Unraveling the projection-stratified anatomical and molecular organization of the Deep Cerebellar Nuclei

Thornton, Staci. Unraveling the projection-stratified anatomical and molecular organization of the Deep Cerebellar Nuclei. 2022, Doctoral Thesis, University of Basel, Faculty of Science.


Official URL: https://edoc.unibas.ch/88170/

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Movement is the main output of the central and peripheral nervous systems. Our daily lives depend on the precise coordination and execution of movements involving multiple limbs. The neuronal circuits which mediate and coordinate movement span throughout the brain and involve the integration of information across the brain’s motor centers to modulate execution, learning, and action planning. Recently, groundbreaking work in the brainstem, cerebellum, and motor cortex have provided a framework for developing methods, designing experiments, and conjuring hypotheses to understand how movement is controlled. Research dedicated to delineating spatially intermingled neuronal populations in the mouse brainstem, gave rise to the work that will be described in this thesis. Specifically, we describe how neurons in the deep cerebellar nuclei (DCN), the sole output of the cerebellum and a structure canonically known as devoted to online motor control, are connected to the brainstem and thalamus. We find a high degree of synaptic specificity with respect to target regions innervated by different subpopulations of DCN neurons. Using a myriad of genetic, viral, and molecular tools, we identify previously uncharacterized anatomical and molecular cell types in the deep cerebellar nuclei and brainstem.
Advisors:Arber, Silvia and Donato, Flavio and Mathis, Mackenzie
Faculties and Departments:05 Faculty of Science > Departement Biozentrum > Neurobiology > Neurobiology (Donato)
09 Associated Institutions > Friedrich Miescher Institut FMI > Neurobiology > Motor circuit function (Arber)
UniBasel Contributors:Arber, Silvia and Donato, Flavio
Item Type:Thesis
Thesis Subtype:Doctoral Thesis
Thesis no:14667
Thesis status:Complete
Number of Pages:88
Identification Number:
  • urn: urn:nbn:ch:bel-bau-diss146673
edoc DOI:
Last Modified:12 Apr 2024 01:30
Deposited On:13 Apr 2022 10:59

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