MSc Neuroimaging for Clinical & Cognitive Neuroscience / Course details

Year of entry: 2025

Course unit details:
Image Analysis

Course unit fact file
Unit code PCHN62121
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 1
Available as a free choice unit? No

Overview

This unit will explore the processing and analysis of both fMRI and M/EEG datasets, covering both theoretical and practical perspectives. Students will gain valuable hands-on experience using SPM to perform these steps, with the aim of consolidating and building upon their knowledge of image analysis theory. In turn, gaining a comprehensive knowledge and understanding of the various stages of analysing neuroimaging and electrophysiological data will provide a firm foundation upon which any future image analysis package can be learnt. The unit will also explore the relative strengths and weaknesses of selecting various imaging parameters and the resulting inferences that can be drawn.  

Aims

The course aims to provide students with a comprehensive knowledge of functional and electrophysiological analysis methodologies. The unit focuses on the processing and statistical analysis of both fMRI and M/EEG. In addition to gaining theoretical knowledge of the image analysis process, students will also learn how to conduct these analyses through hands-on experience with the Statistical Parametric Mapping (SPM) analysis package.  

Learning outcomes

By the end of the course, students will be able to:

  • Understand each stage of the image analysis process
  • Understand the advantages and disadvantages of each analysis parameter
  • Draw appropriate inferences based on the image analysis employed
  • Evaluate the appropriateness of using a particular neuroimage analysis method
  • Consider the various strengths and weaknesses of different analyses techniques
  • Perform all the stages of preprocessing neuroimaging data, using SPM
  • Perform both subject- and group-level statistics on neuroimaging data, using SPM
  • Analyse neuroimaging data for various designs

Teaching and learning methods

The course will be taught through a combination of synchronous lectures and lab sessions (6 x two hours delivered over six weeks), alongside online asynchronous teaching materials. Additional lab sessions and lab support will be available for students, if needed.  

Teaching will be complemented by the availability of notes, slides and recommended reading.

Assessment methods

Method Weight
Written exam 50%
Report 50%

Feedback methods

Formative feedback available.

Study hours

Scheduled activity hours
Lectures 12
Independent study hours
Independent study 138

Teaching staff

Staff member Role
Martyn Mcfarquhar Unit coordinator

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