Institute for Cognitive Neurodynamics, School of Mathematics, East China University of Science and Technology, Shanghai, China; Department of Neurology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
Comput Biol Med. 2023 Nov;166:107500. doi: 10.1016/j.compbiomed.2023.107500. Epub 2023 Sep 17.
Limited by the current experimental techniques and neurodynamical models, the dysregulation mechanisms of decision-making related neural circuits in major depressive disorder (MDD) are still not clear. In this paper, we proposed a neural coding methodology using energy to further investigate it, which has been proven to strongly complement the neurodynamical methodology. We augmented the previous neural energy calculation method, and applied it to our VTA-NAc-mPFC neurodynamical H-H models. We particularly focused on the peak power and energy consumption of abnormal ion channel (ionic) currents under different concentrations of dopamine input, and investigated the abnormal energy consumption patterns for the MDD group. The results revealed that the energy consumption of medium spiny neurons (MSNs) in the NAc region were lower in the MDD group than that of the normal control group despite having the same firing frequencies, peak action potentials, and average membrane potentials in both groups. Dopamine concentration was also positively correlated with the energy consumption of the pyramidal neurons, but the patterns of different interneuron types were distinct. Additionally, the ratio of mPFC's energy consumption to total energy consumption of the whole network in MDD group was lower than that in normal control group, revealing that the mPFC region in MDD group encoded less neural information, which matched the energy consumption patterns of BOLD-fMRI results. It was also in line with the behavioral characteristics that MDD patients demonstrated in the form of reward insensitivity during decision-making tasks. In conclusion, the model in this paper was the first neural network energy computational model for MDD, which showed success in explaining its dynamical mechanisms with an energy consumption perspective. To build on this, we demonstrated that energy consumption levels can be used as a potential indicator for MDD, which also showed a promising pipeline to use an energy methodology for studying other neuropsychiatric disorders.
受当前实验技术和神经动力学模型的限制，重度抑郁症（MDD）相关决策神经回路失调的机制仍不清楚。在本文中，我们提出了一种使用能量的神经编码方法来进一步研究它，该方法已被证明可以很好地补充神经动力学方法。我们扩展了之前的神经能量计算方法，并将其应用于我们的 VTA-NAc-mPFC 神经动力学 HH 模型。我们特别关注在不同多巴胺输入浓度下异常离子通道（离子）电流的峰值功率和能量消耗，并研究了 MDD 组的异常能量消耗模式。结果表明，尽管 MDD 组和正常对照组的中脑腹侧被盖区（VTA）-伏隔核（NAc）-前额叶皮质（mPFC）投射神经元（MSNs）的放电频率、峰值动作电位和平均膜电位相同，但 NAc 区 MSNs 的能量消耗在 MDD 组中低于正常对照组。多巴胺浓度与锥体神经元的能量消耗呈正相关，但不同中间神经元类型的模式不同。此外，MDD 组 mPFC 的能量消耗与整个网络总能量消耗的比值低于正常对照组，表明 MDD 组 mPFC 区域编码的神经信息较少，这与 BOLD-fMRI 结果的能量消耗模式相匹配。这也符合 MDD 患者在决策任务中表现出的奖励不敏感的行为特征。总之，本文中的模型是第一个用于 MDD 的神经网络能量计算模型，它成功地从能量消耗的角度解释了其动力学机制。在此基础上，我们证明了能量消耗水平可以作为 MDD 的一个潜在指标，这也为使用能量方法研究其他神经精神障碍提供了一个很有前途的途径。
