First-level model with CANlab regress( )
The CANlab fmri_data object has a regress( ) method that will run a basic GLM. It returns a data structure with a series of statistic_image objects containing regression parameter estimates ("beta images") and t maps with P values included. These are statistically thresholded for display by default, but can be re-thresholded after running regress( ).
This lab walks you through the steps.
Setting up a first-level analysis: Ingredients
We're setting up for the first-level analysis. We need five things:
1) Brain data. The functional fMRI data, preprocessed and ready for analysis (usually, realigned, warped to MNI space, and smoothed)
- Here, we want "swr*" images, which are Smoothed, Warped, and Realigned
- Other scaling and "denoising" (artifact-removal) steps are sometimes done in preprocessing as well
2) Task information. Onset times, durations, and names for each event type (condition) of interest
- In BIDS standard format, these are in a .tsv file
- We'll create cell arrays called ons, durations, and names (by convention, as used in SPM software)
3) Nuisance covariates. Any custom nuisance covariates we want to add, including
- "Spike" (indicator) regressors for outliers
- Movement parameters and their transformations
- Any other custom regressors, e.g., global CSF or aCompCorr regressors
- We want to be careful about how we choose these, to avoid multicolinearity with task regressors
- We will save these in one matrix, R (by convention, as used in SPM software)
- For BIDS/fmriprep, these are in *_desc-confounds_regressors.tsv
4) Filter. An intended HP filter cutoff in sec
- SPM will add the high-pass filter when it does the analysis
5) Contrast weights for effects (comparisons across conditions) we care about
Find and examine the brain data
Navigate to files
It's helpful to start in a folder with data in it, so you don't have to navigate extensively through the SPM file selection browser.
pwd % where am i now? (in terms of working directory, at least)
Use cd( ) to go to your sample subject directory. Drag and drop, or copy and paste the path from Terminal.
cd('/Users/torwager/Dropbox (Dartmouth College)/COURSES/Courses_Dartmouth/2021_3_Spring_fMRI_Class/Labs_and_Lectures_for_Instructors/Labs_and_assignments/Lab_5/Pinel_data_sample_subject_INSTRUCTOR')
ls % list files here
Find the functional run and examine the data
It's always important to look at your data! Check: Do the brains look like brains? is the range of data values what you expected? Are there crazy outliers? Does the image space match the template space? Are the images relatively homogenous, or do some appear to be really different, i.e., on the same scale?
We'll use CANlab object-oriented tools, specifically the fmri_data object, to load the preprocessed data and make a summary plot. Here, we want "swr*" images, which are Smoothed, Warped, and Realigned...and hopefully thus ready for analysis.
preprocessed_image_name = 'swrsub-sid001567_task-pinel_acq-s1p2_run-03_bold.nii';
% Load the images into an fmri_data object
dat = fmri_data(preprocessed_image_name);
% Plot the images. This will output potential outliers too, but this has been pre-prepped here.
plot(dat);
Load setup file with other variables we need
The file SPM_onsets_and_nuisance_regressors should have everything we need to build the design in SPM! It's always a good idea to save prepared variables before they are entered into SPM, etc.
This .mat file was created by the first_level_spm12 lab, so go back to that if needed. It has the key variables:
- onsets Cell array of onset times for each condition (10 conditions)
- durations Cell array of event durations for each condition
- names Cell array of names for each condition
- R Matrix of nuisance regressors
- C Structure with contrast weights and names
load SPM_onsets_and_nuisance_regressors
Construct design matrix
Now, we've extracted the information in a format we can use to construct a design matrix. Let's try it.
Note that we need to know the TR for this! It's 2 sec. In BIDS formatted data, this can be found in the ".json sidecar" file, in the "RepetitionTime" field. We also need the scan length in sec, which in this case is 300 sec or 5 min.
TR = 2;
X = onsets2fmridesign(onsets, TR, 300);
% let's plot it
plotDesign(onsets, [], TR);