Temporal dissociation of parallel processing in the human subcortical outputs
Temporal dissociation of parallel processing in the human subcortical outputs
复制标题
作者：
Liu, YY;Gao, JH;Fox, PT
许多任务需要根据传入的感觉信息对运动表现进行快速且精细的调整。这种感觉运动适应过程涉及两个平行的皮质下 - 皮质神经回路，分别涉及小脑和基底神经节（1 - 10）。这些分布式回路在人类中是如何在功能上协调的尚未可知。小脑和基底神经节在输入 - 输出组织上表现出非常相似的汇聚（11,12），这为在系统层面研究并行处理提供了一个理想的神经影像模型（13）。在此，我们利用功能性磁共振成像来测量触觉辨别任务期间大脑活动的时间相干性。我们发现，尽管前额叶皮质保持着高水平的激活，但小脑和基底神经节的输出活动呈现出不同的相位模式。此外，小脑活动与辅助运动区的活动显著相关，但与初级运动皮质的活动无关；相比之下，基底神经节活动与初级运动皮质的活动的关联比与辅助运动区的活动的关联更强。这些结果表明小脑和基底神经节的活动在时间上是分开的，这意味着平行的皮质下输出在功能上是独立的。这进一步支持了大规模神经网络与任务相关的动态重构这一观点（14,15）。
Many tasks require rapid and fine-tuned adjustment of motor performance based on incoming sensory information. This process of sensorimotor adaptation engages two parallel subcortico-cortical neural circuits, involving the cerebellum and basal ganglia, respectively(1-10), How these distributed circuits are functionally coordinated has not been shown in humans. The cerebellum and basal ganglia show very similar convergence of input-output organization(11,12), which presents an ideal neuroimaging model for the study of parallel processing at a systems level(13). Here we used functional magnetic resonance imaging to measure the temporal coherence of brain activity during a tactile discrimination task. We found that, whereas the prefrontal cortex maintained a high level of activation, output activities in the cerebellum and basal ganglia showed different phasic patterns. Moreover, cerebellar activity significantly correlated with the activity of the supplementary motor area but not with that of the primary motor cortex; in contrast, basal ganglia activity was more strongly associated with the activity of the primary motor cortex than with that of the supplementary motor area. These results demonstrate temporally partitioned activity in the cerebellum and basal ganglia, implicating functional independence in the parallel subcortical outputs. This further supports the idea of task-related dynamic reconfiguration of large-scale neural networks(14,15).