Intermittent hypoxia and cerebellar development
Data & R analysis code
Background: Apnea of prematurity (AOP) is caused by respiratory control immaturity and affects nearly 50% of premature newborns. This pathology induces perinatal intermittent hypoxia (IH), which leads to neurodevelopmental disorders. The impact on the brain has been well investigated. However, despite its functional importance and immaturity at birth, the involvement of the cerebellum remains poorly understood. Therefore, this study aims to identify the effects of IH on cerebellar development using a mouse model of AOP consisting of repeated 2-min cycles of hypoxia and reoxygenation over 6 h and for 10 days starting on postnatal day 2 (P2).
Results: At P12, IH-mouse cerebella present higher oxidative stress associated with delayed maturation of the cerebellar cortex and decreased dendritic arborization of Purkinje cells. Moreover, mice present with growth retardation and motor disorders. In response to hypoxia, the developing cerebellum triggers compensatory mechanisms resulting in the unaltered organization of the cortical layers from P21 onwards. Nevertheless, some abnormalities remain in adult Purkinje cells, such as the dendritic densification, the increase in afferent innervation, and axon hypomyelination. Moreover, this compensation seems insufficient to allow locomotor recovery because adult mice still show motor impairment and significant disorders in spatial learning.
Conclusions: All these findings indicate that the cerebellum is a target of intermittent hypoxia through alterations of developmental mechanisms leading to long-term functional deficits. Thus, the cerebellum could contribute, like others brain structures, to explaining the pathophysiology of AOP.
Foreword
This website serves as documentation as well as to showcase the data and R analysis code for Leroux S., Rodriguez-Duboc A., Arabo A., Basille-Dugay M., Vaudry D., & Burel D., 2022:
Structure of this website
Each of the files listed in the navigation bar (left side of the screen) contains multiple variables that we analyzed, grouped by thematic. Each file will contain a section for each of those variables. Each of those sections is further divided into three subsections:
Data Exploration
: variable’s summary statistics, distribution, and evolution in time (if applicable).Models & Diagnostics
: quality of fit, diagnostics and predictions for each of the candidate models of that variable.
Each candidate model is split in its own tab.
Effects Analysis
: the chosen model’s coefficients, main effects (Wald \(\chi^2\) and Likelihood Ratio Test), and the marginal means and contrasts for each of its predictor. This subsection also includes the box plots showcased in the article.
Each predictor is split in its own tab.