Archived FMRI pipeline: Difference between revisions

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NOTE: THIS PAGE NEEDS 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 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).


==About fMRI data and file types==
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:
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.
'''Signal preprocessing'''


==Standard Preprocessing Steps & The Pipeline==
1. Preprocessing of anatomical images
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!)


==Parcellation==
2. Preprocessing of functional images
(SECTION TO BE FILLED IN!)


==Wavelet Decomposition==
3. Anatomical standardization of functional images
(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]]
4. Removal of noise signal


For details on the wavelet toolbox in MATLAB, read: [[http://web.mit.edu/1.130/WebDocs/wavelet_ug.pdf]]
'''Network construction'''


==The Motion Problem==
5. Construction of nodes: Parcellation
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. The issue is clearly described and illustrated in the following papers:


Power JD, others and Petersen [[http://imap.humanconnectome.org/hosted/docs/Power-et-al-NeuroImage.pdf]]
6. Construction of links
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion.
NeuroImage 2012.


Satterthwaite, others and Gur [[http://www.sciencedirect.com/science/article/pii/S1053811911014650]]
NB: The pipeline ends once the full, weighted adjacency matrix is defined. Network analyses need to be carried out separately.
Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth.  
NeuroImage 2012.


Van Dijk, others and Buckner [[http://www.sciencedirect.com/science/article/pii/S1053811911008214]]
The influence of head motion on intrinsic functional connectivity MRI.
NeuroImage 2012.


===Motion Task-Froce===
'''The pipeline uses the following software packages:'''
The aim of this group is twofold:


1) We aim to produce a simple diagnostic kit that can be run on pre-existing resting state fMRI data to assess the magnitude of the problem (ie. quantify the amount of motion as well as the confounding effects it may have).
* AFNI (Analysis of Functional NeuroImages - made by the NIH)  


2) We also aim to settle on a single dataset and a set of analyses that will allow the comparison of various denoising methods designed to tackle the problem.
* FSL (FMRIB Software Library - made by the FMRIB in Oxford)  


3) Having settled on the optimal method, we aim to implement it in a user-friendly way.  
* 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]].


This page of the wiki will serve as a repository for useful code and data generated along the way. The task-force will also hold regular meetings to keep efforts integrated, the key ideasemerging from these meetings will be logged below.
Note that it saves the data after each intermediate step in newly created files with relevant prefixes.


====[[Meeting 1: Summary and slides]]====


====[[Meeting 2: Summary and slides]]====
==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:


====[[Useful Code]]====
[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