MSc Neuroimaging for Clinical & Cognitive Neuroscience / Course details
Year of entry: 2021
- View tabs
- View full page
Course unit details:
Advanced Image Analysis
|Unit level||FHEQ level 7 – master's degree or fourth year of an integrated master's degree|
|Teaching period(s)||Semester 2|
|Offered by||School of Biological Sciences|
|Available as a free choice unit?||No|
This module will provide theoretical background and practical instruction on a number of key advanced methods in neuroimaging analysis, including connectivity, multivariate pattern analysis, advanced diffusion and PET methods, and M/EEG source estimation.
Lectures will focus on the theoretical issues underlying the methodology such as anatomical, functional, and effective connectivity; pattern-information mapping vs. mass-univariate modelling; parametric vs. non-parametric statistical methods; and Bayesian approaches to inverse problems. Practical sessions will apply the theory introduced in lectures and provide introductory hands-on training in a number of advanced analysis methods. The course builds on the Image Analysis unit by exploring recent developments in neuroimaging analysis techniques and proposed solutions to specific problems and challenges.
- To provide students with an introduction to, and a working knowledge of, a selection of advanced neuroimaging techniques, focusing on both acquisition and analysis methodology.
- To provide some advanced mathematical, statistical, and neurophysiological background to these methods.
At the end of this unit students will:
- have a solid grasp of a selection of advanced neuroimaging techniques and how they can be used to contribute to research;
- understand some of the mathematical, statistical and neurophysiological factors underlying these advanced methods;
- understand the neurophysiological issues that need to be addressed by the advanced techniques;
- be able to evaluate the use of advanced neuroimaging analysis techniques to answer specific questions about brain function;
- be able to select the most appropriate technique to address the question in hand;
- be able to discuss some of the mathematical and statistical models and assumptions that underlie the methods;
- have some specialised analysis skills.
Teaching and learning methods
The course will be taught through a combination of linked lectures and lab sessions; 12 x two-hour sessions over six weeks. Teaching will be complemented by the availability of notes, slides and recommended reading.
- Two lab reports each worth 50% of the overall mark.
No information available
|Scheduled activity hours|
|Independent study hours|
|Jason Taylor||Unit coordinator|