MLS: MNI152 3D maps for Multiverse Reliability

A collection of 2400 brain maps. Each brain map is a 3D array of values representing properties of the brain at different locations.

### Collection description

<p>Welcome to Analytic Impacts of Group and Individual Reliability estimates collection. This collection is associated Registered Report: https://osf.io/g5wn9. The group-level estimates here are for the Michigan Longitudinal Study (MLS) sample. The estimates are derived from run and session level Monetary Incentive Delay (MID) task data. It features an array Cohen’s D (960 nii.gz files) and ICC estimates (1440 nii.gz files). The group Cohen’s d maps were calculated using d = t/sqrt(N). Since the sample sizes for each image are in the associated file name, you can convert these to t-stat maps if you’d like. Each file in our collection follows a consistent naming convention: subs: Indicates the number of subjects included in the model. ses: Denotes the session number for the repeated measurement. task: Specifies the task or experimental paradigm employed during data acquisition. type: Distinguishes between run-level or session-level estimates. contrast: Represents the specific contrast employed in the analysis. Options include Sgain-Neut, Sgain-Base, Lgain-Neut, and Lgain-Base. mask: Identifies the mask used to constrain input files (MNI152NLin2009cAsym), mot: Describes the type of motion correction applied during preprocessing (four options; see paper for details). mod: Specifies the model type utilized in the analysis, such as AntMod, CueMod, or FixMod (see paper for details). fwhm: Indicates the full width at half maximum (FWHM) of the spatial smoothing kernel applied to the data (five options). stat: Characterizes the statistical metric utilized for analysis, encompassing a range of options including Cohen&#x27;s d for group-level maps, ICC estimates for reliability, mean square between-subject variance (msbtwn), and mean square within-subject variance (mswthn). You can visualize the data on NeuroVault, download them individual or use Nilearn’s fetch_neurvault_ids, whereby you can: Download entire collection: mls_collection = fetch_neurovault_ids(collection_ids=[16606]) Download specific image(s): mls_images = fetch_neurovault_ids(image_ids=[845022,845017]).</p>

欢迎莅临群组与个体可靠性估计分析影响数据集。本数据集与注册报告相关联：https://osf.io/g5wn9。其中群组层面的估计数据针对密歇根纵向研究（MLS）样本。这些估计数据源自运行和会话层面的货币激励延迟（MID）任务数据。该数据集包含一系列 Cohen’s D（960个nii.gz文件）和ICC估计（1440个nii.gz文件）。群组Cohen’s d地图是通过公式 d = t/sqrt(N) 计算得出的。由于每个图像的样本量已包含在相关文件名中，因此若需，您可将其转换为t统计地图。本数据集中的每个文件均遵循一致的命名规范：subs：表示模型中包含的受试者数量。ses：表示重复测量的会话编号。task：指定数据采集期间使用的任务或实验范式。type：区分运行级或会话级估计。contrast：代表分析中使用的特定对比。选项包括Sgain-Neut、Sgain-Base、Lgain-Neut和Lgain-Base。mask：标识用于约束输入文件的掩码（MNI152NLin2009cAsym）。mot：描述预处理过程中应用的运动校正类型（四种选项；详见论文）。mod：指定分析中使用的模型类型，例如AntMod、CueMod或FixMod（详见论文）。fwhm：表示应用于数据的空间平滑核的半最大全宽（FWHM）（五种选项）。stat：描述用于分析的统计指标，包括群组级地图的Cohen’s d、可靠性估计的ICC、组间平均平方差（msbtwn）和组内平均平方差（mswthn）。您可以在NeuroVault上可视化数据，单独下载或使用Nilearn的fetch_neurvault_ids功能进行下载：下载整个数据集：mls_collection = fetch_neurovault_ids(collection_ids=[16606]) 下载特定图像：mls_images = fetch_neurovault_ids(image_ids=[845022,845017])。