MyAV·¶

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: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýExplain the aims of image registration and the basic steps involved in the processÌý
1.02: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýSelect and describe appropriate applications of image registration in neuroscience.Ìý
1.03: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýPerform measurements and or analyses following image registration.
2. Morphology & Volumetry (MV)
2.01: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe the aims of characterising and measuring brain shape.Ìý
2.02: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe the methods used to characterise and measure brain shape, and to determine brain volumes based on MRI data.Ìý
2.03: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe some applications from the neuroscience literature
2.04: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýPerform brain morphometry and volumetry using Statistical Parametric Mapping (SPM)
3. Mapping Brain Activity & Networks (MBAN)
3.01: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe the current understanding of functional activation and the basic mechanisms that underlie its detection using neuroimaging, and in particular BOLD fMRI
3.02: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe and critique applications from the recent neuroscientific literature and in particular some involving multi-modality integration.
3.03: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDesign a valid fMRI experiment and analyze the resulting data using SPM.
4. Multi-Modal Neuroimaging (MMN)
4.01: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDefine and describe examples of multi-modal imaging
4.02: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýList and evaluate strengths and weaknesses of different structural and functional modalities
4.03: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýCompare potential artefacts and their sources for fMRI and DTI
4.04: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýExplain the difference between patient studies and population studies
4.05: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe and select appropriate spatial and temporal co-registration methods for a given phenomenon of interest, and for checking that co-registrationÌý
5. Machine Learning in Neuroradiology (MLN)
5.01: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýExplain, in broad terms, what machine learning is and how it differs from statistical modelling.
5.02: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýUnderstand terminology associated with machine learning:
– Ìý Artificial Intelligence
– Ìý Deep Learning
– Ìý Supervised Learning
– Ìý Unsupervised Learning
5.03: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýDescribe the difference between classification and regression problems and give examples of each in the field of medical imaging.
5.04: Ìý Ìý Ìý Ìý Ìý Ìý Ìý ÌýList and compare different algorithms used in supervised and unsupervised learning tasks and their associated benefits and limitations.