Designing experiments which use magnetic resonance imaging of people, data analysis and interpretation. Use of basic computer coding and application of statistics, and interactive neuroimaging software for scientific communication.
About this paper
|Magnetic Resonance Imaging in Neuroscience
|Semester 1 (On campus)
|Domestic Tuition Fees ( NZD )
|International Tuition Fees
|Tuition Fees for international students are elsewhere on this website.
- Recommended Preparation
- STAT 210 or STAT 310
- May not be credited together with NEUR471 taken in 2021-2023
The course is intended for science majors, especially neuroscience majors, including those with limited programming experience. A computer science or statistics paper would be advantageous but not essential.
Course co-ordinator: Dr Olivia Harrison (firstname.lastname@example.org)
- Teaching staff
- Paper Structure
The first part of the course is composed of a series of lectures that covers the fundamental aspects of functional human neuroimaging coupled with guided practical sessions. This is followed by journal club presentations with additional lectures considering possible applications of MRI to health and disease. Students will then be guided through how to develop and simulate experimental protocols with hands-on support.
The course briefly covers the analysis of other neuroimaging modalities (resting state, structural and diffusion imaging), aspects of which students can choose to incorporate into an experimental design project of their choice. Students are required to present their experimental design to their peers for further discussion.
- Teaching Arrangements
We will use the FSL software package from the University of Oxford's FMRIB Centre. Students are expected to have their own laptop (<5 years old). Lectures are followed by a guided practical session.
Textbooks are not required for this paper. Readings will be supplied.
- Graduate Attributes Emphasised
- Scholarship, Critical thinking, Information literacy, Research.
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete the paper will:
- Understand how brain activity and connectivity are assessed using functional neuroimaging, and why this is important within neuroscience
- Acquire a basic understanding of how mathematical models in neuroimaging are derived and applied, and what can and cannot be inferred regarding underlying neural systems when using these modelling techniques
- Learn that effective collaboration between neuroimagers, physicists, statistical modelers and study participants requires each to have some understanding of the other approaches and perspectives, what is known and what is possible. Students will feel confident in their knowledge of MRI analysis and understand the basics of neuroimaging research
- Learn basic coding, documentation and version control using FSL software which is now a common tool used in neuroimaging
- Learn to use graphics, animation and interactive software tools to communicate MRI analysis techniques to non-specialists