Archived FMRI pipeline: Difference between revisions
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==Preprocessing in SPM== | ==Preprocessing in SPM== | ||
In what follows, we describe the broad stages of fMRI preprocessing, and how they are implemented in SPM | In what follows, we describe the broad stages of fMRI preprocessing, and how they are implemented in SPM. Note that SPM saves the data after each intermediate step in newly created files with relevant prefixes. | ||
===Load the data=== | ===Load the data=== | ||
===Slice-timing Correction=== | ===Slice-timing Correction=== | ||
This step corrects for the time-delay between the acquisition of different slices. In order to correct for this, the user needs to provide the following information: | |||
-the number of slices (e.g. 32) | |||
-TR | |||
-TA | |||
-the order in which slices were acquired (e.g.. bottom to top, interleaved: 2:2:32 1:2:31) | |||
-a reference slice | |||
-a filename prefix (this is used to prefix the new files created at the end of this preprocessing step, saving the slice-time corrected data) | |||
===Rigid body Correction=== | ===Rigid body Correction=== | ||
This step estimates, characterises and broadly corrects for the effect of head-motion. It is also the step at which one can exclude subjects with excessive head-motion (e.g. more than a voxel size). The four options at this stage are | |||
-estimate | |||
-reslices | |||
-estimate and reslice | |||
-realign and unwrap | |||
===Registration to Standard Space=== | ===Registration to Standard Space=== | ||
This is the 'Normalize' option in SPM. Chose source image, template (EPI), method, image size, voxel size (2x2x2). | This is the 'Normalize' option in SPM. Chose source image, template (EPI), method, image size, voxel size (2x2x2). | ||
===Regressing out head motion, CSF, white matter & physiological signals=== | ===Regressing out head motion, CSF, white matter & physiological signals=== | ||
In SPM, this is done under '1st level analysis' -> 'Data and design' -> 'Multiple regression'. | In SPM, this is done under '1st level analysis' -> 'Data and design' -> 'Multiple regression'. | ||
==Parcellation== | ==Parcellation== | ||
==Wavelet Decomposition== | ==Wavelet Decomposition== | ||
Revision as of 11:19, 23 October 2011
About fMRI data and file types
Raw fMRI data is saved in .dcm (dicom) files. Typically these .dcm files correspond to individual slices, and the resulting 3D image (or a time-series of 3D images) is saved in a .nii (nifti) file. Raw dicom files can be transformed into nifti format using SPM (a MATLAB software package implementing Statistical Parametric Mapping for neuroimaging data) or other software such as MRIcro.
Note that, when handling neuroimaging data, you need to take special care that the orientation of the images is correct.
Preprocessing in SPM
In what follows, we describe the broad stages of fMRI preprocessing, and how they are implemented in SPM. Note that SPM saves the data after each intermediate step in newly created files with relevant prefixes.
Load the data
Slice-timing Correction
This step corrects for the time-delay between the acquisition of different slices. In order to correct for this, the user needs to provide the following information:
-the number of slices (e.g. 32) -TR -TA -the order in which slices were acquired (e.g.. bottom to top, interleaved: 2:2:32 1:2:31) -a reference slice -a filename prefix (this is used to prefix the new files created at the end of this preprocessing step, saving the slice-time corrected data)
Rigid body Correction
This step estimates, characterises and broadly corrects for the effect of head-motion. It is also the step at which one can exclude subjects with excessive head-motion (e.g. more than a voxel size). The four options at this stage are -estimate -reslices -estimate and reslice -realign and unwrap
Registration to Standard Space
This is the 'Normalize' option in SPM. Chose source image, template (EPI), method, image size, voxel size (2x2x2).
Regressing out head motion, CSF, white matter & physiological signals
In SPM, this is done under '1st level analysis' -> 'Data and design' -> 'Multiple regression'.
Parcellation
Wavelet Decomposition
The question of smoothing
Required before VBM. Important for finding activations regions. Not used when parcellating (for network analysis).
