Neurovascular Research Lab, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, MA 02129, USA.
Cephalalgia. 2013 Jun;33(8):604-13. doi: 10.1177/0333102412470216.
Spreading depression (SD) is the electrophysiological substrate of migraine aura and a potential trigger for headache. Since its discovery by Leão in 1944, SD has transformed from being viewed as an epiphenomenon into a therapeutic target relevant in the pathophysiology of migraine and brain injury.
Despite decades of research, the underpinnings of SD are still poorly understood, hampering our efforts to selectively block its initiation and spread. Experimental models have nevertheless been useful to measure the likelihood of SD occurrence (i.e. SD susceptibility) and characterize genetic, physiological and pharmacological modulation of SD in search of potential therapies, such as in migraine prophylaxis and stroke. Here, I review experimental SD susceptibility endpoints and surrogates, and minimum essential model requirements to improve their utility in drug screening.
A critical reappraisal of strengths and caveats of experimental models of SD susceptibility is needed to set standards and improve data quality, interpretation and reconciliation.
扩散性抑制（SD）是偏头痛先兆的电生理学基础，也是头痛的潜在触发因素。自 1944 年 Leão 发现以来，SD 已从被视为一种现象转变为偏头痛和脑损伤病理生理学中相关的治疗靶点。
尽管经过几十年的研究，SD 的基础仍知之甚少，这阻碍了我们选择性阻断其起始和传播的努力。然而，实验模型对于测量 SD 发生的可能性（即 SD 易感性）以及表征 SD 在遗传、生理和药理学方面的调节非常有用，以期找到潜在的治疗方法，如偏头痛预防和中风治疗。在这里，我回顾了实验性 SD 易感性终点和替代指标，以及最小必要模型要求，以提高它们在药物筛选中的效用。
需要对 SD 易感性实验模型的优缺点进行批判性评估，以制定标准并提高数据质量、解释和协调。
