INDIVIDUAL COURSE DESCRIPTIONS
INTRODUCTION TO NEUROIMAGING WITH MRI (1ST WEEK OF MICHAELMAS TERM)
This intensive one-week course, organised by Dr Natalie Voets and Dr Janine Bijsterbosch, will introduce students to some of the tools and concepts essential for neuroimaging research. Participants will be introduced to the basics of imaging neuroscience, physics and analysis, including some applications of fMRI. Hands-on practical sessions will include the analysis of some fMRI data. The week also includes a practical component where students will perform a small fMRI study, including designing the experiment and acquiring the data. This week is designed as an introduction to the full Graduate Programme and cannot be attended as a stand-alone course.
REBOOT CAMP WEEKS (1ST WEEK OF BOTH HILLARY AND TRINITY TERMS)
These intensive one-week courses are intended to kick-start the second and third terms. Session typically include:
- Workshops on scientific computing (organised by Dr Tom Marshall and Prof Mark Jenkinson), that aim to introduce different software tools (such as UNIX and Matlab) at a very basic level. No prior knowledge of computing or individual software packages is assumed.
- Lectures and practicals on data, stats and maths (organised by Prof Saad Jbabdi, Prof Jesper Andersson) that introduce the essential mathematical concepts for those doing neuroimaging. Aimed specifically at those who did not do a mathematical degree, as well as those with a mathematical background but without experience in using statistics in practice. It is based around examples that show how statistics and mathematics connects with daily practice when collecting, analysing and interpreting data, with a special focus on neuroimaging.
- Short talks provide introductions to various aspects related to obtaining imaging data at Oxford (organised by Dr Janine Bijsterbosch). These sessions include topics such as open science and reproducibility, ethics, and animal imaging.
CORE GRADUATE PROGRAMME
The core graduate programme consists of three components, each integrating a number of teaching methods (including 1.5 to 2 hour podcasts), and aims to give students a thorough ‘hands-on’ understanding of the topic as well as the theory. It is aimed at a basic to intermediate level, is intended to be taken as a whole, and requires students to register and pay the course fee.
MRI PHYSICS (BASIC LEVEL)
This course, organised by Dr Tom Okell and Dr Mark Chiew, aims to give students a background on how MRI data is acquired, and is run in Michaelmas term. One major goal is to enable researchers to make informed decisions about different aspects of their experimental protocols and the implications of these decisions on the resulting data quality. The first half of the course describes the basic physics underlying MRI acquisition and the second half focuses on the neuroimaging methods most commonly employed in Oxford, with particular focus on functional and diffusion MRI. No physics expertise is assumed.
MRI ANALYSIS (BASIC LEVEL)
Organised by Prof. Mark Jenkinson and Dr Janine Bijsterbosch, this course will introduce the principles and practice behind the analysis of MR images, and is run in Hilary and Trinity terms. This includes image registration, segmentation, detecting structural changes, fMRI analysis and diffusion imaging. The theoretical lectures will be accompanied by tutorials and computer-based practicals using the FSL software package. No prior analysis or mathematical skills (beyond school-level maths) are assumed.
ASSESSMENTS AND TIME COMMITMENT:
The two main courses, MR physics and MRI analysis, each include weekly short practical exercises with questions to be answered, and an end of term exam. Most students should expect to spend 1-2 hours a week viewing podcasts, going over material and preparing for tutorials/practicals in addition to the formal contact hours (typically 2-4 hours per week) for the core courses.
In addition to the main programme there are supplementary courses that anyone is welcome to attend (for free). These span a range of levels from basic to intermediate and advanced levels. The intended audience and level is described for each course below. Advanced level courses vary from year to year and are primarily aimed at methods students (physics/analysis), but are expected to also be useful for interested second- and third-year students with a clinical or neuroscience background, once they have gained some practical experience in neuroimaging. Most first-year students would benefit from attending 50-70% of these module sessions.
INTRODUCTION TO NEUROSCIENCE (BASIC LEVEL, MICHAELMAS TERM)
This course, organised by Dr Rogier Mars, will give students without a biomedical background (e.g., physicists and engineers) an introduction to some key topics in neurology and neuroscience. The focus is on physiological and psychological processes that neuroscientists are interested in, and the limitations of the current methods for measuring them. Topics covered include: Grey matter anatomy and cerebral vasculature; Anatomy of the white matter; Cellular signalling; Brain chemicals; and the Physiology of blood vessels and its relation to BOLD.
ADVANCED MR PHYSICS (ADVANCED LEVEL, HILLARY TERM)
This course, organised by Dr Tom Okell, will cover selected topics in MRI physics to a level beyond that covered on the core graduate programme. A more mathematical and equation-based treatment of topics will be used.
ADVANCED MATHS AND ANALYSIS (ADVANCED LEVEL, HILLARY TERM)
This course, organised by Prof. Saad Jbabdi, introduces key mathematical concepts behind much of the research and software of the Image Analysis Group, to a level beyond that covered on the core graduate programme. A more mathematical and equation-based treatment of topics will be used. Example topics include: Bayesian Modelling; Optimisation; Uni-variate Statistics & Time-Series Analysis; Multi-variate Modelling & Analysis; and Resting-State Analysis & ICA.
ANALYSIS AND INTERPRETATION (BASIC - INTERMEDIATE LEVELS, TRINITY TERM)
This course, organised by Prof. Gwenaelle Douaud, will cover the ways in which analysis results should and should not be interpreted. It focusses on diffusion and functional analysis and examines both clinical and non-clinical research. Best practice tips and known pitfalls are highlighted, with examples provided from the literature. Knowledge of analysis and physics, as covered by the core courses, is assumed.