Altered functional connectivity of the default mode and frontal control networks in patients with insomnia

Sleep issues, like insomnia, are often found as comorbidities alongside other conditions such as Parkinson's disease, chronic pain, anxiety disorders, depressive disorders, and substance abuse disorders.1 The identification of factors which can predict treatment response can be beneficial in the designing of new treatment strategies, in addition to helping to advance personalized medicine within psychiatry. Psychiatric symptoms are caused by dysregulated dynamic cross‐network interactions between the salience (SN), frontoparietal network (FPN, also known as central executive network), and default mode networks (DMNs).2 Alterations in resting‐state brain activity are not only considered to be a consequence of insomnia but also changes in brain networks that maintain insomnia.3 Primary insomnia is the most typical sleep disorder, which is associated with substantial impairment in quality of life,4 and the global prevalence of insomnia is between 10% and 15%.5 During COVID‐19, the problem has become even worse among older people, with 24.4%–26.8% of Chinese adults aged ≥60 years experiencing insomnia in the last month.6 With the limitations of both pharmacological7 and cognitive‐behavioral therapy,8 there is a crucial need for the development of effective, safe, and accessible insomnia treatment options. To optimize treatment outcomes, a potential strategy could be to identify pretreatment neural predictors of treatment response, so as to establish which patients are likely to respond to a given treatment. However, one potentially effective way of searching for biomarkers of treatment outcomes may be to first explore intermediate phenotypes via diagnostic neuroimaging.9

Noninvasive brain stimulation, such as repetitive transcranial magnetic stimulation (rTMS), has been shown to be safe and has the potential to improve insomnia in different types of neurological and neuropsychiatric disorders.10 Initially, the first study has reported that rTMS can improve subjective sleep quality in depression patients.11 Moreover, several studies have reported that rTMS can modulate arousal,12 sleep quality,13 and sleep‐related plasticity.14 Patients with chronic insomnia show abnormal low‐frequency fluctuations (ALFF) in several subregions of the DMN and dorsal attention network (DAN), and ALFF values were positively correlated with the severity of insomnia.15 The potential mechanism by which rTMS improves sleep in patients with insomnia might involve a hyperarousal model in the cerebral cortex16 through anatomical and functional connectivity, affecting metabolic activity17 and hormones18 associated with sleep. Additionally, noninvasive techniques of neurostimulation may be an effective way to reduce cognitive decline associated with aging and neurodegeneration.19

However, there is a lack of biomarkers to predict the effectiveness of rTMS treatments in insomnia. Could resting‐state spontaneous brain activity as a consequence of insomnia and as a potential maintenance mechanism predict the brain response to rTMS intervention? Resting‐state functional magnetic resonance imaging (fMRI) not only provides neural processing information that may serve as a potential target but is also easy to operate in a clinical setting, such as with the application of resting‐state functional connectivity (RSFC).20 The first step in RSFC analysis is to select a region of interest (ROI) for seed‐based analysis based on prior assumptions. Low‐frequency (usually 0.01–0.08 Hz) fluctuations are a steady index for spontaneous activity21 and can help in the identification of suitable ROI. Subsequently, the ALFF has been introduced to detect altered brain states in various diseases, including Alzheimer's disease,22 schizophrenia,23 and insomnia15 Specifically, ALFF altered associated with major psychiatry disease is widely distributed in several DMN subregions.15 However, as these results point to the DMN, is it the local activity of the DMN or the RSFC from the DMN that predicts treatment outcome?

This study aimed to identify a resting‐state fMRI biomarker for insomnia. Benzodiazepines and other sedative‐hypnotic drugs are prescribed to many older people despite a nearly fivefold increase in the risk of adverse cognitive events associated with them.24 Furthermore, the accumulation of side effects from long‐term medication use is an important issue in the management of older people with insomnia,25 and rTMS might be used as a potential tool to address this issue.26 Previous studies have demonstrated that insomnia patients often also have depression and anxiety, and frontoparietal reticular dysfunction is associated with disease duration and anxiety.27 We hypothesized that (1) insomnia patients on medications would show more alert ALFF in the DMN and FPN than healthy participants, and (2) the RSFC of the alerted brain region would predict insomnia severity. By calculating ALFF and RSFC across various brain regions using correlation analysis, we tried to test these two hypotheses. Finally, because treatment adherence is a known predictor of rTMS response,28 we examined whether spontaneous brain activity in those who discontinued treatment differed from spontaneous brain activity in those who continued treatment to control for treatment effects.

The purpose of this study was to investigate fMRI biomarkers that could distinguish insomnia and be responsive to rTMS treatment effects. Three significant findings were identified when comparing chronic insomnia patients to healthy volunteers. Firstly, ALFF values were found to be significantly higher in the PCC and lower in the SPL in insomnia patients as compared to healthy volunteers. Secondly, patients exhibited significantly lower RSFC between PCC/SPL and the prefrontal cortex. Finally, the RSFC between the SPL and FP predicted the treatment effect of rTMS. These results lend support to our first hypothesis that abnormal spontaneous brain activity exists in DMN and FPN regions of insomnia patients. While the second hypothesis was only partially supported, our findings suggest that sleep problems can be primarily predicted through functional connectivity within the FPN. These findings contribute to the identification of a potential biomarker for primary insomnia and highlight the potential of rTMS in targeting the frontal lobes in middle‐aged and older adults.

The ALFF within the DMN and FPN may allow for distinguishing insomnia patients from healthy volunteers. We identified the lower ALFF in the left SPL in insomnia patients, which might be related to the impairment associated with insomnia. The SPL is included in the FPN and DAN and is involved in the spatial orientation function, which enables individuals to remember the location of objects in space and their visual and tactile characteristics.34 Moreover, it plays an important role in the manipulation of information and resetting of working memory.35 One electroencephalography study has shown that women with lower vigilance had higher activity in the right SPL.36 Morphological studies have also reported that primary insomnia patients showed cortical thickening in the left SPL, bilateral insula, and left middle cingulate cortex.37 The PCC is a central node in the DMN, simultaneously communicates with various brain networks (e.g., FPN and DAN), and participates in numerous brain functions (e.g., autobiographical memory retrieval, self‐referential processing, interoception, or imagining the future).38 We found that patients with primary insomnia had significantly higher ALFF in PCC than healthy volunteers. One study has shown that, compared with healthy controls, core DMN regions in insomnia patients showed greater activation in self‐reference‐related tasks.39 In another study, insomnia patients had higher dynamic ALFF in the bilateral hippocampus (including the right insula and putamen), and that correlation was associated with self‐reported anxiety.40 The results of this study demonstrated the potential of ALFF as a diagnostic biomarker by detecting changes in status more accurately.

The RSFC between the DMN and FPN may help differentiate between patients with insomnia and healthy volunteers. Several studies have shown that long‐term chronic insomnia can cause damage to brain networks,41 with DMN being the most commonly reported.15 Previous studies have reported that compared with healthy controls, patients with primary insomnia show higher RSFC between the left insula and the right anterior cingulate cortex42; higher RSFC between the premotor cortex and sensorimotor cortex and lower RSFC between the amygdala, insula, striatum, and thalamus43; higher RSFC within the limbic network but lower RSFC within the DMN44; and higher RSFC between the bilateral middle and left middle frontal gyrus.45 Our results found that this alteration was not highly correlated with insomnia, which is similar to previous studies. One study has shown that damage to RSFC between the DMN and supplementary motor area was correlated with earlier onset of insomnia.46 The seed‐based region‐to‐region RSFC method has demonstrated that insomnia patients showed significantly decreased functional connectivity between the medial prefrontal cortex and right medial temporal lobe and between the left medial temporal lobe and left inferior parietal cortex, which are included in the DMN.47 Another network analysis has also revealed that insomnia patients have decreased RSFC between the anterior and posterior DMN and increased RSFC between the FPN and DAN.39

In insomnia patients, impairment in the FPN may serve as a potential predictor of treatment response to rTMS. Specifically, we discovered a positive association between the severity of insomnia and RSFC between the FP and SPL. Notably, the FPN has been widely implicated in resting‐state brain network abnormalities in insomnia. A meta‐analysis has shown that executive control impairment in insomnia patients is mild to moderate.48 One study using the independent component analysis has found that the RSFC of the right FPN in patients with primary insomnia was decreased.27 Another study has demonstrated less functional connectivity variability between the anterior SN and left FPN.49 Furthermore, the effect of drugs combined with TMS treatment was positively correlated with the connectivity strength of the FPN, indicating that rTMS may be primarily influenced by FPN plasticity. First, our stimulation targets were located in the prefrontal lobe. Second, stimulation of the prefrontal lobe affects a wide range of parietal and subcortical areas.50 The central nervous system regulates sleep homeostasis through cytokine responses that link sleep to the immune system.51 A potential direction of interpretation is that rTMS might enhance sleep quality in insomnia patients through neuroendocrine and autonomic pathways. However, this hypothesis needs to be explored more.

The study had several limitations that need to be considered. First, our patients were recruited from a population that mainly comprised individuals who were in good health and had high socioeconomic status. This might have introduced bias in our study because this sample likely paid more attention to the quality of life and had a high level of social support. Thus, prospective large samples and differentiation of sleep phenotypes and subtypes are required to add information on related genetics. Second, the small sample size of the treatment group did not allow for robust results to predict the therapeutic effect of rTMS. It is true that our results were not focused on the prediction of response to treatment; hence, the description of treatment was not detailed but provided an exploratory map in the results section (Figure 4B). However, the RSFC has the potential to be a predictor of treatment effect. Thus, future exploratory research will require larger samples. Third, the lack of a sham group for baseline comparisons limited our explanation of the therapeutic effect of rTMS for insomnia. Fourth, the PSQI questionnaire, although it is an effective tool for measuring sleep quality over the past month, was not well suited to measuring the effectiveness of insomnia treatment. In future studies, we should consider measuring insomnia severity using the insomnia severity index. Additionally, a single‐item sleep measurement for flexibility and class repetition may also be an option. However, although this study was not focused on the mechanism underlying rTMS treatment, it is an important issue that requires further research. Our assessment of the riskiness of drug use in this study was inadequate, and prospective written documentation of potential side effects, Specifically, choose zolpidem combined with clonazepam increases the risk of epilepsy and falls in elderly patients and requires special attention, should be undertaken in future trials.

HZ and CH conceived and designed the study. HY, QZ, JY, and QL performed the study and collected materials. HZ and HQ analyzed the results. HZ wrote the manuscript. HY and CH helped coordinate the study and reviewed the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by the Introduced Project of the Suzhou Clinical Medical Expert Team (number SZYJTD201725) and Suzhou Science and Tehchnology Bureau (SYSD2020044). The funding agencies did not contribute to the experimental design or conclusions.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The authors would like to thank the participants who have helped us make this research possible.

Zheng H, Zhou Q, Yang J, et al. Altered functional connectivity of the default mode and frontal control networks in patients with insomnia. CNS Neurosci Ther. 2023;29:2318‐2326. doi: 10.1111/cns.14183

Chuan He, Email: he-chuan@outlook.com.

Hailang Yan, Email: 164714565@qq.com.

Data are available upon reasonable request. Some or all data generated or used during the study are available from the corresponding author by request (Hui Zheng;). zh.dmtr@gmail.com