Neurological damage, caused by conditions such as stroke, results in a complex pattern of structural change and significant behavioural dysfunctions; the automated analysis of Magnetic Resonance Imaging (MRI) and discovery of structural-behavioural correlates in such disorders remains challenging. Voxel Lesion Symptom Mapping (VLSM) has been used to associate behaviour with lesion location in MRI but requires lesion masks to be defined on each subject and does not exploit the rich structural information in the images. Tensor Based Morphometry (TBM) has been used to perform voxel-wise structural analysis over the whole brain, and can include behaviour as a co-variate; however interpretation may be confounded by a combination of lesion hyper-intensities and subtle structural remodelling away from the lesion. In this paper, we compare and contrast these techniques in a rodent model of stroke (n=58) to assess their efficacy in a challenging pre-clinical application. The results from the automated techniques are compared with manually derived region-of-interest measures of lesion, cortex, striatum, ventricle and hippocampus, and considered against model power calculations. The automated TBM techniques can successfully detect both lesion and non-lesion effects, and are consistent with manual measurements. They do not require manual segmentation to the same extent as VLSM and should be considered part of the toolkit for unbiased analysis of pre-clinical imaging-based studies.