Neural Mechanisms Underlying Breathing Complexity

Breathing is maintained and controlled by a network of automatic neurons in the brainstem that generate respiratory rhythm and receive regulatory inputs. Breathing complexity therefore arises from respiratory central pattern generators modulated by peripheral and supra-spinal inputs. Very little is known on the brainstem neural substrates underlying breathing complexity in humans. We used both experimental and theoretical approaches to decipher these mechanisms in healthy humans and patients with chronic obstructive pulmonary disease (COPD). COPD is the most frequent chronic lung disease in the general population mainly due to tobacco smoke. In patients, airflow obstruction associated with hyperinflation and respiratory muscles weakness are key factors contributing to load-capacity imbalance and hence increased respiratory drive. Unexpectedly, we found that the patients breathed with a higher level of complexity during inspiration and expiration than controls. Using functional magnetic resonance imaging (fMRI), we scanned the brain of the participants to analyze the activity of two small regions involved in respiratory rhythmogenesis, the rostral ventro-lateral (VL) medulla (pre-Bötzinger complex) and the caudal VL pons (parafacial group). fMRI revealed in controls higher activity of the VL medulla suggesting active inspiration, while in patients higher activity of the VL pons suggesting active expiration. COPD patients reactivate the parafacial to sustain ventilation. These findings may be involved in the onset of respiratory failure when the neural network becomes overwhelmed by respiratory overload We show that central neural activity correlates with airflow complexity in healthy subjects and COPD patients, at rest and during inspiratory loading. We finally used a theoretical approach of respiratory rhythmogenesis that reproduces the kernel activity of neurons involved in the automatic breathing. The model reveals how a chaotic activity in neurons can contribute to chaos in airflow and reproduces key experimental fMRI findings.

呼吸由脑干内的自主神经元网络维持并调控，该网络可产生呼吸节律并接收调控信号输入。因此，呼吸的复杂性源于受外周和脊髓上输入调控的呼吸中枢模式发生器（respiratory central pattern generators）。目前学界对人类呼吸复杂性背后的脑干神经底物仍知之甚少。我们结合实验与理论两种研究手段，对健康人群及慢性阻塞性肺疾病（chronic obstructive pulmonary disease, COPD）患者的相关机制进行解析。COPD是普通人群中最常见的慢性肺部疾病，其主要致病因素为烟草烟雾暴露。在患者群体中，伴随肺过度充气与呼吸肌无力的气流受限，是引发负荷-容量失衡、进而导致呼吸驱动增强的关键因素。令人意外的是，我们发现患者在吸气与呼气阶段的呼吸复杂度均高于健康对照组。我们借助功能磁共振成像（functional magnetic resonance imaging, fMRI）对受试者脑部进行扫描，以分析两个参与呼吸节律发生的微小脑区的活动：延髓嘴侧腹外侧区（rostral ventro-lateral medulla, VL）（前包钦格复合体，pre-Bötzinger complex），以及脑桥尾侧腹外侧区（caudal ventro-lateral pons, VL）（面旁核团，parafacial group）。fMRI结果显示，健康对照组的延髓腹外侧区活动水平更高，提示其吸气过程更为活跃；而患者组的脑桥腹外侧区活动水平更高，提示其呼气过程更为活跃。慢性阻塞性肺疾病患者会激活面旁核团以维持通气功能。当神经网络因呼吸负荷过载而不堪重负时，本研究的发现或与呼吸衰竭的发病机制相关。我们证实，无论在静息状态还是吸气负荷状态下，健康受试者与慢性阻塞性肺疾病患者的中枢神经活动均与气流复杂度存在相关性。我们最终构建了针对呼吸节律发生的理论模型，该模型可复现参与自主呼吸的神经元的核心活动。该模型揭示了神经元的混沌活动如何引发气流混沌，并复现了关键的fMRI实验结果。