The other stuff

Vladimir Litvak

Wellcome Trust Centre for NeuroimagingUCL Institute of Neurology, London, UK

• SPM resources

• Fieldtrip in SPM8

• MEEGTools and Beamforming

• DCM

NormalisationNormalisation

Statistical Parametric MapStatistical Parametric MapImage time-seriesImage time-series

Parameter estimatesParameter estimates

General Linear ModelGeneral Linear ModelRealignmentRealignment SmoothingSmoothing

Design matrix

AnatomicalAnatomicalreferencereference

Spatial filterSpatial filter

StatisticalStatisticalInferenceInference

RFTRFT

p <0.05p <0.05

Software: SPM8

• Open Source academic freeware (under GPL)• Documented and informally supported• Requirements:

– MATLAB: 7.1 (R14SP3) to 7.11 (R2010b)no Mathworks toolboxes required

– Supported platforms (MEX files):

– File Formats:• Images: NIfTI-1 (& Analyze, DICOM)

• Surface meshes: GIfTI• M/EEG: most manufacturers (with FieldTrip’s fileio)

Linux (32 and 64 bit) Windows (32 and 64 bit) Mac Intel (32 and 64 bit)

SPMweb • Introduction to SPM • SPM distribution: SPM2, SPM5, SPM8

• Documentation & Bibliography

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http://www.fil.ion.ucl.ac.uk/spm/

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http://www.fil.ion.ucl.ac.uk/spm/ext/

SPM DocumentationPeer reviewed literaturePeer reviewed literature

SPM Books:SPM Books:Human Brain Function I & IIHuman Brain Function I & IIStatistical Parametric MappingStatistical Parametric Mapping

Online help Online help & function & function descriptionsdescriptions

SPM ManualSPM Manual

SPM Online Bibliography

External Resources

• SPM @ Wikipedia

http://en.wikipedia.org/wiki/Statistical_parametric_mapping

• SPM @ Scholarpedia

http://www.scholarpedia.org/article/SPM

• SPM @ WikiBooks

http://en.wikibooks.org/wiki/SPM

• MRC-CBU Imaging/MEG wiki

http://imaging.mrc-cbu.cam.ac.uk/imaging/CbuImaging

http://imaging.mrc-cbu.cam.ac.uk/meg

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http://www.nitrc.org/projects/spm/

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FieldTripPowered by:

http://fieldtrip.fcdonders.nl/

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

Features

Analysis steps are incorporated in functions

ft_preprocessingft_preprocessing

ft_rejectartifactft_rejectartifact

ft_freqanalysisft_freqanalysis

ft_multiplotTFRft_multiplotTFR ft_freqstatisticsft_freqstatistics

ft_multiplotTFRft_multiplotTFR

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

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

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.

Fieldtrip-SPM8 integration

• Full version of Fieldtrip is contained in SPM8 under /external/fieldtrip.

• Fieldtrip raw, timelock and freq structures can be converted into SPM8 datasets with spm_eeg_ft2spm.

• D.ftraw and D.fttimelock can be used to export SPM dataset to Fieldtrip raw and timelock/freq structs respectively.

• Fieldtrip and SPM share common forward modelling framework. Head models created in SPM can be used in Fieldtrip.

Fieldtrip-SPM8 integration – the future

• Time-frequency analysis will be done using shared code.

• Matlabbatch interface as in SPM will be created for all top-level Fieldtrip function, so Fieldtrip will have GUI for the first time.

• Matlabbatch and distributed computing toolbox from FieldTrip will be combined for easy-to-use job parallelization framework that will work with both FieldTrip and SPM.

•MEEGTools toolbox includes some useful functions contributed by SPM developers and power users.

•Many of these functions combine SPM and FieldTrip functionality.

•Other functions solve system-specific problems that cannot be handled in by the main SPM code.

MEEGTools

• Functions in the beamforming toolbox make it possible to perform source reconstruction using beamforming methods in the time and frequency domains and extract source activity using beamformer spatial filters.

• They make use of SPM-generated forward models (see ‘Source reconstruction’) and (where relevant) generate images that can be entered into the SPM statistics pipeline.

• Some of these functions are based on FieldTrip code and others are being developed by Gareth Barnes at the FIL.

• We are now working on optimizing these functions for Neuromag but this is still in progress.

Beamforming

Time

DCM for fMRI

Single region

z1

u1

a11c

u2

u1

z1

z2

DCM for fMRI

,)(

signal BOLD

qvty

)(

activity

tx

sf

tionflow induc

(rCBF)

s

v

v

q q/vvEf,EEfqτ /α

dHbchanges in

100 )( /αvfvτ

volumechanges in

1

f

q

)1(

fγsxs

signalryvasodilato

s

f

stimulus functionsut

neural state equation

hemodynamic state equations

Estimated BOLD response

Modelled neuralactivity

PredictedBOLD

PredictedBOLD+ noise

=observed data

Multiple regions

u2

u1

z1

z2

z1

z2

u1

a11

a22

c

a21

Modulatory inputs

u2

u1

z1

z2

u2

z1

z2

u1

a11

a22

c

a21

b21

Reciprocal connections

u2

u1

z1

z2

u2

z1

z2

u1

a11

a22

c

a12

a21

b21

Bayes‘ Theorem

)()|()|( pypyp )()|()|( pypyp posterior likelihood ∙ prior

)|( yp )|( yp )(p )(pnew data prior information

Reverend Thomas Bayes1702 - 1761

“Bayes‘ Theorem describes how an ideally rational person processes information."

Wikipedia

Bayesian model inversion

• Knowing the probability of data given the model (which is something we can define) Bayes rule makes it possible to compute the probability of model parameters given the data.

• This requires specifying prior beliefs about the parameters values.

• Bayes rule is a mathematically optimal way to combine prior knowledge and information derived from the data.

• Model parameters will be moved from their prior values only if there is a need for it to fit the data. Thus, in a model with many parameters we can make inferences just about those that are important.

• Bayesian model evidence, approximated by a quantity called ‘free energy’ is a single number combining a measure of ‘goodness of fit’ of a model with ‘complexity penalty’. It allows comparing different models for the same data.

F = - +

Accuracy

Complexity

Summary: the outputs of DCM

• Predicted data as similar as possible to the real data.

• Posterior values of models parameters and posterior precisions (measures of confidence about those values).

• Free energy value (F) which can be used to compare models fitted to the same data.

internal granularlayer

internal pyramidallayer

external pyramidallayer

external granularlayer

AP generation zone synapses

macro-scale meso-scale micro-scale

Daunizeau et al. 2009, NeuroImage

David et al. 2006, NeuroImage

Kiebel et al. 2006, NeuroImage

Moran et al. 2009, NeuroImage

Spatial model

0x

LL

Depolarisation ofpyramidal cells

Spatial model

Sensor data y

Kiebel et al., NeuroImage, 2006Daunizeau et al., NeuroImage, 2009

DCM for steady-state responses

DCM for ERP (+second-order mean-field DCM)

DCM for induced responses

DCM for phase coupling

0 100 200 3000

50

100

150

200

250

0 100 200 300-100

-80

-60

-40

-20

0

0 100 200 300-100

-80

-60

-40

-20

0

time (ms)

input depolarization

time (ms) time (ms)

auto-spectral densityLA

auto-spectral densityCA1

cross-spectral densityCA1-LA

frequency (Hz) frequency (Hz) frequency (Hz)

1st and 2d order moments

DCMs for M/EEG

• Dynamic Causal Modelling (DCM) is an approach combining computational neuroscience and neuroimaging data analysis.

• DCM makes it possible to estimate hidden parameters from observable measurements given a model that links between the two.

• Although there is complex theoretical background behind DCM, its application is straightforward and does not necessarily require mathematical training or programming skills.

Summary

Thanks to

The people who contributed material to this presentation:

• Guillaume Flandin• Stefan Kiebel • Robert Oostenveld• Gareth Barnes• Karl Friston

Thank you for your attention