Multiple sclerosis: 2026 update

Authors

  • Luisa Klotz Department of Neurology, University Hospital Münster, Münster, Germany
  • Mike P. Wattjes Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
  • Tanja Kuhlmann Institute of Neuropathology, University Hospital Münster, Münster, Germany

DOI:

https://doi.org/10.17879/freeneuropathology-2026-9705

Keywords:

Multiple sclerosis, Aging and cellular senescence, Tissue resident memory cells, Biomarkers, Clinical trials

Abstract

Multiple sclerosis (MS) is the inflammatory-demyelinating disease of the CNS with the highest prevalence. Despite significant progress in reducing relapse activity by immunomodulatory or immunosuppressive treatments, there are ongoing challenges in understanding its initiation and predicting disease evolution. Furthermore, since we only partially understand the mechanisms driving disease progression, its successful treatment represents a major challenge. Therefore, this review article summarizes key research findings from the last 18 months, covering advances in the understanding of pathophysiology including the effects of aging on glial and immune cells as well as the functional role of tissue-resident memory cells. Additionally, we discuss advances in imaging technologies and biomarkers as well as the use of AI to predict disease evolution. Finally, we report the most recent findings from clinical trials targeting immune cells within the CNS.

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Published

2026-07-27

How to Cite

Klotz, L., Wattjes, M. P., & Kuhlmann, T. (2026). Multiple sclerosis: 2026 update. Free Neuropathology, 7, 19. https://doi.org/10.17879/freeneuropathology-2026-9705

Issue

Vol. 7 (2026)

Section

Reviews

License

Copyright (c) 2026 Luisa Klotz, Mike P. Wattjes, Tanja Kuhlmann

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Papers are published open access under the Creative Commons BY 4.0 license. This license lets others distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Data included in the article are made available under the CC0 1.0 Public Domain Dedication waiver, unless otherwise stated, meaning that all copyrights are waived.