Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-02-011 ISMRM Abstract

Democratizing Brain Digital Twins: ultra-low-field MRI and EEG enable physiological alpha-band detection

Primary: Physics & Engineering - Low-Field MRI
Secondary: Analysis Methods - Generative Models
469-02-011 · Low Field Applications and Systems · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row J
Keywords: Modelling Neuroscience Simulations In Silico EEG
Accepted
Roberta M Lorenzi 1,2, James A Gholam3, Anita Monteverdi4, Fulvia Palesi1, Claudia Casellato1, Egidio D’Angelo1,4, Derek K Jones3, Marco Palombo3,5, Mara Cercignani3, 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
3Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
4Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy
5School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom
Presenting Author: Roberta M Lorenzi

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. D’Angelo E, Jirsa V. Neurosciences The quest for multiscale brain modeling. Trends Neurosci. 2022;xx(xx):1-14. doi:10.1016/j.tins.2022.06.007 [doi]
2. Sanz Leon P, Knock SA, Woodman MM, et al. The virtual brain: A simulator of primate brain network dynamics. Front Neuroinform. 2013;7(MAY). doi:10.3389/fninf.2013.00010 [doi]
3. Schirner M, Domide L, Perdikis D, et al. Brain simulation as a cloud service: The Virtual Brain on EBRAINS. Neuroimage. 2022;251:118973. doi:10.1016/j.neuroimage.2022.118973 [doi]
4. Monteverdi A, Palesi F, Schirner M, et al. Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias. Front Aging Neurosci. 2023;15(July):1-15. doi:10.3389/fnagi.2023.1204134 [doi]
5. Angiolelli M, Depannemaecker D, Agouram H, et al. The Virtual Parkinsonian patient. NPJ Syst Biol Appl. 2025;11(1). doi:10.1038/s41540-025-00516-y [doi]
6. Sorrentino P, Pathak A, Ziaeemehr A, et al. The virtual multiple sclerosis patient. iScience. 2024;27(7):110101. doi:10.1016/j.isci.2024.110101 [doi]
7. Jirsa V, Wang H, Triebkorn P, et al. Personalised virtual brain models in epilepsy. Lancet Neurol.Elsevier Ltd. 2023;22(5):443-454. doi:10.1016/S1474-4422(23)00008-X [doi]
8. Gholam J, Schmid P, Ametepe J, et al. Diffusion Tensor MRI and Spherical-Deconvolution-Based Tractography on an Ultra-Low Field Portable MRI System. Published online June 4, 2025. http://arxiv.org/abs/2506.04473
9. Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-Minn Human Connectome Project: an overview. Neuroimage. 2013;80:62-79. doi:10.1016/j.neuroimage.2013.05.041 [doi]
10. Smith RE, Tournier JD, Calamante F, Connelly A. Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage. 2012;62(3):1924-1938. doi:https://doi.org/10.1016/j.neuroimage.2012.06.005 [doi]
11. Tournier JD, Smith R, Raffelt D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019;202:116137. doi:10.1016/j.neuroimage.2019.116137 [doi]
12. Palesi F, Lorenzi RM, Casellato C, et al. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Front Cell Neurosci. 2020;14(July):1-11. doi:10.3389/fncel.2020.00240 [doi]
13. Jansen BH, Rit VG. Electroencephalogram and visual evoked potential generation in a mathematical model of coupled cortical columns. Biol Cybern. 1995;73(4):357-366. doi:10.1007/BF00199471 [doi]
14. Amato LG, Vergani AA, Lassi M, et al. Personalized modeling of Alzheimer’s disease progression estimates neurodegeneration severity from EEG recordings. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring. 2024;16(1). doi:10.1002/dad2.12526 [doi]
15. Deb K, Pratap A, Agarwal S, Meyarivan T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. Vol 6.; 2002. doi: 10.1109/4235.996017 [doi]
16. Kumar A, Lin CC, Kuo SH, Pan MK. Physiological Recordings of the Cerebellum in Movement Disorders. Cerebellum.Springer. 2023;22(5):985-1001. doi:10.1007/s12311-022-01473-6 [doi]
17. Lorenzi RM, Palesi F, Casellato C, Gandini Wheeler-Kingshott C, D’Angelo E. Region-specific mean field models enhance simulations of local and global brain dynamics. NPJ Syst Biol Appl. Published online 2025:11:66. doi:10.1038/s41540-025-00543-9 [doi]
18. Park HJ, Kwon JS, Youn T, et al. Statistical parametric mapping of LORETA using high density EEG and individual MRI: Application to mismatch negativities in schizophrenia. Hum Brain Mapp. 2002;17(3):168-178. doi:10.1002/hbm.10059 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/469-02-011