Department of Biological Structure, University of Washington, Seattle, WA, USA.
Graduate Program in Neuroscience, University of Washington, Seattle, WA, USA.
Psychopharmacology (Berl). 2020 Sep;237(9):2569-2588. doi: 10.1007/s00213-020-05577-x. Epub 2020 Jul 9.
Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods.
In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies.
Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species.
Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.
攻击性与神经精神障碍共病，表现出多种临床特征，给患者、照料者和社会带来了巨大负担。这种多样性是因为攻击性是一种复杂的行为，可以从进化的角度分为有动机的（奖励性的）或反应性的（防御性的），每种行为都有其自身的行为特征、功能和神经基础，这些特征和基础可能会因遗传和环境因素而从适应性转变为适应性不良。近年来，人们开发了用于研究有动机攻击性相关行为的临床前动物模型，并确定了指导其进展和表达的神经机制，这方面的研究进展迅速。然而，这些程序的采用通常受到手动评分复杂社会互动的艰巨任务的阻碍。手动观察通常容易受到观察者漂移、分析时间长和评分者间可靠性差的影响，并且与现代神经科学方法所需的采样频率不兼容。
在本文中，我们讨论了近年来在小鼠有动机攻击性的临床前研究方面的进展，并结合我们对机器学习技术在产生自动、稳健的攻击性社会行为评分方面的潜力的看法。我们讨论了在行为药理学研究中实施有效计算机分类的关键考虑因素。
开源自动化分类平台可以与人类观察者的表现相匹配或超过人类观察者的表现，同时消除观察者漂移、偏差和评分者间可靠性的干扰。此外，无监督方法可以识别模型物种中以前未表征的与攻击性相关的行为模式。
开源计算方法的进步有望克服当前手动注释的局限性，同时将计算神经行为学引入更广泛的行为神经科学领域。我们提出，目前可用的开源方法足以克服在临床前攻击性行为研究中广泛采用机器学习的主要限制。
