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<p>This study investigates the influence of the pharmacological nigrostriatal dopaminergic stimulation on the entire brain by analyzing EEG and deep electrodes, placed near the subthalamic nuclei, from 10 Parkinsonian patients before (OFF) and after (ON) L-Dopa administration. We characterize large-scale brain dynamics as the spatio-temporal spreading of aperiodic bursts. We then simulate the effects of L-Dopa utilizing a novel neural-mass model that includes the local dopamine concentration. Whole-brain dynamics are simulated for different dopaminergic tones, generating predictions for the expected dynamics, to be compared with empirical EEG and deep electrode data. To this end, we invert the model and infer the most likely dopaminergic tone from empirical data, correctly identifying a higher Dopaminergic tone in the ON-state, and a lower dopaminergic tone in the OFF-state, for each patient. In conclusion, we successfully infer the dopaminergic tone by integrating anatomical and functional knowledge into physiological predictions, using solid ground truth to validate our findings.</p>

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The Virtual Parkinsonian patient

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月26日

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  • Marianna Angiolelli,
  • Damien Depannemaecker,
  • Hasnae Agouram,
  • Jean Régis,
  • Romain Carron,
  • Marmaduke Woodman,
  • Letizia Chiodo,
  • Paul Triebkorn,
  • Abolfazl Ziaeemehr,
  • Meysam Hashemi,
  • Alexandre Eusebio,
  • Viktor Jirsa,
  • Pierpaolo Sorrentino

摘要

This study investigates the influence of the pharmacological nigrostriatal dopaminergic stimulation on the entire brain by analyzing EEG and deep electrodes, placed near the subthalamic nuclei, from 10 Parkinsonian patients before (OFF) and after (ON) L-Dopa administration. We characterize large-scale brain dynamics as the spatio-temporal spreading of aperiodic bursts. We then simulate the effects of L-Dopa utilizing a novel neural-mass model that includes the local dopamine concentration. Whole-brain dynamics are simulated for different dopaminergic tones, generating predictions for the expected dynamics, to be compared with empirical EEG and deep electrode data. To this end, we invert the model and infer the most likely dopaminergic tone from empirical data, correctly identifying a higher Dopaminergic tone in the ON-state, and a lower dopaminergic tone in the OFF-state, for each patient. In conclusion, we successfully infer the dopaminergic tone by integrating anatomical and functional knowledge into physiological predictions, using solid ground truth to validate our findings.