Modeling Resilience to Damage in Multiple Sclerosis: Plasticity Meets Connectivity

Mario Stampanoni Bassi, Ennio Iezzi, Luigi Pavone, Georgia Mandolesi, Alessandra Musella, Antonietta Gentile, Luana Gilio, Diego Centonze, Fabio Buttari, Mario Stampanoni Bassi, Ennio Iezzi, Luigi Pavone, Georgia Mandolesi, Alessandra Musella, Antonietta Gentile, Luana Gilio, Diego Centonze, Fabio Buttari

Abstract

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system (CNS) characterized by demyelinating white matter lesions and neurodegeneration, with a variable clinical course. Brain network architecture provides efficient information processing and resilience to damage. The peculiar organization characterized by a low number of highly connected nodes (hubs) confers high resistance to random damage. Anti-homeostatic synaptic plasticity, in particular long-term potentiation (LTP), represents one of the main physiological mechanisms underlying clinical recovery after brain damage. Different types of synaptic plasticity, including both anti-homeostatic and homeostatic mechanisms (synaptic scaling), contribute to shape brain networks. In MS, altered synaptic functioning induced by inflammatory mediators may represent a further cause of brain network collapse in addition to demyelination and grey matter atrophy. We propose that impaired LTP expression and pathologically enhanced upscaling may contribute to disrupting brain network topology in MS, weakening resilience to damage and negatively influencing the disease course.

Keywords: brain networks; connectivity; inflammation; long-term potentiation (LTP); multiple sclerosis; resting state functional MRI (rs-fMRI); synaptic plasticity; synaptic scaling.

Conflict of interest statement

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: G.M. reports grants and/or personal fees from Merck, Novartis, Biogen Idec, and Ibsa. D.C. is an Advisory Board member of Almirall, Bayer Schering, Biogen, GW Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi-Genzyme, and Teva and received honoraria for speaking or consultation fees from Almirall, Bayer Schering, Biogen, GW Pharmaceuticals, Merck Serono, Novartis, Roche, Sanofi-Genzyme, and Teva. He is also the principal investigator in clinical trials for Bayer Schering, Biogen, Merck Serono, Mitsubishi, Novartis, Roche, Sanofi-Genzyme, and Teva. His preclinical and clinical research was supported by grants from Bayer Schering, Biogen Idec, Celgene, Merck Serono, Novartis, Roche, Sanofi-Genzyme and Teva. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. F.B. acted as Advisory Board members of Teva and Roche and received honoraria for speaking or consultation fees from Merck Serono, Teva, Biogen Idec, Sanofi, and Novartis and non-financial support from Merck Serono, Teva, Biogen Idec, and Sanofi. M.S.B., E.I., L.P., A.M., A.G., and L.G. declare no conflict of interest.

Figures

Figure 1
Figure 1
Different forms of synaptic plasticity cooperate to promote optimal brain network topology. Left panel: (A) In physiological conditions, the balance between anti-homeostatic and homeostatic plasticity allows the generation of potentiated synapses, associated with selective information processing, and prevents uncontrolled hyperexcitability. (B) Long-term potentiation (LTP) may be specifically involved in generating highly connected nodes (hubs); conversely, synaptic downscaling may be useful for maintaining low connectivity in the peripheral nodes of the network. The fine-tuning between these two forms of synaptic plasticity is required to form brain networks characterized by a scale-free degree distribution. (C) The resulting brain network architecture is characterized by elevated efficiency of information processing and resilience to random damage. Right panel: (A) In multiple sclerosis (MS), neuroinflammation is associated with impaired LTP and pathologically overexpressed synaptic upscaling, leading to uncontrolled neuronal hyperexcitability. (B) Disrupted LTP may selectively reduce hub connectivity, while overexpressed upscaling may contribute to increasing connectivity in the periphery. This is associated with loss of optimal brain network architecture as demonstrated by further random degree distribution. (C) Loss of LTP may selectively disrupt hub connectivity and rich club organization. Conversely, pathologic upscaling may promote increased local connectivity. The resulting brain network architecture dramatically reduces efficiency and impairs the ability to compensate for ongoing brain damage in MS.

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