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皮质感觉运动改变可对痉挛性发声障碍的临床表型和假定基因型进行分类。

Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia.

作者信息

Battistella G, Fuertinger S, Fleysher L, Ozelius L J, Simonyan K

机构信息

Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

出版信息

Eur J Neurol. 2016 Oct;23(10):1517-27. doi: 10.1111/ene.13067. Epub 2016 Jun 27.


DOI:10.1111/ene.13067
PMID:27346568
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5308055/
Abstract

BACKGROUND AND PURPOSE: Spasmodic dysphonia (SD), or laryngeal dystonia, is a task-specific isolated focal dystonia of unknown causes and pathophysiology. Although functional and structural abnormalities have been described in this disorder, the influence of its different clinical phenotypes and genotypes remains scant, making it difficult to explain SD pathophysiology and to identify potential biomarkers. METHODS: We used a combination of independent component analysis and linear discriminant analysis of resting-state functional magnetic resonance imaging data to investigate brain organization in different SD phenotypes (abductor versus adductor type) and putative genotypes (familial versus sporadic cases) and to characterize neural markers for genotype/phenotype categorization. RESULTS: We found abnormal functional connectivity within sensorimotor and frontoparietal networks in patients with SD compared with healthy individuals as well as phenotype- and genotype-distinct alterations of these networks, involving primary somatosensory, premotor and parietal cortices. The linear discriminant analysis achieved 71% accuracy classifying SD and healthy individuals using connectivity measures in the left inferior parietal and sensorimotor cortices. When categorizing between different forms of SD, the combination of measures from the left inferior parietal, premotor and right sensorimotor cortices achieved 81% discriminatory power between familial and sporadic SD cases, whereas the combination of measures from the right superior parietal, primary somatosensory and premotor cortices led to 71% accuracy in the classification of adductor and abductor SD forms. CONCLUSIONS: Our findings present the first effort to identify and categorize isolated focal dystonia based on its brain functional connectivity profile, which may have a potential impact on the future development of biomarkers for this rare disorder.

摘要

背景与目的:痉挛性发声障碍(SD),即喉肌张力障碍,是一种特定任务的孤立性局灶性肌张力障碍,病因和病理生理学尚不明确。尽管该疾病已被描述存在功能和结构异常,但其不同临床表型和基因型的影响仍知之甚少,这使得难以解释SD的病理生理学并识别潜在的生物标志物。 方法:我们结合独立成分分析和静息态功能磁共振成像数据的线性判别分析,来研究不同SD表型(外展型与内收型)和假定基因型(家族性与散发性病例)中的脑组织结构,并表征用于基因型/表型分类的神经标志物。 结果:与健康个体相比,我们发现SD患者的感觉运动网络和额顶叶网络内存在异常功能连接,以及这些网络在表型和基因型方面的不同改变,涉及初级体感皮层、运动前皮层和顶叶皮层。使用左下顶叶和感觉运动皮层的连接性测量,线性判别分析在区分SD患者和健康个体时准确率达到71%。在区分不同形式的SD时,左下顶叶、运动前皮层和右感觉运动皮层测量值的组合在家族性和散发性SD病例之间的判别能力达到81%,而右上顶叶、初级体感皮层和运动前皮层测量值的组合在区分内收型和外展型SD形式时准确率为71%。 结论:我们的研究结果首次尝试基于脑功能连接特征对孤立性局灶性肌张力障碍进行识别和分类,这可能对这种罕见疾病生物标志物的未来发展产生潜在影响。

相似文献

[1]
Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia.

Eur J Neurol. 2016-10

[2]
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Cereb Cortex. 2018-1-1

[3]
Phenotype- and genotype-specific structural alterations in spasmodic dysphonia.

Mov Disord. 2017-4

[4]
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[5]
Brain Activity in Patients With Adductor Spasmodic Dysphonia Detected by Functional Magnetic Resonance Imaging.

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[6]
Altered sensory system activity and connectivity patterns in adductor spasmodic dysphonia.

Sci Rep. 2020-6-23

[7]
Connectome-Wide Phenotypical and Genotypical Associations in Focal Dystonia.

J Neurosci. 2017-8-2

[8]
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Mov Disord. 2019-3-6

[9]
Abnormal activation of the primary somatosensory cortex in spasmodic dysphonia: an fMRI study.

Cereb Cortex. 2010-3-1

[10]
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引用本文的文献

[1]
Neurobiology of Dystonia: Review of Genetics, Animal Models, and Neuroimaging.

Brain Sci. 2025-7-19

[2]
Structural and functional brain alterations in laryngeal dystonia: A coordinate-based activation likelihood estimation meta-analysis.

Hum Brain Mapp. 2024-10

[3]
Deep Brain Stimulation Improves Symptoms of Spasmodic Dysphonia Through Targeting of Thalamic Sensorimotor Connectivity.

Neurosurgery. 2024-1-22

[4]
Temporal Signature of Task-Specificity in Isolated Focal Laryngeal Dystonia.

Mov Disord. 2023-10

[5]
Clinical Implications of Dystonia as a Neural Network Disorder.

Adv Neurobiol. 2023

[6]
Altered brain connectivity in hyperkinetic movement disorders: A review of resting-state fMRI.

Neuroimage Clin. 2023

[7]
Temporal specificity of abnormal neural oscillations during phonatory events in laryngeal dystonia.

Brain Commun. 2022-2-11

[8]
Dystonia Management: What to Expect From the Future? The Perspectives of Patients and Clinicians Within DystoniaNet Europe.

Front Neurol. 2021-6-3

[9]
Deep Learning for the Assisted Diagnosis of Movement Disorders, Including Isolated Dystonia.

Front Neurol. 2021-4-30

[10]
Laryngeal Dystonia: Multidisciplinary Update on Terminology, Pathophysiology, and Research Priorities.

Neurology. 2021-5-25

本文引用的文献

[1]
Brain structural changes in spasmodic dysphonia: A multimodal magnetic resonance imaging study.

Parkinsonism Relat Disord. 2016-4

[2]
Neural correlates of abnormal sensory discrimination in laryngeal dystonia.

Neuroimage Clin. 2015-10-30

[3]
The Functional Connectome of Speech Control.

PLoS Biol. 2015-7-23

[4]
Cortical Silent Period Reveals Differences Between Adductor Spasmodic Dysphonia and Muscle Tension Dysphonia.

Neurorehabil Neural Repair. 2016-3

[5]
Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism.

PLoS One. 2015-6-10

[6]
Abnormal motor cortex excitability during linguistic tasks in adductor-type spasmodic dysphonia.

Eur J Neurosci. 2015-8

[7]
Group differences in MEG-ICA derived resting state networks: Application to major depressive disorder.

Neuroimage. 2015-9

[8]
Monitoring the growth of the neural representations of new animal concepts.

Hum Brain Mapp. 2015-8

[9]
Disintegration of Sensorimotor Brain Networks in Schizophrenia.

Schizophr Bull. 2015-11

[10]
Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

Psychiatry Res. 2015-3-20

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