Effect of Ketamine on Sleep in Treatment-Resistant Depression: A Systematic Review

Hypersomnia as a predictor of response to intravenous ketamine/intranasal esketamine in treatment resistant depression

BACKGROUND

Sleep disturbances are highly prevalent in depressive episodes and are linked to higher mood severity and suicidal behaviors. Slow wave sleep (SWS) and REM sleep are compromised in depression. Current evidence suggests that rapid antidepressant effects of intravenous (IV) ketamine in patients with treatment-resistant depression (TRD) is mediated by its effects on SWS and REM sleep. Sleep phenotypes may help predict ketamine response.

METHOD

In this observational study, we investigated differences in rates of response among sleep phenotypes defined by QIDS-SR in a cohort of patients with TRD (n = 52) treated with IV ketamine or intranasal (IN) esketamine. Also, we explored a neurovegetative symptoms of atypical depression (NVSAD) phenotype and its association between response and change in QIDS-SR following the treatment with IV ketamine/IN esketamine.

RESULTS

94 % of patients reported sleep difficulties and 62 % reported more than one sleep phenotype with middle and early insomnia being the most prevalent. Individuals with baseline hypersomnia showed higher response rates and more pronounced improvements on their QIDS-SR score. Additionally, 15 % of patients presented with NVSAD phenotype; the majority of whom achieved response and had higher reductions on QIDS-SR. A trend towards faster response was identified for hypersomnia and atypical depression phenotypes.

LIMITATIONS

Observational study design and lack of a placebo group.

CONCLUSIONS

Our data indicate that patients with TRD who have baseline hypersomnia and atypical depression features experienced a more substantial reduction in depressive symptoms and are more likely to achieve response with ketamine/esketamine. This could serve as a future predictor for clinical response.

Automatic Sleep Staging Based on Contextual Scalograms and Attention Convolution Neural Network Using Single-Channel EEG

Single-channel EEG based sleep staging is of interest to researchers due to its broad application prospect in daily sleep monitoring recently. We proposed using contextual scalograms as input and developed a convolutional neural network with attention modules named Co-ScaleNet for sleep staging. The contextual scalograms were obtained by combining the same color channels of three original RGB scalograms from consecutive epochs, and a simple and efficient data augmentation was designed according to their various forms. The Co-ScaleNet consists of two main parts. Firstly, three parallel convolutional branches with attention modules correspondingly extract and fuse features from contextual scalograms at the top layers. The remaining part is a stack of lightweight blocks. We achieved an overall accuracy of 87.0% for healthy individuals, 84.7% for depressed patients. And we obtained comparable performance on the public Sleep-EDFx (82.8%), ISRUC (84.6%) and SHHS datasets (87.7%), including a high recall of N1. The contextual scalograms of R channel as input achieved the best performance, which conform to the features of interest in visual scoring. The attention modules improved the recall of N1 and N3. Overall, the contextual scalograms provided a novel scheme for both contextual information extraction and data augmentation. Our study successfully expanded its application to depression datasets, as well as patients with sleep apnea, demonstrating its wide applicability.