MSc Neuroimaging for Clinical & Cognitive Neuroscience / Course details
Year of entry: 2024
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Course unit details:
Image Analysis
Unit code | PCHN62121 |
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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 image analyses aspects of a number of neuroimaging techniques covering both theoretical and practical perspectives. Students will gain valuable hands-on experience with a number of analysis packages, 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, with relative ease.
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 structural neuroimaging and electrophysiological methodologies. The unit focuses on the image analyses aspects of a number of techniques including fMRI, EEG and ERP.
In addition to gaining knowledge of image analysis theory, students will learn how to conduct various aspects of image analysis through hands-on experience with analyses packages.
Learning outcomes
By the end of the course, students will be able to:
- have comprehensive understanding of each stage of image analysis;
- have a well-developed knowledge of 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;
- gain experience of and expertise with all the stages of preprocessing neuroimaging data;
- perform both participant- and group-level statistics on neuroimaging data;
- analyse neuroimaging data for various designs, such as blocked, event-related and mixed designs.
Teaching and learning methods
The course will be taught through a combination of linked lectures and lab sessions (12 x two hours over six weeks). Additional lab sessions and lab support will be available for students.
The course will be supported by a dedicated e-learning package providing step-by-step training and assessment. Students will be required to keep an online diary of their progress for monitoring and assessment.
Teaching will be complemented by the availability of notes, slides and recommended reading.
Assessment methods
Method | Weight |
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Written exam | 50% |
Report | 50% |
Feedback methods
Formative feedback available.
Study hours
Scheduled activity hours | |
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Lectures | 24 |
Independent study hours | |
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Independent study | 126 |
Teaching staff
Staff member | Role |
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Cheryl Capek | Unit coordinator |