在口头Stroop任务中推断潜在的冲突选择的认知状态,使用异质输入歧视式生成式解码模型
Mohammad R Rezaei1,2,3, Haseul Jeoung2, Ayda Gharamani2,4
1Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada.
Journal of neural engineering
|July 20, 2023
概括
一个新的模型从大脑活动和行为中解码认知状态,改善帕金森病患者的冲突处理见解. 这种方法增强了对下核-介质前额叶皮层相互作用的理解.
科学领域:
- 神经科学是一个神经科学.
- 认知科学 认知科学
- 计算精神病学是一种计算精神病学.
背景情况:
- 亚thalamic核 (STN) 和中间前额皮层 (mPFC) 形成一个关键的电路来处理相互矛盾的信息.
- 泰达频段活动 (2-8 Hz) 和STN和mPFC之间的相同步是冲突处理的关键神经标记.
- 需要一个低维的表示,将STN-mPFC神经活动与行为 (认知状态) 联系起来.
研究的目的:
- 开发和验证一种新型模型,即异质输入歧视生成解码器 (HI-DGD),用于推断认知状态.
- 在Stroop任务中,使用神经和行为数据分析帕金森病 (PD) 患者的决策过程.
- 识别神经特征,有助于冲突和非冲突处理在PD.
主要方法:
- 提出HI-DGD模型来整合神经数据 (STN,mPFC) 和行为数据 (响应时间).
- 将模型应用于执行Stroop任务的十名PD患者的数据.
- 使用合成和实验数据验证模型性能,将其与传统方法进行比较.
主要成果:
- HI-DGD模型显著改善了冲突和非冲突试验的认知状态的估计.
- 该模型有效地同时扩散来自神经和行为信号的信息.
- 确定了有助于冲突和非冲突选择的特定神经特征,与现有的实验发现保持一致.
结论:
- HI-DGD模型提供了一种强大的方法,可以从神经和行为数据中推断认知状态.
- 它提供了有关STN-mPFC电路在冲突处理过程中的功能,特别是在PD的宝贵见解.
- 该模型的单试估计能力使其适合于实时应用,如闭环神经调节.
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