Li Yuanxi, Zhang Bing, Liu Jinqi, Wang Rubin
Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China.
Department of Anesthesiology, Obstetrics and Gynecology Hospital of Fudan University, Fudan University, Shanghai, China.
PLoS Comput Biol. 2025 Apr 7;21(4):e1012961. doi: 10.1371/journal.pcbi.1012961. eCollection 2025 Apr.
Numerous experiments have found that the behavioral characteristics of major depressive disorder (MDD) animals are usually associated with abnormal neural activity patterns and brain energy metabolism. However, the relationship among the behavioral characteristics, neural activity patterns and brain energy metabolism remains unknown. In this paper, we computationally investigated this relationship, with a particular focus on how neural energy coding patterns change in MDD brains, in the VTA-NAc-mPFC dopaminergic pathway of the reward system based on our biological neural network model and neural energy calculation model. Interestingly, our results suggested that the neural energy consumption of the whole VTA-NAc-mPFC microcircuit in MDD group was significantly reduced, which was mainly attributed to the decreasing neural energy consumption in the mPFC region. This observation theoretically supported the view of low-level energy consumption in MDD. We also investigated the neural energy consumption patterns of various neuronal types in our VTA-NAc-mPFC microcircuit under the influence of different dopamine concentrations, and found that there were some specific impairments in MDD, which provided some potential biomarkers for MDD diagnosis. More specifically, we found that the actual neural energy consumption of medium spiny neurons (MSNs) in the NAc region was increased in the MDD group, whereas pyramidal neurons in the mPFC region exhibited higher actual neural energy consumption in the NC group. Additionally, in both neuron types, the actual neural energy required to generate an action potential was higher in the MDD group, suggesting that, given the same energy budget, these neurons in the MDD group tended to generate fewer action potentials. To further explore the relationship between neural coding patterns and neural energy coding patterns in the VTA-NAc-mPFC microcircuit, we in addition calculated P-V correlation for each neuronal type, defined as the Pearson's correlation coefficient between membrane potential and neural power. The results showed that the membrane potential and neural power were not perfectly correlated (P-V correlations ranged from 0.6 to 0.9), and dopamine concentration inputs affected the P-V correlations of the MSN, pyramidal neurons and CB interneurons in the mPFC region. These findings suggested that the joint application of the neural coding theory and neural energy coding theory will be superior to the application of any single theory, and this joint application could help discover new mechanisms in neurocircuits of MDD. Overall, our study not only uncovered the neural energy coding patterns for the VTA-NAc-mPFC neural microcircuit, but also presented a novel pipeline for the study of MDD based on the neural coding theory and neural energy coding theory.
众多实验发现，重度抑郁症（MDD）动物的行为特征通常与异常的神经活动模式和脑能量代谢相关。然而，行为特征、神经活动模式和脑能量代谢之间的关系仍不清楚。在本文中，我们基于生物神经网络模型和神经能量计算模型，通过计算研究了这种关系，特别关注MDD大脑中奖励系统的腹侧被盖区-伏隔核-内侧前额叶皮质（VTA-NAc-mPFC）多巴胺能通路中神经能量编码模式是如何变化的。有趣的是，我们的结果表明，MDD组整个VTA-NAc-mPFC微回路的神经能量消耗显著降低，这主要归因于内侧前额叶皮质区域神经能量消耗的减少。这一观察结果在理论上支持了MDD中低水平能量消耗的观点。我们还研究了在不同多巴胺浓度影响下，我们的VTA-NAc-mPFC微回路中各种神经元类型的神经能量消耗模式，发现MDD中存在一些特定的损伤，这为MDD诊断提供了一些潜在的生物标志物。更具体地说，我们发现MDD组伏隔核区域中等棘状神经元（MSNs）的实际神经能量消耗增加，而内侧前额叶皮质区域的锥体神经元在正常对照组中表现出更高的实际神经能量消耗。此外，在这两种神经元类型中，MDD组产生动作电位所需的实际神经能量更高，这表明在相同的能量预算下，MDD组的这些神经元倾向于产生更少的动作电位。为了进一步探索VTA-NAc-mPFC微回路中神经编码模式与神经能量编码模式之间的关系，我们还计算了每种神经元类型的P-V相关性，定义为膜电位与神经功率之间的皮尔逊相关系数。结果表明，膜电位和神经功率并非完全相关（P-V相关性范围为0.6至0.9），并且多巴胺浓度输入会影响内侧前额叶皮质区域MSN、锥体神经元和CB中间神经元的P-V相关性。这些发现表明，神经编码理论和神经能量编码理论的联合应用将优于任何单一理论的应用，并且这种联合应用有助于发现MDD神经回路中的新机制。总体而言，我们的研究不仅揭示了VTA-NAc-mPFC神经微回路的神经能量编码模式，还基于神经编码理论和神经能量编码理论提出了一种研究MDD的新方法。
