Clinical Psychopharmacology and Neuroscience Papers in Press available online.

A comparative investigation of functional connectivity utilizing quantitative electroencephalography in insomnia patients with and without restless leg syndrome
Seo-Young Park 1, Young-Min Park 1,*, Yang Rae Kim 2
1Department of Psychiatry, Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea, 2Kim’s Hue neuropsychiatric clinic, Seoul, Republic of Korea
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 phase-locking value (PLV) 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 (MANCOVA). 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.
Accepted Manuscript [Submitted on 2023-07-08, Accepted on 2023-09-04]