计算代谢精神病学的挑战与前沿。

Anthony G Chesebro, Botond B Antal, Corey Weistuch, Lilianne R Mujica-Parodi
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引用次数: 0

摘要

代谢精神病学面临的主要挑战之一是,导致精神疾病的大脑功能紊乱源于一系列复杂的下游和反馈过程,这些过程跨越多个时空尺度。重要的是,同一回路可能存在多个故障点,每个故障点都会导致不同类型的失调,从而引发不同的下游级联,产生不同的体征和症状。在这里,我们通过研究电路扰动的细微差别如何导致不同的临床结果来说明这一挑战。我们还讨论了计算模型如何进行空间异质整合,并在体外和体内范例之间架起桥梁。通过利用最新的方法学进展和工具,计算模型可以整合跨尺度(如 TCA 循环、离子通道、神经微组装、全脑宏电路)和跨生理系统(如神经、内分泌、免疫、血管)的相关过程,提供一个框架,以超越概念和描述的方式将这些机理过程结合起来,并进行定量和生成。这些都有可能使我们的直觉更加敏锐,从而建立基于电路的个性化诊断和治疗模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Challenges and Frontiers in Computational Metabolic Psychiatry.

One of the primary challenges in metabolic psychiatry is that the disrupted brain functions that underlie psychiatric conditions arise from a complex set of downstream and feedback processes spanning across multiple spatiotemporal scales. Importantly, the same circuit can have multiple points of failure, each of which results in a different type of dysregulation, and thus elicits distinct cascades downstream that produce divergent signs and symptoms. Here, we illustrate this challenge by examining how subtle differences in circuit perturbations can lead to divergent clinical outcomes. We also discuss how computational models can perform the spatially heterogenous integration and bridge in vitro and in vivo paradigms. By leveraging recent methodological advances and tools, computational models can integrate relevant processes across scales (e.g., TCA-cycle, ion channel, neural microassembly, whole-brain macro-circuit) and across physiological systems (e.g., neural, endocrine, immune, vascular), providing a framework that can unite these mechanistic processes in a manner that goes beyond the conceptual and descriptive, to the quantitative and generative. These hold the potential to sharpen our intuitions towards circuit-based models for personalized diagnostics and treatment.

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