多胺化合物的神经效应通过多站点电生理学和可解释的机器学习揭示
Sampath K T Kapanaiah1, Holger Rosenbrock2, Bastian Hengerer2
1Institute of Applied Physiology, Ulm University, Ulm, Germany.
Frontiers in pharmacology
|July 24, 2024
概括
机器学习揭示了神经心理药物化合物的复杂的大脑影响. 这种方法量化了多地点记录的神经变化,有助于理解药物作用和电路层面的复杂性.
科学领域:
- 神经科学是一个神经科学.
- 药理学 药理学是指药理学的学科.
- 机器学习 机器学习
背景情况:
- 神经心理药理学化合物通过相互连接的电路诱导复杂的,广泛的大脑效应.
- 分析这些效应需要估计大脑许多区域的活动和连接.
- 传统的频率统计不适合分析电生理学记录中的有限受试者的众多参数.
研究的目的:
- 提出一种优化,可解释的机器学习 (ML) 方法来分析多站点电生理学记录.
- 用Shapley增量解释 (SHAP) 值量化化合物诱导的神经变化.
- 调查安非他胺和多巴胺抗剂对小鼠神经通信的未知影响.
主要方法:
- 利用基于预测能力和SHAP值的可解释的ML方法.
- 在小鼠的中脊丘脑,前额皮质和海马体 (背部和腹部) 中记录了局部场潜力 (LFP).
- 服用安非他命,克洛沙平,拉克洛普里德或SCH23390以评估它们对神经活动和连接性的影响.
主要成果:
- 鉴定了每个化合物在不同的参数中诱导的可重现的复杂神经生理变化.
- 发现克洛札的作用涉及更多的D1而不是D2受体活性和拉克洛普里德重新配置的三角波段连接.
- 对安非他命和克洛沙平的PFC/vHC和vHC活性观察到类似的thalamic输入下降;安非他命独特地增加了背部海马沟通和thalamic活动.
结论:
- 背部海马沟通尺度与多巴胺受体激活,突出显示神经药理学效应的电路级复杂性.
- 开发的ML方法有助于标准化和改进从多站点电生理学研究 (pEEG/pLFP) 中提取数据.
- 这种方法为剖析药物诱导的神经网络改变提供了一个强大的框架.
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