Image Analysis

PCHN62121 Image Analysis 15 Semester 1 Postgraduate Taught Level 7 Martyn Mcfarquhar Unit coordinator 1 FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' European Credit Transfer & Accumulation System Rating : 7.5 This unit will explore the processing and analysis of both fMRI and M/EEG datasets, covering both theoretical and practical perspectives. Students will gain valuable hands-on experience using SPM to perform these steps, 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. The unit will also explore the relative strengths and weaknesses of selecting various imaging parameters and the resulting inferences that can be drawn.   This unit will explore the processing and analysis of both fMRI and M/EEG datasets, covering both theoretical and practical perspectives. Students will gain valuable hands-on experience using SPM to perform these steps, 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. The unit will also explore the relative strengths and weaknesses of selecting various imaging parameters and the resulting inferences that can be drawn.   The course aims to provide students with a comprehensive knowledge of functional and electrophysiological analysis methodologies. The unit focuses on the processing and statistical analysis of both fMRI and M/EEG. In addition to gaining theoretical knowledge of the image analysis process, students will also learn how to conduct these analyses through hands-on experience with the Statistical Parametric Mapping (SPM) analysis package.   By the end of the course, students will be able to:<ul><li>Understand each stage of the image analysis process</li><li>Understand the advantages and disadvantages of each analysis parameter</li><li>Draw appropriate inferences based on the image analysis employed</li><li>Evaluate the appropriateness of using a particular neuroimage analysis method</li><li>Consider the various strengths and weaknesses of different analyses techniques</li><li>Perform all the stages of preprocessing neuroimaging data, using SPM</li><li>Perform both subject- and group-level statistics on neuroimaging data, using SPM</li><li>Analyse neuroimaging data for various designs</li></ul> The course will be taught through a combination of synchronous lectures and lab sessions (6 x two hours delivered over six weeks), alongside online asynchronous teaching materials. Additional lab sessions and lab support will be available for students, if needed.   Teaching will be complemented by the availability of notes, slides and recommended reading. 1 Written exam 50% 3 Report 50% Formative feedback available. MSc Neuroimaging for Clinical MSc Neuroimaging for Clinical PGDT Taught Component Mandatory N Lectures 12 0 138