概率认知状态建模 (PCSM):解码隐藏的时空动态,以揭示基于任务的fMRI中的串行并行处理,认知需求和串行瓶
1Department of Psychiatry, University of Colorado School of Medicine, Anschutz Medical Campus, Aurora, CO.
bioRxiv : the preprint server for biology
|November 24, 2025
概括
概率认知状态建模 (PCSM) 准确地量化了基于任务的fMRI期间的大脑动态,揭示了认知状态如何支持灵活的任务转换和处理模式.
科学领域:
- 认知神经科学 认知神经科学
- 神经成像分析分析 神经成像分析
- 计算精神病学是一种计算精神病学.
背景情况:
- 了解灵活的认知任务转换和串行并行处理是人类认知的关键.
- 神经成像技术的进步使大脑功能与认知联系起来,促进了量化新出现的认知属性的方法.
研究的目的:
- 引入和验证概率认知状态建模 (PCSM) 以量化基于任务的fMRI期间动态大脑状态.
- 从大脑活动中推导出可解释的认知特性,如处理模式和需求.
主要方法:
- PCSM将BOLD信号的有限冲动响应 (FIR) 建模与高斯混合模型-隐藏马尔科夫模型 (GMM-HMM) 集成在一起.
- 一个具有不同噪声和过渡概率的基准真实模拟验证了PCSM的准确性和稳定性.
主要成果:
- PCSM准确地恢复了地面真实隐藏状态 (>98%) 并提供了稳定的参数估计.
- 值分析可靠地区分了并行,混合和串行处理模式.
- PCSM确定了认知需求,资源可用性和瓶持久性之间的预期关系.
结论:
- PCSM有效地揭示了适应性处理架构背后的动态大脑状态.
- 这个框架可以绘制个人的认知动态,并分析fMRI研究中的认知处理,需求和串行瓶.
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