This course builds on the course Neuroimaging I (DGCN02). It aims to give students a deeper understanding of the imaging methods and analytical tools that are available to cognitive neuroscientists. At the conclusion of the course the student should be able to understand the underlying principles of data acquisition and analysis in fMRI. The course offers a systematic explanation of the acquisition experiment and hands on experience with the most commonly used data analysis techniques. It is mandatory for students wishing to perform an fMRI experiment in their master's project.

Data acquisition. The student will be taught how data are acquired and what the fundamental limitations of the techniques are. The basic principles of MRI will be taught. MRI using the echo planar imaging technique will be explained and modern developments in accelerating data acquisition introduced.Generalised image reconstruction in terms of k-space will be introduced and image acquisition and reconstruction in MRI explained in these terms.The complex nature of the BOLD response used for f(MRI) will be examined. The measurement of connectivity using both resting state fMRI and fibre tracking based on diffusion tensor measures will be introduced.

Data analysis. The main areas covered in this part of the course are: analysing structural MRI data including segmentation and assessment of grey matter volume; analysis of fMRI data both at the single subject and the group level using the general linear model; exploratory data analysis including the use of independent components, and their application to measuring functional connectivity; the assessment of structural connectivity using diffusion-weighted imaging.

Design aspects. An in-depth treatment will be given of the different experimental design types in imaging studies. The advantages and disadvantages of the different design types will be discussed, with emphasis on statistical efficiency. Students will be given the task to design an experiment and practice analysis.