An increased amount of large scale, collaborative biomedical research has recently been conducted on e-Science infrastructures. Such research typically involves conducting comparative analysis on large amounts of data to search for biomarkers for diseases. Running these analysis manually can often be quite cumbersome, labour-intensive and error-prone. Significant work has been invested into automating such analysis with appropriately configured workflows. It is also important for biomedical researchers to validate analysis outcomes, to ensure the reproducibility of the results and to ascertain the ownership of specific scientific results. The detailed, traceable information required for this is often referred to as provenance data. Developing suitable methods and approaches to managing provenance data in large-scale distributed e-Science environments is another important area of research currently being investigated. We present an approach that has been adopted in the neu GRID project, which aims to develop an infrastructure to facilitate research into neurodegenerative disease studies such as Alzheimer's. To facilitate the automation of complex, large-scale analysis in neu GRID, we have adapted CRISTAL, a workflow and provenance tracking solution. The use of CRISTAL has provided a rich environment for neuroscientists to track and manage the evolution of both data and workflows in the neu GRID infrastructure.