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The other stuff Vladimir Litvak Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology, London, UK.

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Presentation on theme: "The other stuff Vladimir Litvak Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology, London, UK."— Presentation transcript:

1 The other stuff Vladimir Litvak Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology, London, UK

2 SPM resources Fieldtrip in SPM8 MEEGTools and Beamforming DCM

3 Normalisation Statistical Parametric Map Image time-series Parameter estimates General Linear Model RealignmentSmoothing Design matrix Anatomical reference Spatial filter Statistical Inference RFT p <0.05

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5 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)

6 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/

7 SPM Toolboxes User-contributed SPM extensions: http://www.fil.ion.ucl.ac.uk/spm/ext/

8 SPM Documentation Peer reviewed literature SPM Books: Human Brain Function I & II Statistical Parametric Mapping Online help & function descriptions SPM Manual

9 SPM Online Bibliography

10 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/

11 SPM Mailing List spm@jiscmail.ac.uk Web home page –http://www.fil.ion.ucl.ac.uk/spm/support/ –Archives, archive searches, instructions Subscribe –http://www.jiscmail.ac.uk/ –email jiscmail@jiscmail.ac.uk –join spm Firstname Lastname Participate & learn –email spm@jiscmail.ac.uk –Monitored by SPMauthors –Usage queries, theoretical discussions, bug reports, patches, techniques, &c… spm@jiscmail.ac.uk http://www.fil.ion.ucl.ac.uk/spm/support/

12 FieldTrip Powered by: http://fieldtrip.fcdonders.nl/

13 What is FieldTrip? A MATLAB toolbox for electrophysiological data analysis

14 Features: high-level functions for electrophysiological 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

15 Features: high-level functions for electrophysiological 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

16 But… X

17 Features Analysis steps are incorporated in functions 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) cfg = [ ] cfg.dataset = ‘Subject01.ds’ cfg.bpfilter = [0.01 150]... rawdata = ft_preprocessing(cfg)

18 FieldTrip toolbox - code reused in SPM8 fieldtrip fileio forwinv private main functions public SPM8 main functions with graphical user interface SPM8 end-user perspective preproc distrib. comput. distrib. comput.

19 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.

20 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.

21 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

22 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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25 Time DCM for fMRI

26 Single region z1z1 u1u1 a 11 c u2u2 u1u1 z1z1 z2z2 DCM for fMRI

27 stimulus functions u t neural state equation hemodynamic state equations Estimated BOLD response

28 Modelled neural activity Predicted BOLD Predicted BOLD + noise = observed data

29 Multiple regions u2u2 u1u1 z1z1 z2z2 z1z1 z2z2 u1u1 a 11 a 22 c a 21

30 Modulatory inputs u2u2 u1u1 z1z1 z2z2 u2u2 z1z1 z2z2 u1u1 a 11 a 22 c a 21 b 21

31 Reciprocal connections u2u2 u1u1 z1z1 z2z2 u2u2 z1z1 z2z2 u1u1 a 11 a 22 c a 12 a 21 b 21

32 Bayes‘ Theorem posterior  likelihood ∙ prior new data prior information Reverend Thomas Bayes 1702 - 1761 “Bayes‘ Theorem describes how an ideally rational person processes information." Wikipedia

33 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.

34 F = - + Accuracy Complexity

35 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.

36 internal granular layer internal pyramidal layer external pyramidal layer external granular layer AP generation zonesynapses macro-scalemeso-scalemicro-scale Daunizeau et al. 2009, NeuroImage David et al. 2006, NeuroImage Kiebel et al. 2006, NeuroImage Moran et al. 2009, NeuroImage

37 Spatial model Depolarisation of pyramidal cells Spatial model Sensor data Kiebel et al., NeuroImage, 2006 Daunizeau et al., NeuroImage, 2009

38  DCM for steady-state responses  DCM for ERP (+second-order mean-field DCM)  DCM for induced responses  DCM for phase coupling 0100200300 0 50 100 150 200 250 0100200300 -100 -80 -60 -40 -20 0 0100200300 -100 -80 -60 -40 -20 0 time (ms) inputdepolarization time (ms) auto-spectral density LA auto-spectral density CA1 cross-spectral density CA1-LA frequency (Hz) 1 st and 2d order moments DCMs for M/EEG

39 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

40 Thanks to The people who contributed material to this presentation: Guillaume Flandin Stefan Kiebel Robert Oostenveld Gareth Barnes Karl Friston

41 Thank you for your attention


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