Surface fmri data processing using BrainVISA (March 4th 2011)

Transcription

1 Surface fmri data processing using BrainVISA (March 4th 2011) Index 1. Introduction and general goal 2. Running the right pipeline 3. Practical details for computing surface processing 4. Visualization of surface results 4.1. Installation of the viewer 4.2. Viewer parameters 5. Where to find all this 1. Introduction and general goal In order to compute surface analysis of fmri data this is the right scheme to follow (from Bertrand Thirion): In this document, we describe how to compute the last steps of this pipeline. Thus, you are supposed to have computed previously: - The T1 BrainVISA pipeline, and - The SPM preprocessing: slice timing, realignment and anatomo-functional coregistration (which generates images with prefix a, for example alocalizer*.img/.hdr ) Note: the examples in this document are computed using data coming from the localizer database of Philippe Pinel and Antonio Moreno.

2 2. Running the right pipeline In BrainVISA: 1) Import SPM preprocessed data. 2) Project images/activations on the cortical surface (2 steps): o Creation of the fmri data projection kernels. o Projection of functional data on the meshes by using the convolution kernels. 3) Copy file "paradigm.csv" in the subject directory /fmri/acquisition1/minf/.

3 4) Adapt and run the python script "script_surface_localizer.py" (extract):

4 5) Visualize surface results (textures): contrasts, T map, z map See below. 3. Practical details for computing surface processing - Run BrainVISA. - Add the database: BrainVISA Préférences Databases Add + Update - Import preprocessed data: BrainVISA fmri Import fmri_images Importation de donnée d'irmf (images SPM/Analyze vers images NIFTI 4D) input = select only the first image a* output = with distortion correction = no The images preprocessed with slice timing, realignment and anatomo-functional coregistration must be selected. - Import the functional mean image (optional): BrainVISA Data management Importation General import input = mean_* output (by hand) = meanaab0036.img

5 - Project images (activations) onto the cortical surface (2 steps): BrainVISA --> Cortical Surface --> IRMf --> projection_to_cortical_surface - Creation of Kernels for fmri data projection (for the two sides: "left" and "right"): Select the white matter mesh. This creates files *KERNEL.dim/.ima (in directory /surface/). - Projection using Convolution Kernels (for the two sides: "left" and "right"). Select: white_mesh = select the mesh fmri_4d_data (the processed functional data) = a* fmri_surface_data = fill in the output This step creates files *Lwhite.tex and *Rwhite.tex (in directory /surface/functional/). - Project functional images on the meshes: BrainVISA fmri Analysis_Pipeline cortical_surface_analysis 4D-NIFTI Raw Functional Projection using Kernels Select : side = Both fct_images = a* fct_mean = mean image This step creates files R_a*.tex et L_a*.tex (in directory /fmri/acquisition1/localizer_1/) - Copy file "paradigm.csv" into the subject directory /fmri/acquisition1/minf/ - Adapt and run the python script "script_surface_localizer.py": >> python script_surface_localizer.py Do this twice (for the right side and for the left side). Lines to be adapted: (lines 24-31) DBPath = "/neurospin/unicog/protocols/irmf/maindatabaselocalizers_pinelmoreno_2008/tests_surf ace_fmri/subjects" Subjects = ["AB070075"] Acquisitions = ["acquisition1"] Sessions = ["localizer_1"] model_id = "localizer_model" side = 'right' #side = 'left' - Finally, update the database: BrainVISA Data management Update/Actualisation des bases de données update the database

6 4. Visualization of surface results For the visualization of the surface results, this new BrainVISA viewer may be used: SurfaceActivationsViewer.py. It displays the (inflated) brain with the curvature of the white matter mesh and the activation contrasts/t map This viewer allows to: - Change the inflation of the brain - Change palettes of both textures at your will (contrast of the curvature or threshold of the activations) 4.1. Installation of the viewer For installing this viewer before next BrainVISA release (which will include it), copy file SurfaceActivationsViewer.py in directory ~/.brainvisa/processes/.

7 4.2. Viewer parameters activations_texture: (.tex/.gii) contrast, T map, z map input_mesh: white matter mesh compute_inflated_mesh: True/False in order to compute the inflated brain mesh (if it has not been computed previously) inflated_mesh: inflated mesh (automatically filled) curvature_texture: (.tex/.gii) curvature (automatically filled) iterations: (500 by default) inflation number of iterations save_inflate_sequence: True/False in order to compute/display the different steps of the inflated brain

8 5. Where to find all this All the tools described in this document are available in the intranet of NeuroSpin: /neurospin/unicog/resources/brainvisa_scripts/surface/ And they are downloadable from the Unicog wiki: Acknowledgements Thank you very much to Bertrand Thirion, Denis Rivière and Antoinette Jobert for their help to implement these tools and to write this document.