PYM0FM-fMRI Data Analysis

Module Provider: Psychology
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Autumn term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2019/0

Module Convenor: Dr Etienne Roesch


Type of module:

Summary module description:

The purpose of this module is to provide students with working knowledge about the brain in the field of cognitive neuroscience, and practical experience of analysing fMRI data. The module interweaves lectures and hands-on experience of data processing, using the University cloud computing infrastructure. Students will familiarise themselves with each step of a typical processing pipeline, learn how to script and automate analyses, and be introduced to the best practices in reproducible neuroimaging.


To introduce recent ideas, techniques and current state of knowledge about the brain, in the field of cognitive neuroscience, and develop practical expertise in the tools and methods used in the analysis of fMRI data. Students will develop a critical appreciation of neuroimaging, with a view to understanding current directions and methodological debates, and supporting a career in the field.

Assessable learning outcomes:

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

  1. To use common neuroanatomical nomenclatures to describe the brain

  2. To understand the main mechanisms underlying brain functioning, including the electrical properties of neurons, neurotransmisters, and vascular coupling mechanisms underlying blood-oxygenation level dependent signal

  3. To understand theoretical issues in fMRI data analysis (e.g., haemodynamic response, motion and other artifacts in the time series, the multiple statistical comparisons problem)

  4. To understand and perform preprocessing of fMRI time series (e.g., realignment, registration to standard space, spatial smoothing)

  5. To set up a general linear model capturing experimental and nuisance effects in data and try out one or more ways of fitting the model to the data

  6. To understand and navigate coordinate system for reporting activations (stereotaxic space)

  7. To make statistical comparison of activation level across the brain between two experimental conditions (contrasts, t-test) for a single subject

  8. To compare activation in two experimental conditions at the group level

Additional outcomes:

This module will provide a valuable introduction to methods of analysis in brain imaging research. It will thus serve as a suitable foundation for students looking to carry out brain imaging experiments in postgraduate studies, or seeking research-based positions in brain imaging laboratories.

Outline content:
This module covers the analysis of FMRI data at both the theoretical
and practical levels. Topics covered include MRI Physics, what FMRI
is actually measuring, preprocessing of FMRI data, and modelling of
FMRI data.

Brief description of teaching and learning methods:

Theoretical content about the brain and the statistical analysis of neuroimaging data is delivered by a lecture at the start of each session. In the second part of each session students learn to use the main tools to analyse fMRI data. Example data sets and the final coursework relate to typical brain functioning.

Contact hours:
  Autumn Spring Summer
Seminars 7.5
Practicals classes and workshops 7.5
Guided independent study: 85
Total hours by term 100
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Report 100

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:
Students will be provided with a data set to independently analyze and report on. The reported analysis should include systematic variation of some processing stages (e.g. degree of spatial smoothing, or comparing different methods of model fitting), and some reporting of relevant methodological literature.

Formative assessment methods:
Students will be able to improve their performance through practical components of the module.

Penalties for late submission:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information:

Assessment requirements for a pass:

Reassessment arrangements:

If a student fails the assignment, an alternative, equivalent assignment can be submitted.  The assignment and date of submission will be by arrangement with the Module Convenor and/or Programme Director. Students should note however that, given the University regulations on failing credits, it may not be in their interests to resubmit the coursework.

Additional Costs (specified where applicable):
1) Required text books:
2) Specialist equipment or materials:
3) Specialist clothing, footwear or headgear:
4) Printing and binding:
5) Computers and devices with a particular specification:
6) Travel, accommodation and subsistence:

Last updated: 10 April 2019


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