Department of Neurology, Northwestern University Feinberg School of Medicine, Ward, Chicago, IL, USA.
Chem Senses. 2018 Sep 22;43(8):583-597. doi: 10.1093/chemse/bjy045.
Nasal inhalation is the basis of olfactory perception and drives neural activity in olfactory and limbic brain regions. Therefore, our ability to investigate the neural underpinnings of olfaction and respiration can only be as good as our ability to characterize features of respiratory behavior. However, recordings of natural breathing are inherently nonstationary, nonsinusoidal, and idiosyncratic making feature extraction difficult to automate. The absence of a freely available computational tool for characterizing respiratory behavior is a hindrance to many facets of olfactory and respiratory neuroscience. To solve this problem, we developed BreathMetrics, an open-source tool that automatically extracts the full set of features embedded in human nasal airflow recordings. Here, we rigorously validate BreathMetrics' feature estimation accuracy on multiple nasal airflow datasets, intracranial electrophysiological recordings of human olfactory cortex, and computational simulations of breathing signals. We hope this tool will allow researchers to ask new questions about how respiration relates to body, brain, and behavior.
鼻腔吸入是嗅觉感知的基础，它会引起嗅觉和边缘脑区的神经活动。因此，我们研究嗅觉和呼吸的神经基础的能力，取决于我们描述呼吸行为特征的能力。然而，自然呼吸的记录本质上是非平稳的、非正弦的和独特的，这使得特征提取难以自动化。缺乏用于描述呼吸行为的免费计算工具，这阻碍了嗅觉和呼吸神经科学的许多方面。为了解决这个问题，我们开发了 BreathMetrics，这是一个开源工具，可以自动提取嵌入在人类鼻气流记录中的全套特征。在这里，我们在多个鼻气流数据集、人类嗅觉皮层的颅内电生理记录和呼吸信号的计算模拟上，严格验证了 BreathMetrics 的特征估计准确性。我们希望这个工具能够让研究人员提出关于呼吸与身体、大脑和行为的关系的新问题。
