Pain ranks among the most frequent nonmotor manifestations of Parkinson's disease (PD), yet the morphofunctional condition of the brainstem pain-modulating nuclei is poorly characterized in experimental models. The periaqueductal gray (PAG) and the raphe magnus nucleus (RMG) form the principal descending antinociceptive circuit and are regarded as secondary targets of PD-related neurodegeneration. In the present exploratory study, a rotenone-induced rat model is used to assess neuronal morphology and metabolic activity in the PAG and the RMG and to test the neuroprotective potential of hydrocortisone (HC). Calcium-dependent acid phosphatase (Ca 2+ -ACP) histochemistry is applied as a dual morphofunctional tool that reports enzymatic metabolic activity together with cellular architecture through lead phosphate deposition. Application of this method to both nuclei under HC treatment in a rotenone PD model has not been reported previously and constitutes the principal novelty of this work. Rotenone exposure produces marked neurodegenerative alterations, which include irregular somatic morphology, nuclear displacement, chromatolysis, dendritic atrophy, and reduced Ca 2+ -ACP activity. Densitometric analysis with one-way analysis of variance (ANOVA) and Tukey's post hoc test, computed with the animal as the unit of analysis ( n = 3 Norm, n = 4 PD, n = 8 PD + HC), shows significant group differences for every parameter measured in both nuclei ( p < 0.001). Hydrocortisone treatment is associated with substantial preservation of somatic architecture, nuclear positioning, and dendritic integrity, and with recovery of Ca 2+ -ACP reactivity to approximately 75 to 80% of control values; the values of the PD + HC group nevertheless remain significantly below those of the intact controls ( p < 0.05). The results indicate that the pain-modulating brainstem nuclei are susceptible to PD-related neurodegeneration and that HC affords substantial although partial protection of neuronal morphology and metabolic activity under neurotoxic conditions. Confirmation in larger, balanced, and mechanistically validated cohorts is required.
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Danielyan et al. (2026) studied this question.
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