The other stuff Vladimir Litvak Wellcome Trust Centre for Neuroimaging UCL Institute of Neurology, London, UK • SPM resources • Fieldtrip in SPM8 • MEEGTools and Beamforming • DCM Image time-series Realignment Spatial filter Design matrix Smoothing General Linear Model Statistical Parametric Map Statistical Inference Normalisation Anatomical reference Parameter estimates RFT p <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): Linux (32 and 64 bit) Windows (32 and 64 bit) Mac Intel (32 and 64 bit) – File Formats: • Images: NIfTI-1 (& Analyze, DICOM) • Surface meshes: GIfTI • M/EEG: most manufacturers (with FieldTrip’s fileio) 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/ SPM Toolboxes • User-contributed SPM extensions: http://www.fil.ion.ucl.ac.uk/spm/ext/ SPM Documentation Peer reviewed literature Online help & function descriptions SPM Books: Human Brain Function I & II Statistical Parametric Mapping SPM 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 • SPM @ NITRC http://www.nitrc.org/projects/spm/ 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… http://www.fil.ion.ucl.ac.uk/spm/support/ spm@jiscmail.ac.uk FieldTrip Powered by: http://fieldtrip.fcdonders.nl/ What is FieldTrip? A MATLAB toolbox for electrophysiological data analysis 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 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 But… Features Analysis steps are incorporated in functions ft_preprocessing cfg = [ ] cfg.dataset = ‘Subject01.ds’ cfg.bpfilter = [0.01 150] ft_rejectartifact ... rawdata = ft_preprocessing(cfg) ft_freqanalysis ft_multiplotTFR ft_freqstatistics ft_multiplotTFR FieldTrip toolbox - code reused in SPM8 SPM8 end-user perspective SPM8 main functions main functions with graphical user interface fileio distrib. forwinv preproc comput. public fieldtrip private 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 •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. Beamforming • 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. DCM for fMRI DCM for fMRI Single region u1 u1 c u2 a11 z1 z1 z2 stimulus functions u t activity neural state equation x (t ) vasodilato ry signal s  x   s  γ ( f  1) f s s hemodynamic state equations flow induc tion (rCBF) f  s f changes in volume τ v  f  v 1 /α v changes in dHb τ q  f E ( f,E 0 ) qE 0  v 1 /α q/v q v BOLD signal y (t )   v, q  Estimated BOLD response Modelled neural activity Predicted BOLD Predicted BOLD + noise = observed data Multiple regions u1 c a11 z1 u2 a21 z2 a22 u1 z1 z2 Modulatory inputs u1 u2 c u1 a11 z1 b21 a21 z2 a22 u2 z1 z2 Reciprocal connections u1 u2 c u1 a11 z1 b21 a12 a21 z2 a22 u2 z1 z2 Bayes‘ Theorem new data p( y | ) prior information p ( ) p ( | y )  p ( y |  ) p ( ) posterior  likelihood ∙ prior Reverend Thomas Bayes 1702 - 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. Accuracy F=- + 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. macro-scale meso-scale micro-scale external granular layer external pyramidal layer internal granular layer internal pyramidal layer AP generation zone Daunizeau et al. 2009, NeuroImage David et al. 2006, NeuroImage Kiebel et al. 2006, NeuroImage Moran et al. 2009, NeuroImage synapses Spatial model Depolarisation of pyramidal cells x0 Sensor data y L  L  Spatial model Kiebel et al., NeuroImage, 2006 Daunizeau et al., NeuroImage, 2009 DCMs for M/EEG input  DCM for ERP (+second-order mean-field DCM) 1st and 2d order moments depolarization 250 0 0 200 -20 -20 150 -40 -40 100 -60 -60 50 -80 -80 0 0 100 200 300 time (ms) auto-spectral density LA -100 0 100 200 300 time (ms) -100 0 100 200 300 time (ms) auto-spectral density CA1 cross-spectral density CA1-LA frequency (Hz) frequency (Hz)  DCM for steady-state responses frequency (Hz)  DCM for induced responses  DCM for phase coupling Summary • 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. 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