Overview

1) Physical and practical principles of MRI imaging, Methods for image pre-processing (e.g. image registration etc.) and basic handling of imaging data; 2)  Practicalities associated with the physiology of MRI contrast in the brain, with a focus on measures of cerebral metabolism and blood flow; 3)  Principles and practical methodologies for measurement of changes in cerebral metabolism; 5)   Practicalities of statistical analyses of functional imaging time-series; 6)   Experimental design for functional imaging; 7) The safety and ethical issues that arise in regards to neuroimaging research and how they apply to the researcher, participants and the wider community.

Assessment Strategy

-threshold -Links to C gradesKnowledge of key areas/principles only • Limited evidence of background study • Answer only poorly focused on ques3on & with some irrelevantmaterial & poor structure • AQempts to present relevant and coherent arguments • Has several factual/computa3onal errors • No original interpreta3on • Only major links between topic are described • Limited problem solving • Many weaknesses in presenta3on & accuracy

-good -Links to B gradesStrong Knowledge • Understands most but not all of subject area • Evidence of background study • Focused answer with good structure • Arguments presented coherently • Mostly free of factual/computa3onal errors • Some limited original interpreta3on • Well known links described between topics • Problems addressed by exis3ng methods/approaches • Good presenta3on, accurate communica3on

-excellent -Links to A gradesComprehensive knowledge • Detailed understanding of the subject area • Extensive background study • Highly focused answer & well-structured • Logically presented & defended arguments • No factual/computa3onal errors • Original interpreta3on • New links to topic are presented • New approach to a problem • Excellent presenta3on with very accurate communica3on

Learning Outcomes

• 1. Understand modern neuroimaging data formats and solid data management and hygiene practices

• 3. Use this knowledge of the GLM to design a good fMRI study

• 4. Be able to pre-process and analyze MRI and fMRI data.

• 5. Be able to report and interpret the results of fMRI studies.

• 6. A thorough understanding of MRI safety

• 7. An understanding of the ethical issues associated with handling modern neuroimaging data.

• Have a solid understanding of practical application of the general linear model (GLM) to analysis of neuroimaging data