Suggested reading/background knowledge
Background knowledge
When we designed this course, we assumed participants have general (but not advanced) background knowledge in the areas listed below. Due to time constraints we are not able to teach these topics during this course, they usually are (and if not should be) covered in general academic training in the neurosciences.
- neuroanatomy, neurophysiology
- statistics: hypothesis testing using the general linear model
- basic physics
- basic math (some algebra)
Suggested reading
When you want to prepare for the course, we suggest the following reading. It is not a requirement, you will hopefully be able to understand most of the course material without it, but it might help.
- An excellent overview paper on fMRI analysis:
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Karl J. Friston. Models of Brain Function in Neuroimaging. Annu. Rev. Psychol. 2005. 56:57–87
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For reference
The content for most of the theoretical image analysis lectures is based on the theory in the following book chapters. After the course if might be worthwile to read them to refresh things or for further study.
- Statistical Parametrical Mapping: the Analysis of Functional Brain Images. Authors: K.J. Friston, J.T. Ashburner, S. Kiebel, T.E. Nichols and W.D. Penny
- Important chapters on spatial preprocessing: Part 2, chapters 4, 5 and 6
- Statistical analysis: Part 3, chapters 8, 9 and 12, 15. Part 4, chapter 18, 20