Longitudinal measures of brain volume are powerful tools to assess the anatomical changes underlying on-going neurodegenerative processes. In different neurological disorders, such as in multiple sclerosis, Alzheimer's disease and Parkinson's disease, the neurodegenerative aspect may result in subtle anatomical brain changes before the appearance of clinical symptoms. Large longitudinal brain imaging datasets are now accessible to investigate such structural changes over time and to evaluate their use as biomarkers of prodromal disease. However, manual segmentation is long and tedious and although automatic methods exist, they are often performed in a cross-sectional manner where each visit is analysed independently. With such analysis methods, bias, error and longitudinal noise may be introduced. MR scanner noise and physiological effects can also introduce additional variability. Therefore, we developed a specific pipeline for longitudinal brain image analysis. To avoid any bias, an individual subject template is created and used as a reference within the pipeline. Then, the pair-wise deformation fields of each visit to the individual template are used to estimate the variation between individual time-points.