Archived FMRI pipeline: Difference between revisions

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NOTE: THIS PAGE NEEDS FILLING IN!
==About fMRI data and file types==
==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.
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 or Freesurfer. (Note that, when handling neuroimaging data, you need to take special care that the orientation of the images is correct.)


Note that, when handling neuroimaging data, you need to take special care that the orientation of the images is correct.
==Standard Preprocessing Steps & The Pipeline==
NOTE THAT THIS IS NOT THE MOST UPTO DATE PIPELINE, which is why we don't link to the relevant code here. We have kept this section for reference, and since the main preprocessing steps are standard, the documentation may be a useful guide.
PLEASE SEE SECTION ON MOTION (below) for the new pipeline (which can be downloaded from the secure wiki).


==Standard Preprocessing Steps & The Pipeline==
The input to the pre-processing pipeline must be provided in nifti (.nii) format (see section above). The following PDF [[http://bcni.psychol.cam.ac.uk/~pv226/fmri_preprocessing.pdf]] describes 6 broad stages of fMRI preprocessing:
In what follows, we describe the broad stages of fMRI preprocessing. Note that the pipeline saves the data after each intermediate step in newly created files with relevant prefixes. (SECTION TO BE FILLED IN!)


====Slice-timing correction====
'''Signal preprocessing'''
This step corrects for the time-delay between the acquisition of different slices.
====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).
====Registration to standard space====


====Regressing out head motion, CSF, white matter & physiological signals====
1. Preprocessing of anatomical images


==Parcellation==
2. Preprocessing of functional images
(SECTION TO BE FILLED IN!)
==Wavelet Decomposition==
(SECTION TO BE FILLED IN!)
A very basic tutorial on wavelets can be found here: [[http://person.hst.aau.dk/enk/ST8/wavelet_tutotial.pdf]]


For details on the wavelet toolbox in MATLAB, read: [[http://web.mit.edu/1.130/WebDocs/wavelet_ug.pdf]]
3. Anatomical standardization of functional images


==The Question of Smoothing==
4. Removal of noise signal
Required before VBM. Important for finding activations regions. Not used when parcellating (for network analysis).


==DVARS and the Motion Problem==
'''Network construction'''
Here are the scripts dvars.py and dvars_mo.py to generate "Petersen plots:" [[http://bcni.psychol.cam.ac.uk/~pv226/dvars.py]] and [[http://bcni.psychol.cam.ac.uk/~pv226/dvars_mo.py]]
Here are the requirements


-python (you have it if you're on mac or linux, but an upgrade to
5. Construction of nodes: Parcellation
2.7.x from python.org is suggested)


-numpy (a version appropriate for your OS and python version, if you
6. Construction of links
haven't installed numpy yet)


-NiBabel, at a command prompt, run
NB: The pipeline ends once the full, weighted adjacency matrix is defined. Network analyses need to be carried out separately.
 sudo easy_install nibabel


Then run the code as such:


1. RMS Delta Displacement plot:
'''The pipeline uses the following software packages:'''


python dvars_mo.py <rigid body motion parameter file>
* AFNI (Analysis of Functional NeuroImages - made by the NIH)
 i.e.     python dvars_mo.py motionXYZ.1D
this will output a file called motionXYZ_dvars.1D


2. %DVARS:
* FSL (FMRIB Software Library - made by the FMRIB in Oxford)


python dvars.py <NIFTI dataset>
* WMTSA (Wavelet Methods for Time-Series Analysis - a Matlab or R program for computing frequency-band specific “wavelet” correlations) . A very basic tutorial on wavelets can be found here: [[http://person.hst.aau.dk/enk/ST8/wavelet_tutotial.pdf]]. For details on the wavelet toolbox in MATLAB, read: [[http://web.mit.edu/1.130/WebDocs/wavelet_ug.pdf]].
 i.e      python dvars.py foobar.nii.gz
this will output a file called foobar_dvars.1D


The input dataset should be at least roughly masked, but otherwise can
Note that it saves the data after each intermediate step in newly created files with relevant prefixes.
be raw or preprocessed data. If data is preprocessed and the mean has
been removed, run like this:


python dvars.py <preprocessed NIFTI dataset> <NIFTI dataset with
non-zero means, 3D or 4D>


The numbers in the fMRI DVARS are in percent signal change, so they
==The Motion Problem==
should be consistent across subjects and vary with the amount of
In short, the 'motion problem' refers to the recent (2012) discovery that even tiny head-motion can lead to severe artifacts in connectvitiy analyses of resting state fMRI data. We have recently set up a 'task-force' to try to better understand and deal with this problem. The task-force holds regular meetings to keep efforts integrated and the key ideas emerging from these meetings will be logged on a parallel (secure) wiki which also contains benchmark data and useful code generated along the way:
motion subjects have. FYI, the baseline of DVARS will be higher with
more thermal noise or continuous noise that you have, so for example,
with people with a lot of cerebral pulsation from vasculature, the
baseline DVARS% will be high.


Enjoy, and let me know if something breaks or if you need help.
[https://wiki.cam.ac.uk/bmuwikisecure/Motion_Task-Froce_Meetings_and_Related_Code Secure Wiki]


==Back To Main Page==
==Back To Main Page==
[[Main Page]]
[[Main Page]]

Latest revision as of 10:53, 10 May 2018

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 or Freesurfer. (Note that, when handling neuroimaging data, you need to take special care that the orientation of the images is correct.)

Standard Preprocessing Steps & The Pipeline

NOTE THAT THIS IS NOT THE MOST UPTO DATE PIPELINE, which is why we don't link to the relevant code here. We have kept this section for reference, and since the main preprocessing steps are standard, the documentation may be a useful guide. PLEASE SEE SECTION ON MOTION (below) for the new pipeline (which can be downloaded from the secure wiki).

The input to the pre-processing pipeline must be provided in nifti (.nii) format (see section above). The following PDF [[1]] describes 6 broad stages of fMRI preprocessing:

Signal preprocessing

1. Preprocessing of anatomical images

2. Preprocessing of functional images

3. Anatomical standardization of functional images

4. Removal of noise signal

Network construction

5. Construction of nodes: Parcellation

6. Construction of links

NB: The pipeline ends once the full, weighted adjacency matrix is defined. Network analyses need to be carried out separately.


The pipeline uses the following software packages:

  • AFNI (Analysis of Functional NeuroImages - made by the NIH)
  • FSL (FMRIB Software Library - made by the FMRIB in Oxford)
  • WMTSA (Wavelet Methods for Time-Series Analysis - a Matlab or R program for computing frequency-band specific “wavelet” correlations) . A very basic tutorial on wavelets can be found here: [[2]]. For details on the wavelet toolbox in MATLAB, read: [[3]].

Note that it saves the data after each intermediate step in newly created files with relevant prefixes.


The Motion Problem

In short, the 'motion problem' refers to the recent (2012) discovery that even tiny head-motion can lead to severe artifacts in connectvitiy analyses of resting state fMRI data. We have recently set up a 'task-force' to try to better understand and deal with this problem. The task-force holds regular meetings to keep efforts integrated and the key ideas emerging from these meetings will be logged on a parallel (secure) wiki which also contains benchmark data and useful code generated along the way:

Secure Wiki

Back To Main Page

Main Page

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