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Structural and functional brain alterations in laryngeal dystonia: A coordinate-based activation likelihood
Nyah Kshatriya1,2, Giovanni Battistella1, Kristina Simonyan1,2,3
1Department of Otolaryngology-Head and Neck Surgery, Massachusetts Eye and Ear and Harvard Medical School, Boston, Massachusetts, USA.
Human Brain Mapping
|September 21, 2024
Summary
Laryngeal dystonia (LD) involves spasms affecting speech. This meta-analysis reveals consistent brain abnormalities in motor cortices, insula, and striatum, supporting LD as a neural network disorder.
Area of Science:
- Neuroscience
- Neurology
- Medical Imaging
Background:
- Laryngeal dystonia (LD) is a focal dystonia impacting speech.
- Previous neuroimaging studies show varied results, hindering clinical application.
- Understanding consistent neural alterations in LD is crucial for developing targeted therapies.
Purpose of the Study:
- To systematically identify and meta-analyze structural and functional brain abnormalities in laryngeal dystonia.
- To consolidate neuroimaging findings for a clearer understanding of LD's neural basis.
- To provide evidence for future therapeutic targets in laryngeal dystonia.
Main Methods:
- Systematic literature search for stereotactic coordinates of abnormalities in LD patients versus controls.
- Coordinate-based activation likelihood estimation (ALE) meta-analysis.
- Inclusion of 21 functional and structural neuroimaging studies (521 LD patients, 448 controls).
Main Results:
- Meta-analysis identified significant abnormalities in bilateral primary motor cortices.
- Abnormalities were also found in the left inferior parietal lobule, left striatum, right insula, and supplementary motor area.
- These findings highlight a distributed neural network dysfunction in LD.
Conclusions:
- Laryngeal dystonia is characterized by widespread neural network abnormalities.
- The identified brain regions serve as key targets for future research and therapeutic interventions.
- This meta-analysis strengthens the view of dystonia as a disorder of neural networks.
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