The Virtual Parkinsonian patient.

IF 3.5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
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
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引用次数: 0

Abstract

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.

虚拟帕金森病人。
本研究通过分析10例帕金森病患者左旋多巴给药前(OFF)和给药后(on)的脑电图和深电极,探讨了黑质纹状体多巴胺能药理学刺激对全脑的影响。我们将大规模脑动力学描述为非周期性爆发的时空传播。然后,我们利用一种包括局部多巴胺浓度的新型神经质量模型模拟左旋多巴的影响。模拟不同多巴胺能音调的全脑动态,生成预期动态的预测,并与经验脑电图和深电极数据进行比较。为此,我们将模型倒置,并从经验数据中推断出最可能的多巴胺能张力,正确识别出每个患者在打开状态下的高多巴胺能张力,以及在关闭状态下的低多巴胺能张力。总之,我们通过将解剖学和功能知识整合到生理预测中,成功地推断出多巴胺能张力,并使用坚实的基础事实来验证我们的发现。
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来源期刊
NPJ Systems Biology and Applications
NPJ Systems Biology and Applications Mathematics-Applied Mathematics
CiteScore
5.80
自引率
0.00%
发文量
46
审稿时长
8 weeks
期刊介绍: npj Systems Biology and Applications is an online Open Access journal dedicated to publishing the premier research that takes a systems-oriented approach. The journal aims to provide a forum for the presentation of articles that help define this nascent field, as well as those that apply the advances to wider fields. We encourage studies that integrate, or aid the integration of, data, analyses and insight from molecules to organisms and broader systems. Important areas of interest include not only fundamental biological systems and drug discovery, but also applications to health, medical practice and implementation, big data, biotechnology, food science, human behaviour, broader biological systems and industrial applications of systems biology. We encourage all approaches, including network biology, application of control theory to biological systems, computational modelling and analysis, comprehensive and/or high-content measurements, theoretical, analytical and computational studies of system-level properties of biological systems and computational/software/data platforms enabling such studies.
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