Distribution of Substantia Nigra Pars Compacta Neurons with Respect to Pars Reticulata Striato-Nigral Afferences: Computer-Assisted Three-Dimensional Reconstructions

Abstract

Following ionophoretic injections of wheat germ agglutinin coupled to horseradish peroxidase into identified functional striatal territories of the rat brain, the distribution of anterogradely labelled striatonigral projections and retrogradely labelled nigrostriatal neurons was studied by three-dimensional modeling. Our models confirmed and extended the organizational scheme of striatonigral projections, already reported by our laboratory. The relationship between striatonigral projections and nigrostriatal neurons was also studied. For each striatal injection site, two subpopulations of labelled nigral neurons were distinguished by their position with respect to the striatal projection field. The first one occupied a proximal position, in register with the labelled striatal projections, while the second one was more distal. The subpopulations of proximal neurons innervating different functional striatal sectors were segregated in the mediolateral, dorsoventral and rostrocaudal directions, while the distal neurons, more scattered, showed a lesser degree of spatial segregation. These results suggest that the substantia nigra might control the flow of cortical information through the striatum via two different modalities based respectively on a closed nigro-striatal loop involving the proximal neurons, and an open loop involving the distal ones. We propose an organization scheme which may account for both the segregation and the intermingling of cell populations innervating distinct striatal sectors. Finally, the contribution of 3D modeling to neuroanatomical studies is discussed, along with the perspectives towards more quantitative and predictive 3D modeling.KeywordsBasal GanglionVentral Tegmental AreaWheat Germ AgglutininNigral NeuronOrganization RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.