Abstract #3142

Multimodal virtual brain: when hd-EEG drives simulations of brain dynamics closer to patients’ reality

Anita Monteverdi¹, Giuseppe Alessandro Calia², Alberto Augello², Paolo Antonino Grasso³, Anna Castelnovo⁴, Matteo Cotta Ramusino⁵, Francesca Conca⁶, Chiara Totaro⁷, Elena Capriglia², Michele Terzaghi^2,8, Lisa Farina⁹, Alfredo Costa^2,5, Anna Pichiecchio^2,9, Stefano Cappa¹⁰, Claudia Gandini Wheeler-Kingshott^2,11, Fulvia Palesi², and Egidio D'Angelo^1,2

¹Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy, ²Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ³Department of Physics and Astronomy, University of Florence, Florence, Italy, ⁴Sleep Medicine Unit, Neurocenter of Italian Switzerland, Lugano, Switzerland, ⁵Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy, ⁶IRCCS Mondino Foundation, Pavia, Italy, ⁷Department of Child Neurology and Psychiatry, IRCCS Mondino Foundation, Pavia, Italy, ⁸Sleep Medicine Centre, IRCCS Mondino Foundation, Pavia, Italy, ⁹Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy, ¹⁰University Institute of Advanced Studies (IUSS), Pavia, Italy, ¹¹NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom

Synopsis

Keywords: Dementia, fMRI (resting state), electroencephalography, digital brain twinss, signal modelling

Motivation: The creation of virtual brain models able to fit individual resting-state fMRI (rs-fMRI) time series is needed, aiming to obtain digital twins of patients useful for clinical translation.

Goal(s): In this work we develop a multimodal virtual brain for a group of mild cognitive impairment patients, a new step towards the creation of patients’ digital twins.

Approach: For each subject hd-EEG data were integrated in the multimodal virtual brain together with structural connectivity to drive brain dynamics simulations.

Results: Multimodal virtual brain revealed its superior ability to fit rs-fMRI time series recorded in patients.

Impact: With its capacity to perform simulations closer to reality, this new model opens new prospectives in the use of virtual brains as digital representations of patients, a crucial tool for the development of personalized interventions.

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