摘要
额叶-基底神经节模型构成运动控制网络,其网络主要由额下回、辅助运动区、初级运动皮层、初级躯体感觉皮层及基底神经节亚区组成,是调节反应抑制能力的主要网络。目前,评测反应抑制能力的范式主要包括Go/No-Go与Stop-signal范式。多项研究发现,这两种实验范式在进行抑制任务时额叶及基底神经节亚区存在不同的激活,提示其抑制机制可能有所不同。其中,Go/No-Go任务是否由超直接通路调控抑制过程还有待探讨,Stop-signal任务则可能需要间接通路、超直接通路实现对抑制过程的调控。运动控制网络与感觉运动网络、默认模式网络之间的交互作用可能在治疗反应抑制缺陷与其他脑功能疾病中发挥调节作用。
The frontal lobe-basal ganglia model constitutes the motor control network,which is mainly composed of the inferior frontal gyrus,supplementary motor area,primary motor cortex,primary somatosensory cortex and the basal ganglia subarea,and is the main network regulating the response inhibition ability.At present,the paradigms to evaluate the inhibition ability of response mainly include Go/No-Go and Stop-signal paradigms.Researches found that the two experimental paradigms had different activations in the frontal lobe and basal ganglia subregion during the inhibition task,suggesting that the inhibition mechanism might be different.Among them,it remains need to be discussed whether the Go/No-Go task is regulated and inhibited by hyperdirect pathway.The Stop-signal task may require the indirect pathway and hyperdirect pathway to realize the regulation of inhibitory process.Meanwhile,the interaction between motor control network,sensorimotor network and default mode network may play a regulatory role in the treatment of response inhibition deficiency and other brain functional diseases.
作者
史冀龙
王君
陈福俊
李懿婷
朱俊全
黄浩洁
侯莉娟
SHI Jilong;WANG Jun;CHEN Fujun;LI Yiting;ZHU Junquan;HUANG Haojie;HOU Lijuan(College of Physical Education and Sports,Beijing Normal University,Beijing 100875,China;State Key Laboratory of Cognitive Neuroscience and Learning,Beijing Normal University,Beijing 100875,China;Exercise Health and Technology Center,Department of Physical Education,Shanghai Jiao Tong University,Bio-X Institutes,Shanghai Jiao Tong University,Shanghai 200240,China)
出处
《生命的化学》
CAS
CSCD
2019年第6期1113-1119,共7页
Chemistry of Life
基金
国家自然科学基金项目(31971095)
上海市科学技术委员会基金项目(18JC1413100)
关键词
反应抑制
额叶-基底神经节模型
运动控制网络
response inhibition
the frontal lobe-basal ganglia model
motor control network