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Alterations of Functional Connectivity in Patients With Restless Legs Syndrome
Kang Min Park1, Keun Tae Kim2, Kyung Wook Kang3
1Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea.
Journal of Clinical Neurology (Seoul, Korea)
|May 19, 2022
Summary
Restless legs syndrome (RLS) is a neurological disorder involving brain network alterations. Studies show changes in functional connectivity in RLS patients, suggesting it
Area of Science:
- Neuroscience
- Neurology
Background:
- Restless legs syndrome (RLS) is a common neurological disorder characterized by an irresistible urge to move the legs, often accompanied by uncomfortable sensations.
- Understanding the neural underpinnings of RLS is crucial for developing effective treatments.
Purpose of the Study:
- To review and synthesize findings from functional connectivity studies in patients with Restless legs syndrome.
- To explore how brain network alterations contribute to RLS pathogenesis and how treatments impact these networks.
Main Methods:
- Systematic review of functional connectivity studies in RLS patients compared to healthy controls.
- Analysis of alterations in specific brain networks, including thalamic, salience, default-mode, and small-world networks.
- Examination of network changes associated with RLS treatments such as repetitive transcranial magnetic stimulation, spinal cord stimulation, and dopaminergic drugs.
Main Results:
- Significant alterations in functional connectivity were identified in RLS patients across multiple brain networks compared to healthy individuals.
- Specific network changes were observed in thalamic, salience, default-mode, and small-world networks.
- RLS treatments, including non-invasive brain stimulation and medications, were associated with modifications in these brain networks.
Conclusions:
- The findings strongly suggest that altered functional connectivity within brain networks is a key component of Restless legs syndrome pathophysiology.
- RLS can be conceptualized as a network disorder, highlighting the importance of network-level analysis in understanding and treating the condition.

