Diffusion Tensor Imaging (DTI) provides insight into the white matter of the human brain, which is affected by Schizophrenia. By comparing a patient group to a control group, the DTI-images are on average expected to be different for white matter regions. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are used to classify the groups. In this work, the number of principal components is optimised for obtaining the minimal classification error. A robust estimate of this error is computed in a cross-validation framework, using different compositions of the data into a training and a testing set. Previously, sequential runs were performed in MATLAB, resulting in long execution times. In this paper we describe an experiment where this application was run on a grid with minimal modifications and user effort. We have adopted a service-based approach that autonomously launches Image Analysis Services onto a campus-wide Condor pool comprising of volunteer resources. This allows high throughput analysis of our data in a dynamic resource pool. The challenge in adopting such an approach comes from the nature of the resources, which change randomly with time and thus require fault tolerance. Through this approach we have reduced the computation time of each dataset from 90 minutes to less than 10. A minimal classification error of 22\% was obtained, using 15 principal components.