five

The NIMH Healthy Research Volunteer Dataset|健康研究数据集|脑成像数据集

收藏
CERN Open Data Portal2024-08-20 更新2024-06-08 收录
健康研究
脑成像
下载链接:
https://openneuro.org/datasets/ds004215
下载链接
链接失效反馈
资源简介:
# The National Institute of Mental Health (NIMH) Research Volunteer (RV) Data Set A comprehensive dataset characterizing healthy research volunteers in terms of clinical assessments, mood-related psychometrics, cognitive function neuropsychological tests, structural and functional magnetic resonance imaging (MRI), along with diffusion tensor imaging (DTI), and a comprehensive magnetoencephalography battery (MEG). In addition, blood samples are currently banked for future genetic analysis. All data collected in this protocol are broadly shared in the OpenNeuro repository, in the Brain Imaging Data Structure (BIDS) format. In addition, task paradigms and basic pre-processing scripts are shared on GitHub. This dataset is unprecedented in its depth of characterization of a healthy population and will allow a wide array of investigations into normal cognition and mood regulation. This dataset is licensed under the [Creative Commons Zero (CC0) v1.0 License](https://creativecommons.org/publicdomain/zero/1.0/). ## Release Notes ### Release v2.0.0 This release includes data collected between 2020-06-03 (cut-off date for v1.0.0) and 2024-04-01. Notable changes in this release: 1. 769 new participants have been added along with re-evaluation data for 15 participants. Total unique participants count is now 1859. 2. `visit` and `age_at_visit` columns added to phenotype files to distinguish between visits and intervals between them. 3. Follow-up online survey data included. 4. Replaced Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) with General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. 5. Discontinued the Perceived Health rating survey. 6. Added Brief Trauma Questionnaire (BTQ) and Big Five personality survey to online screening questionnaires. 7. MRI: - Replaced ADNI-3 resting state sequence with a multi-echo sequence with higher spatial resolution. - Replaced field map scans with a shorter reversed-blipped EPI scan. 8. MEG: - Some participants have 6-minute empty room data instead of the shorter duration empty room acquisition. See the [CHANGES](./CHANGES) file for complete version-wise changelog. ## Participant Eligibility To be eligible for the study, participants need to be medically healthy adults over 18 years of age with the ability to read, speak and understand English. All participants provided electronic informed consent for online pre-screening, and written informed consent for all other procedures. Participants with a history of mental illness or suicidal or self-injury thoughts or behavior are excluded. Additional exclusion criteria include current illicit drug use, abnormal medical exam, and less than an 8th grade education or IQ below 70. Current NIMH employees, or first degree relatives of NIMH employees are prohibited from participating. Study participants are recruited through direct mailings, bulletin boards and listservs, outreach exhibits, print advertisements, and electronic media. ## Clinical Measures All potential volunteers visit [the study website](https://nimhresearchvolunteer.ctss.nih.gov), check a box indicating consent, and fill out preliminary screening questionnaires. The questionnaires include basic demographics, the World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), the DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure, the DSM-5 Level 2 Cross-Cutting Symptom Measure - Substance Use, the Alcohol Use Disorders Identification Test (AUDIT), the Edinburgh Handedness Inventory, and a brief clinical history checklist. The WHODAS 2.0 is a 15 item questionnaire that assesses overall general health and disability, with 14 items distributed over 6 domains: cognition, mobility, self-care, “getting along”, life activities, and participation. The DSM-5 Level 1 cross-cutting measure uses 23 items to assess symptoms across diagnoses, although an item regarding self-injurious behavior was removed from the online self-report version. The DSM-5 Level 2 cross-cutting measure is adapted from the NIDA ASSIST measure, and contains 15 items to assess use of both illicit drugs and prescription drugs without a doctor’s prescription. The AUDIT is a 10 item screening assessment used to detect harmful levels of alcohol consumption, and the Edinburgh Handedness Inventory is a systematic assessment of handedness. These online results do not contain any personally identifiable information (PII). At the conclusion of the questionnaires, participants are prompted to send an email to the study team. These results are reviewed by the study team, who determines if the participant is appropriate for an in-person interview. Participants who meet all inclusion criteria are scheduled for an in-person screening visit to determine if there are any further exclusions to participation. At this visit, participants receive a History and Physical exam, Structured Clinical Interview for DSM-5 Disorders (SCID-5), the Beck Depression Inventory-II (BDI-II), Beck Anxiety Inventory (BAI), and the Kaufman Brief Intelligence Test, Second Edition (KBIT-2). The purpose of these cognitive and psychometric tests is two-fold. First, these measures are designed to provide a sensitive test of psychopathology. Second, they provide a comprehensive picture of cognitive functioning, including mood regulation. The SCID-5 is a structured interview, administered by a clinician, that establishes the absence of any DSM-5 axis I disorder. The KBIT-2 is a brief (20 minute) assessment of intellectual functioning administered by a trained examiner. There are three subtests, including verbal knowledge, riddles, and matrices. ## Biological and physiological measures Biological and physiological measures are acquired, including blood pressure, pulse, weight, height, and BMI. Blood and urine samples are taken and a complete blood count, acute care panel, hepatic panel, thyroid stimulating hormone, viral markers (HCV, HBV, HIV), c-reactive protein, creatine kinase, urine drug screen and urine pregnancy tests are performed. In addition, three additional tubes of blood samples are collected and banked for future analysis, including genetic testing. ## Imaging Studies Participants were given the option to enroll in optional magnetic resonance imaging (MRI) and magnetoencephalography (MEG) studies. ### MRI On the same visit as the MRI scan, participants are administered a subset of tasks from the NIH Toolbox Cognition Battery. The four tasks asses attention and executive functioning (Flanker Inhibitory Control and Attention Task), executive functioning (Dimensional Change Card Sort Task), episodic memory (Picture Sequence Memory Task), and working memory (List Sorting Working Memory Task). The MRI protocol used was initially based on the ADNI-3 basic protocol, but was later modified to include portions of the ABCD protocol in the following manner: 1. The T1 scan from ADNI3 was replaced by the T1 scan from the ABCD protocol. 2. The Axial T2 2D FLAIR acquisition from ADNI2 was added, and fat saturation turned on. 3. Fat saturation was turned on for the pCASL acquisition. 4. The high-resolution in-plane hippocampal 2D T2 scan was removed, and replaced with the whole brain 3D T2 scan from the ABCD protocol (which is resolution and bandwidth matched to the T1 scan). 5. The slice-select gradient reversal method was turned on for DTI acquisition, and reconstruction interpolation turned off. 6. Scans for distortion correction were added (reversed-blip scans for DTI and resting state scans). 7. The 3D FLAIR sequence was made optional, and replaced by one where the prescription and other acquisition parameters provide resolution and geometric correspondence between the T1 and T2 scans. ### MEG The optional MEG studies were added to the protocol approximately one year after the study was initiated, thus there are relatively fewer MEG recordings in comparison to the MRI dataset. MEG studies are performed on a 275 channel CTF MEG system. The position of the head was localized at the beginning and end of the recording using three fiducial coils. These coils were placed 1.5 cm above the nasion, and at each ear, 1.5 cm from the tragus on a line between the tragus and the outer canthus of the eye. For some participants, photographs were taken of the three coils and used to mark the points on the T1 weighted structural MRI scan for co-registration. For the remainder of the participants, a BrainSight neuro-navigation unit was used to coregister the MRI, anatomical fiducials, and localizer coils directly prior to MEG data acquisition. ## Specific Survey and Test Data within Data Set **NOTE:** In the release 2.0 of the dataset, two measures Brief Trauma Questionnaire (BTQ) and Big Five personality survey were added to the online screening questionnaires. Also, for the in-person screening visit, the Beck Anxiety Inventory (BAI) and Beck Depression Inventory-II (BDI-II) were replaced with the General Anxiety Disorder-7 (GAD7) and Patient Health Questionnaire 9 (PHQ9) surveys, respectively. The Perceived Health rating survey was discontinued. ### 1. Preliminary Online Screening Questionnaires | Survey or Test | BIDS TSV Name | | --------------------------------------------------------------------------- | ------------------------------ | | Alcohol Use Disorders Identification Test (AUDIT) | audit.tsv | | Brief Trauma Questionnaire (BTQ) | btq.tsv | | Big-Five Personality | big_five_personality.tsv | | Demographics | demographics.tsv | | Drug Use Questionnaire | drug_use.tsv | | Edinburgh Handedness Inventory (EHI) | ehi.tsv | | Health History Questions | health_history_questions.tsv | | Health Rating | health_rating.tsv | | Mental Health Questions | mental_health_questions.tsv | | World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) | whodas.tsv | ### 2. On-Campus In-Person Screening Visit | Survey | BIDS TSV Name | | -------------------------------------------------------------------------------------------- | ----------------------------- | | Adverse Childhood Experiences (ACEs) | ace.tsv | | Beck Anxiety Inventory (BAI) | bai.tsv | | Beck Depression Inventory-II (BDI-II) | bdi.tsv | | Clinical Variable Form | clinical_variable_form.tsv | | Family Interview for Genetic Studies (FIGS) | figs.tsv | | General Anxiety Disorder-7 (GAD7) | gad7.tsv | | Kaufman Brief Intelligence Test 2nd Edition (KBIT-2) and Vocabulary Assessment Scale (VAS) | kbit2_vas.tsv | | Patient Health Questionnaire 9 | phq9.tsv | | Perceived Health Rating | perceived_health_rating.tsv | | Satisfaction Survey | satisfaction.tsv | | Structured Clinical Interview for DSM-5 Disorders (SCID-5) | scid5.tsv | | Test | BIDS TSV Name | | ---------------------------------------- | --------------------------- | | Acute Care Panel | acute_care.tsv | | Blood Chemistry | blood_chemistry.tsv | | Complete Blood Count with Differential | cbc_with_differential.tsv | | Hematology Panel | hematology.tsv | | Hepatic Function Panel | hepatic.tsv | | Infectious Disease Panel | infectious_disease.tsv | | Lipid Panel | lipid.tsv | | Other Panel | other.tsv | | Urinalysis | urinalysis.tsv | | Urine Chemistry | urine_chemistry.tsv | | Vitamin Levels | vitamin_levels.tsv | ### 3. Optional On-Campus In-Person MRI Visit | Survey | BIDS TSV Name | | ------------------------------ | ------------------- | | MRI Variables | mri_variables.tsv | | NIH Toolbox Cognition Battery | nih_toolbox.tsv | ## Preparation Notes In many of the Clinical Measures data files, there exist `-999` values. `-999` means there was no response though a response was possible. The question may have been skipped over by the participant or the question flow. `-777` appears in the Edinburgh Handedness Inventory (EHI) as well. `-777` means there is no data available for a response. The question was not presented or asked to the participant. The data were prepared using the following tools and filename mappings. ### Clinical Measures Data The `ctdb_clean_up.ipynb` Jupyter Notebook contains the python functions used to clean and convert the spreadsheet downloaded from CTDB to BIDS-standard TSV files as well as their respective data dictionaries converted to BIDS-standard JSON files. ### Biological and Physiological Measures Data The `cris_clean_up.ipynb` Jupyter Notebook contains the Python functions used to clean and convert the spreadsheet with clinical measures to BIDS-standard TSV files and their data dictionaries to BIDS-standard JSON files. ### BIDS-standard MEG Files Data collected by the NIMH MEG Core was converted to BIDS-standard files using the MNE BIDS package. Associated notebooks: `1_mne_bids_extractor.ipynb` & `2_bids_editor.ipynb`. ### BIDS-standard MRI We used the `heudiconv` tool to convert MRI DICOM files to BIDS-standard files with the associated script: `heuristic_rvol.py`. A modified workflow of `pydeface` was used to deface structural scans with the associated notebook: `modified-workflow-pydeface.ipynb` Each participant received either the ADNI3 or the ABCD protocol, not both, during their MRI/MEG visit. T1w scans with acquisition label `fspgr` are ADNI3 protocol sequence and scans with `mprage` acquisition labels are ABCD protocol sequence. ### OpenNeuro BIDS File/Folder Tree Below is a BIDS-compliant file/folder tree as it appears for subjects on OpenNeuro. ```shell sub-ON<subject> └── ses-01 ├── anat │ └── sub-ON<subject>_ses-01_acq-<desc>_run-<index>_<suffix>.<json|nii.gz> ├── asl │ └── sub-ON<subject>_ses-01_run-<index>_asl.<json|nii.gz> ├── dwi │ └── sub-ON<subject>_ses-01_run-<index>_dwi.<bvec|bval|json|nii.gz> ├── fmap │ └── sub-ON<subject>_ses-01_acq-<desc>_dir-<direction>_run-<index>_epi.<bvec|bval|json|nii.gz> ├── func │ └── sub-ON<subject>_ses-01_task-<taskname>_run-<index>_<suffix>.<json|nii.gz> ├── meg │ ├── sub-ON<subject>_ses-01_task-<taskname>_run-01_<meg|coordsystem>.json │ ├── sub-ON<subject>_ses-01_task-<taskname>_run-01_<channels|events>.tsv │ └── sub-ON<subject>_ses-01_task-<taskname>_run-01_meg.ds │ ├── BadChannels │ ├── bad.segments │ ├── ClassFile.cls │ ├── MarkerFile.mrk │ ├── params.dsc │ ├── processing.cfg │ ├── sub-ON<subject>_ses-01_task-<taskname>_run-01_meg.<extension> │ └── sub-ON<subject>_ses-01_task-<taskname>_run-01.xml └── sub-ON<subject>_ses-01_scans.<json|tsv> ``` Definitions: - `<subject>` = subject number - `<taskname>` = task name: `airpuff`, `artifact`, `gonogo`, `haririhammer`, `movie`, `oddball`, `sternberg` - `<desc>` = placeholder for acquisition label for a given suffix - `<direction>` = flipped, unflipped - `<index>` = run number/index - `<suffix>` = placeholder to indicate the scan type - `T1w`: `<desc>` = `fspgr`, `mprage`, `fse`, `highreshippo` - `T2w`: `<desc>` = `abcdcube`, `cube`, `frfse` - `FLAIR`: `<desc>` = `adni2d`, `2d`, `3d`, `t2` - `epi`: `<desc>` = `dwib1000`, `dwi`, `resting` - `T2star` - `bold` - `meg` - `asl` - `<extension>`: indicates meg data files' type = `acq`, `bak`, `hc`, `hist`, `infods`, `meg4`, `newds`, `res4`, `xml`
创建时间:
2022-07-15
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1km分辨率逐月降水量数据集(1901-2023)

该数据集为中国逐月降水量数据,空间分辨率为0.0083333°(约1km),时间为1901.1-2023.12。数据格式为NETCDF,即.nc格式。该数据集是根据CRU发布的全球0.5°气候数据集以及WorldClim发布的全球高分辨率气候数据集,通过Delta空间降尺度方案在中国降尺度生成的。并且,使用496个独立气象观测点数据进行验证,验证结果可信。本数据集包含的地理空间范围是全国主要陆地(包含港澳台地区),不含南海岛礁等区域。为了便于存储,数据均为int16型存于nc文件中,降水单位为0.1mm。 nc数据可使用ArcMAP软件打开制图; 并可用Matlab软件进行提取处理,Matlab发布了读入与存储nc文件的函数,读取函数为ncread,切换到nc文件存储文件夹,语句表达为:ncread (‘XXX.nc’,‘var’, [i j t],[leni lenj lent]),其中XXX.nc为文件名,为字符串需要’’;var是从XXX.nc中读取的变量名,为字符串需要’’;i、j、t分别为读取数据的起始行、列、时间,leni、lenj、lent i分别为在行、列、时间维度上读取的长度。这样,研究区内任何地区、任何时间段均可用此函数读取。Matlab的help里面有很多关于nc数据的命令,可查看。数据坐标系统建议使用WGS84。

国家青藏高原科学数据中心 收录

FAOSTAT Agricultural Data

FAOSTAT Agricultural Data 是由联合国粮食及农业组织(FAO)提供的全球农业数据集。该数据集涵盖了农业生产、贸易、价格、土地利用、水资源、气候变化、人口统计等多个方面的详细信息。数据包括了全球各个国家和地区的农业统计数据,旨在为政策制定者、研究人员和公众提供全面的农业信息。

www.fao.org 收录

Breast Ultrasound Images (BUSI)

小型(约500×500像素)超声图像,适用于良性和恶性病变的分类和分割任务。

github 收录

Beijing Traffic

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

Papers with Code 收录

中国区域地面气象要素驱动数据集 v2.0(1951-2020)

中国区域地面气象要素驱动数据集(China Meteorological Forcing Data,以下简称 CMFD)是为支撑中国区域陆面、水文、生态等领域研究而研发的一套高精度、高分辨率、长时间序列数据产品。本页面发布的 CMFD 2.0 包含了近地面气温、气压、比湿、全风速、向下短波辐射通量、向下长波辐射通量、降水率等气象要素,时间分辨率为 3 小时,水平空间分辨率为 0.1°,时间长度为 70 年(1951~2020 年),覆盖了 70°E~140°E,15°N~55°N 空间范围内的陆地区域。CMFD 2.0 融合了欧洲中期天气预报中心 ERA5 再分析数据与气象台站观测数据,并在辐射、降水数据产品中集成了采用人工智能技术制作的 ISCCP-ITP-CNN 和 TPHiPr 数据产品,其数据精度较 CMFD 的上一代产品有显著提升。 CMFD 历经十余年的发展,其间发布了多个重要版本。2019 年发布的 CMFD 1.6 是完全采用传统数据融合技术制作的最后一个 CMFD 版本,而本次发布的 CMFD 2.0 则是 CMFD 转向人工智能技术制作的首个版本。此版本与 1.6 版具有相同的时空分辨率和基础变量集,但在其它诸多方面存在大幅改进。除集成了采用人工智能技术制作的辐射和降水数据外,在制作 CMFD 2.0 的过程中,研发团队尽可能采用单一来源的再分析数据作为输入并引入气象台站迁址信息,显著缓解了 CMFD 1.6 中因多源数据拼接和气象台站迁址而产生的虚假气候突变。同时,CMFD 2.0 数据的时间长度从 CMFD 1.6 的 40 年大幅扩展到了 70 年,并将继续向后延伸。CMFD 2.0 的网格空间范围虽然与 CMFD 1.6 相同,但其有效数据扩展到了中国之外,能够更好地支持跨境区域研究。为方便用户使用,CMFD 2.0 还在基础变量集之外提供了若干衍生变量,包括近地面相对湿度、雨雪分离降水产品等。此外,CMFD 2.0 摒弃了 CMFD 1.6 中通过 scale_factor 和 add_offset 参数将实型数据化为整型数据的压缩技术,转而直接将实型数据压缩存储于 NetCDF4 格式文件中,从而消除了用户使用数据时进行解压换算的困扰。 本数据集原定版本号为 1.7,但鉴于本数据集从输入数据到研制技术都较上一代数据产品有了大幅的改变,故将其版本号重新定义为 2.0。CMFD 2.0 的数据内容与此前宣传的 CMFD 1.7 基本一致,仅对 1983 年 7 月以后的向下短/长波辐射通量数据进行了更新,以修正其长期趋势存在的问题。2021 年至 2024 年的 CMFD 数据正在制作中,计划于 2025 年上半年发布,从而使 CMFD 2.0 延伸至 2024 年底。

国家青藏高原科学数据中心 收录