Static and temporal dynamic alterations of local functional connectivity in chronic insomnia

doi:10.21203/rs.3.rs-4278831/v1

Background: Several studies have revealed altered intrinsic neural activity in chronic insomnia (CI). However, the temporal variability of intrinsic neural activity in CI is rarely mentioned. This study aimed to explore static and temporal dynamic alterations of regional homogeneity (ReHo) in CI and excavate the potential associations between these changes and clinical characteristics.

Methods: Eighty-seven patients with CI and seventy-eight healthy controls (HCs) were included. Resting-state functional magnetic resonance imaging was performed on all subjects and both static and dynamic ReHo were used to detect local functional connectivity. We then tested the relationship between altered brain regions, disease duration, and clinical scales. The receiver operating characteristic curve analysis was used to reveal the potential capability of these indicators to screen CI patients from HCs.

Results: CI showed increased dynamic ReHo in the right precuneus and decreased static ReHo in the right cerebellum_6. The dynamic ReHo values of the right precuneus were negatively correlated with the self-rating depression score and the static ReHo values of the right cerebellum_6 were positively correlated with the Montreal Cognitive Assessment-Naming scor e. In addition, the combination of the two metrics showed a potential capacity to distinguish CI patients from HCs, which was better than a single metric alone.

Conclusions: The present study has revealed the altered local functional connectivity under static and temporal dynamic conditions in patients with CI, and found the relationships between these changes, mood-related scales, and cognitive-related scales. These may be useful in elucidating the neurological mechanisms of CI and accompanying symptoms.

Chronic insomnia

regional homogeneity

functional connectivity

About 30-35% of the adult population are disturbed by insomnia and its compliances all over the world, and approximately 10-15% of them meet the diagnostic criteria for chronic insomnia (CI) ^1,2, some last for a decade. As long as there is difficulty in falling asleep and/or maintaining sleep, or waking up early, with daytime dysfunctions (fatigue, mental disorders, cognitive decline, etc.), these symptoms occur three times a week and last for more than three months, who can be diagnosed as CI ³. In recent years, the novel coronavirus pandemic has brought an immeasurable impact. Sleep problems loom large as stress and health problems mount ⁴. Long-term insufficient sleep not only increases the risk of cardiovascular and cerebrovascular disease, diabetes, kidney disease, and others, but also increases the probability of suicide and places a heavy burden on society and individuals ^5-7. However, the neural mechanism of CI is still unclear.

Resting-state functional magnetic resonance imaging (rs-fMRI) indirectly reflects the spontaneous activity of the brain in the form of blood oxygen level-dependent (BOLD) signal fluctuations by changing the hemoglobin concentration of brain tissue due to metabolism, which is a non-invasive imaging method ⁸. A large number of studies have shown that spontaneous fluctuations in the low-frequency band represent intrinsic brain activity ⁹. Regional homogeneity (ReHo) is one of the widely used rs-fMRI analysis methods at present by calculating Kendall’s coefficient of concordance to evaluate the regional temporal synchronization of intrinsic brain activity between adjacent voxels, without prior knowledge of brain structure or function, and has high robustness ¹⁰. Several studies have found altered ReHo in certain brain regions such as the fusiform gyrus, cingulate gyrus, frontal lobe, cerebellum, etc., and these altered brain regions have a complex relationship with psychological scores, cognitive scores, and gut microbiome ^11-14.

However, the aforementioned studies assume that the brain BOLD signal is constant and lack attention to the dynamic features of intrinsic brain activity. Intrinsic brain activity changes over time ^15-17. Sliding windows are primarily a dynamic analysis method and have been widely used to explore the temporal variability of brain functional activity in various diseases such as Parkinson's disease, attention deficit hyperactivity disorder, schizophrenia, and major depression ^18-21. Combining ReHo with a sliding window, proposed the dynamic ReHo method to measure the time-varying local functional connectivity. In general, the static ReHo is computed using the sequence of all time points as a whole, while the dynamic ReHo splits the entire time series into different small segments. It has not yet been used to explore neuropathological mechanisms in CI.

Hence, our main objective is to identify static and temporally dynamic intrinsic brain activity and perform multi-view neuroimaging evidence for neural mechanisms in individuals with CI. We used static and dynamic ReHo to evaluate local functional connectivity in CI and further explore its relevance to clinical characteristics. We also used receiver operating characteristic (ROC) analysis to evaluate the ability of static ReHo and dynamic ReHo in distinguishing CI patients and healthy controls (HCs).

Subjects

A total of 173 subjects (93 patients with CI and 80 HCs) diagnosed by the Department of Psychology and Psychiatry of Guangdong Second Provincial General Hospital or recruited from the local community between 2021 and 2023 were included. All subjects were right-handed and had not taken any psychotropic drugs for at least 2 weeks before and during the study to eliminate the effects of the drugs. This study was approved by the Ethics Committee of Guangdong Second Provincial General Hospital. Informed consent was obtained from all participants.

All patients with CI were assessed by two senior psychiatrists based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, version 5. The inclusion criteria for patients with CI were as follows: (a) complaint of difficulty falling asleep, sleep maintenance, and/or early awakening; (b) insomnia occurred at least three times a week for at least 3 months; (c) related daytime dysfunction (e.g., fatigue, emotional disorder, or cognitive impairment); (d) Insomnia Severity Index (ISI) score at least 7 points; and (e) no other sleep disorders (e.g. obstructive sleep apnea or sleep-related movement disorders), severe organic diseases, mental disorders, substance use, or head trauma.

We also recruited 80 age, education, and handedness-matched HCs. The inclusion criteria for the HC group were as follows: (a) good sleep quality; (b) no history of head trauma and no brain lesions, confirmed by conventional T1- or T2-weighted fluid-attenuated inversion-recovery (FLAIR) magnetic resonance imaging; (c) no neuropsychiatric diseases; (d) no pregnant, lactating, or menstruating if female.

Clinical assessments

All patients underwent a series of neuropsychological assessments, including sleep, mood, and cognition-related scales. These scales included the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), Montreal Cognitive Assessment (MoCA; total score, visual-spatial and executive functions, naming, memory, attention, language, abstraction, delay recall, and orientation), Digit Symbol Substitution Test (DSST), and Digit Span Test (DST). HCs completed cognitive-related scales consisting of MoCA, DSST, and DST.

MRI data acquisition

All subjects were scanned using a Philips 3.0T MR imaging system (Ingenia, Philips, The Netherlands) at the Guangdong Second Provincial General Hospital. All subjects were placed in an advanced supine position, wearing earplugs to minimize noise, and secured with tight straps and foam pads to reduce head movement. They were banned from taking caffeine, alcohol, or other psychotropic drugs for 48 hours before the scan and instructed to stay awake and try not to think about anything during the scan. Conventional sequences (T1-weighted images and T2-FLAIR images) were obtained to screen for whether lesion in the brain. The rs-fMRI data were performed using a gradient-echo planar imaging sequence with the following parameters: repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, matrix = 64 × 61, field-of-view (FOV) = 224 mm × 224 mm², flip angle (FA) = 90◦, slice thickness = 3.5 mm with a 1.0 mm gap, interleaved scanning, and 240 volumes in total. Next, high-resolution anatomical images were acquired using a T1-weighted 3D gradient-echo sequence with the following parameters: 185 axial slices, TR = 7.9 ms, TE = 3.6 ms, FA = 8◦, slice thickness = 1.0 mm, no gap, matrix = 256 × 256, and FOV = 256 mm × 256 mm.

Data preprocessing

All images were preprocessed in the Data Processing Assistant for Resting-State fMRI (DPARSF). The preprocessing steps were as follows: (a) remove the first ten volumes to ensure signal stabilization; (b) slice timing; (c) realignment: subjects whose head motion > 2 mm or rotation > 2°in any direction were excluded. Six patients with CI and two healthy controls were excluded due to excessive head motion. (d) spatial normalization to Montreal Neurological Institute space with an isotropic voxel size 3 mm × 3 mm × 3 mm; (e) nuisance covariate regression (Friston’s 24 head motion parameters, white matter signal, and cerebrospinal fluid signal); and (f) Temporal band-pass (0.01–0.10 Hz) filtering.

Static ReHo and dynamic ReHo Calculation

The static ReHo was estimated by computing Kendall’s coefficient of concordance of the times series for each of the 27 nearest neighbor voxels. Then, the images were Z-transformed and spatially smoothed with a Gaussian kernel of 6 mm full-width half-maximum for statistical analysis.

The dynamic ReHo was obtained by using the sliding window method based on the Temporal Dynamic Analysis (TDA) toolkit. The window length and step size are the key factors that affect the algorithm. If the window length is short, the risk of spurious fluctuations increases. The window length is too long to capture its dynamics. In this study, a moderate window length of 30 TR and a shift step size of 1 TR were used and a total of 201 ReHo brain maps were obtained for each participant by calculating the ReHo of the whole brain voxels under each time window. The temporal variability of dynamic ReHo was defined as the standard deviation (SD) of the window-based ReHo maps.

Statistical analysis

The demographic and clinical data between the two groups were compared using the two-sample t-tests or Mann-Whitney U test and chi-square test with SPSS (version 25.0; IBM, Armonk, NY). The threshold for statistical significance was set at p < 0.05, and all hypothesis tests were two-tailed.

To examine the differences between the CI group and HC group in static and dynamic ReHo, a two-sample t-test was employed, with age, education, gender, and mean framewise displacement (FD) as covariates. Multiple comparison correction was performed based on the Gaussian random field theory (GRF, voxel-wise p < 0.005, cluster-wise p < 0.05, two-tailed).

To explore the relationship between altered brain regions and clinical scales in CI, we used the Kolmogorov-Smirnov test to verify whether the data were normally distributed and then selected Spearman correlation or Pearson correlation analysis. P < 0.05 was considered statistically significant.

Finally, binary logistic regression was used to calculate the prediction probabilities of altered static ReHo and dynamic ReHo brain regions, separately and in combination, for diagnosing CI. The receiver operating characteristic (ROC) curves were constructed using the prediction probabilities. The area under the ROC curve (AUC) was calculated to evaluate the diagnostic power of these indicators.

Demographic and clinical data

The demographic and clinical characteristics of all subjects are shown in Table 1. 87 patients with CI and 78 HCs were finally included. No significant differences were found in age, gender, education, and mean FD between the two groups (p > 0.05). There were significant differences between the CI group and HC group in some cognitive scales (DSST, MoCA: total score, visual-spatial and executive, naming, memory, attention, language, abstraction, delayed recall; p < 0.05 ). Patients with CI had lower scores on the cognitive scale than HCs. The DST and MoCA-orientation scores showed no significant difference between the two groups.

Differences in static ReHo and dynamic ReHo between patients with CI and HCs

Compared with the HCs, patients with CI exhibited significantly increased dynamic ReHo in the right precuneus (Table 2 and Figure 1) and decreased static ReHo in the right cerebellum_6 (Table 2 and Figure 2).

Correlation analysis

Pearson correlation analysis showed that the dynamic ReHo values of the right precuneus in patients with CI were negatively correlated with SDS scores (r = -0.296, p = 0.005). Spearman correlation analysis showed that the static ReHo values of the right cerebellum_6 in patients with CI were positively correlated with MoCA-Naming scores (r = 0.234, p = 0.029) (Figure 3).

ROC curve analysis

The ROC curves showed the potential capacity of altered brain regions to diagnose CI in terms of static and dynamic ReHo values. The AUC of the static ReHo value in the right cerebellum_6 was 0.693 (P < 0.0001, 95%CI: 0.614-0.773), and the sensitivity and specificity were 71.3% and 59.0%. The AUC of the dynamic ReHo value in the right precuneus was 0.751 (P < 0.0001, 95%CI: 0.677-0.825), and the sensitivity and specificity were 58.6% and 84.6%. The AUC of the combined of the two indicators was 0.798 (P < 0.0001, 95%CI: 0.731-0.866), and the sensitivity and specificity were 78.2% and 69.2% (Figure 4).

In the current study, we investigated the changes in intrinsic local functional connectivity in individuals with CI under static and temporal dynamic conditions, using static ReHo and dynamic ReHo methods. We found that compared with HCs, patients with CI showed an increase in dynamic ReHo in the right precuneus and a decrease in static ReHo in the right cerebellum_6. The aforementioned regions were correlated with either mood- or cognitive-related scales. Remarkably, the combination of static ReHo and dynamic ReHo exhibited a better potential capacity to distinguish patients with CI from HCs according to the ROC curve analysis. These findings reveal abnormal intrinsic functional connectivity from different aspects and provide a new perspective on understanding the neural mechanisms in CI.

It is well known that the precuneus is a core component of the default mode network ²². Several studies have found disordered structural or functional connectivity of the default mode network in CI and inferred that the default mode network disorder is associated with hyperarousal ^23-25. Using a voxel-wise degree centrality, a study found that the right precuneus was an abnormal intrinsic functional hub in insomnia patients ²⁶. Several seed-based functional connectivity analyses have shown increased functional connectivity between the precuneus and other regions such as the locus coeruleus and thalamus in insomnia ^27,28. In our present study, an increased dynamic ReHo value in the right precuneus was found in the CI group, which may provide temporal variability neuroimaging evidence of default mode network dysfunction in CI. Notably, most studies have shown an increase in intrinsic brain activity of the precuneus, which may suggest the brain is in an excitable state. In addition, the dynamic ReHo values in the right precuneus were negatively correlated with the SDS scores. Previous studies have found that spontaneous local neural activity in the precuneus decreases in people with depression ^21,29. Moreover, abnormal brain function activity in the precuneus has been associated with depression and poor sleep quality in some studies ^30,31. Based on these, we speculate that the increased temporal variability of local functional connectivity in the precuneus may be responsible for preventing depressive symptoms in CI patients.

The cerebellum is traditionally considered a key component of the motor control system, with extensive connections to the cerebral cortex, and is responsible for coordinating many brain functions ³². Currently, the cerebellum is involved in the regulation of the sleep-wake cycle ^33-35. Animal studies have found that sleep and wake affect the ultrastructure of parallel fiber synapses in the cerebellum of mice ³⁶ and connections between the cerebellum and the neocortex were active during sleep spindle in monkeys ³⁷. These indicate that the cerebellum plays a crucial role in the sleep-wake cycle. The results of this study showed that the static ReHo value of the right cerebellum_6 decreased in CI patients. A previous study found abnormalities in the global and nodal topological properties of cerebellum functional connectivity in CI ³⁸. Local intrinsic functional connectivity ^39-41 and grey matter volume ⁴² of the cerebellum decreased in several studies, which is consistent with our results and further demonstrates the role of the cerebellum in sleep regulation. Furthermore, we also found that the static ReHo values of the right cerebellum_6 were positively correlated with the MoCA-naming scores. The cerebellum is involved in higher-order cognitive functions in humans, especially language functions ^43,44. An fMRI and transcranial direct current stimulation study revealed the asymmetric functional distribution of the bilateral cerebellum in bilingual language production, with the right cerebellum biased towards language control ⁴⁵. Combining our findings, we speculate that the decreased intrinsic functional connectivity in the right cerebellum_6 is related to the language cognitive impairment in patients with CI.

Besides that, the ROC curves were used to evaluate the ability of static and dynamic ReHo metrics to distinguish CI patients from HCs. The AUC of dynamic ReHo was relatively high, indicating that the dynamic indicator is superior to the static indicator. These not only highlight the complementary role of dynamic features in the neurological mechanisms of CI but also their potential contribution to disease diagnosis. A previous study found that the classification accuracy of dynamic features was higher than that of static features in Parkinson’s disease, using dynamic and static amplitude of low-frequency fluctuation analyses ⁴⁶. These suggest that dynamic indicators have certain potential value in exploring the pathophysiological mechanisms of various neuropsychiatric diseases. In addition, the analysis of the ROC curves of the combined two metrics showed better diagnostic power than the individual metrics, suggesting that combining different methods may provide a more comprehensive understanding of the neural mechanisms of CI.

There are some limitations in our study. First, we only analyzed dynamic ReHo in a window length of 30TR and a shift step size of 1TR. The results of dynamic ReHo are needed to verify the reliability of using different window lengths and step sizes. Second, it was a cross-sectional study. A longitudinal study is necessary to explore the altered static and dynamic ReHo as the disease progresses. Third, the sample size was not large enough. Future work needs to include more subjects.

We innovatively used the dynamic ReHo to explore the intrinsic functional connectivity in CI. Our results showed an increase in dynamic ReHo in the right precuneus, which was associated with depression scores, and a decrease in static ReHo in the right cerebellum_6, which was associated with language cognitive impairment. These two metrics not only represent different perspectives on intrinsic functional connectivity but also exhibit the potential capacity to distinguish CI patients from HCs. Our findings may provide temporal variability neuroimaging evidence to explore the neuropathological mechanisms of CI.

Acknowledgments

We thank all the patients and volunteers for participating in this study. Also, the authors are highly grateful to the anonymous reviewers for their significant and constructive comments and suggestions, which greatly improve the article.

Funding

This work was supported by the National Natural Science Foundation of China (Nos. 82001792), the Science Foundation of Guangdong Second Provincial General Hospital (No. 3D-A2021009) and the Key basic research projects of Guangzhou, China (No. 202201020385).

Declarations

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Ethical Approval

The ethics committee of Guangdong Second Provincial General Hospital in China approved this study and written informed consent was obtained from all participants.

Competing Interests

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Availability of data and materials

The data are available from the corresponding author upon reasonable request.

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Table 1 Demographic and clinical characteristics of all participants

	CI (N=87)	HC (N=78)	Statistics	P
Age (years)	44.04 ± 15.33	42.52 ± 12.31	0.697	0.487^a
Gender (Female/Male)	63/24	50/28	0.251	0.314^b
Education (years)	13.49 ± 3.09	14.34 ± 3.30	-1.710	0.089^a
Digit Symbol Substitution Test (DSST)	47.41 ± 16.82	52.42 ± 13.09	-2.100	0.036^c
Digit Span Test (DST)	13.24 ± 2.16	13.81 ± 2.10	-1.794	0.090^a
MoCA-Total Score	24.28 ± 3.23	26.97 ± 2.57	-5.835	<0.001^c
MoCA-Visual-spatial and Executive	3.82 ± 1.13	4.40 ± 1.22	-4.513	<0.001^c
MoCA-Naming	2.84 ± 0.43	2.96 ± 0.25	-2.548	0.011^c
MoCA-Memory	3.55 ± 1.37	4.10 ± 1.22	-2.804	0.005^c
MoCA-Attention	5.06 ± 1.19	5.68 ± 0.69	-3.806	<0.001^c
MoCA-Language	2.20 ± 0.70	2.51 ± 0.64	-3.044	0.002^c
MoCA-Abstraction	1.64 ± 0.59	1.79 ± 0.54	-2.236	0.025^c
MoCA-Delayed Recall	2.47 ± 1.41	3.64 ± 0.90	-5.922	<0.001^c
MoCA-Orientation	6.00 ± 0.00	6.00 ± 0.00	0.000	1.000^c
Mean framewise displacement (mm)	0.0887 ± 0.0357	0.0816 ± 0.0333	1.312	0.191^a
Disease duration (months)	57.09 ± 68.46
Pittsburgh Sleep Quality Index (PSQI)	13.17 ± 3.16
Insomnia Severity Index (ISI)	16.37 ± 4.81
Self-Rating Anxiety Scale (SAS)	50.49 ± 11.13
Self-Rating Depression Scale (SDS)	53.68 ± 11.52

Data represent mean ± standard deviation; * indicates that the difference in data between the two groups was statistically significant. ^a independent two sample t-test; ^b Chi-Square test; ^c Mann-Whitney U test. Abbreviations: CI, chronic insomnia; HC, healthy control; MoCA, Montreal Cognitive Assessment.

Table 2 Brain regions showing differences between CI patients and HCs.

Metrics	Brain regions	Cluster sizes	Peak MNI coordinate			t value
			X	Y	Z
Dynamic ReHo	Precuneus_R	110	12	-51	39	4.67
Static ReHo	Cerebellum_6_R	157	12	-75	-21	-4.16

All clusters were identified using Gaussian random field theory for multiple comparisons (voxel-wise p < 0.005, cluster-wise p < 0.05, two tailed). Abbreviations: R, right; MNI, Montreal Neurological Institute; ReHo, regional homogeneity.

No competing interests reported.

Static and temporal dynamic alterations of local functional connectivity in chronic insomnia

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Abstract

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Introduction

Materials and methods

Results

Discussion

Conclusion

Declarations

References

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