实时贝叶斯优化深度大脑刺激,以实现个性化的认知控制增强
bioRxiv : the preprint server for biology
|January 9, 2026
概括
优化算法可以有效地识别深度大脑刺激 (DBS) 参数,以改善大鼠的认知控制. 这项研究表明,神经调节治疗精神和认知障碍的方法更快,更个性化.
科学领域:
- 神经科学是一个神经科学.
- 计算精神病学是一种计算精神病学.
- 生物标志物 生物标志物
背景情况:
- 对精神疾病的深度大脑刺激 (DBS) 参数优化具有挑战性,因为缺乏目标参与的客观读数.
- 认知控制显示出作为DBS治疗疗效的生物标志物的潜力,但对于个体患者的可靠优化仍然没有得到证实.
研究的目的:
- 研究优化算法的有效性,以确定有效的DBS幅度,以增强大鼠模型中的认知控制.
- 通过精确的刺激参数选择来确定最先进的优化是否可以持续改善认知.
主要方法:
- 老鼠执行了一个Set-Shifting任务,这是一个对刺激敏感的认知控制措施.
- 在可变的参数下提供了活跃和非活跃的DBS类刺激.
- 贝叶斯优化用于个性化刺激幅度,将任务性能 (反应时间,精度) 与预定义和传统设置进行比较.
主要成果:
- 在15只大鼠中,急性DBS刺激减少了反应时间,但没有影响精度,证实了先前的发现.
- 在一个由6只老鼠组成的单独队列中,贝叶斯优化成功地确定了刺激幅度,从而减少了所有受试者的反应时间.
结论:
- 优化技术,特别是贝叶斯优化,可以有效地增强与精神病和认知障碍相关的认知标记.
- 这些发现支持个性化,定量驱动的神经调节的可行性,以改善目标参与和治疗疗效.
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