Ideas Lab Collaborative Research: Using Natural Odor Stimuli to Crack the Olfactory Code

创意实验室合作研究：利用自然气味刺激破解嗅觉密码

• 批准号: 1556337
• 负责人: Brian Smith
• 金额: $ 90万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-11-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1556337&HistoricalAwards=false
• 关键词: Ideas; Lab; Collaborative; Research; Using

This project was developed during a NSF Ideas Lab on "Cracking the Olfactory Code" and is jointly funded by the Chemistry of Life Processes program in the Chemistry Division, the Mathematical Biology program in the Division of Mathematical Sciences, the Physics of Living Systems program in the Physics Division, the Neural Systems Cluster in the Division of Integrative Organismal Systems, the Division of Biological Infrastructure, and the Division of Emerging Frontiers. The sense of smell is essential for maintaining quality of life in humans, and its decline can be an important harbinger of neurodegenerative disease. Moreover, since nearly all animals aside from primates rely on olfaction for most survival functions, understanding chemical sensing has immense practical value, for example, in the control of agricultural pests or in training animals to detect odors relevant for bomb, drug and cancer detection. In spite of its importance, the understanding of olfaction lags far behind the other senses, which is in part due to the lack of understanding of the physical space of odors. The understanding of the neural bases of vision and audition were greatly advanced by investigations of the physical dimensions of visual and auditory stimuli. It is therefore likely that a similar in-depth investigation of odor space - how natural odors occur and the backgrounds against which they must be detected - will reveal a new depth of richness of neural representations of odors in the brain. Insects such as the fruit fly and honey bee are excellent models for this research because of the accessibility of their central nervous systems, because of their ease of use under controlled laboratory conditions, and because of the functional similarity of how odors are processed in insect and mammalian brains. This research will characterize how odor flowers and fruits with respect to behavioral value for honey bees (food) and fruit flies (food and egg laying sites). Further monitoring of neural activity in early and later stage processing in the brain, when combined with computational modeling, will reveal significantly richer neural representations than have heretofore been described. This new understanding stands to have an impact on understanding how healthy brains encode sensations and memories of odors and how brains fail under disease conditions. It will also have an impact on understanding how the sense of smell may be built into engineered devices. Finally, both insects are also of economic importance to agriculture for crop pollination (honey bees) and damage to fruit (fruit flies). The PIs will teach and work with undergraduate, graduate and postdoctoral students and especially recruit students from underrepresented groups in science. This research will quantitatively characterize the real-world statistics of multi-component natural odor scenes and investigate how they drive behavior and processing in several brain regions. The focus will be on honey bee as well as fruit fly adults and larva as models, where it will be possible to characterize a library of ethologically relevant natural odors associated with a diversity of behavioral outputs. The work will begin by quantitatively characterizing the detailed statistical properties of natural odor scenes in defined ethological contexts. This will build on the rich literature on identified natural odors in insects and mammals. Naturally occurring plant and fruit odor samples from the natural environments of each insect will be collected and chemically analyzed. Nonlinear dimensionality reduction techniques and approaches based on sparse coding will determine the dimensions of odor space that are most salient for behavioral decisions. Such a quantitative deconstruction of the sensory input would be unprecedented in olfactory neuroscience, and should allow the PIs to effectively and comprehensively drive olfactory circuits for the first time. The hypothesis is that the stimulus dimensions that are most behaviorally relevant to the animal will be most efficiently extracted by the olfactory system. Synthetic odor blends will be specially constructed to vary along relevant sensory dimensions, to probe neural codes and adaptive behaviors in the olfactory system. As in research on the visual system, analysis of such evoked neural responses using statistical methods that take into account natural odor statistics will reveal novel olfactory computations and behaviors that have been previously inaccessible. The project will generate datasets of immediate use and importance to scientists in theoretical biology and mathematics, engineering and biology.

该项目是在一个关于“破解嗅觉代码”的NSF思想实验室中开发的，并由化学过程的化学过程计划共同资助化学过程，数学科学的数学生物学计划，生物系统的物理系统计划，物理系统的物理学计划，物理系统部门，神经系统的神经系统内部的神经系统组合级别的分区，以及分区的分区。嗅觉对于维持人类生活质量至关重要，它的衰落可能是神经退行性疾病的重要预兆。此外，由于除了灵长类动物以外，几乎所有动物都依靠嗅觉来用于大多数生存功能，因此了解化学感应具有巨大的实用价值，例如，在控制农业害虫或训练动物方面可以检测与炸弹，药物和癌症检测有关的气味。尽管其重要性，但对嗅觉的理解远远落后于其他感觉，这部分是由于对气味的物理空间缺乏理解。通过研究视觉和听觉刺激的物理维度，对视觉和试听神经基础的理解大大提高了。因此，可能对气味空间进行类似的深入研究 - 天然气味如何发生以及必须检测到的背景 - 将揭示大脑中气味神经代表的新深度。由于其中枢神经系统的可及性，由于其在受控的实验室条件下易于使用，并且由于昆虫和哺乳动物大脑中的气味如何处理，因此由于其中枢神经系统的可及性，诸如果蝇和蜜蜂等昆虫是这项研究的绝佳模型。这项研究将表征异味的花朵和水果如何相对于蜜蜂（食物）和水果蝇（食物和卵子铺设地点）的行为价值。在大脑的早期和后期处理与计算建模相结合时，对神经活动的进一步监测将揭示出比描述的神经表现能力明显更丰富。这种新的理解会影响理解健康的大脑如何编码气味的感觉和记忆以及在疾病状况下大脑如何失败。这也将对理解如何内置在工程设备中的气味感上产生影响。最后，两种昆虫对农业的授粉（蜜蜂）和对水果的损害（水果蝇）也具有经济重要性。 PI将与本科生，研究生和博士后学生教授和合作，尤其是来自代表性不足的科学群体的学生。这项研究将定量地表征多组分自然气味场景的现实统计数据，并研究它们如何在几个大脑区域驱动行为和处理。重点将放在蜜蜂以及果蝇成年人和幼虫作为模型上，可以在其中表征与各种行为输出相关的伦理学相关自然气味库。这项工作将从定义的伦理学环境中定量表征自然气味场景的详细统计特性开始。这将基于关于昆虫和哺乳动物中鉴定出的天然气味的丰富文献。将收集并化学分析从每种昆虫的自然环境中自然存在的植物和果味样品。基于稀疏编码的非线性尺寸降低技术和方法将决定气味空间的尺寸，这对于行为决策最为明显。嗅觉神经科学中的这种定量解构将是前所未有的，并且应首次使PI有效，全面地驱动嗅觉电路。假设是，嗅觉系统最有效地提取了与动物行为最相关的刺激维度。合成气味混合物将专门构建以沿相关的感觉维度变化，以探测嗅觉系统中的神经代码和适应性行为。与视觉系统的研究一样，使用统计方法对这种诱发的神经反应进行分析，这些方法考虑到自然气味统计，将揭示以前无法访问的新型嗅觉计算和行为。该项目将对理论生物学和数学，工程和生物学的科学家立即使用和重要性生成数据集。