功能性近红外光谱的动态因果建模,使用来自扩散光学断层扫描的空间priors.
Truc Chu1, Kiyomitstu Niioka2, Ippeita Dan2
1Center for Bio-Imaging and Translational Research, Korea Basic Science Institute, Cheongju 28119, Republic of Korea; Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea.
NeuroImage
|March 6, 2026
概括
这项研究通过整合扩散光学断层扫描 (DOT) 数据,通过使用功能近红外光谱学 (fNIRS) 增强了大脑连接分析. 这提高了动态因果建模 (DCM) 的准确性,以了解认知任务期间的大脑网络.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 认知科学 认知科学
背景情况:
- 功能近红外光谱 (fNIRS) 通过血液动力学变化测量大脑活动.
- 现有的fNIRS因果相互作用分析方法经常使用传感器级位置,限制了准确性.
- 动态因果建模 (DCM) 是推断定向大脑连接的强大工具.
研究的目的:
- 通过将分散光学断层扫描 (DOT) 的源级大脑活动本地化纳入fNIRS的DCM扩展.
- 提高从fNIRS数据中推断因果连接的准确性.
- 在响应抑制任务期间调查神经网络调制.
主要方法:
- 为fNIRS开发了一种新的DCM方法,集成DOT衍生源级位置.
- 在Go/No-Go任务期间,将该方法应用于从104名参与者的fNIRS数据.
- 利用贝叶斯模型选择来比较传感器级别与DOT信息源级别模型.
主要成果:
- 使用DOT信息来源位置的DCM模型与传感器级模型相比,显示出更好的证据.
- 有效的连接性分析揭示了右下额 (rIFG) 对运动网络区域的抑制作用.
- 这些发现突出了rIFG在响应抑制中的作用.
结论:
- 拟议的DCM方法通过提供深度依赖源定位来增强从fNIRS数据的因果连接推断.
- 这种方法提供了更准确的网络级大脑活动分析,适用于自然环境.
- 改进的局部化准确度有助于更深入地了解认知功能背后的神经机制.
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