Temporal dissociation of parallel processing in the human subcortical outputs

Yijun Liu, Jia-Hong Gao, Mario Liotti (), Yonglin Pu and Peter T. Fox
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Yijun Liu: Research Imaging Center, University of Texas Health Science Center at San Antonio
Jia-Hong Gao: Research Imaging Center, University of Texas Health Science Center at San Antonio
Mario Liotti: Research Imaging Center, University of Texas Health Science Center at San Antonio
Yonglin Pu: Research Imaging Center, University of Texas Health Science Center at San Antonio
Peter T. Fox: Research Imaging Center, University of Texas Health Science Center at San Antonio

Nature, 1999, vol. 400, issue 6742, 364-367

Abstract: Abstract 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, respectively1,2,3,4,5,6,7,8,9,10. 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 organization11,12, which presents an ideal neuroimaging model for the study of parallel processing at a systems level13. 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 networks14,15.

Date: 1999
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DOI: 10.1038/22547

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