Mind the gap: from neurons to networks to outcomes in multiple sclerosis

MRI studies have provided valuable insights into the structure and function of neural networks, particularly in health and in classical neurodegenerative conditions such as Alzheimer disease. However, such work is also highly relevant in other diseases of the CNS, including multiple sclerosis (MS). In this Review, we consider the effects of MS pathology on brain networks, as assessed using MRI, and how these changes to brain networks translate into clinical impairments. We also discuss how this knowledge can inform the targeting of MS treatments and the potential future directions for research in this area. Studying MS is challenging as its pathology involves neurodegenerative and focal inflammatory elements, both of which could disrupt neural networks. The disruption of white matter tracts in MS is reflected in changes in network efficiency, an increasingly random grey matter network topology, relative cortical disconnection, and both increases and decreases in connectivity centred around hubs such as the thalamus and the default mode network. The results of initial longitudinal studies suggest that these changes evolve rather than simply increase over time and are linked with clinical features. Studies have also identified a potential role for treatments that functionally modify neural networks as opposed to altering their structure. In this Review, Chard and colleagues consider the effects that multiple sclerosis pathology has on brain networks and discuss the ways in which knowledge of brain network changes could inform the targeting of treatments for the condition. Key points Multiple sclerosis (MS) pathology affects neuroaxonal structure (for example, via axonal transection) and function (for example, via demyelination), and does so in both lesions and extra-lesional tissues. Multiple pathological processes can combine to affect neural network function. In individuals with MS, white matter tracts are disrupted by lesions, the cortex is relatively disconnected and the grey matter network topology is more random than in healthy controls. Increases and decreases in connectivity, centred around hubs such as the thalamus and default-mode network, are seen; abnormal connectivity seems to evolve rather than simply progress over time, for example, early increases can be followed by later decreases. Both structural and functional network changes are associated with clinical outcomes, and treatments that modify neural network function — not structure — might s