statistical analysis of m/eeg sensor- and source-level data jason taylor mrc cognition and brain...
Post on 16-Jan-2016
215 Views
Preview:
TRANSCRIPT
Statistical Analysis of M/EEG Sensor- and Source-Level Data
Jason Taylor MRC Cognition and Brain Sciences Unit (CBU)
Cambridge Centre for Ageing and Neuroscience (CamCAN)
Cambridge, UK
jason.taylor@mrc-cbu.cam.ac.uk | http://imaging.mrc-cbu.cam.ac.uk/meg (wiki)
14 May 2012 | London | Thanks to Rik Henson and Dan Wakeman @ CBU
Practical aspects of…
The SPM approach to M/EEG Statistics:
A mass-univariate statistical approach to inference regarding effects in space/time/frequency (using replications across trials or subjects).
• In Sensor Space:
2D Time-Frequency
3D Topography-by-Time
• In Source Space:
3D Time-Frequency contrast images
4-ish-D space by (factorised) time
Alternative approaches: SnPM, PPM, etc.
Overview
Example Datasets
CTF Multimodal Face-Evoked Dataset | SPM8 Manual (Rik Henson)
CTF MEG/ActiveTwo EEG:275 MEG128 EEG3T fMRI (with nulls)1mm3 sMRI
2 sessions
Stimuli:~160 face trials/session~160 scrambled trials/session
Group: N=12 subjects(Henson et al, 2009 a,b,c)
Chapter 37 SPM8 Manual
Neuromag Faces Dataset | Dan Wakeman & Rik Henson @ CBU
Biomag 2010 Award-Winning Dataset!
Neuromag MEG & EEG Data:102 Magnetometers204 Planar Gradiometers70 EEGHEOG, VEOG, ECG1mm3 sMRI
6 sessions
Stimuli: faces & scrambled-face images (480 trials total)
Group: N=18(Henson et al, 2011)
400-600ms
800-1000ms
1700ms
Neuromag Lexical Decision Dataset | Taylor & Henson @ CBU
Neuromag Lexical Decision
Neuromag MEG Data:102 Magnetometers204 Planar GradiometersHEOG, VEOG, ECG1mm3 sMRI
4 sessions
Stimuli:words & pseudowords presented visually(480 trials total)
Group: N=18Taylor & Henson (submitted)
The Multiple Comparison Problem
Where is the effect?: The curse of too much data
The Multiple Comparison Problem
• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.
* Must consider Familywise (vs. per-comparison) Error Rate
• Comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis. RFT correction depends on the search volume.
-> When is there
an effect in time e.g., GFP (1D)?
time
The Multiple Comparison Problem
• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.
* Must consider Familywise (vs. per-comparison) Error Rate
• Comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis. RFT correction depends on the search volume.
-> When/at what frequency
is there an effectan effect in time/frequency (2D)?
time
frequency
• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.
* Must consider Familywise (vs. per-comparison) Error Rate
• Comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis. RFT correction depends on the search volume.
-> When/where
is there an effectin sensor-topographyspace/time (3D)?
The Multiple Comparison Problem
x
y
time
• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.
* Must consider Familywise (vs. per-comparison) Error Rate
• Comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis. RFT correction depends on the search volume.
-> When/where
is there an effectin source space/time (4-ish-D)?
The Multiple Comparison Problem
y
x
time
z
The Multiple Comparison Problem
• The more comparisons we conduct, the more Type I errors (false positives) we will make when the Null Hypothesis is true.
* Must consider Familywise (vs. per-comparison) Error Rate
• Comparisons are often made implicitly, e.g., by viewing (“eye-balling”) data before selecting a time-window or set of channels for statistical analysis. RFT correction depends on the search volume.
Random Field Theory (RFT) is a method for correcting for multiple statistical comparisons with N-dimensional spaces (for parametric statistics, e.g., Z-, T-, F- statistics).
* Takes smoothness of images into account
Worsley Et Al (1996). Human Brain Mapping, 4:58-73
GLM: Condition Effects after removing variance due to confounds
Each trial
Each trial-type (6)
beta_00* image volumes reflect (adjusted) condition effects
Confounds (4)
Henson et al., 2008, NImage
W/in Subject(1st-level) model-> one image per trial-> one covariate value
per trial
Also: Group Analysis (2nd-level)-> one image per subject
per condition-> one covariate value per
subject per condition
Sensor-space analyses
Fac
esS
cram
ble
d
Faces > Scrambled
Where is an effect in time-frequency space?
Kilner et al., 2005, Nsci LettersCTF Multimodal Faces
1 subject (1st-level analysis)1 MEG channel
Morlet wavelet projection
1 t-x-f image per trial
Where is an effect in time-frequency space?
Neuromag Faces
MEG (Mag)
Fac
es >
Scr
amb
led
EEG
100-220ms8-18Hz
Fre
q (H
z)
6
9
0
-500 +1000 Time (ms)
Fre
q (H
z)
6
9
0
-500 +1000 Time (ms)
18 subjects1 channel(2nd-level analysis)
Where is an effect in sensor-time space?
3DTopography-x-Time Image volume (EEG)
1 subjectALL EEG channels
Each trial, Topographic projection (2D) for each time point (1D) = topo-time image volume (3D)
Faces vs Scrambled
CTF Multimodal Faces
x
y
tim
e
Without transformation to Device Space
With transformation to Device Space
Where is an effect in sensor-time space?
Analysis over subjects (2nd Level)NOTE for MEG: Complicated by variability in head-positionSOLUTION(?): Virtual transformation to same position (sessions, subjects) - using Signal Space Separation (SSS; Taulu et al, 2005; Neuromag systems)
Taylor & Henson (2008) BiomagNeuromag Lexical Decision
Stats over 18 subjects, RMS of planar gradiometers
Improved: (i.e., more blobs, larger T values) w/ head-position transform
Other evidence: ICA ‘splitting’ with movement
Where is an effect in sensor-time space?
Analysis over subjects (2nd Level): Magnetometers: Pseudowords vs Words PPM (P>.95 effect>0)
Effect Size
18
-18
fT
Taylor & Henson (submitted)Neuromag Lexical Decision
Source-space analyses
Where is an effect in source space (3D)?
STEPS:
Estimate evoked/induced energy (RMS) at each dipole for a certain time-frequency window of interest. - e.g., 100-220ms, 8-18 Hz - For each condition (Faces, Scrambled) - For each sensor type OR fused modalities
Write data to 3D image - in MNI space - smooth along 2D surface
Smooth by 3D Gaussian
Submit to GLM
Henson et al., 2007, NImage
Where is an effect in source space (3D)?
Henson et al, 2011Neuromag Faces
MEGsensor fusion
E+MEGEEG
RESULTS: Faces > Scrambled
fMRI
Where is an effect in source space (3D)?
with fMRI priorsEEG+MEG+fMRI
fMRI
RESULTS: Faces > Scrambled
with group optimisedfMRI priors
EEG+MEG+fMRI
Henson et al, 2011Neuromag Faces
meanstats
Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* high-dimensional distance between topography vectors
* cut the cluster tree where the solution is stable over longest range of distances
Where and When do effects emerge/disappear in source space (4-ish-D: time factorised)?
Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors (here: Cond effects per t-win)
Where and When do effects emerge/disappear in source space (4-ish-D)?
source timecourses
Taylor & Henson, submittedNeuromag Lexical Decision
Condition x Time-windowInteractions
Factorising time allows you to infer (rather than simply describe) when effects emerge or disappear.
We've used a data-driven method (hierarchical cluster analysis) in sensor space to define time-windows of interest for source-space analyses.
* estimate sources in each sub-time-window
* submit to GLM with conditions & time-windows as factors: Cond x TW interactions
Where and When do effects emerge/disappear in source space (4-ish-D)?
Alternative Approaches
Classical SPM approach: Caveats
Inverse operator induces long-range error correlations (e.g., similar gain vectors from non-adjacent dipoles with similar orientation), making RFT correction conservative
Distributions over subjects/voxels may not be Gaussian (e.g., if sparse, as in MSP)
Alternative Approaches
(Taylor & Henson, submitted; Biomag 2010)
Alternative Approaches
p<.05 FWENon-Parametric Approach (SnPM)
Robust to non-Gaussian distributions
Less conservative than RFT when dfs<20
Caveats:
No idea of effect size (e.g., for power, future expts)
Exchangeability difficult for more complex designs
(Taylor & Henson, Biomag 2010)
SnPM Toolbox by Holmes & Nichols:http://go.warwick.ac.uk/tenichols/software/snpm/
CTF Multimodal Faces
Alternative Approaches
p>.95 (γ>1SD)Posterior Probability Maps (PPMs)
Bayesian Inference
No need for RFT (no MCP)
Threshold on posterior probabilty of an effect greater than some size
Can show effect size after thresholding
Caveats:
Assume Gaussian distribution (e.g., of mean over voxels)
CTF Multimodal Faces
Future Directions
• Extend RFT to 2D cortical surfaces (+1D time)? (e.g., Pantazis et al., 2005, NImage)
• Novel approaches to controlling for multiple comparisons
(e.g., 'unique extrema' in sensors: Barnes et al., 2011, NImage)
• Go multivariate? To identify linear combinations of spatial (sensor or
source) effects in time and space(e.g., Carbonnell, 2004, NImage; but see Barnes et al., 2011)
To detect spatiotemporal patterns in 3D images(e.g., Duzel, 2004, NImage; Kherif, 2003, NImage)
-- The end --
• Thanks for listening
• Acknowledgements:• Rik Henson (MRC CBU)• Dan Wakeman (MRC CBU / now at MGH)• Vladimir, Karl, and the FIL Methods Group
• More info:• http://imaging.mrc-cbu.cam.ac.uk/meg (wiki)
jason.taylor@mrc-cbu.cam.ac.uk
top related