Lydon-Staley David M, Cornblath Eli J, Blevins Ann Sizemore, Bassett Danielle S
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Annenberg School for Communication, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Neuropsychopharmacology. 2021 Jan;46(1):20-32. doi: 10.1038/s41386-020-00805-6. Epub 2020 Aug 28.
Neuropsychopharmacology addresses pressing questions in the study of three intertwined complex systems: the brain, human behavior, and symptoms of illness. The field seeks to understand the perturbations that impinge upon those systems, either driving greater health or illness. In the pursuit of this aim, investigators often perform analyses that make certain assumptions about the nature of the systems that are being perturbed. Those assumptions can be encoded in powerful computational models that serve to bridge the wide gulf between a descriptive analysis and a formal theory of a system's response. Here we review a set of three such models along a continuum of complexity, moving from a local treatment to a network treatment: one commonly applied form of the general linear model, impulse response models, and network control models. For each, we describe the model's basic form, review its use in the field, and provide a frank assessment of its relative strengths and weaknesses. The discussion naturally motivates future efforts to interlink data analysis, computational modeling, and formal theory. Our goal is to inspire practitioners to consider the assumptions implicit in their analytical approach, align those assumptions to the complexity of the systems under study, and take advantage of exciting recent advances in modeling the relations between perturbations and system function.
大脑、人类行为和疾病症状。该领域试图理解影响这些系统的干扰因素，这些干扰因素要么促进更健康，要么导致疾病。为了实现这一目标，研究人员经常进行一些分析，这些分析对受到干扰的系统的性质做出了某些假设。这些假设可以编码到强大的计算模型中，这些模型有助于弥合描述性分析与系统响应的形式理论之间的巨大差距。在这里，我们沿着复杂性的连续统一体回顾一组三个这样的模型，从局部治疗到网络治疗：一般线性模型的一种常用形式、脉冲响应模型和网络控制模型。对于每个模型，我们描述其基本形式，回顾其在该领域的应用，并对其相对优缺点进行坦率评估。该讨论自然地激发了未来将数据分析、计算建模和形式理论相互联系起来的努力。我们的目标是激励从业者考虑其分析方法中隐含的假设，使这些假设与所研究系统的复杂性相一致，并利用最近在建模干扰与系统功能之间关系方面令人兴奋的进展。
