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Holler E, Du Y, Barboi C, Owora A. Prognostic models for predicting insomnia treatment outcomes: A systematic review. J Psychiatr Res 2024; 170:147-157. [PMID: 38141325 DOI: 10.1016/j.jpsychires.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/30/2023] [Accepted: 12/10/2023] [Indexed: 12/25/2023]
Abstract
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.
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Affiliation(s)
- Emma Holler
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN, USA.
| | - Yu Du
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN, USA
| | - Cristina Barboi
- Indiana University School of Medicine, Dept of Anesthesiology and Critical Care Medicine, Indianapolis, IN, USA
| | - Arthur Owora
- Department of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, IN, USA
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Krone LB, Fehér KD, Rivero T, Omlin X. Brain stimulation techniques as novel treatment options for insomnia: A systematic review. J Sleep Res 2023; 32:e13927. [PMID: 37202368 PMCID: PMC10909439 DOI: 10.1111/jsr.13927] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 04/23/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
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.
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Affiliation(s)
- Lukas B. Krone
- University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
- Centre for Experimental NeurologyUniversity of BernBernSwitzerland
- Department of Physiology Anatomy and Genetics, Sir Jules Thorn Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK
- The Kavli Institute for Nanoscience DiscoveryUniversity of OxfordOxfordUK
| | - Kristoffer D. Fehér
- University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
- Geneva University Hospitals (HUG), Division of Psychiatric SpecialtiesUniversity of GenevaGenevaSwitzerland
| | - Tania Rivero
- Medical LibraryUniversity Library of Bern, University of BernBernSwitzerland
| | - Ximena Omlin
- University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland
- Geneva University Hospitals (HUG), Division of Psychiatric SpecialtiesUniversity of GenevaGenevaSwitzerland
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The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder. J Psychiatr Res 2023; 160:56-63. [PMID: 36774831 DOI: 10.1016/j.jpsychires.2023.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/27/2023] [Accepted: 02/04/2023] [Indexed: 02/09/2023]
Abstract
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.
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Lanza G, Fisicaro F, Cantone M, Pennisi M, Cosentino FII, Lanuzza B, Tripodi M, Bella R, Paulus W, Ferri R. Repetitive transcranial magnetic stimulation in primary sleep disorders. Sleep Med Rev 2023; 67:101735. [PMID: 36563570 DOI: 10.1016/j.smrv.2022.101735] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/13/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022]
Abstract
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.
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Affiliation(s)
- Giuseppe Lanza
- Department of Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy; Clinical Neurophysiology Research Unit, Oasi Research Institute-IRCCS, Troina, Italy.
| | - Francesco Fisicaro
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Mariagiovanna Cantone
- Neurology Unit, University Hospital Policlinico "G. Rodolico-San Marco", Catania, Italy; Department of Neurology, Sant'Elia Hospital, ASP Caltanissetta, Caltanissetta, Italy
| | - Manuela Pennisi
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | | | - Bartolo Lanuzza
- Department of Neurology IC and Sleep Research Centre, Oasi Research Institute-IRCCS, Troina, Italy
| | - Mariangela Tripodi
- Department of Neurology IC and Sleep Research Centre, Oasi Research Institute-IRCCS, Troina, Italy
| | - Rita Bella
- Department of Medical and Surgical Science and Advanced Technologies, University of Catania, Catania, Italy
| | - Walter Paulus
- Department of Neurology, Ludwig Maximilians University, Munich, Germany
| | - Raffaele Ferri
- Clinical Neurophysiology Research Unit, Oasi Research Institute-IRCCS, Troina, Italy
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Zhu L, Dang G, Wu W, Zhou J, Shi X, Su X, Ren H, Pei Z, Lan X, Lian C, Xie P, Guo Y. Functional connectivity changes are correlated with sleep improvement in chronic insomnia patients after rTMS treatment. Front Neurosci 2023; 17:1135995. [PMID: 37139515 PMCID: PMC10149758 DOI: 10.3389/fnins.2023.1135995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/28/2023] [Indexed: 05/05/2023] Open
Abstract
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.
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Affiliation(s)
- Lin Zhu
- Department of Neurology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Ge Dang
- Department of Neurology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, United States
| | - Junhong Zhou
- Hebrew Seniorlife, Hinda and Arthur Marcus Institute for Aging Research, Harvard Medical School, Boston, MA, United States
| | - Xue Shi
- Department of Neurology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Xiaolin Su
- Department of Neurology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Huixia Ren
- Department of Geriatrics, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Zian Pei
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Xiaoyong Lan
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | | | - Peng Xie
- NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi Guo
- Department of Neurology, Shenzhen People’s Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, China
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
- *Correspondence: Yi Guo,
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Wang Y, Li M, Li W, Xiao L, Huo X, Ding J, Sun T. Is the insula linked to sleep? A systematic review and narrative synthesis. Heliyon 2022; 8:e11406. [DOI: 10.1016/j.heliyon.2022.e11406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/12/2022] [Accepted: 10/31/2022] [Indexed: 11/08/2022] Open
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The 2021 yearbook of Neurorestoratology. JOURNAL OF NEURORESTORATOLOGY 2022. [DOI: 10.1016/j.jnrt.2022.100008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
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Lu Q, Zhang W, Yan H, Mansouri N, Tanglay O, Osipowicz K, Joyce AW, Young IM, Zhang X, Doyen S, Sughrue ME, He C. Connectomic disturbances underlying insomnia disorder and predictors of treatment response. Front Hum Neurosci 2022; 16:960350. [PMID: 36034119 PMCID: PMC9399490 DOI: 10.3389/fnhum.2022.960350] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 07/19/2022] [Indexed: 01/23/2023] Open
Abstract
ObjectiveDespite 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 methods51 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.ResultsSubjects 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.ConclusionMachine learning analysis into the functional connectome in ID may provide useful insight into diagnostic and therapeutic targets.
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Affiliation(s)
- Qian Lu
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Wentong Zhang
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | - Hailang Yan
- Department of Radiology, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
| | | | - Onur Tanglay
- Omniscient Neurotechnology, Sydney, NSW, Australia
| | | | | | | | - Xia Zhang
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Shenzhen Xijia Medical Technology Company, Shenzhen, China
| | | | - Michael E. Sughrue
- Omniscient Neurotechnology, Sydney, NSW, Australia
- International Joint Research Center on Precision Brain Medicine, XD Group Hospital, Xi’an, China
- Michael E. Sughrue,
| | - Chuan He
- Department of Rehabilitation Medicine, The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, Suzhou, China
- *Correspondence: Chuan He,
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