Departments of Neuroscience, Biomedical Engineering, Electrical Engineering, and Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA.
Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.
Curr Opin Neurobiol. 2024 Jun;86:102881. doi: 10.1016/j.conb.2024.102881. Epub 2024 May 1.
Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions and well-constrained behaviors over short timescales (e.g., trials, hours), many mental health conditions involve complex interactions of mood and cognition that are non-stationary across behavioral contexts and evolve over extended timescales. Here, we discuss opportunities, challenges, and possible future directions in computational psychiatry to quantify non-stationary continuously monitored behaviors. We suggest that this exploratory effort may contribute to a more precision-based approach to treating mental disorders and facilitate a more robust reverse translation across animal species. We conclude with ethical considerations for any field that aims to bridge artificial intelligence and patient monitoring.
在医学领域，长时间研究个体的情绪和认知处理的复杂性是一项艰巨的挑战。虽然系统神经科学的大部分研究都恰当地关注了在短时间尺度上（例如，试验、小时）神经回路功能与良好约束行为之间的联系，但许多心理健康状况涉及情绪和认知的复杂相互作用，这些作用在行为背景下是非稳定的，并在长时间尺度上演变。在这里，我们讨论了计算精神病学中量化非稳定连续监测行为的机会、挑战和可能的未来方向。我们认为，这项探索性工作可能有助于更基于精准的方法来治疗精神障碍，并促进在动物物种之间更稳健的反向翻译。最后，我们对任何旨在弥合人工智能和患者监测的领域的伦理问题进行了讨论。
