2010 Introduction / Overview 6th October 2010 Suz Prejawa & Chris Lambert Wellcome Trust Centre for Neuroimaging, UCL Overview Introduction Whats MfD Programme for 2010 How to prepare your presentation Where to find information and help Experts Overview for dummies
Introduction to MfD 2010 Methods for Dummies 2009 Aim: to give a basic introduction to human brain imaging analysis methods, focusing on fMRI and M/EEG Wednesdays / 13h00 14h00 / FIL Seminar Room Areas covered in MfD Introduction to MfD 2010 Basic Statistics
fMRI (BOLD) EEG / MEG Connectivity VBM & DTI (a new addition)
PROGRAMME 2010 Autumn Introduction to MfD 2010 I. Basic Statistics 20th Oct 17th Nov Linear Algebra & Matrices (Philip Glass & Melanie Boly) T-tests, ANOVAs & Regression (Jennifer Siegel & Varun Sethi)
General Linear Model (Holly Rossiter & Philip Glass) Bayes for beginners (Rik Adams & Yen Yu) Random Field Theory (Rumana Chowdhury & Nagako Murase) Introduction to MfD 2010 II. What are we measuring? Part I: 24th Nov
Basis of the BOLD signal (Louise McDonald & Yen Yu) A nice one week break Introduction to MfD 2010 III. fMRI Analysis 8th Dec 15th Dec Preprocessing: Realigning and un-warping (Matteo Pugnaghi & Rebecca Lawson) Co-registration & spatial normalisation (Rebecca Lawson & Matteo Pugnaghi
Continues after Christmas break Introduction to MfD 2010 PROGRAMME 2009 Winter/ Spring 2010 Introduction to MfD 2010 III. fMRI Analysis (cont.) 12th Jan 2nd Feb Study design and efficiency (Rumana Chowdhury & Robin Carhart Harris) 1st level analysis Design matrix contrasts and inference (Stephane de Brito
& Fiona McNab) 1st level analysis Basis functions, parametric modulation and correlated regressors (Klaartje Heinen & Paul Rogerson) 2nd level analysis between-subject analysis (Fiona McNab & Stephane de Brito) Introduction to MfD 2010 II. What are we measuring? Part II: 9th Feb Basis of the M/EEG signal (Rik Adams & Louise McDonald)
Introduction to MfD 2010 IV. EEG & MEG 16th Feb 23rd Feb Pre-processing and experimental design (Jennifer Siegel & Tabish Saifee) Contrasts, inference and source localisation Introduction to MfD 2010 (Tabish Saifee & Paul Rogerson) V. Connectivity 2nd March 16th March
Intro to connectivity - PPI & SEM (Nagako Murase & Klaartje Heinen) DCM for fMRI theory & practice (Peter Zeidman & Laura Madeley) DCM for ERP / ERF theory & practice (Niall Lally & Holly Rossiter) Introduction to MfD 2010 VI. Structural MRI Analysis 23rd March- 30th March
Voxel Based Morphometry (Laura Madeley & Sabeena Chaudry) Basic DTI (Niall Lally & Sabeena Chaudry) Introduction to MfD 2010 How to prepare your presentation Very important!!!: Read the Presenters guide (http://www.fil.ion.ucl.ac.uk/mfd/guide.pdf)
Remember your audience are not experts The aim of the sessions is to introduce the concepts and explain why they are important to imaging analysis familiarise people with the basic theory and standard methods Time: 45min. + 15min. questions 2 presenters per session Dont just copy last years slides!!!...
Start preparing your talk with your co-presenter at least 2 weeks in advance Talk to the allocated expert 1 week in advance Introduction to MfD 2010 What if I cant make my presentation? If you want to change / swap your topic, try and find someone else to swap with. if you still cant find a solution, then get in touch with Chris, Maria or Suz as soon as possible (at least 3 weeks
before the talk). Introduction to MfD 2010 Where to find help MfD Home Resources http://www.fil.ion.ucl.ac.uk/mfd/page2/page2.html Key papers
Previous years slides Human Brain Function Textbook (online) SPM course slides Cambridge CBU homepage (Rik Hensons slides)
Methods Group Experts Monday Methods Meetings (4th floor FIL, 12.30) SPM email List Introduction to MfD 2010 Experts
Will Penny Head of Methods John Ashburner Dimitris Pinotsis Guillaume Flandin
James Kilner Rosalyn Moran Andre Marreiros Steve Fleming
Vladimir Litvak Chloe Hutton Antoine Lutti Ged Ridgeway
Zoltan Nagy Marta Garrido Introduction to MfD 2010 Contact the expert: discuss presentation and other issues (1 week before talk) Expert will be present in the session Website http://www.fil.ion.ucl.ac.uk/mfd/ Where you can find all the information about MfD 2010:
Programme Contacts Presenters guide Resources (Help) Etc Introduction to MfD 2010 Other helpful courses Matlab for Cognitive Neuroscience (ICN) Run by Klaartje Heinen Jen Marchant [email protected] & [email protected] http://www.icn.ucl.ac.uk/courses/MATLAB-Tutorials/in dex.htm 4.30 pm, Thursday (not every week!) 17 Queen Square, basement seminar room
Physics lecture series Run by FIL physics team Details will be announced 12 Queen Square, Seminar room Introduction to MfD 2010 Overview for Dummies Introduction to MD 2010 Outline SPM & your (fMRI) data Preprocessing Analysis Connectivity
Getting started with an experiment Acronyms Introduction to MfD 2010 Pre-processing Preprocessing Possibilities These steps basically get your imaging data to a state where you can start your analysis Realignment & Unwarping Segmentation and Normalisation Smoothing
Model specification and estimation Analysis Once you have carried out your pre-processing you can specify your design and data The design matrix is simply a mathematical description of your experiment E.g. visual stimulus on = 1 Design matrix General Linear Model visual stimulus off = 0
Inference Contrasts & inference Contrasts allow us to test hypotheses about our data, using t & f tests 1st level analysis: activation over scans (within subject) 2nd level analysis: activation over subjects
Multiple Comparison Problem Random Field Theory SPM Write up and publish Brain connectivity Causal interactions between brain areas, statistical dependencies Functional integration how one region influences another subdivided into: Functional connectivity: correlations among brain systems (e.g.
principal component analysis) Effective connectivity: the influence of one region over another (e.g. psycho-physiological interactions, or Dynamic Causal Modelling) Statistical Parametric Mapping MfD 2010 will focus on the use of SPM8 SPM software has been designed for the analysis of brain imaging data in fMRI, PET, SPECT, EEG & MEG It runs in Matlab just type SPM at the prompt and all will be revealed.
There are sample data sets available on the SPM website to play with Getting started Cogent http://www.vislab.ucl.ac.uk/Cogent/ present scanner-synchronized visual stimuli, auditory stimuli, mechanical stimuli, taste and smell stimuli monitor key presses physiological recordings logging stimulus & scan onset times Try and get hold of one to modify rather than starting from scratch!
People are more than happy to share scripts around. If you need help, talk to Eric Featherstone. Introduction to MfD 2010 Getting started - Setting up your experiment If you need special equipment Peter Aston Physics team special scanning sequences Physics team
They are very happy to help, but contact them in time! Introduction to MfD 2010 Getting started - scanning decisions to be made What are your scanning parameters: how many conditions/sessions/blocks Interstimulus interval Scanning sequence Scanning angle How much brain coverage do you need how many slices what slice thickness
what TR Use the physics wiki page: http:// cast.fil.ion.ucl.ac.uk/pmwiki/pmwiki.php Introduction to MfD 2010 Summary Get you script ready & working with the scanner
Make sure it logs all the data you need for your analysis Back up your data from the stimulus PC! You can transfer it via the network after each scanning session Get a scanning buddy if its your first scanning study Provide the radiographers with tea, biscuits, chocolate etc. Introduction to MfD 2010
Use the project presentations! They are there to help you design a project that will get you data that can actually be analyzed in a meaningful way Introduction to MfD 2010 Acronyms
DCM dynamic causal model DTI diffusion tensor imaging FDR false discovery rate FFX fixed effects analysis FIR finite impulse response FWE family wise error FWHM full width half maximum GLM general linear model GRF gaussian random field theory HRF haemodynamic response
function ICA independent component analysis ISI interstimulus interval
PCA principal component analysis PEB parametric empirical bayes PPI psychophysiological interaction PPM posterior probability map ReML restricted maximum likelihood RFT random field theory RFX random effects analysis ROI region of interest SOA stimulus onset asynchrony SPM statistical parametric mapping VBM voxel-based morphometry
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