Effects of a Secretin Receptor Antagonist on Cerebellar Learning

Eyeblink conditioning (EBC) is an important procedure used to understand the neuronal plasticity that occurs with learning and memory. Delay EBC requires a brainstem-cerebellar circuit while the role of the cerebellum in trace EBC is not as well understood because it requires a more complex neural circuitry involving regions of the medial prefrontal cortex and hippocampus. Secretin is a neuropeptide that is found in high concentrations within the cerebellum. Previous work has shown that blocking secretin’s effects in the cerebellum with intra-cerebellar infusion of relatively large volume of a secretin receptor antagonist impairs delay EBC (Fuchs et al. 2014). Here we study the effect that intra-cerebellar infusion of 0.5 μL secretin receptor antagonist (5-27 secretin) or vehicle prior to training sessions 1 and 2 has on delay and trace EBC in rats. A 600-ms tone CS was used for the delay EBC paradigm and a 300-ms tone CS followed by a 300-ms trace interval was used for the trace EBC paradigm. For delay EBC, the delay vehicle and antagonist groups displayed similar acquisition of conditioned responses (CRs). There was a trend for the trace antagonist group to underperform compared to the trace vehicle group though not quite at a significant level. One explanation for why the results for the delay EBC do not support previous work is that slow learning occurred in the delay vehicle group that may have prevented the effects of secretin receptor antagonist from reaching significance. The trend for the trace antagonist group to display decreased acquisition of CRs suggests that the cerebellum does play an important role in trace EBC. However, in order to better understand the neural circuitry involved in trace EBC, future work should analyze the role that cerebellar secretin itself has on trace EBC

Sleep-Disordered Breathing Risk with Comorbid Insomnia Is Associated with Mild Cognitive Impairment

Featured Application This study aims to identify candidate modifiable risk factors of cognitive disease. Future applications of this study include the optimization of sleep to ameliorate cognitive decline. Introduction: Few studies have evaluated the combined association between SDB with comorbid insomnia and mild cognitive impairment (MCI). To test the hypothesis that SDB with comorbid insomnia is associated with greater odds of MCI than either sleep disorder independently, we used ADNI data to evaluate cross-sectional associations between SDB risk with comorbid insomnia status and MCI. Methods: Participants with normal cognition or MCI were included. Insomnia was defined by self-report. SDB risk was assessed by modified STOP-BANG. Logistic regression models evaluated associations between four sleep disorder subgroups (low risk for SDB alone, low risk for SDB with insomnia, high risk for SDB alone, and high risk for SDB with insomnia) and MCI. Models adjusted for age, sex, BMI, APOE4 genotype, race, ethnicity, education, marital status, hypertension, cardiovascular disease, stroke, alcohol abuse, and smoking. Results: The sample (n = 1391) had a mean age of 73.5 +/- 7.0 years, 44.9% were female, 72.0% were at low risk for SDB alone, 13.8% at low risk for SDB with insomnia, 10.1% at high risk for SDB alone, and 4.1% at high risk for SDB with insomnia. Only high risk for SDB with comorbid insomnia was associated with higher odds of MCI (OR 3.22, 95% CI 1.57-6.60). Conclusion: Studies are needed to evaluate SDB with comorbid insomnia as a modifiable risk factor for MCI

Sleep-Disordered Breathing Risk with Comorbid Insomnia Is Associated with Mild Cognitive Impairment

Introduction: Few studies have evaluated the combined association between SDB with comorbid insomnia and mild cognitive impairment (MCI). To test the hypothesis that SDB with comorbid insomnia is associated with greater odds of MCI than either sleep disorder independently, we used ADNI data to evaluate cross-sectional associations between SDB risk with comorbid insomnia status and MCI. Methods: Participants with normal cognition or MCI were included. Insomnia was defined by self-report. SDB risk was assessed by modified STOP-BANG. Logistic regression models evaluated associations between four sleep disorder subgroups (low risk for SDB alone, low risk for SDB with insomnia, high risk for SDB alone, and high risk for SDB with insomnia) and MCI. Models adjusted for age, sex, BMI, APOE4 genotype, race, ethnicity, education, marital status, hypertension, cardiovascular disease, stroke, alcohol abuse, and smoking. Results: The sample (n = 1391) had a mean age of 73.5 ± 7.0 years, 44.9% were female, 72.0% were at low risk for SDB alone, 13.8% at low risk for SDB with insomnia, 10.1% at high risk for SDB alone, and 4.1% at high risk for SDB with insomnia. Only high risk for SDB with comorbid insomnia was associated with higher odds of MCI (OR 3.22, 95% CI 1.57–6.60). Conclusion: Studies are needed to evaluate SDB with comorbid insomnia as a modifiable risk factor for MCI