FieldTrip, a tool for GUI-less exploration

of brain dynamics

Jan-Mathijs Schoffelen

j.schoffelen@donders.ru.nlPowered by:

Purpose of this talk

Advertisement• of FieldTrip• of some of the scientific work done with FieldTrip

What is FieldTrip?

A MATLAB toolbox for electrophysiological data analysis

Features: high-level functions forelectrophysiological data analysis

Data reading

all commercial MEG systems, many different EEG systems

Preprocessing

filtering, segmenting

Time-locked ERF analysis

Frequency and time-frequency analysis

multitapers, wavelets, welch, hilbert, parametric spectral estimates

Features: high-level functions forelectrophysiological data analysis

Functional connectivity analysis

coherence, phase locking value, granger causality,

and many more

Source reconstruction

beamformers, dipole fitting, linear estimation

Statistical analysis

parametric, non-parametric, channel and source level

All other operations that are required around it

But…

X

Comparison with another well-known toolbox

SPM contra

buttonsmodel-based

GLM, DCM, bayesian model comparison

functionsdata-driven

Comparison with another well-known toolbox

SPMintra

Under the hood a lot of the code is shared

Features

Analysis steps are incorporated in functions

Using functions in an analysis protocol

preprocessingpreprocessing

rejectartifactrejectartifact

freqanalysisfreqanalysis

multiplotTFRmultiplotTFR freqstatisticsfreqstatistics

multiplotTFRmultiplotTFR

cfg = [ ]cfg.dataset = ‘Subject01.ds’cfg.bpfilter = [0.01 150]...rawdata = preprocessing(cfg)

cfg = [ ]cfg.dataset = ‘Subject01.ds’cfg.bpfilter = [0.01 150]...rawdata = preprocessing(cfg)

Typical functions

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]

and determines specific behaviour of the function

Features

Analysis steps are incorporated in functions

Data are represented in standard MATLAB structures

as small as possible

contain all relevant details

Raw data structure

rawData = label: {151x1 cell} trial: {1x87 cell} time: {1x87 cell} fsample: 300 hdr: [1x1 struct] cfg: [1x1 struct]

Event related response

erpData = label: {151x1 cell} avg: [151x900 double] var: [151x900 double] time: [1x900 double] dimord: 'chan_time’ cfg: [1x1 struct]

Features

Analysis steps are incorporated in functions

Data are represented in standard MATLAB structures

as small as possible

contain all relevant details

Data structures bind together analysis blocks

Using functions in an analysis protocol

preprocessingpreprocessing

rejectartifactrejectartifact

freqanalysisfreqanalysis

multiplotTFRmultiplotTFR freqstatisticsfreqstatistics

multiplotTFRmultiplotTFR

cfg = [ ]cfg.method = ‘mtmfft’cfg.foilim = [1 120]...freqdata = freqanalysis(cfg, rawdata)

cfg = [ ]cfg.method = ‘mtmfft’cfg.foilim = [1 120]...freqdata = freqanalysis(cfg, rawdata)

Features

Analysis steps are incorporated in functions

Data are represented in standard MATLAB structures

as small as possible

contain all relevant details

Data structures bind together analysis blocks

Easy to write new functions that operate on these data structures

Output of your own functions can be further processed or plotted with FieldTrip functions

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)

……

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)

……

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)

……

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)

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)

Example use in scripts

subjlist = {‘S01.ds’, ‘S02.ds’, …}triglist = [1 3 7 9]

for s=1:nsubjfor c=1:ncond

cfg = [ ] cfg.dataset = subjlist{s} cfg.trigger = triglist(c) rawdata{s,c} = preprocessing(cfg)

cfg = [ ] cfg.method = ‘mtm’ cfg.foilim = [1 120] freqdata {s,c} = freqanalysis(cfg, rawdata)

endend

Example use in scripts

subjlist = {‘S01.ds’, ‘S02.ds’, …}triglist = [1 3 7 9]

for s=1:nsubjfor c=1:ncond

cfg = [ ] cfg.dataset = subjlist{s} cfg.trigger = triglist(c) rawdata{s,c} = preprocessing(cfg)

filename = sprintf(‘raw%s_%d.mat’, subjlist{s}, condlist(c)); save(filename, ‘rawdata’)

endend

Relevance of analysis scripts

the data and the separate functions are in the hands of the end-users

the scripts depend on the data properties, available memory and programming skills and style

scripts correspond to analysis protocolsscripts can be reviewed by supervisors

scripts are often shared with colleagues

FieldTrip private functions(low-level)

FieldTrip private functions(low-level)

FieldTrip main functions

end-user perspective

FieldTrip toolbox structure - at a glance

FieldTrip toolbox structure - a closer look

fileiofileio forwinvforwinv preprocpreproc privateprivate

FieldTrip main functions

end-user perspective

publicpublic

multivar.multivar.distrib.

comput.distrib.

comput.

FieldTrip toolbox structure - a closer look

fileiofileio forwinvforwinv preprocpreproc privateprivate

end-user perspective

preprocessingpreprocessing

dipolefittingdipolefitting

freqanalysisfreqanalysis sourcestatisticssourcestatistics

……

read_data(…)read_data(…) compute_leadfield(…)compute_leadfield(…)bandpassfilter(…)bandpassfilter(…)

distrib.comput.distrib.

comput.

publicpublic

multivar.multivar.

FieldTrip toolbox structure - a closer look

fileiofileio forwinvforwinv preprocpreproc privateprivate

FieldTrip main functions

end-user perspective

publicpublic

multivar.multivar.distrib.

comput.distrib.

comput.

FieldTrip toolbox - code reused in SPM8

fieldtripfieldtripfileiofileio forwinvforwinv

privateprivate

main functions publicpublicSPM8 main functions

with graphical user interface

SPM8 end-user perspective

preprocpreprocdistrib.

comput.distrib.

comput.

Why use FieldTrip?

contains many algorithms

demonstrated to be an effective tool for analyzing electrophysiological data

active user community

new methods contributed by others

collaboration with other packages

expertise from developers made accessible

expertise from other users made available

Network identification in non-invasive measurements

ROIPower MEG-coherenceCM-coherence

Network identification

all voxel combinations

Network identification

Who is hot....? ...and who is not?

Power

FWHM

Non-homogeneous maps

Simulations

• 2 dipoles in 2 conditions– correlated dipoles

– uncorrelated dipoles

• Reconstruct all-to-all bivariate correlation map with beamformers

• Non-homogeneous smoothing

• 6 dimensional gaussian kernel depending on FWHM– little smoothing for high activations

– more smoothing for low activations

• Many dipole pairs at various SNR etc.

Results: general effects

Results: hit rate

unsmoothed

smoothed

Results: false positives

Summary

• Identification of functionally connected nodes in a neural network based on non-invasive measurements

• Spurious connections / low sensitivity

• Non-homogeneous smoothing improves sensitivity

• …on simulated data

Do you want to give it a try yourself?

http://fieldtrip.fcdonders.nl/

Acknowledgements:

Monkey stuff-Conrado Bosman-Andre Bastos-Robert Oostenveld-Pascal Fries

Non-homogeneous smoothing-Joachim Gross