Amherst FSL Workshop 2018
Click here to register for the workshop! Day 1 is June 1st, and Day 2 is June 8th. Customized videos and exercises will be provided during the workshop.
Below is an annotated agenda for the workshop. This page will be updated during the month of May as more instructional videos are uploaded to help attendees prepare for the course. In addition, we will be using this dataset from openneuro.org for the practicals.
Day 1: fMRI Fundamentals and an Introduction to FSL
(9:30am-10:00am) Unix essentials (Practical, optional)
This session will review Unix commands and concepts used in this course:
- The shell
- Navigating directories and files
- Environmental variables
- Commands and options (or “switches”)
- Shell scripts (in particular, loops and conditionals)
- A web page will be created with a glossary of Unix terms for reference.
- Please see this playlist for an introduction to the Unix terminal, as well as the Surrey Unix tutorial. More videos will be added to give newcomers the tools they need to begin learning Unix and shell scripting.
(10:00am-11:00am) Review of fMRI Data Processing and Analysis (Lecture)
This will be a brief overview of what is done with fMRI data from start to finish in a typical pipeline. This lecture will cover:
- Hemodynamics and the BOLD signal
- The BOLD signal and linearity
- Understanding preprocessing: motion correction, registration, normalization, and smoothing
- From scanner to computer: Converting DICOM files to NIFTI with MRIcroGL (exercise dataset can be downloaded here)
(11:00am-12:00pm) Preprocessing the individual subject (Practical)
This first practical will be a guided hands-on tutorial about how to process fMRI data. We will review the following topics:
- Overview of the GUI
- Brain extraction / skull stripping
- Cost functions and registration of T1 and T2-weighted data
- Boundary-based registration
- Linear and nonlinear warping: When to use which method
- User options: Slice timing correction, temporal derivatives, and smoothing size
- Troubleshooting preprocessing failures
(1:00pm-2:15pm) First-level analysis and the general linear model (Lecture & Practical)
How to set up the GLM for an individual subject and generate parameter estimates.
- Overview of the GLM
- How the GLM relates to fMRI data
- Beta values, parameter estimates, and variability
- Mixed-effects analysis in FSL
- Design matrices and collinearity
- Custom timing files, and how to make the timing from your presentation software FSL-compatible (brief demonstration of E-Prime inline code)
(2:15pm-3:00pm) FSLeyes and viewing results (Practical)
We will tour FSL’s fsleyes data visualization tool, which is useful for understanding fMRI data conceptually – for example, the connection between the canonical HRF and beta weights.
- Fsleyes – the FSL data visualization tool
- Overlays, underlays, and thresholds
- FEAT mode and cluster analysis
(3:00pm-4:00pm) General Q&A*
This is an opportunity to ask questions about any of the topics covered during the day.
*Any issues with FSL installation will also be addressed during this session. A tutorial video will be sent out ahead of the workshop about how to install FSL. If you have any issues, please let me know about them beforehand.
Day 2: Group-Level Analysis, scripting, & advanced topics
(10:00am-11:15am) Group-level analysis (Lecture & Practical)
An overview of how to set up group-level analyses, as well as caveats to be aware of. The lecture will cover the basic mechanisms of group analysis, and correction issues unique to fMRI data.
- Second-level vs. third-level analysis
- Setting up group-level analyses
- T-tests and F-tests: How to set them up and when to use them
- Testing for double dissociations
- ROI analysis with featquery and fslstats
- Correction mechanisms: FWE, FDR, and cluster-forming thresholds
(11:15am-12:00pm) Randomise and non-parametric tests (Lecture & Practical)
This expands upon the group-level analysis lecture by demonstrating different methods for performing inferential statistics. We will also briefly discuss the findings of Eklund et al. (2016).
- Parametric vs. non-parametric assumptions
- Randomise and permutation tests
- Creating contrast files and an overview of Text2Vest
- Using randomize with your data and interpreting the output
(1:00pm-1:45pm) Scripting your analysis (Practical)
Automating analyses is an indispensable skill for the neuroimaging researcher. This practical will introduce the power of loops, which will save you countless hours and reduce the probability of error.
- Review of shell scripting
- Saving the analysis script from the GUI
- Editing the .fsf file
- Looping your analysis over subjects
(1:45pm-3:00pm) FSL’s MELODIC (Lecture & Practical)
Independent Components Analysis (ICA) decomposes neuroimaging data into separate temporal and spatial components. These components can be used to identify networks of brain activity as well as artifacts, enabling you to perform more advanced analyses.
- Overview of functional connectivity
- Networks in the brain
- Introduction to ICA
- Using MELODIC to identify networks
- Comparison of seed-based networks to ICA network identification
- Running first- and second-level analysis on network images
(3:00pm-4:00pm) General Q&A