2024; 22(2): 314-321  https://doi.org/10.9758/cpn.23.1117
A Comparative Investigation of Functional Connectivity Utilizing Electroencephalography in Insomnia Patients with and without Restless Leg Syndrome
Seo-Young Park1, Young-Min Park2, Yang Rae Kim3
1Department of Psychiatry, Inje University College of Medicine, Goyang, Korea
2Psychiatric Clinic in Your Brain and Mind, Goyang, Korea
3Kim’s Hue Neuropsychiatric Clinic, Bucheon, Korea
Correspondence to: Young-Min Park
Psychiatric Clinic in Your Brain and Mind, 1564 Jungang-ro, Ilsanseo-gu, Goyang 10381, Korea
E-mail: medipark@hanmail.net
ORCID: https://orcid.org/0000-0002-4993-1426

Yang Rae Kim
Kim’s Hue Neuropsychiatric Clinic, 272 Gilju-ro, Bucheon 14548, Korea
E-mail: proband@daum.net
ORCID: https://orcid.org/0000-0001-8626-8726
Received: July 13, 2023; Revised: August 30, 2023; Accepted: September 4, 2023; Published online: October 16, 2023.
© The Korean College of Neuropsychopharmacology. All rights reserved.

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Objective: The current study aimed to identify distinctive functional brain connectivity characteristics that differentiate patients with restless legs syndrome (RLS) from those with primary insomnia.
Methods: Quantitative electroencephalography (QEEG) was employed to analyze connectivity matrices using the phaselocking value technique. A total of 107 patients with RLS (RLS group) and 17 patients with insomnia without RLS (primary insomnia group) were included in the study. Demographic variables were compared using t tests and chi-square tests, while differences in connectivity were examined through multiple analyses of covariance. Correlation analysis was conducted to explore the relationship between connectivity and the severity of RLS.
Results: The results indicated significant differences in the primary somatosensory cortex (F = 4.377, r = 0.039), primary visual cortex (F = 4.215, r = 0.042), and anterior prefrontal cortex (F = 5.439, r = 0.021) between the RLS and primary insomnia groups. Furthermore, the connectivity of the sensory cortex, including the primary somatosensory cortex (r = −0.247, p = 0.014), sensory association cortex (r = −0.238, p = 0.028), retrosplenial region (r = −0.302, p = 0.002), angular gyrus (r = −0.258, p = 0.008), supramarginal gyrus (r = −0.230, p = 0.020), primary visual cortex (r = −0.275, p = 0.005) and secondary visual cortex (r = −0.226, p = 0.025) exhibited an inverse association with RLS symptom severity.
Conclusion: The prefrontal cortex, primary somatosensory cortex, and visual cortex showed potential as diagnostic biomarkers for distinguishing RLS from primary insomnia. These findings indicate that QEEG-based functional connectivity analysis shows promise as a valuable diagnostic tool for RLS and provides insights into its underlying mechanisms. Further research is needed to explore this aspect further.
Keywords: Restless legs syndrome; EEG; Phase locking value; Brain; Connectivity; Sensory cortex

Restless legs syndrome (RLS) is a neurological disorder characterized by an irresistible urge to move limbs, accompanied by unpleasant sensory sensations. Its prevalence ranges from 3.9% to 14.3% in the general population and can lead to sleep disturbances and impaired daytime functioning [1]. Although the pathophysiological basis of RLS remains unclear, brain imaging studies have provided insights into potential contributing factors such as reduced central iron storage and abnormalities in the dopamine system [2].

Studies using brain imaging techniques have identified changes in white matter within specific brain regions, including the postcentral gyrus, corpus callosum, and thalamus, in patients with RLS [3], which can affect the somatosensory and limbic systems, leading to alterations in functional connectivity within these areas, either during a task or in the resting state [4]. Resting-state functional magnetic resonance imaging (rs-fMRI) analyses have revealed changes in various brain networks in patients with RLS, suggesting their involvement in the pathophysiology of RLS [5,6].

Rs-fMRI studies have examined the functional connectivity of individuals with primary insomnia compared with healthy controls and highlighted the changes in connectivity within brain regions associated with the default mode network (DMN) and salience network in patients [7,8]. However, findings across studies have been inconsistent owing to variations in symptoms, causes, participants, and measurement tools. Additionally, no study has directly compared the RLS and primary insomnia groups.

In recent years, quantitative electroencephalography (QEEG) and electroencephalography (EEG) have gained attention as valuable tools for investigating functional brain connectivity in neuropsychiatric disorders [9,10]. QEEG is a cost-effective and noninvasive imaging technique that offers accessibility advantages over other methods. QEEG studies of patients with RLS during sleep have revealed various alterations in specific EEG frequency bands. These include an increase in delta power within regions such as the dorsolateral prefrontal cortex, frontal lobe, and temporal lobe. Additionally, a decrease in theta power, indicative of cognitive decline, has been observed along with an elevation in delta and fast alpha power, which is associated with depressive mood [11].

However, research on the clinical application of QEEG functional connectivity analyses is limited. Thus, the present study aimed to use QEEG analysis to identify the distinctive functional brain connectivity characteristics that differentiate patients with RLS from those with primary insomnia; specifically, it examined the differences in brain connectivity between two groups: insomnia with RLS patients (RLS group) and insomnia without RLS patients (primary insomnia group). We hypothesized that the RLS group would exhibit more distinct alterations in brain regions involved in sensory and motor information processing than the primary insomnia group. Identifying these changes has the potential to enhance our understanding of the underlying mechanisms of RLS and facilitate its clinical diagnosis.



This study included 124 participants aged 16−80 who underwent QEEG between January 1, 2015, and February 28, 2022. Among them, 107 individuals were diagnosed with RLS based on clinical interviews conducted by psychiatrists using the diagnostic criteria set by the international restless legs syndrome study group [12]. Additionally, 17 participants were diagnosed with primary insomnia and did not meet the criteria for RLS but exhibited mild symptoms of anxiety and depression. Participants were included in the study regardless of their medication status for their existing medical condition. This retrospective study used data obtained from medical records for analysis.

Patients with drug or alcohol misuse, brain damage, or strokes were excluded. All patients were assessed for the severity of RLS symptoms using the Korean version of the international restless legs scale (K-IRLS) and for the severity of accompanying depression and anxiety using the beck depression inventory (BDI) and beck anxiety inventory (BAI), respectively. This study was approved by the institutional review board of Inje University Ilsan Paik Hospital (2022-04-001).

EEG Recording and Preprocessing

The EEG data were recorded using a custom 32-channel cap (32Ch; Magstim Geodesic Inc.), using an extended 10−20 placement scheme. The participants were instructed to rest comfortably with their eyes closed for 5 minutes while ensuring that they did not fall asleep.

All preprocessing steps were performed. To prepare the data for analysis, a series of preprocessing steps were implemented. The frequencies below 1 Hz and above 60 Hz were filtered out. Resting data were segmented into 2-second epochs and averaged. Visual inspection was conducted to reject gross artifacts, followed by reinter-polation. Independent component analysis was performed to remove ocular and muscle artifacts. Following the preprocessing steps, noise covariance and data covariance were calculated, and clean data were used for subsequent analysis. The head model used was ICBM152, and magnetic resonance imaging registration was conducted on a 3-Shell sphere head model. A dynamic statistical parameter map (dSPM) approach was employed to obtain a spatiotemporal map of brain activity [13].

Brain connectivity was assessed using scaled dSPM values. Based on the Brodmann atlas, connectivity matrices were computed using the phase-locking value (PLV) technique.

EEG Connectivity Analysis

Functional brain connectivity refers to the statistical associations between regions without directional information. In this study, the PLV was chosen as the metric to quantify functional brain connectivity and explore the potential differences between the RLS and primary insomnia groups.

The PLV is a measurement of how well two signals are synchronized. If the PLV value is 1, the signals are completely synchronized [14]. If the PLV value is zero, the signals are independent and not synchronized. We used a modified version of the corrected image phase-locking value (ciPLV) to obtain a more accurate measurement of synchronization between signals [15].

ciPLV was evaluated across different frequency bands, including delta (2−4 Hz), theta (5−7 Hz), alpha (8−12 Hz), beta (13−29 Hz), and gamma (30−60 Hz). We calculated ciPLV for all combinations of brain regions in the network, resulting in connectivity values for each region. These values were obtained by averaging the ciPLVs across the corresponding pairs of nodes.

Statistical Analysis

Prior to conducting parametric statistical comparisons, the normality of the mean PLV values was assessed using the Shapiro–Wilk method. Student’s ttests were performed to compare continuous demographic variables between the RLS and primary insomnia groups. The chi-square test was performed to compare categorical variables. To examine the differences in ciPLV among the groups while accounting for potential confounders, a multiple analysis of covariance was performed. Age, sex, BDI score, BAI score, and RLS severity were included as covariates. The relationship between ciPLV and K-IRLS scores was evaluated by performing correlation analysis. A multiple regression analysis was performed to explore the predictive value of the ciPLV for these clinical scores. Statistical significance was set at a pvalue of 0.05, but the resulting pvalues was not corrected for multiple testing. IBM SPSS (25.0 version; SPSS Inc.) for Windows was used for data analysis.


Patient Cohort and Control Group

This study included 124 participants: 37 were male and 87 were female, with an average age of 46.54 years old. Participants were divided into two groups depending on whether they met the RLS diagnostic criteria: 107 and 17 were in the RLS and primary insomnia groups, with an aver-age age of 46.24 and 48.41 years, respectively. Statistically significant differences in sex, BDI, BAI, and RLS scores were observed between the two groups (p < 0.05) (Table 1).

Comparison between RLS and Control Groups

The results revealed significant differences in functional connectivity during the resting state between the two groups after controlling for potential confounding factors such as age, sex, RLS severity, BDI, and BAI. Specifically, the RLS group exhibited lower connectivity in the anterior prefrontal cortex (Brodmann area, BA 10R) (F = 5.439, p = 0.021) (Fig. 1) and primary visual cortex (BA 17R) (F = 4.215, p = 0.042) than did the primary insomnia group. In contrast, higher connectivity was observed in the primary somatosensory cortex (BA 1L) (F = 4.377, p = 0.039) in the RLS group (Table 2).

Correlation between ciPLV and IRLS Score Verified through Correlation and Multiple Regression Analysis

The association between ciPLV and other clinical scores was verified by performing correlation and multiple regression analyses. ciPLV connectivity on the primary somatosensory cortex (BA 2L) (r = −0.247, p = 0.014), somatosensory association cortex (BA 5L) (r = −0.238, p = 0.028), primary visual cortex (BA 17R) (r = −0.275, p = 0.005), secondary visual cortex (BA 18L) (r = −0.226, p = 0.025), retrosplenial region (BA 29L) (r = −0.302, p = 0.002), angular gyrus (BA 39L) (r = −0.258, p = 0.008) and supramarginal gyrus (BA 40L) (r = −0.230, p = 0.020) were significantly associated with RLS severity in multivariable logistic regression analysis (Fig. 2).


Participants in the RLS group had higher levels of depression and anxiety than those in the primary insomnia group, consistent with the findings of previous studies. RLS, which is often associated with anxiety, can contribute to the development of insomnia, which increases the risk of depression [16,17]. The association between dopamine levels, depression, and RLS is well established. Interestingly, a randomized controlled trial showed that dopamine agonists could effectively alleviate the symptoms of both depression and RLS [18]. However, more research is needed to fully understand how depression and RLS are connected and what causes this relationship [19].

The results revealed that the connectivity of the sensory cortex, including the primary somatosensory cortex, sensory association cortex, retrosplenial region, inferior parietal lobule, and primary visual cortex, in the RLS group was negatively correlated with the severity of RLS symptoms.

Studies have also found changes in the somatosensory cortex, an important part of the brain that processes sensory information such as touch, temperature, and pain, in RLS patients [3,20,21], suggesting a connection between RLS symptoms and sensorimotor network dysfunction.

The retrosplenial region plays a role in spatial information processing and emotional regulation as a secondary association area and also acts as a connecting area between the sensory cortex and the hippocampus, helping to integrate sensory information. Studies on patients with fibromyalgia have shown increased blood flow in this region, indicating a heightened perception of internal sensory signals [22]. Although studies have not conclusively established alterations in connectivity within the retrosplenial region in the RLS group, our research findings suggest that such disruptions in connectivity may play a role in the heightened sensitivity to internal sensations experienced by patients with RLS.

The inferior parietal lobule consists of the angular and supramarginal gyrus, which are involved in processing sensory information. Studies have consistently shown that the parietal lobule, part of the sensory cortex, undergoes connectivity changes in RLS [6,23]. One study used fMRI and discovered that dopamine treatment improved connectivity in the same area, resulting in decreased RLS symptoms [24]. Our study also found a strong link between symptom severity and the parietal lobule in the RLS group, suggesting a role for RLS symptoms in relation to internal sensory changes during rest.

This study found that the anterior prefrontal cortex in the RLS group had lower connectivity than that in the primary insomnia group. The anterior prefrontal cortex is involved in cognitive functions such as decision-making and attention and receives input from dopamine neurons. Disruptions in the mesencephalic dopaminergic system’s A11 cells in patients with RLS may not only affect the spinal pathway but also affect cognitive functions by projecting to the anterior prefrontal cortex. Research has consistently shown that patients with RLS have significant impairments in prefrontal cortical functioning compared to normal controls [25] which are similar to the cognitive difficulties experienced after a night of sleep deprivation [26]. Treatment with dopamine agonists has shown promise for improving cognitive symptoms in patients [27]. Our study supports existing evidence that sleep deprivation can lead to cognitive decline in both RLS and primary insomnia. Additionally, individuals with RLS may have more significant cognitive impairment than those with primary insomnia, potentially owing to dopamine dysregulation.

However, the results of our study differed from our expectations. First, we did not find significant differences in motor cortex-related areas between the RLS and primary insomnia groups, and there was no correlation between symptom severity and these areas. Previous studies observed diurnal variation of DMN in RLS patients, and found that at evening, the connectivity of the thalamus in RLS patients was weaker compared to healthy controls, and this was associated with motor symptoms [23]. In the current study, using day-time QEEG, might not have detected significant differences in motor area connections. Nevertheless, there are other studies showing changes in connectivity in the motor cortex in day-time fMRI [28], so this is still controversial and further research is needed.

Second, our study revealed that the connectivity values in the primary somatosensory cortex were higher in the RLS group than in the primary insomnia group. We also observed a negative correlation between the severity of RLS symptoms and connectivity values. Studies have shown that both RLS and primary insomnia can affect the somatosensory cortex, leading to increased sensitivity to external sensations and sleep disruptions. These changes in connectivity are associated with the duration of insomnia [29]. In summary, our findings indicate altered connectivity in the primary somatosensory cortex in both RLS and primary insomnia, likely as a result of sleep deprivation rather than being specific to RLS.

In contrast, our study revealed that the RLS group had lower connectivity values in the visual cortex than the primary insomnia group. Furthermore, we observed a clear negative correlation between the severity of RLS symptoms and the connectivity of the visual cortex. Research has indicated that characteristic changes in the visual cortex are associated with symptom improvement in the RLS group before and after treatment, suggesting their potential as a biomarker of treatment response. For example, studies have shown that high-frequency repetitive transcranial magnetic stimulation and transcutaneous spinal direct current stimulation can modulate the activity and connectivity of sensory-motor and visual processing areas, leading to symptom relief in patients with RLS [30-32]. The sensorimotor symptoms experienced in RLS can be attributed to a decrease in inhibitory signals to the spinal cord resulting from dopamine dysfunction in the central nervous system, which leads to heightened reflexes, impaired control over motor centers, and increased sensitivity to leg movements and sensory stimuli. The visual cortex may play a role in these symptoms by influencing the inhibitory pathways responsible for their regulation.

In summary, our findings suggest that the functional connectivity of the prefrontal cortex, primary somatosensory cortex, and visual cortex can serve as valuable diagnostic biomarkers for distinguishing RLS from primary insomnia. Further research will contribute to a better understanding of the underlying pathophysiology of RLS.

This study had some limitations, including an unequal distribution of participants between the RLS and primary insomnia groups, which could introduce bias and limit the ability to find significant differences between the groups. In the regions where there were notable differences in connectivity between RLS patients and those with primary insomnia, the data from Brodmann area 1L and 10R in the primary insomnia group didn’t follow a normal distribution. And the result is not corrected for multiple testing. While efforts were made to consider the effects of depression and anxiety by examining their scores in both groups and taking demographic factors such as age and sex into consideration, future studies should employ more rigorous methodologies such as randomization or matching to ensure better comparability between the groups. Additionally, more precise statistical corrections should be employed to ensure the accuracy of the results. Second, no healthy control group was included in the study, which prevented comparisons between participants with insomnia and RLS and healthy individuals. In the current study, we did not examine participants’ history of drug use, which could have significantly influenced the results. For future research, it is essential to include both healthy participants and their drug history to ensure accurate outcome assessment. Finally, the study focused only on changes in connectivity in cortical brain regions and did not examine subcortical areas such as the thalamus and limbic systems, which are relevant to RLS.

Future research should address these limitations and explore whether connectivity in the sensory cortex, particularly in the prefrontal, primary somatosensory, and visual cortices, can serve as a neuroimaging marker for diagnosing RLS in comparison to individuals with primary insomnia. Conducting more targeted investigations will significantly contribute to our understanding of the specific brain regions involved in the mechanisms underlying RLS.



Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Author Contributions

Conceptualization: Young-Min Park, Yang Rae Kim. Formal analysis: Seo-Young Park. Methodology: Young-Min Park, Yang Rae Kim. Software: Seo-Young Park. Writing—original draft: Seo-Young Park. Writing—review and editing: Young-Min Park.

Fig. 1. Top 20% connectivity results of anterior prefrontal cortex (BA 10R) to the entire brain region. (A) RLS, (B) primary insomnia. The figure demonstrates reduced connectivity in RLS patients when compared to those with primary insomnia.
BA, Brodmann area; RLS, restless legs syndrome.
Fig. 2. Association between ciPLV and IRLS scores verified through partial correlation. The relationship between the ciPLV and IRLS scores was examined by performing partial correlation and multiple regression analyses: (A) left primary somatosensory cortex, (B) left somatosensory association cortex, (C) right primary visual cortex, (D) left secondary visual cortex, (E) left retrosplenial region, (F) left angular gyrus, and (G) left supramarginal gyrus. The results revealed a significant negative correlation between RLS score and ciPLV in specific Brodmann areas.
ciPLV, corrected image phase-locking value; IRLS, international restless legs scale; RLS, restless legs syndrome.

Demographic characteristics and assessment of participants

Variable RLS (n = 107) Primary insomnia (n = 17) pvalue
Age 46.2 ± 16.5 48.4 ± 17.1 0.619
Sex (M/F) 28/79 9/8 0.025*
BDI 24.9 ± 11.6 16.2 ± 6.9 < 0.001*
BAI 25.3 ± 14.6 12.2 ± 7.0 < 0.001*

Values are presented as mean ± standard deviation or number only.

RLS, restless legs syndrome; BDI, beck depression inventory; BAI, beck anxiety inventory.

*Significant at p < 0.05, ttest.

MANCOVA results representing the difference between the groups in ciPLVs

Brodmann areas RLS (n = 107) Primary insomnia (n = 17) F pvalue
Brodmann area 1L (left primary somatosensory cortex) 0.044 ± 0.01 0.041 ± 0.02 4.377 0.039
Brodmann area 10R (right anterior prefrontal cortex) 0.036 ± 0.01 0.040 ± 0.01 5.439 0.021
Brodmann area 17R (right visual cortex) 0.021 ± 0.01 0.022 ± 0.01 4.215 0.042

Age, sex, BDI, BAI, and RLS score – adjusted means ± standard error.

MANCOVA, multiple analysis of covariance; ciPLV,corrected image phase-locking value; RLS, restless legs syndrome; BDI, beck depression inventory; BAI, beck anxiety inventory.

The connectivity value in each region was defined as the average ciPLV of the corresponding region for the entire pair of nodes.

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