MRI-based Measurement of Structural and Functional Connectivity in Health and Disease

Abstract

The mammalian brain can be regarded as a highly complex and integrative network that enables mammalian brain function and behaviour. Therefore it is important to study consequences of brain disorders from a network perspective in order to optimally understand pathological processes and recovery mechanisms. In this thesis we used this network perspective to improve our understanding of mechanisms underlying brain damage and recovery after stroke and traumatic brain injury (TBI). In addition, we investigated the effect of ageing on brain network vulnerability to lesions. Our methodologies included structural and functional MRI techniques combined with network analysis tools, such as the minimum spanning tree (MST) approach and Bayesian exponential random graph models (ERGMs). Our studies show that in vivo MRI techniques provide unique tools to longitudinally investigate structural and functional neural networks after brain injury. We found that stroke induces remote alterations in specific tracts (e.g. the corticospinal tract) and networks, including the contralesional hemisphere. Structural and functional networks appeared to be more vulnerable to brain lesions in aged people as well as rats. Advanced approaches like Bayesian ERGMs and MSTs may reveal original or complementary insights in brain network structure and function. Together the studies in this thesis demonstrate the potential of the network analytical approach to investigate the brain in health and disease, the application of which may lead to new insights in disease mechanisms, the identification of diagnostic biomarkers and detection of therapeutic targets.