GLM for fmri data analysis Lab Exercise 1

Transcription

1 GLM for fmri data analysis Lab Exercise 1 March 15, 2013 Medical Image Processing Lab Medical Image Processing Lab GLM for fmri data analysis

2 Outline 1 Getting Started 2 AUDIO 1 st level Preprocessing Model Specification, Review and Estimation Inference and Results 3 The Batch Interface 2 / 26

3 Getting the Software and Data get the SPM software package and the two data sets from or from the the direct links below SPM toolkit Dataset (single subject) For this practical, three files are required: spm8.zip glm data1.zip 3 / 26

4 fmri Data Set AUDIO single subject auditory data, 16 blocks, TR=7s, 1 st level analysis, SPM8 manual Chapter 28, available at 4 / 26

5 Medical Imaging Datatypes DICOM (Digital Imaging and Communications in Medicine) file name suffix.dcm one file per slice meta information stored in every file vendor-specific meta-information standard for archiving in hospital environment incompatible with SPM8 ANALYZE file name suffix header/matrix.hdr,.img header contains voxel-to-world mapping matrix contains the raw data matrix compatible with SPM8 NIfTI (Neuroimaging Informatics Technology Initiative) file name suffix.nii matrix and voxel-to-world mapping in one file standard format of SPM8 5 / 26

6 Start SPM8 extract glm spm8.zip into /tmp verify that there is a folder /tmp/spm8 add SPM8 to the Matlab path and start it alternatively, start fmri mode directly after adding path % s t a r t d i r e c t l y i n fmri mode spm ( ' fmri ' ) ; % add SPM8 path to Matlab path v a r i a b l e addpath ( genpath ( ' /tmp/spm8 ' ) ) ; % s t a r t SPM8 g r a p h i c a l u s e r i n t e r f a c e spm choose fmri 6 / 26

7 SPM8 Interface access the most used functions status messages, progress bar, selections, etc. view results, images, etc. Figure : SPM8 Menu (top-left), Interactive (bottom-left) and Graphics (right) window. 7 / 26

8 File Selection Window one click to select/deselect files Ed button allows manual change or entering of path Done button confirms selection keeps history of previously visited paths regexp-like filtering works also in scripts, e.g. files=spm select; current Matlab path is the default path Figure : SPM select dialog. 8 / 26

9 Inspect the Data AUDIO see SPM8 manual, chapter 28, for detailed tutorial using this data set Decompress glm data1.zip into /tmp. Verify that there is a folder /tmp/glmlab/audio Browse the files, there is one folder containing the structural image (i.e. sm00223) and one folder for the functional volumes (i.e. fm00223). start the check registration tool from command line % check t h e image r e g i s t r a t i o n q u a l i t y s p m c h e c k r e g i s t r a t i o n or click Check Reg button choose one or more images, e.g. one functional and the structural Figure : Functional and structural images before co-registration. 9 / 26

10 Preprocessing Headers, Pre-, and Suffixes SPM functions modify the data in different ways change affine world-to-voxel mapping stored in header *.hdr write new file, often pre- or suffixed Prefixes r* re-sliced and usually also realigned mean* mean image c1* gray matter c2* white matter c3* cerebro-spinal fluid m* bias-field corrected structural image w* warped (non-linearly transformed) image, e.g. into MNI s* spatially smoothed image Suffixes * seg sn.mat non-linear forward transformation from subject to MNI space * seg inv sn.mat non-linear reverse transformation from MNI to subject space 10 / 26

11 Preprocessing Preprocessing Overview Inputs Preprocessing Step Outputs fm00223 *.{img,hdr} Realign (Estimate & Reslice) rfm00223 *.{img,hdr} meanfm {img,hdr} meanfm {img,hdr} sm00223.{img,hdr} sm {img,hdr} sm00223 seg sn.mat rfm00223 *.{img,hdr} Coregister (Estimate) Segmentation Normalise (Write) sm hdr c{1,2,3}sm {img,hdr} sm seg sn.mat sm seg inv sn.mat msm {img,hdr} wmsm {img,hdr} wrfm00223 *.{img,hdr} wrfm00223 *.{img,hdr} Smooth swrfm00223 *.{img,hdr} 11 / 26

12 Preprocessing Spatial Normalization Realignment rigid-body transform (translation and rotation) within modality within subject Co-registration affine transform (translation, rotation and scaling) across different modalities within same or across subjects Normalize non-linear invertible transformation usually to reference space, i.e. MNI 12 / 26

13 Preprocessing 1st Level Analysis Dataset AUDIO /tmp/glmlab/audio/ 13 / 26

14 Preprocessing Realignment of functional volumes Perform realignment of the volumes of the time series to the mean image. select Realign (Est & Res) from the Realign pull-down menu highlight data, select New Session, then highlight the newly created Session option. Select Specify Files and use the SPM file selector to choose all of your functional images fm000*.img. There should be 96 files Save the job file as eg../jobs/realign.m. Press the button in the batch editor headers will be rewritten mean image meanfm img will be created Figure : Rigid body realignment parameters. 14 / 26

15 Preprocessing Co-registration Co-register the structural to the mean functional image. Select Coregister (Estimate) from the Coregister pulldown menu. Highlight Reference Image and then select the mean fmri scan from realignment meanfm img. Highlight Source Image and then select the structural image sm img. Press the Save button (floppy disk symbol) and save the job as coreg.m. Then press the button. Verify that alignment is correct. Figure : Image coregistration result. 15 / 26

16 Preprocessing Segmentation Create tissue probability maps of gray matter (GM), white matter (WM) and cerebro-spinal fluid (CSF), and obtain normalization parameters to warp image into MNI space. Press the Segment button. Highlight the Data field and then select the subjects registered anatomical image sm img. Highlight. Cerebro-Spinal Fluid and choose Native Space. Save the job file as segment.m, and then press button. Open the tissue probability maps c1sm img (GM), c2sm img (WM), c3sm img (CSF), and msm img (bias-field corrected T1). Figure : Segmentation result. 16 / 26

17 Preprocessing Normalized Normalize volumes of time series into MNI space and re-slice to 3mm isotropic voxel size. Select Normalise (Write) from the Normalise pull-down menu. Highlight Data, select New Subject. Highlight Parameter File and select the sm seg sn.mat. Highlight images to write and select all of the realigned functional images rfm000*.img and the structural image sm nii. Open Writing Options, and change Voxel sizes from [2 2 2] to [3 3 3]. Save the job as normalise.m and then press the button. Figure : wmsm img (top) and single subject T1 template /spm8/canonical/single subj T1.nii 17 / 26

18 Preprocessing Smoothing Apply spatial smoothing with isotropic Gaussian filter with 6mm FWHM. Press Smooth button. Select Images to Smooth and then select the spatially normalised files created in the last section i.e. ^wrf.* Highlight FWHM and change [8 8 8] to [6 6 6]. This will smooth the data by 6mm in each direction. Save the job as smooth.m and press the button. Figure : wrfm img (top) and swrfm img (bottom) 18 / 26

19 Model Specification, Review and Estimation Model Specification To avoid T1 effects in the initial scans of an fmri time series we discarding the first cycle (12 scans, 04-15), leaving 84 scans, image files Press Specify 1st-level button. Open the Timing parameters option. Highlight Units for design and select Scans. Highlight Inter-scan interval and enter 7. Highlight Data and Design and select New Subject/Session. Open the newly created Subject/Session option. Highlight Scans and use SPM s file selector to choose the 84 smoothed, normalised func- tional images ie. swrfm00223 { }.img. (use ^s.*). Highlight Conditions and select New condition. Open the newly created Condition option. Highlight Name and enter active. High-light Onsets and enter 6:12:84. Highlight Durations and enter 6. Create a directory named results. Highlight Directory and select the results directory you just created. Save the job as specify.m and press the button. 19 / 26

20 Model Specification, Review and Estimation Review Y = Xβ + ε (1) Y: observed signal, X: regressors, β: parameter estimates, ε: zero-mean Gaussian noise Figure : Design matrix. Figure : Orthogonality. Figure : Explore session / 26

21 Model Specification, Review and Estimation Estimation Press the Estimate button. Highlight the Select SPM.mat option and then choose the SPM.mat file saved in the classical subdirectory. Save the job as estimate.m and press the button. SPM will write a number of files into the selected directory including an SPM.mat file. 21 / 26

22 Inference and Results Contrast Prompt Press Results. Select the SPM.mat file created in the last section. This will invoke the contrast manager. Select Define new contrast. One sided main effects for the active condition (i.e., a one-sided t-test) can be specified as 1 (active > rest) and -1 (rest > active). Select the contrast name e.g., active > rest. Press Done. Figure : Contrast prompt. 22 / 26

23 Inference and Results Thresholded Statistical Parametric Map Select sections from the overlays pulldown menu. Choose wmsm img as background. Figure : Statistical parametric map of AUDIO single subject activation (p<0.05, FWE). 23 / 26

24 The Batch Interface Batch interface allows to define processing pipelines linked by dependencies. Press Batch. Choose menu File then Open File.. Select realign.m, coregister.m and segment.m (in this order). Select the Coregister: Estimate module and then Reference Image. Press Dependency, highlight Realign: Estimate & Reslice: Mean Image, and then OK. Select the Segment module, highlight Data, press Dependency, highlight Coregister: Estimate: Coregistered Images and Press OK. Figure : Batch editor including the first three steps of the preprocessing pipeline. 24 / 26

25 Preprocessing Overview View of the various processing steps as a single box with only few in- and outputs. Inputs Preprocessing Step Outputs fm00223 *.{img,hdr} sm {img,hdr} Preprocessing swrfm00223 *.{img,hdr} 25 / 26

26 Resources SPM8 Documentation, Friston, K.J., Statistical parametric mapping, the analysis of functional brain images. Academic Press. Acknowledgements Thanks to Ahmed Abdulkadir and Jonas Richiardi for helping in putting together this lab exercise. 26 / 26