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研究人类和动物模型中平行行为的挑战。

The challenge of studying parallel behaviors in humans and animal models.

作者信息

Stephens David N, Crombag Hans S, Duka Theodora

机构信息

School of Psychology, University of Sussex, Falmer, Brighton, BN1 9QG, UK.

出版信息

Curr Top Behav Neurosci. 2013;13:611-45. doi: 10.1007/7854_2011_133.

Abstract

The use of animal models is essential in carrying out research into clinical phenomena such as addiction. However, the complexity of the clinical condition inevitably means that even the best animal models are inadequate representations of the condition they seek to mimic. Such mismatches may account for apparent inconsistencies between discoveries in animal models, including the identification of potential novel therapies, and the translation of such discoveries to the clinic. We argue that it is overambitious to attempt to model human disorders such as addiction in animals, and especially in rodents, where "validity" of such models is often limited to superficial similarities, referred to as "face validity" that reflect quite different underlying phenomena and biological processes from the clinical situation. Instead, we suggest a more profitable approach may be to identify (a) well-defined intermediate human behavioral phenotypes that reflect defined, limited aspects of the human clinical disorder, and (b) to develop animal models that are homologous with those discrete human behavioral phenotypes in terms of psychological processes, and underlying neurobiological mechanisms. Examples of current weaknesses and suggestions for more limited approaches that may allow better homology between the test animal and human condition are made.

摘要

使用动物模型对于开展成瘾等临床现象的研究至关重要。然而,临床状况的复杂性不可避免地意味着,即便最好的动物模型也无法充分体现它们试图模拟的状况。这种不匹配可能导致动物模型研究结果(包括潜在新疗法的识别)与这些发现向临床转化之间出现明显的不一致。我们认为,试图在动物(尤其是啮齿动物)身上模拟成瘾等人类疾病是过于雄心勃勃的,在这些动物中,此类模型的“有效性”往往仅限于表面相似性,即所谓的“表面效度”,而这反映的是与临床情况截然不同的潜在现象和生物学过程。相反,我们建议一种更有效的方法可能是:(a) 识别反映人类临床疾病特定、有限方面的明确定义的中间人类行为表型;(b) 开发在心理过程和潜在神经生物学机制方面与那些离散的人类行为表型同源的动物模型。文中给出了当前弱点的示例以及可能使实验动物与人类状况具有更好同源性的更有限方法的建议。

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