Resting-State EEG Reveals Abnormal Microstates Characteristics of Depression with Insomnia

doi:10.21203/rs.3.rs-2092561/v1

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Resting-State EEG Reveals Abnormal Microstates Characteristics of Depression with Insomnia

https://doi.org/10.21203/rs.3.rs-2092561/v1

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published 09 Mar, 2023

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Background: Previous research has revealed various aspects of resting-state EEG for depression and insomnia. However, the EEG characteristics of depressed patients with co-morbid insomniac are rarely studied, especially EEG microstates that capture the dynamic activities of the large-scale brain network.

Methods:To fill the research gaps, this study collected resting-state EEG data from 32 sub-clinical depressions with co-morbid insomnia (CI), 31 comorbid-free depressions (CFD), and 32 healthy controls (HC). Four topographic maps were generated from clean EEG data after clustering and rearrangement. Temporal characteristics were obtained for statistical analysis, including cross-group variance analysis (ANOVA) and intra-group correlation analysis.

Results: The global clustering of all individuals in the EEG microstate analysis revealed the four previously discovered categories of microstates (A, B, C, and D). The occurrence of microstate B was found to be lower in CI than in CFD. The correlation analysis showed that the total PSQI score was negatively correlated with the occurrence of microstate C in CI (r=-0.354, p<.05). Conversely, there was a positive correlation between SDS scores and the duration of microstate C in CFD (r=0.359, p<.05).

Conclusion: The spatiotemporal dynamics of the brain network can vary due to abnormalities in the visual network corresponding to microstate B in patients with depression and insomnia.. Further investigation is needed for microstate change can be related to high arousal and emotional problems in people suffering from depression and insomnia. Microstates may therefore become crucial neurobiological predictors to forecast the likelihood of future cases of depression and insomnia.

EEG Microstates

Insomnia

Depression

Temporal dynamics

One of the most prevalent mental disorders nowadays is depression. The epidemic has resulted in a dramatic rise of 28% in depression worldwide, according to the report of the 75th World Health in 2022 (Director-General's report to Member States at the 75th World Health Assembly – 23 May 2022 (who.int)). Additionally, depression increases the chance of both suicide and disability. Patients who exhibit these symptoms report feeling unenthusiastic, thinking slowly, withdrawing from social situations, and paying more attention to themselves. Along with high rates of depression, sleep disorders are often a consequence. According to epidemiological estimates, between 50% and 90% of depressed people experience sleep problems, and these two conditions frequently co-occur(Steiger & Pawlowski, 2019). To comprehend the probable physiological and psychological processes of depression, it is therefore beneficial to examine depression in patients with concomitant insomnia.

In the DSM-5, sleeplessness is included as a depressive symptom (Psychiatry.org - DSM). Interestingly enough, sleep deprivation (SD) is now one of the treatments for depression, about 45% -60% (Geoffroy & Palagini, 2021) of patients with depression respond positively to this therapy. The antidepressant impact of SD has been explained by the two-process model of sleep regulation, the synaptic plasticity hypothesis, the biological hypothesis of the HPA axis, and proinflammatory cytokines. Additionally, circadian rhythms may be linked to hormones, cytokines, and neurotransmitters that are involved in the pathophysiology and management of depression (Kurczewska et al., 2021). The suprachiasmatic nucleus, a sophisticated biological clock in mammals, regulates its secretion. Circadian rhythm disturbances could be a prodromal sign of mood disorders. The effectiveness of sleep deprivation in treating depression is at odds with the co-morbidity of insomnia and depression. In conclusion, people with depression and insomnia may have a unique circadian rhythm and visual network. Furthermore, these participants are distinct from those who are simply depressed. To examine the relationship between insomnia and depression, we further split depressed patients in this study into those who had symptoms of insomnia and those who did not, as well as creating a healthy control group.

The EEG microstate is a complement and extension of magnetic resonance imaging research because it can comprehend and assess the dynamic state of the entire brain network at the millisecond level (Khanna et al., 2015). The microstate, which is thought of as a reasonably stable spatial stationary piece of the discontinuous instantaneous scalp potential, is a quasi-stable environment created by the instantaneous topographic map of EEG(Lehmann et al., 1987). The relatively steady microstate in space provides a temporal advantage as a result of the benefits of high temporal resolution of EEG signals (Lehmann, 1990). It is believed that microstates are crucial elements of human mind-consciousness processes because they depend on what has just occurred in the brain, so they (Lehmann, 1990; Lehmann et al., 1987). In addition, the resting-state brain network is a structured area of activity in the brain, and changes in its neurodynamics throughout time may be a key indicator of both healthy brain function and mental illnesses (Fox & Raichle, 2007). Microstate thus represents the dynamic actions of a vast brain network in addition to being a neurophysiological measure. Patients with depression may experience various global brain dynamics changes due to Insomnia. As a result, we think of conducting our analysis while the brain is at rest.

In previous spectral studies of resting-state EEG in depression, the activity of α, β, and θ bands in the occipital parietal lobe region increased in depressed patients, suggesting that depression may affect how the brain processes emotions. The α rhythm is considered to be a reliable indicator of depression (Dolsen et al., 2017). And, α asymmetry is primarily present in the frontal lobe, which might predict specific symptoms and how a treatment will turn out (Van der Vinne et al., 2017). Furthermore, γ may be related to emotional fluctuations, a large number of γ may help with emotional stability, and may even be used to foretell the effects of depression (Fitzgerald & Watson, 2018). As for EEG microstate, previous studies on depression mainly focused on severe depression or bipolar disorder. A large number of studies have found that there are aboriginal abnormalities in the indicators of EEG microstate in patients. However, the direction of variation of the microstate anomaly parameters varies inconsistently between studies. The presence of microstate A/B was associated with the intensity of depressive symptoms in a study of individuals with bipolar illness and depression (Damborska et al., 2019). All of the temporal parameters for microstates A through D may be abnormal for people with major depressive disorders (MDD) (Lei et al., 2022; Murphy et al., 2020; Yan et al., 2021). Following therapy, the occurrence and duration of microstate B decreased, whereas the duration and contribution of microstate D increased (Lei et al., 2022). In a recent study on subclinical depression, the occurrence of microstate B was higher, while the duration and contribution of microstate C were significantly lower than those of the non-subclinical depression group(Zhao et al., 2022).

The resting-state EEG study of insomnia showed that during daytime awakening, the power of β, θ, and γ bands of insomnia patients increased, and drowsiness and cortical hyper wake coexisted; the increased EEG activity (such as θ, α, and σ rhythm) of NREM during sleep, which is consistent with the hypothesis of the 24-hour hyperarousal (Zhao et al., 2020; Zhao et al., 2021). There are relatively few studies on the microstate of insomnia. Wei and his colleague discovered that insomnia resulted in a shorter average microstate C length and a higher frequency of microstate D (Wei et al., 2018). It could be associated with the improvement of body awareness and inner sensory sensitivity in insomniacs. Additionally, there was a significant correlation between sleep latency and sleep efficiency and the duration of microstate C (Ding et al., 2022).

A continuum model of abnormality may instead represent the border for mental health issues, according to mounting data. Research on subclinical depression and insomnia is essential for the prevention, detection, and treatment of depression and insomnia due to the existing huge and concealed mental sub-health population. We hope to learn more about the dynamics of the brain networks involved in subclinical depression and insomnia. Therefore, the purpose of this study is to investigate the differences in EEG microstates between healthy individuals in the resting state and subclinical depression, subclinical depression, and sleeplessness. We make the following assumptions: first, in terms of circadian rhythm, subjects with subclinical depression with insomnia have more nocturnal patterns and are more likely to be sleepy during the day; secondly, there are significant differences in the microstate time parameters of CI and CFD; thirdly, microstate time parameters of subjects correlate with the severity of symptoms, such as the scores of depression and insomnia.

Participant

All of the participants were Southwest University students. The experts from the Faculty of Psychology used the Self-Rating Depression Scale (ZUNG, 1965) and Pittsburgh Sleep Scale (Buysse et al., 1989) to evaluate subjects in groups. SDS standard score ≥ 50 points were divided into depressive symptoms (Zung, 1973), PSQI ≥ 8 points indicated poor sleep quality (Liu & Tang, 1996). The criteria of depression with comorbid insomnia (CI) were SDS ≥ 50 and PSQI ≥ 8. The criteria of comorbid-free depression (CFD) were SDS ≥ 50 and PSQI < 8. The criteria of healthy controls (HC) were SDS < 35 and PSQI < 8. The data from the Chinese norm were used to develop the screening criteria(Liu & Tang, 1996; Wang et al., 1999). Other requirements for inclusion include the absence of psychotropic drugs, head trauma, large scars, brain injuries, significant or persistent organic diseases, and comorbidities (such as bipolar disorder, personality disorder, or mood disorder). Finally, the subjects were divided into three groups: CI = 32 subjects with depression with comorbid insomnia; CFD = 31 subjects with comorbid-free depression; HC = 32 healthy subjects.

The Southwest University Ethics Review Committee accepted the study, and it carried out the experiments with its suggestions. Following the “Helsinki Declaration of Principles”, we obtained the subjects' informed consent before the study and provided financial compensation afterward.

Questionnaires

Insomnia Severity Index(ISI), a self-rating scale for insomnia, uses a 5-grade score from 0 to 4, which is used to judge the severity of insomnia (Morin & Press, 1993).

Morningness-Eveningness Questionnaire (MEQ) uses a five-point scale, with scores ranging from 4 to 25 corresponding to changes in diurnal habits from early morning (larks) to nocturnal (owls) (Adan & Almirall, 1991).

Epworth Sleeping Scale (ESS) uses a 0–3 scale to assess recent daytime sleepiness (or average sleep propensity, ASP)(Johns, 1991).

Stanford Sleepiness Scale (SSS) selects one from seven options. The operation is simple and repeatable and is used for subjects to quickly estimate their current state (Hoddes, 1972).

Self-rating Anxiety Scale (SAS) is a simple clinical tool developed by Zung in 1971 to analyze subjective anxiety symptoms of patients over the past week (Zung, 1971).

Eeg Recording And Pro-processing

Before EEG acquisition, participants would complete the questionnaires (SAS, SSS, MEQ, ISI). And resting-state EEG data was then obtained for five minutes. The participants were also requested to close their eyes while remaining awake. If an EEG recording shows a slow wave associated with sleep, we stopped recording (Duan et al., 2021). And the participants were also encouraged to engage in certain waking activities like walking around or washing face with cold water.

EEG recording was done using the 63 lead EEG caps. The sampling frequency for data collection was 500 Hz. The resistance of each electrode was kept below 5 kΩ, and FCz served as the online reference electrode.

The original EEG data preprocessing was completed by EEGLab, and mainly included the following steps: firstly, 1–20 Hz band-pass filtering; secondly, the bad conductors were marked while the adjacent electrodes were replaced with high-quality lead according(rang: 0–8, mean ± std:1.55 ± 1.80); the third step was to remove bad segments(rang: 0–50, mean ± std: 8.24 ± 10.80); finally, ICA was used to remove artifacts. We used the ICLabel to identify eye-related artifacts (rang: 0–5, mean ± std:1.37 ± 1.23), which accounts for activity associated with horizontal eye movements and blinks and vertical eye movements. And the ICLable is an automatic EEG independent component classifier plugin for EEGLAB (Pion-Tonachini et al., 2019).

Microstate Analysis

Clean EEG data were processed in Matlab using Koenig’s a public code and the free EEGLab microstate plug-in(Koenig et al., 2002). The first step was to identify microstates at the individual level (first-level clustering); the second step was to identify microstates at the group level (secondary clustering); the third step was to reorder the microstates based on the template (using the average of the group as the template); and the fourth step was to generate a new dataset and extract each temporal parameter of the microstate for upcoming statistical analysis. We used the duration and occurrence of microstate as our two parameters. While occurrence denotes the percentage of time in each microstate, duration denotes the generally steady duration of a microstate. The variance explained by the model was determined to assess how well the resulting micro-topography correlated with the original EEG data.

Statistical Analysis

To examine the differences among the three groups, we first used SPSS 25.0 to perform variance analysis (ANOVA), and then we ran the Tukey HSD test and applied the Bonferroni correction. Then, within each group, Spearman correlation analyses were performed for questionnaire scores (SDS and PSQI) and EEG microstate parameters. All correlations were at a significant level of α < 0.05.

Demographic and Questionnaires characteristics

The clinical characteristics of the three groups were gathered using Insomnia Severity Index (ISI), Morningness-eveningness Questionnaire (MEQ), Stanford Sleepiness Scale (SSS), Epworth Sleeping Scale (ESS), and Self-rating Anxiety Scale (SAS). However, due to the reasons for rejection or omission, the collecting rates of the five questionnaires were 46%, 57%, 80%, 63%, and 74%, respectively. The one-way ANOVA and Tukey HSD test were used to acquire participant characteristics and psychological data, as shown in Table 1. Concerning age, gender and educational attainment, there were no significant differences among the three groups. Only ISI and SAS show a significant difference in psychological traits (ISI: F(2,40) = 32.34; SAS: F(2,66) = 34.20), with no significant differences found in MEQ, SSS, or ESS. In the post hoc test, CI, CFD scores were higher than HC: ISI (CI-HC), MD = 10.221, p < 0.001; ISI (CFD-HC), MD = 6.721, p < 0.001. SAS (CI-HC), MD = 16.259, p < 0.001; SAS (CFD-HC), MD = 14.494, p < 0.001.

Table 1

Participants' characteristics and psychological data
	CI (n = 32)	CFD (n = 31)	HC (n = 32)	p	Post-hoc
Age (M, SD)	19.73(1.31)	20.03(1.66)	20.03(1.45)	0.68
Sex (female%)	65.63	54.84	59.38	0.47
ISI(M,SD)	12.8(4.21)	9.3(4.54)	2.58(2.37)	< 0.001	CI, CFD > HC
MEQ(M,SD)	12.65(3.05)	12.53(2.19)	14.95(3.61)	0.04
SSS(M,SD)	3.15(0.99)	3.24(1.41)	2.55(1.04)	0.07
ESS(M,SD)	10.28(2.92)	10.27(3.55)	8.37(3.28)	0.098
SAS(M,SD)	49.84(7.21)	48.07(6.64)	33.58(4.26)	< 0.001	CI, CFD > HC
Note. SAS, Self-rating Anxiety Scale; SSS, Stanford Sleepiness Scale; MEQ, Morningness-Eveningness Questionnaire; ISI, Insomnia Severity Index; CI, depression with comorbid insomnia; CFD, comorbid-free depression; HC, healthy control. Data are presented as means (s.d.). The Tukey HSD test was used for post hoc analyses.

Microstate Topographic Map

Figure 1 displays the topographic map. The global explanatory variance of the original data was 80.48%, of which CI, CFD, and HC each contributed 80.84%, 79.97%, and 80.62%. The four microstates’ spatial patterns matched those of earlier research quite well (Koenig et al., 2002; Lei et al., 2022; Yan et al., 2021; Zhao et al., 2022). We marked them as microstates A \ B \ C \ D based on the spatial patterns of earlier studies. The three groups of topographic maps shared a high degree of similarity, and the spatial correction confection ranged from 0.75 (map B, CI vs HC) to 0.99 (map C, CI vs CFD).

Group analysis

The results of ANOVA: only occurrence of the microstate B had a significant difference after Bonferroni correction among the three groups: Duration, A: p = 0.058, B: p = 0.686, C: p = 0.22, D:p = 0.307; Occurrence, A: p = 0.895, B: p = 0.019, C: p = 0.439, D:p = 0.391; Contribution, A: p = 0.808, B: p = 0.095, C: p = 0.643, D: p = 0.817.

The result of pairwise comparison: occurrence of microstate B was significantly smaller in CI when compared with CFD (p = 0.019). The difference in other time parameters was not statistically significant. Spearman correlation results showed that the PSQI score was negatively correlated with the Occurrence of microstate B (r = − 0.276, p < 0.01). In addition, the Occurrence of microstate B in the depression group (CI + CFD) was shorter than that in the healthy control group (HC), but the difference was not significant.

Intra-group analysis

Further intra-group correlation analysis was performed. The results of Spearman correlation analysis showed that the PSQI score in CI was inversely proportional to Occurrence in the C state (r = − 0.354, p < 0.05). The SDS score in CFD was proportional to the duration of C state (r = 0. 359, p < 0.05).

The study showed that the occurrence of microstate B was significantly different between CI and CFD. It was also negatively correlated with PSQI score, indicating that the occurrence of microstate B decreased with worsening sleep quality. Additionally, microstate B may be able to capture the signal differences brought on by various sleep conditions in patients with mild depression. In group analysis, it was found that the occurrence of microstate C in CI was associated with sleep status, while the duration of microstate C in CFD was associated with the severity of depression. In the study of depression and insomnia, it may be necessary to pay more attention to the brain EEG microstate C. However, contrary to our first premise, our study did not discover that CI had a higher proportion of nocturnal personalities or was more prone to daytime sleepiness. It calls for additional research.

Our study found a significant difference between the occurrence of CI and CFD microstate B, which may indicate a shift in the dynamics of the visual network caused by insomnia. In previous studies, it was discovered that the signal sources of microstate B were mainly located in the bilateral occipital cortex, bilateral inferior occipital gyrus, and bilateral cuneiform gyrus (Britz et al., 2010; Custo et al., 2017), and were related to the visual network (Britz et al., 2010). In addition, insomnia is tightly linked to the visual network: first, people with insomnia have worse visual analysis ability and lower processing efficiency (Giora et al., 2017). And the visual pathway may be a major risk factor for insomnia (Dai et al., 2020; Dai et al., 2016). Second, insomniacs' hyperarousal may result in redundant stimulus information and a clogged or overloaded visual pathway (Dai et al., 2020; Nofzinger et al., 2004). Third, persons are more prone to circadian rhythm disruption because defects in the visual circuitry also alter how the SCN processes visual information (Kobayashi et al., 2015; Stinchcombe et al., 2017). The abnormal occurrence of microstate B may be a reflection of the optic nerve's potential activation pattern. We, therefore, hypothesize that there is a substantial variation in the activation trend of visual pathway between CI and CFD, which may lead to a decrease in the ability of CI to process visual information during the hyperarousal state of insomnia. Changes in the microstate may reflect the neurodynamic basis of hyperarousal.

The occurrence of microstate C in CI was connected to the participants' sleep quality, and the duration of microstate C in CFD was related to the severity of depression, according to the intra-group correlation analysis we conducted. However, the results of current microstate studies on depression and insomnia are few and inconsistent: In the study of insomnia, it was found that individuals with insomnia had shorter duration of microstate C (Wei et al., 2018), and that duration was positively connected with both sleep latency and sleep efficiency (Ding et al., 2022) ; In the study of depression, it was found that the duration and contribution of microstate C were significantly lower in the depression group than in the control group (Zhao et al., 2022). And there was a slight correlation between the time parameter of microstate C and the results of the scales used to measure depression (Murphy et al., 2020; Yan et al., 2021).

Studies have revealed a connection between microstate C and the salience network. In the anterior cingulate cortex, bilateral inferior frontal gyrus, and insula, BOLD activity is positively linked with microstate C. As a result, it is believed that microstate C reflects the saliency network's time dynamics properties (Britz et al., 2010; Corbetta et al., 2008). The anterior cingulate gyrus is a crucial part of the attention, learning, language, and movement circuits, while the anterior insular lobe plays a significant role in subjective sensation (Namkung et al., 2017; Uddin, 2015). They have a close anatomical and functional connection,, and regulate the integration of emotion with cognition and motivation (Namkung et al., 2017). Abnormal insular activity and reduced connectivity to the amygdala increase the likelihood that people will not be able to adjust well, and the subjective body consciousness of insomnia patients is abnormal (Wei et al., 2018). In addition, abnormal activity in the anterior cingulate gyrus may harm the reward circuit (Rolls & BE Vogt, 2019). So, negative information is more likely to be transmitted to the anterior cingulate gyrus (Rolls et al., 2019), increasing the likelihood of an internal, negative cognitive bias. Abnormalities in microstate C may reflect the neurodynamics of emotional dissonance and negative cognition, which are associated with an increased risk of depression and insomnia. Therefore, we speculate that microstate C may be an important indicator to predict the risk tendency of depression and insomnia, which needs attention.

Limitations: first, the inclusion criteria mainly refer to the self-rating scales, which are somewhat constrained. It is advisable to include the clinical assessment interview. Second, the spatial resolution of EEG is not high. If the subsequent study can synchronize the study of functional magnetic resonance imaging (fMRI), the results may be more accurate (Chen et al., 2020). Third, a score of > 50 on the SDS, which is not a very high threshold, was the inclusion criterion for depressed participants. However, as our attention was on subclinical individuals, a low threshold was applied. Finally, because this study is a correlation analysis, a causal derivation is not possible. Future research can study specific treatments on microstates B and C using technologies like transcranial direct current stimulation (tDCS), to determine the causal link between microstates and illnesses.

This study provides insights for the application of resting-state EEG microstate dynamics in subclinical depression and insomnia groups. EEG microstates can pick up various signs of depression and insomnia. Neurodynamic changes in large-scale networks, especially visual networks and salience networks. It may be the basis of hyperarousal and emotional disturbance for depression and insomnia patients. Furthermore, we speculate that the visual network may be the focus of research on depressive comorbid insomnia. Microstate may become important neurobiological indicators for predicting the risk of depression and insomnia in the future. However, it is necessary to optimize the experimental design to refine the psychological mechanism corresponding to the time dynamics changes of microstates. Additionally, more evidence is required to support the retest reliability of microstates in studies of psychiatric illnesses.

Acknowledgment

This work has been funded by the National Key Research and Development Program of China (2021YFC2501500) and the National Natural Science Foundation of China (31971028).

Data Availability

Data supporting our fndings are available from the corresponding author, upon reasonable request.

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No competing interests reported.

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published 09 Mar, 2023

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Editorial decision: Major revision
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Resting-State EEG Reveals Abnormal Microstates Characteristics of Depression with Insomnia

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Abstract

Figures

Introduction

Method

Participant

Questionnaires

Eeg Recording And Pro-processing

Microstate Analysis

Statistical Analysis

Results

Demographic and Questionnaires characteristics

Microstate Topographic Map

Group analysis

Intra-group analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Journal Publication

Version 1