Resting-state EEG data before and after cognitive activity across the adult lifespan and a 5-year follow-up.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-09-10 DOI:10.1038/s41597-024-03797-w

Stephan Getzmann, Patrick D Gajewski, Daniel Schneider, Edmund Wascher

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Abstract

This dataset consists of 64-channels resting-state EEG recordings of 608 participants aged between 20 and 70 years, 61.8% female, as well as follow-up measurements after approximately 5 years of 208 participants, starting 2021. The EEG was measured for three minutes with eyes open and eyes closed before and after a 2-hour block of cognitive experimental tasks. The data set is part of the Dortmund Vital Study, a prospective study on the determinants of healthy cognitive aging. The dataset can be used for (1) analyzing cross-sectional resting-state EEG of healthy individuals across the adult life span; (2) generating normalization data sets for comparison of resting-state EEG data of patients with clinically relevant disorders; (3) studying effects of performing cognitive tasks on resting-state EEG and age; (4) exploring intra-individual changes in resting-state EEG and effects of task performance over a time period of about 5 years. The data are provided in Brain Imaging Data Structure (BIDS) format and are available on OpenNeuro.

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成人一生中认知活动前后的静息态脑电图数据以及为期 5 年的随访。

该数据集包括对 608 名年龄在 20 岁至 70 岁之间的参与者（61.8% 为女性）进行的 64 通道静息状态脑电图记录，以及对 208 名参与者从 2021 年开始约 5 年后进行的跟踪测量。在完成 2 小时的认知实验任务前后，分别睁眼和闭眼测量脑电图 3 分钟。该数据集是多特蒙德生命研究（Dortmund Vital Study）的一部分，这是一项关于健康认知老化决定因素的前瞻性研究。该数据集可用于：(1) 分析健康人在整个成年期的横断面静息脑电图；(2) 生成归一化数据集，用于比较临床相关疾病患者的静息脑电图数据；(3) 研究执行认知任务对静息脑电图和年龄的影响；(4) 探索静息脑电图的个体内变化和任务执行对大约 5 年时间的影响。数据以脑成像数据结构（BIDS）格式提供，可在 OpenNeuro 上查阅。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

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.