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Abnormal neural circuits and altered brain network topological properties in patients with chronic unilateral vestibulopathy.

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Title: Abnormal neural circuits and altered brain network topological properties in patients with chronic unilateral vestibulopathy.
Authors: Wang, Yuru (AUTHOR), Li, Kangzhi (AUTHOR), Si, Lihong (AUTHOR), Liu, Shui (AUTHOR), Yang, Xu (AUTHOR)
Source: Neurological Sciences. Aug2025, Vol. 46 Issue 8, p3851-3863. 13p.
Subjects: Large-scale brain networks, Neural circuitry, Functional magnetic resonance imaging, Visual cortex, Topological property
Abstract: Background: Chronic unilateral vestibulopathy (CUVP) is one of the most common causes of chronic dizziness/vertigo. The brain functional mechanisms of CUVP are currently unclear. The study aimed to clarify changes in brain topological properties and subnetwork functional connectivity in CUVP patients, elucidating the neural mechanisms behind their poor dynamic compensation. Methods: A total of 44 participants were included (22 CUVP patients and 22 age- and sex-matched healthy controls). Resting-state functional MRI was performed on all subjects. Network-Based Statistics (NBS) analysis was conducted to identify abnormal neural circuits in CUVP. Graph-theoretical analysis (GTA) was performed to elucidate changes in brain network topological properties. Correlation analysis was conducted to examine the relationship between brain network changes and clinical symptom severity. Results: NBS analysis revealed an abnormal neural network in CUVP patients, with key nodes including the parieto-insular vestibular cortex, sensory-motor cortex, occipital visual cortex, brainstem, and cerebellum. The most significant functional connectivity abnormalities were observed between the brainstem and visual/sensorimotor networks. Graph-theoretical analysis indicated increased characteristic path length, decreased global and local efficiency in CUVP patients. Node properties showed reduced node efficiency and clustering coefficients in multiple nodes within the visual and sensorimotor networks. Correlation analysis indicated that brain network topology and changes in brainstem-sensorimotor network connectivity were negatively correlated with DHI scores. Conclusion: CUVP patients exhibit multisensory integration abnormalities and changes in brain network topology at both the brainstem/cerebellar and cortical levels, which may underlie the potential neural basis for poor vestibular compensation in CUVP patients. [ABSTRACT FROM AUTHOR]
Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Abnormal neural circuits and altered brain network topological properties in patients with chronic unilateral vestibulopathy.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yuru%22">Wang, Yuru</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Kangzhi%22">Li, Kangzhi</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Si%2C+Lihong%22">Si, Lihong</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Shui%22">Liu, Shui</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Xu%22">Yang, Xu</searchLink> (AUTHOR)
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  Data: Background: Chronic unilateral vestibulopathy (CUVP) is one of the most common causes of chronic dizziness/vertigo. The brain functional mechanisms of CUVP are currently unclear. The study aimed to clarify changes in brain topological properties and subnetwork functional connectivity in CUVP patients, elucidating the neural mechanisms behind their poor dynamic compensation. Methods: A total of 44 participants were included (22 CUVP patients and 22 age- and sex-matched healthy controls). Resting-state functional MRI was performed on all subjects. Network-Based Statistics (NBS) analysis was conducted to identify abnormal neural circuits in CUVP. Graph-theoretical analysis (GTA) was performed to elucidate changes in brain network topological properties. Correlation analysis was conducted to examine the relationship between brain network changes and clinical symptom severity. Results: NBS analysis revealed an abnormal neural network in CUVP patients, with key nodes including the parieto-insular vestibular cortex, sensory-motor cortex, occipital visual cortex, brainstem, and cerebellum. The most significant functional connectivity abnormalities were observed between the brainstem and visual/sensorimotor networks. Graph-theoretical analysis indicated increased characteristic path length, decreased global and local efficiency in CUVP patients. Node properties showed reduced node efficiency and clustering coefficients in multiple nodes within the visual and sensorimotor networks. Correlation analysis indicated that brain network topology and changes in brainstem-sensorimotor network connectivity were negatively correlated with DHI scores. Conclusion: CUVP patients exhibit multisensory integration abnormalities and changes in brain network topology at both the brainstem/cerebellar and cortical levels, which may underlie the potential neural basis for poor vestibular compensation in CUVP patients. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Neurological Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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