MSc Neuroimaging for Clinical & Cognitive Neuroscience / Course details

Year of entry: 2021

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Course unit details:
Advanced Image Analysis

Unit code BIOL62152
Credit rating 15
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.

Learning outcomes

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.

Assessment methods

  • Two lab reports each worth 50% of the overall mark.

Feedback methods

No information available

Study hours

Scheduled activity hours
Lectures 24
Independent study hours
Independent study 126

Teaching staff

Staff member Role
Jason Taylor Unit coordinator

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