Ruffini Giulio, Castaldo Francesca, Lopez-Sola Edmundo, Sanchez-Todo Roser, Vohryzek Jakub
Brain Modeling Department, Neuroelectrics, 08035 Barcelona, Spain.
Computational Neuroscience Group, UPF, 08005 Barcelona, Spain.
Entropy (Basel). 2024 Nov 6;26(11):953. doi: 10.3390/e26110953.
Major Depressive Disorder (MDD) is a complex, heterogeneous condition affecting millions worldwide. Computational neuropsychiatry offers potential breakthroughs through the mechanistic modeling of this disorder. Using the Kolmogorov theory (KT) of consciousness, we developed a foundational model where algorithmic agents interact with the world to maximize an Objective Function evaluating affective valence. Depression, defined in this context by a state of persistently low valence, may arise from various factors-including inaccurate world models (cognitive biases), a dysfunctional Objective Function (anhedonia, anxiety), deficient planning (executive deficits), or unfavorable environments. Integrating algorithmic, dynamical systems, and neurobiological concepts, we map the agent model to brain circuits and functional networks, framing potential etiological routes and linking with depression biotypes. Finally, we explore how brain stimulation, psychotherapy, and plasticity-enhancing compounds such as psychedelics can synergistically repair neural circuits and optimize therapies using personalized computational models.
重度抑郁症（MDD）是一种复杂的异质性疾病，影响着全球数百万人。计算神经精神病学通过对这种疾病的机制建模提供了潜在的突破。利用意识的柯尔莫哥洛夫理论（KT），我们开发了一个基础模型，其中算法智能体与世界相互作用，以最大化评估情感效价的目标函数。在这种情况下，抑郁症被定义为持续低效价状态，可能由多种因素引起，包括不准确的世界模型（认知偏差）、功能失调的目标函数（快感缺乏、焦虑）、规划不足（执行缺陷）或不利环境。整合算法、动力系统和神经生物学概念，我们将智能体模型映射到脑回路和功能网络，勾勒出潜在的病因路径，并与抑郁症生物型相联系。最后，我们探讨脑刺激、心理治疗以及诸如迷幻剂等增强可塑性的化合物如何利用个性化计算模型协同修复神经回路并优化治疗。
