PSGY4009

Q+A about assessment, the GLM in more detail

Author

Denis Schluppeck

Published

November 14, 2024

1 Attendance

qr code

2 Today

Assessment

  • A bit more detail on the written assessment
  • General advice on writing, structure

The General Linear Model (GLM)

  • intuition / non-mathematical explanation
  • some of the nitty-gritty (in matlab)
  • demo in fsl

Kinds of designs

  • task, resting state, connectivity, …

3 Learning objectives

By the end of the lecture you should:

  • know what’s expected in coursework
  • have all the information to get started on assessment
  • understand the GLM in principle
  • appreciate some of the technical details of GLM analysis
  • have some knowledge of different kinds of “designs” / approaches to fMRI for neuroscience

4 Assessment

  • Written assignment (max 3000 words) including a 250 word abstract.

  • Details on moodle (2024/25).

The written assignment for this module is an essay about how functional magnetic resonance imaging and/or brain stimulation can be used to study different neuroscience questions. It should cover two topics and/or methods from the course.

5 Not just a literature review

One aim of the assignment is to make you think about the methodological choices the experimenters have to make. After a brief summary of the state of the literature in your area, there should be therefore be a component that talks about how you might extend some previous findings.

6 On moodle

word doc template

7 Guided submission

There are very specific suggestions for how you can tackle each section in turn

overall word limit, 3000w - stick to this limit)

  • Title of project (suggested ~10 words)
  • Lay Summary (max 250 words, one paragraph)
  • Scientific Summary (max 250 words, one paragraph)
  • Background of the project (suggested 600 words)
  • Questions to be answered (suggested 200 words)
  • Plan of investigation (suggested 500 words)
  • Details of data analysis (suggested 500 words)
  • Expected outcomes (suggested 200 words & 1-2 figures)
  • Theoretical & practical implications (suggested 500 words)
  • References

8 Things that you might wonder about:

  • Plan of investigation (suggested 500 words)
  • Details of data analysis (suggested 500 words)
  • Expected outcomes (suggested 200 words & 1-2 figures)

But I don’t have any data (yet?). How to square that circle??

9 What do we look for?

Content

The content of your coursework is (obviously) important - topic choice - methodological details included - facts correct?

… but writing!

  • clear, concise, economic
  • line of argument?
  • structure easy to follow
  • …

10 Strunk & White - Elements of Style

If you haven’t read this little book (26 pages), take the time!

free online PDF of the book

11 The GLM - a quick walk-through

11.1 Some notes for my demo

cd ~/projects/hands-on-brain-data
julia
# using Pluto
# Pluto.run()
# "what_is_linearReg.jl"

12 in matlab

12.1 Some notes for my demo

cd ~/projects/hands-on-brain-data
cd data
X = load('design-3.txt')
y = load('timecourse.txt')
X\y
% regress(), pinv()

13 in fsl

cd ~/projects/hands-on-brain-data-demo
cd data
fsleyes filtered_func_data
fsl &
# simple block design ... stats: 6, (12, 12, 12)

14 in fsl/fsleyes

15 Kinds of designs / approaches

Two directions, in which people elaborate experiments:

  1. tasks, stimuli have become more sophisticated
  2. data analysis methods are changing all the time

16 Tasks

You can find lots of versions of these across all domains of cognitive neuroscience…

  • task-based experiments
    • block designs, event-related designs, mixed, …
    • “continuous” (eg watching movies)
  • resting state fMRI (rsFMRI)

17 Analysis methods

  • GLM, linear regression (the “workhorse” of fMRI analysis)

  • data-driven methods (search for patterns in the data)

    • independent component analysis, ICA (dimensionality reduction techniques)
    • seed-based correlation methods
    • network analysis, …

  • machine learning, decoding

18 GLM

We have just seen a bit more of this in action

19 Resting state fMRI

example rs analysis

Smith et al, 2012, PNAS

20 Decoding, multivariate analysis

Variously: classification, SVM (support vector machines), decoding, even “mind reading”, …

Aim: try to use data - the pattern of activity across many voxels (and trials) - to figure out which stimulus was being displayed.

21 First use, classic reference

Kamitani and Tong, 2005

Kamitani & Tong, 2005

22 Thanks

Hope you found this helpful.

See you soon!

23 Colophon

  • This presentation was made with quarto and revealjs.

  • Uses a font called Atkinson Hyerlegible, which was designed to work better for people with low vision: available via google fonts.