Archived FMRI pipeline
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
The input to the pre-processing pipeline must be provided in nifti (.nii) format (see section above). This PDF [[1]] describe 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. The issue is clearly described and illustrated in the following papers:
Power JD, others and Petersen [[4]] Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 2012.
Satterthwaite, others and Gur [[5]] 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 [[6]] The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 2012.
Motion Task-Froce
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).
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.
3) Having settled on the optimal method, we aim to implement it in a user-friendly way.
This page of the wiki will temporarily 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.
