A Comparative Investigation of Functional Connectivity Utilizing Electroencephalography in Insomnia Patients with and without Restless Leg Syndrome
- Author:
Seo-Young PARK
1
;
Young-Min PARK
;
Yang Rae KIM
Author Information
- Publication Type:Original Article
- From:Clinical Psychopharmacology and Neuroscience 2024;22(2):314-321
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
