ABCD: MNI152 3D maps for Multiverse Reliability

A collection of 6180 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 Adolescent Brain Cognitive Development (ABCD) sample. The estimates are derived from run and session level Monetary Incentive Delay (MID) task data. It features an array Cohen’s D and ICC estimates. 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: abcd_collection = fetch_neurovault_ids(collection_ids=[17171]) Download specific image(s): abcd_images = fetch_neurovault_ids(image_ids=[######, ######])</p>

本数据集汇集了6180份脑部图谱。每一份脑部图谱均由一个三维数值数组构成，用以表征大脑在不同位置的属性。