Archived FMRI pipeline

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Revision as of 12:53, 16 January 2012 by ly266 (talk | contribs) (→‎- TA)
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NOTE: this page needs cleaning up and filling in!

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.

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:

-loading the data
- the number of slices (e.g. 32)
- TR
- TA

TA = TR-TR/sliceNumber

- 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).

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