Park Hae-Jeong, Kang Jiyoung
Center for Systems and Translational Brain Science, Institute of Human Complexity and Systems Science, Yonsei University, Seoul, South Korea.
Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.
Front Comput Neurosci. 2021 Apr 14;15:590019. doi: 10.3389/fncom.2021.590019. eCollection 2021.
The brain is a non-linear dynamical system with a self-restoration process, which protects itself from external damage but is often a bottleneck for clinical treatment. To treat the brain to induce the desired functionality, formulation of a self-restoration process is necessary for optimal brain control. This study proposes a computational model for the brain's self-restoration process following the free-energy and degeneracy principles. Based on this model, a computational framework for brain control is established. We posited that the pre-treatment brain circuit has long been configured in response to the environmental (the other neural populations') demands on the circuit. Since the demands persist even after treatment, the treated circuit's response to the demand may gradually approximate the pre-treatment functionality. In this framework, an energy landscape of regional activities, estimated from resting-state endogenous activities by a pairwise maximum entropy model, is used to represent the pre-treatment functionality. The approximation of the pre-treatment functionality occurs via reconfiguration of interactions among neural populations within the treated circuit. To establish the current framework's construct validity, we conducted various simulations. The simulations suggested that brain control should include the self-restoration process, without which the treatment was not optimal. We also presented simulations for optimizing repetitive treatments and optimal timing of the treatment. These results suggest a plausibility of the current framework in controlling the non-linear dynamical brain with a self-restoration process.
大脑是一个具有自我修复过程的非线性动力系统，该过程可保护大脑免受外部损伤，但往往是临床治疗的瓶颈。为了治疗大脑以诱导所需的功能，制定自我修复过程对于实现最佳的大脑控制是必要的。本研究提出了一种遵循自由能和简并性原理的大脑自我修复过程的计算模型。基于该模型，建立了一个大脑控制的计算框架。我们假定，治疗前的脑回路长期以来一直是根据环境（其他神经群体）对该回路的需求而配置的。由于即使在治疗后这些需求仍然存在，治疗后的回路对需求的反应可能会逐渐接近治疗前的功能。在这个框架中，通过成对最大熵模型从静息态内源性活动估计的区域活动的能量景观，被用来表示治疗前的功能。治疗前功能的近似是通过治疗后回路中神经群体之间相互作用的重新配置来实现的。为了建立当前框架的结构效度，我们进行了各种模拟。模拟结果表明，大脑控制应包括自我修复过程，否则治疗不是最优的。我们还展示了优化重复治疗和治疗最佳时机的模拟。这些结果表明当前框架在通过自我修复过程控制非线性动力大脑方面具有合理性。
