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Open Access Highly Accessed Research article

A resting state network in the motor control circuit of the basal ganglia

Simon Robinson14*, Gianpaolo Basso1, Nicola Soldati2, Uta Sailer3, Jorge Jovicich1, Lorenzo Bruzzone2, Ilse Kryspin-Exner3, Herbert Bauer3 and Ewald Moser45

Author Affiliations

1 Functional Neuroimaging Laboratory, Center for Mind/Brain Sciences, University of Trento, Trento, Italy

2 Telecommunication Engineering, University of Trento, Trento, Italy

3 Faculty of Psychology, University of Vienna, Liebiggasse 5, 1010 Vienna, Austria

4 MR Center of Excellence, Medical University of Vienna, Lazarettgasse 14, 1090 Vienna, Austria

5 Center for Biomedical Engineering and Physics, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria

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BMC Neuroscience 2009, 10:137  doi:10.1186/1471-2202-10-137

Published: 23 November 2009

Abstract

Background

In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond.

Results

An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates.

Conclusion

Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.