Generate surrogate data for each epoch for every EEG electrode (or virtual electrode) Surrogate data constructed to destroy nonlinearity but retain all other structure Get rank of original data against surrogates for each epoch at first level : => Mean rank across epochs Standard Error not required (assuming sta*onary system proper*es)

59 Maximum Sta*s*cs - ROI

60 ROI Null Distribu*ons

61 Extreme Value Sta*s*cs?

62 Combining ROIs?

63 Combining ROIs?

64 wledgements York Neuroimaging Centre Gary Green Gareth Prendergast Michael Simpson Swinburne University of Technology David Liley Susan Rossell