Effectiveness of TES and rTMS for the Treatment of Insomnia: Meta-Analysis and Meta-Regression of Randomized Sham-Controlled Trials

Novel electrical therapy to improve sleep disturbance in patients with autoimmune rheumatic diseases

OBJECTIVES

Sleep disturbance is common in autoimmune rheumatism diseases (ARD) and it plays an important role in activating disease and affects the quality of life. This study aims to evaluate the efficacy and acceptability of the novel electrical therapy on sleep disturbance in ARD patients and its effect on immunologic factors.

METHODS

A total of 51 ARD patients (26 treatment group and 25 control group) with sleep disturbance were enrolled in this study. Sleep parameters and immunological indicators (serum level of 12 cytokines and immune function) were collected. The novel electrical therapy was prescribed for 15-30 min 3-6 times a day. The Pittsburg Sleep Index (PSQI) was assessed before and after 3 months' treatment by Mi Energy equipment. Immune function and serum levels of cytokines of all participants at baseline and after treatment were tested with flow cytometry and flow immunofluorescence, respectively. Correlation analysis was used to analyze the relationship between sleep disturbance and immunologic factors. Multiple linear regression analysis was employed to investigate the risk of sleep disturbance in ARD.

RESULTS

The global score of PSQI (Baseline: 12.81 ± 4.07, After novel electrical therapy: 4.88 ± 2.76) was effectively improved after 3 months of adjuvant therapy by electrical therapy. We also found that serum levels of IL-8 and IL-1β statistically significantly decreased after novel electrical therapy. This adjuvant therapy can also significantly decrease the percentage of CD4 + CD8 + T cell, effector memory CD8 + T cell, Memory CD8 + T cell, Th17 cell, and plasma cell and significantly can increase the percentage of naïve CD8 + T cell, Th2 cell, and Tfh2 cell. Nevertheless, all serum level of 12 cytokines and the percentage of immune cells did not correlate with the PSQI global score except the Tc17 cell. Furthermore, age is an independent risk factor influencing PSQI scores (OR = 1.15, p < 0.05) in patients with autoimmune diseases through multiple linear regression analysis.

CONCLUSIONS

Novel electrical therapy can effectively improve sleep disturbance in patients with ARD. It can also change the serum level of some cytokines (IL-8 and IL-1β) and percentage of immune cells (CD4 + CD8 + T cell, effector memory CD8 + T cell, Memory CD8 + T cell, Th17 cell, naïve CD8 + T cell, Th2 cell, Tfh2 cell, and plasma cell).

Transcranial Magnetic Stimulation of the Default Mode Network to Improve Sleep in Individuals With Insomnia Symptoms: Protocol for a Double-Blind Randomized Controlled Trial

BACKGROUND

Cortical hyperarousal and ruminative thinking are common aspects of insomnia that have been linked with greater connectivity in the default mode network (DMN). Therefore, disrupting network activity within the DMN may reduce cortical and cognitive hyperarousal and facilitate better sleep.

OBJECTIVE

This trial aims to establish a novel, noninvasive method for treating insomnia through disruption of the DMN with repetitive transcranial magnetic stimulation, specifically with continuous theta burst stimulation (cTBS). This double-blind, pilot randomized controlled trial will assess the efficacy of repetitive transcranial magnetic stimulation as a novel, nonpharmacological approach to improve sleep through disruption of the DMN prior to sleep onset for individuals with insomnia. Primary outcome measures will include assessing changes in DMN functional connectivity before and after stimulation.

METHODS

A total of 20 participants between the ages of 18 to 50 years with reported sleep disturbances will be recruited as a part of the study. Participants will then conduct an in-person screening and follow-on enrollment visit. Eligible participants then conduct at-home actigraphic collection until their first in-residence overnight study visit. In a double-blind, counterbalanced, crossover study design, participants will receive a 40-second stimulation to the left inferior parietal lobule of the DMN during 2 separate overnight in-residence visits. Participants are randomized to the order in which they receive the active stimulation and sham stimulation. Study participants will undergo a prestimulation functional magnetic resonance imaging scan and a poststimulation functional magnetic resonance imaging scan prior to sleep for each overnight study visit. Sleep outcomes will be measured using clinical polysomnography. After their first in-residence study visit, participants conduct another at-home actigraphic collection before returning for their second in-residence overnight study visit.

RESULTS

Our study was funded in September 2020 by the Department of Defense (W81XWH2010173). We completed the enrollment of our target study population in the October 2022 and are currently working on neuroimaging processing and analysis. We aim to publish the results of our study by 2024. Primary neuroimaging outcome measures will be tested using independent components analysis, seed-to-voxel analyses, and region of interest to region of interest analyses. A repeated measures analysis of covariance (ANCOVA) will be used to assess the effects of active and sham stimulation on sleep variables. Additionally, we will correlate changes in functional connectivity to polysomnography-graded sleep.

CONCLUSIONS

The presently proposed cTBS protocol is aimed at establishing the initial research outcomes of the effects of a single burst of cTBS on disrupting the network connectivity of the DMN to improve sleep. If effective, future work could determine the most effective stimulation sites and administration schedules to optimize this potential intervention for sleep problems.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04953559; https://clinicaltrials.gov/ct2/show/NCT04953559.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51212.

Connectomic disturbances underlying insomnia disorder and predictors of treatment response

Objective

Despite its prevalence, insomnia disorder (ID) remains poorly understood. In this study, we used machine learning to analyze the functional connectivity (FC) disturbances underlying ID, and identify potential predictors of treatment response through recurrent transcranial magnetic stimulation (rTMS) and pharmacotherapy.

Materials and methods

51 adult patients with chronic insomnia and 42 healthy age and education matched controls underwent baseline anatomical T1 magnetic resonance imaging (MRI), resting-stage functional MRI (rsfMRI), and diffusion weighted imaging (DWI). Imaging was repeated for 24 ID patients following four weeks of treatment with pharmacotherapy, with or without rTMS. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into ID and control groups based on their FC, and derive network and parcel-based FC features contributing to each model. The number of FC anomalies within each network was also compared between responders and non-responders using median absolute deviation at baseline and follow-up.

Results

Subjects were classified into ID and control with an area under the receiver operating characteristic curve (AUC-ROC) of 0.828. Baseline FC anomaly counts were higher in responders than non-responders. Response as measured by the Insomnia Severity Index (ISI) was associated with a decrease in anomaly counts across all networks, while all networks showed an increase in anomaly counts when response was measured using the Pittsburgh Sleep Quality Index. Overall, responders also showed greater change in all networks, with the Default Mode Network demonstrating the greatest change.

Conclusion

Machine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.