Registration is free & open to public
Registration deadline is March 15th
March 22nd-24, 2023
Kincaid 102
Students should bring a laptop that has at least 8GB of RAM, 80GB of memory, and 2.3 GHz of processing power. Basically, any laptop made since 2015 should be fine.
Before the workshop you will need to do the following:
- Install Matlab
- Install SPM
- Install the Conn Toolbox
The course website (under construction) is here.
CHN will provide additional instructions (and support if you run into difficulties) closer to the time of the workshop. If you need to be provided with a laptop please email Ione Fine (ionefine@uw.edu) letting me know whether you’d prefer a Mac or PC and I’ll find one for you.
Wednesday, March 22
10 am – 4 pm Functional connectivity and the CONN toolbox I
Functional connectivity and the CONN toolbox: This will be an introduction to the basic functions of the CONN toolbox, including preprocessing, denoising, and which covariates to include during modeling for both resting-state and task-based connectivity. We will review basic quality assurance checks, how to use the results viewer to create appropriately corrected figures, and how to export the connectivity maps for use with other software packages. In addition, we will learn how to perform analyses between groups, between time-points, and how to correlate individual differences with functional connectivity measures.
3.30-5pm Individual Consultation sessions
email ionefine@uw.edu to book
Thursday, March 23
9 am – 3 pm Functional connectivity and the CONN toolbox II
Functional connectivity and the CONN toolbox Day 2 (Advanced): For researchers who are already familiar with the fundamentals of the CONN toolbox (or have done the workshop on March 22), this module will cover more advanced functions, such as generalized psychophysiological interactions (gPPI), dynamic connectivity, and surface-based connectivity. We will also learn about other options for more advanced users, such as importing custom atlases, and how to script analyses in Matlab.
3-3.45 pm Individual Consultation sessions
email ionefine@uw.edu to book
Friday, March 24.
9 am – 5pm Machine Learning & the Decoding Toolbox
Machine learning: This will be an introduction to The Decoding Toolbox, learning how to use both region-of-interest and searchlight methods to analyze the Haxby et al. 2001 dataset. We will learn about the best practices for conducting unbiased cross-validation, special considerations for preprocessing data for machine learning, how to interpret confusion matrices, and the basics of representational similarity analysis.