基于信号检测论的疼痛分辨力神经指标研究
文献类型：学位论文
| 作者 | 张立波 |
| 答辩日期 | 2023-06 |
| 文献子类 | 博士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 胡理 |
| 关键词 | 神经指标 差异疼痛敏感性 疼痛分辨力 脑电图(EEG) 功能磁共振(fMRI) |
| 学位名称 | 理学博士 |
| 学位专业 | 认知神经科学 |
| 其他题名 | Neural Indicators of Pain Discriminabilitv: A Signal Detection Theory Approach |
| 中文摘要 | Pain imposes enormous health and economic burdens to both individuals and the whole society. To better combat the problem of pain, it is imperative to understand the neural mechanisms of pain and develop reliable objective indicators of pain. However, the majority of previous studies only focus on absolute pain sensitivity, attempting to uncover the neural indicators of the ability to perceive the same painful stimulus as more or less painful. Differential pain sensitivity (or pain discriminability), the ability to distinguish different painful stimuli, is largely overlooked. Notably, objective neural indicators of pain discriminability have important theoretical and clinical implications, such as understanding how the differences in multiple painful stimuli are encoded in the brain and assessing pain discriminability in certain patients. To fill this gap, the present study aimed to reveal neural indicators of pain discriminability using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) techniques. To quantify sensory discriminability, we applied signal detection theory (SDT) to six large datasets (three EEG [Datasets 1}4, total n=461}and two fMRI [Datasets 5}6, total n=399] datasets), in which Datasets 1 and 5 were used for exploration of potential neural indicators of pain discriminability, and others for assessment of the replicability and pain-selectivity of the discovered neural indicators. In each dataset except for Dataset 4, participants received transient stimuli of four sensory modalities (pain [i.e., nociceptive laser stimuli], touch [i.e., non-nociceptive electrical pulses], audition [pure tones], and vision [brief flashes of a grey round disk]) and two intensities (high and low), and reported their perceptual ratings using a 0一10 numeric rating scale, where 0 stood for "no perception" and 10 "the strongest sensation imaginable (in each stimulus modality)". In Dataset 4, participants received laser stimuli of four intensities and then rated their pain intensity ratings using the 0一10 rating scale. EEG results showed that, in Dataset 1，high intensity painful stimuli evoked larger N1，N2, and P2 amplitudes than low intensity painful stimuli, and that the amplitude differences of these three waves in the high and low intensity conditions (high-low) correlated significantly with pain discriminability quantified with AUC. This finding was well replicated in independent Datasets 2 and 3.Results from Dataset 4 further showed that pain-evoked EEG responses consistently encoded pain discriminability when the differences of laser intensity were not too large. On the other hand, even though high intensity tactile, auditory, and visual stimuli also evoked larger N2 and P2 waves than low intensity stimuli, the amplitude differences (high-low) did not reliably correlate with tactile, auditory, and visual discriminability. Further analyses ruled out possible confounding factors: the quantification of sensory discriminability (AUC, d', and rating differences in the high and low intensity conditions [high-low]), perceptual rating differences between modalities, and AUC distribution differences between modalities all had no substantial effect on the main findings. Furthermore, a machine learning-based predictive model with pain-evoked EEG waves as features could predict pain discriminability, but failed to accurately predict tactile, auditory, and visual discriminability. FMRI data revealed similar findings. In Dataset 5, a wide ranges of brain regions including the primary somatosensory cortex (S1), secondary somatosensory cortex (S2), insula, anterior cingulate cortex (ACC), and thalamus were more activated by high intensity painful stimuli than by low intensity painful stimuli, and the differential activations (high-low) in these regions were correlated with pain AUC values. In order to examine the replicability of these findings, region-of-interest (ROI) masks were defined as the significant voxels in the S1，S2, insula, ACC, and thalamus, and then applied to Dataset 6 to extract brain activations in these ROIs. Brain activities in the S 1，thalamus, insula, and ACC were significantly correlated with pain AUC values. On the other hand, brain activations differed between high intensity tactile, auditory, and visual stimuli conditions and low intensity conditions, but the differential activations in few or even no voxels could correlate with corresponding AUC values. Using discriminability measures other than AUC and matching AUC values between different modalities had little effect on the results. In addition, a predictive model based on pain-evoked brain activations could predict pain discriminability, but not tactile, auditory, and visual discriminability. Overall, these results demonstrate that transient pain-evoked brain responses can serve as replicable and selective neural indicators of pain discriminability. These findings also provide a novel interpretation of classical pain-evoked brain response, deepens our understanding of pain processing, and shed light on the effective management of chronic pain. |
| 英文摘要 | 疼痛会对个人和社会都会造成沉重的健康和经济负担。为了更有效地缓解疼痛，深入理解疼痛的神经加工机制、开发客观准确的疼痛敏感性客观指标十分必要。然而，以往研究大多只关注了绝对疼痛敏感性的研究，试图揭示个体对单一疼痛刺激感受性差异的神经指标，却忽略了哪些指标能够反映个体区分多个不同疼痛刺激的能力，即差异疼痛敏感性(或称为疼痛分辨力)。值得注意的是，疼痛分辨力的客观神经指标有着重要的理论和临床意义，如理解疼痛的神经加工机制和评估特殊群体的疼痛分辨力。 为了弥补这一不足，本研究利用脑电图(EEG)和功能磁共振技术(fMRI )技术探究了疼痛分辨力的神经指标。我们在六个大数据集(数据集1 }-4为EEG数据集，n=461;数据集5 }-6为fMRI数据集，n = 399 )中应用信号检测论量化感觉分辨力，并先在数据集1和5中挖掘可能的疼痛分辨力神经指标，再在其余数据集中检验发现的神经指标的可重复性和疼痛选择性。除去数据集4，在其余的五个数据集中，被试均先接受两种不同强度(高和低)、四种不同模态的短暂性感觉刺激(痛觉:激光刺激;触觉:非痛电刺激;听觉:纯音刺激;视觉: 不同灰度圆盘)，再给出0-10的感觉强度评分，其中0代表没有感受，10代表可以想象的最强的感受。在数据集4中，被试接受四种强度的激光刺激，并给 出0-10的疼痛强度评分。 对EEG数据的分析结果显示，在数据集1中，高强度疼痛刺激诱发的N1, N2和P2成分幅值均大于低强度疼痛刺激诱发的幅值，而且高、低强度疼痛刺激 诱发N1,N2和P2幅值差异(高一低)与基于信号检测论的疼痛分辨力指标(AUC ) 存在显著相关。这一发现在独立的数据集2和3中得到了很好的重复。数据集4证实了疼痛诱发脑电响应可在疼痛刺激强度差异不过大时稳定编码疼痛分辨力，进而验证了结果的可推广性。另一方面，虽然不同强度触觉、听觉、视觉刺激诱发的N2, P2幅值也存在差异，但高、低强度下的幅值差异(高一低)却与相应的触觉、听觉、视觉分辨力不存在稳定的相关关系。深入分析排除了可能的混淆因素:分辨力指标选择(AUC, d'、高低刺激强度下的评分差异)、不同模态间的主观评分差异、不同模态间的AUC指标分布差异均对结果没有实质影响。进一步的分析显示，利用疼痛诱发的脑电响应可以建立疼痛分辨力的机器学习预测模型，该模型可有效预测疼痛分辨力，但无法预测触觉、听觉和视觉分辨力。 fMRI研究得到了和EEG研究一致的结果。在数据集5中，初级体感皮层 (S1)、次级体感皮层(S2)、脑岛、前扣带回(ACC )、丘脑等一系列脑区在高强度疼痛刺激下的激活水平都高于低强度疼痛刺激下的激活，而且这些脑区在高、 低强度下的激活差异(高一低)与疼痛分辨力存在显著相关。为评估这一结果的可重复性，以数据集5中S1, S2、脑岛、ACC、丘脑区域内与疼痛分辨力相关的体素为感兴趣区(ROI，提取了数据集6中这些ROI内的脑激活，并与数据集6中的疼痛分辨力做相关分析。结果显示，S1、丘脑、脑岛、ACC内ROI的激活差异(高一低)与疼痛分辨力存在显著相关。另一方面，虽然大量脑区在不同强度的触觉、听觉、视觉刺激下都存在激活差异，但却几乎没有体素与触觉、听觉和视觉分辨力有相关关系。换用不同的分辨力指标和匹配不同模态间的主观评分依然得到了一致的结果。与脑电研究相同，以疼痛诱发脑区激活为特征的机器学习预测模型也可以有效预测疼痛分辨力，但不能预测触觉、听觉、视觉分辨力。 这些结果有力证明了短暂性疼痛诱发脑响应可作为疼痛分辨力有选择性且可重复的神经指标。这一发现为经典疼痛诱发脑响应功能意义提供了新的解释，加深了人们对疼痛感知及其内在机制的理解，也对慢性疼痛的有效管理提供了启不。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/46221]  |
| 专题 | 心理研究所_健康与遗传心理学研究室 |
| 推荐引用方式 GB/T 7714 | 张立波. 基于信号检测论的疼痛分辨力神经指标研究[D]. 中国科学院心理研究所. 中国科学院大学. 2023. |
入库方式： OAI收割
来源：心理研究所
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