神经解码和特征选择方法用于闭环控制回避行为
Jinhan Liu1,2, Rebecca Younk3, Lauren M Drahos3
1Institute of Electrical and Micro Engineering, EPFL, Lausanne, Switzerland.
Journal of neural engineering
|October 17, 2024
概括
研究人员确定了关键的局部场潜在 (LFP) 特性,以预测大鼠的防御行为. 高马功率和区域间相关性准确地解码结和棒压抑制,使精神疾病的实时神经调节成为可能.
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.
- 动物模型 动物模型
背景情况:
- 精神疾病往往涉及过度回避或防御行为.
- 从像局部场潜力 (LFPs) 这样的神经信号预测这些行为对于开发闭环神经调节疗法至关重要.
- 识别编码防御行为的特定LFP特征是一个重大挑战.
研究的目的:
- 识别和评估局部现场潜力 (LFP) 功能来解码大鼠的防御行为.
- 评估机器学习模型在预测结,棒压抑制和运动方面的表现.
- 为了确定最有信息的神经标记,以实时解码防御行为.
主要方法:
- 在子中,在音声冲击调节和灭绝过程中,分析来自内膜皮层和底侧杏仁体的LFP信号.
- 使用了一套全面的光谱,时间和连接神经标记器.
- 在轻度梯度增强机模型中使用了SHapley添加式扩展 (SHAP) 对于特征的重要性,以解码结,条压抑制和运动 (加速计).
主要成果:
- 带功率和通道间带功率比被确定为最佳特征.
- 高马 (80-150赫兹) 功率,功率比率和区域间相关性比其他光谱频段更具信息性.
- 使用最小的计算资源 (例如,<0.051 ms的推断时间) 实现了高的解码精度 (例如,加速计冲动的皮尔森相关性为0.7579) .
结论:
- 局部场势 (LFP) 特性,特别是高马波段功率和区域间连接,可以准确而快速地解码防御行为.
- 这种方法证明了实时解码的可行性,这对于闭环精神病学神经调节至关重要.
- 这些发现为开发针对性干预的基础,以避免和防御行为为特征的疾病.
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