Inhibitory synaptic loss drives network changes in multiple sclerosis: An ex vivo to in silico translational study

M. Huiskamp, S. Kiljan, S. Kulik, M.E. Witte, L.E. Jonkman, J. GJM Bol, G.J. Schenk, H.E. Hulst, P. Tewarie, M.M. Schoonheim, J.J.G. Geurts

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Scopus)


© The Author(s), 2022.Background: Synaptic and neuronal loss contribute to network dysfunction and disability in multiple sclerosis (MS). However, it is unknown whether excitatory or inhibitory synapses and neurons are more vulnerable and how their losses impact network functioning. Objective: To quantify excitatory and inhibitory synapses and neurons and to investigate how synaptic loss affects network functioning through computational modeling. Methods: Using immunofluorescent staining and confocal microscopy, densities of glutamatergic and GABAergic synapses and neurons were compared between post-mortem MS and non-neurological control cases. Then, a corticothalamic biophysical model was employed to study how MS-induced excitatory and inhibitory synaptic loss affect network functioning. Results: In layer VI of normal-appearing MS cortex, excitatory and inhibitory synaptic densities were significantly lower than controls (reductions up to 14.9%), but demyelinated cortex showed larger losses of inhibitory synapses (29%). In our computational model, reducing inhibitory synapses impacted the network most, leading to a disinhibitory increase in neuronal activity and connectivity. Conclusion: In MS, excitatory and inhibitory synaptic losses were observed, predominantly for inhibitory synapses in demyelinated cortex. Inhibitory synaptic loss affected network functioning most, leading to increased neuronal activity and connectivity. As network disinhibition relates to cognitive impairment, inhibitory synaptic loss seems particularly relevant in MS.
Original languageEnglish
Pages (from-to)2010-2019
Number of pages10
Issue number13
Early online date2022
Publication statusPublished - 1 Nov 2022


  • Multiple sclerosis
  • computational modeling
  • inhibitory synaptic loss
  • network function; histopathology

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