Salk Institute for Biological Studies, La Jolla, California, USA.
Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, California, USA.
Ann N Y Acad Sci. 2023 Jul;1525(1):70-87. doi: 10.1111/nyas.14997. Epub 2023 May 2.
A functional interplay of bottom-up and top-down processing allows an individual to appropriately respond to the dynamic environment around them. These processing modalities can be represented as attractor states using a dynamical systems model of the brain. The transition probability to move from one attractor state to another is dependent on the stability, depth, neuromodulatory tone, and tonic changes in plasticity. However, how does the relationship between these states change in disease states, such as anxiety or depression? We describe bottom-up and top-down processing from Marr's computational-algorithmic-implementation perspective to understand depressive and anxious disease states. We illustrate examples of bottom-up processing as basolateral amygdala signaling and projections and top-down processing as medial prefrontal cortex internal signaling and projections. Understanding these internal processing dynamics can help us better model the multifaceted elements of anxiety and depression.
自下而上和自上而下的处理功能相互作用，使个体能够对周围动态的环境做出适当的反应。这些处理方式可以使用大脑的动力系统模型表示为吸引子状态。从一个吸引子状态转移到另一个吸引子状态的转移概率取决于稳定性、深度、神经调质和可塑性的紧张变化。然而，在疾病状态（如焦虑或抑郁）下，这些状态之间的关系如何变化？我们从马尔的计算算法实现的角度描述了自下而上和自上而下的处理，以了解抑郁和焦虑的疾病状态。我们举例说明了基底外侧杏仁核信号和投射的自下而上的处理，以及内侧前额叶皮层内部信号和投射的自上而下的处理。了解这些内部处理动态可以帮助我们更好地对焦虑和抑郁的多方面因素进行建模。
