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Letters to Nature
Nature 400, 364-367 (22 July 1999) | doi:10.1038/22547; Received 5 March 1999; Accepted 1 June 1999
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Temporal dissociation of parallel processing in the human subcortical outputs
Yijun Liu1,2, Jia-Hong Gao1, Mario Liotti1, Yonglin Pu1 & Peter T. Fox1
- Research Imaging Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78284, USA
- Department of Physiology, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, Texas 78284, USA
Correspondence to: Mario Liotti1 Correspondence and requests for materials should be addressed to Y.L. (e-mail: Email: LIUY1@uthscsa.edu).
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.
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