常见良性癫痫儿童认知评估、sMRI和rsfMRI的多维数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Hongzhou Chen, Mengyuan Li, Tian Li, Mai Chen, Chao Jiang, Yudian Tang, Qian Chen, Dazhi Cheng
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引用次数: 0

摘要

最近的研究强调了儿童常见的良性癫痫综合征与部分认知障碍发展之间的潜在关联。我们建立了一个从36名中国儿童志愿者队列中获得的认知、结构磁共振成像(sMRI)和静息状态功能磁共振成像(rsfMRI)数据的综合数据库。这些参与者年龄在5-16岁之间,由经过认证的儿科神经学家专门诊断为具有中央颞叶尖峰的良性癫痫(BECTs)或儿童期缺失癫痫。所有影像学资料均由有资质的临床医生收集,并在磁共振影像学资料收集后1年内收集认知数据。该数据库允许研究人员使用多模态数据进行更详细的分析,以探索患有常见癫痫综合征的儿童认知障碍和大脑机制之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Multidimensional dataset for cognitive assessment, sMRI, and rsfMRI in common benign epileptic children.

Multidimensional dataset for cognitive assessment, sMRI, and rsfMRI in common benign epileptic children.

Multidimensional dataset for cognitive assessment, sMRI, and rsfMRI in common benign epileptic children.

Multidimensional dataset for cognitive assessment, sMRI, and rsfMRI in common benign epileptic children.

Recent studies have highlighted the potential association between common benign epilepsy syndromes in children and the development of partial cognitive impairment. We established a comprehensive database of cognitive, structural magnetic resonance imaging (sMRI), and resting-state functional magnetic resonance imaging (rsfMRI) data obtained from a cohort of 36 Chinese paediatric volunteers. The participants, aged 5-16 years, were specifically diagnosed with either benign epilepsy with centrotemporal spikes (BECTs) or childhood absence epilepsy by certified paediatric neurologists. All imaging data were collected by qualified clinicians, and cognitive data were collected within 1 year of magnetic resonance imaging data collection. This database allows researchers to perform more detailed analyses using multimodal data to explore the relationship between cognitive impairment and brain mechanisms in children with common epilepsy syndromes.

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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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