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基于变压器的长期预测器,用于在帕金森病中预测亚thalamicβ活性

Salvatore Falciglia^1,2, Laura Caffi^3,4,5,6, Claudio Baiata⁷

¹The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy. salvatore.falciglia@santannapisa.it.

NPJ Parkinson's disease

|July 12, 2025

概括

研究人员开发了基于变压器的框架LAURA,用于预测帕金森病患者接受自适应性深度大脑刺激 (aDBS) 的亚thalamicβ功率. 这个工具可以提前六天预测变化,提高了aDBS的有效性,并使远程监控成为可能.

科学领域:

神经科学是一个神经科学.
生物医学工程生物医学工程
人工智能的人工智能

背景情况:

脑下核 (STN) 的深度大脑刺激 (DBS) 是帕金森病 (PD) 的关键治疗方法.
适应性DBS (aDBS) 根据STN局部场势的β频段功率调整刺激,以匹配患者的状态.
目前的aDBS需要经常亲自重新编程,因为β功率波动.

研究的目的:

为在接受aDBS的PD患者中开发出亚thalamicβ功率的预测框架.
为了能够主动调整aDBS治疗,并减少手动重新编程的需要.
调查aDBS设备的远程监控和个性化自动调节的潜力.

主要方法:

使用基于变压器的框架,名为LAURA进行分析.
分析了患有DBS的PD患者的慢性STN局部现场潜在记录.
专注于预测β功率 (12-30 Hz) 随时间的非线性演变.

主要成果:

获得了高预测准确度 (>90%) 的亚thalamicβ功率.
证明了预测能力,可以提前6天.
结果在四名PD患者中一致,并且独立于刺激参数.

结论:

劳拉框架准确地预测了PD患者体下β功率波动.
这种预测能力支持开发aDBS的远程监控和个性化自动调节.
这些发现为通过DBS更适应和更有效地管理帕金森病铺平了道路.

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