the other stuff vladimir litvak wellcome trust centre for neuroimaging ucl institute of neurology,...
TRANSCRIPT
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The other stuff
Vladimir Litvak
Wellcome Trust Centre for NeuroimagingUCL Institute of Neurology, London, UK
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• SPM resources
• Fieldtrip in SPM8
• MEEGTools and Beamforming
• DCM
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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
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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)
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SPMweb • Introduction to SPM • SPM distribution: SPM2, SPM5, SPM8
• Documentation & Bibliography
• SPM email discussion list
• SPM short course• Example data sets• SPM extensions
http://www.fil.ion.ucl.ac.uk/spm/
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SPM Toolboxes• User-contributed SPM extensions:
http://www.fil.ion.ucl.ac.uk/spm/ext/
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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
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SPM Online Bibliography
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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
• SPM @ NITRC
http://www.nitrc.org/projects/spm/
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SPM Mailing List• [email protected]
• Web home page– http://www.fil.ion.ucl.ac.uk/spm/support/– Archives, archive searches, instructions
• Subscribe– http://www.jiscmail.ac.uk/– email [email protected]– join spm Firstname Lastname
• Participate & learn– email [email protected]– Monitored by SPMauthors– Usage queries, theoretical discussions,
bug reports, patches, techniques, &c…
[email protected]://www.fil.ion.ucl.ac.uk/spm/support/
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FieldTripPowered by:
http://fieldtrip.fcdonders.nl/
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What is FieldTrip?
A MATLAB toolbox for electrophysiological data analysis
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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
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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
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But…
X
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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)
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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.
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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.
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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.
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•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
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• 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
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Time
DCM for fMRI
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Single region
z1
u1
a11c
u2
u1
z1
z2
DCM for fMRI
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,)(
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
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Modelled neuralactivity
PredictedBOLD
PredictedBOLD+ noise
=observed data
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Multiple regions
u2
u1
z1
z2
z1
z2
u1
a11
a22
c
a21
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Modulatory inputs
u2
u1
z1
z2
u2
z1
z2
u1
a11
a22
c
a21
b21
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Reciprocal connections
u2
u1
z1
z2
u2
z1
z2
u1
a11
a22
c
a12
a21
b21
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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
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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.
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F = - +
Accuracy
Complexity
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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.
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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
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Spatial model
0x
LL
Depolarisation ofpyramidal cells
Spatial model
Sensor data y
Kiebel et al., NeuroImage, 2006Daunizeau et al., NeuroImage, 2009
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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
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• 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
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Thanks to
The people who contributed material to this presentation:
• Guillaume Flandin• Stefan Kiebel • Robert Oostenveld• Gareth Barnes• Karl Friston
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Thank you for your attention