five

The Stockholm Sleepy Brain Study: Effects of Sleep Deprivation on Cognitive and Emotional Processing in Young and Old

收藏
OpenNeuro2018-03-14 更新2024-12-21 收录
下载链接:
https://openneuro.org/datasets/ds000201
下载链接
链接失效反馈
资源简介:
# The Stockholm SleepyBrain Project # ## Background and Aim ## Sleepiness is a brain state with pervasive effects on cognitive and affective functioning. However, little is known about the functional mechanisms and correlates of sleepiness in the awake brain. This project aimed to investigate overall effects of sleepiness on brain function with particular regard to emotional processing. ## Method and Design ## We investigated the effects of sleep deprivation using a randomized cross-over design. Resting state functional connectivity was investigated using functional magnetic resonance imaging (fMRI). Emotional contagion was studied using concurrent fMRI and electromyography (EMG) of facial muscles in response to emotional expressions and empathy for pain was investigated using pictures of others receiving pain stimuli. To study emotional reappraisal, participants were instructed to actively up-regulate or down-regulate their emotional responses to picture stimuli. The participants were characterized using several rating scales, biometric information, and blood sampling. ## Specific Notes ## ### participants.tsv File ### Subject ID list and subject-level variables. Please refer to the participants.json for guidance on how to interpret specific columns in the participants.tsv file. ### BIDS dataset ### Data were converted from DICOM source files using dcm2niix. The parameters were further extracted from the DICOM files using pydicom and converted to .json format. SeriesDates were anonymized and shifted to pre-1900's years and a subject-based offset added to the month/year that preserves time difference between initial and follow-up visit. T1- and T2-anatomical scans (anat/*_T{1,2}w.nii.gz) were defaced using the pydeface.py software: https://github.com/poldracklab/pydeface (c1ceeb2) ### derivatives Folder ### This folder contains the processed output from the MRIQC protocol. MRIQC is an automated processing pipeline designed to compute many image quality metrics for T1 weighted anatomical and T2* weighted functional scans. For more information please see: https://github.com/poldracklab/mriqc (a5f68f5) Additional derivatives include: - Plots of the fMRI event logs - thumbnail mosaics of the high-resolution T1w and T2w scans used to confirm defacing process. ### sourcedata Folder ### This folder contains the as-provided source files used to create the BIDS dataset files. The only changes made to these source files were to remove any information that could potentially be used to identify the study participants. Specifically: - EyeTrackingLogFiles: Files renamed, "TimeValues" and "TimeStamp" entries changed to "REMOVED" within each file. - PresentationLogFiles: Files renamed, scrubbed of Dates, Subject IDs. These files were used to create the sub-9XXX_ses-{1,2}_task-<taskname>_events.tsv files. - PulseGatingFiles: Files renamed to remove original IDs. - WorkingMemoryTestResults: Files renamed to remove original IDs., subject IDs altered to 9XXX series randomized IDs. Dates removed. Times-of-day left intact. Other data that could not be included in raw form due to its binary nature: - Physiological recordings (EMG): Converted from raw Acknowledge format to compressed .tsv files using the "convert_physio_files.py" script located in the code/ directory within the dataset. The output data are located within the dataset as *_physio.tsv.gz and *_physio.json pairs. ### Diffusion Imaging - use these data with caution ### Diffusion imaging from the following subjects should be used with caution due to suspicious bval/bvecs tables extracted from the source DICOM files: sub-9019 sub-9070 sub-9057 sub-9091 sub-9090 sub-9013 sub-9044 sub-9050 sub-9067 sub-9035 sub-9035 sub-9073 sub-9083 sub-9037 sub-9007 sub-9053 sub-9066 sub-9012 sub-9082 sub-9077 sub-9076 sub-9099 sub-9001 ### Raw Polysomnography Data ### Raw polysomnography data is available upon request. Please contact Gustav Nilsonne at gustav.nilsonne@ki.se to request this data. ### Known Issues ### -sub-9066/ses-1/func/sub-9066_ses-1_task-hands_events.tsv does not have all of the columns present in the other events files. It only has 'onset', 'duration' and 'condition'.
创建时间:
2018-03-14
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

中国空气质量数据集(2014-2020年)

数据集中的空气质量数据类型包括PM2.5, PM10, SO2, NO2, O3, CO, AQI,包含了2014-2020年全国360个城市的逐日空气质量监测数据。监测数据来自中国环境监测总站的全国城市空气质量实时发布平台,每日更新。数据集的原始文件为CSV的文本记录,通过空间化处理生产出Shape格式的空间数据。数据集包括CSV格式和Shape格式两数数据格式。

国家地球系统科学数据中心 收录

中国区域交通网络数据集

该数据集包含中国各区域的交通网络信息,包括道路、铁路、航空和水路等多种交通方式的网络结构和连接关系。数据集详细记录了各交通节点的位置、交通线路的类型、长度、容量以及相关的交通流量信息。

data.stats.gov.cn 收录

Breast Cancer Dataset

该项目专注于清理和转换一个乳腺癌数据集,该数据集最初由卢布尔雅那大学医学中心肿瘤研究所获得。目标是通过应用各种数据转换技术(如分类、编码和二值化)来创建一个可以由数据科学团队用于未来分析的精炼数据集。

github 收录

中国交通事故深度调查(CIDAS)数据集

交通事故深度调查数据通过采用科学系统方法现场调查中国道路上实际发生交通事故相关的道路环境、道路交通行为、车辆损坏、人员损伤信息,以探究碰撞事故中车损和人伤机理。目前已积累深度调查事故10000余例,单个案例信息包含人、车 、路和环境多维信息组成的3000多个字段。该数据集可作为深入分析中国道路交通事故工况特征,探索事故预防和损伤防护措施的关键数据源,为制定汽车安全法规和标准、完善汽车测评试验规程、

北方大数据交易中心 收录

熟肉制品在全国需求价格弹性分析数据

为更好了解各市对熟肉制品的市场需求情况,本行业所有企业对相关熟肉制品需求弹性数据进行采集计算。如果熟肉制品需求量变动的比率大于价格变动的比率,那么熟肉制品需求富有弹性,说明顾客对于熟肉制品价格变化的敏感程度大,弹性越大,需求对价格变化越敏感,本行业所有企业可以在该市适当的降低熟肉制品价格来获得较多的收益。如果熟肉制品需求缺乏弹性,本行业所有企业可以在该市适当的提高熟肉制品价格来获得较多的收益。该项数据对本行业所有企业在全国的市场营销决策有重要意义。1.数据采集:采集相关熟肉制品在某一时间段全国的的需求数据和价格数据,按照市级进行整理归纳,得到该熟肉制品的需求量变动数值和价格变化数值。 2.算法规则:对采集得到的数据按照如下公式进行计算:需求弹性系数Ed=-(△Q/Q)÷(△P/P),得到需求弹性系数。式中:Q表示产品的需求量,单位为份;P表示产品的价格,单位为元;△Q表示需求量同比变动值,单位为份;△P表示价格同比变动值,单位为元。取需求弹性系数的绝对值|Ed|作为分析数据时的参考系数。 3.数据分析:根据|Ed|的数值可分析该熟肉制品的需求价格弹性。(1)|Ed|=1(单位需求价格弹性),说明需求量变动幅度与价格变动幅度相同;(2)1<|Ed|(需求富有弹性),说明需求量变动幅度大于价格变动幅度;(3)|Ed|<1(需求缺乏弹性),说明需求量变动幅度小于价格变动幅度。

浙江省数据知识产权登记平台 收录