fMRI-Analysis: Patient Specific Processing
Last updated
Last updated
Lead-DBS looks for files named res*.nii inside your patient folders. You can for instance put these files into the patient's folder:
rest_on.nii (e.g. a rs-fMRI acquisition acquired during stimulation)
rest_off.nii (e.g. one acquired in DBS off state)
Please note that all files need to begin with res* and need to end with .nii.
The above example shows a patient directory with files needed to run fMRI & dMRI analyses. Please note that only one run of resting state fMRI needs to be present. You also don't need dti.\ files to assess functional connectivity *
These files should be 4D nifti files containing fMRI data acquired at rest. As with any standard names, filenames can be changed by editing the file ea_prefs.m inside your Lead-DBS installation.
If your data is composed of multiple sessions, a session vector called res*_sessvec.mat needs to be put into the patient folder as well. If your files are named as in the example given above, please name the session vectors
rest_on_sessvec.mat (specifying sessions of the rest_on.nii file)
rest_off_sessvec.mat (specifying sessions of the rest_off.nii file).
If you simply put one file (e.g. rest.nii) inside the folder, place only one session vector file (rest_sessvec.mat) inside the folder. If you acquired the whole scan in a single session, you don't have to provide any sessvec file at all. The res*_sessvec.mat files should contain one n x 1 sized variable named sessvec, n being the number of volumes acquired in the session. A hypothetical rs-fMRI scan consting of three sessions, each consisting of 5 volumes should be modeled by a sessvec-vector of:
sessvec=[1 1 1 1 1 2 2 2 2 2 3 3 3 3 3];
To begin the analysis, open the Lead-Connectome Settings by clicking on the Settings button next to the Lead-Connectome checkbox. A new window pops up.
Choose a parcellation scheme from the popup menu on the top.
Then check Compute connectivity matrix under the panel Functional connectivity. Additionally check Compute graph-metrics if you wish to write out graph-theory files. If the latter is the case, check which graph-metrics you wish to calculate and export in the Graph theory metrics (node-wise) panel. Please note that the Structure-Function similarity index may only be calculated if a structural connectivity matrix is present or being calculated, too.
Enter the correct TR (repetition time) of the rs-fMRI acquisition.
Press Save and close.
Check the Lead Connectome checkbox and uncheck all other checkboxes.
Check the Normalize checkbox if you haven't performed normalization on this subject before.
Press Run
Lead-DBS Connectome will now calculate the connectivity matrix and export the grey-matter time series from your rs-fMRI acquisition. Files will be written into
patient_folder/connectomics/Name_of_selected_parcellation/
Advanced parameters – as e.g. whether you wish to perform global signal regression – may be changed by editing the ea_prefs.m file inside your Lead-DBS installation folder. Look for entries in the prefs.lc.func struct.
If you are curious about what exactly happens to the data in the preprocessing steps, you can examine the function ea_extract_timecourses.m. As in many subfunctions of Lead-DBS, there is a hidden flag called vizz defined in the first lines of the code. If you set it to 1 and run the above, you will see a figure showing the time courses in each step of the processing pipeline.
Lead-Connectome GUI with settings for fMRI-whole brain assessment & graph-theory metric calculation.
This figure shows the fMRI connectivity matrix of a patient based on the AICHA reordered (Joliot 2015) parcellation scheme. The two prominent off-diagonal lines show high connectivity between homologue areas (left-/right) of the parcellation map. Middle and lower/right blue areas (with lower overall connectivity) show subcortical areas. In this example, global signal regression has been performed. Pearson's correlation coefficients range from ~-0.6 to 1.
