Presentation for the Biomag 2016 satellite meeting "From raw MEG to publication - How to perform MEG group analysis with free academic software" Corresponding MATLAB scripts can be found on https://github.com/fieldtrip/Wakeman-and-Henson-2015

1. From raw MEG to publication:
how to perform group analysis
with FieldTrip
Robert Oostenveld
Donders Institute, Radboud University, Nijmegen, NL
NatMEG, Karolinska Institute, Stockholm, SE

2. Presentation outline
FieldTrip background
Strategy for making analysis pipelines
Single subject pipeline
Highlight: Reading and converting files
Highlight: Anatomical coregistration
Group analysis pipeline

3. M/EEG signal characteristics considered during analysis
timecourse of activity
-> ERP
spectral characteristics
-> power spectrum or TFR
spatial distribution of activity over the head
-> source reconstruction
interaction between regions in the brain
-> source reconstruction and connectivity

4. Channels show a superposition of the source activity
Some analyses can be done at the channel level
Some should be done at the source level
analyses
interpretations or hypotheses
require

5. FieldTrip basics
dataout = functionname(cfg, datain, …)
functionname(cfg, datain, …)
dataout = functionname(cfg)
the “cfg” argument is a configuration structure, e.g.
cfg.channel = {‘C3’, C4’, ‘F3’, ‘F4’}
cfg.foilim = [1 70]

6. Using functions in an analysis protocol
ft_preprocessing
ft_rejectartifact
ft_freqanalysis
ft_multiplotTFR ft_freqstatistics
ft_multiplotTFR
cfg = [ ]
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)

7. Using functions in an analysis protocol
ft_preprocessing
ft_rejectartifact
ft_freqanalysis
ft_multiplotTFR ft_freqstatistics
ft_multiplotTFR
cfg = [ ]
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)

8. Event related response
timelockData =
avg: [151x900 double]
var: [151x900 double]
label: {151x1 cell}
time: [1x900 double]
dimord: 'chan_time’
cfg: [1x1 struct]

9. Example use in scripts
cfg = []
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)
cfg = []
cfg.method = ‘montecarlo’
cfg.statistic = ‘indepsamplesT’
cfg.design = [1 2 1 2 2 1 2 1 1 2 ... ]
...
freqstat = ft_freqstatistics(cfg, freqdata)
ft_preprocessing
ft_freqanalysis
ft_freqstatistics
ft_topoplotTFR
…

10. Example use in scripts
cfg = []
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)
cfg = []
cfg.method = ‘montecarlo’
cfg.statistic = ‘indepsamplesT’
cfg.design = [1 2 1 2 2 1 2 1 1 2 ... ]
...
freqstat = ft_freqstatistics(cfg, freqdata)
ft_preprocessing
ft_freqanalysis
ft_freqstatistics
ft_topoplotTFR
…

11. Example use in scripts
cfg = []
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)
cfg = []
cfg.method = ‘montecarlo’
cfg.statistic = ‘indepsamplesT’
cfg.design = [1 2 1 2 2 1 2 1 1 2 ... ]
...
freqstat = ft_freqstatistics(cfg, freqdata)
ft_preprocessing
ft_freqanalysis
ft_freqstatistics
ft_topoplotTFR
…

12. Example use in scripts
cfg = []
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)
cfg = []
cfg.method = ‘montecarlo’
cfg.statistic = ‘indepsamplesT’
cfg.design = [1 2 1 2 2 1 2 1 1 2 ... ]
...
freqstat = ft_freqstatistics(cfg, freqdata)
ft_preprocessing
ft_freqanalysis
ft_freqstatistics
ft_topoplotTFR
…

13. Example use in scripts
cfg = []
cfg.dataset = ‘Subject01.ds’
cfg.bpfilter = [0.01 150]
...
rawdata = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
...
freqdata = ft_freqanalysis(cfg, rawdata)
cfg = []
cfg.method = ‘montecarlo’
cfg.statistic = ‘indepsamplesT’
cfg.design = [1 2 1 2 2 1 2 1 1 2 ... ]
...
freqstat = ft_freqstatistics(cfg, freqdata)
ft_preprocessing
ft_freqanalysis
ft_freqstatistics
ft_topoplotTFR
…

14. Example use in scripts
subj = {‘S01.ds’, ‘S02.ds’, …}
trig = [1 3 7 9]
for s=1:nsubj
for c=1:ncond
cfg = []
cfg.dataset = subj{s}
cfg.trigger = trig(c)
rawdata{s,c} = ft_preprocessing(cfg)
cfg = []
cfg.method = ‘mtmfft’
cfg.foilim = [1 120]
freqdata{s,c} = ft_freqanalysis(cfg, rawdata{s,c})
end
end

15. Example use in scripts
subj = {‘S01.ds’, ‘S02.ds’, …}
trig = [1 3 7 9]
for s=1:nsubj
for c=1:ncond
cfg = []
cfg.dataset = subj{s}
cfg.trigger = trig(c)
rawdata = ft_preprocessing(cfg)
filename = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c));
save(filename, ‘rawdata’)
end
end

16. Example use in scripts
subj = {‘S01.ds’, ‘S02.ds’, …}
trig = [1 3 7 9]
for s=1:nsubj
for c=1:ncond
cfg = []
cfg.dataset = subj{s}
cfg.trigger = trig(c)
cfg.outputfile = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c));
rawdata = ft_preprocessing(cfg)
end
end

17. Example use in distributed computing
subj = {‘S01.ds’, ‘S02.ds’, …}
trig = [1 3 7 9]
for s=1:nsubj
for c=1:ncond
cfgA{s,c} = []
cfgA{s,c}.dataset = subj{s}
cfgA{s,c}.trigger = trig(c)
cfgA{s,c}.outputfile = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c))
cfgB{s,c} = []
cfgB{s,c}.dataset = subj{s}
cfgB{s,c}.trigger = trig(c)
cfgB{s,c}.inputfile = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c));
cfgB{s,c}.outputfile = sprintf(‘freq%s_%d.mat’, subj{s}, trig(c));
end
end
dfeval(@ft_preprocessing, cfgA)
dfeval(@ft_freqanalysis, cfgB)

18. Example use in distributed computing
subj = {‘S01.ds’, ‘S02.ds’, …}
trig = [1 3 7 9]
for s=1:nsubj
for c=1:ncond
cfgA{s,c} = []
cfgA{s,c}.dataset = subj{s}
cfgA{s,c}.trigger = trig(c)
cfgA{s,c}.outputfile = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c))
cfgB{s,c} = []
cfgB{s,c}.dataset = subj{s}
cfgB{s,c}.trigger = trig(c)
cfgB{s,c}.inputfile = sprintf(‘raw%s_%d.mat’, subj{s}, trig(c));
cfgB{s,c}.outputfile = sprintf(‘freq%s_%d.mat’, subj{s}, trig(c));
end
end
qsubcellfun(@ft_preprocessing, cfgA)
qsubcellfun(@ft_freqanalysis, cfgB)

19. FieldTrip is a toolbox
the data and the separate functions are all in
your hands
you write a script or set of scripts that represents
the analysis pipeline
scripts correspond to analysis protocols
scripts can be reviewed by supervisors
scripts are often reused and shared with colleagues
scripts can be published/released

20. File importing with FieldTrip
highlight 1

21. FieldTrip private functions
(low-level)
FieldTrip main functions
end-user
perspective
FieldTrip toolbox structure - at a glance

22. FieldTrip toolbox structure - a closer look
fileio forward preproc private
FieldTrip main functions
end-user
perspective
utilities
…

23. external
EEGLAB
external
SPM
preproc
FieldTrip toolbox structure - a closer look
fileio forward private
end-user
perspective
ft_read_data(…) ft_compute_leadfield(…)
ft_preproc_bandpassfilter(…)
utilitiesFieldTrip main functions

24. SPM under the hood
preprocfileio forward
end-user
perspective
ft_read_data(…) ft_compute_leadfield(…)
ft_preproc_bandpassfilter(…)
fieldtrip
SPM
… private

25. EEGLAB under the hood
fileio forward
end-user
perspective
ft_read_data(…) ft_compute_leadfield(…)
fieldtrip
EEGLAB
… private

26. The fileio (input/output) mini toolbox
In FieldTrip primarily
ft_definetrial(cfg)
ft_preprocessing(cfg)
ft_read_mri
Provides a consistent interface (API) to datasets
from 7 MEG systems and >30 EEG systems
ft_read_event
ft_read_header
ft_read_data
ft_read_xxx(filename, …)

27. highlight 2
Coregistration between MEG/EEG and anatomy
1) anatomical landmarks (lpa, rpa, nas)
2) HPI/HCL coil locations
3) scalp surface points

28. Lab specific conventions for landmarks and markers
Landmarks:
anatomically recognizeable points on a head
Markers (or fiducials):
points that are visible in multiple modalities,
e.g. HPI coils or vitamin E capsules

29. Sharing anonymous imaging data
Facial details have been removed,
e.g. using ft_defacevolume or
ft_defacemesh.
Nasion is missing, the outline of the
nose is missing, sometimes also the
ears.

30. Coregistration
1. Redo the coregistration using limited data
2. Trust the coregistration that was provided
FieldTrip BrainStorm

31. Presentation outline
FieldTrip background
Strategy for making analysis pipelines
Single subject pipeline
Highlight: Reading and converting files
Highlight: Anatomical coregistration
Group analysis pipeline

32. Making a group analysis pipeline
Start with version control
> git init
Write the pipeline for a single subject
> git commit
Manage subject differences
> git commit
Run for all subjects
> git commit
Do group analysis
> git commit
Share your pipeline along with paper and data
> git push

33. Wrap up and topics for discussion
Managing and sharing pipelines
- version control, provenance, consistency,
reproducibility
Preventing “data dredging”, “double dipping” and
biassed analyses
Sharing data for (re)analysis
- BIDS: Brain Imaging Data Structure now also
for MEG (see poster Mo-P012)
- Provide sufficient detail, but not all
http://datacolada.org
https://aspredicted.org