Abstract

The autonomic brainstem generates and modifies breathing rhythm by integrating inputs from chemo‐ and mechanosensors in the viscera while coordinating descending outputs from higher CNS structures. Hypoglossal motoneurons (XII MNs) receive inputs from respiratory premotor neurons in the medulla. Previous studies in rodents have demonstrated significant changes in breathing control during the first three weeks of life, with a putative sensitive period at 10 to 13 days post‐birth (P10–P13) characterized by pronounced changes in neurotransmitters, receptors, excitation‐inhibition balance, and breathing physiology [Li et al., J Physiol, 577:957–770, 2006]. However, age‐dependent morphological changes of XII MNs during the first three weeks post‐birth and especially during this sensitive period, have not been thoroughly studied. In this study, we comprehensively characterize and quantify the postnatal morphological changes in rat XII MNs. We hypothesized that morphological changes occur in XII MN morphology and arbor complexity corresponding to the functionally‐defined sensitive period observed at P10–P13. To test this hypothesis, we used innovative statistical approaches to quantify and compare developmental changes in Golgi‐Cox stained XII MNs at nine postnatal ages between P1–P21. Soma size increased ~40% from P1 to P21, with no significant change in shape. However, dendritic arborization increased in extent and complexity. Dendritic branching of developing neurons significantly increased from P1 through P13, with the greatest increase at P10–P13 based on the Sholl method. For many parameters, three age groups 1) P1–P5, 2) P7–P12, and 3) P13–P21 were used as possible windows of development. We also found that at specific ages certain parameters such as soma size and dendrite distance were non‐normally distributed. Our detailed characterization of XII MN morphological development establishes a foundation for the study and elucidation of morphological changes caused by maternal and perinatal conditions using a rigorous statistical approach.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

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