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The intrinsic and extrinsic connectome of subregions of the basal ganglia


Oliver Schmitt (Department of Anatomy, Uni Rostock), Peter Eipert (Department of Anatomy, Uni Rostock), Konstanze Philipp (Department of Anatomy, Uni Rostock), Richard Kettlitz (Department of Anatomy, Uni Rostock), Andreas Wree (Department of Anatomy, Uni Rostock)

The motoric part of the basal ganglia (BG) network in the rat receives input from the primary motor cortex and consists of the caudate putamen complex, lateral and medial globus pallidus, substantia nigra, subthalamic nucleus and some thalamic nuclei (parafascicular, ventromedial, mediodorsal, ventrolateral, lateral habenula). Most of these classical components are directly (monosynaptically) interconnected. In a metastudy of 2200 tract-tracing publications of the rat central nervous system much more regions were found that are directly connected to functionally important regions of the motoric BG. In this contribution extrinsic and intrinsic connectivity of the BG has been analyzed. Using conventional global and local graph evaluation methods, new approaches of vulnerability and pathway analysis as well as techniques of visual analytics revealed new patterns of reciprocal connections. The unilateral intrinsic BG consists of 14 nodes which are connected by 122 edges resulting in a line density of 67.033% and an average cluster coefficient of 0.735. The average path length is 1.335. It was found that the accumbens nucleus has most ipsilateral and contralateral inputs while the lateral agranular prefrontal cortex has most ipsi- and contralateral outputs. The caudate putamen complex has the largest eigenvector centrality and the lowest Shapley rate. This indicates its importance for the intrinsic network structure of the BG. The substantia nigra pars compacta has a relative high rank with regard to vulnerability, however, the substantia nigra reticular part and the medial globus pallidus are more important to preserve network structure following removal of these nuclei.
Preferred presentation format: Poster
Topic: Computational neuroscience