Niedernhuber Maria, Streicher Joaquim, Leggenhager Bigna, Bekinschtein Tristan A
Cambridge Consciousness and Cognition Lab, Department of Psychology, University of Cambridge, Cambridge, UK.
Department of Psychology, University of Zurich, Zurich, Switzerland.
J Pain Res. 2024 Jul 15;17:2393-2405. doi: 10.2147/JPR.S449173. eCollection 2024.
Fluctuations of chronic pain levels are determined by a complex interplay of cognitive, emotional and perceptual variables. We introduce a pain tracking platform composed of wearable neurotechnology and a smartphone application to measure and predict chronic pain levels and its interplay with other dimensions of experience.
Our method measures, dynamically and at home, pain strength, phenomenal and neural time series collected with an online tool and low-density EEG. Here we used data from a single participant who performed an attention task at home for a period of 20 days to investigate the role of attention to different bodily systems in chronic pain.
We show a relationship between emotions and pain strength while allocating attention to the heartbeat, the breathing, the affected or the unaffected limb. We found that pain was maximal when attending to the affected limb and decreased when the participant focused on his breathing or his heartbeat. These results provide interesting insights regarding the role of attention to interoceptive signals in chronic pain. We found power changes in the delta, theta, alpha and beta (but not in the gamma) band between the four attention conditions. However, there was no reliable association of these changes to pain intensity ratings. Theta power was higher when attention was directed to the unaffected limb compared to the others. Further, the pain ratings, when attending to unaffected limb, were associated with alpha and theta power band changes.
Overall, we demonstrate that our neurophysiology and experience tracking platform can capture how body attention allocation alters the dynamics of subjective measures and its neural correlates. This research approach is proof of concept for the development of personalized clinical assessment tools and a testbed for behavioural, subjective and biomarkers characterization.
慢性疼痛水平的波动由认知、情感和感知变量的复杂相互作用决定。我们推出了一个由可穿戴神经技术和智能手机应用程序组成的疼痛追踪平台,用于测量和预测慢性疼痛水平及其与其他体验维度的相互作用。
我们的方法在家中动态测量疼痛强度、通过在线工具和低密度脑电图收集的现象学和神经时间序列。在这里,我们使用了一名参与者在家中进行为期20天的注意力任务的数据,以研究对不同身体系统的注意力在慢性疼痛中的作用。
我们展示了在关注心跳、呼吸、受影响或未受影响的肢体时,情绪与疼痛强度之间的关系。我们发现,当关注受影响的肢体时疼痛最大,而当参与者专注于呼吸或心跳时疼痛会减轻。这些结果为关注内感受信号在慢性疼痛中的作用提供了有趣的见解。我们发现四种注意力条件之间在δ、θ、α和β(但不是γ)波段存在功率变化。然而,这些变化与疼痛强度评分之间没有可靠的关联。与其他情况相比,当注意力指向未受影响的肢体时,θ功率更高。此外,在关注未受影响的肢体时,疼痛评分与α和θ功率波段变化相关。
总体而言,我们证明了我们的神经生理学和体验追踪平台可以捕捉身体注意力分配如何改变主观测量及其神经关联的动态。这种研究方法是开发个性化临床评估工具的概念验证,也是行为、主观和生物标志物特征描述的试验台。