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Overview

Students will carry out most of the work in the Imaging Computer Lab where students will analyse imaging data acquired at the Ïã¸ÛÁùºÏ²Ê¹ÒÅÆ×ÊÁÏ Imaging Unit. These will include a dataset of 20 young and 20 older, healthy participants, who underwent structural and resting state scans, diffusion tensor and perfusion imaging, as well as magnetic resonance spectroscopy. Where convenient data from publicly accessible databases will also be used. The students will spend the initial 9 weeks analysing different image sequences, in the process familiarizing themselves with a number of imaging techniques and analytical pathways. In the final two weeks, the students will carry out a group level analysis or a patient based analysis, either using data previously processed by themselves and other students in the class, or an entirely novel dataset.

1. Structural Imaging. T1 and T2 weighted structural scans will be used for multi-spectral tissue classification. Students will need to co-register multispectral images and normalize them using an atlas representative target image. They are expected to generate classified volumes and provide basic statistics concerning CSF. white and grey matter volumes. (Analytical package: FSL and SPM).

2. Diffusion Tensor Imaging (DTI). Physical principles of DTI will be reviewed. Students will need to register DTI images to structural images using non-linear warping to correct for Eddy currents and susceptibility artefacts. Mean diffusity and anisotropy maps will be computed and seed based white matter tract highlighted. (Analytical package: FSL).

3. Functional connectivity. Neuro-physiological correlates of slow BOLD fluctuations and spatial-temporal BOLD coherence will be reviewed. Analysis of BOLD coherence: seed- based correlation approach and independent components analysis will be carried out on pre-processed BOLD time-series acquired in the resting state. The aim is to compute resting state networks, including default mode, attention and language networks. (Analytical package: FSL and SPM).

4. Perfusion imaging. Overview of PET based techniques for estimation of cerebral blood flow. Contrast based vs. non-contrast based estimation of local cerebral blood flood. ASL Images will be analysed to estimate cerebral blood flow. (Analytical package: FSL and SPM).

5. Spectroscopy. Physical principles. Students will be expected to compute the concentrations of main neuro-metabolites, including glutamate and GABA in a single voxel. Analysis will include corrections for partial volume effects. (Analytical package: SPM and Tarquin/jMRUI).

6. Ethical implications regarding incidental findings, and possible impact of advanced techniques are introduced and discussed. Discussion will include the ethical implications that arise from the ability to map brain pathways, patterns and metrics of neural connectedness and how these measures may be used in diagnosis, the potential for incidental findings of clinical importance in research participants and how to deal with these findings, and the wider implications of neuroimaging findings and how it may be applied in the wider community.

Learning Outcomes

• 1) Knowledge of imaging relevant aspects of neuroanatomy, neurophysiology and neuropathology

• 2) Understanding the physical principles underlying structural and metabolic MRI

• 3) Able to process structural, functional connectivity, perfusion and spectral MR data

• 4) Ability to choose imaging protocols and analysis techniques adequate to study a specific physiological or disease process (e.g. age related atrophy vs. traumatic bleeds)

• 5) Ability to think clearly and critically about the ethical issues raised by advances in neuroimaging

• 6) Ability to choose proper analytic tool to determine MR correlates of disease markers