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

• Published in: Nature reviews. Neurology Vol. 17; no. 3; pp. 173 - 184
• Main Authors: Chard, Declan T, Alahmadi, Adnan A S, Audoin, Bertrand, Charalambous, Thalis, Enzinger, Christian, Hulst, Hanneke E, Rocca, Maria A, Rovira, Àlex, Sastre-Garriga, Jaume, Schoonheim, Menno M, Tijms, Betty, Tur, Carmen, Gandini Wheeler-Kingshott, Claudia A M, Wink, Alle Meije, Ciccarelli, Olga, Barkhof, Frederik
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.03.2021
• Subjects: Alzheimer Disease - physiopathology; Alzheimer Disease - therapy; Brain - physiopathology; Cognitive science; Health aspects; Humans; Inflammation - physiopathology; Multiple sclerosis; Multiple Sclerosis - physiopathology; Multiple Sclerosis - therapy; Nerve Net - physiopathology; Neural circuitry; Neural networks; Neurons; Neurons - physiology; Neuroscience; Pathology; Patient outcomes; United Kingdom
• ISSN: 1759-4758; 1759-4766
• DOI: 10.1038/s41582-020-00439-8

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.