Since the classical work Wernicke it has been understood that connections in the brain are important for higher-order behaviours. This disconnection hypothesis has been revisited numerous times over the years however there is still no clear consensus on a methodology which can accurately capture widespread disconnection in various patient populations. Much of the previous literature focused on local measures of diffusion but in recent years there has been a shift towards methods which capture long-range disconnection. The main focus of this thesis was to investigate the utility of Anatomical Connectivity Maps (ACMs) to measure global disconnection, as a complementary methodology to local diffusion measures. With a view to clinical utility, Chapter 2 tested whether ACM and other structural connectivity measures (fractional anisotropy, mean diffusivity, Tract-Based Spatial Statistics or the Tractotron toolkit) were able to identify behavioural outliers with similar lesions in post-stroke aphasia. No method was successful, suggesting these measures may not be suitable for single-subject analysis. Conversely, Chapter 3 validated the utility of ACMs at a group-level for identifying long-range disconnection using the well-established Papez-circuit. Once validated, pseudo-ACMs were used to systematically compare, for the first time, how different temporal lobe resection techniques affected long-range disconnection by selectively removing tissue associated with each resection from the tracking mask. This demonstrated that both height and length of resection were important factors affecting post-surgical structural connectivity. As this analysis was undertaken in a healthy dataset, Chapter 4 compared these pseudo-ACMs to disconnection patterns in real patients and found that broad, group-level disconnection was emulated using pseudo-ACMs. As these changes did not relate strongly to behaviour, pre- and post-surgical diffusion scans are necessary to disentangle connectivity changes in patients which may relate to recovery post-surgery or their chronic epilepsy. Chapter 5 then applied these novel techniques to see whether they could improve the way we define and understand deficits post-stroke. Hierarchical cluster analysis identified four groups of patients based on their lesion profile which were found to have distinct structural disconnection patterns. Pseudo-ACMs were used as a proxy for acute diffusion data, and were compared to chronic patient data. Remote increases and decreases in connectivity were identified which may be longitudinal changes relating to recovery, but they did not relate well to behaviour. Therefore future work would be needed to formally validate this approach with real data, where potentially this method can be an important tool in neuroscience research. Finally, Chapter 6 used pseudo-ACMs to demonstrate that damage following a left hemispheric middle cerebral artery (MCA) stroke can be explained by the underlying neurovasculature of the MCA. This highlights the importance of incorporating the underlying neurovasculature rather than single voxels. By using these vascular territories, disconnection associated with a lesion was predicted. Although this Chapter did not incorporate behaviour, it highlights the utility of ACM and pseudo-ACMs when there is no scope for the collection of diffusion data in the patient population.