A multi-modal neuroimaging data release for Meige Syndrome and Facial Paralysis Research.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-14 DOI:10.1038/s41597-025-04383-4
Peng Gao, Jixin Luan, Aocai Yang, Manxi Xu, Kuan Lv, Pianpian Hu, Hongwei Yu, Zeshan Yao, Guolin Ma
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Abstract

The sharing of multimodal magnetic resonance imaging (MRI) data is of utmost importance in the field, as it enables a deeper understanding of facial nerve-related pathologies. However, there is a significant lack of multi-modal neuroimaging databases specifically focused on these conditions, which hampers our comprehensive knowledge of the neural foundations of facial paralysis. To address this critical gap and propel advancements in this area, we have released the Multimodal Neuroimaging Dataset of Meige Syndrome, Facial Paralysis, and Healthy Controls (MND-MFHC). This dataset includes detailed clinical assessments of 53 individuals with facial paralysis (FP), 31 patients with Meige syndrome (MS), and 102 healthy controls (HC). To promote open access, the BIDS-formatted data and associated quality control reports can be accessed through the Science Data Bank (SciDB). By sharing this comprehensive dataset, our aim is to facilitate further research and exploration into the intricate neural mechanisms underlying facial nerve-related pathologies.

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Meige综合征和面瘫研究的多模态神经影像学数据发布。
多模态磁共振成像(MRI)数据的共享在该领域至关重要,因为它可以更深入地了解面神经相关病理。然而,目前缺乏专门针对这些疾病的多模态神经影像学数据库,这阻碍了我们对面瘫神经基础的全面了解。为了解决这一关键差距并推动这一领域的进步,我们发布了Meige综合征、面瘫和健康对照(MND-MFHC)的多模态神经成像数据集。该数据集包括53名面瘫(FP)患者、31名Meige综合征(MS)患者和102名健康对照(HC)患者的详细临床评估。为了促进开放获取,可以通过科学数据库(SciDB)访问bids格式的数据和相关的质量控制报告。通过共享这个全面的数据集,我们的目标是促进进一步研究和探索复杂的神经机制背后的面神经相关病理。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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