Diffusion tensor imaging (DTI) is a specialist MRI modality that can identify microstructural changes or abnormalities in the brain. It can also be used to show fibre tract pathways. Both of these features were used in this thesis. Firstly, standard imaging analysis techniques were used to study the effects of mild, repetitive closed head injury on a group of professional boxers. Such data is extremely rare, so the findings of regions of brain abnormalities in the boxers are important, adding to the body of knowledge about more severe traumatic brain injury. The author developed a novel multivariate analysis technique which was used on the same data. This new technique proved to be more sensitive than the standard univariate methods commonly used. An important part of diagnosing and monitoring brain damage involves the use of biomarkers. A novel investigation of whether diffusion parameters obtained from DTI data could serve as bio-markers of cognitive impairment in Parkinson's disease was conducted. This also involved developing a multivariate approach, which displayed increased sensitivity compared with any of the component parameters used singly, and suggested these diffusion measures could be robust bio-markers of cognitive impairment. Fibre tract connectivity between regions of the brain is also a potentially valuable measure for diagnosis and monitoring brain integrity. The feasibility of this was investigated in a multi-modal MRI study. Functional MRI (fMRI) identifies regions of activation associated with a particular task. DTI can then find the pathway of the fibre bundles connecting these regions. The feasibility of using regional connectivity to interrogate brain integrity was investigated using a single healthy volunteer. Fibre pathways between regions activated and deactivated by a working memory paradigm were determined. Though the results are only preliminary, they suggest that this line of research should be continued.

Diffusion tensor imaging (DTI) is a specialist MRI modality that can identify microstructural changes or abnormalities in the brain. It can also be used to show fibre tract pathways. Both of these features were used in this thesis. Firstly, standard imaging analysis techniques were used to study the effects of mild, repetitive closed head injury on a group of professional boxers. Such data is extremely rare, so the findings of regions of brain abnormalities in the boxers are important, adding to the body of knowledge about more severe traumatic brain injury. The author developed a novel multivariate analysis technique which was used on the same data. This new technique proved to be more sensitive than the standard univariate methods commonly used. An important part of diagnosing and monitoring brain damage involves the use of biomarkers. A novel investigation of whether diffusion parameters obtained from DTI data could serve as bio-markers of cognitive impairment in Parkinson's disease was conducted. This also involved developing a multivariate approach, which displayed increased sensitivity compared with any of the component parameters used singly, and suggested these diffusion measures could be robust bio-markers of cognitive impairment. Fibre tract connectivity between regions of the brain is also a potentially valuable measure for diagnosis and monitoring brain integrity. The feasibility of this was investigated in a multi-modal MRI study. Functional MRI (fMRI) identifies regions of activation associated with a particular task. DTI can then find the pathway of the fibre bundles connecting these regions. The feasibility of using regional connectivity to interrogate brain integrity was investigated using a single healthy volunteer. Fibre pathways between regions activated and deactivated by a working memory paradigm were determined. Though the results are only preliminary, they suggest that this line of research should be continued.