Approved research

Template Based Automatic Analysis of Age Related White Matter Change

Lay summary

Progress towards understanding changes in brain structure in diseases associated with ageing, such as Alzheimer's Disease (AD), are hampered by a lack understanding of ?normal? ageing. Characterising ?normal? brain ageing would allow the focus to be shifted to areas that appear abnormal. Aims: 1. Stratify individuals according to markers of ?normal? cognitive ageing from UKBioBank data. 2. Apply Tract-Based Automatic Analysis (TBAA) to characterize ?normal? white-matter microstructural features. 3. Identify white matter regions where age significantly associates with diffusion-relevant microstructural features. 4. Incorporate macroscopic and microscopic structural information to create age specific normal adult brain templates. This research seeks to use the clinical, cognitive and imaging data from UKBioBank to study the mechanisms of age related changes in brain structure and use them as a platform to better diagnosis. This aim is completely consistent with UKBioBank's aims. Providing this mechanistic information will help to identify new therapeutic interventions and possible lifestyle changes. Stratifying age related changes in white matter structure into more homogenous categories will provide better 'disease' targets for mechanism based research because there will be less aggregation of individuals with diverse aetiologies within the same heterogeneous category of ageing. We will create digital atlases of the brain using MRI scans from people without disease so these may be used to better highlight subtle changes in the brain that are associated with conditions like Alzheimer?s Disease. We will align the brains and measure features in the white matter, the electrical circuitry of the brain, along each cable bundle. The output of these measurements is a set of data for each participant that can be compared. We will investigate the relationships of these white matter features with respect to age, sex and behavioral variables such as cognitive performance. We are interested in the subgroup of UKBioBank data that includes brain imaging (MRI) data. From that data we will identify cognitively normal adults across the lifespan for further analysis.