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논문 기본 정보

자료유형
학술저널
저자정보
Seo-Young Park (Inje University) Young-Min Park (Psychiatric Clinic in Your Brain and Mind) Yang Rae Kim (Kim’s Hue Neuropsychiatric Clinic)
저널정보
대한정신약물학회 Clinical Psychopharmacology and Neuroscience Clinical Psychopharmacology and Neuroscience Vol.22 No.2
발행연도
2024.5
수록면
314 - 321 (8page)
DOI
10.9758/cpn.23.1117

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초록· 키워드

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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 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 con ducted 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 bio markers 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.

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