Jabakhanji Rami, Foss Jennifer M, Berra Hugo H, Centeno Maria V, Apkarian A Vania, Chialvo Dante R
Department of Physiology, Northwestern University Feinberg School of Medicine, 303 East Chicago Ave, Chicago IL, 60611, USA.
Mol Pain. 2006 Jan 5;2:1. doi: 10.1186/1744-8069-2-1.
Most current methods for assessing pain in animals are based on reflexive measures and require constant interaction between the observer and the animal. Here we explore two new fully automated methods to quantify the impact of pain on the overall behavior of the organism. Both methods take advantage of the animals' natural preference for a dark environment. We used a box divided into two compartments: dark and bright. In the motoric operant task, "AngleTrack", one end of the box was raised so that the animals had to climb uphill to go from the light to the dark compartment. In the thermal operant task, "ThermalTrack", the floor of the dark compartment was heated to a given temperature, while the light compartment remained at 25 degrees C. Rats were individually placed in the light box and their crossing between chambers monitored automatically for 30 minutes. The angle of the box, or the temperature of the dark compartment, was altered to challenge the animals' natural preference. We test the hypothesis that different models of pain (inflammatory or neuropathic) can be differentiated based on performance on these devices. Three groups of rats were tested at five different challenge levels on both tasks: 1) normal, 2) neuropathic injury pain (Spared Nerve Injury), and 3) inflammatory pain (intraplantar injection of Carrageenan). We monitored the position of the animals as well as their rate of switching between compartments. We find significant differences between the three groups and between the challenge levels both in their average position with respect to time, and in their switching rates. This suggests that the angle-track and thermal-track may be useful in assessing automatically the global impact of different types of pain on behavior.
目前大多数评估动物疼痛的方法都基于反射性测量,并且需要观察者与动物之间持续互动。在此,我们探索了两种全新的全自动方法,以量化疼痛对生物体整体行为的影响。这两种方法都利用了动物对黑暗环境的天然偏好。我们使用了一个分为两个隔室的箱子:黑暗和明亮隔室。在运动操作性任务“AngleTrack”中,箱子的一端被抬高,这样动物必须爬坡才能从明亮隔室进入黑暗隔室。在热操作性任务“ThermalTrack”中,黑暗隔室的地板被加热到给定温度,而明亮隔室保持在25摄氏度。将大鼠单独放置在明亮的箱子中,并自动监测它们在两个隔室之间的穿梭情况,持续30分钟。改变箱子的角度或黑暗隔室的温度,以挑战动物的天然偏好。我们检验了这样一个假设,即可以根据在这些装置上的表现来区分不同类型的疼痛模型(炎症性或神经性)。在这两项任务中,对三组大鼠在五个不同的挑战水平下进行了测试:1)正常组,2)神经性损伤疼痛组( spared nerve injury,保留神经损伤),3)炎症性疼痛组(足底注射角叉菜胶)。我们监测了动物的位置以及它们在隔室之间的切换速率。我们发现,在平均位置随时间的变化以及切换速率方面,三组之间以及不同挑战水平之间均存在显著差异。这表明AngleTrack和ThermalTrack可能有助于自动评估不同类型疼痛对行为的整体影响。