Institute for Biomedical Engineering (ETH Zurich)‏ & Computational Neuroeconomics Group (Univ. of Zurich) Z URICH SPM C OURSE 2011 Batch Programming of fMRI Data Analysis Lars Kasper & Christoph Mathys

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Overview 2Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)  Introduction & Example Dataset  General fMRI Data Analysis Workflow with SPM  Quality Assessment of Raw Data  Spatial Preprocessing  Statistical Design: The General Linear Model  Results: Analyzing Contrast & Reporting  Within-Subject Batching (Single Subject)  Subject-independent Analysis Steps  Subject-independent Data Flow (Dependencies)  Subject-related data  Between-Subject-Batching (Multiple Subject)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Overview 3Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)  Introduction & Example Dataset  General fMRI Data Analysis Workflow with SPM  Quality Assessment of Raw Data  Spatial Preprocessing  Statistical Design: The General Linear Model  Results: Analyzing Contrast & Reporting  Within-Subject Batching (Single Subject)  Subject-independent Analysis Steps  Subject-independent Data Flow (Dependencies)  Subject-related data  Between-Subject-Batching (Multiple Subject)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 Kasper/Mathys (18-Feb-11)4Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS What is batch processing?  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, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 3 flavors of batching – Goals of this tutorial After finishing this session, you will be able to analyze fMRI datasets using  the Graphical User Interface (GUI) of SPM: 2.The Batch Editor of SPM 3.A template Matlab.m-script file to batch very flexibly 6Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Introducing the Dataset  Rik Henson‘s famous vs non-famous faces dataset  Includes a manual with step-by-step instruction for analysis (homework ;-))  Download from SPM homepage (available for SPM5, but works fine with SPM8) 7Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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) 8Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 9Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Overview 10Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)  Introduction & Example Dataset  General fMRI Data Analysis Workflow with SPM  Quality Assessment of Raw Data  Spatial Preprocessing  Statistical Design: The General Linear Model  Results: Analyzing Contrast & Reporting  Within-Subject Batching (Single Subject)  Subject-independent Analysis Steps  Subject-independent Data Flow (Dependencies)  Subject-related data  Between-Subject-Batching (Multiple Subject)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Spatial Preprocessing – Realign  sd 11Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11) FORMAT P = spm_realign (P,flags) GUI Batch Editor Batch File

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Spatial Preprocessing – Unwarp 12Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11) uw_params= spm_uw_estimate (P,uw_est_flags); spm_uw_apply (uw_params,uw_write_ flags); GUI Batch Editor Batch File

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 spm8-folder, sub-folder man/manual.pdf  Rik Henson‘s famous vs non-famous faces dataset  Included in SPM manual, chapter 29, with step-by-step instruction for analysis  Available for SPM5, but works fine with SPM8 13Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Overview 14Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)  Introduction & Example Dataset  General fMRI Data Analysis Workflow with SPM  Quality Assessment of Raw Data  Spatial Preprocessing  Statistical Design: The General Linear Model  Results: Analyzing Contrast & Reporting  Within-Subject Batching (Single Subject)  Subject-independent Analysis Steps  Subject-independent Data Flow (Dependencies)  Subject-related data  Between-Subject-Batching (Multiple Subject)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS General Workflow for the batch interface 15Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11) Top-down approach Specify subject-independent data/analysis steps Specify subject-independent file-dependencies (data flow) Specify subject-related data (e.g. event-timing)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 16Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 17Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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 18Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Overview 19Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)  Introduction & Example Dataset  General fMRI Data Analysis Workflow with SPM  Quality Assessment of Raw Data  Spatial Preprocessing  Statistical Design: The General Linear Model  Results: Analyzing Contrast & Reporting  Within-Subject Batching (Single Subject)  Subject-independent Analysis Steps  Subject-independent Data Flow (Dependencies)  Subject-related data  Between-Subject-Batching (Multiple Subject)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Between-Subject-Batching (Multiple Subject) Make sure, parameters to be adjusted have an X (clear value) for the single subject template Specify a meta-job with Run batch Create one run for every subject and add missing parameter values (in right order) 20Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Resources and Useful Literature  All step-by-step instructions can be found in the SPM manual, chapter 40  Also multiple-session and multiple subjects processing included  The SPM helpline/mailing list  E.g. bug precluding the batch-file selector form working was fixed here, but not in the updates yet bin/webadmin?A2=ind1001&L=SPM&P=R39357  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, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS Many, many thanks to  Klaas Enno Stephan  The SPM developers (FIL methods group) 22Computational Neuroeconomics (Prof. Stephan, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)

Z URICH SPM C OURSE 2011 B ATCH P ROGRAMMING OF F MRI D ATA A NALYSIS 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, USZ) / MR-Technology (Prof. Prüssmann, IBT)‏Kasper/Mathys (18-Feb-11)