疼痛大鼠皮质功能连接的格兰杰因果分析
Granger causality analysis of rat cortical functional connectivity in pain.
机构信息
School of Aeronautics and Astronautics, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China. Department of Psychiatry, New York University School of Medicine, New York, NY 10016, United States of America.
出版信息
J Neural Eng. 2020 Feb 7;17(1):016050. doi: 10.1088/1741-2552/ab6cba.
OBJECTIVE
The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are two of the most important cortical brain regions encoding the sensory-discriminative and affective-emotional aspects of pain, respectively. However, the functional connectivity of these two areas during pain processing remains unclear. Developing methods to dissect the functional connectivity and directed information flow between cortical pain circuits can reveal insight into neural mechanisms of pain perception.
APPROACH
We recorded multichannel local field potentials (LFPs) from the S1 and ACC in freely behaving rats under various conditions of pain stimulus (thermal versus mechanical) and pain state (naive versus chronic pain). We applied Granger causality (GC) analysis to the LFP recordings and inferred frequency-dependent GC statistics between the S1 and ACC.
MAIN RESULTS
We found an increased information flow during noxious pain stimulus presentation in both S1[Formula: see text]ACC and ACC[Formula: see text]S1 directions, especially at theta and gamma frequency bands. Similar results were found for thermal and mechanical pain stimuli. The chronic pain state shares common observations, except for further elevated GC measures especially in the gamma band. Furthermore, time-varying GC analysis revealed a negative correlation between the direction-specific and frequency-dependent GC and animal's paw withdrawal latency. In addition, we used computer simulations to investigate the impact of model mismatch, noise, missing variables, and common input on the conditional GC estimate. We also compared the GC results with the transfer entropy (TE) estimates.
SIGNIFICANCE
Our results reveal functional connectivity and directed information flow between the S1 and ACC during various pain conditions. The dynamic GC analysis support the hypothesis of cortico-cortical information loop in pain perception, consistent with the computational predictive coding paradigm.
目的
初级躯体感觉皮层（S1）和前扣带皮层（ACC）是分别编码疼痛感觉辨别和情感情绪方面的两个最重要的皮质脑区。然而，这些区域在疼痛处理过程中的功能连接尚不清楚。开发用于剖析皮质疼痛回路之间的功能连接和定向信息流的方法可以深入了解疼痛感知的神经机制。
方法
我们在自由行为大鼠中记录了来自 S1 和 ACC 的多通道局部场电位（LFPs），在各种疼痛刺激（热与机械）和疼痛状态（未受刺激与慢性疼痛）下进行记录。我们将格兰杰因果关系（GC）分析应用于 LFP 记录，并推断了 S1[Formula: see text]ACC 和 ACC[Formula: see text]S1 方向之间的频率依赖 GC 统计信息。
主要结果
我们发现，在有害性疼痛刺激呈现期间，S1[Formula: see text]ACC 和 ACC[Formula: see text]S1 方向的信息流增加，特别是在 theta 和伽马频带。对于热和机械性疼痛刺激也有类似的结果。慢性疼痛状态有共同的观察结果，但在伽马频带中，GC 测量值进一步升高。此外，时变 GC 分析表明，方向特异性和频率依赖性 GC 与动物的爪撤回潜伏期之间存在负相关。此外，我们使用计算机模拟研究了模型失配、噪声、缺失变量和共同输入对条件 GC 估计的影响。我们还将 GC 结果与转移熵（TE）估计进行了比较。
意义
我们的结果揭示了在各种疼痛条件下 S1 和 ACC 之间的功能连接和定向信息流。动态 GC 分析支持疼痛感知中皮质-皮质信息循环的假设，与计算预测编码范式一致。
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