Integrating the Physical Environment Within a Population Neuroscience Perspective.

Lindsey Smith
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Abstract

Population neuroscience recognises the role of the environment in shaping brain, behaviour, and mental health. An overview of current evidence from neuroscientific and epidemiological studies highlights the protective effects of nature on cognitive function and stress reduction, the detrimental effects of urban living on mental health, and emerging concerns relating to extreme weather events and eco-anxiety. Despite the growing body of evidence in this area, knowledge gaps remain due to inconsistent measures of exposure and a reliance on small samples. In this chapter, attention is given to the physical environment and population-level studies as a necessary starting point for exploring the long-term impacts of environmental exposures on mental health, and for informing future research that may capture immediate emotional and neural responses to the environment. Key data sources, including remote sensing imagery, administrative, sensor, and social media data, are outlined. Appropriate measures of exposure are advocated for, recognising the value of area-level measures for estimating exposure over large study samples and spatial and temporal scales. Although integrating data from multiple sources requires consideration for data quality and completeness, deep learning and the increasing availability of high-resolution data present opportunities to build a more complete picture of physical environments. Advances in leveraging detailed locational data are discussed as a subsequent approach for building upon initial observations from population studies and improving understanding of the mechanisms underlying behaviour and human-environment interactions.

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从人群神经科学的角度整合物理环境。
人口神经科学认识到环境在塑造大脑、行为和心理健康方面的作用。对目前神经科学和流行病学研究证据的综述强调了自然对认知功能和减轻压力的保护作用、城市生活对心理健康的不利影响,以及与极端天气事件和生态焦虑有关的新问题。尽管该领域的证据越来越多,但由于暴露的测量方法不一致以及对小样本的依赖,知识差距依然存在。在本章中,我们将关注物理环境和人口层面的研究,将其作为探索环境暴露对心理健康的长期影响的必要起点,并为未来的研究提供信息,以捕捉对环境的直接情绪和神经反应。本文概述了主要的数据来源,包括遥感图像、行政管理、传感器和社交媒体数据。提倡采用适当的暴露测量方法,承认区域级测量方法对于估算大量研究样本和时空尺度的暴露的价值。虽然整合多种来源的数据需要考虑数据的质量和完整性,但深度学习和越来越多的高分辨率数据为建立更完整的物理环境图景提供了机会。本文讨论了在利用详细定位数据方面取得的进展,将其作为一种后续方法,用于在群体研究的初步观察基础上,加深对行为和人类与环境相互作用机制的理解。
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来源期刊
Current topics in behavioral neurosciences
Current topics in behavioral neurosciences Neuroscience-Behavioral Neuroscience
CiteScore
4.80
自引率
0.00%
发文量
103
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