Khan Academy is a great free resource for all sorts of topics.
- Their series on linear algebra is particularly useful and relevant to our needs.
- The Fourier series and the
- statistics one videos may also prove useful (h/t [Sam Jones] ( ??? ) ).
If you feel that your background in mathematics and signal processing is a bit weak please have a look at these slides. This file was put together by Joana Leitao and covers several topics that are important to be familiar with in neuroimaging:
- basic linear algebra
- ordinary least square solution for the general linear model
- the BOLD response and convolution: what is a linear time invariant system and why is matters when doing a fMRI study ?
- how to do t-test and ANOVAS within a general linear model
If you need to dust up your knowledge about MRI.
- the e-MRI course
- MRI fundamentals on coursera
- A Magnetic Resonance Imaging Lab from UCSD
- MRI question is not a course but a VERY VERY comprehensive FAQ on MRI.
There are also blog posts series on practicalfmri (and on its companion winnower account) that cover
- the physics of fMRI artefacts
- the type of artefacts you might encounter and what to look for
- a list of potential confounds to the BOLD signal
- quality control on MRI and fMRI
- the Braindr web app designed by Anisha Keshavan to expose yourself to some good, bad and ugly MRI structural images
http://the-brain-box.blogspot.com/2015/05/what-does-fmri-measure.html http://blogs.discovermagazine.com/neuroskeptic/2014/02/03/non-bold-signal-fmri/#.UwNqBdvxBXT Matthias Nau [???] EPI guide https://matthiasnau.com/epi_guide.pdf
There are quite a few courses for fMRI analysis out there that I am aware of.
- On coursera A few courses on coursera with notably
- Tor Wager's and Martin Lindquist's 2 parts MOOC (part 1 and part 2)
- and the Introduction to Neurohacking In R
- Others
- the fMRI Image Acquisition and Analyses course from the mind research network is an IRL course but links to useful resources too
- the functional MRI methods - psych 214 from Berkeley
If you are going to do some multivariate analysis, it is likely you will need to
know a bit lot of machine learning. I did find that that
this class on coursera
covered a lot of ground. It is not specific to neuroimaging but gives you a good
overview of the basic concept you need to understand.
Worst case it will let you understand why John von Neumann said
With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.
https://fmrif.nimh.nih.gov/public/other-courses/mvpa https://dartbrains.org/intro.html
There is one on the rMRI website.
Neurohackademy is more than a neuroimaging course: it is broader in scope as it covers reproducibility and open science issues in neuroimaging. It is also very practical and definitely python oriented. To know more, see this post by Tal Yarkoni about the 2018 edition of Neurohackademy.
Most of the main analysis packages on top of the IRL courses usually have one video series that works as a course +/- tutorial.
Nipype is best viewed as a way to create and run software-agnostic preprocessing/analysis-pipeline. It becomes very powerful when you need to use different softwares in your analysis.
Tim Van Mourik and a few other people have developed tool to facilitate building pipelines with nipype:
- Porcupine stands for "PORcupine Creates Ur PipelINE" which is probably the worst recursive acronym with bad capitalisation and annoying use of slang. This software allows researchers to build pipelines using a GUI and generates the code that is needed to run the pipeline created.
- Giraffe is web-based "Graphical Interface for Reproducible Analysis oF workFlow Experiments" that can take advantage of Porcupine to create pipelines.
Some of those are clearly not specific to neuroimaging but are well worth going through even if you are a PI.
- If you have no idea what the distribution of p-value would look like if there were only noise in your data, then the odds are you will learn at least one thing in Daniel Lakens course on how to improve your statistical inferences. Most likely you will learn more than one thing.
Daniel also has a blog blog is very useful of stats related knowledge. Similarly Guillaumme Rousselet's has a series of posts on his blog where you learn more about robust statistics and how to improve your data visualizations.
There is a MOOC on open-science is still under construction but on top of an insane list of resources has the module 5 already up and running to teach you how to use github and zenodo to create a time stamped screenshot of your code to link to in your papers.
Stat 159/259 - Reproducible and Collaborative Data Science from Berkeley has a hands-on and integrated approach to GIT, jupyter notebooks, Python and more...