This module has been developed to give students a good theoretical and practical knowledge of the main neuroimaging data analysis methods for the quantification of brain activity, structural connectivity and morphology, and of the application of multimodal imaging in research and clinical practice.
At the end of this module, students will be able to:
1. Anatomical Data Fusion (ADF)
1.01:                Explain the aims of image registration and the basic steps involved in the process 
1.02:                Select and describe appropriate applications of image registration in neuroscience. 
1.03:                Perform measurements and or analyses following image registration.
2. Morphology & Volumetry (MV)
2.01:                Describe the aims of characterising and measuring brain shape. 
2.02:                Describe the methods used to characterise and measure brain shape, and to determine brain volumes based on MRI data. 
2.03:                Describe some applications from the neuroscience literature
2.04:                Perform brain morphometry and volumetry using Statistical Parametric Mapping (SPM)
3. Mapping Brain Activity & Networks (MBAN)
3.01:                Describe the current understanding of functional activation and the basic mechanisms that underlie its detection using neuroimaging, and in particular BOLD fMRI
3.02:                Describe and critique applications from the recent neuroscientific literature and in particular some involving multi-modality integration.
3.03:                Design a valid fMRI experiment and analyze the resulting data using SPM.
4. Multi-Modal Neuroimaging (MMN)
4.01:                Define and describe examples of multi-modal imaging
4.02:                List and evaluate strengths and weaknesses of different structural and functional modalities
4.03:                Compare potential artefacts and their sources for fMRI and DTI
4.04:                Explain the difference between patient studies and population studies
4.05:                Describe and select appropriate spatial and temporal co-registration methods for a given phenomenon of interest, and for checking that co-registration 
5. Machine Learning in Neuroradiology (MLN)
5.01:                Explain, in broad terms, what machine learning is and how it differs from statistical modelling.
5.02:                Understand terminology associated with machine learning:
–   Artificial Intelligence
–   Deep Learning
–   Supervised Learning
–   Unsupervised Learning
5.03:                Describe the difference between classification and regression problems and give examples of each in the field of medical imaging.
5.04:                List and compare different algorithms used in supervised and unsupervised learning tasks and their associated benefits and limitations.