The Virtual Parkinsonian Patient: the effects of L-dopa and Deep brain Stimulation on whole-brain dynamics
Marianna Angiolelli*1,2, Gabriele Casagrande1, Letizia Chiodo2, Damien Depannemaecker1, Viktor Jirsa1, Pierpaolo Sorrentino1,3
1Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France
2Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy
3Department of Biomedical Sciences, University of Sassari, Sassari, Italy
*Email: marianna.angiolelli@unicampus.it
IntroductionParkinson’s disease is a progressive neurodegenerative disease characterized by the loss of dopaminergic neurons in the substantia nigra. The primary treatment for PD involves the administration of levodopa, but long-term use of it is associated with complications, necessitating alternative therapeutic strategies. One approach is deep brain stimulation (DBS), a neuromodulatory treatment that delivers electrical stimulation to specific brain regions (most often,the subthalamic nucleus). While DBS can be an effective therapy, optimal stimulation parameters are specific to each patient, and finding them can be challenging. Today, parameter tuning is based on a trial-and-error process, which is time-consuming, exhausting for the patient, requires a highly skilled dedicated team, and has very high chances of missing the optimal setting.
MethodsTo predict the effects of DBS on large-scale brain dynamics, we employed a mean-field neural mass model based on the adaptive quadratic integrate-and-fire (aQIF) framework [1], where Dopamine is included. The model was extended to incorporate an external current simulating a biphasic stimulation, mimicking DBS effects. Each brain region was modeled as a neural mass, with connectivity based on individual structural connectomes. The model includes excitatory, inhibitory, and neuromodulatory connections. EEG and deep electrode recordings in the STN validated the predictions. A Bayesian inversion with DNN inferred the neural state in ON/OFF conditions, quantifying parameter uncertainty.
ResultsFirst, we investigate different conditions varying simulations of L-Dopa administration and then changing DBS parameters analyzing large-scale brain activity and its impact on neural avalanche (spontaneous bursts of activations) topological properties. For all patients, we correctly infer that the dopaminergic tone is higher given the dynamics observed after administration of L-Dopa, and lower before the administration of L-Dopa [2]. The same approach enables us to tell apart pre- and post-DBS states across multiple patients, quantifying the effects of stimulation on large-scale brain dynamics.
DiscussionThis work provides a framework to understand how L-Dopa and DBS influence large-scale neural activity, offering insights into mechanisms and optimization for PD treatment. Unlike most PD models focusing on beta-range activity [3], we emphasize aperiodic activities instead, which have only received limited attention in Parkinson’s disease thus far. Furthermore, we focus on large-scale dynamics and efficient parameter estimation with uncertainty, rather than fitting a cost function. This approach explicitly accounts for Dopamine levels and stimulation amplitude, bridging pathophysiology, and personalized clinical predictions of clinical effectiveness.
Acknowledgements
The project leading to this publication has received funding from the Excellence Initiative of Aix-Marseille Université - A*Midex, a French “Investissements d’Avenir programme” AMX-21-IET-017
References
[1] Depannemaecker, D., Duprat, C., Casagrande, G., Saggio, M., Athanasiadis, A. P., Angiolelli, M., ... & Jirsa, V. (2024). A next generation neural mass model with neuromodulation. bioRxiv, 2024-06.
[2] Angiolelli, M., Depannemaecker, D., ... & Sorrentino, P. (2024). The Virtual Parkinsonian Patient. medRxiv, 2024-07.
[3] Meier, J. M., Perdikis, D., Blickensdörfer, A., ... & Ritter, P. (2022). Virtual deep brain stimulation: Multiscale co-simulation of a spiking basal ganglia model and a whole-brain mean-field model with The Virtual Brain. Experimental Neurology, 354, 114111.
