# fMRI Lab for 17th March ###### With me (Will), Marisa, Balazs, & Doran --- ### Aim: To get acquainted with the tools needed to generate fMRI results --- # Plan #### 1. Tools #### 2. Introduce experiment #### 3. View the raw data #### 4. Explore the design file #### 5. Preprocessing #### 6. Modelling task events #### 7. View the results #### 8. Multi-subject analysis and automation --- ### Tools for neuroimaging  ###### FSL (FMRIB Software Library) is a free suite of applications from Oxford's Functional Magnetic Resonance Imaging of the Brain (FMRIB) laboratory. ###### http://mriquestions.com/best-fmri-software.html ----  ###### FEAT (FLAME), BET, FSLeyes ###### https://fsl.fmrib.ox.ac.uk/fsl/fslwiki --- # The Experiment Before looking at the data we will describe the experiment. ### Aim: To find the neural correlates of word-generation ###### Data and more info: https://www.fmrib.ox.ac.uk/primers/intro_primer/ExBox11/IntroBox11.html ---- This dataset is from an event-related language experiment and has three different types of events: ---- ###### - Word-generation events (WG): Here the subject is presented with a noun, say for example "car" and his/her task is to come up with a pertinent verb (for example "drive") and then "think that word in his/her head". The subject was explicitly instructed never to say or even mouth a word to prevent movement artefacts. ###### - Word-shadowing events (WS): Here the subject is presented with a verb and is instructed to simply "think that word in his/her head". ###### - Null-events (N): These are events where nothing happens, i.e. the cross-hair remains on the screen and no word is presented. The purpose of these "events" is to supply a baseline against which the other two event types can be compared. ---- ##### Within one session, the events were presented at a constant ISI (Inter Stimulus Interval) of 6 seconds. For example, the first 72 seconds (twelve events) in this session may have looked like: ### N-WS-N-WS-N-WS-N-WG-N-WS-WG-N --- ## The Data #### - `home/fsluser/fsl_course_data/ExBox11` - Navigate to this directory, using the `Terminal` (or using the `files` GUI) - use `cd your_path` to change working directory - use `ls` to list files in working directory - keep this terminal window open for later --- ## The raw neuroimaging data  - Let's view the raw 4D data for the subject ---- #### 1. Open program 'FSLEyes' - Type `fsleyes` in Terminal/Command Line and press enter - Or, open it from the FSL GUI #### 2. Add functional data - `file` -> `add from file` -> Find: `home/fsluser/fsl_course_data/ExBox11/fmri.nii.gz` #### 3. View in Movie Mode --- ## The Analysis - To get from this raw data to the results, an analysis was conducted using `FEAT`. - We will look at the details of this analysis now. ---- ## Analysis with FEAT #### 1. Open FEAT GUI - Type `fsl` in terminal & `Enter` -> Click FEAT button #### 2. Load the design file (`.fsf`) - `Load` -> `ExBox11/fmri.feat/design.fsf` ---- ##### You can think of the design file as a recipe that turns your ingrediants (raw Nifti data) into a full meal (results). Having this recipe saved is useful because it means you can fully reproduce the meal again. ###### Because this dataset has already been analyzed with FEAT, a design file already exists - but for your experiments, you will need to create one yourself using this software. ##### So, let's explore the design file tab by tab. ---- ## `Data`  ---- ## `Misc`  - Use `Baloon help` if you want to find info on parameters ---- ## `Pre-stats`  - Many preprocessing options available - e.g. highpass filtering ---- ## High-pass filtering  - Remove low-frequency (slow, < 100s) signal changes - Important due to _scanner drift_ - Let's look at the effect of HPF ---- #### 1. Open `FSLEyes` #### 2. Add `fmri.feat/filtered_func_data.nii.gz` #### 3. View the two time series - `View` -> `Timeseries` - Click `Plotting mode` drop-down -> `Demeaned` #### 4. Find example of high-pass filtering - e.g., voxel coordinate `24,33,15` starts low and drifts upwards in unfiltered image ---- ## `Registration`  ---- ## `Stats`  1. Go to `Full model setup` ---- ## Model Set up ### `Explanatory Variables (EVs)`  ###### - Each EV models a different effect ----  - View contents of these 3 column .txt files 1. Open previous terminal window 2. Type `ls` and Enter to view files in working directory 3. Type `cat word_generation.txt` and `Enter` (`cat` will print the contents of the txt file) ---- - 1st column = start time of stimulus (s) - 2nd column = duration (s) - 3rd column = intensity (1 = on) ----  - Do the same for `word_shadowing.txt` ##### These 3 column txt files contain all the information about the timing of events in the scanner ---- ### Convolving  ---- ### `Contrasts & F-tests`  ---- ### Contrasts ##### Contrasts are the way in which we express questions (alternative hypotheses). - For example, the question of whether a positive effect exists (that's associated with an EV called β1) can be expressed as β1 > 0. - Or to test if the effect size associated with one regressor is larger than another can be expressed as β1 > β2. ---- ### Contrasts & F-tests  - Question: What do you think each 5 contrasts are testing? - Tip: EV1 = word generation & EV2 = word shadowing ---- #### Contrast Meanings ##### - `Generation`: this tests for when there was greater activation during word generation compared to baseline. ##### - `Shadowing`: this tests for when there was greater activation during word shadowing compared to baseline. ##### - `Mean`: this tests for when the average activation during generation and shadowing was greater than baseline. ##### - `Shad > Gen`: this tests for when there was greater activation during word shadowing compared to word generation. ##### - `Gen > Shad`: this tests for when there was greater activation during word generation compared to word shadowing. ---- ### `View design`  ###### (If error occurs on class laptops, refer to the above picture) ----  ###### - After all that set-up, FEAT is now able to create a model of predicted activity for each explanatory variable (i.e. word generation / shadowing) across time. ###### - GLM will compare this model to actual BOLD activity and reveal those voxels whose activity is well explained by model i.e involved in word generation and/or shadowing. ---- ## `Post-stats (thresholding)`  - Important for controlling false positives --- ### Running the analysis #### 2 Options: - Simply press `Go` - Or, type `feat <design.fsf>` in to command line. - ...or view ready-made results! --- ## Viewing the results  ---- ## Viewing the results 1. View results directory - `Applications` -> `Files` -> `fsl_course_data/ExBox11/fmri.feat` 2. View results report - Double click to open `report.html` 3. See the report on registration, pre-stats and stats - these are good sanity checks ---- ## Post-stats - `Post-stats` is where the real results are 1. What contrasts show significant efffect? - Each contrast has an associated results image 2. View cluster based statistics - Click on any contrast image --- ### Let's look at the last two results (contrasts Shad>Gen; Gen>Shad) in 3D using FSLeyes ---- ### Viewing the results in 3D 1. Open an anatomical image on which to overlay functional results ###### - Add `fmri.feat/example_func.nii.gz` 2. Overlay statistical map of contrast 4 (`fmri.feat/thresh_zstat4; Shad > Gen`) and contrast 5 (`fmri.feat/thresh_zstat5; Gen > Shad`) 3. Apply different colour maps to each ###### - To change a maps colour, select it in the overlay list and then change the colour map at the top ---- 4. Load unthresholded versions - Add `fmri.feat/stats/zstat5` - not all significant but still potentially useful --- ## Automating this process - This was a single subject analysis - imagine doing this for 50+ participants - We can use the command line for more than file navigation - We can use it to automate this process - Our design file - `design.fsf` - is simply a text file - lot's of potential --- # Summary - Hopefully now be acquainted with: 1. Bash commands in Terminal 2. fMRI analysis software FEAT 3. fMRI data/results visualization with FSLEyes - Code is Key! ---- # fMRI Analysis Resources ---- ## Ginette Mumford's FSL FEAT youtube tutorials https://www.youtube.com/watch?v=lCwewJJPd5U&list=PLB2iAtgpI4YHlH4sno3i3CUjCofI38a-3&ab_channel=mumfordbrainstats - This is a great series which takes you from start to finish of a task-based fMRI analysis using FSL's FEAT software ---- ## Andrew Jahn's FSL videos https://www.youtube.com/watch?v=9ionYVXUQn8&list=PLOPaMln1VugP5sSKaa_KMmrrVXMJTMrLi&ab_channel=AndrewJahn - More examples of scripting + FSL ---- ## MIT General Linear Model for fMRI - 3 part lecture series that explains the general linear model nicely. These were the videos that made the GLM and task-design click. - Theoretical understanding of the principles of the method ---- ## FSL's FEAT tutorial https://www.fmrib.ox.ac.uk/primers/intro_primer/ExBox11/IntroBox11.html - Example data here https://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/feat1/index.html - More explation here ---- https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT/UserGuide - The master guide to FEAT ---- ## GLM Hanbook https://www.fmrib.ox.ac.uk/primers/appendices/glm.pdf