1. lars@iew.uzh.ch kasper@biomed.ee.ethz.ch Institute for Biomedical Engineering (ETH Zurich)‏ and Empirical Research in Economics (Univ. of Zurich)‏ Z URICH SPM C OURSE 2009 fMRI Single Subject Analysis & Batch Programming Lars Kasper

2. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Overview 2Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)  Quality Assessment of Raw Data  Spatial Preprocessing  Realign and Unwarp  Coregister  General Linear Model: The Design Matrix  Estimating the Model  Results: Defining and Analyzing Contrasts  Reporting and Summarizing  Outlook: What to do with a lot of single subject results

3. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING RealignmentSmoothing Normalisation General linear model Statistical parametric map (SPM) Image time-series Parameter estimates Design matrix Template Kernel Gaussian field theory p <0.05 Statisticalinference Overview of SPM

4. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Goals of this tutorial After finishing this session, you should be able to Analyze single subject fMRI datasets using  the Graphical User Interface (GUI) of SPM 2.The Batch Editor of SPM 3.A template Matlab.m-file to batch very flexibly 4Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

5. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Batch processing of data  Repeats same data analysis for many subjects (>=2)  Not prone to human errors, reproducible what was done  e. g. jobs mat-files  Runs automatically, no supervision needed  Researcher can concentrate on assessing the results  CAVEAT: Tempting to forget about all analysis steps in between which could lead to errors in your conclusions  Therefore: Always make sure, that meaningful results were created at each step  Using Display/CheckReg to view raw data, preprocessed data  Using spm_print to save reported supplementary data output  If anything went wrong, use debugging 5Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

6. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Introducing the Dataset  Rik Henson‘s famous vs non-famous faces dataset http://www.fil.ion.ucl.ac.uk/spm/data/face_rep/face_rep_SPM5.html  Includes a manual with step-by-step instruction for analysis (homework ;-))  Download from SPM homepage (available for SPM5, but works fine with SPM8b) 6Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

7. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Introducing the Dataset  Factorial 2 x 2 design to investigate repetition suppression  Question: Influence of repeated stimulus presentation on brain activity (accomodation of response)?  Each stimulus (pictures of faces) presented twice during a session  Condition Rep, Level: 1 or 2  lag between presentations randomized  26 Famous and 26 non-famous faces to differentiate between familiarity (long-term memory) and repetition  Condition Fam, Level F(amous) and N(onfamous)  Task: Decision whether famous or nonfamous (button-press) 7Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

8. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Introducing the Dataset: Published Results a.Right Fusiform face area  Repetition suppression for familiar/famous faces b.Left Occipital face area (posterior, occip. extrastriate)  Repetition suppression for familiar AND unfamiliar faces c.Posterior cingulate and bilateral parietal cortex  Repetition enhancement 8Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

9. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Overview 9Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)  Quality Assessment of Raw Data  Spatial Preprocessing  Realign and Unwarp  Coregister  General Linear Model: The Design Matrix  Estimating the Model  Results: Defining and Analyzing Contrasts  Reporting and Summarizing  Outlook: What to do with a lot of single subject results

10. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Overview 10Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)  Quality Assessment of Raw Data  Spatial Preprocessing  Realign and Unwarp  Coregister  General Linear Model: The Design Matrix  Estimating the Model  Results: Defining and Analyzing Contrasts  Reporting and Summarizing  Outlook: What to do with a lot of single subject results

11. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Spatial Preprocessing – Realign  sd 11Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09) FORMAT P = spm_realign (P,flags) GUI Batch Editor Batch File

12. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Spatial Preprocessing – Unwarp 12Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09) uw_params= spm_uw_estimate (P,uw_est_flags); spm_uw_apply (uw_params,uw_write_ flags); GUI Batch Editor Batch File

13. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Uh…this takes ages…  Now you can probably value the benefits of batch processing. If you are still keen on doing all that by hand (good exercise!), refer to the following  The SPM manual  Most current version in your spm8b-folder, sub-folder man/manual.pdf  Rik Henson‘s famous vs non-famous faces dataset http://www.fil.ion.ucl.ac.uk/spm/data/face_rep/face_rep_SPM5.html  Included in SPM manual, chapter 29, with step-by-step instruction for analysis  Available for SPM5, but works fine with SPM8b 13Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

14. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Overview 14Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)  Quality Assessment of Raw Data  Spatial Preprocessing  Realign and Unwarp  Coregister  General Linear Model: The Design Matrix  Estimating the Model  Results: Defining and Analyzing Contrasts  Reporting and Summarizing  Outlook: What to do with a lot of single subject results

15. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Overview 15Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)  Quality Assessment of Raw Data  Spatial Preprocessing  Realign and Unwarp  Coregister  General Linear Model: The Design Matrix  Estimating the Model  Results: Defining and Analyzing Contrasts  Reporting and Summarizing  Outlook: What to do with a lot of single subject results

16. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING General Workflow for the batch interface 16Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09) Top-down approach  Specify subject-independent data/analysis steps  Specify subject-independent file-dependencies (data flow) 3.Specify subject-related data (e.g. event-timing)

17. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING 1. The subject-independent analysis parts  Load all modules first (in right order!)  Then specify details (where Xs are found) which are subject independent  TR  Nslices  model factors  contrasts of interest 17Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

18. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING 2. Data-flow specification (subject-independent dependencies)  Specify, which results of which steps are input to another step (DEP-sign)  e.g. smoothed images needed for model spec  Afterwards save this job as template.mat file 18Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

19. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING 3. Add subject-dependent data/information  Essentially go to all X‘s and fill in appropriate values  e.g. the.mat-file of the conditions onsets/durations  Save this job as subject-batch file & Run 19Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

20. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING 4. Making it multi-subject 1.Make sure, parameters to be adjusted have an X (clear value) for the single subject template 2.Specify a meta-job with Run batch 3.Create one run for every subject and add missing parameter values (in right order) 20Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

21. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Resources and Useful Literature  All step-by-step instructions can be found in the SPM manual, chapter 35  Also multiple-session and multiple subjects processing included  Batch templates are in your spm path:  Configured subject-independent analysis steps /man/batch/face_single_subject_template_nodeps.m  With dependencies included /man/batch/face_single_subject_template.m  With multiple subjects /man/batch/face_multi_subject_template.m 21Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

22. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Many, many thanks to  Klaas Enno Stephan  The SPM developers (FIL methods group) 22Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

23. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Extending the batchfile with SPM GUI functions  Debugging  Generally a good idea to find out, how things work in SPM  Crucial for batch-programming using a.m-file  Here: debug spm.m by setting a breakpoint  If called function found, use edit.m to look at the %comments in the file 23Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)

24. Z URICH SPM C OURSE 2009 S INGLE S UBJECT A NALYSIS B ATCH P ROGRAMMING Tuning the engine – Matlab workspace variables  e.g. to manipulate SPM.mat or jobs by hand  also important during debugging, how variables are defined and changed 24Computational Neuroeconomics (Prof. Stephan, IEW) / MR-Technology (Prof. Prüssmann, IBT)‏Lars Kasper (11-Feb-09)