Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
664-01-011 ISMRM Abstract

Causal and functional dynamics of visuomotor network demonstrate excitatory/inhibitory alterations in multiple sclerosis

Accepted
Gökçe KORKMAZ 1,2, Roberta M Lorenzi1,2, Francesca Ravera3, Adnan Alahmadi4, Anita Monteverdi5, Baris Kanber2,6, Ferran Prados Carrasco2,7,8, Fulvia Palesi1, Egidio D’Angelo1,5, Ahmed Toosy2,9, Claudia A Gandini Wheeler-Kingshott
1Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
2NMR Research unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
3Department of Physics, University of Pavia, Pavia, Italy
4Radiologic Sciences, Faculty of Applied Medical Sciences,, King Abdulaziz University, Jeddah, Saudi Arabia
5Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy
6Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
7Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
8e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
9Department of Brain Repair and Rehabilitation, Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom
Presenting Author: Gökçe KORKMAZ

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Filippi, M., Brück, W., Chard, D., Fazekas, F., Geurts, J. J., Enzinger, C., ... & Rocca, M. A. (2019). Association between pathological and MRI findings in multiple sclerosis. The Lancet Neurology, 18(2), 198-210.
2. Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273–1302. https://doi.org/10.1016/S1053-8119(03)00202-7 [doi]
3. Sanz Leon, P. et al. The virtual brain: a simulator of primate brain network dynamics. Front. Neuroinform. 7, 385–430 (2013).
4. Fleischer, V., Muthuraman, M., Anwar, A. R., Gonzalez-Escamilla, G., Radetz, A., Gracien, R. M., ... & Groppa, S. (2020). Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study. Scientific reports, 10(1), 806.
5. Martí-Juan, G., Sastre-Garriga, J., Martinez-Heras, E., Vidal-Jordana, A., Llufriu, S., Groppa, S., ... & Pareto, D. (2023). Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study. Cerebral Cortex, 33(12), 7322-7334.
6. Sorrentino, P., Pathak, A., Ziaeemehr, A., Lopez, E. T., Cipriano, L., Romano, A., ... & Hashemi, M. (2024). The virtual multiple sclerosis patient. Iscience, 27(7).
7. Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology, 33(11), 1444-1444.
8. Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., ... & Gorgolewski, K. J. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature methods, 16(1), 111-116.
9. Klein, A., & Tourville, J. (2012). 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in neuroscience, 6, 171.
10. Palesi, F., De Rinaldis, A., Castellazzi, G., Calamante, F., Muhlert, N., Chard, D., ... & Gandini Wheeler-Kingshott, C. A. (2017). Contralateral cortico-ponto-cerebellar pathways reconstruction in humans in vivo: implications for reciprocal cerebro-cerebellar structural connectivity in motor and non-motor areas. Scientific reports, 7(1), 12841.
11. Lorenzi, R. M., Korkmaz, G., Alahmadi, A. A., Monteverdi, A., Casiraghi, L., D’Angelo, E., ... & Gandini Wheeler-Kingshott, C. A. (2025). Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action. The Cerebellum, 24(4), 115.
12. Li, B., Daunizeau, J., Stephan, K. E., Penny, W., Hu, D., & Friston, K. (2011). Generalised filtering and stochastic DCM for fMRI. neuroimage, 58(2), 442-457.
13. Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009). Bayesian model selection for group studies. Neuroimage, 46(4), 1004-1017.
14. Wasserman, L. (2000). Bayesian model selection and model averaging. Journal of mathematical psychology, 44(1), 92-107.
15. Wong, K. F. & Wang, X. J. A recurrent network mechanism of time integration in perceptual decisions. J. Neurosci. 26, 1314–1328 (2006).
16. Zeidman, P., Jafarian, A., Seghier, M. L., Litvak, V., Cagnan, H., Price, C. J., & Friston, K. J. (2019). A guide to group effective connectivity analysis, part 2: Second level analysis with PEB. NeuroImage, 200, 12–25. https://doi.org/10.1016/j.neuroimage.2019.06.032 [doi]
17. Bhattacharyya, P. K., Phillips, M. D., Stone, L. A., Bermel, R. A., & Lowe, M. J. (2013). Sensorimotor cortex gamma-aminobutyric acid concentration correlates with impaired performance in patients with MS. American Journal of Neuroradiology, 34(9), 1733-1739.

Cite this abstract