Electroencephalographic connectivity predicts clinical response to repetitive transcranial magnetic stimulation in patients with insomnia disorder

The effects of transcranial magnetic stimulation on patients with chronic insomnia: a prospective functional near-infrared spectroscopy study

BACKGROUND

Chronic insomnia (CI) is a prevalent disorder associated with prefrontal cortex (PFC) dysfunction, and it can significantly impair quality of life and psychological well-being. This study explores the use of functional near-infrared spectroscopy (fNIRS) as a tool for rapid screening of CI and for evaluating the efficacy of repetitive transcranial magnetic stimulation (rTMS) in improving brain function and clinical symptoms in CI patients.

METHODS

A total of 60 participants were enrolled, including 30 healthy controls (HCs) and 30 CI patients. CI patients received a two-week intervention of 1 Hz rTMS targeting the right dorsolateral prefrontal cortex (DLPFC). Symptom severity was assessed using the Pittsburgh Sleep Quality Index (PSQI), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). Hemodynamic differences between CI patients and HCs during a verbal fluency task (VFT) were analyzed using fNIRS.

RESULTS

HCs exhibited significantly lower PSQI, SAS, and SDS scores compared to CI patients (p < 0.05). During the VFT, oxygenated hemoglobin (oxy-Hb) levels in the DLPFC and medial prefrontal cortex (mPFC) were significantly higher in HCs than in CI patients (p < 0.05). Following rTMS treatment, CI patients demonstrated significantly increased activation in relevant brain regions compared to pre-treatment levels.

CONCLUSION

fNIRS has shown considerable potential as a valuable tool for the screening and diagnosis of CI. Compared to HCs, CI patients exhibited significantly weaker PFC responses during the VFT. rTMS treatment was found to alleviate symptoms in CI patients, likely due to its ability to enhance PFC activation.

The change of MRI indexes of brain glymphatic function and sleep status before and after repeated transcranial magnetic stimulation in insomnia disorder patients

Introduction

We aim to explore the associations between brain glymphatic function and sleep status in insomnia disorder patients (IDs), and to investigate whether repeated transcranial magnetic stimulation (rTMS) can improve sleep and glymphatic function.

Methods

In this prospective study, consecutive IDs were enrolled and randomly classified into true rTMS group and sham rTMS group. Age- and sex-matched healthy control participants (HCs) were enrolled and examined between January 2023 and December 2023. Neuropsychological test included the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), the Montreal Cognitive Assessment (MOCA), and the Minimum Mental State Examination (MMSE). Sleep status was accessed using questionnaires and polysomnography (PSG) in all participants. Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index of MRI was used to evaluate brain glymphatic function.

Results

28 ID patients of true rTMS group, 9 ID patients of sham rTMS group, and 20 control participants were included. Before rTMS, both of the true/sham rTMS groups had lower DTI-ALPS than HCs. Multivariate linear regression models indicated that N2 sleep duration and the arousal index were independently associated with DTI-ALPS in all IDs. After rTMS, compared with the sham rTMS group, the sleep questionnaires, total sleep time, N2 sleep duration, the arousal index of PSG, and DTI-ALPS reflected that the treatment improved the sleep status and glymphatic function of IDs.

Conclusion

Our research implies that N2 sleep duration and the arousal index were independently associated with lymphatic function. The rTMS therapy can improve glymphatic function and sleep status in IDs.

Insomnia and emotional dysfunction: Altered brain network connectivity across sleep and wakefulness states

OBJECTIVE

To investigate the relationship between insomnia-induced sleep disturbance and emotional dysfunction and whether the brain network functional connectivity patterns during either wakefulness or sleep states functioned as a mediator in this relationship.

METHODS

Twenty participants with non-clinical insomnia disorder (ID) and 20 normal controls (NC) were recruited and underwent resting-state Electroencephalography (EEG) recordings during wakefulness and sleep stages. Functional connectivity was analyzed using coherence (COH) across multiple frequency bands. The relationships between COH metrics and self-reported emotional measures and the potential mediation effects were investigated.

RESULTS

The ID group revealed sleep stage-specific alterations in brain network functional connectivity, with enhanced alpha connectivity being observed in non-rapid eye movement (NREM) sleep and increased delta connectivity in both phasic and tonic rapid eye movement (REM) sleep stages. Increased alpha band connectivity in anterior-posterior networks during wakefulness was associated with emotional regulation difficulties and depressive symptoms. Mediation analyses showed that alpha and delta band connectivity between frontal-occipital regions mediated the relationship between insomnia and emotional dysregulation.

CONCLUSIONS

These findings reveal the pattern of functional connectivity is differently changed in insomnia disorder across wakefulness and sleep states, and such connectivities play a mediation rolein relationship between chronic sleep disruption and emotional regulation in insomnia disorder. These findings provide a novel insight into the neurophysiological mechanisms linking sleep disruption to emotional dysfunction and suggest these aberrant functional connectivity patterns as potential neurophysiological targets for insomnia intervention.

Pilot randomized controlled trial of transcranial magnetic stimulation for the treatment of insomnia in cancer survivors: An efficacy, safety, and feasibility therapy

Nearly 60% of cancer survivors experience insomnia symptoms, which is 2-3 times higher than the general population. This study examined the efficacy, safety, and feasibility of repetitive transcranial magnetic stimulation (rTMS) for the treatment of insomnia in cancer survivors. Sixty-six cancer survivors with insomnia were randomly assigned to receive rTMS (n = 22), Sham-rTMS (n = 21), and CBT-I (n = 23) treatment for a 6-week period. Participants completed assessments at baseline, 3 weeks, and 6 weeks, respectively. The primary outcome was the change in Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Index (PSQI) from baseline to 6 weeks. The secondary outcome included the change in Hospital Anxiety and Depression Scale (HADS) and Epworth Sleeping Scale (ESS). The generalized estimating equations (GEE) analysis showed a significant difference in reduced ISI (β = -4.58, 95% CI -8.25, -0.91, p = .009) and PSQI (β = -2.35, 95% CI -4.63, -0.07, p = .041) between intervention rTMS and Sham-rTMS, respectively. A significant between-group difference was also observed in reduced ESS (β = -4.65, 95% CI -8.24, -1.06, p = .006). However, the GEE analysis showed that there was no difference between rTMS and CBT-I for relieving insomnia symptoms and daytime sleepiness. After the 6-week treatment, rTMS, Sham-rTMS, and CBT-I demonstrated 60.0%, 28.6%, and 61.5% response rates for insomnia severity and 66.7%, 35.7%, and 53.8% for sleep quality improvement. The rate of adverse events was 9.1%, 0%, and 4.3% in the rTMS, Sham-rTMS, and CBT-I groups, respectively, and no serious adverse events were reported. Given the critical role of good sleep for cancer prognosis, there is an urgent need to increase access to evidence-based treatment for insomnia in cancer survivors. TMS offers an efficacy, safety, and feasibility therapy.

Effect of combined treatment with transcranial direct current stimulation and repetitive transcranial magnetic stimulation compared to monotherapy for the treatment of chronic insomnia: a randomised, double-blind, parallel-group, controlled trial

BACKGROUND

Chronic insomnia increases the risk of various health problems and mental illness. Existing research suggests promise for both transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS) in treating chronic insomnia individually. However, the combined effects of tDCS and rTMS on this condition remain unclear. This study aimed to verify the efficacy and safety of tDCS combined with rTMS for the treatment of adult patients with chronic insomnia.

METHODS

This was a randomised double-blind parallel-group controlled study. Overall, 157 participants with chronic insomnia were randomly assigned to one of three neurotherapy regimens: tDCS + rTMS, sham tDCS + rTMS, or tDCS + sham rTMS. All groups received 20 treatment sessions over 4 consecutive weeks. The primary outcome was the change in patients' sleep as assessed by the Pittsburgh Sleep Quality Index (PSQI) at 2 weeks, 4 weeks, and 3 months of follow-up. The secondary outcome was the assessment of different dimensions of depression and anxiety in patients through the Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA), as well as the occurrence of adverse events.

RESULTS

Throughout the intervention and after the 3-month follow-up, the tDCS + rTMS group had significantly reduced total PSQI scores compared with the other two groups [tDCS + rTMS, 9.21 vs. sham tDCS + rTMS, 10.03; difference - 1.10; 95% confidence interval (CI), - 1.82 to - 0.38; p = 0.003; tDCS + rTMS, 9.21 vs. tDCS + sham rTMS, 10.76; difference - 2.14; 95% CI, - 2.90 to - 1.38; p < 0.001; sham tDCS + rTMS, 10.03 vs. tDCS + sham rTMS, 10.76; difference - 1.04; 95% CI, - 1.82 to - 0.26; p = 0.010), indicating improved overall sleep quality. Total HAMD and insomnia factor scores were significantly lower in the tDCS + rTMS group than in the other two groups after treatment (p < 0.05). Notably, no adverse events or serious adverse reactions were observed during the study period.

CONCLUSIONS

Combining tDCS with rTMS effectively relieved insomnia symptoms, achieving a significant therapeutic effect after 2-week of intervention, and demonstrating the persistence of treatment effects in later follow-up, emphasising the advantages of combination therapy in improving treatment stability and long-term benefits, reflecting the rapid and effective augmentation of combination therapy. This combined therapy may serve as a safe and effective treatment for adults with chronic insomnia.

TRIAL REGISTRATION

This study was registered as a clinical trial with the China Clinical Trial Registration Center (ChiCTR2100052681).

Prognostic models for predicting insomnia treatment outcomes: A systematic review

OBJECTIVE

To identify and critically evaluate models predicting insomnia treatment response in adult populations.

METHODS

Pubmed, EMBASE, and PsychInfo databases were searched from January 2000 to January 2023 to identify studies reporting the development or validation of multivariable models predicting insomnia treatment outcomes in adults. Data were extracted according to CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) guidelines and study quality was assessed using the Prediction model study Risk Of Bias Assessment Tool (PROBAST).

RESULTS

Eleven studies describing 53 prediction models were included and appraised. Treatment response was most frequently assessed using wake after sleep onset (n = 10; 18.9%), insomnia severity index (n = 10; 18.9%), and sleep onset latency (n = 9, 17%). Dysfunctional Beliefs About Sleep (DBAS) score was the most common predictor in final models (n = 33). R2 values ranged from 0.06 to 0.80 for models predicting continuous response and area under the curve (AUC) ranged from 0.73 to 0.87 for classification models. Only two models were internally validated, and none were externally validated. All models were rated as having a high risk of bias according to PROBAST, which was largely driven by the analysis domain.

CONCLUSION

Prediction models may be a useful tool to assist clinicians in selecting the optimal treatment strategy for patients with insomnia. However, no externally validated models currently exist. These results highlight an important gap in the literature and underscore the need for the development and validation of modern, methodologically rigorous models.

Brain stimulation techniques as novel treatment options for insomnia: A systematic review

Despite the success of cognitive behavioural therapy for insomnia and recent advances in pharmacotherapy, many patients with insomnia do not sufficiently respond to available treatments. This systematic review aims to present the state of science regarding the use of brain stimulation approaches in treating insomnia. To this end, we searched MEDLINE, Embase and PsycINFO from inception to 24 March 2023. We evaluated studies that compared conditions of active stimulation with a control condition or group. Outcome measures included standardized insomnia questionnaires and/or polysomnography in adults with a clinical diagnosis of insomnia. Our search identified 17 controlled trials that met inclusion criteria, and assessed a total of 967 participants using repetitive transcranial magnetic stimulation, transcranial electric stimulation, transcutaneous auricular vagus nerve stimulation or forehead cooling. No trials using other techniques such as deep brain stimulation, vestibular stimulation or auditory stimulation met the inclusion criteria. While several studies report improvements of subjective and objective sleep parameters for different repetitive transcranial magnetic stimulation and transcranial electric stimulation protocols, important methodological limitations and risk of bias limit their interpretability. A forehead cooling study found no significant group differences in the primary endpoints, but better sleep initiation in the active condition. Two transcutaneous auricular vagus nerve stimulation trials found no superiority of active stimulation for most outcome measures. Although modulating sleep through brain stimulation appears feasible, gaps in the prevailing models of sleep physiology and insomnia pathophysiology remain to be filled. Optimized stimulation protocols and proof of superiority over reliable sham conditions are indispensable before brain stimulation becomes a viable treatment option for insomnia.

The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder

BACKGROUND

It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes.

METHODS

In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects.

RESULTS

In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID.

CONCLUSION

rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.

Repetitive transcranial magnetic stimulation in primary sleep disorders

Repetitive transcranial magnetic stimulation (rTMS) is a widely used non-invasive neuromodulatory technique. When applied in sleep medicine, the main hypothesis explaining its effects concerns the modulation of synaptic plasticity and the strength of connections between the brain areas involved in sleep disorders. Recently, there has been a significant increase in the publication of rTMS studies in primary sleep disorders. A multi-database-based search converges on the evidence that rTMS is safe and feasible in chronic insomnia, obstructive sleep apnea syndrome (OSAS), restless legs syndrome (RLS), and sleep deprivation-related cognitive deficits, whereas limited or no data are available for narcolepsy, sleep bruxism, and REM sleep behavior disorder. Regarding efficacy, the stimulation of the dorsolateral prefrontal cortex bilaterally, right parietal cortex, and dominant primary motor cortex (M1) in insomnia, as well as the stimulation of M1 leg area bilaterally, left primary somatosensory cortex, and left M1 in RLS reduced subjective symptoms and severity scale scores, with effects lasting for up to weeks; conversely, no relevant effect was observed in OSAS and narcolepsy. Nevertheless, several limitations especially regarding the stimulation protocols need to be considered. This review should be viewed as a step towards the further contribution of individually tailored neuromodulatory techniques for sleep disorders.

Functional connectivity changes are correlated with sleep improvement in chronic insomnia patients after rTMS treatment

Background

Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used as a treatment modality for chronic insomnia disorder (CID). However, our understanding of the mechanisms underlying the efficacy of rTMS is limited.

Objective

This study aimed to investigate rTMS-induced alterations in resting-state functional connectivity and to find potential connectivity biomarkers for predicting and tracking clinical outcomes after rTMS.

Methods

Thirty-seven patients with CID received a 10-session low frequency rTMS treatment applied to the right dorsolateral prefrontal cortex. Before and after treatment, the patients underwent resting-state electroencephalography recordings and a sleep quality assessment using the Pittsburgh Sleep Quality Index (PSQI).

Results

After treatment, rTMS significantly increased the connectivity of 34 connectomes in the lower alpha frequency band (8-10 Hz). Additionally, alterations in functional connectivity between the left insula and the left inferior eye junction, as well as between the left insula and medial prefrontal cortex, were associated with a decrease in PSQI score. Further, the correlation between the functional connectivity and PSQI persisted 1 month after the completion of rTMS as evidenced by subsequent electroencephalography (EEG) recordings and the PSQI assessment.

Conclusion

Based on these results, we established a link between alterations in functional connectivity and clinical outcomes of rTMS, which suggested that EEG-derived functional connectivity changes were associated with clinical improvement of rTMS in treating CID. These findings provide preliminary evidence that rTMS may improve insomnia symptoms by modifying functional connectivity, which can be used to inform prospective clinical trials and potentially for treatment optimization.

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.