The aim of this module is to provide an understanding of issues of design and analysis which are specific to neuroimaging. Students will get hands-on practical experience of the acquisition, design and analysis of neuroimaging data.
Module learning outcomes
Be able to show a in-depth understanding of the methods of and issues involved in data collection and analysis in fMRI and MEG
Demonstrate the ability to independently perform neuroimaging analyses in fMRI and MEG
Be able to show a critical understanding of the relative merits of fMRI and MEG as tools for cognitive and behavioural neuroscience
Be able to report and critically evaluate the results of fMRI and MEG analyses
Module content
Lectures
Signal, Noise and Preprocessing of fMRI data
Experimental Design in fMRI
Statistical Analysis in fMRI: Basic Analyses
Statistical Analysis in fMRI: Advanced Approaches
Signal, noise and preprocessing of MEG data
MEG Sensor space analysis
MEG Source Space Analysis
Statistical Analysis in MEG: Advanced Approaches
Practicals
fMRI Experimentation and Images
fMRI Single Subject (1st Level) Analysis
fMRI Group (2nd Level) Analysis
fMRI Time-Series and MVPA Analysis
MEG Experimentation and data pre-processing
MEG Sensor Space Analysis
MEG Source Space Analysis
MEG Group Level Analysis
Assessment
Task
Length
% of module mark
Coursework - extensions not feasible/practicable Weekly quiz
N/A
10
Essay/coursework Practical Report
N/A
90
Special assessment rules
None
Reassessment
Task
Length
% of module mark
Essay/coursework Practical Report
N/A
90
Module feedback
The marks on all assessed work will be provided on e-vision.
Indicative reading
Sample Reading:
Functional Magnetic Resonance Imaging by Huettel, Song, McCarthy (3rd edition)